Efficacy of an Individually Tailored, Internet-Mediated Physical Activity Intervention in Older Adults: A Randomized Controlled Trial

Efficacy of a Wearable Activity Tracker With Step-by-Step Goal-Setting on Older Adults' Physical Activity and Sarcopenia Indicators: Clustered Trial

BACKGROUND

Smart wearable technology has potential benefits for promoting physical activity and preventing sarcopenia.

OBJECTIVE

The purpose of this study was to explore the efficacy of a wearable activity tracker with 2-stage goal-setting for daily steps on older adults' physical activity and sarcopenia indicators.

METHODS

The study used a clustered trial design and was conducted in March to June 2022. Participants were community-dwelling adults older than 60 years who were recruited from 4 community centers in Taipei City. The intervention was designed with 2-stage goals set to 5000 steps/day in the first 4 weeks and 7500 steps/day in the final 4 weeks while wearing a commercial wearable activity tracker. Data were collected by self-reported questionnaires, a body composition analyzer, a handle grip tester, and 5 sit-to-stand tests.

RESULTS

All 27 participants in the experimental group and 31 participants in the control group completed the 8-week intervention. Total and light-intensity physical activities, skeletal muscle index, and muscle strength increased, while sedentary time, BMI, and the waist circumference of participants decreased in the experimental group, with significant group-by-time interactions compared to the control group.

CONCLUSIONS

A wearable activity tracker with gradual goal-setting is an efficient approach to improve older adults' physical activity and sarcopenia indicators. Smart wearable products with behavioral change techniques are recommended to prevent sarcopenia in older adult populations.

A systematic review on eHealth technology personalization approaches

Despite the widespread use of personalization of eHealth technologies, there is a lack of comprehensive understanding regarding its application. This systematic review aims to bridge this gap by identifying and clustering different personalization approaches based on the type of variables used for user segmentation and the adaptations to the eHealth technology and examining the role of computational methods in the literature. From the 412 included reports, we identified 13 clusters of personalization approaches, such as behavior + channeling and environment + recommendations. Within these clusters, 10 computational methods were utilized to match segments with technology adaptations, such as classification-based methods and reinforcement learning. Several gaps were identified in the literature, such as the limited exploration of technology-related variables, the limited focus on user interaction reminders, and a frequent reliance on a single type of variable for personalization. Future research should explore leveraging technology-specific features to attain individualistic segmentation approaches.

Comparative effectiveness of interventions on promoting physical activity in older adults: A systematic review and network meta-analysis

Background

Despite the well-established health benefits of physical activity, a large population of older adults still maintain sedentary life style or physical inactivity. This network meta-analysis (NMA) aimed to compare the effectiveness of wearable activity tracker-based intervention (WAT), electronic and mobile health intervention (E&MH), structured exercise program intervention (SEP), financial incentive intervention (FI) on promoting physical activity and reducing sedentary time in older adults.

Methods

The systematic review based on PRISMA guidelines, a systematic literature search of PubMed, Web of Science, Google Scholar, EMbase, Cochrane Library, Scopus were searched from inception to December 10th 2022. The randomized controlled trials (RCT) were included. Two reviewers independently conducted study selection, data extraction, risk of bias and certainty of evidence assessment. The effect measures were standard mean differences (SMD) and 95% confidence interval (CI) in daily steps, moderate-to-vigorous physical activity (MVPA) and sedentary time.

Results

A total of 69 studies with 14,120 participants were included in the NMA. Among these included studies, the results of daily steps, MVPA and sedentary time was reported by 55, 25 and 15 studies, respectively. The NMA consistency model analysis suggested that the following interventions had the highest probability (surface under the cumulative ranking, SUCRA) of being the best when compared with control: FI + WAT for daily steps (SUCRA = 96.6%; SMD = 1.32, 95% CI:0.77, 1.86), WAT + E&MH + SEP for MVPA (SUCRA = 91.2%; SMD = 0.94, 95% CI: 0.36, 1.52) and WAT + E&MH + SEP for sedentary time (SUCRA = 80.3%; SMD = -0.50, 95% CI: -0.87, -0.14). The quality of the evidences of daily steps, MVPA and sedentary time was evaluated by very low, very low and low, respectively.

Conclusions

In this NMA, there's low quality evidence that financial incentive combined with wearable activity tracker is the most effective intervention for increasing daily steps of older adults, wearable activity tracker combined with electronic and mobile health and structured exercise program is the most effective intervention to help older adults to increase MVPA and reduce sedentary time.

The Importance of Activating Factors in Physical Activity Interventions for Older Adults Using Information and Communication Technologies: Systematic Review

Background

In an aging population, it is important to activate older adults in taking care of their own health. Increasing physical activity is one way to avoid or lessen age-related physical and mental impairments. Interest in the use of information and communication technology (ICT) tools to promote physical activity among older adults is growing considerably. Such tools are suitable for communicating activation factors-skills, knowledge, and motivation-by integrating a variety of behavior change techniques (BCTs) to enhance physical activity. Although activation factors have been incorporated into physical activity interventions using ICT, little is known about the actual integration methods used in such interventions or about the effects of activation factors on influencing behavior change.

Objective

The first aim of this study was to identify which of the activation factors were covered in physical activity-promoting ICT interventions for older adults and which BCTs were used to address them. The second objective was to classify the user interaction interfaces and delivery modes that were used to promote these activation factors.

Methods

The search engines of PubMed, Web of Science, and ScienceDirect were used to search for and identify articles examining the effectiveness of ICT interventions for promoting physical activity in older adults. References and related data were selected, extracted, and reviewed independently by 2 reviewers. The risk of bias was assessed, and any conflict was addressed by a third separate reviewer. Selected articles included older adults aged ≥55 years without pre-existing medical diseases and other physical or mental conditions that could hinder movement.

