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Elnaggar A, von Oppenfeld J, Whooley MA, Merek S, Park LG. Applying Mobile Technology to Sustain Physical Activity After Completion of Cardiac Rehabilitation: Acceptability Study. JMIR Hum Factors 2021; 8:e25356. [PMID: 34473064 PMCID: PMC8446842 DOI: 10.2196/25356] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 05/24/2021] [Accepted: 07/04/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Many patients do not meet the recommended levels of physical activity after completing a cardiac rehabilitation (CR) program. Wearable activity trackers and mobile phone apps are promising potential self-management tools for maintaining physical activity after CR completion. OBJECTIVE This study aims to evaluate the acceptability of a wearable device, mobile app, and push messages to facilitate physical activity following CR completion. METHODS We used semistructured interviews to assess the acceptability of various mobile technologies after participation in a pilot randomized controlled trial. Intervention patients in the randomized controlled trial wore the Fitbit Charge 2, used the Movn mobile app, and received push messages on cardiovascular disease prevention and physical activity for over 2 months. We asked 26 intervention group participants for feedback about their experience with the technology and conducted semistructured individual interviews with 7 representative participants. We used thematic analysis to create the main themes from individual interviews. RESULTS Our sample included participants with a mean age of 66.7 (SD 8.6) years; 23% (6/26) were female. Overall, there were varying levels of satisfaction with different technology components. There were 7 participants who completed the satisfaction questionnaires and participated in the interviews. The Fitbit and Movn mobile app received high satisfaction scores of 4.86 and 4.5, respectively, whereas push messages had a score of 3.14 out of 5. We identified four main themes through the interviews: technology use increased motivation to be physically active, technology use served as a reminder to be physically active, recommendations for technology to improve user experience, and desire for personal feedback. CONCLUSIONS By applying a wearable activity tracker, mobile phone app, and push messages, our study showed strong potential for the adoption of new technologies by older adults to maintain physical activity after CR completion. Future research should include a larger sample over a longer period using a mixed methods approach to assess the efficacy of technology use for promoting long-term physical activity behavior in older adults.
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Affiliation(s)
- Abdelaziz Elnaggar
- Department of Community Health Systems, School of Nursing, University of California San Francisco, San Francisco, CA, United States
| | | | - Mary A Whooley
- Veterans Affairs Medical Center, San Francisco, CA, United States.,Department of Medicine, University of California San Francisco, San Francisco, CA, United States.,Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA, United States
| | - Stephanie Merek
- Veterans Affairs Medical Center, San Francisco, CA, United States
| | - Linda G Park
- Department of Community Health Systems, School of Nursing, University of California San Francisco, San Francisco, CA, United States.,Veterans Affairs Medical Center, San Francisco, CA, United States
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Wang C, Qi H. Visualising the knowledge structure and evolution of wearable device research. J Med Eng Technol 2021; 45:207-222. [PMID: 33769166 DOI: 10.1080/03091902.2021.1891314] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
In recent years, the literature associated with wearable devices has grown rapidly, but few studies have used bibliometrics and a visualisation approach to conduct deep mining and reveal a panorama of the wearable devices field. To explore the foundational knowledge and research hotspots of the wearable devices field, this study conducted a series of bibliometric analyses on the related literature, including papers' production trends in the field and the distribution of countries, a keyword co-occurrence analysis, theme evolution analysis and research hotspots and trends for the future. By conducting a literature content analysis and structure analysis, we found the following: (a) The subject evolution path includes sensor research, sensitivity research and multi-functional device research. (b) Wearable device research focuses on information collection, sensor materials, manufacturing technology and application, artificial intelligence technology application, energy supply and medical applications. The future development trend will be further studied in combination with big data analysis, telemedicine and personalised precision medical application.
