Early Human Development 90 (2014) 665–672

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Child and environmental factors associated with leisure participation in adolescents born extremely preterm Noémi Dahan-Oliel a,b, Barbara Mazer b,c, Désirée B. Maltais d,e, Patricia Riley a, Line Nadeau d,e, Annette Majnemer a,b,⁎ a

Montreal Children's Hospital, Montreal, Canada School of Physical and Occupational Therapy, McGill University, Montreal, Canada Jewish Rehabilitation Hospital, Laval, Canada d Department of Rehabilitation, University of Laval, Quebec City, Canada e Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Quebec City, Canada b c

a r t i c l e

i n f o

Article history: Received 6 June 2014 Received in revised form 7 August 2014 Accepted 11 August 2014 Keywords: Adolescents Preterm Leisure participation Child factors Environment

a b s t r a c t Background: Developmental impairments persist among adolescents born extremely preterm, and these individuals are at an increased risk for chronic disease later in life. Participating in active and positive leisure activities may act as a buffer against negative outcomes, but involvement in active-physical and skill-based activities is low in youth born preterm. Aims: To explore the child and environmental determinants of leisure participation among adolescents born extremely preterm. Study design: Cross-sectional study. Subjects: Participants were recruited from the hospital's Neonatal Follow-Up Program and included 128 adolescents born preterm (mean gestational age: 26.5 weeks). Outcome measures: Leisure participation was assessed using the Children's Assessment of Participation and Enjoyment. Potential determinants were assessed using standardized tests and questionnaires. Selected factors were entered into five separate multivariable regression models. Results: Child and environmental factors contributed between 21% (skill-based) and 52% (active physical) of the adjusted variance for participation intensity. Lower gestational age was associated with greater participation in recreational activities. Male sex, higher maternal education and better motor competence were associated with involvement in active-physical activities. Being older and feeling socially accepted were associated with participation in social activities. Families oriented to hobbies and higher maternal education were associated with participation in skill-based activities. Preference was the strongest determinant of participation in all five leisure activities. Conclusions: Activities should be adapted to individual skill level, include family and peers, foster social acceptance and be driven by the adolescent's preferences. Although certain factors cannot be modified, they can be used to identify adolescents at risk for low participation. © 2014 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Abbreviations: CAPE, Children's Assessment of Participation and Enjoyment; CASE, Child and Adolescent Scale of Environment; DAILY, Determinants of Active Involvement in Leisure for Youth; DMQ, Dimensions for Mastery Questionnaire; FES-4th edition, Family Environment Scale — 4th edition; ICF-CY, International Classification of Functioning, Disability and Health, Children & Youth Version; MABC-2, Movement Assessment Battery for Children, 2nd version; MUHC, McGill University Health Centre; NNFU, Neonatal Follow-up Program; PAC, Preferences for Activities of Children; SDQ, Strengths and Difficulties Questionnaire; SPP, Self-Perception Profile; SSS, Social Support Scale; Vineland-II, Vineland Adaptive Behavior Scale, Second edition. ⁎ Corresponding author at: School of Physical & Occupational Therapy, Faculty of Medicine, McGill University, 3654 Promenade Sir-William-Osler, Davis House, Room D26, Montreal, QC H3G 1Y5, Canada. Tel.: +1 514 398 4501. E-mail address: [email protected] (A. Majnemer).

http://dx.doi.org/10.1016/j.earlhumdev.2014.08.005 0378-3782/© 2014 Elsevier Ireland Ltd. All rights reserved.

Traditionally, the consequences of disease were portrayed as causing either morbidity or mortality [1] with the focus of service provision being specifically impairment-based, and rarely included participation-based goals which families tend to prioritize. The World Health Organization has recognized participation as a key concept in its International Classification of Functioning, Disability and HealthChild and Youth version in 2007 (ICF-CY) [2]. This bio-psycho-social model illustrates how functioning with its components Body Functions, Body Structures and Activities and Participation is seen in relation to a child's health condition, as well as Personal and Environmental Contextual Factors (Fig. 1). Using such a framework in pediatric health care may

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Fig. 1. International Classification of Functioning, Disability and Health (2).

