LIFE SPAN AND DISABILITY

LIFE SPAN AND DISABILITY Vol. XIX / n. 1 / January - June 2016

Psychology Social issues Education Rehabilitation Habilitation

ISSN 2035-5963 ● The varied impact of psychological disability across the lifespan in Australia ● The role of age, cognitive functioning and gender on the “attentional activity rate” ● Mortality in People with Intellectual Disability in India: Correlates of Age and Settings ● Parental and teachers attachment in children at risk of ADHD and with ADHD ● Properties of the Italian version of the Body Weight Image and Self-Esteem in a non-clinical sample

Vol. XIX / n. 1 / January - June 2016

Journal promoted by the Department of Psychology Institute for Research on Mental Retardation and Brain Aging “Oasi Maria SS.” - Troina

LIFE SPAN AND DISABILITY Psychology, Social issues, Education, Rehabilitation, Habilitation Founded in 1998, Life Span And Disability promotes interdisciplinary research on psychological, social, educational, rehabilitative and neuro-psychological aspects of the human life span. The aim is to disseminate scientific studies tapping on cognitive, emotional and interpersonal – transient or permanent – problems that may occur during the individual’s life span (e.g., adolescence, unemployment, retirement, fertility drop, normal and pathological aging), causing uneasiness or permanent disability. Neuropsychological and social aspects of Intellectual Disability, as well as rehabilitation strategies to improve the cognitive and adaptive functions and the quality of life of these persons are the target of scientific papers included in the Journal. Attention is focused on potential or residual skills and competences that might be enhanced to promote individual’s fulfillment and cognitive development. Personal skills and abilities are also considered from educational, social, environmental point of views, in relation with the bio-psychological bases and/or data derived from empirical research. Both quantitative and qualitative methodological approaches are welcomed. Authors’ contributions received by our editorial office are submitted to members of the scientific committee or external experts, for a blind peer-review and approval process. Life Span is a six-monthly Journal, published in English online. The Journal welcomes research contributes with original theoretical, methodological or empirical studies; diagnostic or intervention instruments (introduction of innovative intervention methods, questionnaires, diagnostic and intervention scales, validation of instruments); relevant case-reports; reviews on specific topics. Website: www.lifespan.it Editors in chief: Santo Di Nuovo (Catania) / Renzo Vianello (Padova) / Serafino Buono (Troina) International scientific committee: • Valeria Abusamra (Buenos Aires) • Ottavia Albanese (Milan) • Giulia Balboni (Aosta) • Marco Bertelli (Florence) • Pietro Boscolo (Padua) • Daniela Brizzolara (Pisa) • Fredi Büchel (Geneva) • Gerhard Buettner (Frankfurt) • Raquel Casado Muñoz (Burgos) • Marcello Cesa-Bianchi (Milan) • Cesare Cornoldi (Padua) • Luigi Croce (Brescia) • Larry Dana (New York) • Rossana De Beni (Padua) • Maurizio Elia (Troina) • Raffaele Ferri (Troina) • Rosa Ferri (Rome) • Rafi Feuerstein (Jerusalem) • Carl Haywood (Nashville and New York) • Dario Ianes (Trento) • Edward Janicki (Chicago) • Olga Jerman (Pasadena) • Mike Kerr (Wales) • Giulio Lancioni (Bari) • Silvia Lanfranchi (Padua) • Rosalba Larcan (Messina) • Fernando Lezcano Barbero (Burgos) • Daniela Lucangeli (Padua) • Ruth Luckasson (Albuquerque) • Francesco Marucci (Rome) • Michèle Mazzocco (Baltimore) • Paolo Moderato (Parma) • Enrico Molinari (Milan) • Sebastiano A. Musumeci (Troina) • Anna Maria Pepi (Palermo) • Michael Powers (Glastombury) • Geoffrey M. Reed (Geneva) • Carlo Ricci (Rome) • Johannes Rojahn (Fairfax) • Corrado Romano (Troina) • Luis Salvador (Cadiz) • Shekhar Saxena (Geneva) • Salvatore Soresi (Padua) • Giacomo Stella (Modena) • Elena Tanti Burlo (Malta) • Dua Tarun (Geneva) • John A. Tsiouris (New York) • David Tzuriel (Ramat-Gan) • Henny M. J. van Schrojenstein Lantman-de Valk (Maastricht) • Stefano Vicari (Rome) • Paolo Vitali (Montreal) • Benedetto Vitiello (Bethesda) • Patricia Walsh (Dublin) EDITORIAL BOARD: Tommasa Zagaria (coordinator) Maria Teresa Amata / Angelica Carrubba / Santina Città / Francesco Di Blasi / Simonetta Panerai / Damiana Schillaci / Marinella Zingale / Rosa Zuccarello The English texts have been revised by Philip Doughty © Associazione Oasi Maria SS. - IRCCS All rights reserved

LIFE SPAN AND DISABILITY

Journal promoted by the Department of Psychology Institute for Research on Mental Retardation and Brain Aging “Oasi Maria SS.” - Troina

© copyright 2016 Associazione Oasi Maria SS. - IRCCS 94018 Troina (En) - via Conte Ruggero, 73 Tel. 0935 653530 - Fax 0935 650234

Cover & editing by Damiana Schillaci

Published online: June 2016

Summary

The varied impact of psychological disability across the lifespan in Australia James Athanasou

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The role of age, cognitive functioning and gender on the “attentional activity rate” Elena Commodari

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Mortality in people with Intellectual Disability in India: correlates of age and settings Ram Lakhan & Thomas M. Kishore

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Parental and teachers attachment in children at risk of ADHD and with ADHD Olga Liverta Sempio, Rosa Angela Fabio, Paola Tiezzi & Clemente Cedro

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Properties of the italian version of the Body Weight Image and Self-Esteem (B-WISE) in a non-clinical sample Marianna Alesi & Annamaria Pepi

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Life Span and Disability XIX, 1 (2016), 7-20

The varied impact of psychological disability across the lifespan in Australia

James Athanasou1

Abstract The purpose of this report is to examine the extent of psychological disability and its impact throughout the lifespan. The report is based on a secondary analysis of the official survey of Psychological Disability by the Australian Bureau of Statistics. Psychological disability encompasses abroad continuum of developmental, cognitive and psychiatric disorders. It affects 3.4 per cent of the population (around 770,000 Australians) and accounts for one-fifth of all persons with a disability. Depression and mood affective disorders are its major components. It does feature as an independent condition but in 88% of cases exists in conjunction with other long-term health factors. This disability increases monotonically across the age groups and rises dramatically from ages 65 and over. The proportion at the extremes of age is moderated by gender. Almost all persons with a psychiatric disability (96.5%) experienced a restriction in their daily living activities or some form of schooling or employment restriction. A tentative framework for the study of psychological disability across the lifespan is introduced. Keywords: Psychological disability; psychological disorders; Australia. Received: February 24, 2015; Revised: December 16, 2015; Accepted: December 23, 2015 © 2016 Associazione Oasi Maria SS. - IRCCS 1

University of Sydney, Faculty of Health Sciences. E-mail: [email protected].

Correspondence to: James Athanasou, Faculty of Health Sciences, 75 East Street LIDCOMBE NSW 2141 AUSTRALIA Acknowledgments: The helpful comments of two anonymous reviewers are gratefully acknowledged.

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Life Span and Disability Athanasou J. ________________________________________________________________________________________________________________________________

1. Introduction Psychological disability with its various forms of incapacity, frailty or debility has long been a feature of the broad landscape of disabling conditions in Australia (Australian Bureau of Statistics, 2015). Although the specific conditions have been diagnosed (American Psychiatric Association, 2013) and disability has been reconstructed in social terms (Shakespeare & Watson, 1997) this has not always emphasized the resulting limitations (Hughes & Patterson, 1997). For the most part, the psychological substrate that is common to a wide variety of conditions and which effects thoughts, feelings or actions has not been emphasized coherently since the first official statistical data was collected in 1967 (Australian Bureau of Statistics, 2008). For instance, most previous studies (e.g., Andrews, Henderson, & Hall, 2001) have emphasized mental illness. The purpose of this report is to examine the extent of psychological disability or behavioral disorders and their impact across the lifespan. By way of background, this study also adopts the official definitions of the Government Statistician in conjunction with the World Health Organization. Mental health is viewed as ―a state of emotional and social well-being‖ (Australian Bureau of Statistics, 2008, p. 4). Mental illness, on the other hand, relates to ―a clinically diagnosable disorder that significantly interferes with an individual’s cognitive, emotional or social abilities‖ (Australian Bureau of Statistics, 2008, p. 4). The phrase ―psychological disability‖ is an umbrella term that encompasses restrictions in everyday activities or an illness for which supervision is required and that is due to a nervous or emotional conditions, mental illness, brain injury, intellectual developmental disorders, autism and related disorders, dementia and Alzheimer’s disease (Australian Bureau of Statistics, 2015). Mulvany (2000) has emphasized an impairment-based approach and commented generally: ―The experience of severe mental disorder is frequently associated with economic hardship, unemployment, a breakdown in social relationships and a lowered standard of living‖ (pp. 582-583). She advocated an evaluation of the psychological, social and physical restrictions associated with traditional psychiatric classifications. The concern of this paper is to explore such restrictions. Disability status in Australia has been characterized according to a series of graded categories from no reported disability to profound core activity limitations (Australian Bureau of Statistics, 2013). The core activity limitations are communication, mobility and self-care. Limitation refers to a

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Psychological disability in Australia ________________________________________________________________________________________________________________________________

person needing help with, or using aids or equipment for the activity and the overall level is determined by their highest level of limitation in these activities. The categories are defined as profound, severe, moderate or mild: (a) profound - the person is unable to do, or always needs help with, a core activity task; (b) severe - the person sometimes needs help with a core activity task, or the person has difficulty understanding or being understood by family or friends, or the person can communicate more easily using sign language or other non-spoken forms of communication; (c) moderate - the person needs no help, but has difficulty with a core activity task; and (d) mild - the person needs no help and has no difficulty with any of the core activity tasks, but uses aids or equipment, or has one or more of the following limitations (cannot easily walk 200 meters, cannot walk up and down stairs without a handrail, cannot easily bend to pick up an object from the floor, cannot use public transport, can use public transport, but needs help or supervision, needs no help or supervision, but has difficulty using public transport). This paper is derived from the recently released official survey of psychological disability by the Australian Bureau of Statistics (2015) and all references to the unpublished data are from that survey. The results are used to direct policy and planning in the area of disability pursuant to Australia’s ratification of the United Nations Convention of the Rights of Persons with Disabilities in 2008 (United Nations, 2006, 61st session, Item 67b), namely ―…appropriate measures to promote the physical, cognitive and psychological recovery, rehabilitation and social reintegration of persons with disabilities…‖ (United Nations, 2006, p. 14). Various epidemiological studies have indicated that psychological disability is not distributed randomly. Lorant, Deliege, Eaton, Robert, Philippot and Ansseau (2003) reported a meta-analysis of 60 studies in which low socioeconomic status was linked with depression. It was not clear however, whether depression was a cause or an effect. Kringlen, Torgensen and Cramer (2001) surveyed the prevalence of mental disorder in Oslo. They found higher prevalence in women than men whereas Waghorn and colleagues (Waghorn, Saha, Harvey, Morgan, Waterreus, Bush, et al., 2012) found that 60% of participants with psychotic disorders were male in an Australian survey of 1825 participants. They reported that psychotic disorder peaked in the age groups 25-34 years (Tab. 1, p. 777). The interaction between disabilities was highlighted by White, Chant, Edwards, Townsend and Waghorn (2005) who undertook a secondary analysis of the Disability Ageing and Carers Survey in 1998. They found co-morbid

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Life Span and Disability Athanasou J. ________________________________________________________________________________________________________________________________

relationships between intellectual disability and psychotic disorder (1.3%), depressive disorder (8%) and anxiety disorder (14%). The employment limitations of psychological disability were also highlighted in Waghorn et al. (2012), who found that only 22.4% of people with psychotic disorders (such as schizophrenia, schizoaffective disorder, bipolar affective disorder, depressive psychosis, delusional disorders) in Australia were employed. On the other hand, Sanderson and Andrews (2002) noted that there were some mental disorders that were not associated with a disability. Using a Composite International Diagnostic Interview they screened for personality disorder, neurasthenia and psychosis in 10,641 Australian participants in the National Survey of Mental Health and Well-Being. Disability was assessed using the 12-item Short Form (Ware, Kosinski, & Keller, 1996). They concluded that ―All mental disorders except alcohol abuse were significantly associated with some disability, although not all associations were significant after comorbid conditions — both physical and mental — were taken into account.‖ (p. 83).

2. Aims Accordingly, this study explores these issues and addresses the following questions: (a) what is the incidence of psychological disability; (b) is the incidence of psychological disability higher in men than women; (c) is comorbidity a feature of psychological disability; (d) are there demographic and socio-economic factors associated with psychological disability across the lifespan; and (e) are there employment implications for those with a psychological disability?

3. Method 3.1. Participants The results for this report were derived from the seventh national survey of Disability, Ageing and Carers Australian Bureau of Statistics, (2013). This is a stratified, random, household survey of 27,400 private dwellings and 500 non-private dwellings. It includes urban and rural areas but excluded indigenous community collection districts located in very remote regions of Australia. The final sample comprised 68,802 persons from households and 10,362 persons from cared accommodation.

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Psychological disability in Australia ________________________________________________________________________________________________________________________________

3.2. Procedures A multi-stage sampling approach was used (state and territory then geographical strata then statistical local areas then population census collection districts then 27,400 private dwellings, 500 non-private dwellings and approximately 1,000 health establishments). Trained interviewers screened households for a person with a disability or persons aged 65 years and over, using a computer-assisted personal interview procedure. Interviewers were prompted to check the branching of questions and responses through programmed algorithms. Proxy interviews with another member of the household were undertaken for those unable to answer questions due to incapacity, illness or other misadventure. Basic social and demographic data were collected and a copy of the household questionnaire is available freely upon request. Questions were wide-ranging and related to the personal and social aspects of disability, the restrictions in daily living, the assistance required as well as schooling or employment restrictions. The range of questions in the cared accommodation was restricted as there was no access through a proxy interview or the content of some questions was not relevant. Participation in such household surveys is mandated in accordance with the Census and Statistics Act, 1905. Participants are advised of the confidentiality and privacy of the data collection. Published data is released with randomly adjusted cell values through a process called ―perturbation‖ and this is done in order to ensure that individuals, families, households or dwellings are not capable of being identified.

4. Analysis As this was census data only a secondary analysis utilizing descriptive statistics (histograms, cross-tabulations) are reported but tests for the statistical significance of the difference in proportions were computed (z-test). Further details are provided in the results section.

5. Results Psychological disability affects 3.4 per cent of the population (around 770,000 Australians) and accounts for one-fifth of all persons with a disability. The major category of psychological disability is depression and mood affective disorders (see Fig. 1). The distribution of disability is complicated by the fact that respondents may report more than one condition

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Life Span and Disability Athanasou J. ________________________________________________________________________________________________________________________________

as well as other multiple long-term health conditions. It may feature as an independent condition or more likely exists in conjunction with other longterm health factors. For instance, 88% of persons have both a psychological disability and another disability as well. Figure 1 - Psychological disabilities in Australia (N = 867.700) Alzheimer's disease Dementia Autism and related disorders Intellectual developmental disorder Nervous tension/stress Phobic and anxiety disorders Depression/mood affective disorders (excluding postnatal depression) 0%

10%

20%

30%

40%

Note: respondents may report multiple conditions.

5.1. The age and gender background of psychological disability Laypersons might have a general idea that psychological disabilities may ebb and flow with the exigencies of life. The fact is that the proportion affected in each age group actually increases monotonically and rises dramatically from ages 65 and over (see Fig. 2). Figure 2 - Psychological disability as a proportion of the overall population (N = 22,875,200)

Proportion

0,200 0,150 0,100 0,050 0,000 0-14

12

15-24

25-34

35-44

45-54

55-64

65-74

75-84 85 years and over

Psychological disability in Australia ________________________________________________________________________________________________________________________________

Proportion of psychological disability

Figure 3 - Proportion of males and females with psychological disabilities across age groups. 0,18 0,16 0,14 0,12 0,10 0,08

0,06 0,04 0,02 0,00 0-14

15-24

25-34

35-44

Males

45-54

55-64

65-74

75-84 85 years and over

Females

The effects of age, however, are moderated markedly by gender. For the most part, the proportion of males with a psychological disability compared to females is not comparable at the ends of the age distribution (see Fig. 3). For instance, psychological disability is three times higher for males compared to females in the age group 0-14 years, whereas for females aged 85 years and over psychological disability is twice as high as that for males. Naturally these differences in proportions are all statistically significant (z-tests, p < .001) in part because of the massive sample size. This applies whether one uses all males in the population as the benchmark or even the number of males with a psychological disability. 5.2. Extent of disability Almost all persons with a psychiatric disability (96.5%) experience a restriction in their daily living activities or some form of schooling or employment restriction. The breakdown by categories for males and females with psychological disabilities is shown in Figure 4 and indicates that for most, this restriction was profound.

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Life Span and Disability Athanasou J. ________________________________________________________________________________________________________________________________

Figure 4 - Extent of limitations or restrictions for persons with psychological disabilities (N = 770,500). 350 300 Thousands

250

200 150 100 50 0 Profound

Severe

Moderate

Mild

School work

Nil

Limitations or restrictions

The limitations and the areas of activity where assistance is needed extend well beyond cognitive or emotional tasks. They include self-care (45% all percentages rounded); mobility (59%); oral communication (2%); cognitive or emotional tasks (88%); health care (51%); reading or writing tasks (30%); private transport (45%); household chores (43%); property maintenance (44%); and meal preparation (25%). Only 4% did not need assistance or experience any difficulty with one of these listed areas of activity. It must be somewhat encouraging that 99% of respondents said that their needs for assistance were fully met (49%) or partly met (also 49%). The assistance provided to persons with a psychological disability who live in households is divided between (a) informal providers such as a partner, parent, child, other relative or friend; or (b) formal providers of assistance such as government, private non-profit organizations or private commercial organizations. Informal providers overlap but the burden falls mainly on partners (177,800) and parents (176,300). Private commercial organizations (219,900) dominate the formal assistance scene, followed by government services (186,200). There is considerable double-counting in these figures as multiple sources of assistance are involved.

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Psychological disability in Australia ________________________________________________________________________________________________________________________________

5.3. Educational and employment implications The proportion of persons aged 5-20 years with a psychological disability who are not currently attending school is 32% compared with 24% for those with no disability. This disadvantage is also visible in post-compulsory education and training (higher education, TAFE, business college, industry skills centre) where 6% of those with a psychological disability are studying compared with 14% for those with no disability. The difficulties experienced are vastly different from persons with other disabilities. They centred on fitting in socially, learning difficulties and communication difficulties that epitomize the inherent nature of a psychological disability (see Fig. 5). The long-term implications of these social, learning and communication difficulties are evident in post-school achievements. Fewer persons with psychological difficulties obtain degrees, advanced diplomas and Certificate III/IV. Considerably more have no educational attainment when compared with the proportion of persons with other disabilities or no disability (see Fig. 6).