Results

In total, 368 records were retrieved, and 13 studies met all inclusion criteria. Articles differed in terms of themes, timescales, user interaction interfaces, and outcome measures; therefore, a quantitative data synthesis was not feasible. Motivation was the most promoted activation factor among all trials (33 times). An app and a smartwatch were used in the majority of intervention groups (7/20, 35%) for tracking physical activity and receiving personalized feedback based on the individual goals. Skills (25 times) and knowledge (17 times) were the next most commonly addressed activation factors. Face-to-face interaction was the most used approach to targeting users' skills, including providing instructions on how to perform a behavior and exchanging knowledge via education on the health consequences of insufficient physical activity. Overall, integrating all 3 activation factors and using multiple user interaction interfaces with a variety of delivery modes proved the most effective in improving physical activity.

Conclusions

This study highlights commonly used BCTs and preferred modes of their delivery. So far, only a limited number of available BCTs (21/102, 21%) have been integrated. Considering their effectiveness, a larger variety of BCTs that address skills, knowledge, and motivation should be exploited in future ICT interventions.

Physical Activity Intervention for Leisure-Time Activity Levels Among Older Adults: A Cluster Randomized Trial

Importance

Current randomized trial evidence of the effects of physical activity interventions in older adults is mainly from developed countries, with little reliable evidence from low- and middle-income countries, such as China, where race, culture, and lifestyles differ substantially from those in Western populations.

Objective

To evaluate the effects of a multilevel intervention for increasing leisure-time activity levels in Chinese older adults.

Design, Setting, and Participants

This cluster randomized trial was conducted from May 2021 to May 2023, including an 8-week intervention period and a follow-up period of 24 months. Eight villages in China were randomly assigned to the intervention (4 villages) or the control (4 villages) group. Potentially eligible participants were 60 years or older. A total of 511 older adults from the selected 8 villages were enrolled.

Interventions

The intervention group received an 8-week socioecological model-based intervention, comprising individual, interpersonal, and community-level components, whereas the control group did not.

Main Outcome and Measure

The primary outcome was the change in leisure-time activity at 8 weeks, measured by the Physical Activity Scale for the Elderly (PASE). Possible PASE scores for leisure-time activity range from 0 to 502, with higher scores indicating higher activity levels.

Results

A total of 511 older adults from 8 villages were recruited and randomly allocated to the intervention (240 participants, 4 villages) or control (271 participants, 4 villages) groups. The mean (SD) age was 70.95 (5.72) years, and 284 (55.6%) were female participants. Seven serious adverse events (unrelated deaths) were reported. Participants in the intervention group increased leisure-time activity more than those in the control group, with a mean difference in PASE scores of 13.74 points (95% CI, 8.58-18.91 points) between the groups at 8 weeks (P < .001). Significant differences in leisure-time activity were also found over 24 months (mean difference in scores at 4 weeks, 11.66 points; 95% CI, 6.41-16.90 points; P < .001; at 6 months, 12.35 points; 95% CI, 7.19-17.50 points; P < .001; at 12 months, 11.55 points; 95% CI, 6.32-16.78 points; P < .001; and at 24 months, 14.51 points; 95% CI, 9.28-19.75 points; P < .001).

Conclusions and Relevance

In this cluster randomized trial, the multilevel intervention was effective in promoting leisure-time activity over 24 months of follow-up in older adults from China. This finding suggests that implementation of such interventions could be an important step in addressing physical inactivity in older adults in low- and middle-income countries.

Trial Registration

Chinese Clinical Trial Registry Identifier: ChiCTR2100045653.

Increasing Lifestyle Walking by 3000 Steps per Day Reduces Blood Pressure in Sedentary Older Adults with Hypertension: Results from an e-Health Pilot Study

Increasing daily steps by an additional 3000 steps/day on 5 days/week equates to ~150 min/week of aerobic physical activity to meet the physical activity guidelines; however, its effectiveness for blood pressure control in older adults with hypertension is unknown. A 20-week, single-arm, pilot e-health lifestyle walking intervention was conducted in 21 sedentary older adults (73 ± 5 years old) with hypertension (13 female, 8 male) to investigate the effectiveness of increasing daily steps by an additional 3000 steps/day for blood pressure control. The intervention consisted of two phases, with behavior change assistance provided during the first active phase (weeks 1-10) to help reach step goals and minimal assistance provided during the second self-maintenance phase (weeks 11-20). Nineteen participants (91%) completed both the 10- and 20-week assessments. The participants wore the pedometer for ≥10 h on 97% of the days over 20 weeks. They significantly increased average steps/day from 3899 ± 2198 at baseline to 6512 ± 2633 at 10 weeks and 5567 ± 2587 at 20 weeks. After 20 weeks, both systolic (137 ± 10 to 130 ± 11 mm Hg, p < 0.001) and diastolic (81 ± 6 to 77 ± 6 mm Hg, p = 0.01) blood pressure improved. The response was consistent in participants with (n = 8) and without (n = 13) anti-hypertensive medication. The results of our lifestyle walking intervention are encouraging for reducing blood pressure in older adults with hypertension; however, larger randomized, controlled trials need to be performed to confirm these findings.

Frail2Fit study protocol: a feasibility and acceptability study of a virtual multimodal intervention delivered by volunteers to improve functional outcomes in older adults with frailty after discharge from hospital

INTRODUCTION

Physical activity (PA) and replete nutritional status are key to maintaining independence and improving frailty status among frail older adults. In response to the COVID-19 pandemic, healthcare has increasingly turned to virtual modes of delivery and there is interest in the use of trained volunteers to deliver PA and nutrition interventions. We aim to evaluate the feasibility and acceptability of training hospital volunteers to deliver an online intervention, comprising exercise, behaviour change and nutrition support, to older people with frailty after discharge from hospital.

METHODS

We will use a quasi-experimental mixed methods approach. Hospital volunteers (n=6) will be trained to deliver an online, 3-month, multimodal intervention to frail (Clinical Frailty Scale ≥5) adults ≥65 years (n=30) after discharge from hospital. Feasibility will be assessed by determining the number of volunteers recruited, trained and retained at the end of the study; the proportion of intervention sessions delivered; participant recruitment, retention and adherence to the intervention. To determine the acceptability of the intervention, interviews will be conducted among a purposive sample of older adults, and volunteers. Secondary outcomes will include physical function, appetite, well-being, quality of life, anxiety and depression, self-efficacy for managing chronic disease and PA. Outcomes will be measured at baseline, 3 months and 6 months.