promote client-centered care, identify the factors enhancing or impeding participation, and support the development of tailored programs to remove potential barriers to participation. The role of the family and social environment (e.g. home, schools, recreation settings) in defining participation from early childhood to adolescence is acknowledged in the ICF-CY. Leisure refers to the positive ways individuals fill their free time, and encompasses sedentary, passive activities such as watching TV, active pursuits such as sports, or activities focusing on skill-building and competencies such as playing a musical instrument. Leisure also includes hobbies or activities related to socialization, chores and employment. Additionally, an important aspect of leisure is that of free choice. Adolescence is a time of growing autonomy, peer interactions and increased opportunities to make decisions [3]. Participation in preferred and selected leisure pursuits is experienced positively and may contribute to adolescent development. Leisure provides constructive opportunities for adolescents, with benefits ranging from better physical, social and emotional health [4] to educational successes [5]. Evidence has shown that participation in leisure activities, particularly physical activity, declines during adolescence [6] and into adulthood [7]. Adolescence is an important time when a child gradually takes on new life roles and becomes increasingly independent, preparing for successful transition to adulthood. Maintaining participation levels at this time is therefore essential for maximizing health and ensuring optimal integration into society. Being at risk for or having a disability may add an additional barrier to engaging in leisure, as participation levels in children and youth with physical and other developmental disabilities are known to be lower than in typically developing peers [8,9]. Preterm birth is a global problem, affecting families worldwide [10]. Complex neurodevelopmental problems in preterm survivors have been shown to persist into adolescence [11] and adulthood [12], which may limit activities and restrict participation in the home, at school and in the community. Minor motor incoordination often persists in adolescents born preterm [13] and may co-exist with cognitive deficits [14] and behavioral problems [15]. Furthermore, children born very preterm have higher rates of social withdrawal and peer victimization than term-born peers [16]. Leisure participation may be critical in fostering independence and fitness, promoting psychosocial well-being and preventing chronic health conditions in the preterm population, especially as they transition from adolescence into adulthood. Motor development, cognitive skills and other childrelated factors are associated with participation levels of children and youth with physical disabilities [17,18], but have yet to be validated in

adolescents born preterm who may have a wide range of severity of impairments. Furthermore, preferences for activities is a strong determinant of leisure participation in adolescents with cerebral palsy [17]. Family functioning, socio-economic background and environmental factors are also found to influence leisure opportunities and participation in children with disabilities [17], yet quantitative studies on the contextual factors which positively or negatively influence leisure participation in adolescents born preterm are lacking [19]. Reasons for engaging in leisure may be multiple and complex, but gaining a better understanding of the factors that are associated with leisure participation in adolescents born preterm will enable the design of responsive and effective intervention programs and health promotion initiatives aimed at increasing participation. Currently, there is no evidence on the determinants of leisure participation in adolescents born preterm. Therefore, specific child and environmental factors were included in this study based on clinical relevance and on a conceptual model of leisure determinants in children with disabilities [20] which was validated in adolescents with cerebral palsy [17]. The purpose of this study was to identify the child and environmental factors associated with leisure participation in adolescents born extremely preterm.

2. Methods 2.1. Procedures Ethical approval was obtained from the Montreal Children's Hospital Research Ethics Board. This cross-sectional study is part of a broader study entitled “Determinants of Active Involvement in Leisure for Youth — DAILY living with disability” which includes adolescents born extremely preterm as well as adolescents born with a congenital heart defect. This paper reports on adolescents born extremely preterm and included youth between 12 and 20 years of age who were born at ≤ 29 weeks of gestational age and eligible at birth for the Neonatal Follow-up Program were recruited. Exclusion criteria included documented genetic syndrome or chromosomal anomaly. Details on recruitment have been described previously [21]. Informed consent was obtained from the primary caregiver as well as assent from able adolescents. Standardized assessments, self-report and parent-report questionnaires on leisure participation and potential child and environmental determinants were completed during a three-hour visit at the hospital or at the participant's home. All questionnaires have been translated to French using forward and back translation.

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2.2. Dependent variable

2.3. Independent variables

Leisure participation was assessed using the Children's Assessment of Participation and Enjoyment (CAPE) [22] by a trained clinician using a semi-structured interview. The CAPE assesses participation in five types of leisure activities (recreational, active-physical, social, skill-based and self-improvement) outside of mandated school activities in children and youth with or without disabilities aged six to 21 years. Scoring includes diversity (number of activities within each subscale), intensity (how often), with whom, where, and enjoyment level. Scores for diversity and intensity of participation in this study were highly related (Spearman's r ≥ 0.9), therefore CAPE intensity scores for each of the five activity types were used as the primary outcomes to explore the determinants of leisure participation. Internal consistency (Cronbach's alpha: 0.30–0.84) and test–retest reliability (ICCs: 0.64–0.86) have been reported, and content and construct validity were demonstrated [20].

