531469 research-article2014
HPQ0010.1177/1359105314531469Journal of Health PsychologySousa et al.
Article
Lifestyle and treatment adherence among overweight adolescents
Journal of Health Psychology 1–11 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1359105314531469 hpq.sagepub.com
Pedro Sousa1, Pedro Gaspar1, Helena Fonseca2,3 and Filomena Gaspar4
Abstract This study evaluated the influence of overweight adolescents’ lifestyle on the adherence to weight control, and identifies their predictors. Participants were 94 adolescents, aged 12–18 years, attending a Paediatric Obesity Clinic. Lifestyle was assessed using the “Adolescent Lifestyle Profile” and treatment adherence through the “Therapeutic Adherence to Weight Control Questionnaire.” Adherence to weight control was associated with various lifestyle domains. Several predictors were identified for lifestyle and adherence to weight control among overweight adolescents. A broad array of intercorrelations and predictors were identified and should be taken into account when designing adolescent weight control interventions.
Keywords adherence to weight control, adolescents, lifestyle, overweight, predictors
Introduction The prevalence of overweight and obesity among adolescents has dramatically increased, both in developed and developing countries (Carmo et al., 2006; Huang et al., 2013; Machado et al., 2011; Oude Luttikhuis et al., 2009; Padez et al., 2005; World Health Organization (WHO), 2002, 2006). Childhood and adolescence are critical periods for establishing a pattern of healthy behaviors and adoption of a healthier lifestyle (Commission of the European Communities, 2005, 2007). By the age of 15 years, many adolescents show a reliable level of competence in metacognitive understanding of decision-making, creative problem-solving, correctness of choice, and commitment to a course of action (Mann
et al., 1989). There is evidence that the implementation of strategies to prevent or reduce obesity prevalence may lead to significant gains in health outcomes (Pereira and Mateus, 2003; Steele et al., 2008). It is important to emphasize the central role of lifestyle in the understanding 1Polytechnic
Institute of Leiria, Portugal de Lisboa, Portugal 3Hospital de Santa Maria (HSM), Portugal 4School of Nursing of Lisbon, Portugal 2Universidade
Corresponding author: Pedro Sousa, Escola Superior de Saúde, Polytechnic Institute of Leiria, Campus 2, Morro do Lena, 2411-901 Leiria, Portugal. Email: [email protected]
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of obesity and behavior change processes (Brown et al., 2009). According to WHO (1986, 1988), lifestyle is defined both as a set of mediating structures which reflect the activities, attitudes, and social values, and as a cluster of behavioral patterns, depending on age, education, economic and social factors, among others. The adoption of unhealthy diets and weight-control behaviors often leads to an energy imbalance (NeumarkSztainer et al., 2012; Vivier and Tompkins, 2008; Zeller and Modi, 2008). Several studies have shown that a sedentary lifestyle may be responsible for weight gain, especially among younger children (Dietz, 2001; Giammattei et al., 2003; Wrotniak et al., 2004). Padez et al. (2009) studied the association between sleep duration, overweight, and body fat in a sample of Portuguese children and found that the prevalence of overweight and body fat percentage decreased with a long-lasting sleep pattern. Treating overweight patients implies adherence to behavioral changes and a healthier lifestyle (Elfhag and Rossner, 2005; Sousa, 2010; Walpole et al., 2011). There is little data on adherence rates to weight loss in interventions targeting adolescents and on their measurement and evaluation (França et al., 2013). It has been found that the majority of obese persons do not remain in weight-loss programs for a long time. Among those who remain, the majority do not lose weight and those who do, tend to return to their previous weight after some time (Brambilla et al., 2010). It is estimated that there is a 50% dropout rate among obese adolescents, and that less than 5% of those who achieve weight loss are able to maintain the weight loss after 5 years (França et al., 2013). Nogueira and Zambon (2013) analyzed potential reasons for nonadherence to follow-up at a specialized outpatient clinic for obese children and adolescents. Patients reported that poor adherence to the program was mainly due to the programs being time consuming and patients having to miss schools and other activities for coming to the clinic. Other reasons were refusal to proceed with the treatment, dissatisfaction with the results, change of health
service, difficulty in scheduling follow-up appointments, and spending too much time in the waiting room. The success of any intervention in this field lies in the control of individual needs, encouragement in the adoption of a health-promoting lifestyle, and promotion of adherence to the weight-control treatment and behavioral change strategies (Brown et al., 2009; Elfhag and Rossner, 2005; Sousa, 2010; Walpole et al., 2011). There is evidence that treatment adherence is critical for an effective weight-control management. Therefore, focusing on adherence can be the best investment for being successful (Davin and Taylor, 2009; WHO, 2003). Several aspects of health behaviors have shown to be influenced by gender (Conner et al., 2004; Ho et al., 2005). Previous studies also found relevant results in this domain. There is some evidence that social support mediates the relationship between weight and psychopathology in adolescence (Freitas-Rosa et al., 2013). Geographic location, gender, and nutritional status also seem to play an important role in body image concerns among adolescents (Laus et al., 2013). Therefore, these predictors should be taken into consideration in designing an intervention. The primary objective of this research was to investigate the association between body mass index (BMI) z-score, lifestyle, and adherence to weight control among overweight adolescents. A secondary objective was to identify potential predictors for both lifestyle and adherence to weight control.
Methods Study design and participants A cross-sectional correlational study was conducted. It was hypothesized that a higher BMI z-score would be associated with a worse health-promoting lifestyle and a worse adherence to weight control. It was further hypothesized that a greater health-promoting lifestyle was associated with a better adherence to weight control among overweight adolescents. We further hypothesized that lifestyle and
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Sousa et al. adherence to weight control were influenced by demographic, anthropometric, behavioral, and clinical variables. Study participants were enrolled through an ongoing longitudinal study on adolescent obesity and monitored by a quarterly survey. This study was conducted with the data from the baseline evaluation. Survey methods have been described in detail elsewhere (Sousa et al., 2013b). Participants (n = 94) were adolescents included in a Paediatric Obesity Management Program in Portugal, aged between 12 and 18 years, fulfilling the Centers for Disease Control’s (CDC) criteria for overweight (BMI percentile ≥ 85th). Exclusion criteria were the presence of severe psychopathology, inability to communicate in writing, pregnancy, and having been proposed for bariatric surgery. The program consisted of clinical assessment, medical, psychological, nutritional, and physical activity counseling. Sample recruitment had the support of the clinical staff. All eligible adolescents with appointments between 1 January and 31 December 2012 were included.
Procedures This study was approved by the Ethical Committee for Health (Lisbon, Portugal) in January 2012 and founded by the Foundation for Science and funded by the Fundação para a Ciência e a Tecnologia (Portugal) (PTDC/DTP-PIC/0769/2012). All eligible adolescents and respective parents signed an informed consent where the study objectives were explained. Confidentiality and voluntary participation were assured. Those who signed the informed consent were given a brochure with a summary of relevant information and e-contacts needed to enable them to fill out the data collection instruments online. The option of responding to the initial questionnaire on paper was also provided at the clinic.
