Acceptability and Effects of Commercially Available Activity Trackers for Chronic Pain Management Among Older African American Adults

Electronic Wearable Device Use for Physical Activity in Older Adults: A Qualitative Study

Innovative solutions to help older adults increase physical activity are critically important. In this qualitative study, we explored older adults' acceptance, capability, and experiences of using three different types of electronic wearable devices over a period of 4-24 weeks for self-monitoring and promoting physical activity. We conducted 23 semistructured interviews with older adults who participated in three physical activity intervention studies. Two researchers analyzed the data using NVivo version 12, applying a directed content analysis that was partially guided by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Six themes emerged: (1) device learning, (2) hedonic motivation, (3) habit and adherence, (4) facilitating conditions, (5) effort expectancy, and (6) performance expectancy. Although most older adults (95.8%) from this study were first-time users, they reflected positive experiences and generally enjoyed using electronic wearable devices. Participants reported issues related to electronic wearable device functionalities that can be improved to better enhance user experience and motivate increased physical activity. Future research should explore the role of electronic wearable devices in older adults' physical activity with an emphasis on behavioral change over time.

Characterizing terminology applied by authors and database producers to informatics literature on consumer engagement with wearable devices

OBJECTIVE

Identifying consumer health informatics (CHI) literature is challenging. To recommend strategies to improve discoverability, we aimed to characterize controlled vocabulary and author terminology applied to a subset of CHI literature on wearable technologies.

MATERIALS AND METHODS

To retrieve articles from PubMed that addressed patient/consumer engagement with wearables, we developed a search strategy of textwords and Medical Subject Headings (MeSH). To refine our methodology, we used a random sample of 200 articles from 2016 to 2018. A descriptive analysis of articles (N = 2522) from 2019 identified 308 (12.2%) CHI-related articles, for which we characterized their assigned terminology. We visualized the 100 most frequent terms assigned to the articles from MeSH, author keywords, CINAHL, and Engineering Databases (Compendex and Inspec together). We assessed the overlap of CHI terms among sources and evaluated terms related to consumer engagement.

RESULTS

The 308 articles were published in 181 journals, more in health journals (82%) than informatics (11%). Only 44% were indexed with the MeSH term "wearable electronic devices." Author keywords were common (91%) but rarely represented consumer engagement with device data, eg, self-monitoring (n = 12, 0.7%) or self-management (n = 9, 0.5%). Only 10 articles (3%) had terminology from all sources (authors, PubMed, CINAHL, Compendex, and Inspec).

DISCUSSION

Our main finding was that consumer engagement was not well represented in health and engineering database thesauri.

CONCLUSIONS

Authors of CHI studies should indicate consumer/patient engagement and the specific technology investigated in titles, abstracts, and author keywords to facilitate discovery by readers and expand vocabularies and indexing.

Physical activity monitoring-based interventions in geriatric patients: a scoping review on intervention components and clinical applicability

OBJECTIVE

To identify and analyze the components applied in interventions using physical activity (PA) monitoring in geriatric patients and determine their feasibility and applicability.

METHODS

A systematic search in six databases (PubMed, Embase, SPORTDiscus, CINAHL, Web of Science, and GeroLit) was conducted to identify studies reporting interventions that included the application of a PA monitor in adults aged ≥ 60 years with a clinical diagnosis. PA monitor interventions were analyzed regarding their feedback, goal-setting and behavior change technique (BCT) components. To determine the feasibility and applicability of interventions, the participants' adherence to the intervention, their experience as well as adverse events were analyzed.

RESULTS

Seventeen eligible studies, applying 22 interventions, were identified. Studies included a total of 827 older patients with a median age of 70.2 years. In thirteen interventions (59%), the PA monitor was embedded in a structured behavioral intervention, an indication-specific intervention or usual care. Most frequently applied intervention components were goal setting and self-monitoring (n = 18), real-time PA monitor feedback complemented by feedback from the study team (n = 12), use of further BCTs (n = 18), and regular counseling with the study team (n = 19). Comprehensive information on the participants' intervention adherence and experience were reported for 15 (68%) and 8 (36%) interventions, respectively.

