mHealth Physical Activity Interventions Research Articles

Perspectives of People with Stroke, Caregivers and Healthcare Professionals on an Adaptive mHealth Intervention for Physical Activity in the Prevention of Secondary Stroke: A Qualitative Study.

Engaging in regular physical activity (PA) is associated with lower mortality following stroke, and PA reduces the chance of recurrent stroke. Despite recent guidelines to optimise PA following stroke, people with stroke are known to be less active than their age-matched counterparts. Given the heterogenous nature of stroke, adaptive PA interventions are recommended for people with stroke. Empirical data is lacking on adaptive PA or behavioural change interventions following stroke. Suggested strategies in the prevention of stroke recommend the use of mobile health (mHealth) interventions in the primary prevention of stroke. A structured stakeholder consultation process is key to successful implementation of complex interventions. This paper reports the findings of our consultation process to inform the development of an adaptive mHealth PA. We used a qualitative study design to explore the perspectives of key stakeholders on the development of an adaptive PA intervention delivered via mHealth post-stroke. Healthcare workers, carers and people with stroke participated in semi-structured one-to-one or focus group interviews. A reflexive thematic analysis was undertaken on transcribed interviews; key themes and sub-themes were developed using coding and summarised by two researchers, then reviewed by the full research team. Twenty-eight stakeholders were interviewed and three main themes were identified; Key feature of a mHealth intervention, delivering a mHealth intervention, Challenges to development and use. There was widespread agreement across stakeholder groups that an adaptive mHealth PA intervention following stroke would be beneficial to people with stroke, following discharge from acute care. Our consultation supports the development of an adaptive PA programme that addresses specific impairments that can hinder exercise participation after stroke.

141 Perspectives of key stakeholders on an adaptive physical activity mHealth intervention in the prevention of secondary stroke: a qualitative study

Abstract Background Engaging in regular Physical Activity (PA) is associated with lower mortality post-stroke, and PA reduces the chance of recurrent stroke. Despite recent guidelines to optimise PA post-stroke, people with stroke are known to be less active than their age-matched counterparts. Given the heterogenous nature of stroke, PA interventions, which are adaptive to individual performance, are recommended. Empirical data is lacking on the design and delivery of adaptive PA interventions post-stroke. Suggested strategies recommend the use of mobile health (mHealth) interventions. The aim of this study is to explore the perspectives of key stakeholders on the design and development of an adaptive mHealth PA intervention post-stroke. Methods People with stroke, healthcare professionals and carers participated in semi-structured one-to-one or focus group interviews. A reflexive thematic analysis was undertaken on transcribed interviews and key themes and sub-themes were developed. Results Twenty-eight interviews were conducted across stakeholder groups including people with stroke (n = 12, mean age 55 years), healthcare professionals (n = 10) and carers (n = 6, mean age 67 years). Three main themes relevant to the design and development of the intervention were identified: (1) Key features of a mHealth PA intervention, (2) Pragmatic intervention delivery, (3) Challenges to development and use. Conclusion There was widespread agreement across stakeholder groups that an adaptive mHealth PA intervention post-stroke would be beneficial following discharge from acute care. Our data supports the development of individually adapted programs, to increase PA post-stroke. These findings present new perspectives across stakeholder groups on the design and delivery of an adaptive, mHealth PA post-stroke. Taken together with existing quantitative evidence, these findings will inform the development of a feasibility trial, examining patient and process outcomes, to increase PA levels post-stroke.