2.3.1. Child factors Age, sex and gestational age were obtained from hospital records. Motor competence, cognitive ability and functional status were evaluated using standardized assessments. Participants were evaluated by an occupational or physical therapist using the Movement Assessment Battery for Children—Second Edition (MABC-2) [23] in three areas of motor competence: manual dexterity, aiming and catching, and balance. It is designed for children between 3 and 16 years of age, but has been successfully used in young adults born preterm [24]. A psychologist administered the Leiter-R brief IQ [25] to provide an estimate of global intellectual level. Functional status in communication, daily living skills, and socialization was assessed by a psychologist using the Vineland Adaptive Behavior Scales, Second Edition (Vineland-II) [26] during a semi-structured interview with the parent. Evaluators were blind to participants' medical history, and the results of other

Table 1 Child factors correlated with participation intensity scores. Independent variables

N (%) or mean (SD)

Correlations with CAPE intensity scores Recreational

Sex (n = 128) Male (reference female) Age (n = 128) Gestational age (n = 128) Cognitive ability (Leiter-R) (n = 127) Motor competence (MABC-2) (n = 126) Total score (1–19) Manual dexterity Aiming and catching Balance Functional status (Vineland-II) (n = 123) Adaptive behavior composite Socialization Communication Daily living skills Activity preferences (PAC) (1–3) (n = 127) Recreational Active-physical Social activities Skill-based Self-improvement Behavior (SDQ) (0–10) (n = 118) Emotional symptoms Peer problems Motivation (DMQ—parent) (1–5) (n = 117) Object-oriented persistence Social persistence with adults Gross motor persistence Total persistence Total mastery motivation General competence Motivation (DMQ—child) (1–5) (n = 121) Object-oriented persistence Social persistence with children Gross motor persistence Total persistence Mastery pleasure Total mastery motivation General competence Self-perception (SPP) (1–4) (n = 120) Social acceptance Athletic competence

61 (47.7%) 16.0 (2.40) 26.54 (1.79) 91.92 (18.68) 7.33 (3.19) 6.82 (3.22) 7.83 (3.48) 9.29 (3.67) 92.67 (16.22) 98.93 (17.06) 91.22 (15.54) 91.58 (17.69)

Active-physical

Social

Skill-based

Self-improvement

.06 −.05 −.23⁎⁎ .04

.31⁎⁎ .10 .03 .22⁎

−.15 .31⁎⁎ .07 .23⁎

−.10 .06 −.03 −.01

−.18⁎ −.07 .02 .35⁎⁎

.01 −.02 .04 −.06

.31⁎⁎ .19⁎ .42⁎⁎ .17

.20⁎ .15 .20⁎ .14

.02 .03 .03 −.09

.04 .08 .01 .01

.11 .05 .10 .15

.35⁎⁎ .33⁎⁎ .32⁎⁎ .29⁎⁎

.32⁎⁎ .34⁎⁎ .32⁎⁎ .27⁎⁎

2.06 (0.34) 2.08 (0.45) 2.60 (0.35) 2.00 (0.51) 1.89 (0.38)

.51⁎⁎

2.54 (2.40) 2.29 (1.93)

.14 .02

3.30 (0.80) 3.60 (0.74) 3.15 (0.88) 3.38 (0.57) 3.51 (0.54) 3.25 (0.91)

−.02 .23⁎ .03 .09 .09 .04

.17 .06 .39⁎⁎ .27⁎⁎ .26⁎⁎ .22⁎

.12 .03 .26⁎⁎ .17 .14 .16

3.36 (0.62) 3.47 (0.72) 3.34 (0.88) 3.41 (0.59) 3.96 (0.82) 3.52 (0.58) 3.35 (0.69)

.12 .12 .12 .12 .13 .13 .04

.21⁎ .23⁎ .49⁎⁎ .35⁎⁎

−.02 .19⁎ .25⁎⁎ .19⁎ .23⁎ .22⁎

3.05 (0.67) 2.58 (0.86)

.03 .13

.38⁎⁎ .44⁎⁎

.56⁎⁎

−.24⁎ −.06

.16 .33⁎⁎ .24⁎⁎

.47⁎⁎

−.04 −.25⁎⁎

.12 .14 .13 .02

.35⁎⁎

−.20⁎ −.03 .04 −.13 .12 .08 .04 .05 .01 .19⁎

.13

.15 .14 .09 .14 .04

.42⁎⁎ .17

.17 .01

.36⁎⁎ .31⁎⁎ .35⁎⁎ .37⁎⁎

.37⁎⁎ −.03 −.02 .31⁎⁎ .12 .16 .26⁎⁎ .22⁎ .21⁎ .13 .14 .04 .15 .26⁎⁎ .20⁎ .04 .10 −.05

CAPE — Children Assessment of Participation and Enjoyment, DMQ — Dimensions of Mastery Questionnaire, Leiter-R — Leiter-R brief IQ, MABC-2 — Movement Assessment Battery for Children-Second Edition, PAC — Preferences for Activities, SDQ — Strengths and Difficulties Questionnaire, SPP — Self-Perception Profile, Vineland-II — Vineland Adaptive Behavior Scales, Second Edition. ⁎ p b 0.05. ⁎⁎ p b 0.01.