Percentage

Figure 5 - Difficulties experienced at school or institution because of psychological conditions or other disabilities 70 60 50 40 30 20 10 0

Psychological Disability

Other disability

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Life Span and Disability Athanasou J. ________________________________________________________________________________________________________________________________

Figure 6 - Occupational groups of persons with psychological conditions and those with no disability Labourers Machinery Operators Drivers Sales Clerical Administrative Community Personal Service Technicians Trades Professionals Managers 0

10

20

30

40

Percentage No disability

Psychological disability

Psychological disability has a major effect on labor force status. Only 8% of those with a psychological disability are working full-time compared with 34% of those with other disabilities or 55% of those with no disability. Just on 71% of those with a psychological disability are not formally in the labor force. The pattern of employment across occupational groups is reasonable similar save for that of laborers (see Fig. 7). Laborers account for just under 30% of all those employed with a psychological disability compared 14% of all those without a disability. Figure 7 - Highest level of educational attainment for persons with psychological conditions, other disabilities and those with no disability 20,0 15,0 10,0 5,0 0,0 Graduate Dip Postgraduate Degree

Bachelor Degree

Psychological disability

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Advanced Diploma / Diploma

Certificate III / IV No educational attainment

Other disability

No disability

Psychological disability in Australia ________________________________________________________________________________________________________________________________

Figure 8 - A framework for the study of psychological disability

6. Discussion and Conclusions The burden of psychological disability in Australia is not only widespread but also deep in its personal and social consequences. These findings are generalizations and mask the underlying situation for each individual. The results confirm the value of adopting a restriction-based approach to describing individual disability that emphasizes the everyday limitations. It supports the view of Mulvany (2000) but also complements the utility of a medical approach (American Psychiatric Association, 2013) as well as the attempts to view disability as a social construct (Wendell, 1996). This report has explored the limitations and for the most part made comparisons between those persons with a psychological disability and those with other disabilities or persons with no disability. The picture that has been painted is of almost uniform disadvantage. Although psychological disability is a heterogeneous category of conditions, the common substrate is that of disturbed mood, cognition and behavior. The major dominance of depressive disorders within this categorization was highlighted and far surpasses all other categories. It is followed by anxiety disorders, then stress. All of these conditions contributed uniquely to restrictions in the core activities. Differences between males and females in the distribution of psychological disability especially in the youngest and oldest age groups were obvious. They were not explained by the data and this points to a limitation of a secondary and descriptive analysis. Another limitation is the lack of representation of indigenous communities in the most remote areas of Australia.

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Life Span and Disability Athanasou J. ________________________________________________________________________________________________________________________________

The impact of psychological disability was reported to be overwhelmingly profound in the restrictions that it imposed on everyday life. These restrictions in communication, self-care and mobility were wideranging. They extended to educational consequences with lower levels of educational achievement (Athanasou, 2014). Accordingly it was no surprise that full-time employment was lower than for those with other disabilities. Moreover, the distribution was skewed towards those at the lower skill levels. This reflected earlier findings and reports (Athanasou, 1999, 2015). A generalized and holistic model of psychological disability is presented in Figure 8. This is merely a starting point and overarching framework for studying the varied impact of psychological disability. In this model the condition is mediated by age and gender and results in limitations in core activities at varying levels. These have a flow-on effect to education and employment. Despite the limitations of a self-report format with likely lower levels of reporting disability there is evidence that there are profound personal, social and medical aspects of psychological disability throughout Australia. This poses major challenges for rehabilitation. Key competencies are required for professionals (a) to deal with depression and mood affective disorders; (b) to cope with a population that spans the entire age range; (c) to provide assistance to individuals to overcome restrictions in their daily living activities especially in areas such as cognitive or emotional tasks; and (d) to intervene to ensure social justice in education, training and employment.

References American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing Andrews, G., Henderson, S., & Hall, W. (2001). Prevalence, comorbidity, disability and service utilisation. British Journal of Psychiatry, 178, 145153. Athanasou, J. (1999). Disability and employment in Australia. Some positives and negatives. Australian Journal of Career Development, 8 (3), 18-22.

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Athanasou, J. (2014). The impact of disability status on education and work in Australia. Australian Journal of Career Development, 23 (2), 100-104. Athanasou, J. A. (2015). Living, working and earning for people with disabilities in Australia. Australian Journal of Career Development, 24 (3), 178-183. Australian Bureau of Statistics (2008) National survey of mental health and wellbeing: Summary of results, Catalogue No. 4326.0. Canberra. Australian Bureau of Statistics (2013) Disability, ageing and carers, Australia, Catalogue No. 4430.0. Canberra. Australian Bureau of Statistics (2015) Survey of disability, ageing and carers: Psychological disability, 2012 – Australia, Catalogue No. 4433.0.55.004. Canberra. Hughes, B., & Patterson, K. (1997). The social model of disability and the disappearing body: towards a sociology of impairment. Disability and Society, 12 (3), 325-40. Kringlen, E., Torgensen, S., & Cramer, V. (2001). A Norwegian psychiatric epidemiological study. American Journal of Psychiatry, 158 (7), 1091-1098. Lorant, V., Deliege, D., Eaton, W., Philippot, P., & Ansseau, M. (2003). Socioeconomic inequalities in depression: A meta-analysis. American Journal of Epidemiology, 157 (2), 98-112. Mulvany, J. (2000). Disability, impairment or illness? The relevance of the social model of disability to the study of mental disorder. Sociology of Health & Illness, 22 (5), 582-601. Sanderson, K., & Andrews, G. (2002). Prevalence and severity of mental health-related disability and relationship to diagnosis. Psychiatric Services, 53 (1), 80-86. Shakespeare, T., & Watson, N. (1997). Defending the social model. Disability and Society, 12 (2), 293-300.

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United Nations (2006). Convention on the rights of persons with disabilities and optional protocol. New York: Author. Waghorn, G., Saha, S., Harvey, C., Morgan, V. A., Waterreus, A., Bush, R., Castle, D., Galletly, C., Stain, H. J., Neil, A. L., McGorry, P., & McGrath, J. (2012). ―Earning and learning‖ in those with psychotic disorders: The second Australian national survey of psychosis. Australian and New Zealand Journal of Psychiatry, 46 (8), 774-785. Ware, J. E., Kosinski, M., & Keller, S. D. (1996). A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Medical Care, 34, 220–233. Wendell, S. (1996). The rejected body: Feminist philosophical reflections on disability. New York: Routledge. White, P., Chant, P., Edwards, N., Townsend, C., & Waghorn, G. (2005). Prevalence of intellectual disability and comorbid mental illness in an Australian community sample. Australian and New Zealand Journal of Psychiatry, 39 (5), 395-400.

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Life Span and Disability XIX, 1 (2016), 21-43

The role of age, cognitive functioning and gender on the “attentional activity rate”

Elena Commodari1

Abstract The attentional response times influence the capacity to manage cognitive and behavioral activities. This study investigated the agerelated changes of the “attentional activity rate”, i.e., the speed of the attentional responses in elderly adults. The role of cognitive functioning and gender were also analysed. Participants were 240 old adults aged from 65 to 85 years. The response times during the execution of tasks measuring “selective attention”, “focused attention”, “divided attention” and “alternating attention” were evaluated. Results showed a decreasing of the speed of the attentional responses with increasing age. The age-related changes of the “attentional activity rate” did not involve all aspects of attention, and presented different characteristics in males and females. Interestingly, the level of cognitive functioning did not directly contribute to the speed of execution of attentional tasks when divided and selective attention were involved. Considering that the speed Received: August 26, 2015; Revised: March 21, 2016; Accepted: June 10, 2016 © 2016 Associazione Oasi Maria SS. - IRCCS 1

Department of Educational Sciences, University of Catania. E-mail: [email protected]. Phone: +390952508025 The author reports no declaration of interest. Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent: Informed consent was obtained from all individual participants included in the study.

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Life Span and Disability Commodari E. ________________________________________________________________________________________________________________________________

at which a mental activity is executed contributes to determine patterns of mental inefficiency in the old adults, the findings of the presents studies could be relevant for the developmental, clinical and experimental fields. Keywords: Aging; Gender; Attention; Response times.

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Age, Gender and Attention ________________________________________________________________________________________________________________________________

1. Introduction “Activity rate” is the speed at which mental activities are performed (Lezak, Howieson, Bigler, & Trane, 2015). Literature reports a slowing of all mental activities in old age (Salthouse, 1985, 1996a, 1996b; Valeriani, Ranghi, & Giaquinto, 2003; Leclerc & Kensinger, 2008; Slessor, Miles, Bull, & Phillips, 2010). Salthouse (1985) assumed that the reduction of the speed of execution of cognitive operations is one of the main factors that contribute to the age-related differences in cognitive functioning. According to the “processing-speed” theory, two distinct mechanisms are responsible for the relation between speed and cognition: the “limited time mechanism” and the “simultaneity mechanism” (Salthouse, 1996a, 1996b). The first mechanism concerns the restriction of the time factor in performing later operations when a large percentage of available time is occupied by the execution of early operations. The second mechanism involves the possibility that the products of early processing may be lost by the time that later processing is completed. Processing deficits could be related to discrepancies between the time course of loss of information and the speed with which mental operations are executed (Salthouse, 1996a, 1996b). The age-related effects on different speed measures can vary in magnitude (Salthouse & Coon, 1993, 1994). Although increased age is associated with slower performance in a wide range of tasks, there are many contrasting opinions on the grade to which age-related slowing is specific to particular processes or reflects larger and more general influences (Salthouse, 2000). The effect of age on the speed of information processing shows up in delayed reaction times and longer than average total performance times in the absence of a specific disability. Slowing of mental activities reduces the auditory span and performance accuracy. Moreover, it contributes to determining the memory lapses of an elderly person. In this regards, as similarly occurred in the 1990’s, Luszcz and Bryan (1999) demonstrated that the reduction in the speed of information processing is a fundamental contributor to normal age-related memory loss. The decrease in the speed of information processing also involves attention skills. Attention is an essential component of complex cognitive processes, such as language, reasoning and problem-solving. It is a multifaceted process (Butter, 1987; Posner & Rothbart, 2007; Rueda, Posner, & Rothbart, 2007; Peterson & Posner, 2012) and includes several activities involving different brain systems (Benton & Silvan, 1984; Raz & Buhle, 2006). There is not a unique definition of attention and its aspects. In this

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Life Span and Disability Commodari E. ________________________________________________________________________________________________________________________________

work, “selective attention” is the ability to ward-off distracting stimuli (Sohlberg & Mateer, 1989; Lezak et al., 2015); “focused attention” is the ability to respond discretely to specific stimuli (Sohlberg & Mateer, 1989); “divided attention” is the capacity to maintain two attentional focuses contemporarily. It is related to the optimal allocation of resources between different sets of input by splitting (Hahn, Wolkenberg, Thomas, Ross, Myers, Heishman et al., 2008); “alternating attention” is the ability to shift alternatively focus and tasks (Lezak et al., 2015). It is the rapid shifting of the attentional focus, given the inability to process all available information in parallel (Parasuraman, 1998). Each of these processes implies distinct neural structures and serves different functions in everyday behavior (Rueda et al., 2007; Petersen & Posner, 2012). The age effect on the individual attention network has been examined using several experimental manipulations (e.g., Brink & McDowd, 1999; Zhou, Fan, Lee, Wang, & Wang, 2011). However, the results of these studies are not univocal. Brink and McDowd (1999) suggested that the agerelated differences in the distinct attentional processes demanded by different tasks are not depending on general slowing. Moreover, several differences by age in the execution of tasks involving different aspects of attention were found. Some studies reported relatively intact alerting and orienting in older adults (Mahoney, Verghese, Goldin, Lipton, & Holtzer, 2010). The immediate span of attention is also a relatively effortless process. It tends to be resistant to the effects of aging and of many brain disorders. However, it slowly decreases after 50 years old. Huttermann, Bock and Memmert (2012) showed that the age-related decrease in attention span depends on the viewing condition, and it is greater when one of the two tasks is steadily fixated. According to these authors, a fixated task engages attention and thus withdraws it from the periphery. They found that elderly adults presented more difficulties than younger ones in situations that required attention in the visual periphery, and hypothesized that the effects of age on attention span are not uniform but are depending on the presence or absence of eye movements and of a steadily fixated task. Selective attention for spatial tasks is conserved in old age while selective attention for visual search tasks significantly decreases after 60-65 years old (Madden, Turkington, Provenzale, Denny, Langley, Hawk, et al., 2002; Greenwood & Parasuraman, 2004; Commodari, 2006). These observations are consistent with those obtained from brain studies. Posterior brain attention systems responsible for selecting spatial locations are relatively

24

Age, Gender and Attention ________________________________________________________________________________________________________________________________

well preserved with advancing age. However, aging is associated with the decline in the efficiency of the neural mechanisms supporting both context and conflict processing (e.g., Mittenberg, Seidenberg, O’Leary, & Di Giulio 1989). Some studies have shown that older people are penalized by a divided attention situation (e.g., McDowd & Craik, 1988). Salthouse, Fristoe, Uneweaver and Coon (1995) demonstrated that two different explanation could be found on this evidence. According to the first explanation, aging influences complex cognitive processes, such as those involved in the execution of the tasks that require divided attention, more than elementary processes; according to the second explanation, aging directly influences elementary processes and the effect of aging on complex cognitive activities is indirect. However, age-related effects on dual-task performance were reduced when single-task performances were taken into account (Salthouse, 2000). As seen in most cognitive skills, gender differences in attention have been observed (e.g., Feng, Spence, & Pratt, 2007) and these differences persist in the old age (Commodari & Guarnera, 2010). Some authors (e.g., Gur & Gur, 2002; Munro, Winicki, Schretlen, Gower, Turano, Muñoz, et al., 2012) showed that gender differences play a significant role in the aging process. Women presented lower age-related cognitive decline than men (Gur & Gur, 2002). Gender differences in patterns of cognitive test performance have been attributed to many factors, such as sex hormones or sexual dimorphisms in brain structure, that change with normal aging (Munro et al., 2012). Males outperform females in tasks involving spatial attention. Gender differences were also observed in visual recognition tasks, such as symbol substitution tasks (Mittenberg et al., 1989), and in visual selective attention task (Merritt, Hirshman, Wharton, Stangl, Devlin, & Lenz; 2007). The role of gender in the functional organization of the hemispheres in conditions of focusing of attention during the memorization of competitively presented verbal information has also been studied (Razumnikova & Vol’f , 2007).

2. Research aim Although many studies analyzed the effect of age on the attentional performances, the majority of the studies assessed single aspects of attention such as divided or selective attention (Parasuraman, Nestor, & Greenwood, 1989; West, 2004). Moreover, only a little number of researchers studied the

25

Life Span and Disability Commodari E. ________________________________________________________________________________________________________________________________

speed of the attentional responses (e. g., Salthouse, 1996a, 1996b; Madden et al., 2002; Commodari & Guarnera, 2010). This study aimed at addressing some of these limitations. It analyzed the effect of aging on the speed at which attention skills operated and investigated all the key aspects in which attention is articulated. The first aim of this study was to investigate the age-related changes in the “attentional activity rate,” i.e., the speed of the attentional response, in aging. The rate of execution of tasks that involved different aspects of attention was measured, with the purpose to analyze whether the reduction of speed processing related to aging influenced all aspects of attention. “Selective attention,” “divided attention,” “focused attention,” and “alternating attention” were assessed. Second, the study investigated the presence of differences by gender in the speed at which attentional tasks were executed. Gender influences the speed at which a cognitive activity is executed (Aartsen, Martin, & Zimprich, 2004; De Frias, Nilsson, & Herlitz, 2006) and several differences related to gender during aging are observed (e.g., Gur & Gur, 2002). The study aimed to assess whether the changes in the attentional activity rate in aging involved the same aspects of the attention skills in males and females. Third, the study aimed to verify whether the level of cognitive functioning influenced the “attentional activity rate”. Age, in fact, is not the only variable that contributes to the slowing of mental performances. Cognitive slowing is also a typical manifestation of cognitive impairment. The elderly with cognitive deterioration are slower in the execution of mental activities as compared to the cognitively healthy elderly (Gorus, De Raedt, Lambert, Lemper, & Mets, 2008; Phillips, Rogers, Haworth, Bayer, & Tales, 2013; Castellano, Guarnera, & Di Nuovo, 2015). For this reason, one of the purposes of this study was to investigate “attentional activity rate” in elderly adults who present different levels of cognitive functioning.

3. Method 3.1. Participants Participants were 240 adults aged from 65 to 85 years old (males: 116; females: 124; age mean: 75.27 standard deviation: 6.79) in apparently good physical and psychological health. The participants were recruited in three recreational centers in a large town of Italy, during five days. The participants were selected according to the following criteria. All the 300

26

Age, Gender and Attention ________________________________________________________________________________________________________________________________

persons aged more than 65 who frequented the recreational centers were invited to participate in the study. Of these, 38 subjects who referred serious physical or psychological diseases and/or presented significant limitations of daily activities, as indicated by the manager of each center, were excluded from the study. The remaining 262 subjects participated in the test administration. During test administration, 22 participants failed the training session. These participants were excluded from research. The 240 participants did not report significant physical problems or cognitive disorders and were able to participate in the activities of the recreational centers. However, after the attentional tasks administration, all participants were assessed for cognitive impairment. The choice to test for cognitive impairment on the participants depended on the evidence that elderly subjects who appear to be healthy and intact have cognitive impairment, which cannot be identified without extensive examination (Valdois, Joanette, Poissant, Ska, & Dehaut, 1990). Thus, a typical “normal” group of the elderly person probably includes, at least, a few people with mild and non-apparent cognitive disorders. About the education level, participants had, at least, a middle school level of education: 101 subjects graduated middle school (53 males, 48 females), 95 graduated high school (49 males, 46 females), 44 had a university degree (26 males, 18 females). However, the “Milan Overall Dementia Assessment” scores (MODA, Spinnler & Tognoni, 1987; Brazzelli, Capitani, Della Sala, Spinnler, & Zuffi, 1994a, 1994b), which evaluate cognitive functioning, were corrected for the academic grade. 3.2. Measures The “attentional activity rate” was measured through several computerized tasks, which were included in a multi-task computerized battery (Di Nuovo, 2009). The tasks assessed the speed of attentional responses. Each task measured a different aspect of attention. A multiple task measuring auditory, visual and visual-spatial recognition measured “focused attention;” a computerized version of the classic Stroop test measured “selective attention;” a multiple barrage task assessed “alternating attention;” finally, a simultaneous double task assessed “divided attention.” For each, task, the scores were the median reaction time. Reaction times were counted in second. The scores for each task were the median response times in seconds.