Effects of an mHealth physical activity intervention to prevent osteoporosis in premenopausal women. A randomized controlled trial

ObjectivesIt is critical to develop prevention strategies for osteoporosis that reduce the burden fractures place on individuals and health systems. This study evaluated the effects of an mHealth intervention that delivered and monitored a non-supervised exercise program on bone mineral density (BMD). DesignRandomized controlled trial. Methods60 premenopausal women aged 35–50 years were divided into an intervention group (IG) and a control group (CG). The IG followed a 6-month intervention aimed at increasing osteogenic physical activity, guided by two daily goals: walking at least 10,000 steps and completing 60 impacts over 4 g of acceleration. These goals were monitored using a wearable accelerometer linked to an mHealth app. The CG maintained their regular lifestyle and wore the accelerometer without feedback. BMD was estimated using dual-energy X-ray absorptiometry (DXA) scans at baseline and after 6 months, with group-by-time analyses conducted using ANCOVA. The intervention's impact on physical fitness and activity habits was also evaluated. Results46 participants completed the study (IG = 24; CG = 22). The IG showed significant improvements compared to the CG in femoral neck BMD (IG: +0.003 ± 0.029 g/cm2 vs CG: −0.027 ± 0.031 g/cm2), trochanter BMD (IG: +0.004 ± 0.023 g/cm2 vs CG: −0.026 ± 0.030 g/cm2), and total hip BMD (IG: +0.006 ± 0.043 g/cm2 vs CG: −0.040 ± 0.048 g/cm2). The IG also demonstrated significant improvements in physical fitness measures, including peak torque and power at various speeds and positions. No adverse events related to the intervention were reported. ConclusionsThis non-supervised physical activity intervention delivered by wearable-technology and an mHealth app was effective in improving BMD, suggesting its potential for osteoporosis prevention.

Frailty and comorbidities among young adult cancer survivors enrolled in an mHealth physical activity intervention trial.

The physical frailty phenotype identifies individuals at risk for adverse health outcomes but has rarely been assessed among young adult cancer survivors (YACS). This study describes frailty status among YACS participating in a physical activity (PA) intervention trial. YACS were categorized at baseline using the 5-item FRAIL scale: fatigue; weight loss; illness; ambulation; resistance. Chi-square tests compared frailty and non-cancer comorbidities by characteristics. Prevalence rates (PRs) for the independent associations between characteristics, frailty, and comorbidities were estimated using modified Poisson regression models. Among 280 YACS (82% female; mean (M) age = 33.4±4.8 years, M=3.7±2.4 years post-diagnosis), 11% frail, 17% prefrail; the most frequent criteria were fatigue (41%), resistance (38%), and ambulation (14%). Compared to BMI <25, higher BMI was associated with increased likelihood of frailty (BMI 25-30, PR: 2.40, 95% CI: 1.38-4.17; BMI > 30, PR: 2.95, 95% CI: 1.71-5.08). Compared to 0, ≥ 30min/week of moderate-to-vigorous PA was associated with reduced frailty (PR: 0.39, 95% CI: 0.25-0.60). Most YACS (55%) reported ≥ 1 comorbidity, most frequently depression (38%), thyroid condition (19%), and hypertension (10%). Comorbidities were more common for women (59% vs. 37%) and current/former smokers (PR: 1.71, 95% CI: 1.29-2.28). Prevalence of frailty and comorbidities in this sample was similar to other YACS cohorts and older adults without cancer and may be an indicator of accelerated aging and increased risk for poor outcomes. Assessment of frailty may help identify YACS at increased risk for adverse health outcomes.

Evaluating the Feasibility, Acceptability, and Engagement of an mHealth Physical Activity Intervention for Adults With Spinal Cord Injury Who Walk: A Randomized Controlled Trial.

The purpose of this study was to test a partnered, self-determination theory-informed mobile health intervention called SCI Step Together, using an 8-week randomized controlled trial design. The aim of SCI Step Together is to increase the quantity and quality of physical activity (PA) among adults with spinal cord injury (SCI) who walk. The SCI Step Together program provides PA modules and PA self-monitoring tools and facilitates peer and health coach support. Process, resource, management, and scientific feasibility were assessed, and participants completed questionnaires at baseline, mid-, and postintervention to assess determinants and outcomes of PA. Interviews were conducted to evaluate acceptability. Results suggest that the program demonstrated good feasibility, acceptability, and engagement. The intervention group (n = 11) had greater fulfillment of basic psychological needs and knowledge (p = .05) than the control group (n = 9). There were no significant interaction effects for other outcomes. The SCI Step Together program is feasible and acceptable and efficacious for improving some psychosocial variables. Results may inform SCI mobile health programs.