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Table 2 Environmental factors correlated with participation intensity scores. Independent variables

Correlations with CAPE intensity scores N (%) or mean (SD)

Maternal education (reference ≤ high school) (n = 121) Family climate (FES) (n = 121) Intellectual/cultural orientation (19–69) Active recreational orientation (23–69) Social support (SSS) (1–4) (n = 120) Classmate support Teacher support Friend support Environmental barriers (CASE) (0–100) (n = 119)

Recreational

Active-physical

73 (53%)

.08

.32⁎⁎

55.30 (9.62) 52.56 (10.70)

.04 .08

3.27 (0.62) 3.34 (0.60) 3.66 (0.55) 40.59 (10.67)

−.11 .05 .12 −.02

Social

Skill-based

Self-improvement

.22⁎

.24⁎⁎

.13

−.03 .25⁎⁎

.01 .08

.26⁎⁎ .26⁎⁎

.19⁎ .04

.25⁎⁎ −.01 .21⁎

.35⁎⁎ .01 .29⁎⁎ −.17

−.08

.09 −.01 .18⁎ −.06

−.01 .23⁎ .08 −.11

CAPE — Children Assessment of Participation and Enjoyment, CASE — Child and Adolescent Scale of Environment, FES — Family Environment Scale, SSS — Social Support Scale. ⁎ p b 0.05. ⁎⁎ p b 0.01.

testers' evaluations. The Strengths and Difficulties Questionnaire (SDQ) [27] parent-report version was used to measure behavior in children and youth. The Dimensions of Mastery Questionnaire (DMQ) [28] child- and parent-report versions were used to assess mastery motivation in gross motor tasks, social activities, mastery pleasure, negative reactions to failure, and general competence. Self-perception was measured using Harter's Self-Perception Profile (SPP) using a self-report format [29]. The child (b 14 years) and adolescent (≥14 years) versions were used. Items are scored 1 (least adequate self-judgment) to 4 (most adequate self-judgment). The Preferences for Activities of Children [34] was used to evaluate which activities the adolescent most prefers or has interest in performing, without regards to actual performance. Fifty-five leisure activities were rated by the adolescent as 1, (would not like to do at all), 2 (would sort of like to do) or 3 (would really like to do). Mean preference scores for each of the five activity types were calculated. Domain and total scores were used for the MABC-2, Vineland-II, SDQ and DMQ.

2.3.2. Environmental factors Maternal education was obtained through a parent questionnaire and was dichotomized to ≤ High school and N High school (College, University, Graduate degree). Family climate was assessed using the Family Environment Scale—Fourth Edition (FES) [30] which measures family social environments in three dimensions (relationship, personal growth, and system maintenance) through parent-report, and yields 10 scores. The Social Support Scale (SSS) for Children and Adolescents [31] examines support from parents, teachers, friends and classmates through self-report. Scale scores are calculated for each of these four domains, with higher scores denoting more support. The Child and Adolescent Scale of Environment (CASE) [32] assesses the physical, social and attitudinal environment barriers of children and youth with disabilities as reported by parents. A total score out of 100 is calculated with a higher score denoting a greater difficulty.

2.4. Data analysis Descriptive statistics were used to highlight the sample's main characteristics and participation levels. Pearson correlations for continuous variables and Spearman correlations for dichotomous variables (sex and maternal education) were used to determine the strength of associations between child and environmental factors with the five CAPE intensity scores. Effect sizes of the correlations were described as follows: small (r = 0.1–0.3), medium (r = 0.3–0.5) and large (r N 0.5) [33]. To identify the factors associated with leisure participation in the five activity types, multivariate linear regression modeling was used. Factors were included based on clinical relevance, supported by literature in children with disabilities, and confirmed by univariate regression analyses. Age, sex, gestational age and maternal education were included a priori, as these variables are known to potentially influence child development. Additional selected independent variables were entered into five separate multivariate linear regression models. For independent variables with both total and subdomain scores, multivariate regression analysis was used to guide selection of the particular score which explained the largest variance in the dependent variable. Assumptions for normality, linearity and homogeneity of variance were met. Multicollinearity was verified by variance inflation factor and tolerance (46). Missing data were treated with listwise deletion. Final models were validated using bootstrapping, with repeated samples of the same size as the original, with replacement. Two thousand replications were produced to estimate bootstrap confidence intervals [34]. Statistical significance was set at p ≤ 0.05. SPSS 18.0 statistical software was used. 3. Results This study included 128 adolescents (mean age 16.0 years, range: 12.0–20.0 years) with a mean gestational age of 26.5 weeks (range:

Table 3 Regression model for intensity of participation in recreational activities. Child factors

Age Sex (reference female) Gestational age Mastery motivation—social persistence with adults (DMQ—parent) Preferences for recreational activities (PAC) Environmental factors Maternal education (reference ≤ high school) Family climate-achievement orientation (FES)