Measures Data were collected during 2012 from different sources: clinical files (demographic, anthropometric, behavioral, and clinical variables) and
self-report instruments (AWCQ - Adherence to Weight Control Questionnaire, ALP - Adolescent Lifestyle Profile). The instruments are described in detail in the study protocol (Sousa et al., 2013a). Demographic variables. The demographic variables considered were age, gender, parental profession (grouped into classes 1 to 3, according to the differentiation degree), and parental education (higher education, third cycle, second cycle, first cycle, none). Anthropometric variables. Anthropometric data (BMI percentile, BMI z-score, and waist circumference percentile) were measured by trained health professionals at the clinic. The BMI cut-offs endorsed by the CDC (Kuczmarski et al., 2000) were used. Behavioral variables. Behavioral variables were weekly physical activity (h/w), screen time (h/w), family support and weight-loss motivation (2 Likert-type questions, range 1–5), and body image (sequence of seven silhouettes that evolve progressively from thinness to overweight). Clinical variables. Clinical variables included time elapsed since the first visit (in months), age at onset of obesity, and blood pressure percentiles. AWCQ. This instrument was developed and validated by Sousa et al. (2013a). This screening tool measures “Treatment Adherence to Weight Control” (TAWC) and the “Risk of Non-Adherence to Weight Control” (RNAWC) in adolescents, with a five-point Likert-type format. The TAWC (29 items) includes four subscales: SEA (Self-Efficacy and Adherence Behaviors), PPI (Parental’/Providers’ Influence), FSI (Friends’/School’s Influence), and PB (Perceived Benefits). The RNAWC (7 items) presented a one-factor solution. Both scales presented good reliability values (.908 and .770) and a five-point Likert-type format. A high TAWC score corresponds to a greater
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treatment adherence. Furthermore, a high RNAWC score corresponds to a greater risk of nonadherence. ALP. This instrument was designed to measure the frequency of health-promoting behaviors in adolescents (early, middle, and late). The Portuguese version of ALP was validated by Sousa et al. (2013c), from the original version of Hendricks et al. (2006). The Portuguese version is a 36-item summated behavior rating scale that employs a four-point Likert-type response format, organized into seven factors (Health Responsibility, Physical Activity, Nutrition, Positive Life Perspective, Interpersonal Relationship, Stress Management, and Spiritual Health). This model showed adequate adjustment indices: CMIN/DF (chi-square/degree of freedom) = 1.667, CFI (comparative fit index) = .807, GFI (goodness-offit index) = .822, RMR (root mean square residual) = .051, RMSEA (root mean square error of approximation) = .053, PNFI (Parsimony Normed Fit Index) = .575, PCFI (Parsimony Comparative Fit Index) = .731. The scale has a high reliability score (α = .866), subscale reliability values between .492 and .747. A high ALP score corresponds to a healthier lifestyle.
Data analysis Missing data at baseline were determined using the expectation-maximization method (by including data from the second wave of the ongoing longitudinal study). Descriptive statistics, including measures of frequency, central tendency, and distribution were used to describe the sample characteristics and study variables. Nonparametric tests were used in inferential statistics, due to a non-normal distribution of the data. Spearman correlation, Mann–Whitney U test, and Kruskal–Wallis with Bonferroni correction were used to assess the associations between the variables and test the hypothesis. All analyses were conducted using the SPSS v.18 software. A p value of .05 was used to control the type I error rate.
Results The characteristics of the sample are described in Table 1. The mean BMI z-score was 2.065 (standard deviation (SD) = 0.377), corresponding to a mean BMI percentile of 97.362 (SD = 2.193). Parents with a higher education were a minority (6.40% fathers; 10.00% mothers) and most parents worked in “services and sales” (25.60% fathers; 28.20% mothers). The high percentage of unemployed parents (11.50% fathers; 9.00% mothers) was noteworthy. Statistically significant differences between genders were only found for screen time (U = 157.000, p = .033), with boys spending more time in front of screens (24.820 ± 11.709 vs 17.625 ± 10.454). The overall lifestyle score was 2.604 (SD = 0.386), using a four-point scale. The analyses of the subscales showed the highest values for “Interpersonal Relationship” and “Positive Life Perspective” and the lowest for “Spiritual Health,” “Health Responsibility,” and “Physical Activity.” Regarding weight-control adherence (scale range: 1–5), the overall nonadherence score was 2.506 ± 0.864, while for the overall treatment adherence score, the value rose to 3.730 ± 0.576. Of note are the high levels of “Perceived Benefits” and “Parents’/Providers’ Influence.”
Association between BMI z-score and health-promoting lifestyle Spearman correlations between these two constructs were calculated. Weak and nonsignificant correlations were found between BMI z-score and the various subscales of lifestyle (p > .05) (Table 2).
Association between BMI z-score and adherence to weight control Spearman correlations between BMI z-score and adherence to weight control were weak and nonsignificant for the various subscales (p > .05) (Table 2).
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Sousa et al. Table 1. Descriptive statistics. Variables
Boys (N = 46)
Girls (N = 48)
Total (N = 94)
Age, years
M
SD
M
SD
M
14.457
1.441
13.896
1.533
14.170
2.157 95.692 92.000
0.360 14.643 3.114
1.989 96.980 91.848
0.364 2.177 3.337
2.065 97.362 91.797
5.278 24.820 4.060 3.708 5.714
4.411 11.709 .982 1.151 0.854
3.596 17.625 3.789 3.632 5.371
2.410 10.454 0.976 1.116 0.877
4.497 21.622 3.943 3.674 5.524
26.214 6.097 77.694 43.861
28.536 3.451 26.819 30.286
19.878 6.969 76.171 38.114
23.603 3.554 21.942 26.680
23.084 6.496 77.000 40.250
3.266 4.450 3.415 4.230 3.724 2.676
0.866 0.481 0.844 0.775 0.578 0.955
3.197 4.494 3.542 4.241 3.736 2.344
0.807 0.683 0.777 0.975 0.581 0.741
3.230 4.473 3.480 4.236 3.730 2.506
2.228 2.598 2.862 2.940 2.924 2.915 1.828 2.599
0.578 0.611 0.423 0.642 0.524 0.580 0.646 0.375
2.319 2.240 2.710 3.200 3.255 2.982 1.801 2.610
0.594 0.705 0.540 0.571 0.520 0.505 0.640 0.400
2.274 2.417 2.785 3.072 3.091 2.949 1.815 2.604
Anthropometric variables BMI z-score BMI percentile Waist circumference percentile Behavioral variables Weekly physical activity (h/w) Screen time (h/w) Family support Weight-loss motivation Body image silhouette Clinical variables Time elapsed since first visit (months) Age at onset of obesity (years) Systolic blood pressure percentile Diastolic blood pressure percentile Adherence to weight control Self-efficacy/adherence behaviors Parents’/providers’ influence Friends/school influence Perceived benefits TAWC total score Risk of nonadherence Lifestyle Health responsibility Physical activity Nutrition Positive life perspective Interpersonal relationship Stress management Spiritual health ALP total score
ALP: Adolescent Lifestyle Profile; BMI: body mass index; SD: standard deviation; TAWC: Treatment Adherence to Weight Control.
Association between health-promoting lifestyle and adherence to weight control The analysis of the correlation between lifestyle and adherence showed that adolescents with higher rates of health responsibility, also
presented higher rates of self-efficacy and adherence behaviors (rs = .469, p < .0001), were more influenced by parents and health professionals (rs = .219, p = .036) and showed higher indices of overall treatment adherence (rs = .387, p = < .0001). Individuals with higher levels of physical activity showed
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Table 2. Correlations between lifestyle, adherence to weight control, and BMI z-score. r Spearman
HR
Adherence to weight control .469** Self-Efficacy/ adherence behaviours .219* Parents’/providers’ influence .187 Friends/school influence Perceived benefits −.008 TAWC total score .387** Risk of nonadherence −.195 BMI z-score −.036
PA
N
PLP
IR
SM
SH .289**
Total ALP
.528**
.299**
.378**
.109
.306**
.510**
.030
.251*
.448**
.174
.314**
−.001
.146
.026
.181
.073
.259*
.065
.163
−.011 .391** −.151 −.040
.102 .278** −.158 .001
.169 .416** −.242* −119
−.004 .136 −.150 −.111
.046 .362** −.124 .015
.022 .212* −.101 .054
.079 .454** −.229* −.047
.279**
ALP: Adolescent Lifestyle Profile; BMI: body mass index; HR: health responsibility; IR: interpersonal relations; N: nutrition; PA: physical activity; PLP: positive life perspective; SH: spiritual health; SM: stress management; TAWC: Treatment Adherence to Weight Control. *p < .05; **p < .01.