CONCLUSION

The components included in PA monitoring-based interventions varied considerably especially regarding the extent, frequency, and content of feedback, goal setting and BCTs counseling. Future research should evaluate which components are most effective and clinically applicable to promote physical activity in geriatric patients. To be able to precisely analyze the effects, trials should seek to report details on intervention components, adherence and adverse events, while future reviews may use the findings of this scoping review to conduct analyses with less heterogeneity in study characteristics and intervention strategies.

The effect of m-health applications on self-care improvement in older adults: A systematic review

PARTICIPANTS

Four electronic databases were searched on March 6, 2020 including Scopus, PubMed, ISI, and Embase.

METHODS

Our search consisted of concepts of "self-care," "elderly" and "Mobile device." English journal papers and, RCTs conducted for individuals older than 60 in the last 10 years were included. A narrative approach was used to synthesize the data due to the heterogeneous nature of the data.

RESULTS

Initially, 3047 studies were obtained and finally 19 studies were identified for deep analysis. 13 outcomes were identified in m-health interventions to help older adults' self-care. Each outcome has at least one or more positive results. The psychological status and clinical outcome measures were all significantly improved.

CONCLUSION

According to the findings, it is not possible to draw a definite positive decision about the effectiveness of interventions on older adults because the measures are very diverse and have been measured with different tools. However, it might be declared that m-health interventions have one or more positive results and can be used along with other interventions to improve the health of older adults.

Willingness to Use Mobile Health Devices in the Post-COVID-19 Era: Nationwide Cross-sectional Study in China

BACKGROUND

Despite the increased development and use of mobile health (mHealth) devices during the COVID-19 pandemic, there is little knowledge of willingness of the Chinese people to use mHealth devices and the key factors associated with their use in the post-COVID-19 era. Therefore, a more comprehensive and multiangle investigation is required.

OBJECTIVE

We aimed to probe Chinese attitudes regarding the use of mHealth and analyze possible associations between the attitude of willingness to use mHealth devices and some factors based on the socioecological model.

METHODS

A survey was conducted using quota sampling to recruit participants from 148 cities in China between June 20 and August 31, 2022. Data from the survey were analyzed using multiple stepwise regression to examine the factors associated with willingness to use mHealth devices. Standardized regression coefficients (β) and 95% CIs were calculated using multiple stepwise regression.

RESULTS

The survey contained a collection of 21,916 questionnaires and 21,897 were valid questionnaires, with a 99.91% effective response rate. The median score of willingness to use mHealth in the post-COVID-19 era was 70 points on a scale from 0 to 100. Multiple stepwise regression results showed that the female gender (β=.03, 95% CI 1.04-2.35), openness personality trait (β=.05, 95% CI 0.53-0.96), higher household per capita monthly income (β=.03, 95% CI 0.77-2.24), and commercial and multiple insurance (β=.04, 95% CI 1.77-3.47) were factors associated with the willingness to use mHealth devices. In addition, people with high scores of health literacy (β=.13, 95% CI 0.53-0.68), self-reported health rating (β=.22, 95% CI 0.24-0.27), social support (β=.08, 95% CI 0.40-0.61), family health (β=.03, 95% CI 0.03-0.16), neighbor relations (β=.12, 95% CI 2.09-2.63), and family social status (β=.07, 95% CI 1.19-1.69) were more likely to use mHealth devices.

CONCLUSIONS

On the basis of the theoretical framework of socioecological model, this study identified factors specifically associated with willingness of the Chinese people to use mHealth devices in the post-COVID-19 era. These findings provide reference information for the research, development, promotion, and application of future mHealth devices.