Physical Activity Trends Among Adults in a National Mobile Health Program: A Population-Based Cohort Study of 411,528 Adults.

Physical inactivity is a global public health challenge, and effective, large-scale interventions are needed. We examined the effectiveness of a population-wide mobile health (mHealth) intervention in Singapore, National Steps Challenge Season 3 (NSC3) and 2 booster challenges (Personal Pledge and Corporate Challenge). The study includes 411,528 participants. We used regression discontinuity design and difference-in-difference with fixed-effects regression to examine the association of NSC3 and the additional booster challenges on daily step counts. Participants tended to be female (58.5%), with an average age of 41.5 years (standard deviation, 13.9) and body mass index (weight (kg)/height (m)2) of 23.8 (standard deviation, 4.5). We observed that NSC3 was associated with a mean increase of 1,437 steps (95% confidence interval (CI): 1,408, 1,467) per day. Enrollments in Personal Pledge and Corporate Challenge were associated with additional mean increases of 1,172 (95% CI: 1,123, 1,222) and 896 (95% CI: 862, 930) steps per day, respectively. For NSC3, the associated mean increase in the step counts across different sex and age groups varied, with greater increases for female participants and those in the oldest age group. We provide real-world evidence suggesting that NSC3 was associated with improvements in participants' step counts. Results suggest NSC3 is an effective and appealing population-wide mHealth physical activity intervention.

MHealth Physical Activity Intervention for Individuals With Spinal Cord Injury: Planning and Development Processes.

BackgroundInterventions to support physical activity participation among individuals with spinal cord injury (SCI) are required given this population’s low levels of physical activity and extensive barriers to quality physical activity experiences.ObjectiveThis study aimed to develop a mobile health intervention, called SCI Step Together, to improve the quantity and quality of physical activity among individuals with SCI who walk.MethodsOur overarching methodological framework was the Person-Based approach. This included the following 4 steps: conduct primary and secondary research (step 1); design intervention objectives and features (step 2a); conduct behavioral analysis and theory (step 2b); create a logic model (step 3); and complete the SCI Step Together program content and integrated knowledge translation (IKT; step 4), which occurred throughout development. The partnership approach was informed by the SCI IKT Guiding Principles. Three end users pilot-tested the app and participated in the interviews.ResultsStep 1 identified issues to be addressed when designing intervention objectives and features (step 2a) and features were mapped onto the Behavior Change Wheel (step 2b) to determine the behavior change techniques (eg, action planning) to be included in the app. The logic model linked the mechanisms of action to self-determination theory (steps 2/3). Interviews with end users generated recommendations for the technology (eg, comparing physical activity levels with guidelines), trial (eg, emailing participants’ worksheets), and intervention content (eg, removing graded tasks; step 4).ConclusionsUsing the SCI IKT Guiding Principles to guide partner engagement and involvement ensured that design partners had shared decision-making power in intervention development. Equal decision-making power maximizes the meaningfulness of the app for end users. Future research will include testing the acceptability, feasibility, and engagement of the program. Partners will be involved throughout the research process.Trial RegistrationClinicalTrials.gov: NCT05063617; https://clinicaltrials.gov/ct2/show/NCT05063617

IMPACT: A Randomized Controlled Trial of an mHealth Physical Activity Intervention for Young Adult Cancer Survivors