higher rates of self-efficacy/adherence behaviors (rs = .528, p < .0001) and overall treatment adherence (rs= .391, p < .0001). Better nutrition rates appear to be associated with higher levels of self-efficacy and adherence behaviors (rs = .299, p = .004), higher parental and providers’ influence (rs = .251, p = .016), and higher overall treatment adherence (rs = .278, p = .007). Regarding the positive life-perspective subscale, a positive association was found between self-efficacy and adherence behaviors (rs = .378, p < .0001), parental and providers’ influence (rs = .448, p < .0001), and overall treatment adherence (rs = .416, p < .0001). However, the positive life perspective showed a negative correlation with risk of nonadherence (rs = −.242, p = .020). Presenting better stress management appears to contribute to higher rates of self-efficacy and adherence behaviors (rs = .306, p = .003), parental and providers’ influence (rs = .314, p = .002), as well as friends and school influence (rs = .259, p = .013), and even higher overall treatment adherence (rs = .362, p < .0001). Moreover, the higher the rates of spiritual health, the higher the self-efficacy and adherence behaviors (rs = .289, p = .005) and the overall treatment adherence (rs = .212, p = .044).
Finally, the higher the overall lifestyle score, the higher the self-efficacy and adherence behaviors (rs = .510, p < .0001), parental and providers’ influence (rs = .279, p = .007) and the overall treatment adherence (rs = .454, p < .0001). On the other hand, the higher the overall lifestyle score, the lower the risk of nonadherence (rs = −.229, p = .028) (Table 2).
Lifestyle predictors Demographic variables. Significant gender differences were found, with males showing higher rates of physical activity (2.620 ± 0.599 vs 2.228 ± 0.707, p = .013) and females exhibiting higher rates of interpersonal relationships (3.255 ± 0.526 vs 2.933 ± 0.526, p = .003) and a more positive life perspective (3.200 ± 0.571 vs 2.940 ± 0.642, p = .050). Behavioral variables. Adolescents with higher rates of weekly physical activity presented higher scores of health responsibility (rs = .307, p = .005), better nutrition (rs = .372, p = .008), and higher overall lifestyle score (rs = .389, p = .001). On the other hand, higher rates of physical inactivity were associated with worse nutrition (rs = −.344, p = .002) and poorer interpersonal relationships (rs = −.377, p = .014).
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Sousa et al. Table 3. Lifestyle predictors. Variables Demographic variables Age (rs) Gender (U) Mother’s education (H) Father’s education (H) Mothers’ profession (H) Father’s profession (H) Anthropometric variables BMI percentile (rs) Waist circumference percentile (rs) Behavioral variables Weekly physical activity (rs) Screen time (rs) Family support (rs) Weight loss motivation (rs) Body image silhouette (rs) Clinical variables Time elapsed since first visit (rs) Age at onset of obesity (rs)
HR
PA
N
PLP
IR
SM
SH
Total ALP
.072 977 2.395 1.256 .417 1.766
.163 760.0* 2.665 1.533 3.424 2.841
.194 931 2.315 4.057 2.276 .831
.080 833.0* 6.519 2.928 1.932 6.327
.127 702.5** 5.996 1.848 2.459 .242
−.044 993 2.562 4.070 2.356 .698
.107 1038.5 5.883 .763 1.786 2.838
.148 1037.5 3.652 .697 .603 .284
.741 −.150
.715 −.125
.992 .023
.275 −.057
.307 −.090
.889 .022
.624 −.016
.665 −.087
.307* −.197 .250 .198 −.200
.527** −.226 .245 .278 −.142
.372** −.344* .142 .204 −.027
.220 −.109 .005 .155 −.152
.211 −.377* −.008 .019 −.085
.089 −.097 −.185 −.097 −.150
.021 .185 .210 .164 −.120
.389** −.241 .180 .235 −.184
−.023 −.017
.110 .003
.359** −.143
.147 .009
.160 .104
.147 −.118
−.079 .041
.165 −.042
ALP: Adolescent Lifestyle Profile; HR: Health Responsibility; IR: Interpersonal Relations; N: Nutrition; PA: Physical Activity; PLP: Positive Life Perspective; SH: Spiritual Health; SM: Stress Management. rs: Spearman correlation; U: Mann–Whitney U test; H: Kruskal–Wallis test. *p < .05; **p < .01.
Clinical variables. The longer the time elapsed since the first visit, the higher the nutrition score (rs = .375, p = .001) (Table 3).
Adherence to weight-control predictors Demographic variables. Significant differences were found between educational levels of both parents regarding parental influence on adherence (mother: Higher Education = 4.875 ± 0.222 vs second cycle = 4.313 ± 0.521, p = .018; father: Higher Education = 4.875 ± 0.177 vs third cycle = 4.251 ± 0.695, p = .037). Significant differences were also found between parental occupation regarding parental influence on adherence (Executives and intellectual professions = 4.886 ± 0.163 vs Inactive workers = 4.417 ± 0.793, p = .037). Behavior variables. Adolescents who presented higher rates of family support tended to develop
a lower risk of nonadherence to weight control (rs = −.430, p = .003). Clinical variables. Higher waist percentiles were associated with an increase in the risk of nonadherence to weight control (rs = .258, p = .050). A weak negative correlation was found between time elapsed since the first visit and the selfefficacy/adherence behaviors score (rs = −.270, p = .036) (Table 4).
Discussion With its focus on weight control, this study identified a set of lifestyle and treatment adherence predictors. It has long been understood that lifestyle intervention is essential for successful treatment. A systematic review of Brown et al. (2009), aiming to determine the long-term effectiveness of lifestyle interventions in the prevention of overweight and morbidity, showed a significant positive impact on
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Table 4. Adherence to weight control predictors. Variables Demographic variables Age (rs) Gender (U) Mother’s education (H) Father’s education (H) Mothers’ profession (H) Father’s profession (H) Anthropometric variables BMI z-score (rs) BMI percentile (rs) Waist circumference percentile (rs) Behavioral variables Weekly physical activity (rs) Screen time (rs) Family support (rs) Weight loss motivation (rs) Body image silhouette (rs) Clinical variables Time elapsed since first visit (rs) Age at onset of obesity (rs)
SEA
PPI
FSI
PB
Total TAWC
RNAWC
.008 1020.500 .418 .141 1.517 2.702
−.109 877.500 11.960* 10.185* 4.048 8.464*
−.053 993.500 4.666 2.468 .447 .660
−.165 981.000 3.071 2.190 2.177 1.406
−.021 1015.000 1.505 1.545 .951 1.729
−.046 838.000 1.976 9.192 4.223 3.936
−.034 −.034 −.105
.003 .000 .095
.148 .145 −.027
.212 .211 .032
.056 .054 −.087
.067 .067 .258*
.223 −.202 .295 .297 −.079
.097 .005 .226 .034 .025
−.028 −.048 .080 .065 .008
.064 .101 −.170 −.269 .119
.162 −.081 .247 .159 −.040
−.175 .141 −.430** −.205 .032
−.270*
−.054
−.182
−.048
−.207
.083
.120
.132
.155
.047
.171
.095
FSI: Friends’/School’s Influence; PB: Perceived benefits; PPI: Parents’/Providers’ Influence; RNAWC: Risk of Non-Adherence to Weight Control; SEA: Self-Efficacy & Adherence behaviours; TAWC: Treatment Adherence to Weight Control. rs: Spearman correlation; U : Mann–Whitney U test; H: Kruskal–Wallis test. *p < .05; **p < .01.
