ObjectivesAdherence to lifestyle interventions is crucial for the treatment of obesity. However, there is little research about adherence to lifestyle interventions in persons around retirement age. The objectives of this study are (1) to identify factors associated with the adherence to resistance training and a hypocaloric diet and (2) to describe the association between adherence and changes in body composition outcome parameters.DesignThis secondary data analysis included three randomized controlled trials.Setting & participantsThe inclusion criteria of the participants were an age of 55–75 years, a BMI ≥ 25 kg/m2 and receiving both a hypocaloric diet and resistance training. All participants were residing in the community.MeasurementsAdherence to hypocaloric diet was measured through the mean dietary intake on the basis of a 3-day dietary record. If the participant consumed at least 600 kcal less than the individual caloric requirements, they were considered adherent. Adherence to resistance training was achieved if ≥67% of the recommended training sessions were attended over the course of the study periods.Results232 participants were included, 47.0% female, mean age 64.0 (±5.5) years. 80.2% adhered to resistance training and 51.3% adhered to a hypocaloric diet. Older age (Beta 0.41; 95% CI 0.05, 0.78; p = 0.028) and male sex (Beta 7.7; 95% CI 3.6, 11; p < 0.001) were associated with higher resistance training adherence. A higher BMI at baseline (Beta 6.4; 95% CI 3.6, 9.2; p < 0.001) and male sex (Beta 65; 95% CI 41, 88; p < 0.001) were associated with higher adherence to hypocaloric diet.ConclusionWe identified several associated factors (sex, age and BMI at baseline) that should be considered to promote adherence in future lifestyle intervention studies in persons around retirement age. We recommend including behavior change techniques in lifestyle interventions and consider sex-specific interventions to improve the adherence of women.
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Objective We examined whether the role of maternal education in children's unhealthy snacking diet is moderated by other socio-economic indicators. Methods Participants were selected from the Amsterdam Born Children and their Development cohort, a large ongoing community-based birth cohort. Validated Food Frequency Questionnaires (FFQ) (n = 2782) were filled in by mothers of children aged 5.7±0.5yrs. Based on these FFQs, a snacking dietary pattern was derived using Principal Component Analysis. Socio-economic indicators were: maternal and paternal education (low, middle, high; based on the highest education completed) household finance (low, high; based on ability to save money) and neighbourhood SES (composite score including educational level, household income and employment status of residents per postal code). Cross-sectional multivariable linear regression analysis was used to assess the association and possible moderation of maternal education and other socio-economic indicators on the snacking pattern score. Analyses were adjusted for children's age, sex and ethnicity. Results Low maternal education (B 0.95, 95% CI 0.83;1.06), low paternal education (B 0.36, 95% CI 0.20;0.52), lower household finance (B 0.18, 95% CI 0.11;0.26) and neighbourhood SES (B -0.09, 95% CI -0.11;-0.06) were independently associated with higher snacking pattern scores (p<0.001). The association between maternal education and the snacking pattern score was somewhat moderated by household finance (p = 0.089) but remained strong. Children from middle-high educated mothers (B 0.44, 95% CI 0.35;0.52) had higher snacking pattern scores when household finance was low (B 0.49, 95% CI 0.33;0.65). Conclusions All socio-economic indicators were associated with increased risk of unhealthy dietary patterns in young children, with low maternal education conferring the highest risk. Yet, within the group of middle-high educated mothers, lower household finance was an extra risk factor for unhealthy dietary patterns. Intervention strategies should therefore focus on lower educated mothers and middle-high educated mothers with insufficient levels of household finance.
DOCUMENT
Physical activity is crucial in human life, whether in everyday activities or elite sports. It is important to maintain or improve physical performance, which depends on various factors such as the amount of physical activity, the capability, and the capacity of the individual. In daily life, it is significant to be physically active to maintain good health, intense exercise is not necessary, as simple daily activities contribute enough. In sports, it is essential to balance capacity, workload, and recovery to prevent performance decline or injury.With the introduction of wearable technology, it has become easier to monitor and analyse physical activity and performance data in daily life and sports. However, extracting personalised insights and predictions from the vast and complex data available is still a challenge.The study identified four main problems in data analytics related to physical activity and performance: limited personalised prediction due to data constraints, vast data complexity, need for sensitive performance measures, overly simplified models, and missing influential variables. We proposed end investigated potential solutions for each issue. These solutions involve leveraging personalised data from wearables, combining sensitive performance measures with various machine learning algorithms, incorporating causal modelling, and addressing the absence of influential variables in the data.Personalised data, machine learning, sensitive performance measures, advanced statistics, and causal modelling can help bridge the data analytics gap in understanding physical activity and performance. The research findings pave the way for more informed interventions and provide a foundation for future studies to further reduce this gap.
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