Pedometer-Based Behavioral Change Program Can Improve Dependency in Sedentary Older Adults: A Randomized Controlled Trial

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.

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.

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.

Comprehensive geriatric intervention in community-dwelling older adults: a cluster-randomized controlled trial

BACKGROUND

In longevity societies, one of the most serious social issues is sarcopenia and/or frailty. Preventing them is important for maintaining independence and quality of life in the older population. This study investigated the effect of a self-monitoring comprehensive geriatric intervention programme (CGIP) on physical function and muscle size in community-dwelling older adults. We compared the effects of a CGIP using weekly class-styled (CS) sessions and a home-based (HB) programme.

METHODS

The 526 participants were randomized into one of two groups (CS 251, HB 275) based on their residential districts. We conducted a 12 week CGIP, which consisted of low-load resistance exercise, physical activity increments, oral function improvements, and a nutritional guide. All participants were encouraged to attend two 90 min lectures that included instructions on the CGIP. They were provided with exercise materials (triaxial-accelerometers/pedometers, ankle weights, and elastic bands) and diary logs. The CS group attended 90 min weekly sessions and independently executed the programme on other days, whereas the HB group only received instructions on how to execute the programme. Physical functions, such as knee extension strength (KES), normal and maximum walking speed, the timed up-and-go test, and anterior thigh muscle thickness (MT), were measured and analysed using intention-to-treat analysis before and after the 12 week intervention.

RESULTS

Of the 526 participants identified, 517 (CS 243 age 74.0 ± 5.4 women 57.2%, HB 274 age 74.0 ± 5.6 women 58.8%) were enrolled. Nine (CS 8, HB 1) were excluded from the analysis because they did not participate in the pre-intervention measurements. Both interventions significantly improved KES (CS 18.5%, HB 10.6%), normal walking speed (CS 3.7%, HB 2.8%), and MT (CS 3.2%, HB 3.5%). Greater improvement of KES was observed in the CS group (P = 0.003). Maximum walking speed (CS 4.7%, HB 1.8%; P = 0.001) and timed up-and-go (CS -4.7%, HB -0.2%; P < 0.001) significantly improved in the CS group only.

CONCLUSIONS

The intervention was effective in preventing sarcopenia and/or frailty. Most physical functions and MT improved after both interventions. The HB intervention is cost-effective and may help prevent sarcopenia and/or frailty in the large older population.

Behavior Change Techniques Incorporated in Fitness Trackers: Content Analysis

Background

The use of fitness trackers as tools of self-management to promote physical activity is increasing. However, the content of fitness trackers remains unexplored.

Objective

The aim of this study was to use the Behavior Change Technique Taxonomy v1 (BCTTv1) to examine if swim-proof fitness trackers below Aus $150 (US$ 105) incorporate behavior change techniques (BCTs) that relate to self-management strategies to increase physical activity and reduce sedentary behavior and to determine if content of the fitness trackers correspond to physical activity guidelines.

Methods

A total of two raters used the BCTTv1 to code 6 fitness trackers that met the inclusion criteria. The inclusion criteria were the ability to track activity, be swim proof, be compatible with Android and Apple operating systems, and cost below Aus $150.

Results

All fitness trackers contained BCTs known to promote physical activity, with the most frequently used BCTs overlapping with self-management strategies, including goal setting, self-monitoring, and feedback on behavior. Fitbit Flex 2 (Fitbit Inc) contained the most BCTs at 20. Huawei Band 2 Pro (Huawei Technologies) and Misfit Shine 2 (Fossil Group) contained the least BCTs at 11.

Conclusions

Fitness trackers contain evidence-based BCTs that overlap with self-management strategies, which have been shown to increase physical activity and reduce sedentary behavior. Fitness trackers offer the prospect for physical activity interventions that are cost-effective and easily accessed by a wide population.

Fear of falling among community-dwelling older adults: A scoping review to identify effective evidence-based interventions

Fear of falling presents a significant problem for many older adults by reducing physical function and increasing the risk of future falls. Several different types of interventions have improved fear of falling and a summary of efficacious interventions will help clinicians recommend treatment options. Using the Arksey and O'Malley Framework for scoping reviews, the purpose of this review was to identify efficacious interventions for treating fear of falling among community-dwelling older adults in order to provide a list of potential treatment options for care providers. A total of 45 publications were identified for inclusion in this review.