27

Life Span and Disability Commodari E. ________________________________________________________________________________________________________________________________

The “Focused Attention” task comprised three sub-tasks. Each sub-task measured the auditory, visual and visual-spatial “focused attention,” respectively. The “auditory focused attention” sub-task required subjects to recognize an auditory target among vocal distractors. Participants had to press a particular computer key following a vocal stimulus. The stimuli were letters (vowels and consonants). The visual sub-task required the recognition of a visual target among a group of distractors (images of common objects) appearing in the sequence. Participants had to press a key when a visual target appears on the screen of the computer. The visual-spatial sub-task was a computerized version of the symbols barrage test. Participants had to delete one symbol in a set of stimuli. The screen showed a set of stimuli which were sequentially ringed. Subjects had to press a computer key when the target is circled. “Selective Attention” task was a computerized version of the Stroop test. It comprised two tasks. The first task was the baseline. Participants had to press a computer key of the same color as the character used to print a word that appeared on the screen. The second task was the interference task. It required subjects to name the color of the ink used to print a word describing a different color, e.g., “red,” written using blue ink. “Divided Attention” task was a simultaneous double task. This task required the subject recognized a visual target and an auditory target contemporarily. “Alternating Attention” task was a multiple barrage task. It was a computerized adjustment of a non-verbal cancelation test. It was composed of two sub-tasks. Each task required the search and cancelation of different targets presented in sets of 90 stimuli. In the first task (verbal task), the stimuli were letters, in the second task (visual-spatial task), the stimuli were small squares with a variously oriented code. This task required a continual shift of attentional focus. For each set of stimuli, there were three targets. Characteristics psychometric of these tasks were satisfactory. Reliability test-retest on the standardization sample at an interval of 15-20 days was from .88 to .92. Moreover, concurrent validity (r values comprised between .80 and .90 for the different tests) was satisfactory (Di Nuovo, 2009). Preliminarily to this research, test-retest reliability on a sample of old persons in apparent good health was calculated. Reliability values for the single subtests were comprised between .83 and .92. Cognitive functioning has been evaluated through the MODA (Spinnler & Tognoni, 1987; Brazzelli et al., 1994a, 1994b). The MODA is a rapid rating scale generating a global score from 0 to 100. The MODA permits to detect the presence of cognitive impairment and measures the severity of the condition. MODA scores allow distinguish subjects with a normal cognitive

28

Age, Gender and Attention ________________________________________________________________________________________________________________________________

functioning from subjects with cognitive impairment. Moreover, it assesses the risk of developing cognitive impairment. The scores are adjusted for age and education. The MODA meets the requirements for a reliable bedside cognitive screening instrument (Spinnler & Tognoni, 1987; Brazzelli et al.,1994a). According to the authors, the correlation between the MODA and the Mini-Mental State Examination was .63 in controls and .84 in patients with Alzheimer's dementia. The MODA test-retest reliability was .83 (Brazzelli et al., 1994a). 3.3. Procedures A psychologist tested the participants during an individual session in a room of the recreational centers. The room was illuminated with overhead fluorescent lighting. In the room, there was a table with a computer. First, the psychologist administered the “attentional activity rate” tasks in an individual session. Only the participants who were able to execute the training session completed the test administration. The participants who failed the training session of at least two tasks were excluded from the study, and the test session has been interrupted. After two days, the psychologist tested the participants for cognitive impairment in a second session. Participants were seated approximately 57 cm from the monitor of a computer. The tasks required pressing specific computer keys, according to the instruction of the psychologist. The participants used their preferred hand. The psychologist instructed the subjects to maintain fixation on the centre of the screen before start each task and to respond as fast as they accurately can, as soon they detected the appearance of the target stimuli. A training session preceded the administration of each task. However, the tasks were easy and did not require any previous experience in the use of the computer. 3.4. Statistical analysis Several statistical analyses were conducted. Preliminarily the MODA scores distribution was calculated for the entire sample. The choice to assess this variable, although the participants were in apparent good health and intact, depended on the results of previous studies on elderly people. These studies (e.g., Valdois et al., 1990) showed that often the old individuals present undiagnosed cognitive impairment, and a typical “normal” group of

29

Life Span and Disability Commodari E. ________________________________________________________________________________________________________________________________

the elderly person probably includes some people with mild and nonapparent cognitive disorders. Second, means, standard deviation, and t-test values by age were calculated. The aim of these analyses was to assess whether the young-old and old-old participants presented significant differences in the speed of responses during the execution of tasks which involved different aspects of attention, Third, differences by gender were evaluated. These analyses aimed to assess whether old male and females significantly differed in the “attentional activity rate.” Differences in the “attentional activity rate”, with respect to the level of cognitive functioning were also measured. Finally, several regression analyses were conducted. The aim of these analyses was to identify the contribution of the variables “age” and “level of cognitive functioning” on the speed of attentional responses in aging, both in the males and females. These analyses were conducted separately for the two genders because of the results of the t-test analysis, which showed that men and women performed differently in the attentional tasks. Moreover, the findings of previous studies also showed significant gender differences in attention skills both in young and in old people (e.g., Merritt et al., 2007). 3.4.1. Means and standard deviation of the MODA scores Table 1 shows the means and standard deviation of the MODA scores for the entire sample. As expected, the cognitive assessment revealed that some of the participants obtained a score indicating a condition of cognitive decline. The participants were divided into three groups, according to their MODA score. The first group comprised the 184 subjects who presented an MODA score indicating the absence of cognitive deterioration (normal group). The second group included the 32 subjects who obtained an MODA score showing a borderline condition about the risk of developing a pathological cognitive decline (group at risk of pathology). Finally, the third group comprised the 24 subjects who obtained an MODA score indicating a condition of cognitive decline (pathological group). Table 1 - Cognitive functioning of the participants Participants Normal cognitive functioning (MODA scores ≥ 89) Borderline with respect to a risk of cognitive impairment (MODA scores range 88.9-85.6) Cognitive impairment (MODA scores range ≤ 85.5) TOTAL

30

N 184

M 91.53

SD 1.03

24

87.45

.73

32

75.03

6.30

240

Age, Gender and Attention ________________________________________________________________________________________________________________________________

3.4.2. Means, standard deviation, t test values by age The means, standard deviation and t test values by age were calculated, with the aim to evaluate whether age equally contributes to slowing of the “attentional activity rate”, and whether the slowing involved all the aspects of attention skills. Participants were divided into two groups with respect to their age. The first group comprised the subjects aged from 65 to 75 years old (132 subjects), the second group included the subjects aged more than 76 years old (108 subjects). Table 2 presents the results of these analyses. t test analysis showed significant differences by age in the “focused attention” (auditory task; t = -5.21, p < .001), “selective attention” (t = 2.80 ; p < .001) and “alternating attention” (symbol task t = 5.23, p < .001) scores. In all these tasks, the younger participants were faster as compared to the older participants. Interestingly, no significant differences by age were observed in the “focused attention” (visual and visual-spatial) and the “alternating attention” (verbal) scores. Table 2 - Means, standard deviations and t test for the “attentional activity rate” scores by age levels Age

M

SD

65-75

.50

.25

> 75

.70

.35

65-75

.43

.09

> 75

.46

.16

65-75

.37

.14

> 75

.41

.21

65-75

.66

.25

> 75

.85

.37

65-75

1.03

.12

> 75

1.40

.84

65-75

1.23

.35

> 75

1.12

.20

65-75

126.15

28.96

> 75

131.77

42.39

65-75

201.06

82.52

> 75

150.15

64.49

t

p

-5.21

< .001

-1.42

.15

-1.37

.17

-4.77

< .001

-4.97

< .001

2.80

< .001

-1.21

.22

5.23

< .001

Focused Attention Auditory Visual Visual-spatial Divided attention Selective Attention Base line task Color word interference task Alternating attention Verbal Visual- spatial

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Life Span and Disability Commodari E. ________________________________________________________________________________________________________________________________

3.4.3. Means, standard deviation, and t test values by “gender” The means, standard deviation and t test values by gender were calculated. Table 3 shows the results of the descriptive analyses and t test values. Results showed significant differences by gender in all the scores, except “selective attention” scores. Table 3 - Means, standard deviations and t test for the “attentional activity rate” scores by gender Gender

M

SD

M

.73

.39

F

.46

.13

M

.48

.17

F

.41

.03

M

.43

.24

F

.35

.08

M

.85

.44

F

.65

.06

M

1.41

.80

F

.99

.09

M

109.66

38.83

F

146.47

20.04

M

151.92

73.20

F

202.70

76.53

M

30.36

2.01

F

32.13

2.05

t

p

7.06

< .001

4.66

< .001

3.46

< .001

4.88

< .001

5.66

< .001

-9.31

< .001

-5.24

< .001

-6.73

< .001

Focused Attention Auditory Visual Visual-spatial Divided attention task Selective Attention Base line task Color word interference task Alternating attention Verbal Visual spatial

3.4.4. Means, standard deviation and t test values by “cognitive functioning” Table 4 presents the means, standard deviation and ANOVA for the “activity rate” scores by “cognitive functioning”. ANOVA showed that the normal group was faster as compared to the others groups of participants. In particular, the results showed that the subjects who obtained an MODA score indicating the absence of cognitive impairment were speedier than the others participants in several tasks. Significant differences by “cognitive impairment” were observed in the following tasks: “focused attention” (auditory task: F = 9.26; p < .001; visual task: F = 2.93, p = .05; visualspatial task: F = 4.19, p = .01) and “alternating attention” (verbal task:

32

Age, Gender and Attention ________________________________________________________________________________________________________________________________

F = 7.80, p < .001; spatial task: F = 23.73; p < .001). Post hoc analysis with Bonferroni correction confirmed these results. Post hoc analysis showed that these three groups of subjects differed in all the scores. Table 4 - Means, standard deviations and ANOVA for each “activity rate” score by cognitive functioning level Cognitive functioning

M

SD

.64

.35

.43

.05

.44

.04

.45

.14

.40

.03

.41

.06

.41

.20

.32

.02

.34

.01

.77

.36

.64

.05

.68

.00

1.24

.67

1.01

.09

1.07

.13

131.57

38.18

131.99

19.69

102.14

16.77

164.75

77.29

260.08

55.81

171.70

47.78

F

p

9.26

< .001

2.93

.05

.4.19

.01

2.61

.07

2.72

.06

7.80

< .001

23.73

< .001

Focused attention

Auditory

Visual

Visual-spatial

Absence of cognitive impairment Risk of cognitive impairment Pathological impairment Absence of cognitive impairment Risk of cognitive impairment Pathological impairment Absence of cognitive impairment Risk of cognitive impairment Pathological impairment

Divided attention Absence of cognitive impairment Risk of cognitive impairment Pathological impairment Selective attention Absence of cognitive impairment Risk of cognitive impairment Pathological impairment Alternating attention

Verbal

Visual-Spatial

Absence of cognitive impairment Risk of cognitive impairment Pathological impairment Absence of cognitive impairment Risk of cognitive impairment Pathological impairment

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Life Span and Disability Commodari E. ________________________________________________________________________________________________________________________________

3.4.5. Regression analyses According to the results of the t-test analysis, which showed that males and females presented different performances in the attentional “activity rate” tasks, and of the previous literature in this field, regression analyses were calculated separately on the subsamples of males and females. Age and level of cognitive functioning were regressed for each attentional activity rate scores. The results (Tab. 5) showed that age and level of cognitive functioning, as measured by MODA, were significant predictors of the majority of the scores. However, age and cognitive functioning differently influenced the attentional response times in the males and females. Table 5 - Regressions analyses for males and females using age and MODA scores as independent variables and the “attentional activity rate” scores as dependent variables Dependent Variables

Focused attention Auditory task Focused attention

Indipendent Variables MODA Age scores t t

R2

F

M

-.94

-.41

.00

1.86

F

-.41

-3.63**

.51

66.58**

M

.26

.11

.02

2.50

Gender

Visual task

F

-1.6

-4.92**

.31

28.94**

Focused attention

M

-2.87*

-5.03**

.16

12.68**

F

.52

-.30

-.01

.33

M

1.26

-.34

.01

1.73

Visual-spatial task Divided attention Selective attention Alternating attention verbal task Alternating attention spatial task

F

2.54*

-.92

. 15

12.35**

M

-4.68**

1.07

.27

22.51**

F

1.74

1.50

.01

1.57

M

-.008

2.26*

.04

3.98*

F

-.20

-6.64**

.43

48.37

M

.54

4.32

.16

12.54**

F

-.35

2.86

.14

11.30**

*p < .05; **p < .001

The regression analyses on the subsample of the males showed that the independent variables were significant predictors for several attentional response time scores. Both age and level of cognitive functioning were significant predictors for the visual-spatial “focused attention” (F = 12.682, R2 = 1.83, p < .001). The t values that permit us to evaluate the contribution

34

Age, Gender and Attention ________________________________________________________________________________________________________________________________

of the variables to the model showed that the level of the cognitive functioning was the better predictor for the visual-spatial focused attention (t = -5.03, standardized coefficient B = -5.33, p < .001). Age was also a significant predictor for the “selective attention” (F = 22.54, R2 = .27, p < .001; t = -4.68, standardized coefficient B = .46) and verbal “alternating attention” (F = 3.98, R2 = .06, p < .05; t = 2.26, standardized coefficient B = .25) scores. The MODA scores, which evaluated the level of cognitive functioning, were significant predictors of the visual-spatial “alternating” attention scores (F = 12.54; R2 = .18, p < .001; standardized coefficient B = .45). Regression analyses on the subsamples of the females showed that age and MODA scores were significant predictors for the auditory “focused attention” scores (F = 66.58, R2 = .53, p < .001; t = 4.43, standardized coefficients B = .42, p < .001; t = -3.63, standardized coefficient B = -.34, p < .001, respectively). Age was a significant predictor for the “divided attention” scores (F = 12.35, R2 = .17, p < .001; standardized coefficient B = .31). Finally, the “level of cognitive functioning” was a significant predictor for the visual “focused attention”, and verbal “alternating attention” scores (F = 28.94, t = -4.42; R2 = .32, standardized coefficient B = -.55, p < .001; F = 48.37, p < .001, t = -6.64, standardized coefficient B = -6.64, p < .001, respectively). It is known that cognitive impairment influences all behavioral and mental performances. For this reason, the contribution of this variable in determining “attentional activity rate” was expected. Nevertheless, the level of cognitive functioning was not a significant predictor of the speed at which “divided attention” and “selective attention” operated. Of interest, the regression analyses on the partial samples of males and females showed a different role of the variables age and level of cognitive functioning on the attentional activity rate in the two genders.

4. Discussion “Activity rate” plays a pivotal role in determining the quality of cognitive performances (Brittain, La Marche, & Reeder, 1991; Salthouse, 1996a, 1996b; Wang, Fu, Greenwood, Luo, & Parasuraman, 2012). The speed of mental activity influences the execution of cognitive and behavioral activities. The decreasing speed of the attentional responses significantly contributes to reducing daily autonomy and capacity to manage cognitive and behavioral activities. In particular, the high speed of the attentional

35

Life Span and Disability Commodari E. ________________________________________________________________________________________________________________________________

responses plays a pivotal role in the quality of those cognitive and behavioural performances that require fast orienting, the capacity to select a stimulus, and the ability to avoid distracting stimuli. Results of this study have contributed to better investigate the effect of age on the attentional responses times. First, results showed that “attentional activity rate” decays with aging. This was not a surprising result. Mental response slowing is commonly observable in aging. Effect of age on the response times is consistent with the well-known Processing Speed Theory of cognitive aging (Salthouse, 1996a, 1996b). According to this theory, the reaction times were longer in older people than in younger, especially under the high perceptual load condition. The speed of early perceptual processing is compromised with increasing age, and this effect is greater in the complex task. However, although the reduction in speed at which mental activities are executed may reflect the generalized cognitive slowing in old people (Wang et al., 2012), these results are not unequivocal (Brink & McDowd, 1999). The reduction of the speed at which attention operates did not involve all the aspects of attention skills. The older subjects who participated in the study were slower in the execution of tasks requiring “focused attention,” “selective attention” and “divided attention.” The other aspects of the “attentional activity rate” resisted to the aging effects. Several differences by gender were also found. Results showed genderrelated differences in the majority of the “attentional activity rate” scores, except the “selective attention” scores. Gender did not influence the ability to resist to interfering stimuli. Gender differences in cognitive domains are well documented in the literature (Aartsen et al., 2004; Razumnikova & Vol’f, 2007). Results of the study confirmed that these differences persist in old age. ANOVA analysis for “level of cognitive functioning” showed that the level of cognitive functioning influenced the “attentional activity rate”. Participants without cognitive impairment were faster in responses as compared to those that were in a condition of cognitive impairment or at risk of developing cognitive impairment. Of interest, the cognitive decline did not influence the “attentional activity rate” in tasks involving “focused attention”, “divided attention”, and “selective attention”. The absence of cognitive impairment did not directly contribute to the attentional response times when “focused,” “divided” and “selective” attention were involved. The regression analyses supported these considerations. These analyses were separately conducted for males and females with the goal to investigate

36

Age, Gender and Attention ________________________________________________________________________________________________________________________________

whether the individual contribution of the variables “age” and “level of cognitive functioning” is the same or different in the two genders. The regression analyses conducted on the partial samples of the males and females showed that both age and level of the cognitive functioning were significant predictors of the majority of the scores. General cognitive functioning influences all behavioural and mental performances, and it is not surprising the contribution of this variable in determining the attentional response times. Nevertheless, the level of the cognitive functioning was not a significant predictor of the response times in tasks that measure the ability to maintain a dual focus and the ability to control interfering stimuli. Moreover, the effects of age and level of cognitive functioning on the “attentional activity rate” were different in the two genders. With regard to the males, results of the present study showed the following results. Age influenced both the speed to focus visual-spatial stimuli and the speed in avoiding interfering stimuli and shifting attention, whereas the level of cognitive functioning was a significant predictor of the speed at which visual-spatial “alternating attention” and visual spatial “focused attention” operated. With regard to the females, results of this research showed that age was a significant predictor of the attentional activity rate when the auditory “focused attention” and “divided attention” were involved. The level of cognitive functioning was a significant predictor for the attentional response times when the auditory and visual “focused attention,” and verbal “alternating attention” were involved. All these results showed that gender differences contribute to determining the attentive response times at each age and level of cognitive functioning. Moreover, sensory and motor changes characterise ageing, independently from the quality of the cognitive functioning. These issues permit to formulate some interesting considerations. The speed of information processing plays a pivotal role in several daily activities. However, in a condition of cognitive normality, the attentional response times did not depend on higher or lower cognitive efficiency but prevalently on gender and age. This result could have interesting practical relevance. It is accepted that coping defects can arise from some neurological, psychiatric, and psychological variables. Moreover, it is well known that deficit in attention plays a pivotal role in determining patterns of mental inefficiency. For this reason, the effect of age on the speed at which attention skills operate should be highly considered in the applicative field. Issues of this study showed

37

Life Span and Disability Commodari E. ________________________________________________________________________________________________________________________________

that, in a condition of cognitive normality, the increasing of age contribute to reducing the attentional speed. This reduction was relatively independent of the quality of the cognitive functioning but was influenced by the gender. The central role of age and gender in the slowdown of the “attentional activity rate” might be considered especially if a person executes day-to-day activities (for example work activities) that involve high attention level.

