Dataset Information

Association between behavioral phenotypes and sustained use of smartphones and wearable devices to remotely monitor physical activity.

ABSTRACT: Smartphones and wearable devices can be used to remotely monitor health behaviors, but little is known about how individual characteristics influence sustained use of these devices. Leveraging data on baseline activity levels and demographic, behavioral, and psychosocial traits, we used latent class analysis to identify behavioral phenotypes among participants randomized to track physical activity using a smartphone or wearable device for 6 months following hospital discharge. Four phenotypes were identified: (1) more agreeable and conscientious; (2) more active, social, and motivated; (3) more risk-taking and less supported; and (4) less active, social, and risk-taking. We found that duration and consistency of device use differed by phenotype for wearables, but not smartphones. Additionally, "at-risk" phenotypes 3 and 4 were more likely to discontinue use of a wearable device than a smartphone, while activity monitoring in phenotypes 1 and 2 did not differ by device type. These findings could help to better target remote-monitoring interventions for hospitalized patients.

SUBMITTER: Fendrich SJ

PROVIDER: S-EPMC8563736 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Association between behavioral phenotypes and sustained use of smartphones and wearable devices to remotely monitor physical activity.

Fendrich Sarah J SJ Balachandran Mohan M Patel Mitesh S MS

Scientific reports 20211102 1

Smartphones and wearable devices can be used to remotely monitor health behaviors, but little is known about how individual characteristics influence sustained use of these devices. Leveraging data on baseline activity levels and demographic, behavioral, and psychosocial traits, we used latent class analysis to identify behavioral phenotypes among participants randomized to track physical activity using a smartphone or wearable device for 6 months following hospital discharge. Four phenotypes we ...[more]

PMID: 34728746

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Project description:BACKGROUND:There is growing interest in using wearable devices to remotely monitor patient behaviors. However, there has been little evaluation of how often these technologies are used to monitor sleep patterns over longer term periods, particularly among more high-risk patients. OBJECTIVE:The goal of the research was to evaluate the proportion of time that patients with ischemic heart disease used wearable devices to monitor their sleep and identify differences in characteristics of patients with higher versus lower use. METHODS:We evaluated wearable device data from a previously conducted clinical trial testing the use of wearable devices with personalized goal-setting and financial incentives. Patients with ischemic heart disease established a sleep baseline and were then followed for 24 weeks. The proportion of days that sleep data was collected was compared over the 24 weeks and by study arm. Characteristics of patients were compared to groups with high, low, or no sleep data. RESULTS:The sample comprised 99 patients with ischemic heart disease, among which 79% (78/99) used the wearable device to track their sleep. During the 6-month trial, sleep data were collected on 60% (10,024/16,632) of patient-days. These rates declined over time from 77% (4292/5544) in months 1 and 2 to 58% (3188/5544) in months 3 and 4 to 46% (2544/5544) in months 5 and 6. Sleep data were collected at higher rates among the intervention group compared with control (67% vs 55%, P<.001). In the main intervention period (months 3 and 4), patients with higher rates of sleep data were on average older (P=.03), had a history of smoking (P=.007), and had higher rates of commercial health insurance (P=.03). CONCLUSIONS:Among patients with ischemic heart disease in a physical activity trial, a high proportion used wearable devices to track their sleep; however, rates declined over time. Future research should consider larger evaluations coupled with behavioral interventions. TRIAL REGISTRATION:ClinicalTrials.gov NCT02531022; https://clinicaltrials.gov/ct2/show/NCT02531022.