weight, blood pressure reduction, type 2 diabetes, and metabolic syndrome risk. The analysis of the intercorrelations between lifestyle, adherence to weight control, and overweight revealed that BMI z-score was not the main factor responsible for lifestyle and treatment adherence, making it necessary to find other factors that may contribute to this variance. Unlike previous research, it was not possible to endorse the assumption that an effective overweight management implies patients’ adherence to behavioral changes and a healthier lifestyle (Elfhag and Rossner, 2005; Sousa, 2010). Our results indicate that adherence to weight control is closely related to lifestyle. There is some indication that health responsibility, physical activity, nutrition, positive life perspective, stress management, and spiritual health, all contribute to several domains of adherence,
including self-efficacy/adherence behaviors, parental/providers’ influence, friends/school influence, and risk of nonadherence. These results underscore the importance of behavioral change and lifestyle as pillars of adherence to weight control. Based on our results, some lifestyle predictors were identified. The lifestyle domains which scored lower were “Physical Activity,” “Health Responsibility,” and “Spiritual Health.” Brownell et al. (2010) had already noted that the concept of personal health responsibility is a fundamental concept in social and political approaches to obesity, pointing out that the fight against obesity should imply the promotion of personal and collective responsibility. Furthermore, our findings suggest that adolescents with higher scores of health responsibility tend to score higher for weekly physical
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Sousa et al. activity. In this domain, we found interesting gender differences, with boys showing higher rates of physical activity, while girls showed higher interpersonal skills. Higher rates of weekly physical activity were associated with higher rates of health responsibility, a better nutrition, and a higher overall lifestyle score. On the other hand, higher rates of sedentary lifestyle were associated with lower interpersonal skills and nutrition quality. Earlier studies stressed the importance of promoting physical activity among overweight adolescents (Stankov et al., 2012; Ullrich-French and McDonough, 2013). However, overweight adolescents experience several barriers to participation in physical activity, such as physical discomfort and fatigue. Moreover, stigmatization and peer discrimination can negatively reinforce their negative selfperception, negatively impacting their psychosocial development and increasing their psychosocial vulnerability. In this research, the “adherence to weight control” domains that scored higher were benefit perception and recognition of parental/providers’ influence. Another important result was the fact that parents with a more differentiated profession and higher education seemed to be able to influence more positively the adolescents’ adherence to weight control. These data are in line with the WHO (2006) report, which states that there is an association between overweight and obesity and lower socioeconomic status, which in turn contributes to an increase of inequalities in health. Findings suggest a positive association between the waist circumference percentile and the risk of nonadherence. Interestingly, our findings indicate that the longer the time elapsed since the first visit, the lower the selfefficacy for adherence. On the one hand, we are aware that behavioral changes are complex, demanding, and usually require long periods of treatment/monitoring. On the other hand, and according to the results, it seems that adolescents, as time goes by, start to believe less in treatment success and in their ability for change.
Results also indicate that the higher the waist circumference percentile, the higher the risk of nonadherence. Adherence to weight control is a critical component for successful obesity management (Walpole et al., 2011). The problem of nonadherence remains a challenge for both health professionals and researchers and is responsible for a significant number of people not getting the maximum benefit from obesitymanagement programs, leading to poor health outcomes, reduced quality of life, and increased healthcare costs (Dulmen et al., 2007). The strengths of this study include the use of obesity-specific instruments, usually more sensitive than generic instruments, thereby reducing the noise of medical comorbidities (Zeller and Modi, 2008). One limitation of the study is the nonrandomized sample that prevents the generalization of results. Moreover, the limited amplitude of BMI percentiles did not allow a full exploration of potential differences in psycho-social variables according to the overweight degree (Zeller and Modi, 2008). A third limitation is the analysis of separate models for each correlation, which may have led to misleading interpretations, as an intercorrelation among the different variables is expected. Future research should use a larger and randomized sample, with a larger BMI variance, as well as use complementary techniques of multivariate analysis.
Conclusion These results underline the importance of behavioral change and the adoption of a healthier lifestyle as pillars for adherence to weight control. Tailored obesity-management programs should be designed not only according to adolescent health needs but also taking into account the broad array of predictors that have been identified. Acknowledgements We gratefully acknowledge the clinical staff of the Paediatric Obesity Clinic for their dedication. We also thank all the adolescents and parents for their participation and collaboration.
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Funding This work was partially funded by Fundação para a Ciência e a Tecnologia (PTDC/DTP-PIC/0769/2012) and supported by the Polytechnic Institute of Leiria, Portugal, and the Department of Paediatrics at Hospital de Santa Maria, Lisbon, Portugal.
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França S, Sahade V, Nunes M, et al. (2013) Adherence to nutritional therapy in obese adolescents: A review. Nutrición Hospitalaria 28(4): 988–998. Freitas-Rosa M, Goncalves S and Antunes H (2013) The relationship between weight status and psychopathology in adolescence: The role of social support. Journal of Health Psychology. Epub ahead of print 12 April. DOI: 10.1177/1359105313482162. Giammattei J, Blix G, Marshak H, et al. (2003) Television watching and soft drink consumption: Associations with obesity in 11- to 13-yearold schoolchildren. Archives of Pediatrics and Adolescent Medicine 157(9): 882–886. Hendricks C, Murdaugh C and Pender N (2006) The Adolescent Lifestyle Profile: Development and psychometric characteristics. Journal of National Black Nurses Association 17(2): 1–5. Ho R, Davidson G and Ghea V (2005) Motives for the adoption of protective health behaviours for men and women: An evaluation of the psychosocial-appraisal health model. Journal of Health Psychology 10(3): 373–395. Huang D, Lanza H, Wright-Volel K, et al. (2013) Developmental trajectories of childhood obesity and risk behaviors in adolescence. Journal of Adolescence 36: 139–148. Kuczmarski, R., Ogden, C., & Grummer-Strawn, L. (2000). CDC growth charts: United States. Advanced Data 314: 1–27. Laus M, Miranda V, Almeida S, et al. (2013) Geographic location, sex and nutritional status play an important role in body image concerns among Brazilian adolescents. Journal of Health Psychology 18: 332–338. Machado M, Alves M and Couceiro M (2011) Saúde Infantil e Juvenil em Portugal: Indicadores do Plano Nacional de Saúde. Acta Pediátrica Portuguesa 42(5): 195–204. Mann L, Harmoni R and Power C (1989) Adolescent decision-making: The development of competence. Journal of Adolescence 12(3): 265–278. Neumark-Sztainer D, Wall M, Story M, et al. (2012) Dieting and unhealthy weight control behaviors during adolescence: Associations with 10-year changes in body mass index. Journal of Adolescent Health 50(1): 80–86. Nogueira T and Zambon M (2013) Reasons for non-adherence to obesity treatment in children and adolescents. Revista Paulista de Pediatria 31(3): 338–343.