Purpose: The IMPACT trial evaluated a theory-based mobile physical activity (PA) intervention on total PA (primary) and moderate-to-vigorous (MVPA) at 6 months in a nationwide sample of young adult cancer survivors (YACS). Methods: YACS (N=280) were randomized to either an intervention group or a self-help (control) group. All participants received digital tools (activity tracker, smart scale, Facebook group) and an individual videochat session. Intervention participants also received a 6-month mHealth program with components to promote increased PA (behavioral lessons, adaptive goal-setting, tailored feedback, tailored text messages, Facebook group prompts). PA was assessed via accelerometry and online questionnaire (Godin Leisure Time Exercise Questionnaire) at baseline and 6 months. Using linear mixed models and an intention-to-treat approach, we tested for group differences in changes from baseline to 6 months, adjusting for education, time since diagnosis, age, and accelerometer wear time. Results: Of 280 YACS (M=33.4 (SD 4.8) yrs, 81.8% women, 23.2% racial/ethnic minority individuals), 92.9% completed 6-month measures. Device-measured total PA min/wk (i.e., sum of light, moderate, and vigorous PA) increased from a mean of 1974.3 (SD=673.9) to 2024.3 (686.7) at 6 months in the intervention group (p=.26) and from 1814.9 (704.5) to 1877.7 (758.2) in the control group (p=.43), with no difference between groups (p=.84). Both groups increased MVPA min/wk over 6 months; increases were 24.7 min/wk (95% CI: 14.8, 34.6; p&amp;lt;.0001) in the intervention versus 11.4 min/wk (95% CI: 1.4, 21.4; p=.02) in the control (p=.07 between groups). Increases in MVPA were 99.7% and 41.6% over baseline in the intervention and control groups, respectively. Increases in self-reported total PA were significant; 123.3 min/wk (95 CI%: 94.5, 152.1; p&amp;lt;.0001) in the intervention versus 83.40 (95% CI: 49.30, 117.50; p&amp;lt;.0001) in the control (p=.08 between groups). Conclusions: While both groups increased total PA over 6 months, the intervention doubled the increase in MVPA min/wk relative to the control group, which is associated with important health benefits. Future research should examine moderators of effects to identify for whom, and under what conditions, the effectiveness varied.

Measurement of Adherence to mHealth Physical Activity Interventions and Exploration of the Factors That Affect the Adherence: Scoping Review and Proposed Framework.

BackgroundMobile health (mHealth) is widely used as an innovative approach to delivering physical activity (PA) programs. Users’ adherence to mHealth programs is important to ensure the effectiveness of mHealth-based programs.ObjectiveOur primary aim was to review the literature on the methods used to assess adherence, factors that could affect users’ adherence, and the investigation of the association between adherence and health outcomes. Our secondary aim was to develop a framework to understand the role of adherence in influencing the effectiveness of mHealth PA programs.MethodsMEDLINE, PsycINFO, EMBASE, and CINAHL databases were searched to identify studies that evaluated the use of mHealth to promote PA in adults aged ≥18 years. We used critical interpretive synthesis methods to summarize the data collected.ResultsIn total, 54 papers were included in this review. We identified 31 specific adherence measurement methods, which were summarized into 8 indicators; these indicators were mapped to 4 dimensions: length, breadth, depth, and interaction. Users’ characteristics (5 factors), technology-related factors (12 factors), and contextual factors (1 factor) were reported to have impacts on adherence. The included studies reveal that adherence is significantly associated with intervention outcomes, including health behaviors, psychological indicators, and clinical indicators. A framework was developed based on these review findings.ConclusionsThis study developed an adherence framework linking together the adherence predictors, comprehensive adherence assessment, and clinical effectiveness. This framework could provide evidence for measuring adherence comprehensively and guide further studies on adherence to mHealth-based PA interventions. Future research should validate the utility of this proposed framework.