ANALYSIS

Descriptive statistics will be used to describe feasibility and adherence to the intervention. Secondary outcomes at baseline will be compared at 3 and 6 months. Interviews will be transcribed verbatim and analysed using thematic analysis.

ETHICS AND DISSEMINATION

Health Research Authority ethical approval was obtained on 30 May 2022 (reference: 22/WA/0155). Results will be disseminated through peer-reviewed journal articles, volunteer organisations, National Health Service communication systems and social media platforms. A toolkit will be developed to facilitate roll out of volunteer training.

TRIAL REGISTRATION NUMBER

NCT05384730.

The effectiveness of wearable activity trackers for increasing physical activity and reducing sedentary time in older adults: A systematic review and meta-analysis

Background

Traditional interventions such as education and counseling are successful in increasing physical activity (PA) participation, but are usually labor and resource intensive. Wearable activity trackers can objectively record PA and provide feedback to help users to achieve activity goals and are an increasingly popular tool among adults used to facilitate self-monitoring of PA. However, no reviews systematically explored the roles of wearable activity trackers in older populations.

Methods

We searched PubMed, Web of Science, Google Scholar, Embase, Cochrane Library, and Scopus from inception to September 10, 2022. Randomized controlled trials were included. Two reviewers independently conducted study selection, data extraction, risk of bias, and certainty of evidence assessment. A random-effects model was used to evaluate the effect size.

Results

A total of 45 studies with 7144 participants were included. A wearable activity tracker was effective in increasing daily steps (standard mean differences (SMD) = 0.59, 95% confidence interval (CI) (0.44, 0.75)), weekly moderate-to-vigorous PA (MVPA) (SMD = 0.54, 95% CI (0.36, 0.72)), and total daily PA (SMD = 0.21, 95% CI (0.01, 0.40)) and reducing sedentary time (SMD = -0.10, 95% CI (-0.19, -0.01)). Subgroup analysis showed that the effectiveness of wearable activity trackers for daily steps was not influenced by participants and intervention features. However, wearable activity trackers seemed more effective in promoting MVPA of participant's age <70 than participant's age ≥70. In addition, wearable activity trackers incorporated with traditional intervention components (e.g. telephone counseling, goal setting, and self-monitoring) could better promote MVPA than alone use. Short-term interventions potentially achieve better MVPA increase than long-term.

Conclusion

This review showed that wearable activity trackers are an effective tool to increase PA for the old population and also favor reducing sedentary time. When used together with other interventions, wearable activity trackers can achieve better MVPA increase, especially in the short term. However, how to more effectively improve the effectiveness of wearable activity trackers is an important direction of future research.

Do physical activity interventions combining self-monitoring with other components provide an additional benefit compared with self-monitoring alone? A systematic review and meta-analysis

Objective

To determine the net effect of different physical activity intervention components on step counts in addition to self-monitoring.

Design

A systematic review with meta-analysis and meta-regression.

Data sources

Five databases (PubMed, Scopus, Web of Science, ProQuest and Discus) were searched from inception to May 2022. The database search was complemented with backward and forward citation searches and search of the references from relevant systematic reviews.

Eligibility criteria

Randomised controlled trials comparing an intervention using self-monitoring (active control arm) with an intervention comprising the same treatment PLUS any additional component (intervention arm).

Data extraction and synthesis

The effect measures were mean differences in daily step count. Meta-analyses were performed using random-effects models, and effect moderators were explored using univariate and multivariate meta-regression models.

Results

Eighty-five studies with 12 057 participants were identified, with 75 studies included in the meta-analysis at postintervention and 24 at follow-up. At postintervention, the mean difference between the intervention and active control arms was 926 steps/day (95% CI 651 to 1201). At a follow-up, the mean difference was 413 steps/day (95% CI 210 to 615). Interventions with a prescribed goal and involving human counselling, particularly via phone/video calls, were associated with a greater mean difference in the daily step count than interventions with added print materials, websites, smartphone apps or incentives.

Conclusion

Physical activity interventions that combine self-monitoring with other components provide an additional modest yet sustained increase in step count compared with self-monitoring alone. Some forms of counselling, particularly remote phone/video counselling, outperformed other intervention components, such as websites and smartphone apps.

PROSPERO registered number

CRD42020199482.

Possible Impact of a 12-Month Web- and Smartphone-Based Program to Improve Long-term Physical Activity in Patients Attending Spa Therapy: Randomized Controlled Trial

Background

Lack of physical activity (PA) and sedentary behaviors are leading risk factors for noncommunicable diseases (NCDs). Web- and smartphone-based interventions are effective in increasing PA in older adults and in patients with NCD. In many countries, spa therapy, commonly prescribed to patients with NCD, represents an ideal context to initiating lifestyle changes.

Objective

This study aimed to evaluate, in patients attending spa therapy, the effectiveness of an intervention combining a face-to-face coaching and, when returning home, a web- and smartphone-based PA program on the achievement of PA guidelines (PAG) 12 months after the end of spa therapy.

Methods

This was a 12-month, prospective, parallel-group randomized controlled trial. Patients were enrolled during spa therapy and randomized 1:1 to intervention or control group who received PA usual advice. From the end of spa therapy, PA, weight, waist circumference, and quality of life of the participants were assessed by phone every 2 months. Primary outcome was meeting PAG (PA ≥600 metabolic equivalent of task) at 12 months. Secondary outcomes were meeting current PAG at 6 months; sedentary time, weight, waist circumference, PA, and quality of life at 6 and 12 months. Objective use data of the web- and smartphone-based PA program were collected. Analytic methods included intention to treat and constrained longitudinal data analyses.

