To assess the reporting quality of interventions aiming at promoting physical activity (PA) using a wearable activity tracker (WAT) in patients with infammatory arthritis (IA) or hip/knee osteoarthritis (OA). A systematic search was performed in eight databases including PubMed, Embase and Cochrane Library) for studies published between 2000 and 2022. Two reviewers independently selected studies and extracted data on study characteristics and the reporting of the PA intervention using a WAT using the Consensus on Exercise reporting Template (CERT) (12 items) and Consolidated Standards of Reporting Trials (CONSORT) E-Health checklist (16 items). The reporting quality of each study was expressed as a percentage of reported items of the total CERT and CONSORT E-Health (50% or less=poor; 51–79%=moderate; and 80–100%=good reporting quality). Sixteen studies were included; three involved patients with IA and 13 with OA. Reporting quality was poor in 6/16 studies and moderate in 10/16 studies, according to the CERT and poor in 8/16 and moderate in 8/16 studies following the CONSORT E-Health checklist. Poorly reported checklist items included: the description of decision rule(s) for determining progression and the starting level, the number of adverse events and how adherence or fdelity was assessed. In clinical trials on PA interventions using a WAT in patients with IA or OA, the reporting quality of delivery process is moderate to poor. The poor reporting quality of the progression and tailoring of the PA programs makes replication difcult. Improvements in reporting quality are necessary.
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Purpose: The purposes of this study were, first, to (re)design the user-interface of the activity tracker known as the MOX with the help of input from elderly individuals living independently and, second, to assess the use of and experiences with the adapted Measure It Super Simple (MISS) activity tracker in daily life. Methods: The double diamond method, which was used to (re)design the user-interface, consists of four phases: discover, define, develop, and deliver. As a departure point, this study used a list of general design requirements that facilitate the development of technology for the elderly. Usage and experiences were assessed through interviews after elderly individuals had used the activity tracker for 2 weeks. Results: In co-creation with thirty-five elderly individuals (65 to 89-years-old) the design, feedback system, and application were further developed into a user-friendly interface: the Measure It Super Simple (MISS) activity. Twenty-eight elderly individuals (65 to 78-years-old) reported that they found the MISS activity easy to use, needed limited help when setting the tracker up, and required limited assistance when using it during their daily lives. Conclusions: This study offers a generic structured methodology and a list of design requirements to adapt the interface of an existing activity tracker consistent with the skills and needs of the elderly. The MISS activity seemed to be successfully (re)designed, like the elderly who participated in this pilot study reported that anyone should be able to use it.
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Despite the increased use of activity trackers, little is known about how they can be used in healthcare settings. This study aimed to support healthcare professionals and patients with embedding an activity tracker in the daily clinical practice of a specialized mental healthcare center and gaining knowledge about the implementation process. An action research design was used to let healthcare professionals and patients learn about how and when they can use an activity tracker. Data collection was performed in the specialized center with audio recordings of conversations during therapy, reflection sessions with the therapists, and semi-structured interviews with the patients. Analyses were performed by directed content analyses. Twenty-eight conversations during therapy, four reflection sessions, and eleven interviews were recorded. Both healthcare professionals and patients were positive about the use of activity trackers and experienced it as an added value. Therapists formulated exclusion criteria for patients, a flowchart on when to use the activity tracker, defined goals, and guidance on how to discuss (the data of) the activity tracker. The action research approach was helpful to allow therapists to learn and reflect with each other and embed the activity trackers into their clinical practice at a specialized mental healthcare center.
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Physical activity monitoring with wearable technology has the potential to support stroke rehabilitation. Little is known about how physical therapists use and value the use of wearable activity monitors. This cross-sectional study explores the use, perspectives, and barriers to wearable activity monitoring in day-to-day stroke care routines amongst physical therapists. Over 300 physical therapists in primary and geriatric care and rehabilitation centers in the Netherlands were invited to fill in an online survey that was developed based on previous studies and interviews with experts. In total, 103 complete surveys were analyzed. Out of the 103 surveys, 27% of the respondents were already using activity monitoring. Of the suggested treatment purposes of activity monitoring, 86% were perceived as useful by more than 55% of the therapists. The most recognized barriers to clinical implementation were lack of skills and knowledge of patients (65%) and not knowing what brand and type of monitor to choose (54%). Of the non-users, 79% were willing to use it in the future. In conclusion, although the concept of remote activity monitoring was perceived as useful, it was not widely adopted by physical therapists involved in stroke care. To date, skills, beliefs, and attitudes of individual therapists determine the current use of wearable technology.