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Sousa et al. Oude Luttikhuis H, Baur L, Jansen H, et al. (2009) Interventions for treating obesity in children. Cochrane Database of Systematic Reviews 1: CD001872. Padez C, Mourão I, Moreira P, et al. (2005) Prevalence and risk factors for overweight and obesity in Portuguese children. Acta Paediatrica 94(11): 1550–1557. Padez C, Mourão I, Moreira P, et al. (2009) Long sleep duration and childhood overweight/obesity and body fat. American Journal of Human Biology 21: 371–376. Pereira J and Mateus C (2003) Custos indirectos associados à obesidade em Portugal. Revista Portuguesa de Saúde Pública 3: 65–80. Sousa C (2010) O papel da mudança comportamental no tratamento da obesidade. MSc Dissertation, University of Porto, Porto. Sousa P, Fonseca H and Gaspar P (2013a) Measuring adherence to weight control in adolescents: Development and validation of a self-report questionnaire. Atención Primaria 45(Suppl. I): 70–71. Sousa P, Fonseca H, Gaspar P, et al. (2013b) Internet-based intervention programme for obese adolescents and their families (Next. Step): Research protocol of a controlled trial. Journal of Advanced Nursing. Epub ahead of print 11 September. DOI: 10.1111/jan.12245. Sousa P, Gaspar P, Fonseca H, et al. (2013c) Health promoting behaviours in adolescence: Psychometric properties of the Adolescent Lifestyle Profile PT version. Atención Primaria 45(Suppl. I): 140. Stankov I, Olds T and Cargo M (2012) Overweight and obese adolescents: What turns them off physical activity? International Journal of Behavioral Nutrition and Physical Activity 9: e53. Steele R, Nelson T and Jelalian E (2008) Pediatric obesity: Trends and epidemiology. In: Jelalian E and Steele R (eds) Handbook of Childhood and Adolescent Obesity. New York: Springer, pp. 3–10.
Ullrich-French S and McDonough M (2013) Correlates of long-term participation in a physical activity-based positive youth development program for low-income youth: Sustained involvement and psychosocial outcomes. Journal of Adolescence 36(2): 279–288. Vivier P and Tompkins C (2008) Health consequences of obesity in children and adolescents. In: Jelalian E and Steele RG (eds) Handbook of Childhood and Adolescent Obesity. New York: Springer, pp. 11–24. Walpole B, Dettmer E, Morrongiello B, et al. (2011) Motivational interviewing as an intervention to increase adolescent self-efficacy and promote weight loss: Methodology and design. BMC Public Health 11: 459. World Health Organization (WHO) (1986) Ottawa Charter for Health Promotion. Geneva: WHO. World Health Organization (WHO) (1988) Adelaide Recommendations on Healthy Public Policy. Copenhagen: WHO. World Health Organization (WHO) (2002) Diet, Nutrition and the Prevention of Chronic Diseases (Report of the joint WHO/FAO expert consultation). Geneva: WHO. World Health Organization (WHO) (2003) Adherence to Long-Term Therapies: Evidence for Action. Geneva: WHO. World Health Organization (WHO) (2006) The European Charter on Counteracting Obesity. Istanbul: WHO. Wrotniak B, Epstein L, Paluch R, et al. (2004) Parent weight change as a predictor of child weight change in family-based behavioral obesity treatment. Archives of Pediatrics and Adolescent Medicine 158: 342–347. Zeller M and Modi A (2008) Psychosocial factors related to obesity in children and adolescents. In: Jelalian E and Steele R (eds) Handbook of Childhood and Adolescent Obesity. New York: Springer, pp. 25–42.
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HPQ0010.1177/1359105314531469Journal of Health PsychologySousa et al.
Article
Lifestyle and treatment adherence among overweight adolescents
Journal of Health Psychology 1–11 © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1359105314531469 hpq.sagepub.com
Pedro Sousa1, Pedro Gaspar1, Helena Fonseca2,3 and Filomena Gaspar4
Abstract This study evaluated the influence of overweight adolescents’ lifestyle on the adherence to weight control, and identifies their predictors. Participants were 94 adolescents, aged 12–18 years, attending a Paediatric Obesity Clinic. Lifestyle was assessed using the “Adolescent Lifestyle Profile” and treatment adherence through the “Therapeutic Adherence to Weight Control Questionnaire.” Adherence to weight control was associated with various lifestyle domains. Several predictors were identified for lifestyle and adherence to weight control among overweight adolescents. A broad array of intercorrelations and predictors were identified and should be taken into account when designing adolescent weight control interventions.
Keywords adherence to weight control, adolescents, lifestyle, overweight, predictors
Introduction The prevalence of overweight and obesity among adolescents has dramatically increased, both in developed and developing countries (Carmo et al., 2006; Huang et al., 2013; Machado et al., 2011; Oude Luttikhuis et al., 2009; Padez et al., 2005; World Health Organization (WHO), 2002, 2006). Childhood and adolescence are critical periods for establishing a pattern of healthy behaviors and adoption of a healthier lifestyle (Commission of the European Communities, 2005, 2007). By the age of 15 years, many adolescents show a reliable level of competence in metacognitive understanding of decision-making, creative problem-solving, correctness of choice, and commitment to a course of action (Mann
et al., 1989). There is evidence that the implementation of strategies to prevent or reduce obesity prevalence may lead to significant gains in health outcomes (Pereira and Mateus, 2003; Steele et al., 2008). It is important to emphasize the central role of lifestyle in the understanding 1Polytechnic
Institute of Leiria, Portugal de Lisboa, Portugal 3Hospital de Santa Maria (HSM), Portugal 4School of Nursing of Lisbon, Portugal 2Universidade
Corresponding author: Pedro Sousa, Escola Superior de Saúde, Polytechnic Institute of Leiria, Campus 2, Morro do Lena, 2411-901 Leiria, Portugal. Email: [email protected]
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of obesity and behavior change processes (Brown et al., 2009). According to WHO (1986, 1988), lifestyle is defined both as a set of mediating structures which reflect the activities, attitudes, and social values, and as a cluster of behavioral patterns, depending on age, education, economic and social factors, among others. The adoption of unhealthy diets and weight-control behaviors often leads to an energy imbalance (NeumarkSztainer et al., 2012; Vivier and Tompkins, 2008; Zeller and Modi, 2008). Several studies have shown that a sedentary lifestyle may be responsible for weight gain, especially among younger children (Dietz, 2001; Giammattei et al., 2003; Wrotniak et al., 2004). Padez et al. (2009) studied the association between sleep duration, overweight, and body fat in a sample of Portuguese children and found that the prevalence of overweight and body fat percentage decreased with a long-lasting sleep pattern. Treating overweight patients implies adherence to behavioral changes and a healthier lifestyle (Elfhag and Rossner, 2005; Sousa, 2010; Walpole et al., 2011). There is little data on adherence rates to weight loss in interventions targeting adolescents and on their measurement and evaluation (França et al., 2013). It has been found that the majority of obese persons do not remain in weight-loss programs for a long time. Among those who remain, the majority do not lose weight and those who do, tend to return to their previous weight after some time (Brambilla et al., 2010). It is estimated that there is a 50% dropout rate among obese adolescents, and that less than 5% of those who achieve weight loss are able to maintain the weight loss after 5 years (França et al., 2013). Nogueira and Zambon (2013) analyzed potential reasons for nonadherence to follow-up at a specialized outpatient clinic for obese children and adolescents. Patients reported that poor adherence to the program was mainly due to the programs being time consuming and patients having to miss schools and other activities for coming to the clinic. Other reasons were refusal to proceed with the treatment, dissatisfaction with the results, change of health
service, difficulty in scheduling follow-up appointments, and spending too much time in the waiting room. The success of any intervention in this field lies in the control of individual needs, encouragement in the adoption of a health-promoting lifestyle, and promotion of adherence to the weight-control treatment and behavioral change strategies (Brown et al., 2009; Elfhag and Rossner, 2005; Sousa, 2010; Walpole et al., 2011). There is evidence that treatment adherence is critical for an effective weight-control management. Therefore, focusing on adherence can be the best investment for being successful (Davin and Taylor, 2009; WHO, 2003). Several aspects of health behaviors have shown to be influenced by gender (Conner et al., 2004; Ho et al., 2005). Previous studies also found relevant results in this domain. There is some evidence that social support mediates the relationship between weight and psychopathology in adolescence (Freitas-Rosa et al., 2013). Geographic location, gender, and nutritional status also seem to play an important role in body image concerns among adolescents (Laus et al., 2013). Therefore, these predictors should be taken into consideration in designing an intervention. The primary objective of this research was to investigate the association between body mass index (BMI) z-score, lifestyle, and adherence to weight control among overweight adolescents. A secondary objective was to identify potential predictors for both lifestyle and adherence to weight control.