References Aartsen, M., Martin, M., & Zimprich, D. (2004). Gender differences in level and change in cognitive functioning: results from the longitudinal aging study. Gerontology, 50, 35-8. Benton, A. L., & Sivan, A. B. (1984). Problems and conceptual issues in neuropsychological research in aging and dementia. Journal of Clinical Neuropsychology, 6, 57-64. Brazzelli, M., Capitani, E., Della Sala, S., Spinnler, H., & Zuffi, M. (1994a). Neuropsychological instrument adding to the description of patients with suspected cortical dementia: the Milan overall dementia assessment. Journal of Neurology, Neurosurgery, and Psychiatry, 57, 1510-1517. Brazzelli, M., Capitani, E., Della Sala, S., Spinnler, H., & Zuffi, M. (1994b). MODA Milan Overall Dementia Assessment. Firenze: Giunti OS. Brink, J. M., & McDowd, J. M. (1999). Aging and selective attention: an issue of complexity or multiple mechanisms? Journal of Gerontology, 54B (1), 30-33. Brittain, J. L., La Marche, J. A., & Reeder, K. P. (1991). The effects of age and IQ on Paced Auditory Serial Addition Task (PASAT) performance. Clinical Neuropsychology, 5, 163-175. Butter, C. M. (1987). Varieties of attention and disturbances of attention: a neuropsychological analysis. In: Jeannerod M. (Ed.), Neurophysiological and Neuropsychological Aspects of Spatial Neglect (pp. 1-25). North Holland: Elsevier.

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Castellano, S., Guarnera M., & Di Nuovo, S. (2015). Imagery in Healthy and in Cognitively Impaired Aging. Clinical Gerontologist, 38 (2), 103-113. Commodari, E. (2006). I cambiamenti delle funzioni attentive negli adulti. In S. Di Nuovo (Ed.), La valutazione dell'attenzione (pp. 115-119). Milano: Franco Angeli. Commodari, E., & Guarnera, M. (2010). Attention and aging. Aging Clinical and Experimental Research, 20 (6), 578-584. De Frias, C., Nilsson, L. G., & Herlitz A. (2006). Sex differences in declarative memory and visual spatial ability are robust in cross-sectional studies. Aging Neuropsychological Cognition, 13, 574-587. Di Nuovo, S. (2009). Attenzione e concentrazione. Trento: Erickson. Feng, J., Spence, J., & Pratt, J. (2007). Playing and action video game reduces gender differences in spatial cognition. Psychological Science, 18 (10), 350-355. Gorus, E., De Raedt., R., Lambert, M., Lemper, J. C., & Mets, T. (2008). Reaction times and performance variability in normal aging, mild cognitive impairment, and Alzheimer's disease. Journal of Geriatric Psychiatry and Neurology, 21 (3), 204-218. Greenwood, P., & Parasuraman, R. (2004). The scaling of spatial attention in visual search and its modification in healthy aging. Perception and Psychophysics, 66, 3-22. Gur, R., & Gur R. (2002). Gender differences in aging: cognition, emotions, and neuroimaging studies. Dialogues in Clinical Neuroscience, 4 (2), 197210 Hahn, B., Wolkenberg, F. A., Thomas, J., Ross T. J., Myers, C. S., Heishman, S. J., Stein, D. J., Kurup, P. K., & Stein, E. A. (2008). Divided versus selective attention: evidence for common processing mechanisms. Brain Research, 1215, 137-146.

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Hüttermann, S., Bock, O., & Memmert, D., (2012). The breadth of attention in old age. Ageing Research, 4:e10. Leclerc, C. M & Kensinger, E. A. (2008). Effect of age on detection of emotional information. Psychology and Aging, 23, 209-215. Lezak, M. D., Howieson D. B., Bigler E. D., & Trane D. (2015). Neuropsychological assessment. Oxford, NY: Oxford University Press. Luszcz, M. A., & Bryan, J. (1999). Toward understanding age-related memory loss in late adulthood. Gerontology, 45 (1), 2-9. Madden, D., Turkington, T., Provenzale, J., Denny, L. L, Langley, L.K., Hawk, T. C., & Coleman, R. E. (2002). Aging and attentional guidance during visual search: functional neuroanatomy by positron emission tomography. Psychology, 17, 24-43. Mahoney, J., Verghese, J. Goldin, Y., Lipton, R., & Holtzer, R. (2010). Alerting, orienting, and executive attention in older adults. Journal of International Neuropsychological Society, 16 (5), 877-889. McDowd, J. M.& Craik, F. I. (1988). Effects of aging and task difficulty on divided attention performance. Journal of Experimental Psychology: Human Perception and Performance, 14 (2), 267-280. Merritt, P., Hirshman H., Wharton W., Stangl, B., Devlin, G., & Lenz A. (2007). Evidence for gender differences in visual selective attention. Personality and Individual Differences, 43, 597-609. Mittenberg, W., Seidenberg, M., O’Leary, D. S., & Di Giulio, D. V. (1989). Changes in cerebral functioning associated with normal aging. Journal of Clinical and Experimental Neuropsychology, 11, 918-932. Munro, C. A, Winicki, J. M., Schretlen, D. J. Gower, E. W., Turano K. A., Muñoz, B., Keay, L., Bandeen-Roche, K., & West, S. K. (2012). Sex differences in cognition in healthy elderly individuals. Aging, Neuropsychology, and Cognition: A Journal on Normal and Dysfunctional Development, 19 (6), 759-768.

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Parasuraman, R. (1998). The attentive brain: issues and prospects. In: Parasuraman R, (Ed.) The Attentive Brain (pp. 3-15). Cambridge, Massachusetts: MIT Press. Parasuraman, R., Nestor, P., & Greenwood, P. (1989). Sustained-attention capacity in young and older adults. Psychological Aging, 4, 339-345. Petersen, S. E., & Posner M. I. (2012). The attention system of the human brain: 20 years after. Annual Review of Neuroscience, 35, 75-89. Phillips, M., Rogers, P., Haworth, J., Bayer, A., & Tales, A. (2013). IntraIndividual Reaction Time Variability in Mild Cognitive Impairment and Alzheimer’s Disease: Gender, Processing Load and Speed Factors. PLoS ONE, 8, 6.e65712. Posner, M, I., & Rothbart M. K. (2007). Research on attention networks as a model for the integration of psychological science. Annual Review of Psychology 58,1-23. Raz, A., & Buhle, J. (2006). Typologies of attentional networks. Nature review, Neuroscience, 7 (5), 367-379. Razumnikova, O. M. & Vol’f, R. V. (2007). Gender differences in interhemisphere interactions during distributed and directed attention, Neuroscience and Behavioral Physiology, 37 (5), 429-434. Rueda, M. R., Posner, M. I., & Rothbart, M. K. (2007). The development of executive attention: contributions to the emergence of Self-Regulation. Developmental Neuropsychology, 28 (2), 573-594. Salthouse, T. A. (1985). A theory of cognitive aging Amsterdam: NorthHolland. Salthouse, T. A. (1996a). The Processing-Speed Theory of Adult Age Differences in Cognition. Psychological Review, 103 (3), 403-428 Salthouse, T. A. (1996b). Influence of working memory on adult age differences in matrix reasoning. British Journal of Psychology, 84, 171-199.

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Salthouse, T. A. (2000). Aging and measures of processing speed. Biological Psychology, 54, 35-54. Salthouse, T. A., & Coon, V. E. (1993). Influence of task-specific processing speed on age differences in memory. Journal of Gerontology: Psychological Sciences, 48, 245-255. Salthouse, T. A., & Coon, V. E. (1994). Interpretation of differential deficits: The case of aging and mental arithmetic. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 1172-1182. Salthouse, T. A., Fristoe, N. M., Uneweaver, T. A., & Coon V. E. (1995). Aging of attention: Does the ability to divide decline? Memory and Aging, 23 (1), 59-71. Slessor, G., Miles, L. K., Bull, R. & Phillips, L. K. (2010). Age-Related Changes in Detecting Happiness: Discriminating Between Enjoyment and Nonenjoyment Smile. Psychology and Aging, 25 (1), 246-250. Sohlberg, M., & Mateer C. A. (1989). Introduction to cognitive rehabilitation. New York: Guilford. Spinnler, H., & Tognoni, S. (1987). Standardizzazione e taratura italiana di test neuropsicologici. Italian Journal of Neurological Sciences, 6, 71-95. Valdois, S., Joanette, Y., Poissant, A., Ska, B. & Dehaut, F. (1990). Heterogeneity in the cognitive profile of normal elderly. Journal of Clinical and Experimental Neuropsychology , 12, 587-598. Valeriani, M., Ranghi, F., & Giaquinto, S. (2003). The effects of aging on selective attention to touch: a reduced inhibitory control in elderly subjects? International Journal of Psychophysiology, 49, 75-87. Wang, Y., Fu, S., Greenwood, P., Luo, Y., & Parasuraman, R. (2012). Perceptual load, voluntary attention, and aging: An event-related potential study. International Journal of Psychophysiology, 84, 17-25.

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West, R. (2004). The effects of aging on controlled attention and conflict processing in the Stroop task. Journal of Cognitive Neuroscience, 16, 10313. Zhou, S. S., Fan, J., Lee, T. M, Wang, C. Q., & Wang, K. (2011). Age related differences in attentional networks of alerting and executive control in young, middle-aged, and older Chinese adults, Brain Cognition, 75 (2), 205-210.

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Life Span and Disability XIX, 1 (2016), 45-56

Mortality in people with Intellectual Disability in India: correlates of age and settings

Ram Lakhan1 & Thomas M. Kishore2

Abstract Background: The life expectancy of people with intellectual disability (ID) has increased across the world with the advancements in public health policies and in the medical sciences. Still, people with ID have higher risks of dying prematurely due to associated medical and genetic conditions. However, there is no estimation of the mortality and life expectancy rate available for the ID population in India. In this context, this study was aimed to investigate the mortality rate in ID and its association with age in rural and urban settings in India. Method: Secondary data from the Disability Report for 2003 of the National Sample Survey Organization (NSSO) is used for analysis. Spearman correlation distinguishing age and rural and urban settings, t-test to measure the difference between rural and urban contexts and simple regression with age groups and settings, are carried out. Results: Age was highly associated with the mortality rate in ID. This association was strongly positive in both rural (ϱ =.954, p = .001) and urban ID adults Received: August 26, 2015; Revised: March 21, 2016; Accepted: June 10, 2016 © 2016 Associazione Oasi Maria SS. - IRCCS 1

Department of Epidemiology and Biostatistics, Jackson State University. Phone: 001-601-8992599 E-mail: [email protected]. 2 Department of Clinical Psychology, NIMHANS, Bangalore, India. The authors report no declaration of interest. Acknowledgment: Author sincerely thanks all the anonymous researchers who surveyed the Indian population for NSSO report on disability, the sole source of this study.

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Life Span and Disability Lakhan R. & Kishore T. M. ________________________________________________________________________________________________________________________________

(ϱ = .957, p = .001). However, the mortality rate did not differ statistically between rural and urban adults (t = 2.16, p = .062). For every one-year increase in the age of the population, the mortality rate was found to increase by 3.3 and 3.0 persons per 100,000 in rural and urban ID adults, respectively. Conclusion: This data analysis demonstrates a high mortality rate which significantly increases from the beginning of adulthood in ID Population. Further research is needed to support the findings of this study, and discover determinates of higher mortality rate or low life-span of the ID population in India. Keywords: Age; India; Intellectual disability; Mortality rate; Life expectancy.

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1. Introduction Similar to the prevalence rate, greater knowledge of the mortality rate or life span for the ID population is highly important issue in the field of public health and rehabilitation policies in India. Unfortunately, there is scarcity of results from such studies on the nation. However, a few studies have been conducted on the prevalence rate of ID, which have attempted to minimize the gap in knowledge, but the issue of the mortality rate remains unaddressed in scientific literature (Maulik & Harbour 2010; Girimaji & Srinath, 2010; Cohen & Brown, 2012; Nahar, Kotecha, Puri, Pandey, & Verma, 2013). A clearer understanding regarding the mortality rate is needed to organize appropriate services to reduce the mortality rate and increase the quality of life in people with ID (Eyman, Grossman, Tarjan, & Miller, 1987; McGuigan, Hollins, & Attard, 1995; Janicki, Dalton, Henderson, & Davidson, 1999; Patja, Iivanainen, Vesala, Oksanen, & Ruoppila, 2000). With the advancements in public health, science and medical technology, the overall life span of people has increased in both the economically developed world as well in presently developing nations (Oeppen & Vaupel, 2002; Bloom, 2011; Turnock, 2012). Life expectancy rate for the ID population has also improved across the world (Cooper, Melville, & Morrison, 2004). Now, many people with ID are living longer lives similar to their age-matched counterparts (Janicki, 2010). Governmental and nongovernmental agencies, including World Health Organization is bringing in appropriate changes in the Health Policy, and promoting life care and rehabilitation services, with specific targets in Asian pacific-zone countries (Janicki, 2009, 2010). In such a scenario, it is imperative to have data on the longevity of ID victims. It is highly important to collect systematic data on these issues in India to build a knowledge base for creating appropriate rehabilitation and health services and understanding risk factors deriving from a higher mortality rate and associated policy developments (Fujiura, Park, & Rutkowski‐Kmitta, 2005). However, there are no systematic research efforts to understand the life expectancy of the ID population in India. Therefore, the issue of the mortality rate becomes extremely important for research. Intellectual disability is often associated with secondary disabilities or disorders such as cerebral palsy, epilepsy and other neurological and genetic disorders (van Schrojenstein Lantman-De Valk, Metsemakers, Haveman, & Crebolder, 2000; Jansen, Krol, Groothoff, & Post, 2004; Katz, 2009; Chavan & Rozatkar, 2014). Chronic diseases such as cardiovascular, respiratory,

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urinary diseases and neoplasm are frequently reported and are associated with early death in this population (Patja, Eero, & Iivanainen, 2001; Patja, Mölsä, & Iivanainen, 2001). The burden of associated conditions, poor health care, poverty, and dependency on others for their personal care make the ID population highly vulnerable to poor quality of life and lowered life span compared to their age-matched peers (Ouellette‐Kuntz, 2005). In such cases, the ID population with coexisting conditions requires greater attention and care compared to ID people without the comorbidity (Datta, Russell, & Gopalakrishna, 2002). Among the ID population, people with severe disability have higher rate of comorbid conditions, thus they are more threatened for survival (Patja et al, 2000; Arvio & Sillanpää, 2003). But, with improved health care, the infant and child mortality rate has declined in India and this fact should have influenced the life span of the ID population positively (Bloom, 2011; UNICEF, 2011). Nonetheless, we need to interpret these findings in the light that the majority of the Indian population with ID live in rural areas, where the health facilities are more inadequate than the urban areas. Hence, we need to know if there is an urban-rural divide with regard to the longevity of the people with ID in order to take appropriate measures to enhance longevity, particularly quality living. In this context, this study is primarily aimed to estimate the mortality rate in ID adults above age 18 years, correlated to age, and prediction of the mortality rate on the basis of age in rural and urban settings.

2. Material and Method This research is based on secondary data that was obtained from a disability report published by the National Sample Survey Organization (NSSO, 2002). This was the first time that the Indian Ministry of Social Justice had covered the ID population in its survey for disabilities. In this report, ID was represented by the term mental disability/mental retardation. A Survey was conducted at national level in which a total of 70,302 households were covered of those 45,571 households were urban and 24,731 were rural. It was a random sampling, and sample size was calculated by applying the formula for cross-sectional research design. The survey did not report data on mortality or life span of ID people, which was calculated by the researchers of this paper. The Spearman correlation was considered to estimate the strength and direction of the association with the age of ID adults in rural and urban

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population. A Student t-test was used to verify the difference between the mortality rates in two settings. In order to observe the likelihood of mortality in the adult population, a regression analysis was carried out between age groups and mortality rates in both settings, where age was considered an independent variable. We have used Statistical Package for Social Sciences (SPSS) version-21, for statistical analysis.