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Purpose: The purpose of this study was to validate optimized algorithm parameter settings for step count and physical behavior for a pocket worn activity tracker in older adults during ADL. Secondly, for a more relevant interpretation of the results, the performance of the optimized algorithm was compared to three reference applications Methods: In a cross-sectional validation study, 20 older adults performed an activity protocol based on ADL with MOXMissActivity versus MOXAnnegarn, activPAL, and Fitbit. The protocol was video recorded and analyzed for step count and dynamic, standing, and sedentary time. Validity was assessed by percentage error (PE), absolute percentage error (APE), Bland-Altman plots and correlation coefficients. Results: For step count, the optimized algorithm had a mean APE of 9.3% and a correlation coefficient of 0.88. The mean APE values of dynamic, standing, and sedentary time were 15.9%, 19.9%, and 9.6%, respectively. The correlation coefficients were 0.55, 0.91, and 0.92, respectively. Three reference applications showed higher errors and lower correlations for all outcome variables. Conclusion: This study showed that the optimized algorithm parameter settings can more validly estimate step count and physical behavior in older adults wearing an activity tracker in the trouser pocket during ADL compared to reference applications.
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This study offers an overview of the natural development of the use of an activity tracker, as well as the relative importance of a range of determinants from literature. Decay is exponential but slower than may be expected from existing literature. Many factors have a small contribution to sustained use. The most important determinants are technical condition, age, user experience, and goal-related factors. This finding suggests that activity tracking is potentially beneficial for a broad range of target groups, but more attention should be paid to technical and user experience–related aspects of activity trackers.
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Aim: The aim of this study was to describe the experience with commercially available activity trackers embedded in the physiotherapy treatment of patients with a chronic disease. Methods: In a qualitative study, 29 participants with a chronic disease participated. They wore an activity tracker for two to eight weeks. Data were collected using 23 interviews and discussion with 6 participants. A framework analysis was used to analyze the data. Results: The framework analysis resulted in seven categories: purchase, instruction, characteristics, correct functioning, sharing data, privacy, use, and interest in feedback. The standard goal of the activity trackers was experienced as too high, however the tracker still motivated them to be more active. Participants would have liked more guidance from their physiotherapists because they experienced the trackers as complex. Participants experienced some technical failures, are willing to share data with their physiotherapist and, want to spend a maximum of e50,-. Conclusion: The developed framework gives insight into all important concepts from the experiences reported by patients with a chronic disease and can be used to guide further research and practice. Patients with a chronic disease were positive regarding activity trackers in general. When embedded in physiotherapy, more attention should be paid to the integration in treatment.
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Aim: The aim of this study was to describe the experience with commercially available activity trackers embedded in the physiotherapy treatment of patients with a chronic disease. Methods: In a qualitative study, 29 participants with a chronic disease participated. They wore an activity tracker for two to eight weeks. Data were collected using 23 interviews and discussion with 6 participants. A framework analysis was used to analyze the data. Results: The framework analysis resulted in seven categories: purchase, instruction, characteristics, correct functioning, sharing data, privacy, use, and interest in feedback. The standard goal of the activity trackers was experienced as too high, however the tracker still motivated them to be more active. Participants would have liked more guidance from their physiotherapists because they experienced the trackers as complex. Participants experienced some technical failures, are willing to share data with their physiotherapist and, want to spend a maximum of €50,-. Conclusion: The developed framework gives insight into all important concepts from the experiences reported by patients with a chronic disease and can be used to guide further research and practice. Patients with a chronic disease were positive regarding activity trackers in general. When embedded in physiotherapy, more attention should be paid to the integration in treatment.
