Introduction: The objectives of this review paper were to synthesize the data from randomized controlled trials in the literature to come to a conclusion on the effects of e-health interventions on promoting physical activity in older people. Methods: The Medline, CINAHL, Embase, PsycINFO, and SportDiscus databases were searched for articles about studies that 1) recruited subjects with a mean age of > 50 years, 2) tested e-health interventions, 3) employed control groups with no or less advanced e-health strategies, 4) measured physical activity as an outcome, 5) were published between 1st January 2008 and 31st May 2019, and 6) employed randomized controlled trials. The risk of bias in individual studies was assessed using the Physiotherapy Evidence Database scale. To examine the effects of the interventions, variables quantifying the amount of physical activity were extracted. The within-group effects of individual studies were summarized using Hedges g and 95% confidence intervals. Between-group effects were summarized by meta-analyses using RevMan 5.0 with a random effect model. Results: Of the 2810 identified studies, 38 were eligible, 25 were included in the meta-analyses. The within-group effect sizes (Hedges g) of physical activity in the intervention group at T1 ranged from small to large: physical activity time (0.12 to 0.84), step counts (− 0.01 to 11.19), energy expenditure (− 0.05 to 0.86), walking time (0.13 to 3.33), and sedentary time (− 0.12 to − 0.28). The delayed effects as observed in T2 and T3 also ranged from small to large: physical activity time (0.24 to 1.24) and energy expenditure (0.15 to 1.32). In the meta-analysis, the betweengroup effect of the e-health intervention on physical activity time measured by questionnaires, physical activity time measured by objective wearable devices, energy expenditure, and step counts were all significant with minimal heterogeneity. Conclusion: E-health interventions are effective at increasing the time spent on physical activity, energy expenditure in physical activity, and the number of walking steps. It is recommended that e-health interventions be included in guidelines to enhance physical activity in older people. Further studies should be conducted to determine the most effective e-health strategies.

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Chronic illness has a significant effect on quality of life, and a critical measure of health outcomes. Correlations between pain, depressive symptoms, fatigue, and physical function and their direct and indirect effects on quality of life were determined among 200 Thai older population with knee osteoarthritis using the Numeric Rating Scale, the Multidimensional Assessment of Fatigue, and the Thai Geriatric Depression Scale‐15. Physical function was investigated by using the Timed Up and Go test. Path analysis revealed significant negative direct paths from pain, depressive symptoms, fatigue, and Timed Up and Go test results to quality of life. Pain, fatigue, and the Timed Up and Go test results had indirect effects on quality of life, while depressive symptoms had no indirect effect on quality of life. Increases in pain, depressive symptoms, fatigue, and Timed Up and Go test results were associated with reductions in quality of life. An understanding of the complex relationship among these variables is beneficial for designing an appropriate intervention for improving quality of life among older people with knee osteoarthritis.