Consumer Health Informatics for Racial and Ethnic Minoritized Communities: Minor Progress, Major Opportunities

OBJECTIVE

By reducing barriers to accessing health services and by supporting health management, consumer health informatics has the potential to reduce health disparities. Yet, technologies are still being designed without considerations for racial and ethnic minoritized populations. This paper reviews consumer health informatics research within this population to assess for whom and how such technologies are being designed.

METHODS

We searched four databases from January 2020- December 2021 for literature focused on consumer health informatics and racial and ethnic minoritized populations. We extracted information about the study population, geographic location, stage of the design lifecycle, culturally tailored approaches, community engagement strategies, and considerations for the social determinants of health.

RESULTS

Twenty articles were included in the review. Most of the included literature were original research articles that tested health management interventions focused on one racial or ethnic minoritized population primarily within a confined geographic area within the United States. Seven studies described the extent to which an intervention was culturally tailored, including modifying the content, interface, functionality, and platform. Community engagement strategies varied, but few articles employed robust approaches. Lastly, seven studies detailed considerations for the social determinants of health, including providing hardware to access interventions and incorporating information about community-based resources within an intervention.

CONCLUSIONS

There has been moderate progress in consumer health informatics focused on racial and ethnic minoritized populations and many opportunities remain for these technologies to be used as an approach to address health disparities. Future research should utilize community engagement strategies to design interventions that are attune to multiple racial and ethnic minoritized populations across geographic regions in addition to numerous intersectional identities and multiple co-morbidities.

Fitbit Activity, Quota-Based Pacing, and Physical and Emotional Functioning Among Adults With Chronic Pain

Physical activity can improve function in patients with chronic pain, however, adherence is low, in part due to inconsistent activity patterns. Smart wearable activity trackers, such as Fitbits, may help promote activity. In our program for chronic pain, we examined: 1) Fitbit activity patterns (ie, step count, moderate-to-vigorous physical activity (MVPA), sedentary behavior), and 2) whether achievement of weekly, individualized Fitbit step goals was associated with functional outcomes. We conducted a secondary analysis of Fitbit data from our 10-week mind-body activity program for chronic pain (GetActive-Fitbit arm, N = 41). Participants self-reported emotional and physical function and completed performance-based and accelerometer-based assessments. From week 1 to week 10, 30% of participants increased >800 steps; 32.5% increased MVPA; and 30% decreased sedentary behavior. Only step count significantly changed across time with mean steps peaking at week 8 (M = +1897.60, SD = 467.67). Fitbit step goal achievement was associated with improvements in anxiety (ß = -.35, CI [-2.80, -.43]), self-reported physical function (ß = -.34, CI [-5.17, 8.05]), and performance-based physical function (ß = .29, CI [-71.93, 28.38]), but not accelerometer-based physical function or depression. Adhering to individualized Fitbit step goals in the context of a mind-body activity program may improve anxiety and self-reported and performance-based physical function. PERSPECTIVE: We examine Fitbit activity patterns and the association between quota-based pacing and functional outcomes within a mind-body activity program for adults with chronic pain. Complementing quota-based pacing and coping skills with Fitbits may be a useful approach to promote activity engagement and behavior change in chronic pain populations.

Perceptions on wearable sensor-based interventions for monitoring of opioid therapy: A qualitative study

Prescription opioid use is a risk factor for the development of opioid use disorder. Digital solutions, including wearable sensors, represent a promising opportunity for health monitoring, risk stratification and harm reduction in this treatment space. However, data on their usability and acceptability in individuals using opioids is limited. To address this gap, factors that impact usability and acceptability of wearable sensor-based opioid detection were qualitatively studied in participants enrolled in a wearable sensor-based opioid monitoring research study. At the conclusion of the monitoring period, participants were invited to take part in semi-structured interviews developed based on the technology acceptance model. Thematic analysis was conducted first using deductive, then inductive coding strategies. Forty-four participants completed the interview; approximately half were female. Major emergent themes include sensor usability, change in behavior and thought process related to sensor use, perceived usefulness in sensor-based monitoring, and willingness to have opioid use patterns monitored. Overall acceptance for sensor-based monitoring was high. Aesthetics, simplicity, and seamless functioning were all reported as key to usability. Perceived behavior changes related to monitoring were infrequent while perceived usefulness in monitoring was frequently projected onto others, requiring careful consideration regarding intervention development and targeting. Specifically, care must be taken to avoid stigma associated with opioid use and implied misuse. The design of sensor systems targeted for opioid use must also consider the physical, social, and cognitive alterations inherent in the respective disease processes compared to routine daily life.