Project description:ObjectivesTo evaluate whether commercially available 'off-the-shelf' wearable technology can improve patient rehabilitation outcomes, and to categorise all wearables currently being used to augment rehabilitation, including the disciplines and conditions under investigation.DesignSystematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020 statement checklist, and using the Grading of Recommendations, Assessment, Development and Evaluation approach.Data sourcesEmbase, MEDLINE, Web of Science and the Cochrane Library were searched up to and including July 2023.Eligibility criteriaWe included trials and observational studies evaluating the use of consumer-grade wearables, in real patient cohorts, to aid physical therapy or rehabilitation. Only studies investigating rehabilitation of acute events with defined recovery affecting adult patients were included.Data extraction and synthesisTwo independent reviewers used a standardised protocol to search, screen and extract data from the included studies. Risk of bias was assessed using the Cochrane Methods Risk of Bias in Randomised Trials V.2 and Risk of Bias in Non-Randomised Studies of Interventions tools for randomised controlled trials (RCTs) and observational studies, respectively.ResultsEighteen studies encompassing 1754 patients met eligibility criteria, including six RCTs, six quasi-experimental studies and six observational studies. Eight studies used wearables in Orthopaedics, seven in Stroke Medicine, two in Oncology and one in General Surgery. All six RCTs demonstrated that wearable-driven feedback increases physical activity. Step count was the most common measure of physical activity. Two RCTs in orthopaedics demonstrated non-inferiority of wearable self-directed rehabilitation compared with traditional physiotherapy, highlighting the potential of wearables as alternatives to traditional physiotherapy. All 12 non-randomised studies demonstrated the feasibility and acceptability of wearable-driven self-directed rehabilitation.ConclusionThis review demonstrates that consumer-grade wearables can be used as adjuncts to traditional physiotherapy, and potentially as alternatives for self-directed rehabilitation of non-chronic conditions. Better designed studies, and larger RCTs, with a focus on economic evaluations are needed before a case can be made for their widespread adoption in healthcare settings.Prospero registration idCRD42023459567.

Project description:BackgroundPhysical inactivity is a global health issue, and mobile health (mHealth) apps are expected to play an important role in promoting physical activity. Empirical studies have demonstrated the efficacy and efficiency of app-based interventions, and an increasing number of apps with more functions and richer content have been released. Regardless of the success of mHealth apps, there are important evidence gaps in the literature; that is, it is largely unknown who uses what app functions and which functions are associated with physical activity.ObjectiveThis study aims to investigate the use patterns of apps and wearables supporting physical activity and exercise in a Japanese-speaking community sample.MethodsWe recruited 20,573 web-based panelists who completed questionnaires concerning demographics, regular physical activity levels, and use of apps and wearables supporting physical activity. Participants who indicated that they were using a physical activity app or wearable were presented with a list of app functions (eg, sensor information, goal setting, journaling, and reward), among which they selected any functions they used.ResultsApproximately one-quarter (n=4465) of the sample was identified as app users and showed similar demographic characteristics to samples documented in the literature; that is, compared with app nonusers, app users were younger (odds ratio [OR] 0.57, 95% CI 0.50-0.65), were more likely to be men (OR 0.83, 95% CI 0.77-0.90), had higher BMI scores (OR 1.02, 95% CI 1.01-1.03), had higher levels of education (university or above; OR 1.528, 95% CI 1.19-1.99), were more likely to have a child (OR 1.16, 95% CI 1.05-1.28) and job (OR 1.28, 95% CI 1.17-1.40), and had a higher household income (OR 1.40, 95% CI 1.21-1.62). Our results revealed unique associations between demographic variables and specific app functions. For example, sensor information, journaling, and GPS were more frequently used by men than women (ORs <0.84). Another important finding is that people used a median of 2 (IQR 1-4) different functions within an app, and the most common pattern was to use sensor information (ie, self-monitoring) and one other function such as goal setting or reminders.ConclusionsRegardless of the current trend in app development toward multifunctionality, our findings highlight the importance of app simplicity. A set of two functions (more precisely, self-monitoring and one other function) might be the minimum that can be accepted by most users. In addition, the identified individual differences will help developers and stakeholders pave the way for the personalization of app functions.

Dataset Information

Association between behavioral phenotypes and sustained use of smartphones and wearable devices to remotely monitor physical activity.

Publications

Association between behavioral phenotypes and sustained use of smartphones and wearable devices to remotely monitor physical activity.

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