3. Results The NSSO report does not have any information on mortality or life span of ID in India. To estimate the mortality rate, the prevalence rate of the age interval (15 to 19 years, mean age = 17) is taken as standard prevalence at the beginning of adulthood (bold in Tab. 1). Then the prevalence rates of the following age groups were subtracted from the standard prevalence rate of the mean age group of 17 years to construct the table for the mortality rate (Tab. 1). Table 1 - Estimated mortality on basis of prevalence in different age intervals Prevalence and mortality, case per 100,000 Age groups

Mean age

0 to 14 15 to 19 20 to 24 25 to 29 30 to 34 35 to 39 40 to 44 45 to 49 50 to 54 55 to 59 60 above

7 17 22 27 32 37 42 47 52 57 62

Rural Prevalence Mortality 107 172 141 31 105 67 91 81 64 108 39 133 23 149 23 149 17 155 11 161

Urban Prevalence Mortality 131 164 137 27 87 77 86 78 93 71 46 118 50 114 25 139 12 152 7 157

In ID adults, the mortality rate found positively associated with their chronological age in rural (ϱ = .954, p < .001) and urban (ϱ = .957, p < .001) population. Mortality rates between urban and rural population does not differ statistically (t = 2.16, p = .062) (Fig. 1 and Tab. 2). The ANOVA, F = 70.65, p < .001, allows the regression analysis in ID adults in rural and

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urban, F = 77.02, p = < .001 population. Analysis demonstrates that in rural, (R2 = .910), and urban (R2 = .917) 91% of mortality rate in ID adults is explainable by their age. Regression predicts that the mortality rate increases by 3.20 in rural and 3.02 in urban, with the increase of age by one year in the population (Fig. 1, Tab. 2 and 3). Table 2 - Mortality association with age in ID adults (above 18 years) separated by rural and urban setting Settings rural urban

M + SD 114.89 + 45.98 103.67 + 43.38

ϱ

p .001 .001

.954 .957

t

p

2.16

.062

Table 3 - Mortality estimation on basis of age in rural and urban settings Predictor variable Age

Dependent variable: Setting rural urban

Model (ANOVA)

Regression

F

p

R2

B0

B1

70.65 77.02

.001 .001

.910 .917

-19.65 -23.73

3.20 3.03

Figure 1 - Estimated mortality rate by age

4. Discussion According to the NSSO survey report, the prevalence rate of ID increases in the children’s population from the age of birth until adulthood. This situation may give a false impression that the higher rate of ID is acquired

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during the post-natal period. However, this may not be true. It may be due to many other factors, such as stigma, poor awareness, cultural beliefs, one’s personal attitude and also the slower process of self identification and the poor referral practices in India (Scior, 2011; Kishore & Basu, 2011). On the contrary, at the beginning of adulthood, the prevalence rate drops, alternatively a high mortality rate is observed at this transitional stage in life. Although it is evident in literature that those people with ID are the victim of lower life expectancy compared to the general population (Patja et al., 2000; Lavin, McGuire, & Hogan, 2006). In European studies, the average life expectancy of ID population with moderate level of ID is observed around 45 years (Katz, 2003; Lavin et al., 2006), however, people with a milder form of ID are living almost a life equal to that of the general population (Janicki, 2009). The data on average life expectancy and its difference between rural and urban settings, if any, for the Indian ID population are unavailable for the comparison. Our study indicates that mortality rates sharply increase from the beginning of adulthood, with higher rates in the rural areas for any age group. But the rural-urban differences were not statistically significant. It may imply that on rates of mortality per se, the environment has no impact. However, one might assume that in rural areas, many more adults with ID will pass as neuro-typical and are not included in the statistical surveys. Some further comment might be that this undercount may have created a bias and therefore may have affected the results. Rather, age was highly associated with the mortality rate in ID. The association between age and mortality was strongly positive both in rural and urban areas. With the increase of every 1 (one) year of age in the population, the mortality rate increases by 3.3 and 3.0 persons per 100,000 in rural and urban ID adults respectively. It is clear that people with ID from rural areas do not have higher mortality rates, even where the health care facilities are not adequate as compared to the urban settings. These findings imply that we need to create a necessary infrastructure to provide services to address the health needs of adults with ID, in general. There is also a need for services to address the overall needs of adults with ID that help them lead an independent and quality life. Findings of the present study corroborate with other studies which have demonstrated lower life span for ID especially where disability is moderate and severe (Eyman, Grossman, Chaney, & Call, 1990; Patja et al., 2000; Katz, 2003; Lavin et al., 2006). People with a profound level of disability are subject to a further shorter life span due to their associated conditions,

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and the severe risks posed from other medical needs and reduced mobility (Eyman, Call, & White, 1989; Eyman et al., 1990).

5. Strengths & Limitations This is the first research article of this kind that has attempted to predict the mortality rate of ID adults in rural and urban populations in India. The findings are based on the small data set that was collected almost one and half decades ago, in a cross-sectional manner. Looking at the nature of this data and the way it was collected by untrained surveyors on the basis of parental interviews and enumerator’s judgment, without adopting any standard-used instruments; we can infer that the majority of cases screened in this survey have had moderate and severe levels of ID (NSSO, 2002). People with milder and borderline ID might have been excluded. Considering the limitations of the data, findings of this study should be read carefully and possibly in the light of other relevant studies. However, irrespective of methodological limitations, this research is able to provide an approximate idea of mortality rate in the ID adult population.

6. Conclusion This analysis shows a strong relationship between the chronological age and mortality rate in the Indian ID population. The mortality rate significantly increases from the beginning of adulthood, which is an important issue for the public health. However, due to the nature of crosssectional data, this analysis is unable to investigate temporal relationships with the factors causing a higher rate of mortality in Indian ID adults. The issues of mortality and consequently relative life span are highly crucial; thus this study suggests further research in this area.

References Arvio, M., & Sillanpää, M. (2003). Prevalence, aetiology and comorbidity of severe and profound intellectual disability in Finland. Journal of Intellectual Disability Research, 47 (2), 108-112. Bloom D. E. (2011). Population dynamics in India and implications for economic growth. WDA-Forum, University of St. Gallen.

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Chavan, B. S., & Rozatkar, A. R. (2014). Intellectual disability in India: Charity to right based. Indian journal of psychiatry, 56 (2), 113-16. Cohen, L., & Brown, R. I. (2012). Mapping Future Research in Disabilities Research Initiatives in Intellectual Disabilities in India: Report of a National Interdisciplinary Meeting. Journal of Policy and Practice in Intellectual Disabilities, 9 (2), 151-155. Cooper, S. A., Melville, C., & Morrison, J. (2004). People with intellectual disabilities: their health needs differ and need to be recognised and met. British Medical Journal, 329 (7463), 414-415. Datta, S. S., Russell, P. S. S., & Gopalakrishna, S. C. (2002). Burden among the caregivers of children with intellectual disability associations and risk factors. Journal of Intellectual Disabilities, 6 (4), 337-350. Eyman, R. K., Grossman, H. J., Tarjan, G., & Miller, C. R. (1987). Life expectancy and mental retardation. A longitudinal study in a state residential facility (Monograph No. 7). Washington DC: American Association on Mental Deficiency. Eyman, R. K., Call, T. L., & White, J. F. (1989). Mortality of elderly mentally retarded persons in California. Journal of Applied Gerontology, 8 (2), 203-215. Eyman, R. K., Grossman, H. J., Chaney, R. H., & Call, T. L. (1990). The life expectancy of profoundly handicapped people with mental retardation. New England Journal of Medicine, 323 (9), 584-589. Fujiura, G. T., Park, H. J., & Rutkowski‐Kmitta, V. (2005). Disability statistics in the developing world: A reflection on the meanings in our numbers. Journal of Applied Research in Intellectual Disabilities, 18 (4), 295-304. Girimaji, S. C., & Srinath, S. (2010). Perspectives of intellectual disability in India: epidemiology, policy, services for children and adults. Current opinion in psychiatry, 23 (5), 441-446.

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Janicki, P., Dalton, A. J., Henderson, C. M., Davidson, P. W. (1999). Mortality and morbidity among older adults with intellectual disability: health services considerations. Disability & Rehabilitation, 21 (5-6), 284294. Janicki, M. P. (2009). The Aging Dilemma: Is Increasing Longevity Among People With Intellectual Disabilities Creating a New Population Challenge in the Asia‐Pacific Region? Journal of Policy and Practice in Intellectual Disabilities, 6 (2), 73-76. Janicki, M. P. (2010). Aging Adults with Intellectual Disabilities: Perspectives on Emerging Service Concerns. Journal of Special Education and Rehabilitation, 11 (1-2), 114-129. Jansen, D. E., Krol, B., Groothoff, J. W., & Post, D. (2004). People with intellectual disability and their health problems: a review of comparative studies. Journal of Intellectual Disability Research, 48 (2), 93-102. Katz, R. T. (2009). Are children with Cerebral Palsy and developmental disability living longer? Journal of Developmental and Physical Disabilities, 21 (5), 409-424. Katz, R. T. (2003). Life expectancy for children with cerebral palsy and mental retardation: implications for life care planning. NeuroRehabilitationAn Interdisciplinary Journal, 18 (3), 261-270. Kishore, M. T., & Basu, A. (2011). Early concerns of mothers of children later diagnosed with autism: Implications for early identification. Research in Autism Spectrum Disorders, 5 (1), 157-163. Lavin, K. E., McGuire, B. E., & Hogan, M. J. (2006). Age at death of people with an intellectual disability in Ireland. Journal of Intellectual Disabilities, 10 (2), 155-164. Maulik P. K. & Harbour C. K. (2010). Epidemiology of Intellectual Disability. In J. H. Stone, & M. Blouin (Eds.), International Encyclopedia of Rehabilitation. Center for International Rehabilitation Research Information and Exchange, Buffalo. Available online: http://cirrie.buffalo.edu/encyclope dia/en/article/144/ (retrieved 9 July 2014).

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McGuigan, S. M., Hollins, S., & Attard, M. (1995). Age‐specific standardized mortality rates in people with learning disability. Journal of Intellectual Disability Research, 39 (6), 527-531. Nahar, R., Kotecha, U., Puri, R. D., Pandey, R. M., & Verma, I. C. (2013). Survival analysis of down syndrome cohort in a tertiary health care center in India. The Indian Journal of Pediatrics, 80 (2), 118-123. NSSO (2002) National Sample Survey Organization. Ministry of Statistics and Programme Implementation Government of India. Disabled Persons in India, NSS 58th round (July-December 2002) Report No. 485 (58/26/1) December 2003, New Delhi. Available online: http://www.domainb.com/economy/general/2005/pdf/Disability_in_India.pdf (retrieved 9 July 2015). Oeppen, J., & Vaupel, J. W. (2002). expectancy. Science, 296 (5570), 1029-1031.

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Turnock B. J. (2012). Public Health What It Is and How It Works. 5th Ed. Jones & Bartlett Learning, Burlington. UNICEF (2011). The Situation of Children in India A Profile 2011. Available online: http://www.unicef.org/india/The_Situation_of_Children_i n_India_-__A_profile_20110630_.pdf (retrieved 9 July 2015). van Schrojenstein Lantman-De Valk, H. M., Metsemakers, J. F., Haveman, M. J., & Crebolder, H. F. (2000). Health problems in people with intellectual disability in general practice: a comparative study. Family practice, 17 (5), 405-407.

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Life Span and Disability XIX, 1 (2016), 57-77

Parental and teachers attachment in children at risk of ADHD and with ADHD

Olga Liverta Sempio1, Rosa Angela Fabio2, Paola Tiezzi3 & Clemente Cedro4

Abstract Parents of children with Attention Deficit Hyperactivity Disorder (ADHD) experience more stress than parents of nonclinical controls. One of the factors that is highly significant in the study of parenting is attachment. Attachment has a quality that transcends the day-to-day interactions between parent and child. The style of attachment in children with ADHD was examined in this study. The main hypothesis is that ADHD children would be characterized by greater insecure attachment patterns than control children; secondly, it extends our current knowledge and attempts to understand if the pattern of insecure attachment developed with family caregivers would be present also with school caregivers. A sample of 72 children (36 young children aged 4-5 years: 12 at risk of ADHD-I, 12 at risk of ADHD-C and 12 controls; and 36 older children aged 7 years: 12 with ADHD-I, 12 with ADHD-C and 12 controls) was tested on both Family Separation Anxiety Test (F-SAT) and School Separation Anxiety Test (S-SAT) measures of attachment. Results showed that the ADHD-I and ADHD-C groups scored lower than controls on both SAT scales. There was also a strong positive correlation between the ADHD children’s scores on the School and Family Received: September 19, 2015; Revised: April 15, 2016; Accepted: June 10, 2016 © 2016 Associazione Oasi Maria SS. - IRCCS

1

Department of Psychology, Catholic University of Milano, Italy. E-mail: [email protected]. Department of Cognitive Science, University of Messina, Messina, Italy. E-mail: [email protected] 3 Department of Psychology, Catholic University of Milano, Italy. E-mail: [email protected]. 4 Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina. E-mail: [email protected] 2

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Separation Anxiety Tests. These findings suggest the attachment deficit may be an important but currently underestimated factor in the diagnosis of ADHD and that the family attachment patterns can predict the school attachment patterns. Keywords: ADHD; Attachment; Family SAT; Teachers SAT.

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1. Introduction A substantial amount of literature exists documenting the cognitive, emotive and behavioural deficits present in Attention Deficit Hyperactivity Disorder (ADHD) (Pennington & Ozonoff, 1996; DuPaul & Heckert, 1997; Rothbart & Bates, 1998; Purdie, Hattie & Carroll, 2002; Gagliano, Lamberti, Siracusano, Ciuffo, Boncoddo, Maggio, et al., 2014; Fabio, Gullà, & Errante, 2015). However, studies differ in the emphasis placed on various aspects of the disorder. Due in part to these different perspectives, attempts to validate a unitary diagnosis of ADHD characterized by unique behavioural and neuropsychological functioning, neurochemical substrates or common psychiatric, psychosocial or neuropsychological outcomes have had limited success (Bonafina, Newcorn, McKay, Koda, & Halpherin, 2000). In this context, it has been proposed that the attachment theory may offer an important perspective on the development of ADHD (Stiefel, 1997; Erdman, 1998; Clarke, Ungerer, Chahoud, Johnson, & Stiefel, 2002). According to Bowlby’s Attachment Theory, attachments develop from the need for security and safety that are acquired through life, and are usually directed towards a few specific individuals (Bowlby, 1969, 1988). The goal of attachment behaviour is to form and maintain an affectionate bond with a primary caregiver, usually the mother, throughout childhood and adulthood. This parent-infant interaction is referred to as “exploration from a secure base” (Ainsworth, Bell, & Stayton, 1974). The nature of the response given by the attachment figure, either positive or negative, is very important as the child encodes this information and incorporates into what is described as an internal working model. Whereas a positive interaction will lead to an internalized feeling of security (Bowlby, 1988; Crittenden, 1990; 2008), negative interaction will lead to insecurity and results in behavior that is avoidant, ambivalent or disorganized (Lyons-Ruth, 1996). An infant labelled “avoidant” generally gives the impression of being independent and selfsufficient; he differs from the securely attached child in that he seems unaffected by separation from their mother and either rejected or avoided her when she returned. Infants labelled “ambivalent” are more likely to cling to their mother in an unfamiliar environment and less willing to explore on their own; when separated from their mother they appear anxious, agitated and tearful (Ainsworth & Wittig, 1969). Finally, infants labelled “disorganized” (Main & Solomon, 1990) apparently lack a consistent strategy for organizing their comfort-seeking behaviour with the mother.

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Their disorganized reactions include apprehension, helplessness and depression. As above-mentioned, it is clear that early parent-child relationship serve as the foundation for the emergence of self-regulation skills that are strongly impaired in ADHD children. Some support for the relationship between attachment impairment and ADHD comes from clinical studies (Cavallina, Pazzagli, Ghiglieri, & Mazzeschi, 2015). Pinto, Turton, Hughes, White and Gillberg (2006) find a link between scores for disorganized attachment at 1 year and later teacherrated symptoms of ADHD. Also Green, Stanley and Peters (2007) obtained the same results with another tool. The authors investigated the relationship of child attachment representation, psychopathology, and maternal atypical parenting in a high-risk sample using the Manchester Child Attachment Story Task (MCAST). Disorganized attachment showed a high prevalence and independent associations with attention deficit symptomatology and maternal expressed emotions. In a longitudinal study Carlson, Jacobitz and Sroufe (1995) show that maternal intrusiveness assessed when infants were six months old was a more powerful predicted distractibility in early childhood, and hyperactivity in middle childhood, than did biological or temperament factors. Similarly Stiefel (1997) has linked the emergence of symptoms in ADHD to a lack of sustained parental attention during the child first year of life. Clarke and colleagues (2002) compared the quality of attachment in 5-10 years old boy with ADHD and a group of same-age normal controls. They used a broad based attachment assessment with three measures of representational models of attachment and the self: 1) the S.A.T. (Separation Anxiety Test) which assess children’s verbal responses to hypothetical separation (Hansburg, 1972); 2) the self interview (Cassidy, 1988), which assesses children verbal descriptions of themselves in relation to significant others; 3) attachment-based rating of Family Drawings (Fury, 1996), which provide non-verbal assessment of the attachment relationship. Their results showed that children with ADHD were characterized by greater insecurity than control children. Specifically, the results suggest the presence of an anxious-ambivalent or disorganized attachment style in children with ADHD. Other evidence on the relationship between attachment impairment and ADHD comes from the striking similarities between the developmental outcomes of insecure attachment and the difficulties seen in children with ADHD (Erickson, Sroufe, & Egeland, 1985; Jacobson & Wille, 1986; Lyons-Ruth, Alpern, & Repacholi, 1993). Olson (1996) has reviewed the evidence regarding attachment anomalies and

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over-activity. Insecurity of early mother-child attachment is related to teacher ratings of behavioural impulsivity and interpersonal hostility in preschool age children. Summarizing, the causes of ADHD are likely to stem from a combination of biological, often genetically determined neurochemical disturbances, and environmental disadvantages, and ADHD may be best conceptualized as a disorder of self-regulation, involving a generalized difficulty in the inhibition of cognitive, affective and motor functions (Olson, 1996; Barkley, 1997; Fabio & Urso, 2014; Fabio & Caprì, 2015; Fabio, Castriciano & Rondanini, 2015). These impairments in self-regulation may also have its roots in strained interactions with early caregivers and disrupted primary attachments (Olson, 1996; Sandberg & Barton, 1996; Stiefel, 1997). In this perspective the children with disrupted attachment have not learned how to regulate their negative arousal and their emotions, and so they are not able to self-regulate their behaviours and their cognitive processes. On the other hand, the security of the mother-child attachments has been shown to be related to a toddler’s willingness to comply with the mother request and engage in positive and constructive problem solving. Secure mother-infant attachments have also been found to predict cognitive self-regulation and the ability to delay gratification at the time the child enters school (Olson, Roese, & Zanna, 1990). The difficulty of ADHD to regulate their arousal continues throughout life in the formation of social relationships and the behaviour outside the family continues to reflect relationship expectation. Wiener and Daniels (2015) report that the same self-regulation deficit showed in a family setting, takes place in a school setting. The authors report on a qualitative study of the school experiences of adolescents with attention-deficit hyperactivity disorder (ADHD) in the context of quantitative research on teacher attitudes and practices, adolescent self-appraisals, and social and family relationships. The findings of the authors suggest that teachers of adolescents with ADHD in this case know about the nature of the disorder, understand that students' difficulties with organization and academic performance are not typically intentional, use evidence-based interventions to support students, and provide the monitoring and scaffolding needed for academic achievement. In the present study these qualitative results (Wiener & Daniels, 2015) are investigated with an instrument that is a semi-projective test developed to assess the representation of attachment in children, based on their responses to pictures of teacher-child separation experiences.

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2. Aims and hypothesis The purpose of the present investigation was twofold. Initially we sought to replicate and to confirm the results of Clarke et al.’s (2002) exploratory study, using two homogeneous age groups (one group of 4 to 5 year olds and one group of 7 years old). At a general level we aimed to support the findings of Clarke that ADHD children would be characterized by greater insecurity than control children. More specifically, we hypothesized that due to the high level of arousal and temperament in combined hyperactivity/impulsivity disorder (ADHD-C) children, they would show lower levels of self-reliance and attachment security than those with a predominantly inattentive type disorder (ADHD/I). The second and main aim is to extend our current knowledge on the relationship between attachment and ADHD and tries to understand if an insecure pattern of attachment with a family caregiver could be generalized to school caregivers.

