Comparison of Veteran experiences of low-cost, home-based diet and exercise interventions

Holtz, Bree; Krein, Sarah L.; Bentley, Douglas; Hughes, M. E.; Giardino, Nicholas D.; Richardson, Caroline R.

doi:10.1682/jrrd.2013.04.0088

Cited by 19 publications

(15 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Asynchronous features from the communication category (eg, online forums) were reported to support increases in usage and engagement [ 52 , 79 , 109 , 117 , 119 , 120 ], whereas studies that included synchronous features (eg, online video and chat rooms) for meetings [ 16 , 41 , 62 - 65 ] mostly reported no effect or reduced engagement when compared with controls (face-to-face). Interestingly, it was reported that female participants tended to engage in online group discussions more than their male counterparts [ 69 ]. Online forums have also been found to encourage usage over longer periods [ 109 ].…”

Section: Resultsmentioning

confidence: 99%

Peer-Based Social Media Features in Behavior Change Interventions: Systematic Review

Elaheebocus¹,

Weal²,

Morrison³

et al. 2018

J Med Internet Res

View full text Add to dashboard Cite

BackgroundIncorporating social media features into digital behavior change interventions (DBCIs) has the potential to contribute positively to their success. However, the lack of clear design principles to describe and guide the use of these features in behavioral interventions limits cross-study comparisons of their uses and effects.ObjectiveThe aim of this study was to provide a systematic review of DBCIs targeting modifiable behavioral risk factors that have included social media features as part of their intervention infrastructure. A taxonomy of social media features is presented to inform the development, description, and evaluation of behavioral interventions.MethodsSearch terms were used in 8 databases to identify DBCIs that incorporated social media features and targeted tobacco smoking, diet and nutrition, physical activities, or alcohol consumption. The screening and review process was performed by 2 independent researchers.ResultsA total of 5264 articles were screened, and 143 articles describing a total of 134 studies were retained for full review. The majority of studies (70%) reported positive outcomes, followed by 28% finding no effects with regard to their respective objectives and hypothesis, and 2% of the studies found that their interventions had negative outcomes. Few studies reported on the association between the inclusion of social media features and intervention effect. A taxonomy of social media features used in behavioral interventions has been presented with 36 social media features organized under 7 high-level categories. The taxonomy has been used to guide the analysis of this review.ConclusionsAlthough social media features are commonly included in DBCIs, there is an acute lack of information with respect to their effect on outcomes and a lack of clear guidance to inform the selection process based on the features’ suitability for the different behaviors. The proposed taxonomy along with the set of recommendations included in this review will support future research aimed at isolating and reporting the effects of social media features on DBCIs, cross-study comparisons, and evaluations.

show abstract

Section: Resultsmentioning

confidence: 99%

Peer-Based Social Media Features in Behavior Change Interventions: Systematic Review

Elaheebocus¹,

Weal²,

Morrison³

et al. 2018

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…They viewed these technologies not to be effective for sleep -which is one of the benefits of use from many of these consumer devices and apps. This could be because many devices have sleep tracking as a 'secondary' function [38].…”

Section: Discussionmentioning

confidence: 99%

Health Care Provider Perceptions of Consumer-Grade Devices and Apps for Tracking Health: A Pilot Study (Preprint)

Holtz¹,

Vasold²,

Cotten³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Stay Strong programs both lasted 12 months. Designs were informed by experiences of veterans in a prior study [ 39 ] and by the self-regulation theory and the information-motivation-behavioral skills model [ 40 - 43 ], which describe processes of behavior change mediated through goal attainment and skills mastery, and acknowledges the central role of self-efficacy in sustaining change in PA [ 41 , 44 - 47 ]. Our published protocol provides more details about behavior change techniques incorporated into the programs [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Effect of Adding Telephone-Based Brief Coaching to an mHealth App (Stay Strong) for Promoting Physical Activity Among Veterans: Randomized Controlled Trial

et al. 2020

View full text Add to dashboard Cite

Background Though maintaining physical conditioning and a healthy weight are requirements of active military duty, many US veterans lose conditioning and rapidly gain weight after discharge from active duty service. Mobile health (mHealth) interventions using wearable devices are appealing to users and can be effective especially with personalized coaching support. We developed Stay Strong, a mobile app tailored to US veterans, to promote physical activity using a wrist-worn physical activity tracker, a Bluetooth-enabled scale, and an app-based dashboard. We tested whether adding personalized coaching components (Stay Strong+Coaching) would improve physical activity compared to Stay Strong alone. Objective The goal of this study is to compare 12-month outcomes from Stay Strong alone versus Stay Strong+Coaching. Methods Participants (n=357) were recruited from a national random sample of US veterans of recent wars and randomly assigned to the Stay Strong app alone (n=179) or Stay Strong+Coaching (n=178); both programs lasted 12 months. Personalized coaching components for Stay Strong+Coaching comprised of automated in-app motivational messages (3 per week), telephone-based human health coaching (up to 3 calls), and personalized weekly goal setting. All aspects of the enrollment process and program delivery were accomplished virtually for both groups, except for the telephone-based coaching. The primary outcome was change in physical activity at 12 months postbaseline, measured by average weekly Active Minutes, captured by the Fitbit Charge 2 device. Secondary outcomes included changes in step counts, weight, and patient activation. Results The average age of participants was 39.8 (SD 8.7) years, and 25.2% (90/357) were female. Active Minutes decreased from baseline to 12 months for both groups (P<.001) with no between-group differences at 6 months (P=.82) or 12 months (P=.98). However, at 12 months, many participants in both groups did not record Active Minutes, leading to missing data in 67.0% (120/179) for Stay Strong and 61.8% (110/178) for Stay Strong+Coaching. Average baseline weight for participants in Stay Strong and Stay Strong+Coaching was 214 lbs and 198 lbs, respectively, with no difference at baseline (P=.54) or at 6 months (P=.28) or 12 months (P=.18) postbaseline based on administrative weights, which had lower rates of missing data. Changes in the number of steps recorded and patient activation also did not differ by arm. Conclusions Adding personalized health coaching comprised of in-app automated messages, up to 3 coaching calls, plus automated weekly personalized goals, did not improve levels of physical activity compared to using a smartphone app alone. Physical activity in both groups decreased over time. Sustaining long-term adherence and engagement in this mHealth intervention proved difficult; approximately two-thirds of the trial’s 357 participants failed to sync their Fitbit device at 12 months and, thus, were lost to follow-up. Trial Registration ClinicalTrials.gov NCT02360293; https://clinicaltrials.gov/ct2/show/NCT02360293 International Registered Report Identifier (IRRID) RR2-10.2196/12526

show abstract

Comparison of Veteran experiences of low-cost, home-based diet and exercise interventions

Cited by 19 publications

References 28 publications

Peer-Based Social Media Features in Behavior Change Interventions: Systematic Review

Peer-Based Social Media Features in Behavior Change Interventions: Systematic Review

Health Care Provider Perceptions of Consumer-Grade Devices and Apps for Tracking Health: A Pilot Study (Preprint)

Effect of Adding Telephone-Based Brief Coaching to an mHealth App (Stay Strong) for Promoting Physical Activity Among Veterans: Randomized Controlled Trial

Contact Info

Product

Resources

About