A Physical Activity Intervention in Older African Americans: The PAACE Pilot Randomized Controlled Trial

INTRODUCTION

Older African Americans have low levels of physical activity, which increases the risk of aging-related health conditions. This study aimed to determine the effectiveness of, and satisfaction with, a community-engaged physical activity intervention targeting older African Americans.

METHODS

Fifty-six older African Americans were randomized to a successful aging group (SAG) or a physical activity group (PAG) for 12 wk. The PAG consisted of two weekly group exercise sessions and two to three home-based exercise sessions per week. The SAG consisted of weekly group educational sessions related to various aspects of healthy aging. Physical activity was measured by ActiGraph accelerometers and the Community Healthy Activities Model Program for Seniors Activities Questionnaire in both groups, and a Fitbit was continuously worn by PAG participants.

RESULTS

Group session attendance was 93% and 86% in the PAG and SAG, respectively. The increase in ActiGraph-derived steps per day (1085.3 ± 265.6 vs 34.7 ± 274.3; P = 0.008) and daily minutes of moderate-to-vigorous physical activity (MVPA; 6.2 ± 1.6 vs 0.3 ± 1.7; d = 0.68; P = 0.01), and self-reported Community Healthy Activities Model Program for Seniors Activities Questionnaire MVPA bouts (3.5 ± 0.77 vs 0.33 ± 0.79; P < 0.001) were significantly greater within the PAG than the SAG. ActiGraph-derived minutes of daily MVPA and steps as well as Fitbit-derived steps per day were significantly greater on days when PAG participants engaged in group sessions compared with days when they self-reported home-based exercise or no exercise ( P < 0.016). Participants in both study arms reported high levels of satisfaction (>4- on 5-point Likert scale).

CONCLUSIONS

The intervention increased physical activity, was well attended, and resulted in high satisfaction. Future studies should assess long-term sustainability in this population.

mHealth Apps Using Behavior Change Techniques to Self-report Data: Systematic Review

Background

The popularization of mobile health (mHealth) apps for public health or medical care purposes has transformed human life substantially, improving lifestyle behaviors and chronic condition management.

Objective

This review aimed to identify behavior change techniques (BCTs) commonly used in mHealth, assess their effectiveness based on the evidence reported in interventions and reviews to highlight the most appropriate techniques to design an optimal strategy to improve adherence to data reporting, and provide recommendations for future interventions and research.

Methods

We performed a systematic review of studies published between 2010 and 2021 in relevant scientific databases to identify and analyze mHealth interventions using BCTs that evaluated their effectiveness in terms of user adherence. Search terms included a mix of general (eg, data, information, and adherence), computer science (eg, mHealth and BCTs), and medicine (eg, personalized medicine) terms.

Results

This systematic review included 24 studies and revealed that the most frequently used BCTs in the studies were feedback and monitoring (n=20), goals and planning (n=14), associations (n=14), shaping knowledge (n=12), and personalization (n=7). However, we found mixed effectiveness of the techniques in mHealth outcomes, having more effective than ineffective outcomes in the evaluation of apps implementing techniques from the feedback and monitoring, goals and planning, associations, and personalization categories, but we could not infer causality with the results and suggest that there is still a need to improve the use of these and many common BCTs for better outcomes.