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Affiliation(s)
- Chen Wang
- Department of Health informatics and Management, School of Health Humanities, Peking University, Beijing, China
| | - Huiying Qi
- Department of Health informatics and Management, School of Health Humanities, Peking University, Beijing, China
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Olsen MK, Stechuchak KM, Hung A, Oddone EZ, Damschroder LJ, Edelman D, Maciejewski ML. A data-driven examination of which patients follow trial protocol. Contemp Clin Trials Commun 2020; 19:100631. [PMID: 32913914 PMCID: PMC7471618 DOI: 10.1016/j.conctc.2020.100631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 07/24/2020] [Accepted: 08/02/2020] [Indexed: 11/25/2022] Open
Abstract
Protocol adherence in behavioral intervention clinical trials is critical to trial success. There is increasing interest in understanding which patients are more likely to adhere to trial protocols. The objective of this study was to demonstrate the use of a data-driven approach to explore patient characteristics associated with the lowest and highest rates of adherence in three trials assessing interventions targeting behaviors related to lifestyle and risk for cardiovascular disease. Each trial included a common set of baseline variables. Model-based recursive partitioning (MoB) was applied in each trial to identify participant characteristics of subgroups characterized by these baseline variables with differences in protocol adherence. Bootstrap resampling was conducted to provide optimism-corrected c-statistics of the final solutions. In the three trials, rates of protocol adherence varied from 56.9% to 87.5%. Evaluation of heterogeneity of protocol adherence via MoB in each trial resulted in trees with 2–4 subgroups based on splits of 1–3 variables. In two of the three trials, the first split was based on pain in the past week, and those reporting lower pain were less likely to be adherent. In one of these trials, the second and third splits were based on education and employment, where those with lower education levels and who were employed were less likely to be adherent. In the third trial, the two splits were based on smoking status and then marriage status, where smokers who were married were least likely to be adherent. Optimism-corrected c-statistics ranged from 0.54 to 0.63. Model-based recursive partitioning can be a useful approach to explore heterogeneity in protocol adherence in behavioral intervention trials. An important next step would be to assess whether patterns hold in other similar studies and samples. Identifying subgroups who are less likely to be adherent to an intervention can help inform modifications to the intervention to help tailor the intervention to these subgroups and increase future uptake and impact. Trial registration ClinicalTrials.gov identifiers: NCT01828567, NCT02360293, and NCT01838226.
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Affiliation(s)
- Maren K Olsen
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, USA.,Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Karen M Stechuchak
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, USA
| | - Anna Hung
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, USA.,DCRI, Duke University, Durham, NC, USA
| | - Eugene Z Oddone
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, USA.,Division of General Internal Medicine, Department of Medicine, Duke University, Durham, NC, USA
| | - Laura J Damschroder
- Ann Arbor VA HSR&D Center for Clinical Management Research, Ann Arbor, MI, USA.,VA PROVE QUERI, Ann Arbor, MI, USA
| | - David Edelman
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, USA.,Division of General Internal Medicine, Department of Medicine, Duke University, Durham, NC, USA
| | - Matthew L Maciejewski
- Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, USA.,Division of General Internal Medicine, Department of Medicine, Duke University, Durham, NC, USA.,Department of Population Health Sciences, Duke University, Durham, NC, USA
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Damschroder LJ, Buis LR, McCant FA, Kim HM, Evans R, Oddone EZ, Bastian LA, Hooks G, Kadri R, White-Clark C, Richardson CR, Gierisch JM. Effect of Adding Telephone-Based Brief Coaching to an mHealth App (Stay Strong) for Promoting Physical Activity Among Veterans: Randomized Controlled Trial. J Med Internet Res 2020; 22:e19216. [PMID: 32687474 PMCID: PMC7435619 DOI: 10.2196/19216] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 06/11/2020] [Accepted: 07/07/2020] [Indexed: 02/06/2023] Open
Abstract
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
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Affiliation(s)
- Laura J Damschroder
- Veterans Affairs Center for Clinical Management Research, Ann Arbor Healthcare System, Ann Arbor, MI, United States
| | - Lorraine R Buis
- University of Michigan, Department of Family Medicine, Ann Arbor, MI, United States
| | - Felicia A McCant
- Veterans Affairs Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, United States
| | - Hyungjin Myra Kim
- Veterans Affairs Center for Clinical Management Research, Ann Arbor Healthcare System, Ann Arbor, MI, United States
| | - Richard Evans
- Veterans Affairs Center for Clinical Management Research, Ann Arbor Healthcare System, Ann Arbor, MI, United States
| | - Eugene Z Oddone
- Veterans Affairs Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, United States.,Division of General Internal Medicine, Department of Medicine, Duke University Medical Center, Durham, NC, United States
| | - Lori A Bastian
- Veterans Affairs Pain Research, Informatics, Multimorbidities, and Education Center, Veterans Affairs Connecticut, West Haven, CT, United States.,Division of General Internal Medicine, Department of Medicine, Yale University, West Haven, CT, United States
| | - Gwendolyn Hooks
- Veterans Affairs Center for Clinical Management Research, Ann Arbor Healthcare System, Ann Arbor, MI, United States
| | - Reema Kadri
- University of Michigan, Department of Family Medicine, Ann Arbor, MI, United States
| | - Courtney White-Clark
- Veterans Affairs Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, United States
| | | | - Jennifer M Gierisch
- Veterans Affairs Durham Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans Affairs Health Care System, Durham, NC, United States.,Division of General Internal Medicine, Department of Medicine, Duke University Medical Center, Durham, NC, United States.,Department of Population Health Sciences, Duke University Medical Center, Durham, NC, United States
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