Recruitment of young adult cancer survivors into a randomized controlled trial of an mHealth physical activity intervention

PurposeFew studies have recruited young adult cancer survivors (YACS) from around the USA into remotely-delivered behavioral clinical trials. This study describes recruitment strategies used in the IMproving Physical Activity after Cancer Treatment (IMPACT) study, a 12-month randomized controlled trial of a mobile physical activity intervention for YACS.MethodsWe conducted formative work to guide development of recruitment messages and used a variety of methods and channels to recruit posttreatment YACS (diagnosed ages 18–39, participating in < 150 min/week of moderate-to-vigorous intensity activity). We used targeted social media advertisements, direct mailings, clinical referrals, and phone calls to potentially eligible individuals identified through local tumor registries. We also asked community organizations to share study information and advertized at a national conference for YACS.ResultsThe final sample of 280 participants (23% identified as racial/ethnic minority individuals, 18% male, mean 33.4 ± 4.8 years) was recruited over a 14-month period. About 38% of those who completed initial screening online (n = 684) or via telephone (n = 63) were randomized. The top recruitment approach was unpaid social media, primarily via Facebook posts by organizations/friends (45%), while direct mail yielded 40.7% of participants. Other social media (paid advertisements, Twitter), email, clinic referrals, and conference advertisements each yielded 3% or fewer participants. The most cost-effective methods per participant recruited were unpaid social media posts and direct mailings.ConclusionsThe IMPACT trial successfully met enrollment goals using a national strategy to recruit physically inactive YACS. Our approaches can inform recruitment planning for other remotely-delivered intervention trials enrolling YACS.Trial registrationClinicalTrials.govNCT03569605. Registered on 26 June 2018.

An mHealth-Facilitated Personalized Intervention for Physical Activity and Sleep in Community-Dwelling Older Adults.

This randomized controlled pilot trial tested the preliminary effect of a 24-week mHealth-facilitated, personalized intervention on physical activity (PA) and sleep in 21 community-dwelling older adults. The intervention included a personalized exercise prescription, training, goal setting, and financial incentives. mHealth strategies, including self-monitoring, motivational messages, activity reminders, and phone coaching, were used to facilitate PA participation. PA and sleep were measured using actigraphy and questionnaires at baseline and 8-, 16-, and 24-week visits. Participants in the intervention group had lower objective PA levels at 24 weeks than at 8 and 16 weeks, although levels of PA remained higher than at baseline. Compared with the control group, the intervention increased PA at 8, 16, and 24 weeks; improved subjective sleep quality at 16 and 24 weeks; and increased actigraphy-measured sleep duration and sleep efficiency at 24 weeks. mHealth PA interventions may benefit PA and sleep in older adults. Strategies for maintaining long-term PA behavioral changes are needed.

A meta-analysis of the overall effect of mHealth physical activity interventions for weight loss and the moderating effect of behavioral change theories, techniques, and mobile technologies

Mobile technologies offer the potential for efficacious and cost-effective lifestyle interventions for weight loss. Extant research indicates that mobile health (mHealth) lifestyle interventions are potentially effective and practical methods of weight loss, but it is less known what intervention characteristics are associated with weight loss effects. This meta-analysis examined the effectiveness of mHealth physical activity interventions for weight loss compared with non-technology/usual care interventions and the moderating effects of behavioral change theories, techniques, and mobile technologies. A total of 24 studies were identified based on inclusion criteria. Weight loss was the primary outcome. The results showed a medium significant effect size ( d = 0.395; 95% CI = 0.243, 0.546; Z = 5.107, p &lt; 0.001; N = 5146) favoring mHealth interventions. Interventions were significantly more effective when wearable devices were used ( QB = 4.102, df = 1, p &lt; 0.05) and when feedback was employed ( QB = 4.566, df = 1, p &lt; 0.05). The implications for future mHealth intervention design are discussed.

Smartphone-Based Interventions for Physical Activity Promotion: Scoping Review of the Evidence Over the Last 10 Years.