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BACKGROUND: A lack of physical activity is considered to cause 6% of deaths globally. Feedback from wearables such as activity trackers has the potential to encourage daily physical activity. To date, little research is available on the natural development of adherence to activity trackers or on potential factors that predict which users manage to keep using their activity tracker during the first year (and thereby increasing the chance of healthy behavior change) and which users discontinue using their trackers after a short time.OBJECTIVE: The aim of this study was to identify the determinants for sustained use in the first year after purchase. Specifically, we look at the relative importance of demographic and socioeconomic, psychological, health-related, goal-related, technological, user experience-related, and social predictors of feedback device use. Furthermore, this study tests the effect of these predictors on physical activity.METHODS: A total of 711 participants from four urban areas in France received an activity tracker (Fitbit Zip) and gave permission to use their logged data. Participants filled out three Web-based questionnaires: at start, after 98 days, and after 232 days to measure the aforementioned determinants. Furthermore, for each participant, we collected activity data tracked by their Fitbit tracker for 320 days. We determined the relative importance of all included predictors by using Random Forest, a machine learning analysis technique.RESULTS: The data showed a slow exponential decay in Fitbit use, with 73.9% (526/711) of participants still tracking after 100 days and 16.0% (114/711) of participants tracking after 320 days. On average, participants used the tracker for 129 days. Most important reasons to quit tracking were technical issues such as empty batteries and broken trackers or lost trackers (21.5% of all Q3 respondents, 130/601). Random Forest analysis of predictors revealed that the most influential determinants were age, user experience-related factors, mobile phone type, household type, perceived effect of the Fitbit tracker, and goal-related factors. We explore the role of those predictors that show meaningful differences in the number of days the tracker was worn.CONCLUSIONS: This study offers an overview of the natural development of the use of an activity tracker, as well as the relative importance of a range of determinants from literature. Decay is exponential but slower than may be expected from existing literature. Many factors have a small contribution to sustained use. The most important determinants are technical condition, age, user experience, and goal-related factors. This finding suggests that activity tracking is potentially beneficial for a broad range of target groups, but more attention should be paid to technical and user experience-related aspects of activity trackers.
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Study selection: Randomized controlled trials published after 2007 with (former) healthcare patients ≥ 21 years of age were included if physical activity was measured objectively using a wearable monitor for both feedback and outcome assessment. The main goal of included studies was promoting physical activity. Any concurrent strategies were related only to promoting physical activity. Data extraction: Effect sizes were calculated using a fixed-effects model with standardized mean difference. Information on study characteristics and interventions strategies were extracted from study descriptions. Data synthesis: Fourteen studies met the inclusion criteria (total n = 1,902), and 2 studies were excluded from meta-analysis. The overall effect size was in favour of the intervention groups (0.34, 95% CI 0.23–0.44, p < 0.01). Study characteristics and intervention strategies varied widely. Conclusion: Healthcare interventions using feedback on objectively monitored physical activity have a moderately positive effect on levels of physical activity. Further research is needed to determine which strategies are most effective to promote physical activity in healthcare programmes. Lay Abstract Wearable technology is progressively applied in health care and rehabilitation to provide objective insight into physical activity levels. In addition, feedback on physical activity levels delivered by wearable monitors might be beneficial for optimizing their physical activity. A systematic review and meta-analysis was conducted to evaluate the effectiveness of interventions using feedback on objectively measured physical activity in patient populations. Fourteen studies including 1902 patients were analyzed. Overall, the physical activity levels of the intervention groups receiving objective feedback on physical activity improved, compared to the control groups receiving no objective feedback. Mostly, a variety of other strategies were applied in the interventions next to wearable technology. Together with wearable technology, behavioral change strategies, such as goal-setting and action planning seem to be an important ingredient to promote physical activity in health care and rehabilitation. LinkedIn: https://www.linkedin.com/in/hanneke-braakhuis-b9277947/ https://www.linkedin.com/in/moniqueberger/
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