Conclusions

Personalization, associations, and goals and planning techniques were the most used BCTs in effective trials regarding adherence to mHealth apps. However, they are not necessarily the most effective since there are studies that use these techniques and do not report significant results in the proposed objectives; there is a notable overlap of BCTs within implemented app components, suggesting a need to better understand best practices for applying (a combination of) such techniques and to obtain details on the specific BCTs used in mHealth interventions. Future research should focus on studies with longer follow-up periods to determine the effectiveness of mHealth interventions on behavior change to overcome the limited evidence in the current literature, which has mostly small-sized and single-arm experiments with a short follow-up period.

Digital Health Interventions for Musculoskeletal Pain Conditions: Systematic Review and Meta-analysis of Randomized Controlled Trials

Background

Digital health solutions can provide populations with musculoskeletal pain with high-reach, low-cost, easily accessible, and scalable patient education and self-management interventions that meet the time and resource restrictions.

Objective

The main objective of this study was to determine the effectiveness of digital health interventions for people with musculoskeletal pain conditions (ie, low back pain, neck pain, shoulder pain, knee pain, elbow pain, ankle pain, and whiplash).

Methods

A systematic review and meta-analysis was conducted. We searched PubMed and Cochrane Central Register of Controlled Trials (from 1974 to August 2021) and selected randomized controlled trials of digital health interventions in the target population of patients with musculoskeletal pain with a minimum follow-up of 1 month. A total of 2 researchers independently screened and extracted the data.

Results

A total of 56 eligible studies were included covering 9359 participants, with a mean follow-up of 25 (SD 15.48) weeks. In moderate-quality evidence, digital health interventions had a small effect on pain (standardized mean difference [SMD] 0.19, 95% CI 0.06-0.32), disability (SMD 0.14, 95% CI 0.03-0.25), quality of life (SMD 0.22, 95% CI 0.07-0.36), emotional functioning (SMD 0.24, 95% CI 0.12-0.35), and self-management (SMD 0.14, 95% CI 0.05-0.24).

Conclusions

Moderate-quality evidence supports the conclusion that digital health interventions are effective in reducing pain and improving functioning and self-management of musculoskeletal pain conditions. Low-quality evidence indicates that digital health interventions can improve the quality of life and global treatment. Little research has been conducted on the influence of digital health on expenses, knowledge, overall improvement, range of motion, muscle strength, and implementation fidelity.

Trial Registration

PROSPERO CRD42022307504; https://tinyurl.com/2cd25hus

Wearing the Future—Wearables to Empower Users to Take Greater Responsibility for Their Health and Care: Scoping Review

Background

Wearables refer to devices that are worn by individuals. In the health care field, wearables may assist with individual monitoring and diagnosis. In fact, the potential for wearable technology to assist with health care has received recognition from health systems around the world, including a place in the strategic Long Term Plan shared by the National Health Service in England. However, wearables are not limited to specialist medical devices used by patients. Leading technology companies, including Apple, have been exploring the capabilities of wearable health technology for health-conscious consumers. Despite advancements in wearable health technology, research is yet to be conducted on wearables and empowerment.

Objective

This study aimed to identify, summarize, and synthesize knowledge on how wearable health technology can empower individuals to take greater responsibility for their health and care.

Methods

This study was a scoping review with thematic analysis and narrative synthesis. Relevant guidance, such as the Arksey and O’Malley framework, was followed. In addition to searching gray literature, we searched MEDLINE, EMBASE, PsycINFO, HMIC, and Cochrane Library. Studies were included based on the following selection criteria: publication in English, publication in Europe or the United States, focus on wearables, relevance to the research, and the availability of the full text.

Results

After identifying 1585 unique records and excluding papers based on the selection criteria, 20 studies were included in the review. On analysis of these 20 studies, 3 main themes emerged: the potential barriers to using wearables, the role of providers and the benefits to providers from promoting the use of wearables, and how wearables can drive behavior change.