BackgroundSeveral reviews of mobile health (mHealth) physical activity (PA) interventions suggest their beneficial effects on behavior change in adolescents and adults. Owing to the ubiquitous presence of smartphones, their use in mHealth PA interventions seems obvious; nevertheless, there are gaps in the literature on the evaluation reporting processes and best practices of such interventions.ObjectiveThe primary objective of this review is to analyze the development and evaluation trajectory of smartphone-based mHealth PA interventions and to review systematic theory- and evidence-based practices and methods that are implemented along this trajectory. The secondary objective is to identify the range of evidence (both quantitative and qualitative) available on smartphone-based mHealth PA interventions to provide a comprehensive tabular and narrative review of the available literature in terms of its nature, features, and volume.MethodsWe conducted a scoping review of qualitative and quantitative studies examining smartphone-based PA interventions published between 2008 and 2018. In line with scoping review guidelines, studies were not rejected based on their research design or quality. This review, therefore, includes experimental and descriptive studies, as well as reviews addressing smartphone-based mHealth interventions aimed at promoting PA in all age groups (with a subanalysis conducted for adolescents). Two groups of studies were additionally included: reviews or content analyses of PA trackers and meta-analyses exploring behavior change techniques and their efficacy.ResultsIncluded articles (N=148) were categorized into 10 groups: commercial smartphone app content analyses, smartphone-based intervention review studies, activity tracker content analyses, activity tracker review studies, meta-analyses of PA intervention studies, smartphone-based intervention studies, qualitative formative studies, app development descriptive studies, qualitative follow-up studies, and other related articles. Only 24 articles targeted children or adolescents (age range: 5-19 years). There is no agreed evaluation framework or taxonomy to code or report smartphone-based PA interventions. Researchers did not state the coding method, used various evaluation frameworks, or used different versions of behavior change technique taxonomies. In addition, there is no consensus on the best behavior change theory or model that should be used in smartphone-based interventions for PA promotion. Commonly reported systematic practices and methods have been successfully identified. They include PA recommendations, trial designs (randomized controlled trials, experimental trials, and rapid design trials), mixed methods data collection (surveys, questionnaires, interviews, and focus group discussions), scales to assess app quality, and industry-recognized reporting guidelines.ConclusionsSmartphone-based mHealth interventions aimed at promoting PA showed promising results for behavior change. Although there is a plethora of published studies on the adult target group, the number of studies and consequently the evidence base for adolescents is limited. Overall, the efficacy of smartphone-based mHealth PA interventions can be considerably improved through a more systematic approach of developing, reporting, and coding of the interventions.

An mHealth Physical Activity Intervention for Latina Adolescents: Iterative Design of the Chicas Fuertes Study.

BackgroundOnly 3% of Latina teens meet the national physical activity (PA) guidelines, and these habits appear to persist into adulthood. Developing effective interventions to increase PA in Latina teens is necessary to prevent disease and reduce disparities. Mobile technologies may be especially appropriate for this population, but mobile health (mHealth) intervention content must be designed in collaboration with the target population. ObjectiveThis study aims to develop an mHealth PA intervention for Latina adolescents using a multistage iterative process based on the principles of human-centered design and multiple iterations of the design phase of the IDEAS (Integrate, Design, Assess, Share) framework. MethodsOn the basis of the feedback from a previous pilot study, the planned intervention included visual social media posts and text messaging, a commercial wearable tracker, and a primarily visual website. The development of the requested mHealth intervention components was accomplished through the following 2 phases: conducting focus groups with the target population and testing the usability of the final materials with a youth advisory board (YAB) comprising Latina adolescents. Participants for focus groups (N=50) were girls aged 13-18 years who could speak and read in English and who were recruited from local high schools and after-school programs serving a high proportion of Latinos. Facilitated discussions focused on experience with PA and social media apps and specific feedback on intervention material prototypes and possible names and logos. Viable products were designed based on their feedback and then tested for usability by the YAB. YAB members (n=4) were Latinas aged 13-18 years who were not regularly active and were recruited via word of mouth and selected through an application process.ResultsThe focus group discussions yielded the following findings: PA preferences included walking, running, and group fitness classes, whereas the least popular activities were running, swimming, and biking. Most participants (n=48, 96%) used some form of social media, with Instagram being the most favored. Participants preferred text messages to be sent no more than once per day, be personalized, and be positively worded. The focus group participants preferred an intervention directly targeting Latinas and social media posts that were brightly colored, included girls of all body types, and provided specific tips and information. Modified intervention materials were generally perceived favorably by the YAB members, who provided suggestions for further refinement, including the shortening of texts and the incorporation of some Spanish phrases. ConclusionsLatina teens were generally enthusiastic about an mHealth PA intervention, provided that the materials were targeted specifically to them and their preferences. Through multiple iterations of development and feedback from the target population, we gained insight into the needs of Latina teens and joined with industry partners to build a viable final product.