3. Method 3.1. Participants The participants in this study were selected from a database containing 600 children attending Kindergarten public schools (girls and boys aged 4-5 years) and 450 children attending primary public schools (girls and boys aged 7 years) in a district of Lombardy, Italy. The final sample included 72 children, divided into two age groups. In the young age group there were 36 children aged 4-5 years: 12 at risk of ADHD-I (4 females, 8 males), 12 at risk of ADHD-C (1 females, 11 males) and 12 normally achieving controls (10 females, 2 males). In the older age group there were 36 children aged 7 years: 12 with ADHD-I (5 females, 7 males), 12 with ADHD-C (2 females, 10 males) and 12 normally achieving control, participants (3 females, 9 males). All the children lived with their biological parents. Two children with ADHD-HI were excluded from the study. The reason was the low representativeness of this subtype. A worldwide meta-analysis of 86 studies in children and adolescents and 11 studies in adults indicated that the predominantly hyperactive type of ADHD was the least common subtype in all samples (Willcutt, 2012).

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3.1.1. Children at risk of ADHD Children belonging to this group were diagnosed as children at risk of ADHD using an Italian version of the Praecox Deficit Attention Teacher Scale (PDATS, DuPaul, Power, Anastopoulos, & Reid, 1998), a teachers interview translated by Marcotto, Paltenghi and Cornoldi (2002). The PDATS corresponds to the symptom domain of ADHD as described in the DSM-V (American Psychiatric Association, 2013) with nine items belonging to the dimension of inattention and nine items to the dimension of hyperactivity-impulsivity. Items were rated on a 4-point scale (0 = never or rarely, 1 = sometimes, 2 = often, 3 = very often). The cut-off for the criterion score for inclusion of 32 (16 for the inattention dimension and 16 for the hyperactivity-impulsivity dimension) was taken from the standardized and validated version of the scale applied to kindergarten children by Marcotto and colleagues (2002). Children at risk of ADHD were defined as those with a positive rating of 2 or 3 on five or six items on either the inattention or hyperactive-impulsive subscales. Of the children at risk of ADHD, 12 met the DSM IV criteria for the inattentive subtype (ADHD-I) and 12 for the combined subtype (ADHD-C). Only 2 children with ADHD-HI subtype were excluded from further analysis. To determine inclusion in the clinical groups, teachers were asked to complete Pelham’s (1977) Disruptive Behaviour Disorder Rating Scale (DBDS; Italian translation by Marzocchi, Oosterlaan, De Meo, Di Pietro, Pizzica, & Cavolina, 2001). A specialized psychologist examined the children with high DBDS scores to determine those children that had a pervasive and chronic disorder (over 6 months of disorder). No child had any history of brain damage, epilepsy, psychosis or anxiety disorder. Of the 38 children selected as being “at risk of ADHD”, 10 had to be excluded from the study either because no parental consent was forthcoming (n = 6), or because they scored an IQ of less than 85 on the Raven’s Progressive Matrices (n = 4). Only 24 of the remaining 28 children were chosen. 3.1.2. Children with ADHD As above, children in this group were diagnosed as ADHD using the Italian version of the PDATS (DuPaul et al., 1998; Marcotto et al., 2002) and analyzed by a specialized psychologist. Of this group, 12 met the DSM-5 criteria for the inattentive subtype (ADHD-I) and 12 met the criteria for the combined subtype (ADHD-C). Only 2 children with ADHD-HI subtype were excluded from further analysis. This diagnosis was confirmed using the Italian version of the DBDS (Marzocchi et al., 2001), completed

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by teachers. As before, the number of symptoms recorded in the DBD was used by the specialist psychologist to determine those children whose disorder was both pervasive and chronic (over 6 months of disorder). None of the children had any history of mental retardation, brain damage, epilepsy, psychosis or anxiety disorder. From the 31 children selected as ADHD, 7 were excluded either because no parental consent was forthcoming (n = 5), or because they scored an IQ of less than 85 on the Raven’s Progressive Matrices (n = 2). Table 1 - Demographic characteristics of the groups ADHD-I

ADHD-C

Controls

df

F

p

4-5 years old Children N of boys/girls

8/4

11/1 56.00 (5.01)

10/2

Age in months M (SD)

54.10 (6.00)

58.00 (4.07)

IQ M (SD)

98.20 (6.20) 102.00 (6.30) 104.00 (7.20)

PDATS - hyperactivity M (SD)

5.10 (3.80)

18.80 (2.49)

2.00 (2.90)** 2.33 2.71

.01

PDATS - distractibility M (SD)

5.01 (2.50)

18.88 (3.14)

2.10 (2.4)** 2.33 2.81

.01

6-7 years old children N of boys/girls Age M (SD) IQ M (SD)

7/5 84.16 (7.02)

10/2 79.50 (5.20)

9/3 81.30 (7.02)

100.10 (6.70) 102.20 (7.10) 105.20 (4.60)

DATS - distractibility M (SD)

17.16 (2.90)

18.28 (4.50)

7.60 (4.20)** 2.33 3.21

.01

DATS - hyperactivity M (SD)

7.19 (4.10)

19.83 (4.40)

3.70 (4.10)** 2.33 2.21

.01

** p < .01

3.1.3. Normally achieving control participants These children were recruited from the same secondary schools and kindergarten as the two clinical groups and the four groups were matched for sex, IQ (Ravens Matrices) and age. Demographic and clinical characteristics of ADHD and control children are summarized in Table 1. The whole Raven’s test was administered to estimate children’s IQ. As expected, the ADHD and the control group differed significantly on DATS (distractibility subscale), F(2,59) = 21.60, p < .001 and on the DATS (hyperactivity subscale), F(2,59) = 34.11, p < .001. Least square difference post hoc analysis reveals that 4-5 years old ADHD-C children show higher PDATS scores than controls, both in distractibility and in hyperactivity subscale, respectively t(21) = 3.21, p < .01, t(21) = 2.87, p < .01 and ADHD-I children show higher PDATS scores than controls, only in distractibility subscale, t(21) = 3.87, p < .01. Least square difference post

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hoc analysis reveals also that 6-7 years old ADHD-C children show higher PDATS scores than controls, both in distractibility and in hyperactivity subscale, respectively t(23) = 4.22, p < .01, t(21) = 4.87, p < .01 and ADHDI children show higher PDATS scores than Controls, only in distractibility subscale, t(21) = 4.81, p < .01. 3.2. Procedure For both clinical participants and controls, approval for study was obtained from the parents. They were also asked to complete PDAPS (Praecox Deficit Attention Parent Scale) and DAPS (Deficit Attention Parent Scale). These scales contain the same items of PDATS and DATS referred to family interaction. The correlations between PDAPS and PDATS and between DAPS and DATS were very high (r = .81). Each child was seen individually in a quiet room in either their kindergarten or primary school on three occasions (separated by approximately one week). Each session lasted approximately 30 minutes. Each child was administered the Raven test in one of the sessions and the two versions of SAT on two separate sessions. The order of administration of tests was counterbalanced across the three sessions. 3.3. Instruments 3.3.1. Family Separation Anxiety Test This test, also used by Clarke et al. (2002), is a semi-projective test developed to assess the attachment representation of children based on their responses to pictures of parent-child separation experiences. In this study the Klagsbrun and Bowlby (1976) adaptation for 4-7 years olds was used. For each child the following scenes were presented, one at time: 1) Parents going out for the evening, leaving child at home; 2) A child’s first day at school, at the point of separation from the mother; 3) Parents going away for the weekend, leaving the child with an aunt and uncle; 4) A child left in the park by his parents and told to play by alone; 5) Parents going away for 2 weeks, leaving a child at home; 6) A mother putting a child to bed and about to go out the door. After each picture was described, the child was asked the following questions (as used in the standard administration of SAT): (1) How does the

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child feel? (2) Why does the child feel (happy/sad)? (3) What is the child going to do? For clarification purposes, prompts were given when necessary. The scoring indices for the Seattle Version of SAT (Slough, Goyette, & Greenberg, 1988) were used, with responses allocated to one of 21 categories that were assigned weighted scores and combined to yield three factors: 1) Attachment: the child’s ability to express vulnerability or need about severe separation, computed on a scale of 1 to 4, high scores indicate secure attachment themes; 2) Self-reliance: the child’s ability to express self confidence about handling the mild separation, computed on a scale of 1 to 4, high scores indicate high ability to express self-reliance; 3) Avoidance: the child’s degree of avoidance in discussing the separation, computed on a scale of 1 to 3, high scores indicate high levels of avoidance. 3.3.2. School Separation Anxiety Test This test (Liverta Sempio & Marchetti, 1999; Liverta Sempio, Marchetti, & Lecciso, 1999) is a semi-projective test developed to assess the representation of attachment in children, based on their responses to pictures of teacher-child separation experiences. Each child was presented with a series of scenes, one at time. The test is structurally and semantically based on the Family Separation Anxiety Test (Clarke et al., 2002) and uses the same procedure and scoring indices.

4. Results The results are first discussed showing the Family Separation Anxiety Test and the School Separation Anxiety Test reliabilities, secondly the differences between the groups are presented and finally the correlations between the Family SAT and School SAT are showed. 4.1. Family and School SAT reliabilities The principal investigator scored all verbatim transcripts anonymously. In order to establish inter-rater reliability, a sample (30% of the clinical and control transcripts) was also scored independently by an additional experienced rater. The index of inter-rater agreement (number of responses in each category type identified by the first rater/number of responses in

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each category type identified by the second rater x 100) was high, 87% for the Attachment factor items, 91% for the Self-Reliance factor items and 89% for the Avoidance factor. Discrepancies between raters were resolved through negotiation and these revised criteria were used for rating subsequent transcripts. Table 2 - Between group comparisons on the Family and School Separation Anxiety Test ADHD-I

ADHD-C

Controls

M (SD)

M (SD)

M (SD)

df

F

p

4-5 years old Family Separation Anxiety Test Global index

20.33 (11.44) 22.83 (8.45) 30.91 (3.36) 2.33 5.15

.01

Attachment

5.50 (3.03)

5.58 (2.86)

7.50 (2.67) 2.33 1.87

.17

Self-Reliant

8.16 (3.92)

9.08 (2.74) 11.75 (2.26) 2.33 4.38

.02

9.83 (3.97)

6.33 (.65) 2.33 5.28

.01

26.25 (4.39) 27.55 (4.71) 30.08 (4.33) 2.33 4.59

.01

Avoidant

11. 33 (5.34)

6-7 years old Family Separation Anxiety Test Global index Attachment

7.25 (1.95)

6.91 (2.51)

9.08 (2.50) 2.33 3.58

.03

Self-Reliant

9.08 (1.44)

9.66 (.88)

9.91 (2.23) 2.33 1.92

.16

Avoidant

8.08 (2.46)

7.98 (2.06)

6.91 (1.71) 2.33 5.12

.01

19.50 (10.46) 19.66 (9.95) 29.00 (4.76) 2.33 3.58

.05

4-5 years old School separation Anxiety Test Global index Attachment

5.41 (2.84)

5.00 (2.12)

7.83 (2.58) 2.33 3.52

.05

Self-Reliant

7.41 (3.37)

7.58 (3.31)

9.75 (3.01) 2.33

.83

.44

11.33 (5.06) 10.93 (4.44) 6.58 (1.72) 2.33 1.07

.35

25.83 (3.18) 25.00 (4.02) 29.75 (4.90) 2.33 4.59

.01

Avoidant 6-7 years old School separation Anxiety Test Global index Attachment

6.58 (1.44)

5.91 (1.83)

8.58 (2.39) 2.33 6.21

.01

Self-Reliant

9.41 (0.99)

9.58 (1.31) 10.88 (2.10) 2.33 2.02

.15

Avoidant

9.16 (2.20)

8.50 (2.40)

.49

7.00 (1.90) 2.33 0.72

There were significant positive correlation between the Attachment and Self-Reliance scales for ADHD-I, ADHD-C and control group; r(24) = .488, p = .017; r(24) = .42, p = .047 and r(24) = .37, p = .06, respectively. However, the Avoidant scale was negatively correlated with the Attachment

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and Self-Reliant scales for ADHD-I, ADHD-C and control group, r(24) = -.72, p < .01 and r(24) = -.82, p < .01; r(24) = -.59, p < .01 and r(24) = -.69, p < .01; r(24) = -.41, p = .51 and r(24) = -.40, p = .053, respectively. The Attachment and Self-Reliance School scales were correlated for ADHD-I, ADHD-C and control group, r(24) = .46, p = .02, r(24) = .61, p < .01 and r(24) = .36, p = .062, respectively. The Avoidance scale was negatively correlated to Attachment and Self-Reliance scales for ADHD-I, ADHD-C and control group, r(24) = -.78, p < .01 and r(24) = -.82, p < .01; r(24) = -.60, p < .01 and r(24) = -.86, p < .01; r(24) = -.41, p = .51 and r(24) = -.39, p = .055, respectively. 4.2. Differences between the groups Table 2 shows the means and standard deviations of the ADHD and control groups on each of the two attachment measures (Family SAT and School SAT). A 2 (ages: 4-5 years vs 6-7 years) x 3 (groups: ADHD-I vs ADHD-C vs Control) ANOVA was carried out. Significance was tested at the alpha level of .05. 4.2.1. Family Separation Anxiety Test In the first ANOVA, with Family SAT as the dependent variable, age shows significant effect, F(1,66) = 4.43, p = .03, this means that the older children had a higher SAT score than the younger. There was also a significant main effect of group, F(1,66) = 6.19, p = .003. From table 2, we can see that both the ADHD-I and ADHD-C groups obtained poorer scores than the control group. This result was confirmed by post hoc analysis revealing significant differences between both the ADHD-I and ADHD-C groups and Controls (t(24) = 6.58, p = .002, t(24) = 5.62, p = .007 respectively) but no difference between the two clinical groups (t(24)= .95, p = .64). Further separate ANOVAs were applied with the three components of the family SAT, with reference to the attachment component, we found a significant effect of age, F(1,66) = 6.37, p = .014; the older group showed higher levels of attachment. Group had also a significant effect, F(1,66) = 4.60, p = .013; both the ADHD-I and ADHD-C groups obtained poorer scores than controls. Post hoc analysis confirmed that there were significant differences between the both the ADHD-I and the ADHD-C groups and controls (t(24) = 1.91, p = .013, t(24) = 2.04, p = .009 respectively) but no differences between the two clinical group (t(24) = .12,

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p = .87). With reference to the self-reliance component, there was a significant main effects of group, F(1,66) = 5.00, p = .009. This result indicates that the ADHD-I and ADHD-C groups obtained poorer scores than controls. Post hoc analysis confirmed this, showing that there were significant differences between the ADHD-I and the ADHD-C groups and controls t(24) = 2.20, p = .003, t(24) = 1.45, p = .044 respectively) but no differences between the two clinical groups (t(24) = .75, p = .29). Finally, with reference to the avoidance component, there was a significant main effect of age, F(1,66) = 4.33, p = .041, the older group scored lower than the younger age group. There was also a significant main effect of group, F(1,66) = 6.29, p = .003. Post hoc analysis confirmed that there were significant differences between both the ADHD-I and the ADHD-C groups and controls (t(24)= 3.08, p = .001, t(24)= 2.25, p = .015 respectively) but no differences between the two clinical groups (t(24) = .83, p = .35). 4.2.2. School Separation Anxiety Test With reference to school SAT performance we obtained a significant main effects of age, F(1,66) = 6.56, p = .013: the older group scored significantly higher than the younger group, and a significant effect of group, F(1,66) = 8.07, p = .001, such that both the ADHD-I and ADHD-C groups obtained poorer scores than controls. Post hoc analysis confirmed that there were significant differences between the ADHD-I and the ADHDC groups and controls (t(24) = 6.7, p = .001, t(24) = 7.04, p = .001 respectively) but no differences between the two clinical groups (t(24) = 1.9, p = .86).The three sub-scales of SAT were separately analysed in three 2 x 3 ANOVA. In the attachment component, we found a significant main effect of group, F(1,66) = 8.81, p = .0001 suggesting that both the ADHD-I and ADHD-C groups scored more poorly than controls. Post hoc analysis confirmed this, revealing that there were significant differences between the ADHD-I and the ADHD-C groups and controls (t(24) = 2.20, p = .002, t (24) = 2.75, p = .001 respectively) but no differences between the two clinical groups (t(24) = .69, p = .43). There was no significant effect for age, F(1,66) = 2.77, p = .10. With reference to the self-reliance scale, we obtained a significant main effect of age, F(1,66) = 7.22, p = .009, with higher scores for the older age group. There was also a significant main effect of group, F(1,66) = 3.46, p = .037; both the ADHD-I and ADHD-C groups obtained poorer scores than controls. Post hoc analysis confirmed that there were significant differences between the ADHD-I and the ADHDC groups and controls (t(24) = 1.75, p = .02, t(24) = 1.58, p = .035

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respectively) but no differences between the two clinical groups (t(24) = .75, p = .82). Finally an analysis of avoidance component revealed a significant main effects of age, F(1,66) = 4.24, p = .043, such that the older children scored lower on this scale than the younger children. There was also a significant main effect of group, F(1,66) = 5.60, p = .006. Post hoc analysis confirmed that there were significant differences between the ADHD-I and the ADHD-C groups and controls (t(24) = 2.75, p = .005, t(24) = 2.70, p = .005 respectively) but no differences between the two clinical groups (t(24) = .04, p = .96). 4.3. Correlation between School and Family Separation Anxiety Test Scores on the School and Family versions of the Separation Anxiety Test were strongly correlated in all the groups, r(72) = .70, p < .001, suggesting that children with high levels of attachment security within the family also have a high level of attachment security at school. Moreover, this strong positive correlation between scores on the two tests was reflected in correlations between the sub-scales for attachment, r(72) = .68, p < .001; Self-reliance r(72) = .52, p < .001, and avoidance, r(72) = .64, p < .001.