Parameter estimate

Bootstrap

Beta

95% CI

p

Estimate

BCa — 95% CI

−0.03 0.075 −0.141 0.187 1.458

−0.10, 0.04 −0.25, 0.40 −0.24, −0.05 −0.03, 0.41 1.0, 1.90

0.361 0.644 0.004 0.097 ≤0.001

−0.03 0.08 −0.14 0.18 1.45

−0.09, 0.03 −0.23, 0.39 −0.23, −0.05 −0.06, 0.42 1.03, 1.86

0.241 −0.010

−0.08, 0.56 −0.03, 0.01

0.144 0.341

0.24 −0.01

−0.08, 0.55 −0.03, 0.01

Adjusted r2 = 0.34, n = 117. DMQ — Dimensions of Mastery Questionnaire, FES — Family Environment Scale, PAC — Preferences for Activities. Bold values indicate significance at pb0.05.

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Table 4 Regression model for intensity of participation in active-physical activities. Child factors

Age Sex (reference female) Gestational age Cognitive ability (Leiter-R) Motor competence—aiming and catching (MABC-2) Functional status—adaptive behavior composite (Vineland-II) Mastery motivation—gross motor persistence (DMQ—parent) (DMQ—child) Behavior—emotional symptoms (SDQ) Self-perception- athletic competence (SPP) Preferences for active-physical activities (PAC) Environmental factors Maternal education (reference ≤ high school) Family climate- active recreational orientation (FES) Social support- classmate support (SSS)

Parameter estimate

Bootstrap

Beta

95% CI

p

Estimate

BCa – 95% CI

0.005 0.325 −0.011 0.004 0.055 0.003

−0.05, 0.06 0.06, 0.60 −0.09, 0.07 −0.01, 0.02 0.02, 0.09 −0.01, 0.01

0.877 0.02 0.779 0.605 0.007 0.611

0.003 0.33 −0.01 0.002 0.05 0.003

−0.06, 0.07 0.06, 0.60 −0.10, 0.06 −0.01, 0.01 0.02, 0.09 −0.01, 0.02

0.126 0.178 −0.001 0.027 0.548

−0.06, 0.31 −0.03, 0.39 −0.06, 0.05 −0.24, 0.21 0.21, 0.89

0.175 0.091 0.961 0.806 0.03

0.13 0.18 −0.004 0.02 0.55

−0.05, 0.33 −0.06, 0.40 −0.06, 0.05 −0.23, 0.27 0.21, 0.89

0.303 0.005 0.116

0.03, 0.57 −0.01, 0.02 −0.09, 0.32

0.03 0.462 0.267

0.31 0.004 0.11

0.06, 0.54 −0.01, 0.02 −0.12, 0.34

Adjusted r2 = 0.52, n = 110. DMQ — Dimensions of Mastery Questionnaire, FES — Family Environment Scale, Leiter-R — Leiter-R brief IQ, MABC-2 — Movement Assessment Battery for Children-Second Edition, PAC — Preferences for Activities, SDQ — Strengths and Difficulties Questionnaire, SPP — Self-Perception Profile, SSS — Social Support Scale, Vineland-II — Vineland Adaptive Behavior Scales, Second Edition. Bold values indicate significance at pb0.05.

22.4–29.6 weeks), and mean birth weight of 898.6 g (range: 490–1445 g). 3.1. Description of leisure participation in adolescents born preterm Participation levels were highest in social and recreational activities, and lowest in active-physical and skill-based activities. Boys participated in more active-physical activities (p = 0.01) and more often (p b 0.001), whereas girls had higher participation levels in social and self-improvement activities (p b 0.05). Older adolescents engaged in more social activities and more frequently (p b 0.01) than younger adolescents. An in-depth description of leisure participation in this sample is reported elsewhere [21]. 3.2. Univariate correlations with intensity of participation Male sex was moderately correlated with participation intensity in active-physical activities and being female had small correlations with participation intensity in self-improvement activities. Of interest, gestational age had a small and negative correlation with participation in recreational activities. Several child factors were moderately correlated with participation intensity. These include motor competence and

mastery motivation for participation intensity in active-physical activities, and cognitive ability for participation intensity in selfimprovement activities. Functional status was moderately correlated with the intensity of participation in active-physical, social and self-improvement activities. Perceiving oneself as socially accepted was moderately correlated with intensity of participation in active-physical and social activities, as was athletic competence with active-physical and close friendship with social activities. Activity preferences for recreational and activephysical activities had the strongest correlations with intensity of participation in those activities. Table 1 provides the correlations between the child factors with participation intensity. Several environmental factors, including family climate and friend support, were modestly correlated with participation intensity in active-physical, social, skilled-based and self-improvement activities (r between 0.18 and 0.29). Classmate support was moderately correlated (r = 0.35) with participation intensity in social activities, and maternal education was moderately correlated (r = 0.32) with participation intensity in active-physical activities. Physical, social and attitudinal barriers, as measured by the CASE, were not significantly associated with participation intensity in any activity. Table 2 provides the correlations between the environmental factors with participation intensity.

