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. 2023 Oct 17;11:1228632. doi: 10.3389/fpubh.2023.1228632

Table 3.

Studies examining the association between parental SES and physical activity (PA).

SN Authors Methodology description
Study design Sample size (n) Continent/country Age range SES measure Method of analysis Findings Key strengths and limitations Quality score
1 Moore and Littlecott (2015) Cross-sectional 9,194 Wales 11–16 years SES Mixed-effects logistic regression models Adolescents from lower SES backgrounds exhibited a higher likelihood of engaging in physical activity (OR: 1.13, 95% CI 1.08, 1.18) compared to their peers from higher SES. Lower SES backgrounds might have fewer opportunities for indoor entertainment or screen time due to limited access to electronic devices, television, or gaming consoles. However, inappropriate measures of the data and methodological constraints make inconclusive statements about the relationship between high SES and physical activity. 8
2 Hankonen et al. (2017) Cross-sectional 659 Finland 16–19 years SES COM-B model Adolescents with a lower SES were observed to face an elevated risk of physical inactivity in comparison to their counterparts with a higher SES. Financial instability or housing insecurity can contribute to increased sedentary behaviors and decreased motivation for physical activity. However, the utilization of self-report measures to assess behavior, abilities, and environmental factors is susceptible to bias. 8
3 Richter et al. (2009a) Cross-national survey 97,721 Europe (n = 30), Asia (n = 1), North America (n = 2) 13–15 years SES The multilevel logistic regression model Adolescents with a lower SES demonstrated a significantly higher likelihood of engaging in physical activity when compared to their peers from higher SES backgrounds, with odds ratios of (OR: 1.34, 95% CI, 1.26–1.43) for boys and (OR: 1.57,95% CI, 1.45–1.71) for girls. Lower SES backgrounds might be more likely to walk or bike to school or other destinations due to limited access to private transportation, thereby increasing their overall physical activity levels. However, the findings were influenced by the way physical activity is measured or reported leading to unusual results. 8
4 Yannakoulia et al. (2016) Cross-sectional, National representative 11,717 Greece 3–18 years SES Classification–regression tree analysis (CART) model. Children and adolescents from higher SES backgrounds tended to dedicate more time to sports activities, with children spending an average of 3.3 h (SD 1.9) compared to 2.7 h (SD 2.4) per week, and adolescents engaging for an average of 4.5 h (SD 3.2) versus 3.1 h (SD 3.0) per week for those from lower SES. Higher SES communities may have better access to sports facilities, parks, and recreation programs, creating a supportive environment for sports participation. However, a low response rate might distort the findings 8
5 Poulain et al. (2019) Longitudinal 2,492 German 3–18 years SES Mixed-effect model Adolescents with mothers having higher SES had greater odds of being physically active (OR: 1.83, 95% CI: 1.45–2.31) than those with lower SES.
Similarly, children with higher SES (OR: 1.42, 95% CI: 1.14–1.77) were more engaged in physical activities than those with lower SES.
Families may place a stronger emphasis on physical fitness and active lifestyles. However, this study may have limited representativeness across socioeconomic classes, potentially limiting its broader applicability. 9
6 Yang (2021) Cross-sectional 1,040 South Korea 10–11 years SES Multiple linear regression model Children from higher parental SES engaged in more weekly physical activity (β: 0.08, p < 0.05) than those from lower parental SES Parenting role modeling may serve as positive role models for their children. However, this study limits the causal inferences. 8
7 Krist et al. (2017) Cross-sectional 1,523 Germany 12–13 years SES A generalized linear mixed model with a logit Children and adolescents from low parental SES had lower odds of physical activity (OR low: 0.90, 95% CI: 0.63–1.29) than those from high parental SES Lack of role models may be fewer visible role models who prioritize and engage in physical activity. However, due to the structure of the questionnaire, the findings were overestimated. 8
8 Park and Hwang. (2017) Cross-sectional 72,435 South-Korea 13 to 18 years SES Multivariate logistic regression Adolescents with low parental SES had a higher risk of no physical activity (OR: 1.425, 95% CI: 1.336–1.521) than those with high parental SES financial instability can contribute to increased sedentary behaviors and decreased motivation for physical activity. Nevertheless, there are certain methodological limitations in the study 8
9 Henriksen et al. (2016) Cross-sectional 6,269 Denmark 11–15 years Parental social class Logistic regression Children and adolescents from low parental social class face a higher risk of physical inactivity (OR: 2.10, 95% CI: 1.39–3.18) than those from high parental social class. Time Constraints which can limit the time available for physical activities. However, this phenomenon is not conducive to casual interpretations. 6
10 de Buhr and Tannen (2020) Cross-sectional 4,294 German 6–13 years SES Spearman’s Rho correlations Children from higher parental SES were more physically active (r: 0.079, 95% CI: 0.025–0.132) than those from lower parental SES. Education and awareness seem to a protective factor and make them more physically active. However, interpreting adolescent PA patterns obtained from self-reports can be challenging due to the potential influence of social desirability bias. 8
11 Pavon et al. (2010) Cross-sectional 3,259 Nine European countries (Sweden, Greece, Italy, Spain, Hungary, Belgium, France, Germany, and Austria) 12–17 years SES One-way analysis of covariance Adolescents with high parental SES exhibited significantly better physical fitness (p < 0.05) than those with low parental SES. Access to resources appears to have a positive impact on promoting physical activity. However, methodological issues might to a risk of inconclusive findings. 6
12 Falese et al. (2021) Cross-sectional 10,510 Six European cities (Namur, Tampere, Hannover, Latina, Amersfoort, and Coimbra). 14–17 years Parental income and parental education The multilevel multivariable linear regression model Adolescents with higher parental education levels were more likely to engage in more vigorous physical activity (OR: 2.7, 95% CI: 0.3–5.1), and those with higher parental income had increased physical activity (OR: 4.7, 95% CI: 2.8–6.6) compared to their counterparts. Awareness and knowledge lead them to encourage themselves to be physically active. However, Self-reported measures of physical activity intensity may lead to either overestimation or underestimation 8
13 Simetin et al. (2011) Cross-sectional 3,296 Croatia 11–15 years SES Binary logistic regression Children and adolescents with high parental SES had greater odds of engaging in physical activity (OR children: 1.8, 95% CI: 1.3–2.5; OR adolescents: 1.3, 95% CI: 0.9–1.8) than those with low parental SES Access to resources and facilities increases the likelihood of physical activity. However, self-reporting bias and lack of causation limit conclusions on the high SES and PA. 6
14 Al Sabbah et al. (2007) Cross-sectional 8,885 Palestine 12–18 years Maternal education Logistic regression Adolescents with high maternal education were more likely to engage in more physical activities (OR: 1.26, 95% CI: 1.09–1.46) than those with low maternal education. Health literacy encourages them to promote physical activity for a healthy lifestyle. Nevertheless, self-reported measurements of physical activity might result in either an overestimation or underestimation. 6
15 Lazzeri et al. (2014) Cross-sectional 3,291 Italy 11–15 years Parental income Logistic regression Children and adolescents with high parental income were more likely to fall short of physical activity guidelines (OR children: 1.30, 95% CI: 0.48–3.55; OR adolescents: 5.0, 95% CI: 0.66–37.6) compared to those with low parental income Access to digital devices and entertainment options that can potentially reduce physical activity levels. However, the finding may be influenced by measurement errors in physical activity data 6