5. Discussion The findings of this study are consistent in their support of the hypothesis that ADHD is associated with an insecure internal working model of attachment (Erdman, 1998). As predicted, children with ADHD scored more poorly than controls on all the three scales of scholastic and parental versions of the SAT. Summarizing each result of the sub-scales, 1) Attachment: ADHD children expressed lower appropriate level of concern, fear, or feeling of sadness about severe separation than controls; 2) Self-reliance: ADHD children expressed lower self confidence and feeling of well-being about handling the mild separation than controls; 3) Avoidance: ADHD children expressed higher a degree of avoidance in discussing the separation than controls. In this sense, our findings replicate those of Clarke and colleagues (2002). The second important finding is that the hyperactivity-impulsivity dimension does not appear to be determinant in the magnitude of insecurity. Factors such as temperament and arousal that have previously been shown to predict many aspects of children’s development are not, on their own, powerful predictors of a insecurity pattern of behaviour. Rather as Sanson,

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Oberklaid, Pedlow and Prior (1991) pointed out, they seem to have a significant impact only when other risk factors, such as poor parenting, economic hardship or difficulties of attachment are also present (Wiener & Daniels, 2015). Data of the present study revealed a strong positive correlation between School Separation Anxiety Test and Family Separation Anxiety Test. According to Clarke et al. (2002) it was not clear whether the quality of care giving contributes directly to the development of ADHD related problems or if the child’s challenging behaviours lead to disturbance in interactions. The new contribution coming from the present study is that children generalize from a pattern of attachment originating within the family context to other contexts such as the school environment. Moreover it supports the hypothesis that the difficulty of ADHD to regulate their arousal continues throughout life in the formation of social relationships and the behaviour outside the family continues to reflect relationship expectation. In sum, we suggest that the symptoms of ADHD are the result of a complex mix of child, family and environmental influences, although the distinct contribution of each is not yet clear. Arousal, temperament and behaviour are three of the childhood factors that have been linked to later social competence and wellbeing and it is reasonable to suggest that similar factors may be seen involved in the vulnerability to attachment problems. Ladnier and Massanari (1999) propose a cycle to explain the way in which the relationship between child and caregiver can lead to ADHD. The cycle begins when the child is expects a strong negative emotion such as anger, sadness, loneliness, or fear. Lacking the capacity for self-control, the child attempts to relate with the caregiver through intrusive, demanding, attentionseeking behaviours. The caregiver begins to feel irritation and resentment and responds with criticism or physical violence (hitting). The child then reacts by attempting to ignore the caregiver and becomes defiant or coercive and raises the level of his acting-out behaviours. This leads to an increase in the level of conflict by the parents and so the cycle is perpetuated. The same pattern of escalating conflict is re-enacted at school where children with ADHD are often punished for their behaviour.

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References Ainsworth, M. D. S., & Wittig, B. A. (1969). Attachment and exploratory behavior of one-year olds in a strange situation. In B. M. Foss (Ed.), Determinants of infant behavior (Volume 4, pp. 111-136). London: Methuen. Ainsworth, M. D. S., Bell, S. M., & Stayton, D. J. (1974). Infant-mother attachment and social development: “socialisation” as a product of reciprocal responsiveness to signals. In M. P. Richards (Ed.), The Integration of a child into a social world (pp. 99-135). London: Cambridge University Press. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing. Barkley, R. A. (1997). Defiant Children: A Clinician’s Manual for Assessment and Parent Training. New York: Guilford Press. Bonafina, M. A., Newcorn, J. H., McKay, K. E., Koda, V. H., & Halperin, J. M. (2000). ADHD and reading disabilities. A cluster analytic approach for distinguishing subgroups. Journal of Learning Disabilities, 33, 297-307. Bowlby, J. (1969). Attachment. Attachment and loss: Vol. 1. Loss. New York: Basic Books. Bowlby, J. (1988). A secure base: Parent-child attachment and healthy human development. London: Routledge. Carlson, E., Jacobvitz, D., & Sroufe, L. A. (1995). A developmental investigation of inattentiveness and hyperactivity. Child Development, 66, 37-54. Cassidy, J. (1988). Child-mother attachment and the self in six-year-olds. Child Development, 59, 121-134.

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Cavallina, C., Pazzagli, C., Ghiglieri, V., & Mazzeschi, C. (2015). Attachment and parental reflective functioning features in ADHD: enhancing the knowledge on parenting characteristics. Frontiers in Psychology, 6, 1-6. Clarke, L. ,Ungerer, J., Chahoud, K., Johnson, S., & Stiefel, I. (2002). Attention Deficit Hyperactivity Disorder is associated with attachment insecurity. Clinical Child Psychology and Psychiatry, 7 (2), 179-198. Crittenden, P. M. (2008). Raising Parents. Attachment, Parenting and Child Safety. Devon: Willan Publishing. Crittenden, P. M. (1990). Internal representational models of attachment relationships. Infant Mental Health Journal, 11, 259-277. DuPaul, G. J., Power, T. J., Anastopoulos, A. D., & Reid, R. (1998). ADHD Rating Scale IV: checklists, norms, and clinical interpretation. New York: Guilford Press. DuPaul, G. J., & Heckert, T. L. (1997). The effects of school-based interventions for attention-deficit hyperactivity disorder: A meta-analysis. School Psychology Review, 26, 5-27. Erdman, P. (1998). Conceptualising ADHD as a contextual response to parental attachment. America Journal of Family Therapy, 26 (1), 177-185. Erickson, M. F., Sroufe, L. A., & Egeland, B. (1985). The relationship between quality of attachment and behavior problems in preschool in high risk sample. Monographs of the Society for Research in Child Development, 50 (1-2), 147-166. Fabio, R. A., & Caprì, T. (2015). Autobiographical Memory in ADHD Subtypes. Journal of Developmental and Intellectual Disability, 6, 26-36. Fabio, R. A., Gullà, I., & Errante, A. (2015). Emotion and eye movements: eye tracker and mnestic parameters. In M. Sakakibara (Ed.), Memory consolidation (pp. 235-258). New York: Nova Science Publisher.

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Fabio R. A., & Urso, M. F. (2014). The analysis of Attention Network in ADHD, attention problems and typically developing subjects. Life Span and Disability, 17, 199-221. Fabio, R. A., Castriciano, C., & Rondanini, A. (2015). ADHD: Auditory and Visual Stimuli in Automatic and Controlled Processes. Journal of Attention Disorder, 19, 771-778. Fury, G. (1996). The relation between infant attachment history and representations of relationships in school-aged family drawings (Unpublished doctoral dissertation). University of Minnesota. Gagliano, A., Lamberti, M., Siracusano, R., Ciuffo, M., Boncoddo, M., Maggio, R., Rosina, S., Cedro, C., & Germanò, E. (2014). A comparison between children with ADHD and children with epilepsy in self-esteem and parental stress level. Clinical Practice & Epidemiology in Mental Health, 10, 176-83. Green, J., Stanley, C., & Peters, S. (2007). Disorganized attachment representation and atypical parenting in young school age children with externalizing disorder. Attachment & Human Development, 9 (3), 207-222. Hansburg, H. G. (1972). Adolescent separation anxiety: Vol. 1. A method for the study of adolescent separation problems. Springfield, IL: Charles C. Thomas. Jacobson, J. L., & Wille, D. E. (1986). The influence of attachment pattern on developmental changes in peer interaction from the toddler to the preschool period. Child Development, 57, 338-347. Klagsbrun, M., & Bowlby, J. (1976). Responses to separation from parents: a clinical test for young children. British Journal of Projective Psychology and Personality study, 21, 7-26. Ladnier, R. D., & Massanari, A. E. (1999). Treating ADHD as attachment deficit hyperactivity disorder. In T. M. Levy (Ed.), Handbook of attachment inventions (pp. 27-65). San Diego: Academic Press.

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Liverta Sempio, O., & Marchetti, A. (1999). SAT Scuola: prova di ansia da separazione riferita alla relazione con l’insegnante. Università Cattolica del Sacro Cuore, Milano – Università degli Studi, Urbino. Liverta Sempio, O., Marchetti, A., & Lecciso, F. (1999). Il SAT Famiglia e il SAT Scuola: strumenti di misura dell’ansia da separazione da genitori e insegnanti. Università Cattolica del Sacro Cuore, Milano. Lyons-Ruth, K. (1996). Attachment relationships among children with aggressive behavior problems: The role of disorganized early attachment patterns. Journal of Consulting and Clinical Psychology, 64 (1), 64-73. Lyons-Ruth, K., Alpern, L., & Repacholi, B. (1993) Disorganized infant attachment classification and maternal psychosocial problems as predictors of hostile-aggressive behavior in the preschool classroom. Child Development, 64, 572-585. Main, M., & Solomon, J. (1990). Procedures for identifying infants as disorganized/disoriented during the Ainsworth strange situation. In M.T. Greenberg, D. Cichetti & E. M. Cummings (Eds.), Attachment in the Preschool Years: Theory, Research and Intervention (pp. 121-160). Chicago: University of Chicago Press. Marcotto, E., Paltenghi, B., & Cornoldi, C. (2002). La scala IPDDAI: contributo per la costruzione di uno strumento per l’identificazione precoce del disturbo da deficit di attenzione e/o iperattività. Difficoltà di apprendimento, 8 (2), 153-172. Marzocchi, G. M., Oosterlaan, J., De Meo, T., Di Pietro, M., Pizzica, S., Cavolina, P., & Zuddas, A. (2001). Disruptive Behaviour Disorder Rating Scale for teacher (Italian version). Giornale di Neuropsichiatria dell’Età Evolutiva, 21, 378-393. Olson, S. (1996). Developmental perspectives. In S. Sandberg (Ed.), Hyperactivity disorders of childhood (pp. 149-194). Cambridge, UK: Cambridge University Press.

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Olson, J. M., Roese, N. J., & Zanna, M. P. (1990). Expectancies. In E. T. Higgins & A. W. Kruglanski (Eds.), Social psychology: Handbook of basic principles (pp. 211-238). New York: Guilford Press. Pelham, W. E. (1977). Withdrawal of a stimulant drug and concurrent behavioral intervention in the treatment of a hyperactive child. Behavior Therapy, 8, 473-479. Pennington, B. F., & Ozonoff, S. (1996). Executive functions and developmental psychopathology. Journal of Child Psychology and Psychiatry, 37, 51-87. Pinto, C., Turton, P., Hughes, P., White, S., & Gillberg, C. (2006). ADHD and infant disorganized attachment a prospective study of children next-born after stillbirth. Journal of attention disorders, 10, 83-91. Purdie, N., Hattie, J., & Carroll, A. (2002). A review of the Research on Interventions for Attention Deficit Hyperactivity Disorder: What works best? Review of Educational Research, 72 (1), 61-99. Rothbart, M. K., & Bates, J. E. (1998). Temperament. In W. Damon (Series Ed.) & N. Eisenberg (Volume Ed.), Handbook of child psychology: Vol. 3: Social, Emotional and Personality Development (5th ed., pp. 105-176). New York: Wiley. Sandberg, S., & Barton, J. (1996). Historical development . In S. Sandberg (Ed.), Monographs in child and adolescent psychiatry: Hyperactivity disorders of childhood (pp. 1-25). Cambridge University Press. Sanson, A., Oberklaid, F., Pedlow, R., & Prior, M. (1991). Risk indicators: assessment of infancy predictors of preschool behavioural maladjustment. Journal of Child Psychology & Psychiatry, 32, 609-626. Slough, N., Goyette, M., & Greenberg, M. (1988). Scoring Indices for the Seattle Version of the Separation Anxiety Test. University of Washington. Stiefel, I. (1997). Can disturbance in attachment contribute to attention deficit hyperactivity disorder? A case discussion. Clinical Child Psychology and Psychiatry, 2, 45-64.

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Wiener, L., Daniels, J. (2015). School Experiences of Adolescents With Attention-Deficit/Hyperactivity Disorder. Journal of Learning Disabilities, doi: 10.1177/0022219415576973 Willcutt, E. G. (2012). The prevalence of DSM-IV attentiondeficit/hyperactivity disorder: a meta-analytic review. Neurotherapeutics, 9, 490-499.

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Life Span and Disability XIX, 1 (2016), 79-98

Properties of the italian version of the Body Weight Image and Self-Esteem (B-WISE) in a non-clinical sample

Marianna Alesi1& Annamaria Pepi2

Abstract This study aims at assessing psychometric properties of the Italian version of the Body Weight Image and Self-Esteem (B-WISE) originally developed by Awad and Voruganti (2004) with psychiatric patients. The subjects were 1,033 non-clinical Italians with an average chronological age of 27.49 years (SD = 8.91). With regard to gender, there were 547 females and 491 males. Participants were administered self-report instruments: the B-WISE Questionnaire (Awad and Voruganti, 2004) and the Rosenberg Self-Esteem Scale (1965). The exploratory factor analysis revealed the existence of 3 factors explaining the 48.03% of total variance. With regard to internal consistency, our study showed a not satisfactory internal consistency of B-WISE by obtaining weak values of α ranging from .43 to .45. We found significant differences on body image between males and females. Males showed a higher body satisfaction. Moreover they revealed positive significant associations between body-image and selfesteem. Received: April 2, 2015; Revised: March 22, 2016; Accepted: April 23, 2016 © 2016 Associazione Oasi Maria SS. - IRCCS 1 Department of Psychology, University of Palermo, Italy. E-mail: [email protected]. 2 Department of Psychology, University of Palermo, Italy. E-mail: [email protected]. Correspondence to: Marianna Alesi, Department of Psychology, University of Palermo, V.le delle Scienze Edificio 15, 90135, Palermo, Italy. Tel. +09123897702; Fax: +0916513825 Acknowledgments We would like to thank Dr. Awad for his authorization to use the B-Wise free of charge in our study and to translate it to Italian.

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Results encouraged the use of the B-WISE questionnaire and highlighted the need for this measure to be employed in order to compare clinical and non-clinical samples. In particular, the B-WISE could be used as an instrument of screening useful in the early identification of high body dissatisfaction cases. Keywords: Body Image; Self-Esteem; Psychometric Properties.

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1. Introduction Considerable research has documented the key role of body image satisfaction for mental and physical health as well as for a better quality of life (Wilson, Latner, & Hayashi, 2013). Body image is a issue historically investigated by clinical psychologists and psychiatrists because of its relevance to eating disorders as well as in weight gains associated with consumption of medicines in psychiatric diseases. Nevertheless, developmental and educational psychologists have recently taken interest in self-concept and self-esteem associated with body image in the light of clinical emergencies concerning children’s obesity and subsequent attention given towards the psychological well-being of overweight children (Donnellan, Trzesniewski, Conger, & Conger, 2007; Alesi & Pepi, 2013). Body image represents a multi-dimensional construct describing personal perceptions concerning one’s own body. Consequently it involves cognitive, affective and behavioral components. As Cash (2004) stated in his editorial article introducing the new scientific journal, Body Image: An International Journal of Research, “body image is body images” (p. 1). The author argues that our everyday experiences are largely influenced by multiple beliefs on personal appearance. Individuals affirm the source of their attention on the body on the basis of their individual differences, such as thoughts, feelings and behavior, as well as on the basis of psychosocial contraints such as socio-cultural pressures of media and peers. Individual perceptions of body image are assumed to change throughout life span in conjunction with growth and gender differences (Young-Hyman, Schlundt, Herman-Wenderoth, & Bozylinski, 2003; Pepi, Faria, & Alesi, 2006; Gaspar, Amaral, Oliveira, & Borges, 2011) Body image beliefs develop from early childhood when children evaluate their appereance by comparing with their peer group their performance on a variety of tasks (Donnellan et al., 2007). Children establish from ages 6 to 11 the link between body mass index (B.M.I.) and body image and associate the higher B.M.I. to the more negative body image (Xanthopulos, Borradaile, Hayes, Sherman, Veur, Grundy et al., 2011). Judgements and feelings of personal worth appear from early developmental phases when the child is required to verify the evaluation of himself by comparison with actual performance on a variety of tasks including motor, physical, social and learning tasks (Alesi, Rappo, & Pepi, 2012). Children with an adequate level of physical self-esteem are more likely to accept their perceived body

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image and less dependent upon external contingencies regarding appearance or social acceptance. This sensitivity increases by age and becomes more evident during adolescence when concerns about weight and shape are given considerable importance with possible negative consequences of body image (Wertheim & Paxton, 2011). Specifically, girls tend to prefer the stereotype of thin body and boys adopt the muscular stereotype for the body (Shiver, Harrist, Page, Hubbs-Tait, Moulton, & Topham, 2013). These gender differences are consistent with the “objectification theory”; girls show a higher level of body dissatisfaction than boys. Young women are generally considered more dissatisfied with their bodies, worrying about their appearance and looks and commonly showing lower self-esteem than men (Mellor, Fuller-Tyszkiewicz, McCabe, & Ricciardelli, 2010). However, Forbes, Adams-Curtis, Rade and Jaberg (2001) emphasize the generalization of this phenomenon extended to whole populations and identify a different kind of body dissatisfaction for women and men; for women dissatisfaction concerns the body shape and could have severe clinical consequences such as eating disorders, while for men it concerns the pervasive use of anabolic steroids. Cultural and social symbols for ideal body images are differentiated: for females the standard is the thin figure considered more attractive and for males the standard is a muscular and mesomorphic body representing power and success (Mellor et al., 2010). Parents, peers and media tend to support the ideal of thinness for girls and often associate it with success in employment and romantic relationships (Murnen, 2011). However, over the last years men, too, attribute an increasing relevance to their appearance as shown by the growth in the beauty market for men (Griffin & Kirby, 2007; Grossbard, Lee, Neighbors, & Larimer, 2009). Body image concerns both for women and men are closely related to B.M.I.. A great amount of research confirms the role of B.M.I. in influencing body image and reveals a negative correlation between higher B.M.I and elevated body dissatisfaction with higher rates of association in non-clinical samples (Milligan & Pritchard, 2006). B.M.I. revealed to be a significant risk factor for body dissatisfaction; overweight individuals were found to be more likely to develop body image worries being perceived as less attractive than thin individuals (NeumarkSztainer, Wall, Larson, Eisenberg, & Loth, 2011).

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1.1. The Assessment of Body Image This increasing research interest raises some methodological issues regarding the availability of instruments able to measure body satisfaction in a non-clinical population during life-span. A broad range of instruments and methods is now available to measure body image in clinical conditions. This methodological variety stems from the terminological overlapping which characterizes the definition of body image; Thompson (2004) e.g. identified 14 terms to label different dimensions of the construct. Consequently, a large number and variety of measures have been developed. Therefore three main categories of measures can be identified: 1) Selfreport questionnaires; 2) Figure rating scales; 3) Computer based techniques (See Tab. 1). First, self-report questionnaires and interviews measure the subjective perception of body image. Examples are: the Eating Disorders Inventory (Gardner, Taylor, & Polivy, 1983), the Body Attitude Test (Probst, Van Coppenolle, & Vandereycken, 1997), the Body Uneasiness Test (Cuzzolaro, Vetrone, Marano, & Battacchi, 1999), the Body Shape Questionnaire (Cooper, Taylor, Cooper, & Fairburn, 1987), the Body Cathexis Scale (Orlandi, Covezzi, Galeazzi, & Guaraldi, 2006). Second, the method of Figure Rating Scale was originally developed by Stunkard, Sorenson and Schulsinger (1983). It consists of nine black and white body size silhouettes ranging from very thin to very obese. Participants are asked to choose the figure representing what they think to be their actual body size, their ideal body size and the body size they believe to be able to maintain over a prolonged period of time. Finally, computer based techniques are built on image distortion of the whole body or body parts. Subjects are showed a human shape and are asked to modify its size until it corresponds to his/her view of his/her body size. Examples are: Body Image Assessment Software or BIAS (Letosa-Porta, Ferrer-Garcia, & Gutierrez-Maldonado, 2005), Anamorphic Micro (Shafran & Fairburn, 2002).