Table 5 Regression model for intensity of participation in social activities. Child factors

Age Sex (reference female) Gestational age Cognitive ability (Leiter-R) Motor competence- aiming and catching (MABC-2) Functional status—adaptive behavior composite (Vineland-II) Behavior—peer problems (SDQ) Mastery motivation—gross motor persistence (DMQ—parent) (DMQ—child) Self-perception—social acceptance (SPP) Preferences for social activities (PAC) Environmental factors Maternal education (reference ≤ high school) Social support—classmate support (SSS)

Parameter estimate

Bootstrap

Beta

95% CI

p

Estimate

BCa — 95% CI

0.09 −0.155 −0.055 0.0 −0.003 −0.001 −0.065

0.01, 0.17 −0.55, 0.24 −0.16, 0.05 −0.01, 0.01 −0.06, 0.05 −0.02, 0.02 −0.17, 0.04

0.03 0.436 0.315 0.930 0.930 0.933 0.205

0.09 −0.15 −0.05 −0.00 −0.00 −0.00 −0.07

0.01, 0.17 −0.55, 0.20 −0.17, 0.05 −0.01, 0.01 −0.06, 0.05 −0.02, 0.02 −0.17, 0.03

0.052 0.044 0.453 0.887

−0.19, 0.29 −0.18, 0.27 0.10, 0.81 0.32, 1.45

0.667 0.703 0.01 0.002

0.05 0.04 0.44 0.89

−0.19, 0.28 −0.20, 0.30 0.14, 0.73 0.35, 1.40

0.327 −0.076

−0.05, 0.70 −0.46, 0.31

0.087 0.695

0.33 −0.07

−0.08, 0.72 −0.40, 0.32

adjusted r2 = 0.32, n = 110. DMQ — Dimensions of Mastery Questionnaire, Leiter-R — Leiter-R brief IQ, MABC-2 — Movement Assessment Battery for Children-Second Edition, PAC — Preferences for Activities, SDQ — Strengths and Difficulties Questionnaire, SPP — Self-Perception Profile, SSS — Social Support Scale, Vineland-II — Vineland Adaptive Behavior Scales, Second Edition. Bold values indicate significance at pb0.05.

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Table 6 Regression model for intensity of participation in skill-based activities. Child factors

Age Sex (reference female) Gestational age Behavior—emotional symptoms (SDQ) Mastery motivation—social persistence with children (DMQ—child) Preferences for skill-based activities (PAC) Environmental factors Maternal education (reference ≤ high school) Family climate—active recreational orientation (FES) Social support—friend support (SSS)

Parameter estimate

Bootstrap

Beta

95% CI

p

Estimate

BCa — 95% CI

−0.003 −0.177 −0.015 −0.014 0.105 0.361

−0.05, 0.04 −0.33, 0.10 −0.08, 0.05 −0.06, 0.03 −0.04, 0.25 0.14, 0.59

0.903 0.11 0.626 0.537 0.142 0.002

−0.00 −0.11 −0.01 −0.02 0.11 0.36

−0.05, 0.04 −0.39, 0.09 −0.09, 0.05 −0.06, 0.04 −0.04, 0.24 0.06, 0.56

0.259 0.013 −0.067

0.05, 0.47 0, 0.02 −0.27, 0.14

0.015 0.007 0.514

0.25 0.01 −0.05

0.07, 0.50 0, 0.02 −0.35, 0.19

Adjusted r2 = 0.21, n = 112. DMQ — Dimensions of Mastery Questionnaire, FES — Family Environment Scale, PAC — Preferences for Activities, SDQ — Strengths and Difficulties Questionnaire, SSS — Social Support Scale.

3.3. Multivariate regression models for participation intensity Child and environmental factors contributed between 21% (skillbased) and 52% (active-physical) of the adjusted variance for participation intensity (Tables 3 to 7). Lower gestational age and preference for recreational activities were significantly associated with greater participation intensity in recreational activities (Table 3). Better motor competence in aiming and catching, higher maternal education, being male and preferring active-physical activities were significantly associated with participation intensity in active-physical activities (Table 4). Increasing age, perceiving oneself as being socially accepted, and preferring social activities were significantly associated with intensity of participation in social activities (Table 5). Higher maternal education, family orientation to leisure participation and preference for skillbased activities were significantly associated with greater participation in these activities (Table 6). In the multivariate regression model for self-improvement activities, preference for these activities was the only significant independent variable (Table 7). 4. Discussion The extent to which the child and environmental factors included in this study explained participation intensity varied depending on the type of leisure activity. These differences imply that the importance of a factor in determining or explaining participation intensity in a given activity may depend on the activity's underlying demands. Overall, the child and environmental factors in this study best explained participation intensity in active-physical activities (52% of the variance explained). For these types of activities (e.g. team sports, martial arts,