Results

The study sample included 228 participants (n=176, 77.2% females) with a mean age of 62.4 (SD 6.7) years and a mean BMI of 28.2 (SD 4.2) kg/m². Approximately 53.9% (123/228) of the participants were retired. No group differences were found for any baseline variable. At 12 months, the proportion of patients achieving PAG was significantly higher in intervention group than in the control group (81% vs 67% respectively, odds ratio 2.34, 95% CI 1.02-5.38; P=.045). No difference between intervention and control group was found neither in achieving PAG at 6 months nor for sedentary time, weight, and waist circumference at 6 and 12 months. Regarding quality of life, the physical component subscale score was significantly higher at 12 months in the intervention group than in the control group (mean difference: 4.1, 95% CI 1.9-6.3; P<.001). The mean duration use of the program was 7.1 (SD 4.5) months. Attrition rate during the first 2 months was 20.4% (23/113) whereas 39.8% (45/113) of the participants used the program for at least 10 months.

Conclusions

PA increased in both the intervention group and the control group. However, at 12 months, more participants met PAG in the intervention group compared with the controls. This indicates that the web- and smartphone-based program could have maintained PA in the intervention group. In addition, a spa therapy seems to be an ideal time and framework to implement PA education.

Trial Registration

ClinicalTrials.gov NCT02694796; https://clinicaltrials.gov/ct2/show/NCT02694796

The Effectiveness of a Computer-Tailored Web-Based Physical Activity Intervention Using Fitbit Activity Trackers in Older Adults (Active for Life): Randomized Controlled Trial

BACKGROUND

Physical activity is an integral part of healthy aging; yet, most adults aged ≥65 years are not sufficiently active. Preliminary evidence suggests that web-based interventions with computer-tailored advice and Fitbit activity trackers may be well suited for older adults.

OBJECTIVE

The aim of this study was to examine the effectiveness of Active for Life, a 12-week web-based physical activity intervention with 6 web-based modules of computer-tailored advice to increase physical activity in older Australians.

METHODS

Participants were recruited both through the web and offline and were randomly assigned to 1 of 3 trial arms: tailoring+Fitbit, tailoring only, or a wait-list control. The computer-tailored advice was based on either participants' Fitbit data (tailoring+Fitbit participants) or self-reported physical activity (tailoring-only participants). The main outcome was change in wrist-worn accelerometer (ActiGraph GT9X)-measured moderate to vigorous physical activity (MVPA) from baseline to after the intervention (week 12). The secondary outcomes were change in self-reported physical activity measured by means of the Active Australia Survey at the midintervention point (6 weeks), after the intervention (week 12), and at follow-up (week 24). Participants had a face-to-face meeting at baseline for a demonstration of the intervention and at baseline and week 12 to return the accelerometers. Generalized linear mixed model analyses were conducted with a γ distribution and log link to compare MVPA and self-reported physical activity changes over time within each trial arm and between each of the trial arms.

RESULTS

A total of 243 participants were randomly assigned to tailoring+Fitbit (n=78, 32.1%), tailoring only (n=96, 39.5%), and wait-list control (n=69, 28.4%). Attrition was 28.8% (70/243) at 6 weeks, 31.7% (77/243) at 12 weeks, and 35.4% (86/243) at 24 weeks. No significant overall time by group interaction was observed for MVPA (P=.05). There were no significant within-group changes for MVPA over time in the tailoring+Fitbit group (+3%, 95% CI -24% to 40%) or the tailoring-only group (-4%, 95% CI -24% to 30%); however, a significant decline was seen in the control group (-35%, 95% CI -52% to -11%). The tailoring+Fitbit group participants increased their MVPA 59% (95% CI 6%-138%) more than those in the control group. A significant time by group interaction was observed for self-reported physical activity (P=.02). All groups increased their self-reported physical activity from baseline to week 6, week 12, and week 24, and this increase was greater in the tailoring+Fitbit group than in the control group at 6 weeks (+61%, 95% CI 11%-133%).

CONCLUSIONS

A computer-tailored physical activity intervention with Fitbit integration resulted in improved MVPA outcomes in comparison with a control group in older adults.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry ACTRN12618000646246; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12618000646246.

The Effect of Wearable and Smartphone Applications on Physical Activity, Quality of Life, and Cardiovascular Health Outcomes in Overweight/Obese Adults: A Systematic Review and Meta-analysis of Randomized Controlled Trials

BACKGROUND

Advances in the device and smartphone technology have resulted in a convenient option for providing physical activity strategies; this is especially important during the coronavirus disease 2019 pandemic.

OBJECTIVE

The purpose of this meta-analysis is to evaluate the efficacy of wearable and smartphone-based interventions to promote physical activity and improve quality of life and cardiovascular health outcomes among overweight/obese adults.

DATA SOURCES

We searched relevant databases up to 18 November 2021 for conducting a systematic review and meta-analysis of randomized controlled trials lasting 4 or more weeks that investigated the impacts of wearables and smartphone applications on physical activity, quality of life and health outcomes.

RESULTS

Twenty-six studies including 2373 participants were included. There was a significant pooled standard mean differences (SMD) for the comparison between intervention versus control in steps per day (SMD: 0.54; p = 0.0003), moderate-to-vigorous physical activity (SMD: 0.47; p = 0.02), quality of life (SMD: 0.33; p = 0.0006), body weight (mean difference (MD), -1.61 kg; p = 0.009), and BMI (MD, -0.59 kg/m²; p = 0.04). There were no significant differences between the intervention and control groups for systolic and diastolic blood pressure and resting heart rate (all p > 0.05).

CONCLUSION

Our findings suggest that wearable and smartphone-based interventions are effective strategies in promoting physical activity and can provide a direct contact line to health professionals.

Nurse-led health coaching programme to improve hospital-to-home transitional care for stroke survivors: A randomised controlled trial

OBJECTIVE

To evaluate the effects of a nurse-led health coaching programme for stroke survivors and family caregivers in hospital-to-home transition care.

METHODS

A total of 140 dyads of stroke survivors and their family caregivers were recruited and randomly assigned to either the intervention group (received a 12-week nurse-led health coaching programme) or the usual care group. The primary outcome was self-efficacy, and secondary outcomes were quality of life (QoL), stroke-related knowledge, and caregiver-related burden. The outcomes were measured at baseline, 12 and 24 weeks.