Multilevel mHealth Intervention Increases Physical Activity of Older Adults Living in Retirement Community.

Regular physical activity is crucial for healthy aging, but older adults are the least active age group. This study explored the feasibility, acceptability, and efficacy of a multilevel mHealth intervention for increasing physical activity of older adults living in a retirement community. Participants included 54 older adults (mean age = 81.2y, 77.8% female, 98.1% white) living in a retirement community. Participants received a Fitbit Zip and access to a multilevel mHealth physical activity intervention (MapTrek Residential) for 8weeks. Physical activity (in steps per day) and intervention compliance (days worn) were measured objectively with the Fitbit for 12weeks (8-wk intervention plus 4-wk follow-up). Psychosocial outcomes (social support, self-efficacy, and outcome expectations) were assessed at baseline and 8weeks. Acceptability outcomes were assessed with an open-ended process evaluation survey and focus groups. Descriptive statistics and linear mixed models were used to examine intervention effects. Participants increased daily steps from 5438 steps per day at baseline (95% CI, 4620 to 6256) to 6201 steps per day (95% CI, 5359 to 7042) at week 8 (P < .0001) but this was not maintained at 12weeks (P = .92). Our multilevel mHealth physical activity intervention was effective for increasing physical activity older adults over 8weeks. Additional research focused on maintaining physical activity gains with this approach is warranted.

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

BackgroundMobile health (mHealth) interventions can increase physical activity (PA); however, their long-term impact is not well understood.ObjectiveThe 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.MethodsWe 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.ResultsOf 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.ConclusionsmHealth 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.

A Technology-Based Physical Activity Intervention for Patients With Metastatic Breast Cancer (Fit2ThriveMB): Protocol for a Randomized Controlled Trial.

BackgroundIncreased incidence and life expectancy have resulted in a growing population of patients with metastatic breast cancer, and these patients experience high rates of morbidity and premature mortality. Increased physical activity (PA) is consistently associated with improved health and disease outcomes among early-stage survivors. However, there is a paucity of research on PA in patients with metastatic breast cancer, and existing PA interventions have exhibited low feasibility because of their focus on intense PA and/or requirement of on-site visits. Mobile health (mHealth)–based PA interventions may be particularly useful for patients with metastatic breast cancer because they allow for remote monitoring, which facilitates individual tailoring of PA recommendations to patients’ abilities and may minimize participant burden. However, no studies have examined mHealth PA interventions in patients with metastatic breast cancer.ObjectiveWe aim to address these critical research gaps by testing a highly tailored technology-based intervention to promote PA of any intensity (ie, light, moderate, or vigorous) by increasing daily steps in patients with metastatic breast cancer. The primary aim of this study is to test the feasibility and acceptability of the Fit2ThriveMB intervention. We will also examine outcome patterns suggesting the efficacy of Fit2ThriveMB on symptom burden, quality of life, and functional performance.MethodsThe Fit2ThriveMB trial is a two-arm pilot randomized controlled trial that will compare the effects of a smartphone-delivered, home-based PA intervention and an attention-control education condition on PA and quality of life in low-active female patients with metastatic breast cancer. A subsample (n=25) will also complete functional performance measures. This innovative trial will recruit 50 participants who will be randomized into the study’s intervention or control arm. The intervention will last 12 weeks. The Fit2ThriveMB intervention consists of a Fitbit, coaching calls, and the Fit2ThriveMB smartphone app that provides self-monitoring, a tailored goal-setting tool, real-time tailored feedback, app notifications, and a group message board. Assessments will occur at baseline and post intervention.ResultsThe Fit2ThriveMB study is ongoing. Data collection ended in February 2021.ConclusionsData from this study will provide the preliminary effect sizes needed to assemble an intervention that is to be evaluated in a fully powered trial. In addition, these data will provide essential evidence to support the feasibility and acceptability of using a technology-based PA promotion intervention, a scalable strategy that could be easily integrated into care, among patients with metastatic breast cancer.Trial RegistrationClinicalTrials.gov NCT04129346; https://clinicaltrials.gov/ct2/show/NCT04129346International Registered Report Identifier (IRRID)DERR1-10.2196/24254