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Table 1 - The three main categories of measures with advantages and disadvantages

Self-report questionnaires

Figure rating scales

Computer based techniques

Measures Eating Disorders Inventory (Gardner, Taylor, & Polivy, 1983); Body Attitude Test (Probst, Van Coppenolle, & Vandereycken, 1997); Body Uneasiness Test (Cuzzolaro, Vetrone, Marano, & Battacchi, 1999); Body Shape Questionnaire (Cooper, Taylor, Cooper, & Fairburn, 1987); Body Cathexis Scale (Orlandi, Covezzi, Galeazzi, & Guaraldi, 2006). Figure Rating Scale (Stunkard, Sorenson, & Schulsinger, 1983). Body Image Assessment Software or BIAS (LetosaPorta, Ferrer-Garcia, & GutierrezMaldonado, 2005); Anamorphic Micro (Shafran & Fairburn, 2002).

Advantages Description of own perspective. Easy and quick administration to large samples. Responses are easily quantifiable and analizable

Disadvantages Subjective perceptions and feelings. Memory bias. Social desirability bias. Qualitative measure Validity problems

Simple and enjoyable measure

Validity and reliability problems Learning bias

Richness of interface Simple and enjoyable measure Online scoring

Validity and reliability problems

This broad variety of instruments generates often the difficulty for researchers and clinicians to select and utilize suitable measures. For example, in Italy few instruments evaluate the influence of sociocultural factors on body image in non-clinical samples (Stefanile, Matera, Nerini, & Pisani, 2011). The majority of tools is employed for clinical goals.

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Consequently, the present study aims to translate to the Italian context and validate the Body Weight Image and Self-Esteem (Awad & Voruganti, 2004) which presents the main advantage to tap socio-cultural influences on individual perception of the body. Awad and Voruganti (2004) have developed and validated a questionnaire called the Body Weight Image and Self-Esteem, originally abbreviated B-WISE, to measure the psychosocial impact of weight gains in psychiatric populations. The researched issues concerned the impaired quality of life aspects, such as bad self-image and lower levels of selfesteem, lower general functioning, social stigmatization, discrimination, associated with the weight changes. It was articulated in 12 items and had an acceptable internal consistency (alpha = .79). This instrument had two main benefits: it was short, quick and easy to administer. Recently Al-Halabi, Garcia-Portilla, Saiz, Fonseca, Bobes-Bascaran, Galvàn and colleagues (2012) evaluated the psychometric properties of the Spanish version of the B-WISE in patients with psychiatric disorders. Their factor analysis revealed 3 factors explaining 50.93% of the total variance. The internal consistency ranged from .55 to .73. Moreover, Probst, Davy, Raepsaet, Knapen, Simons and De Hert (2010) enlarged the population and verified the psychometric properties of the B-WISE both in clinical (N = 412) and non-clinical samples (N = 800). Their findings confirmed a tri-factor structure of the questionnaire. On the whole, the psychometric properties were less satisfactory in the non-clinical group. Given these theoretical premises, this study aims to extend the analysis of the psychometric properties of the B-WISE in a large non-clinical sample. We choose to investigate this questionnaire for three main reasons: 1) to replicate first trial to widen its utilization to include non-clinical populations (Probst et al., 2010); 2) for its focus on socio-cultural factors influencing personal perception of the body; 3) for its inherent characteristics to result easy and quick to complete. In particular, our goal is to investigate its factor structure through the exploratory analysis factor and analyze consistency and validity of the questionnaire by employing variables such as gender, B.M.I. and Selfesteem. Following hypotheses were tested in this research: H 1. The B-Wise maintains the tri-factor structure found in previous research (Probst et al., 2010; Al-Halabi et al., 2012).

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H 2. Body image is better in women because individual perceptions are shaped by gender differences, as demonstrated in most literature (Murnen, 2011; Wertheim & Paxton, 2011; Flament, Hill, & Buchholz, 2012). H 3. Body image, body weight and self-esteem are related, as widely showed by previous research (Mellor et al., 2010; Smolak, 2011).

2. Method 2.1. Sample The subjects in this study were 1,033 Italians with an average chronological age of 27.49 years (SD = 8.91; range 15-46 years), of which 49.5% were students. With regard to gender, there were 547 females and 491 males. The medium socio-economic level was predominant, based on parameters such as family size, parents’ academic careers and jobs. 2.2. Instruments and procedure Participants were previously provided all information to assure the correct compilation and confidentiality and anonymity being guaranteed. At the beginning of the sampling phase participants were given a Questionnaire articulated in two sections: 1) The socio-demographic section aimed at analyzing socio-cultural background by evaluating parameters such as family size, academic history and jobs, hobbies in free time; 2) The anthropometric section aimed at investigating self-reported body weight (BW) and height to calculate the B.M.I. Then all participants were administered a self-report instruments battery that included the B-WISE Questionnaire (Awad & Voruganti, 2004) and the Rosenberg Self-Esteem Scale (Rosenberg, 1965). The B-WISE or Body Weight, Image and Self-Esteem Evaluation Questionnaire (Awad & Voruganti, 2004) consisted of twelve items related both to the feelings and thoughts concerning body weight and psychosocial adjustments in the preceding 2 weeks. Six items were expressed in a positive way (I am upset with my present weight; Generally, I am feeling good about myself) and the other six in a negative way (I dislike the way I look; I am avoiding friends and relatives because I am out of shape). We employed the Italian version of B-WISE obtained by previous translation from English to Italian and then by back-translation from Italian into English to ensure

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maximum linguistic and cultural coherence between the two versions (Van de Vijver & Hambleton, 1996). The original administration procedure was used. Subjects were asked to read each sentence carefully and answer these questions as it applied to them at the present time by choosing between the options Never, Sometimes and Most of Time. Scores were from 3 to 1 points for each positive item and from 1 to 3 point for each negative item; the maximum score (3) indicated a higher positive perception of body image. Total score ranged from 12 to 36 with higher scores revealing higher satisfaction. The Self-Esteem Scale (Rosenberg, 1965) consisted of ten items related to the way people feel about themselves. Five items were expressed in a positive way (I am satisfied with myself; I feel I have a certain number of qualities) and the other five in a negative way (Sometimes I think I’m worthless; I feel I have few things to be proud of ). Subjects were asked to express their degree of agreement, and responses to each item were made on a scale from 1 (Totally agree) to 6 (Totally disagree). The scoring parameters of the evaluation were from 6 points to 1 point for each positive item and from 1 point to 6 points for each negative item. Higher global scores revealed a higher level of self-esteem. The internal consistency of the scale yielded alpha coefficient of .77. We used a previous Italian adaptation (Pepi et al., 2006). The above-mentioned 6 points were reduced to 3 points when utilizing the statistical factoring. Instruments were collectively administered and the task did not last more than 20 minutes.

3. Data Analysis 3.1. Descriptive Statistics and Item Analyses Means and standard deviations for each item of the B-WISE and the total score are given in Table 2. Values are reported both for the whole sample and for the two groups differentiated on the basis of the gender. The mean total B-WISE score was 27.33 (SD = 2.92) with significant differences between males and females (t = 5.78; p < .001). Specifically males (M = 27.92; SD = 2.84) manifested higher body image level than women (M = 26.88; SD = 2.91). The comparison of mean scores revealed significant differences between boys and girls on the following items: n. 1 I am upset with my present weight (t = 4.29; p < .01), n. 2 I feel active and energetic (t = 2.45; p < .001), n. 5 I dislike the way I look (t = 5.23; p < .001), n. 6 I

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am self-conscious in the company of others because of my weight (t = 1.52; p < .05), n. 7 I am reminded of my body shape and appearance during the day (t = - 1.73; p < .05). Only in the item n. 7, women scored higher than men. Table 2 - Means of the B-WISE scores in the total sample, in the men group and in the women group Items 1. 2. 3. 4.

I am upset with my present weight I feel active and energetic I am going out to enjoy myself more often I am not able to control my hunger and craving for food 5. I dislike the way I look 6. I am self-conscious in the company of the others because of my weight 7. I am reminded of my body shape and appearance during the day 8. I am avoiding friends and relatives because I am out of shape 9. I know why I put on weight, and I know how to lose it 10. I believe that excess weight is not good for my general health 11. I am taking steps to control my weight 12. Generally, I am feeling good about myself

Total sample N = 1,033 M SD 2.27 (.69) 2.64 (.54) 2.32 (.68) 2.32 (.75)

M 2.35 2.68 2.39 2.38

SD (.68) (.51) (.67) (.74)

M 2.17 2.60 2.26 2.24

SD (.68) (.56) (.66) (.76)

2.26 2.72

(.63) (.54)

2.36 2.75

(.63) (.54)

2.16 2.70

(.62) (.54)

1.87

(.64)

1.84

(.64)

1.91

(.63)

2.93

(.27)

2.94

(.28)

2.93

(.27)

2.18

(.83)

2.20

(.82)

2.17

(.84)

1.37

(.56)

1.38

(.57)

1.36

(.56)

1.98 2.60

(.71) (.56)

2.00 2.69

(.71) (.50)

1.98 2.52

(.70) (.59)

Men N = 491

Women N = 547

Tables 3, 4 and 5 show the discriminant power of the 12 items by the choices of answer alternatives. Globally we can observe that items 1, 4, 9 and 11 show minor rate of differences between the choices. More clearly the items 1 and 4 present similar rates of choice for the opposite alternatives of answer Never and Most of Time. The item 9 shows similar rates of choice for the alternatives of answer Most of Time and Sometimes. The item 11 shows similar rates of choice for the alternatives of answer Never and Sometimes. This distribution of answer alternatives is similar among the total sample, the both men’s and women’s groups.

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Table 3 - Choices of answer alternatives in the total sample Items 1 2 3 4 5 6 7 8 9 10 11 12

Sometimes 14.1% 3% 12% 17.4% 10.4% 4.4% 27.6% .7% 27% 67.5% 26% 3.6%

Most of Time 45.3% 29.7% 44.2% 33.5% 52.8% 19% 57.8% 5.1% 27.1% 28.3% 49.9% 32.5%

Never 40.7% 67.3% 43.7% 48.9% 36.5% 76.5% 14.5% 94% 45.3% 4.2% 24.2% 63.9%

Table 4 - Choices of answer alternatives in the men group Items 1 2 3 4 5 6 7 8 9 10 11 12

Sometimes 11.8% 2% 11% 15.7% 8% 4.9% 30.3% .8% 26.1% 66.9% 26.1% 2%

Most of Time 40.1% 27.3% 39.1% 30.5% 47.1% 15.3% 56% 4.7% 28.5% 28.8% 49.3% 26.5%

Never 48.1% 70.7% 49.9% 53.8% 44.9% 79.8% 13.6% 94.5% 45.4% 4.3% 24.6% 71.5%

Table 5 - Choices of answer alternatives in the women group Items 1 2 3 4 5 6 7 8 9 10 11 12

Sometimes 16.1% 3.9% 13% 19.1% 12.6% 3.9% 25.2% .6% 28.4% 68.1% 25.9% 5%

Most of Time 49.9% 31.8% 48.9% 36.3% 58.3% 22.4% 59.4% 5.6% 26.4% 27.8% 50.4% 38%

Never 34% 64.3% 38.1% 44.6% 29.1% 73.7% 15.4% 93.9% 45.2% 4.1% 23.7% 57%

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3.2. Internal structure To identify dimensions underlying the B-WISE among Italian participants, Exploratory Factor Analysis was performed using SPSS 20. Criteria for evaluating the adequacy of each factor extracted included the following: (1) eigenvalues > 1; (2) examination of the scree plot; (3) item loadings > .30. Oblimin rotation was used because dimensions were expected to be related to one another. The Principal Component Analysis identified 3 factors that explain the 48.03% of the variance (Tab. 6). The mean sampling adequacy (Bartlett’s test) was 2047.91 (p < .001) and the Kaiser-Meyer-Olkin (KMO) was .597. The three factors had eigenvalues of 2.49, 1.78 and 1.50 respectively. The first component, Body Image Distress, had 6 items and accounted for 20.73% of the total variance. The second component, Well-being and activity, had 3 items and explained 14.82% of the variance. The third component, named Weight control, had 3 items and accounted for 12.48% of the variance. Table 6 - Principal component analysis, oblimin rotation (n = 1,030) Items 2 12 3 4 1 5 9 11 10 8 6 7

I Component .70 .67 .61 .60 .49 .48

II Component

III Component

.83 .70 -.53 -.67 -.65 .59

3.3. Internal consistency The Italian version of B-WISE demonstrated a moderate internal consistency. Cronbach’s Alpha was of .45 for the whole sample, of .43 for the men group and of .44 for women group. The average inter-item correlation was of .19 for the total sample, of .13 for the men group and of .20 for women group. The Guttman Split-Half Coefficient was of .14 for the whole sample, of .17 for the men group and of .09 for the women group. Finally in the total group the item-total correlations were all significant (p < .001) ranging from .11 to .57; in the men group the item-total

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correlations were all significant (p < .001) ranging from .14 to .57, in the women group the item-total correlations were all significant (p < .001) ranging from .27 to .61. 3.4. Convergent Validity To assess the convergent validity, correlations between B-WISE, B.M.I. (Body Mass Index) and Self-Esteem Scale were calculated. In the total sample (Tab. 7) we found a positive significant correlation (p < .01) between B-WISE and Self-esteem (r = .31); in the men group (Tab. 8) we found a positive significant (p < .01) correlation between B-WISE and Self-esteem (r = .27) and a negative significant correlation between B-WISE and B.M.I (r = -.11). Finally in the women group (Tab. 9) we found a positive correlation (p < .01) only between B-WISE and Self-esteem (r = .33) Table 7 - Correlations between B-WISE, B.M.I. and Self-esteem in the total sample (1) B-WISE (2) B.M.I. (3) Self-esteem ** p < .01

(1) 1 -.00 .31**

(2)

(3)

1 -.04

1

Table 8 - Correlations between B-WISE, B.M.I. and Self-esteem in the men group (1) B-WISE (2) B.M.I. (3) Self-esteem ** p < .01

(1) 1 -.11** .20**

(2)

(3)

1 -.08

1

Table 9 - Correlations between B-WISE, B.M.I. and Self-esteem in the women group (1) B-WISE (2) B.M.I. (3) Self-esteem ** p < .01

(1) 1 .00 .33**

(2)

(3)

1 -.05

1

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4. Discussion The aim of this study was to assess the psychometric properties of the Italian version of the Body Weight Image and Self-Esteem (B-WISE) originally developed by Awad and Voruganti (2004). Our study differs mainly from previous research using B-WISE (Awad & Voruganti, 2004; Probst et al., 2010; Al-Halabi et al., 2012) in the sampling. We involved only non-clinical patients and our sample was more large being composed of 1,030 subjects subdivided into two groups, men and women. First, significant differences in body image between males and females were revealed showing higher positive body perception among males. This is consistent with other research findings obtained in previous studies on gender differences which have generally associated the highest self-image to boys. In particular, the boys’ satisfaction is documented to be higher, less inclined to temporal fluctuations and seems to be more influenced by the social ideal emphasizing a sterotyped body related to powerness and muscularity. While the girls’ body-image is more closely defined by thinness (Mellor et al., 2010; Flament, Hill, & Buchholz, 2012). Research in this field has looked at gender stereotypes from the school age (Pepi et al., 2006). With concern to internal structure, we decided to use exploratory factor analysis because we have only one previous study which tried to use the BWISE with non-clinical participants. Results obtained in this study revealed a tri-factor internal structure which explained the 48.03% of total variance. This value could be considered acceptable. This structure was similar to that previously obtained by Al-Halabi et al. (2012). These three factors named Body Image Distress and Awareness, Well-being and activity and Weight, involved emotional, cognitive and behavioral components of body image. With regard to internal consistency, our study confirmed results obtained by Probst and colleagues (2010), which showed a not satisfactory internal consistency of the B-WISE. Specifically we obtained weak values of α ranging from .43 to .45, given the Cronbach’s Alpha reliability is within an acceptable range around .70. This internal consistency was lower than the value obtained in previous studies involving psychiatric patients. We agree with Probst and colleagues (2010) that it could be attributed to a different variability between clinical and non-clinical samples. With concern the validity, we correlated B-WISE with B.M.I. and Self-esteem scores obtained by Rosenberg questionnaire (1965). We found significant associations between body-image and self-esteem: the subject who declared himself

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satisfied with his own body showed positive self-worth. This corroborated results previously obtained which had found that individuals with higher level of self-esteem showed higher level of body image. Only in the males group we found a negative correlation between B-WISE and B.M.I revealing that the higher B.M.I. is associated to lower level of body satisfaction. This association confirms the influence of cultural and models aimed at generalizing the “objectification theory” to males (Mellor et al., 2010). To conclude, the results obtained encourage the utilization of the BWISE questionnaire, but highlight the need to discuss limitations of the study. The first source of weakness in this study is the similarity between the items of the B-WISE and the Rosenberg self-esteem questionnaire regarding self-esteem which could have caused theoretical overlappings and the consequent problems of internal validity. Another source of weakness is the self-report approach in which respondents may not respond truthfully because they are not able to evaluate their body perception or because they tend to present themselves in a more socially acceptable manner. Nevertheless, B-WISE could be used as an instrument of screening for its main benefits, to be short, quick and easy to administer and for its nature of “state measure” aimed at assessing the body perception at the present specific point in time. In this way, it could be useful in the early identification of the cases of high body dissatisfaction to be further analyzed by the use of more sophisticated measures. Analyses of previous studies suggest to us the need to compare the psychometric properties of the investigated instrument between clinical and non-clinical samples. It’s necessary now to verify our results with the hypothesis that its relatively modest properties are due to the typical composition of our sample and not to the translation from the American to the Italian version. B-WISE was originally intended for schizophrenic patients and it’s worth further analyzing the nature of the body image in psychiatric populations. This brings to mind the need for a deeper discussion concerning the nature of the “body image” as a multidimensional construct involving perceptual, cognitive and emotional dimensions. The effect could be the “(mis)measurement of body image”. In these terms it may be the case to quote the ten strategies suggested by Thompson (2004) in order to improve body image assessment for applied and research purposes: 1. To specify the label of dimension of body image of interest; 2. To employ multiple measures of body image; 3. to select instruments on the basis of their reliability and validity; 4. to use instruments with appropriate samples; 5.

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assess reliability and validity in the involved sample; 6. to adapt instruments for one’s own purposes; 7. to determine the need of a state or trait measure; 8. to plan carefully the instructional protocol; 9. to analyse data by participants characteristics; 10. to consider differences of clinical and statistical significance. Finally is important to acknowledge that future research is needed to build on this study. The main goal is to provide a richer and more complete understanding of how the body image develops across the ages.

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