RESULTS

Stroke survivors in the intervention group demonstrated a significant improvement in self-efficacy at 12 weeks (x̅: 24.9, 95%CI: 20.2-29.6, p < 0.001) and at 24 weeks (x̅: 23.9, 95%CI: 19.2-28.6, p < 0.001) compared to the usual care group. Findings also demonstrated significant increases in stroke survivors' QoL, stroke-related knowledge, and reduction in unplanned hospital readmissions and caregiver-related burden. There were no statistically significant changes in other outcomes between the two groups.

CONCLUSION

The nurse-led health coaching programme improved health outcomes for both stroke survivors and their caregivers.

PRACTICE IMPACTION

Findings from the study suggest that nurse-led health coaching should be incorporated into routine practice in hospital-to-home transitional care for stroke survivors and their caregivers.

The Effect of a Mobile and Wearable Device Intervention on Increased Physical Activity to Prevent Metabolic Syndrome: Observational Study

Background

Research on whether wearable devices and app-based interventions can effectively prevent metabolic syndrome (MetS) by increasing physical activity (PA) among middle-aged people living in the rural areas of South Korea remains insufficient.

Objective

The aim of this study was to determine whether mobile and wearable device interventions can improve health indicators, including PA, in MetS risk groups in rural South Korea.

Methods

In this clinical trial, performed from December 2019 to June 2020, participants were asked to use a wearable device (GalaxyWatch Active1) alone (standard intervention) or the wearable device and mobile app (Yonsei Health Korea) (enhanced intervention). Clinical measures and International Physical Activity Questionnaire (IPAQ) scores were evaluated initially and after 6 months. The number of steps was monitored through the website. The primary outcome was the difference in PA and clinical measures between the enhanced intervention and standard intervention groups. The secondary outcome was the decrease in MetS factors related to the change in PA.

Results

A total of 267 participants were randomly selected, 221 of whom completed the 6-month study. Among the 221 participants, 113 were allocated to the enhanced intervention group and 108 were allocated to the standard intervention group. After 6 months, the body weight and BMI for the enhanced intervention group decreased by 0.6 (SD 1.87) and 0.21 (SD 0.76), respectively (P<.001). In both groups, systolic blood pressure, diastolic blood pressure, waist circumference, and glycated hemoglobin A_1c (HbA_1c) decreased (P<.001). The total PA was approximately 2.8 times lower in the standard intervention group (mean 44.47, SD 224.85) than in the enhanced intervention group (mean 124.36, SD 570.0). Moreover, the enhanced intervention group achieved the recommended level of moderate to vigorous physical activity (MVPA), whereas the standard intervention group did not (188 minutes/week vs 118 minutes/week). Additionally, the number of participants in the enhanced intervention group (n=113) that reached 10,000 daily steps or more after the intervention increased from 9 (8.0%) to 26 (23.1%) (P=.002), whereas this number did not increase significantly in the standard intervention group (n=108), from 8 (7.4%) to 16 (14.8%) (P=.72). The number of participants without any MetS factors increased by 12 (11%) and 8 (7%) in the enhanced and standard intervention group, respectively.

Conclusions

PA monitoring and an intervention using wearable devices were effective in preventing MetS in a rural population in Korea. Blood pressure, waist circumference, and HbA_1c were improved in both intervention groups, which were effective in reducing MetS factors. However, only the participants in the enhanced intervention group continuously increased their MVPA and step counts above the recommended level to prevent MetS. Body weight and BMI were further improved, and a higher number of participants with zero MetS factors was attained from the enhanced intervention.

Trial Registration

Clinical Research Information Service KCT0005783; https://cris.nih.go.kr/cris/search/detailSearch.do/16123

“It is Easy to do Nothing and Easy to Sit Down”: Perceptions of Physical Activity and Sedentary Behaviors During Pre-retirement

This study explored the insights of old age pre-retirement employees towards physical activity and sedentary behavior. A quota sampling of 20 participants from within the Civil Service in Malta were invited to an interview. Participants who were included met the statutory requirement for retirement within the subsequent 6 months to 1 year. Semi-structured interviews were conducted using a narrative approach. Structural narrative analysis and reflective thematic analysis were used. The story structure highlighted the significance of the individual experiences on the perceptions towards future physical activity during retirement. Two themes were identified using the thematic analysis, influencers, and perceptions. Triangulation identified that sedentary behavior was not part of the narration. The transition from work to retirement is a unique and personal experience and therefore when promoting an active lifestyle, the individual experience and past behaviors must be actively considered.

Remote, Unsupervised Functional Motor Task Evaluation in Older Adults across the United States Using the MindCrowd Electronic Cohort

COVID-19 has impacted the ability to evaluate motor function in older adults, as motor assessments typically require face-to-face interaction. One hundred seventy-seven older adults nationwide completed an unsupervised functional upper-extremity assessment at home. Data were compared to data from an independent sample of community-dwelling older adults (N = 250) assessed in lab. The effect of age on performance was similar between the in-lab and at-home groups. Practice effects were also similar. Assessing upper-extremity motor function remotely is feasible and reliable in community-dwelling older adults. This test offers a practical solution for telehealth practice and other research involving remote or geographically isolated individuals.

Remote, unsupervised functional motor task evaluation in older adults across the United States using the MindCrowd electronic cohort

The COVID-19 pandemic has impacted the ability to evaluate motor function in older adults, as motor assessments typically require face-to-face interaction. This study tested whether motor function can be assessed at home. One hundred seventy-seven older adults nationwide (recruited through the MindCrowd electronic cohort) completed a brief functional upper-extremity assessment at home and unsupervised. Performance data were compared to data from an independent sample of community-dwelling older adults (N=250) assessed by an experimenter in-lab. The effect of age on performance was similar between the in-lab and at-home groups for both the dominant and non-dominant hand. Practice effects were also similar between the groups. Assessing upper-extremity motor function remotely is feasible and reliable in community-dwelling older adults. This test offers a practical solution in response to the COVID-19 pandemic and telehealth practice and other research involving remote or geographically isolated individuals.