Multilevel mobile health approach to improve cardiovascular health in resource-limited communities with Step It Up: a randomised controlled trial protocol targeting physical activity

IntroductionAlthough physical activity (PA) reduces cardiovascular disease (CVD) risk, physical inactivity remains a pressing public health concern, especially among African American (AA) women in the USA. PA interventions focused on...

Off-Policy Estimation of Long-Term Average Outcomes With Applications to Mobile Health

Due to the recent advancements in wearables and sensing technology, health scientists are increasingly developing mobile health (mHealth) interventions. In mHealth interventions, mobile devices are used to deliver treatment to individuals as they go about their daily lives. These treatments are generally designed to impact a near time, proximal outcome such as stress or physical activity. The mHealth intervention policies, often called just-in-time adaptive interventions, are decision rules that map an individual’s current state (e.g., individual’s past behaviors as well as current observations of time, location, social activity, stress, and urges to smoke) to a particular treatment at each of many time points. The vast majority of current mHealth interventions deploy expert-derived policies. In this article, we provide an approach for conducting inference about the performance of one or more such policies using historical data collected under a possibly different policy. Our measure of performance is the average of proximal outcomes over a long time period should the particular mHealth policy be followed. We provide an estimator as well as confidence intervals. This work is motivated by HeartSteps, an mHealth physical activity intervention. Supplementary materials for this article are available online.

Preferences for mHealth physical activity interventions during chemotherapy for breast cancer: a qualitative evaluation.

Physical activity has been shown to decline over the course of chemotherapy in breast cancer survivors; yet it may reduce treatment-related side effects and emerging evidence indicates it may improve disease outcomes. Mobile health (mHealth) interventions may be an effective, scalable strategy to increase physical activity during treatment. However, little is known about breast cancer patients' interests and preferences for these interventions. It is important to understand patients' interests and preferences prior to development of mHealth physical activity interventions to increase their relevance and efficacy. Breast cancer survivors (n = 30) participated in a semi-structured phone interview and were asked about barriers and facilitators to physical activity during chemotherapy as well as their preferences on a range of potential mHealth intervention features. Transcribed interviews were coded and key themes were analyzed using an iterative, inductive approach. Five key themes were extracted from the interviews: (1) need for education about physical activity during chemotherapy; (2) treatment side effects inhibit physical activity; (3) a structured, home-based, tech-supported program with in-person elements is most feasible; (4) need for a personalized, highly tailored intervention; and (5) importance of social support from other breast cancer survivors, friends, and family. Breast cancer survivors are interested in mHealth physical activity interventions during chemotherapy, but preferences for intervention content and delivery varied. Future work should engage patients and survivors in intervention development and testing.