Methods Study design and participants A cross-sectional correlational study was conducted. It was hypothesized that a higher BMI z-score would be associated with a worse health-promoting lifestyle and a worse adherence to weight control. It was further hypothesized that a greater health-promoting lifestyle was associated with a better adherence to weight control among overweight adolescents. We further hypothesized that lifestyle and
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Sousa et al. adherence to weight control were influenced by demographic, anthropometric, behavioral, and clinical variables. Study participants were enrolled through an ongoing longitudinal study on adolescent obesity and monitored by a quarterly survey. This study was conducted with the data from the baseline evaluation. Survey methods have been described in detail elsewhere (Sousa et al., 2013b). Participants (n = 94) were adolescents included in a Paediatric Obesity Management Program in Portugal, aged between 12 and 18 years, fulfilling the Centers for Disease Control’s (CDC) criteria for overweight (BMI percentile ≥ 85th). Exclusion criteria were the presence of severe psychopathology, inability to communicate in writing, pregnancy, and having been proposed for bariatric surgery. The program consisted of clinical assessment, medical, psychological, nutritional, and physical activity counseling. Sample recruitment had the support of the clinical staff. All eligible adolescents with appointments between 1 January and 31 December 2012 were included.
Procedures This study was approved by the Ethical Committee for Health (Lisbon, Portugal) in January 2012 and founded by the Foundation for Science and funded by the Fundação para a Ciência e a Tecnologia (Portugal) (PTDC/DTP-PIC/0769/2012). All eligible adolescents and respective parents signed an informed consent where the study objectives were explained. Confidentiality and voluntary participation were assured. Those who signed the informed consent were given a brochure with a summary of relevant information and e-contacts needed to enable them to fill out the data collection instruments online. The option of responding to the initial questionnaire on paper was also provided at the clinic.
Measures Data were collected during 2012 from different sources: clinical files (demographic, anthropometric, behavioral, and clinical variables) and
self-report instruments (AWCQ - Adherence to Weight Control Questionnaire, ALP - Adolescent Lifestyle Profile). The instruments are described in detail in the study protocol (Sousa et al., 2013a). Demographic variables. The demographic variables considered were age, gender, parental profession (grouped into classes 1 to 3, according to the differentiation degree), and parental education (higher education, third cycle, second cycle, first cycle, none). Anthropometric variables. Anthropometric data (BMI percentile, BMI z-score, and waist circumference percentile) were measured by trained health professionals at the clinic. The BMI cut-offs endorsed by the CDC (Kuczmarski et al., 2000) were used. Behavioral variables. Behavioral variables were weekly physical activity (h/w), screen time (h/w), family support and weight-loss motivation (2 Likert-type questions, range 1–5), and body image (sequence of seven silhouettes that evolve progressively from thinness to overweight). Clinical variables. Clinical variables included time elapsed since the first visit (in months), age at onset of obesity, and blood pressure percentiles. AWCQ. This instrument was developed and validated by Sousa et al. (2013a). This screening tool measures “Treatment Adherence to Weight Control” (TAWC) and the “Risk of Non-Adherence to Weight Control” (RNAWC) in adolescents, with a five-point Likert-type format. The TAWC (29 items) includes four subscales: SEA (Self-Efficacy and Adherence Behaviors), PPI (Parental’/Providers’ Influence), FSI (Friends’/School’s Influence), and PB (Perceived Benefits). The RNAWC (7 items) presented a one-factor solution. Both scales presented good reliability values (.908 and .770) and a five-point Likert-type format. A high TAWC score corresponds to a greater
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treatment adherence. Furthermore, a high RNAWC score corresponds to a greater risk of nonadherence. ALP. This instrument was designed to measure the frequency of health-promoting behaviors in adolescents (early, middle, and late). The Portuguese version of ALP was validated by Sousa et al. (2013c), from the original version of Hendricks et al. (2006). The Portuguese version is a 36-item summated behavior rating scale that employs a four-point Likert-type response format, organized into seven factors (Health Responsibility, Physical Activity, Nutrition, Positive Life Perspective, Interpersonal Relationship, Stress Management, and Spiritual Health). This model showed adequate adjustment indices: CMIN/DF (chi-square/degree of freedom) = 1.667, CFI (comparative fit index) = .807, GFI (goodness-offit index) = .822, RMR (root mean square residual) = .051, RMSEA (root mean square error of approximation) = .053, PNFI (Parsimony Normed Fit Index) = .575, PCFI (Parsimony Comparative Fit Index) = .731. The scale has a high reliability score (α = .866), subscale reliability values between .492 and .747. A high ALP score corresponds to a healthier lifestyle.
Data analysis Missing data at baseline were determined using the expectation-maximization method (by including data from the second wave of the ongoing longitudinal study). Descriptive statistics, including measures of frequency, central tendency, and distribution were used to describe the sample characteristics and study variables. Nonparametric tests were used in inferential statistics, due to a non-normal distribution of the data. Spearman correlation, Mann–Whitney U test, and Kruskal–Wallis with Bonferroni correction were used to assess the associations between the variables and test the hypothesis. All analyses were conducted using the SPSS v.18 software. A p value of .05 was used to control the type I error rate.
Results The characteristics of the sample are described in Table 1. The mean BMI z-score was 2.065 (standard deviation (SD) = 0.377), corresponding to a mean BMI percentile of 97.362 (SD = 2.193). Parents with a higher education were a minority (6.40% fathers; 10.00% mothers) and most parents worked in “services and sales” (25.60% fathers; 28.20% mothers). The high percentage of unemployed parents (11.50% fathers; 9.00% mothers) was noteworthy. Statistically significant differences between genders were only found for screen time (U = 157.000, p = .033), with boys spending more time in front of screens (24.820 ± 11.709 vs 17.625 ± 10.454). The overall lifestyle score was 2.604 (SD = 0.386), using a four-point scale. The analyses of the subscales showed the highest values for “Interpersonal Relationship” and “Positive Life Perspective” and the lowest for “Spiritual Health,” “Health Responsibility,” and “Physical Activity.” Regarding weight-control adherence (scale range: 1–5), the overall nonadherence score was 2.506 ± 0.864, while for the overall treatment adherence score, the value rose to 3.730 ± 0.576. Of note are the high levels of “Perceived Benefits” and “Parents’/Providers’ Influence.”
Association between BMI z-score and health-promoting lifestyle Spearman correlations between these two constructs were calculated. Weak and nonsignificant correlations were found between BMI z-score and the various subscales of lifestyle (p > .05) (Table 2).
Association between BMI z-score and adherence to weight control Spearman correlations between BMI z-score and adherence to weight control were weak and nonsignificant for the various subscales (p > .05) (Table 2).