Long-term Effectiveness of mHealth Physical Activity Interventions: Systematic Review and Meta-analysis of Randomized Controlled Trials

BACKGROUND

Mobile health (mHealth) interventions can increase physical activity (PA); however, their long-term impact is not well understood.

OBJECTIVE

The primary aim of this study is to understand the immediate and long-term effects of mHealth interventions on PA. The secondary aim is to explore potential effect moderators.

METHODS

We performed this study according to the Cochrane and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched PubMed, the Cochrane Library, SCOPUS, and PsycINFO in July 2020. Eligible studies included randomized controlled trials of mHealth interventions targeting PA as a primary outcome in adults. Eligible outcome measures were walking, moderate-to-vigorous physical activity (MVPA), total physical activity (TPA), and energy expenditure. Where reported, we extracted data for 3 time points (ie, end of intervention, follow-up ≤6 months, and follow-up >6 months). To explore effect moderators, we performed subgroup analyses by population, intervention design, and control group type. Results were summarized using random effects meta-analysis. Risk of bias was assessed using the Cochrane Collaboration tool.

RESULTS

Of the 2828 identified studies, 117 were included. These studies reported on 21,118 participants with a mean age of 52.03 (SD 14.14) years, of whom 58.99% (n=12,459) were female. mHealth interventions significantly increased PA across all the 4 outcome measures at the end of intervention (walking standardized mean difference [SMD] 0.46, 95% CI 0.36-0.55; P<.001; MVPA SMD 0.28, 95% CI 0.21-0.35; P<.001; TPA SMD 0.34, 95% CI 0.20-0.47; P<.001; energy expenditure SMD 0.44, 95% CI 0.13-0.75; P=.01). Only 33 studies reported short-term follow-up measurements, and 8 studies reported long-term follow-up measurements in addition to end-of-intervention results. In the short term, effects were sustained for walking (SMD 0.26, 95% CI 0.09-0.42; P=.002), MVPA (SMD 0.20, 95% CI 0.05-0.35; P=.008), and TPA (SMD 0.53, 95% CI 0.13-0.93; P=.009). In the long term, effects were also sustained for walking (SMD 0.25, 95% CI 0.10-0.39; P=.001) and MVPA (SMD 0.19, 95% CI 0.11-0.27; P<.001). We found the study population to be an effect moderator, with higher effect scores in sick and at-risk populations. PA was increased both in scalable and nonscalable mHealth intervention designs and regardless of the control group type. The risk of bias was rated high in 80.3% (94/117) of the studies. Heterogeneity was significant, resulting in low to very low quality of evidence.

CONCLUSIONS

mHealth interventions can foster small to moderate increases in PA. The effects are maintained long term; however, the effect size decreases over time. The results encourage using mHealth interventions in at-risk and sick populations and support the use of scalable mHealth intervention designs to affordably reach large populations. However, given the low evidence quality, further methodologically rigorous studies are warranted to evaluate the long-term effects.

Effect of eHealth to increase physical activity in healthy adults over 55 years: A systematic review and meta-analysis

To estimate the effect of eHealth interventions on increasing physical activity (PA) in healthy adults over 55 years, a systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. MEDLINE, Cochrane, Web of Science, SPORTDiscus, and Scopus databases were searched, from inception to February 2020, for experimental studies reporting the effect of eHealth interventions on steps/day, daily moderate-to-vigorous physical activity (MVPA min/day), PA min/week, and MVPA min/week among adults over 55 years. The DerSimonian and Laird method was used to compute a pooled effect size (ES) estimate and the respective 95% confidence interval (95% CI). Eighteen studies were included in this meta-analysis with adults whose age ranged from 58 to 74.2 years. The interventions lasted between four and 52 weeks. The ES estimates of eHealth interventions on increasing PA were 0.59 (95% CI: 0.15-1.02) for steps/day, 0.49 (95% CI: 0.17-0.80) for daily MVPA, 0.13 (95% CI: 0.01-0.24) for total weekly PA and 0.31 (95% CI: 0.13-0.48) for weekly MVPA. Considering clinical improvements, the mean change difference estimates were an increase of 1616.28 steps/day (95% CI: 386.25-2846.31), 7.41 minutes of daily MVPA (95% CI: 3.24-11.57), 40.54 minutes of total weekly PA (95% CI: -8.71 to 89.79) and 56.35 minutes of weekly MVPA (95% CI: 17.43-95.27). In conclusion, eHealth interventions are effective in increasing PA levels among adults over 55 years, resulting in increased steps/day, MVPA min/day, PA min/week and MVPA min/week.

The effects of step-count monitoring interventions on physical activity: systematic review and meta-analysis of community-based randomised controlled trials in adults

Background

Step-count monitors (pedometers, body-worn trackers and smartphone applications) can increase walking, helping to tackle physical inactivity. We aimed to assess the effect of step-count monitors on physical activity (PA) in randomised controlled trials (RCTs) amongst community-dwelling adults; including longer-term effects, differences between step-count monitors, and between intervention components.

Methods

Systematic literature searches in seven databases identified RCTs in healthy adults, or those at risk of disease, published between January 2000–April 2020. Two reviewers independently selected studies, extracted data and assessed risk of bias. Outcome was mean differences (MD) with 95% confidence intervals (CI) in steps at follow-up between treatment and control groups. Our preferred outcome measure was from studies with follow-up steps adjusted for baseline steps (change studies); but we also included studies reporting follow-up differences only (end-point studies). Multivariate-meta-analysis used random-effect estimates at different time-points for change studies only. Meta-regression compared effects of different step-count monitors and intervention components amongst all studies at ≤4 months.

Results

Of 12,491 records identified, 70 RCTs (at generally low risk of bias) were included, with 57 trials (16,355 participants) included in meta-analyses: 32 provided change from baseline data; 25 provided end-point only. Multivariate meta-analysis of the 32 change studies demonstrated step-counts favoured intervention groups: MD of 1126 steps/day 95%CI [787, 1466] at ≤4 months, 1050 steps/day [602, 1498] at 6 months, 464 steps/day [301, 626] at 1 year, 121 steps/day [− 64, 306] at 2 years and 434 steps/day [191, 676] at 3–4 years. Meta-regression of the 57 trials at ≤4 months demonstrated in mutually-adjusted analyses that: end-point were similar to change studies (+ 257 steps/day [− 417, 931]); body-worn trackers/smartphone applications were less effective than pedometers (− 834 steps/day [− 1542, − 126]); and interventions providing additional counselling/incentives were not better than those without (− 812 steps/day [− 1503, − 122]).