Older adults' satisfaction of wearing consumer-level activity monitors

There is a growing body of evidence to suggest that consumer-level activity monitors are a valid means of measuring physical activity in older adults. Understanding whether older adults are satisfied with wearing these activity monitors is an important step to ensuring that devices can be successfully implemented in clinical and research settings. Twenty-five older adults (mean age = 72.5 years, standard deviation = 4.9) wore two consumer-level activity monitors (Misfit Shine and Fitbit Charge HR) for seven consecutive days. After the week, participants were asked for their views and satisfaction of wearing each device, measured in part by the Quebec User Evaluation of Satisfaction with assistive Technology. Participants were generally satisfied with most aspects of the devices, though they were significantly more satisfied with the Misfit Shine. Participants were critical about their ability to adjust the fit of both the Misfit Shine and Fitbit Charge HR. Interestingly, the perceived satisfaction with the device was not associated with participants' consideration of wearing the device again. Future research needs to consider whether the design of consumer-level activity monitors are best suited for older adults.

A Smart Insole to Promote Healthy Aging for Frail Elderly Individuals: Specifications, Design, and Preliminary Results

Background

Older individuals frequently experience reversible “frailty syndrome,”, increasing incidence of disability. Although physical exercise interventions may delay functional decline, there are difficulties in implementing them and performing seamless follow-up at home. Very few technological solutions attempt to address this challenge and improve individual participation.

Objective

Our objectives are to (1) develop a technological solution designed to support active aging of frail older persons, (2) conduct a first laboratory evaluation of the device, and (3) design a multidimensional clinical trial to validate our solution.

Methods

We conducted a first phase of multidisciplinary meetings to identify real end users and health professional’s unmet needs, and to produce specifications for the architecture of the solution. In a second phase, we performed laboratory tests of the first proposed prototype (a smart insole) with 3 healthy volunteers. We then designed an ongoing clinical trial to finalize the multidimensional evaluation and improvement of the solution.

Results

To respond to the needs expressed by the stakeholders (frailty monitoring and adherence improvement), we developed a prototype of smart shoe insole to monitor key parameters of frailty during daily life and promote walking. It is a noninvasive wireless insole, which automatically measures gait parameters and transmits information to a remote terminal via a secure Internet connection. To ensure the solution’s autonomy and transparency, we developed an original energy harvesting system, which transforms mechanical energy produced by the user’s walking movement into electrical energy. The first laboratory tests of this technological solution showed good reliability measures and also a good acceptability for the users. We have planned an original iterative medical research protocol to validate our solution in real life.

Conclusions

Our smart insole could support preventive strategies against disability in primary care by empowering the older patients without increasing the busy health professional’s workload.

Trial Registration

Clinicaltrials.gov NCT02316600; https://clinicaltrials.gov/ct2/results?term=NCT02316600&Search=Search. Accessed: 2015-05-13 . (Archived by WebCite at http://www.webcitation.org/6YUTkObrQ).

Effects of a behavioral program on exercise adherence and exercise self-efficacy in community-dwelling older persons

Background. This study determines the effects of a behavioral program on exercise adherence (step counts) and level of exercise self-efficacy (ESE) in community-dwelling older persons. Methods. Sixty-three participants (age = 63.8 ± 4.5 years) were enrolled in this controlled quasi-experimental study. They were divided into 3 groups: (1) EBG performed a 6-week exercise intervention followed by a 5-week behavioral program, (2) EG performed exercise intervention similar to EBG, and (3) control group (CG) did not receive any interventions. Step counts were measured based on the scores recorded by a pedometer while ESE was measured by a self-reported ESE scale. Results. Data analysis showed significant differences due to time effect (F(1,2) = 39.884, P < 0.01, and η = .399); time and group interactions (F(2,60) = 112.683, P < 0.01, and η = .790); and between-group effect (F(2,60) = 12.524, P < 0.01, and η = .295) for step counts. As for ESE, significant differences were also found for time effect (F(2,4) = 66.628, P < 0.05, and η = .526); time and group interactions (F(2,60) = 4.562, P = 0.014, and η = .132); and between-group effect (F(2,60) = 13.632, P < 0.05, and η = .312). EBG presented with significantly higher mean changes for both step counts and ESE compared to other groups (all P < 0.05). Conclusion. This study suggests that the addition of a behavioral program is superior as compared to exercising alone on increasing exercise adherence and level of self-efficacy in older persons.