Mobile Health Interventions for Physical Activity, Sedentary Behavior, and Sleep in Adults Aged 50 Years and Older: A Systematic Literature Review

Elavsky, Steriani; Knapová, Lenka; Klocek, Adam; Ŝmahel, David

doi:10.1123/japa.2017-0410

Cited by 73 publications

(64 citation statements)

References 88 publications

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“…Persuasive technology is a branch of mHealth in which the aim is to use digital technology to guide users to change their attitudes and behavior by enhancing the effects of behavioral change techniques [ 24 ]. mHealth interventions show promise in encouraging older people to increase their levels of physical activity as reported in systematic reviews [ 25 - 28 ]. Because there is a lack of properly designed trials in this area, none of these systematic reviews drew conclusions on whether the mHealth is more effective or can enhance the effect of the conventional intervention to promote physical activity in older people.…”

Section: Introductionmentioning

confidence: 99%

“…In the previous trials, most of the mHealth interventions that were included in the reviews used only websites, DVDs, and texting instead of face-to-face physical activity training and health education [ 25 - 28 ]. Recently, studies have shown that the newly developed technologies, including wearable devices and social media, showed promising effects in promoting physical activity in many populations (eg, young people, cancer survivors) [ 33 - 35 ].…”

Section: Introductionmentioning

confidence: 99%

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Effects of an mHealth Brisk Walking Intervention on Increasing Physical Activity in Older People With Cognitive Frailty: Pilot Randomized Controlled Trial

Kwan¹,

Lee²,

Lee³

et al. 2020

JMIR Mhealth Uhealth

105

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Background Cognitive frailty is the coexistence of physical frailty and cognitive impairment and is an at-risk state for many adverse health outcomes. Moderate-to-vigorous physical activity (MVPA) is protective against the progression of cognitive frailty. Physical inactivity is common in older people, and brisk walking is a feasible form of physical activity that can enhance their MVPA. Mobile health (mHealth) employing persuasive technology has been successful in increasing the levels of physical activity in older people. However, its feasibility and effects on older people with cognitive frailty are unclear. Objective We aimed to identify the issues related to the feasibility of an mHealth intervention and the trial (ie, recruitment, retention, participation, and compliance) and to examine the effects of the intervention on cognitive function, physical frailty, walking time, and MVPA. Methods An open-label, parallel design, randomized controlled trial (RCT) was employed. The eligibility criteria for the participants were age ≥60 years, having cognitive frailty, and having physical inactivity. In the intervention group, participants received both conventional behavior change intervention and mHealth (ie, smartphone-assisted program using Samsung Health and WhatsApp) interventions. In the control group, participants received conventional behavior change intervention only. The outcomes included cognitive function, frailty, walking time, and MVPA. Permuted block randomization in 1:1 ratio was used. The feasibility issue was described in terms of participant recruitment, retention, participation, and compliance. Wilcoxon signed-rank test was used to test the within-group effects in both groups separately. Results We recruited 99 participants; 33 eligible participants were randomized into either the intervention group (n=16) or the control (n=17) group. The median age was 71.0 years (IQR 9.0) and the majority of them were females (28/33, 85%). The recruitment rate was 33% (33/99), the participant retention rate was 91% (30/33), and the attendance rate of all the face-to-face sessions was 100% (33/33). The majority of the smartphone messages were read by the participants within 30 minutes (91/216, 42.1%). ActiGraph (58/66 days, 88%) and smartphone (54/56 days, 97%) wearing compliances were good. After the interventions, cognitive function improvement was significant in both the intervention (P=.003) and the control (P=.009) groups. The increase in frailty reduction (P=.005), walking time (P=.03), step count (P=.02), brisk walking time (P=.009), peak cadence (P=.003), and MVPA time (P=.02) were significant only in the intervention group. Conclusions Our mHealth intervention is feasible for implementation in older people with cognitive impairment and is effective at enhancing compliance with the brisk walking training program delivered by the conventional behavior change interventions. We provide preliminary evidence that this mHealth intervention can increase MVPA time to an extent sufficient to yield clinical benefits (ie, reduction in cognitive frailty). A full-powered and assessor-blinded RCT should be employed in the future to warrant these effects. Trial Registration HKU Clinical Trials Registry HKUCTR-2283; http://www.hkuctr.com/Study/Show/31df4708944944bd99e730d839db4756

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of an mHealth Brisk Walking Intervention on Increasing Physical Activity in Older People With Cognitive Frailty: Pilot Randomized Controlled Trial

Kwan¹,

Lee²,

Lee³

et al. 2020

JMIR Mhealth Uhealth

105

View full text Add to dashboard Cite

show abstract

“…E-health interventions have also been implemented among older people, and their effects on promoting physical activity have been evaluated in clinical trials. A few systematic reviews have shown that many of them employed different e-health strategies, and many individual trials have shown that many e-health interventions are effective at increasing physical activity but some are not [25,26]. The number of trials included in these reviewers was small and therefore the effects of e-health interventions were not concluded in these reviews.…”

Section: Introductionmentioning

confidence: 99%

The effect of e-health interventions promoting physical activity in older people: a systematic review and meta-analysis

Kwan

Salihu

Lee

et al. 2020

Eur Rev Aging Phys Act

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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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“…Some studies have considered publications in a short time span (e.g., [57]), while others have considered specific problem areas such as pain [58], cardiovascular disease [14], or balance and fall risk assessments [59]. In addition, some research has focused on interventions that use mobile technology to alter physical activity, sedentary behavior, and sleep in older adults [60]. The evidence collected in previous research is limited to technologies targeting a specific condition or promoting activities as contrast to the present work in which we focus on technology for health management in general.…”

Section: Discussionmentioning

confidence: 99%

Monitoring Older Adults’ Health Information Using Mobile Technology: A Systematic Literature Review

Cajamarca

Herskovic

Rossel

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Mobile health technologies are becoming more common to assist older people in independent living and self-management of illnesses. Although many mobile health technologies can be beneficial to older users, there remains a dearth of evidence-informed guidance to develop such technologies. The objective of this study was to conduct a systematic review to (1) determine which types of mobile health technologies have been used to monitor health in older adults, (2) determine whether these devices allow older users to visualize their data and to complement automatically gathered sensor information with subjective information or data from other sources, and (3) determine which health information about older adults is usually monitored. We performed a focused systematic literature review of the ACM Digital Library database, including papers specifically assessing the implementation of mobile health technologies (e.g., wearables), and other mobile computerized equipment for independent older adults. Our results show that the most commonly used device is the smartphone, that the monitored data is usually acceleration, heart rate and position, and that only 30.4% of studies evaluate devices for older adults with older adult participants.

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Mobile Health Interventions for Physical Activity, Sedentary Behavior, and Sleep in Adults Aged 50 Years and Older: A Systematic Literature Review

Cited by 73 publications

References 88 publications

Effects of an mHealth Brisk Walking Intervention on Increasing Physical Activity in Older People With Cognitive Frailty: Pilot Randomized Controlled Trial

Effects of an mHealth Brisk Walking Intervention on Increasing Physical Activity in Older People With Cognitive Frailty: Pilot Randomized Controlled Trial

The effect of e-health interventions promoting physical activity in older people: a systematic review and meta-analysis

Monitoring Older Adults’ Health Information Using Mobile Technology: A Systematic Literature Review

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