Conclusions

Step-count monitoring leads to short and long-term step-count increases, with no evidence that either body-worn trackers/smartphone applications, or additional counselling/incentives offer further benefit over simpler pedometer-based interventions. Simple step-count monitoring interventions should be prioritised to address the public health physical inactivity challenge.

Systematic review registration

PROSPERO number CRD42017075810.

The effectiveness of a wearable activity tracker (WAT)-based intervention to improve physical activity levels in sedentary older adults: A systematic review and meta-analysis

BACKGROUND

The evidence shows that WAT-based interventions enhance the physical activity (PA) levels of young people by sustainably delivering behavior change techniques (BCTs). These results may not be replicable among older adults. This paper aims to evaluate the effectiveness of WAT-based interventions in improving PA levels in sedentary older adults.

METHODS

Eight electronic databases were searched for randomized controlled trials published January 2008 to December 2018. BCTs delivered by WAT aimed at increasing PA levels using step counts or time spent on moderate-to-vigorous (MVPA) exercise as an outcome were eligible for inclusion.

RESULTS

In nine out of the ten included studies, higher PA levels were seen in the intervention group than in the control group. One study where the participants' mean age was 80+ showed no significant increase in PA levels. Significant effects were also demonstrated from the meta-analysis, which included four studies using a passive control (i.e., the usual care or health information) on step counts (n = 207, Hedges g = 1.27, 95 % CI = 0.51-2.04, p = 0.001) and two studies on MVPA (n = 83, Hedge's g = 1.23, 95 % CI = 0.75-1.70, p < 0.001). A non-significant effect was found on step counts (n = 201, Hedge's g = 0.22, 95 % CI = -0.62 to 1.06, p = 0.61) in three studies that used an active control comparison group (i.e., traditional pedometer).

CONCLUSIONS

A WAT-based intervention is effective at improving PA levels among older adults over the short term when compared with the usual care or health information. However, when compared with a traditional pedometer or when used among old-old adults, the results were inconclusive.

Requirements for (web-based) physical activity interventions targeting adults above the age of 65 years - qualitative results regarding acceptance and needs of participants and non-participants

Background

It remains unclear how physical activity (PA) interventions need to be designed to reach older adults and to be widely accepted in this target group. The aim of this study was to assess the acceptance of a web-based PA program, including individual intervention components as well as relevant contextual factors, and to specify requirements for future interventions.

Methods

Two hundred sixty-six participants of a PA intervention completed a questionnaire covering individual program components (content, structure, and context). Further, 25 episodic guided interviews focusing on reasons for (non-) participation were conducted with 8 participants and 17 non-participants. Following qualitative content analysis, different requirements were identified and organized based on the social-ecological model, resulting in a profile of requirements.

Results

Based on the participants’ and non-participants’ statements, six different levels of requirements affecting acceptance of and successful participation in a web-based PA intervention were identified. The individual fit was influenced by an interaction of different factors at the intrapersonal, sociocultural, content, spatial, digital and organizational levels. Several age- and gender-specific requirements were noted in the interviewed older adults. Men and women, as well as younger (< 70 years) and older (≥70 years) adults differed in terms of perceived enjoyment and benefits of socializing while exercising together, the time expenditure perceived to be acceptable, previous digital skills, as well as in perceptions that ambience and accessibility of exercise facilities in the neighborhood were important.

Conclusions

To motivate older adults to engage in PA and address different needs in terms of life circumstances and quality of life as well as differences in technical affinity, different requirement profiles should be included in the process of intervention development and implementation. Participatory development loops and modular offer formats are recommended for this.

Can consumer wearable activity tracker-based interventions improve physical activity and cardiometabolic health in patients with chronic diseases? A systematic review and meta-analysis of randomised controlled trials

Background

To date, it is unclear if consumer wearable activity trackers (CWATs), with or without behaviour multi-component strategies, effectively improve adherence to physical activity and health outcomes under free living conditions in populations with chronic diseases. Therefore, we systematically evaluated the efficacy of CWAT-based interventions to promote physical activity levels and cardiometabolic health in populations with chronic diseases.

Methods

Randomised controlled trials were collected from five bibliographic databases (PubMed, Embase, Web of Science, The Cochrane Central Register of Controlled Trials and CINAHL). Studies were eligible for inclusion if they evaluated a CWAT-based counselling intervention versus control intervention among patients with chronic respiratory diseases, type 2 diabetes mellitus, cardiovascular diseases, overweight/obesity, cognitive disorders, or sedentary older adults. Data were pooled using a random-effects model.

Results

After deduplication 8147 were identified of which 35 studies met inclusion criteria (chronic respiratory diseases: 7, type 2 diabetes mellitus: 12, cardiovascular diseases: 6, overweight/obesity: 3, cognitive disorders: 1, sedentary older adults: 6). Compared to control groups, CWAT-based interventions significantly increased physical activity by 2123 steps per day (95% confidence interval [CI], [1605–2641]; p < 0.001). In addition, CWAT-based interventions in these populations significantly decreased systolic blood pressure (− 3.79 mm Hg; 95% CI: [− 4.53, − 3.04] mm Hg; p < 0.001), waist circumference (− 0.99 cm; 95% CI: [− 1.48, − 0.50] cm; p < 0.001) and low-density lipoprotein cholesterol concentration (− 5.70 mg/dl; 95% CI: [− 9.24, − 2.15] mg/dl; p = 0.002).

Conclusion

CWAT-based interventions increase physical activity and have beneficial effects on important health-related outcomes such as systolic blood pressure, waist circumference and LDL cholesterol concentration in patients with chronic diseases.