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Sousa et al. Table 1. Descriptive statistics. Variables
Boys (N = 46)
Girls (N = 48)
Total (N = 94)
Age, years
M
SD
M
SD
M
14.457
1.441
13.896
1.533
14.170
2.157 95.692 92.000
0.360 14.643 3.114
1.989 96.980 91.848
0.364 2.177 3.337
2.065 97.362 91.797
5.278 24.820 4.060 3.708 5.714
4.411 11.709 .982 1.151 0.854
3.596 17.625 3.789 3.632 5.371
2.410 10.454 0.976 1.116 0.877
4.497 21.622 3.943 3.674 5.524
26.214 6.097 77.694 43.861
28.536 3.451 26.819 30.286
19.878 6.969 76.171 38.114
23.603 3.554 21.942 26.680
23.084 6.496 77.000 40.250
3.266 4.450 3.415 4.230 3.724 2.676
0.866 0.481 0.844 0.775 0.578 0.955
3.197 4.494 3.542 4.241 3.736 2.344
0.807 0.683 0.777 0.975 0.581 0.741
3.230 4.473 3.480 4.236 3.730 2.506
2.228 2.598 2.862 2.940 2.924 2.915 1.828 2.599
0.578 0.611 0.423 0.642 0.524 0.580 0.646 0.375
2.319 2.240 2.710 3.200 3.255 2.982 1.801 2.610
0.594 0.705 0.540 0.571 0.520 0.505 0.640 0.400
2.274 2.417 2.785 3.072 3.091 2.949 1.815 2.604
Anthropometric variables BMI z-score BMI percentile Waist circumference percentile Behavioral variables Weekly physical activity (h/w) Screen time (h/w) Family support Weight-loss motivation Body image silhouette Clinical variables Time elapsed since first visit (months) Age at onset of obesity (years) Systolic blood pressure percentile Diastolic blood pressure percentile Adherence to weight control Self-efficacy/adherence behaviors Parents’/providers’ influence Friends/school influence Perceived benefits TAWC total score Risk of nonadherence Lifestyle Health responsibility Physical activity Nutrition Positive life perspective Interpersonal relationship Stress management Spiritual health ALP total score
ALP: Adolescent Lifestyle Profile; BMI: body mass index; SD: standard deviation; TAWC: Treatment Adherence to Weight Control.
Association between health-promoting lifestyle and adherence to weight control The analysis of the correlation between lifestyle and adherence showed that adolescents with higher rates of health responsibility, also
presented higher rates of self-efficacy and adherence behaviors (rs = .469, p < .0001), were more influenced by parents and health professionals (rs = .219, p = .036) and showed higher indices of overall treatment adherence (rs = .387, p = < .0001). Individuals with higher levels of physical activity showed
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Table 2. Correlations between lifestyle, adherence to weight control, and BMI z-score. r Spearman
HR
Adherence to weight control .469** Self-Efficacy/ adherence behaviours .219* Parents’/providers’ influence .187 Friends/school influence Perceived benefits −.008 TAWC total score .387** Risk of nonadherence −.195 BMI z-score −.036
PA
N
PLP
IR
SM
SH .289**
Total ALP
.528**
.299**
.378**
.109
.306**
.510**
.030
.251*
.448**
.174
.314**
−.001
.146
.026
.181
.073
.259*
.065
.163
−.011 .391** −.151 −.040
.102 .278** −.158 .001
.169 .416** −.242* −119
−.004 .136 −.150 −.111
.046 .362** −.124 .015
.022 .212* −.101 .054
.079 .454** −.229* −.047
.279**
ALP: Adolescent Lifestyle Profile; BMI: body mass index; HR: health responsibility; IR: interpersonal relations; N: nutrition; PA: physical activity; PLP: positive life perspective; SH: spiritual health; SM: stress management; TAWC: Treatment Adherence to Weight Control. *p < .05; **p < .01.
higher rates of self-efficacy/adherence behaviors (rs = .528, p < .0001) and overall treatment adherence (rs= .391, p < .0001). Better nutrition rates appear to be associated with higher levels of self-efficacy and adherence behaviors (rs = .299, p = .004), higher parental and providers’ influence (rs = .251, p = .016), and higher overall treatment adherence (rs = .278, p = .007). Regarding the positive life-perspective subscale, a positive association was found between self-efficacy and adherence behaviors (rs = .378, p < .0001), parental and providers’ influence (rs = .448, p < .0001), and overall treatment adherence (rs = .416, p < .0001). However, the positive life perspective showed a negative correlation with risk of nonadherence (rs = −.242, p = .020). Presenting better stress management appears to contribute to higher rates of self-efficacy and adherence behaviors (rs = .306, p = .003), parental and providers’ influence (rs = .314, p = .002), as well as friends and school influence (rs = .259, p = .013), and even higher overall treatment adherence (rs = .362, p < .0001). Moreover, the higher the rates of spiritual health, the higher the self-efficacy and adherence behaviors (rs = .289, p = .005) and the overall treatment adherence (rs = .212, p = .044).
Finally, the higher the overall lifestyle score, the higher the self-efficacy and adherence behaviors (rs = .510, p < .0001), parental and providers’ influence (rs = .279, p = .007) and the overall treatment adherence (rs = .454, p < .0001). On the other hand, the higher the overall lifestyle score, the lower the risk of nonadherence (rs = −.229, p = .028) (Table 2).
Lifestyle predictors Demographic variables. Significant gender differences were found, with males showing higher rates of physical activity (2.620 ± 0.599 vs 2.228 ± 0.707, p = .013) and females exhibiting higher rates of interpersonal relationships (3.255 ± 0.526 vs 2.933 ± 0.526, p = .003) and a more positive life perspective (3.200 ± 0.571 vs 2.940 ± 0.642, p = .050). Behavioral variables. Adolescents with higher rates of weekly physical activity presented higher scores of health responsibility (rs = .307, p = .005), better nutrition (rs = .372, p = .008), and higher overall lifestyle score (rs = .389, p = .001). On the other hand, higher rates of physical inactivity were associated with worse nutrition (rs = −.344, p = .002) and poorer interpersonal relationships (rs = −.377, p = .014).
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Sousa et al. Table 3. Lifestyle predictors. Variables Demographic variables Age (rs) Gender (U) Mother’s education (H) Father’s education (H) Mothers’ profession (H) Father’s profession (H) Anthropometric variables BMI percentile (rs) Waist circumference percentile (rs) Behavioral variables Weekly physical activity (rs) Screen time (rs) Family support (rs) Weight loss motivation (rs) Body image silhouette (rs) Clinical variables Time elapsed since first visit (rs) Age at onset of obesity (rs)
HR
PA
N
PLP
IR
SM
SH
Total ALP
.072 977 2.395 1.256 .417 1.766
.163 760.0* 2.665 1.533 3.424 2.841
.194 931 2.315 4.057 2.276 .831
.080 833.0* 6.519 2.928 1.932 6.327
.127 702.5** 5.996 1.848 2.459 .242
−.044 993 2.562 4.070 2.356 .698
.107 1038.5 5.883 .763 1.786 2.838
.148 1037.5 3.652 .697 .603 .284
.741 −.150
.715 −.125
.992 .023
.275 −.057
.307 −.090
.889 .022
.624 −.016
.665 −.087
.307* −.197 .250 .198 −.200
.527** −.226 .245 .278 −.142
.372** −.344* .142 .204 −.027
.220 −.109 .005 .155 −.152
.211 −.377* −.008 .019 −.085
.089 −.097 −.185 −.097 −.150
.021 .185 .210 .164 −.120
.389** −.241 .180 .235 −.184
−.023 −.017
.110 .003
.359** −.143
.147 .009
.160 .104
.147 −.118
−.079 .041
.165 −.042
ALP: Adolescent Lifestyle Profile; HR: Health Responsibility; IR: Interpersonal Relations; N: Nutrition; PA: Physical Activity; PLP: Positive Life Perspective; SH: Spiritual Health; SM: Stress Management. rs: Spearman correlation; U: Mann–Whitney U test; H: Kruskal–Wallis test. *p < .05; **p < .01.
Clinical variables. The longer the time elapsed since the first visit, the higher the nutrition score (rs = .375, p = .001) (Table 3).