snow sports, individual physical activities), those with an increased risk of low engagement are adolescent females, adolescents with poor motor competence and low preference for physical activity and those from low socio-economic backgrounds. Skill-based activities (e.g. art lessons, swimming lessons, learning to dance, playing a musical instrument), on the other hand, had the most unexplained variance (79% unexplained). That being so, the results of this study indicate that adolescents with low preference for structured skill-building activities, those from low socio-economic backgrounds and from families not oriented to leisure participation are less likely to be engaged in skillbased activities. Understanding what may determine participation for this group is important for three reasons. First, studies on adolescents have consistently shown that regular involvement in active-physical and in structured skill-building activities is associated with better physical and emotional health, and with higher educational and occupational attainment later in life [5,35]. Second, active involvement in positive leisure activities may be especially beneficial for the preterm population, as these individuals are at increased risk for chronic noncommunicable diseases compared to peers born at term [36]. Third, adolescents and young adults born preterm have significantly lower levels of participation in physical activities than peers born at term [12,19] and they participate less in active-physical and skill-based activities than in sedentary, unstructured and social-based leisure activities [21]. Several child factors were associated with participation in different activity types. Sex was significantly associated with participation intensity in active-physical activities in adolescents born extremely preterm, consistent with the existing evidence showing lower participation rates

Table 7 Regression model for intensity of participation in self-improvement activities. Child factors

Age Sex (reference female) Gestational age Cognitive ability (Leiter-R) Functional status—adaptive behavior composite (Vineland-II) Behavior—prosocial (SDQ) Mastery motivation—object oriented persistence (DMQ—parent) Mastery motivation—mastery pleasure (DMQ—child) Preferences for self-improvement activities (PAC) Environmental factors Maternal education (reference ≤ high school) Family climate—intellectual cultural orientation (FES) Social support—teacher support (SSS)

Parameter estimate

Bootstrap

Beta

95% CI

p

Estimate

BCa – 95% CI

−0.063 −0.195 −0.059 0.009 −0.005 −0.037 0.112 0.147 0.955

−0.14, 0.01 −0.75, −0.03 −0.16, 0.04 0, 0.02 −0.02, 0.01 −0.14, 0.07 −0.15, 0.37 −0.07, 0.33 0.48, 1.44

0.10 0.272 0.237 0.096 0.463 0.493 0.389 0.180 ≤0.001

−0.06 −0.20 −0.06 0.01 −0.01 −0.04 0.11 0.15 0.94

−0.14, 0.02 −0.53, 0.13 −0.15, 0.05 0, 0.02 −0.02, 0.01 −0.12, 0.06 −0.15, 0.36 −0.11, 0.37 0.41, 1.62

−0.21, 0.52 −0.01, 0.03 −0.02, 0.53

0.396 0.294 0.073

0.16 0.01 0.25

−0.26, 0.57 −0.01, 0.03 −0.01, 0.55

0.157 0.009 0.252

Adjusted r2 = 0.27, n = 110. DMQ — Dimensions of Mastery Questionnaire, Leiter-R — Leiter-R brief IQ, FES — Family Environment Scale, PAC — Preferences for Activities, SDQ — Strengths and Difficulties Questionnaire, SSS — Social Support Scale, Vineland-II — Vineland Adaptive Behavior Scales, Second Edition. Bold values indicate significance at pb0.05.