The TTCYB Study Protocol: A Tailored Print Message Intervention to Improve Cardiovascular Patients' Lifestyles

This article describes the development of the "Time to Change Your Behavior" (TTCYB) study protocol, a theory-based, tailored print message intervention to improve compliance with the self-care regimen in patients with cardiovascular diseases. A design with a baseline measurement and two follow-ups at six and 12 months will be applied. At baseline and the six-month follow-up, patients will complete self-report questionnaires evaluating lifestyle habits and socio-demographic and psychological variables; at the 12-month follow-up, patients will answer a telephone interview assessing lifestyle habits. After the baseline measurement, patients will be randomized into one of three groups: (1) the tailored group, which will receive tailored health brochures; (2) the "non-tailored" group, which will receive non-tailored health brochures; or (3) the usual care group, which will receive no print information materials. The effectiveness of the intervention will be assessed through patients' judgments of the brochures and changes in lifestyle. The role of socio-demographic and psychological variables as potential moderators of the materials' effectiveness will be explored. If the TTCYB is efficacious, it will have implications for the design and implementation of tailored communication programs. Concepts from this study can be potentially extended to primary prevention among high-risk groups.

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

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 between-group 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.

Health coaching to improve self-care of informal caregivers of adults with chronic heart failure - iCare4Me: Study protocol for a randomized controlled trial

BACKGROUND

Persons with chronic heart failure are living longer. These patients typically live in the community and are cared for at home by informal caregivers. These caregivers are an understudied and stressed group.

METHODS

We are conducting a two-arm, randomized controlled trial of 250 caregivers of persons with chronic heart failure to evaluate the efficacy of a health coaching intervention. A consecutive sample of participants is being enrolled from both clinic and hospital settings at a single institution affiliated with a large medical center in the northeastern US. Both the intervention and control groups receive tablets programmed to provide standardized health information. In addition, the intervention group receives 10 live coaching sessions delivered virtually by health coaches using the tablets. The intervention is evaluated at 6-months, with self-care as the primary outcome. Cost-effectiveness of the intervention is evaluated at 12-months. We are also enrolling heart failure patients (dyads) whenever possible to explore the effect of caregiver outcomes (self-care, stress, coping, health status) on heart failure patient outcomes (number of hospitalizations and days in the hospital) at 12-months.

DISCUSSION

We expect the proposed study to require 5 years for completion. If shown to be efficacious and cost-effective, our virtual health coaching intervention can easily be scaled to. support millions of caregivers worldwide.

Effect of interventions using physical activity trackers on physical activity in people aged 60 years and over: a systematic review and meta-analysis

Background

Older people are at high risk of physical inactivity. Activity trackers can facilitate physical activity. We aimed to investigate the effect of interventions using activity trackers on physical activity, mobility, quality of life and mental health among people aged 60+ years.

Methods

For this systematic review, we searched eight databases, including MEDLINE, Embase and CENTRAL from inception to April 2018. Randomised controlled trials of interventions that used activity trackers to promote physical activity among people aged 60+ years were included in the analyses. The study protocol was registered with PROSPERO, number CRD42017065250.

Results

We identified 23 eligible trials. Interventions using activity trackers had a moderate effect on physical activity (23 studies; standardised mean difference (SMD)=0.55; 95% CI 0.40 to 0.70; I2=86%) and increased steps/day by 1558 (95% CI 1099 to 2018 steps/day; I2=92%) compared with usual care, no intervention and wait-list control. Longer duration activity tracker-based interventions were more effective than short duration interventions (18 studies, SMD=0.70; 95% CI 0.47 to 0.93 vs 5 studies, SMD=0.14; 95% CI −0.26 to 0.54, p for comparison=0.02). Interventions that used activity trackers improved mobility (three studies; SMD=0.61; 95% CI 0.31 to 0.90; I2=10%), but not quality of life (nine studies; SMD=0.09; 95% CI −0.07 to 0.25; I2=45%). Only one trial included mental health outcomes and it reported similar effects of the activity tracker intervention compared with control.

Conclusions

Interventions using activity trackers improve physical activity levels and mobility among older people compared with control. However, the impact of activity tracker interventions on quality of life, and mental health is unknown.

Physical activity monitors to enhance amount of physical activity in older adults - a systematic review and meta-analysis

Background

The body of evidence related to the effect of physical activity monitor-based interventions has grown over the recent years. However, the effect of physical activity monitor-based interventions in older adults remains unclear and should be systematically reviewed.

Objective

The objective of this systematic review was to estimate the effect of physical activity monitor-based interventions on physical activity behavior in participants aged 65 and above. Subsequently we explored the effect on body mass index, physical capacity, and health-related quality of life and finally the impact of patient- and intervention characteristics.

Methods

Searches in MEDLINE, EMBASE, SPORTDiscus, CINAHL, and CENTRAL were performed on April 26, 2018. No publication date filters were applied. References of eligible studies were scrutinized and relevant journals were hand-searched. Randomized controlled trials and randomized cross-over trials investigating the effect of a physical activity monitor-based intervention on physical activity were included. Studies were included if the mean age of the participants was above 65 years, and participants could walk independently with or without walking aids. The Cochrane handbook was used as a template for extracting data and the RoB 2.0 tool was used to assess risk of bias. Random-effects meta-analysis using Hedges g, were used to pool the study results. The main outcome of this study was physical activity.

Results

Twenty-one studies with 2783 participants were included. The median participant age in the studies was 70.5 years, the median percentage of male participants was 42%, and the median baseline daily step count was 5268. Physical activity monitor-based interventions had a moderate effect (SMD = 0.54, 95% CI: 0.34 to 0.73) compared to control interventions, corresponding to an average increase of 1297 steps per day in the intervention groups. No impact of patient and intervention characteristics on the effect estimates were found.

Short conclusion

Low quality of evidence was found for a moderate effect of physical activity monitor-based interventions on physical activity compared with control interventions. More studies with higher research methodology standards are required.

PROSPERO registration

CRD42018083648.

Electronic supplementary material

The online version of this article (10.1186/s11556-019-0213-6) contains supplementary material, which is available to authorized users.