Conclusions

Considerable literature findings suggest that wearables can empower individuals by assisting with diagnosis, behavior change, and self-monitoring. However, greater adoption of wearables and engagement with wearable devices depend on various factors, including promotion and support from providers to encourage uptake; increased short-term investment to upskill staff, especially in the area of data analysis; and overcoming the barriers to use, particularly by improving device accuracy. Acting on these suggestions will require investment and constructive input from key stakeholders, namely users, health care professionals, and designers of the technology. As advancements in technology to make wearables viable health care devices have only come about recently, further studies will be important for measuring the effectiveness of wearables in empowering individuals. The investigation of user outcomes through large-scale studies would also be beneficial. Nevertheless, a significant challenge will be in the publication of research to keep pace with rapid developments related to wearable health technology.

A randomized trial to promote physical activity in adult pre-hypertensive and hypertensive patients

The purpose of the pilot study was to determine the effectiveness of wearable activity trackers alone or in combination with behaviour change strategies for promoting physical activity (PA) among individuals with pre-hypertension or hypertension. A sample of 44 adults (68% female and mean age 55) were randomized to receive either a Fitbit Charge HR 3 alone (FB) or the Fitbit in combination with behaviour change strategies (i.e., goal setting, behaviour goal review, adaptive feedback) delivered by a trained health coach (FB+) for 12 weeks. Moderate and vigorous PA (MVPA), steps, and sedentary time (ST) were assessed using ActiGraph wGT3X-BT. The FB+ group significantly increased PA [+1854 (2518) steps/day, p < .01] and MVPA [+26 (34) mins/day, p < .05], and decreased their ST [-63 (73) mins/day, p < .01]. The FB group significantly increased  MVPA [+11 (16) mins/day, p < .05], and decreased their ST [-87 (117) mins/day, p < .01]. Participants in FB+ had a significantly greater increase in MVPA/day compared to FB only with a between-group effect size of 0.6 (p < .05). Using Fitbit for self-monitoring is effective in increasing PA and reducing ST among pre-hypertensive and hypertensive participants. Additional behaviour change support amplified the intervention effectiveness for promoting MVPA.

A Pilot Study of a Chronic Pain Self-Management Program Delivered by Community Health Workers to Underserved African American Older Adults

OBJECTIVE

African American older adults living in disadvantaged communities are disproportionately burdened by disabling pain. To address their needs, we tested the feasibility and potential effects of a cognitive-behavioral chronic pain self-management program delivered by community health workers.

DESIGN

A single-group, pre-post evaluation of the STEPS-2 (Seniors using Technology to Engage in Pain Self-management) intervention, in which participants learned pain-management skills through web-based videos. They were also given wearable activity trackers to facilitate incremental increases in walking. In weekly telephone calls, community health workers helped participants apply skills and set goals.

SUBJECTS/SETTING

Thirty-one adults in Detroit, Michigan (97% African American, 97% female, mean 68.7 years), with chronic musculoskeletal pain.

METHODS

Participants completed telephone surveys at baseline and eight weeks. We measured changes in PROMIS pain interference and pain intensity, as well as Patient Global Impression of Change in pain and functioning. Feasibility indicators included participant engagement and satisfaction, and fidelity to session protocols by community health workers.

RESULTS

Participants on average completed 6.6/7 sessions, and 100% agreed or strongly agreed that they improved their understanding of pain management. Average community health worker fidelity score was 1.79 (0 to 2 scale). Pain interference decreased from baseline to post-program (T-score 61.6 to 57.3, P=.000), as did pain intensity (0 to 10 scale, 6.3 to 5.1, P=.004). Approximately 90% of participants reported that pain and function were at least "a little better" since baseline.

CONCLUSIONS

An intervention combining mobile health tools with support from community health workers holds promise for improving pain outcomes among underserved older adults.

Patterns of Use and Key Predictors for the Use of Wearable Health Care Devices by US Adults: Insights from a National Survey

BACKGROUND

Despite the growing popularity of wearable health care devices (from fitness trackes such as Fitbit to smartwatches such as Apple Watch and more sophisticated devices that can collect information on metrics such as blood pressure, glucose levels, and oxygen levels), we have a limited understanding about the actual use and key factors affecting the use of these devices by US adults.

