Background: Dependency in activities of daily living (ADL) might be caused by multidimensional frailty. Prevention is important as ADL dependency might threaten the ability to age in place. Therefore, this study aimed to assess whether protective factors, derived from a systematic literature review, moderate the relationship between multidimensional frailty and ADL dependency, and whether this differs across age groups. Methods: A longitudinal study with a follow–up after 24 months was conducted among 1027 communitydwelling people aged ≥65 years. Multidimensional frailty was measured with the Tilburg Frailty Indicator, and ADL dependency with the ADL subscale from the Groningen Activity Restriction Scale. Other measures included socio-demographic characteristics and seven protective factors against ADL dependency, such as physical activity and non-smoking. Logistic regression analyses with interaction terms were conducted. Results: Frail older people had a twofold risk of developing ADL dependency after 24 months in comparison to non-frail older people (OR=2.12, 95% CI=1.45–3.00). The selected protective factors against ADL dependency did not significantly moderate this relationship. Nonetheless, higher levels of physical activity decreased the risk of becoming ADL dependent (OR=0.67, 95% CI=0.46–0.98), as well as having sufficient financial resources (OR=0.49, 95% CI=0.35–0.71). Conclusion: Multidimensional frail older people have a higher risk of developing ADL dependency. The studied protective factors against ADL dependency did not significantly moderate this relationship.
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Background. Children with developmental coordination disorder (DCD) face evident motor difficulties in daily functioning. Little is known, however, about their difficulties in specific activities of daily living (ADL). Objective. To (a) investigate differences between children with DCD and their typically developing peers, for ADL performance, learning, and participation, and (b) explore the predictive values of these aspects. Design. Cross-sectional study.Methods: Both in a clinical sample of children diagnosed with DCD (n=25, age range 5-8 years; 21 males) and typically developing peers (25 matched controls), parents completed the DCDDaily-Q. Differences in scores between the groups were investigated using T-tests for performance and participation, and Pearson’s Chi-square for learning. Multiple regression analyses were performed to explore the predictive values of performance, learning, and participation. Results. Compared to peers, children with DCD showed poor performance of ADL (p≤.005 for all items), delays in learning of ADL p≤.002 for all items), and less frequent participation in some ADL (p=.001 for mean total scores, p≤.05 for 7 out of 23 items). Children with DCD demonstrated heterogeneous patterns of performance (poor in 10-80% of the items) and learning (delayed in 0-100% of the items). In the DCD group, delays in learning of ADL were a predictor for poor performance of ADL (p=.001), and poor performance of ADL was a predictor for less frequent participation in ADL compared to peers (p=.040). Limitations. A limited number of children with DCD was addressed in this study.Conclusions. This study highlights the impact of DCD on children’s daily lives and the need for tailored intervention.
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Purpose: The purpose of this study was to validate optimized algorithm parameter settings for step count and physical behavior for a pocket worn activity tracker in older adults during ADL. Secondly, for a more relevant interpretation of the results, the performance of the optimized algorithm was compared to three reference applications Methods: In a cross-sectional validation study, 20 older adults performed an activity protocol based on ADL with MOXMissActivity versus MOXAnnegarn, activPAL, and Fitbit. The protocol was video recorded and analyzed for step count and dynamic, standing, and sedentary time. Validity was assessed by percentage error (PE), absolute percentage error (APE), Bland-Altman plots and correlation coefficients. Results: For step count, the optimized algorithm had a mean APE of 9.3% and a correlation coefficient of 0.88. The mean APE values of dynamic, standing, and sedentary time were 15.9%, 19.9%, and 9.6%, respectively. The correlation coefficients were 0.55, 0.91, and 0.92, respectively. Three reference applications showed higher errors and lower correlations for all outcome variables. Conclusion: This study showed that the optimized algorithm parameter settings can more validly estimate step count and physical behavior in older adults wearing an activity tracker in the trouser pocket during ADL compared to reference applications.
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