Adherence to weight-control predictors Demographic variables. Significant differences were found between educational levels of both parents regarding parental influence on adherence (mother: Higher Education = 4.875 ± 0.222 vs second cycle = 4.313 ± 0.521, p = .018; father: Higher Education = 4.875 ± 0.177 vs third cycle = 4.251 ± 0.695, p = .037). Significant differences were also found between parental occupation regarding parental influence on adherence (Executives and intellectual professions = 4.886 ± 0.163 vs Inactive workers = 4.417 ± 0.793, p = .037). Behavior variables. Adolescents who presented higher rates of family support tended to develop
a lower risk of nonadherence to weight control (rs = −.430, p = .003). Clinical variables. Higher waist percentiles were associated with an increase in the risk of nonadherence to weight control (rs = .258, p = .050). A weak negative correlation was found between time elapsed since the first visit and the selfefficacy/adherence behaviors score (rs = −.270, p = .036) (Table 4).
Discussion With its focus on weight control, this study identified a set of lifestyle and treatment adherence predictors. It has long been understood that lifestyle intervention is essential for successful treatment. A systematic review of Brown et al. (2009), aiming to determine the long-term effectiveness of lifestyle interventions in the prevention of overweight and morbidity, showed a significant positive impact on
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Table 4. Adherence to weight control predictors. Variables Demographic variables Age (rs) Gender (U) Mother’s education (H) Father’s education (H) Mothers’ profession (H) Father’s profession (H) Anthropometric variables BMI z-score (rs) BMI percentile (rs) Waist circumference percentile (rs) Behavioral variables Weekly physical activity (rs) Screen time (rs) Family support (rs) Weight loss motivation (rs) Body image silhouette (rs) Clinical variables Time elapsed since first visit (rs) Age at onset of obesity (rs)
SEA
PPI
FSI
PB
Total TAWC
RNAWC
.008 1020.500 .418 .141 1.517 2.702
−.109 877.500 11.960* 10.185* 4.048 8.464*
−.053 993.500 4.666 2.468 .447 .660
−.165 981.000 3.071 2.190 2.177 1.406
−.021 1015.000 1.505 1.545 .951 1.729
−.046 838.000 1.976 9.192 4.223 3.936
−.034 −.034 −.105
.003 .000 .095
.148 .145 −.027
.212 .211 .032
.056 .054 −.087
.067 .067 .258*
.223 −.202 .295 .297 −.079
.097 .005 .226 .034 .025
−.028 −.048 .080 .065 .008
.064 .101 −.170 −.269 .119
.162 −.081 .247 .159 −.040
−.175 .141 −.430** −.205 .032
−.270*
−.054
−.182
−.048
−.207
.083
.120
.132
.155
.047
.171
.095
FSI: Friends’/School’s Influence; PB: Perceived benefits; PPI: Parents’/Providers’ Influence; RNAWC: Risk of Non-Adherence to Weight Control; SEA: Self-Efficacy & Adherence behaviours; TAWC: Treatment Adherence to Weight Control. rs: Spearman correlation; U : Mann–Whitney U test; H: Kruskal–Wallis test. *p < .05; **p < .01.
weight, blood pressure reduction, type 2 diabetes, and metabolic syndrome risk. The analysis of the intercorrelations between lifestyle, adherence to weight control, and overweight revealed that BMI z-score was not the main factor responsible for lifestyle and treatment adherence, making it necessary to find other factors that may contribute to this variance. Unlike previous research, it was not possible to endorse the assumption that an effective overweight management implies patients’ adherence to behavioral changes and a healthier lifestyle (Elfhag and Rossner, 2005; Sousa, 2010). Our results indicate that adherence to weight control is closely related to lifestyle. There is some indication that health responsibility, physical activity, nutrition, positive life perspective, stress management, and spiritual health, all contribute to several domains of adherence,
including self-efficacy/adherence behaviors, parental/providers’ influence, friends/school influence, and risk of nonadherence. These results underscore the importance of behavioral change and lifestyle as pillars of adherence to weight control. Based on our results, some lifestyle predictors were identified. The lifestyle domains which scored lower were “Physical Activity,” “Health Responsibility,” and “Spiritual Health.” Brownell et al. (2010) had already noted that the concept of personal health responsibility is a fundamental concept in social and political approaches to obesity, pointing out that the fight against obesity should imply the promotion of personal and collective responsibility. Furthermore, our findings suggest that adolescents with higher scores of health responsibility tend to score higher for weekly physical
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Sousa et al. activity. In this domain, we found interesting gender differences, with boys showing higher rates of physical activity, while girls showed higher interpersonal skills. Higher rates of weekly physical activity were associated with higher rates of health responsibility, a better nutrition, and a higher overall lifestyle score. On the other hand, higher rates of sedentary lifestyle were associated with lower interpersonal skills and nutrition quality. Earlier studies stressed the importance of promoting physical activity among overweight adolescents (Stankov et al., 2012; Ullrich-French and McDonough, 2013). However, overweight adolescents experience several barriers to participation in physical activity, such as physical discomfort and fatigue. Moreover, stigmatization and peer discrimination can negatively reinforce their negative selfperception, negatively impacting their psychosocial development and increasing their psychosocial vulnerability. In this research, the “adherence to weight control” domains that scored higher were benefit perception and recognition of parental/providers’ influence. Another important result was the fact that parents with a more differentiated profession and higher education seemed to be able to influence more positively the adolescents’ adherence to weight control. These data are in line with the WHO (2006) report, which states that there is an association between overweight and obesity and lower socioeconomic status, which in turn contributes to an increase of inequalities in health. Findings suggest a positive association between the waist circumference percentile and the risk of nonadherence. Interestingly, our findings indicate that the longer the time elapsed since the first visit, the lower the selfefficacy for adherence. On the one hand, we are aware that behavioral changes are complex, demanding, and usually require long periods of treatment/monitoring. On the other hand, and according to the results, it seems that adolescents, as time goes by, start to believe less in treatment success and in their ability for change.
Results also indicate that the higher the waist circumference percentile, the higher the risk of nonadherence. Adherence to weight control is a critical component for successful obesity management (Walpole et al., 2011). The problem of nonadherence remains a challenge for both health professionals and researchers and is responsible for a significant number of people not getting the maximum benefit from obesitymanagement programs, leading to poor health outcomes, reduced quality of life, and increased healthcare costs (Dulmen et al., 2007). The strengths of this study include the use of obesity-specific instruments, usually more sensitive than generic instruments, thereby reducing the noise of medical comorbidities (Zeller and Modi, 2008). One limitation of the study is the nonrandomized sample that prevents the generalization of results. Moreover, the limited amplitude of BMI percentiles did not allow a full exploration of potential differences in psycho-social variables according to the overweight degree (Zeller and Modi, 2008). A third limitation is the analysis of separate models for each correlation, which may have led to misleading interpretations, as an intercorrelation among the different variables is expected. Future research should use a larger and randomized sample, with a larger BMI variance, as well as use complementary techniques of multivariate analysis.
Conclusion These results underline the importance of behavioral change and the adoption of a healthier lifestyle as pillars for adherence to weight control. Tailored obesity-management programs should be designed not only according to adolescent health needs but also taking into account the broad array of predictors that have been identified. Acknowledgements We gratefully acknowledge the clinical staff of the Paediatric Obesity Clinic for their dedication. We also thank all the adolescents and parents for their participation and collaboration.
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Journal of Health Psychology
Funding This work was partially funded by Fundação para a Ciência e a Tecnologia (PTDC/DTP-PIC/0769/2012) and supported by the Polytechnic Institute of Leiria, Portugal, and the Department of Paediatrics at Hospital de Santa Maria, Lisbon, Portugal.
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![[Predictor variables for low adherence to a lifestyle modification program of overweight treatment in primary health care].](/assets/img/hold.png)