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in physical activity for adolescent girls [37]. Age was positively associated only with participation intensity in social activities among adolescents born preterm. Although longitudinal studies have reported declining levels of physical activity with increasing age during adolescence [7], the cross-sectional design of the present study does not allow confirming those findings in adolescents born preterm. Motor competence was a significant determinant of participation intensity in active-physical activities among adolescents born preterm, similar to other studies among adolescents with cerebral palsy [17]. This finding is extremely important clinically, as significant motor impairments have been found to persist throughout childhood [13] and into young adulthood [24] in preterm survivors. Providing individualized physical activities adapted to skill level may encourage participation. Saigal and colleagues' [12] hypothesis that preference for a less physically active lifestyle may be associated with limited participation in sports in adolescents and young adults born preterm is confirmed by the findings of this study, as activity preference was shown to be strongly correlated with participation intensity in active-physical activities. Interestingly, lower gestational age was significantly associated with intensity of participation only in recreational activities, with adolescents born earlier participating more frequently in recreational activities than other adolescents born prematurely. Recreational activities (e.g. watching TV, playing video games) tend to be unstructured, passive and sedentary, and may be linked to negative outcomes, such as obesity, social withdrawal and diminished physical activity [38], suggesting that efforts should be directed at addressing the barriers to physical activity (i.e. poor motor competence, low maternal education, preference for a less physically active lifestyle) in order to increase active participation in adolescents born extremely preterm. Perceiving oneself as being socially accepted was also a significant determinant of participation intensity in social activities. These findings support the results of other studies that have shown that young adults who are born with extremely low birth weight are more shy, less sociable and have lower emotional well-being compared to normal birth weight adults [39]. Fostering positive self-perception in adolescents born preterm may therefore be of benefit for enhancing participation levels and for contributing to emotional and psychosocial well-being. Maternal education was significantly associated with participation in active-physical and skill-based activities, implying that adolescents from lower socio-economic backgrounds are at risk for low participation in such activities. This finding reinforces the need for affordable leisure opportunities for youth. In order to increase participation levels, leisure programs for youth should be adapted to individual skill level, include family and peer involvement, and be offered in the school system to indirectly counteract socio-economic status of the parents [40]. These recommendations are in line with the present study in which family climate was an important determinant of participation in structured skill-building activities among adolescents born preterm. This study has a number of strengths and limitations. It is the first study to explore the child and environmental factors associated with participation in five leisure activity types in adolescents born preterm. Several child and environmental factors cannot be readily modified (e.g. motor competence, cognitive ability, maternal education), but they can be useful to identify those adolescents most at risk for low engagement in specific activity types. Although a variety of factors were included and accounted for up to about half of the variance for participation in active-physical activities, identifying additional contributing factors for participation in skill-based and self-improvement activities is required to better understand this complex behavior. In the present study, due to sample size, the number of variables that could be entered in the regression models was limited; therefore identifying the contribution of other potential factors (e.g. parenting style, availability of leisure activities, transportation) was not possible. Because the independent variables collected through the self-report and parent-report questionnaires were personal perceptions not predictable and not related to severity of prematurity, missing data

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was dealt with listwise deletion, rather than with imputation. No correction was done for multiple comparisons; since this study aimed to examine the association of various factors with leisure participation, the impact of multiple comparisons on significance levels did not have an important effect on the findings. This study was cross-sectional; therefore the direction of the association between preference and actual participation, which was the most consistent finding across activities types, cannot be implied. Perhaps the next step is to understand which factors determine preferences for active-physical and skillbased activities in order to influence healthy leisure choices in adolescents born preterm. Future studies should validate the factors associated with leisure participation in this population using quantitative and qualitative designs and explore the effectiveness of health promotion initiatives encouraging leisure participation in this population. 5. Conclusion Our findings indicate that leisure activities should be adapted to individual skill level, include family and peers, and be affordable. Fostering social acceptance and helping families to understand the value of leisure should be targeted to increase participation. Early encouragement to actively participate in a wide range of positive opportunities may promote healthy choices in adolescence and optimize actual involvement in leisure in this high-risk population. Funding source All phases of this study were supported by a Canadian Institutes of Health Research grant # MOP-102720. Noémi Dahan-Oliel received doctoral fellowships from the Fonds de la Recherche du Québec-Santé (2010–2013) and the Montreal Hospital Research InstituteFoundation of Stars (2009–2010). This work was supported by infrastructure from the Montreal Children's Hospital-Research Institute and Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal, both of which are funded by the Fonds de la Recherche du Québec-Santé. Financial disclosure The authors have no financial relationships relevant to this article to disclose. Conflict of interest statement The authors have no conflicts of interest to disclose. Acknowledgments We are grateful to the adolescents and their families for their participation in this study. Many thanks to Dr. May Khairy for her recruitment advice, Patricia Grier, RN for making all the recruitment calls, Joey Waknin, Marie-Linda Boghdady and Christopher Saunders for their invaluable help in coordinating this project and Gevorg Chilingaryan for statistical assistance. Thanks to our testers Dr. Keiko Shikako-Thomas, Shira Vasilevsky, Melissa Turner, Rena Birnbaum, Marie-Elaine Lafrance, Corinne Mercier, Rochelle Rein, Nathalie Bilodeau, Dr. Marie Brossard Racine, Anna Radzioch and Dr. Catherine Zygmuntowicz. References [1] World Health Organization. International Statistical Classification of Diseases and Related Health Problems; 1992 [10th revision Geneva]. [2] World Health Organization. International Classification of Functioning, Disability and Health: Children and Youth Version; 2007 [Geneva]. [3] Arnett J. Emerging adulthood: a theory of development from the late teens through the twenties. Am Psychol 2000;55:469–80.

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