OBJECTIVE

The main objective of this study was to examine the use of wearable health care devices and the key predictors of wearable use by US adults.

METHODS

Using a national survey of 4551 respondents, we examined the usage patterns of wearable health care devices (use of wearables, frequency of their use, and willingness to share health data from a wearable with a provider) and a set of predictors that pertain to personal demographics (age, gender, race, education, marital status, and household income), individual health (general health, presence of chronic conditions, weight perceptions, frequency of provider visits, and attitude towards exercise), and technology self-efficacy using logistic regression analysis.

RESULTS

About 30% (1266/4551) of US adults use wearable health care devices. Among the users, nearly half (47.33%) use the devices every day, with a majority (82.38% weighted) willing to share the health data from wearables with their care providers. Women (16.25%), White individuals (19.74%), adults aged 18-50 years (19.52%), those with some level of college education or college graduates (25.60%), and those with annual household incomes greater than US $75,000 (17.66%) were most likely to report using wearable health care devices. We found that the use of wearables declines with age: Adults aged >50 years were less likely to use wearables compared to those aged 18-34 years (odds ratios [OR] 0.46-0.57). Women (OR 1.26, 95% CI 0.96-1.65), White individuals (OR 1.65, 95% CI 0.97-2.79), college graduates (OR 1.05, 95% CI 0.31-3.51), and those with annual household incomes greater than US $75,000 (OR 2.6, 95% CI 1.39-4.86) were more likely to use wearables. US adults who reported feeling healthier (OR 1.17, 95% CI 0.98-1.39), were overweight (OR 1.16, 95% CI 1.06-1.27), enjoyed exercise (OR 1.23, 95% CI 1.06-1.43), and reported higher levels of technology self-efficacy (OR 1.33, 95% CI 1.21-1.46) were more likely to adopt and use wearables for tracking or monitoring their health.

CONCLUSIONS

The potential of wearable health care devices is under-realized, with less than one-third of US adults actively using these devices. With only younger, healthier, wealthier, more educated, technoliterate adults using wearables, other groups have been left behind. More concentrated efforts by clinicians, device makers, and health care policy makers are needed to bridge this divide and improve the use of wearable devices among larger sections of American society.

Digital Phenotyping Self-Monitoring Behaviors for Individuals With Type 2 Diabetes Mellitus: Observational Study Using Latent Class Growth Analysis

Background

Sustained self-monitoring and self-management behaviors are crucial to maintain optimal health for individuals with type 2 diabetes mellitus (T2DM). As smartphones and mobile health (mHealth) devices become widely available, self-monitoring using mHealth devices is an appealing strategy in support of successful self-management of T2DM. However, research indicates that engagement with mHealth devices decreases over time. Thus, it is important to understand engagement trajectories to provide varying levels of support that can improve self-monitoring and self-management behaviors.

Objective

The aims of this study were to develop (1) digital phenotypes of the self-monitoring behaviors of patients with T2DM based on their engagement trajectory of using multiple mHealth devices, and (2) assess the association of individual digital phenotypes of self-monitoring behaviors with baseline demographic and clinical characteristics.

Methods

This longitudinal observational feasibility study included 60 participants with T2DM who were instructed to monitor their weight, blood glucose, and physical activity using a wireless weight scale, phone-tethered glucometer, and accelerometer, respectively, over 6 months. We used latent class growth analysis (LCGA) with multitrajectory modeling to associate the digital phenotypes of participants’ self-monitoring behaviors based on their engagement trajectories with multiple mHealth devices. Associations between individual characteristics and digital phenotypes on participants’ self-monitoring behavior were assessed by analysis of variance or the Chi square test.

Results

The engagement with accelerometers to monitor daily physical activities was consistently high for all participants over time. Three distinct digital phenotypes were identified based on participants’ engagement with the wireless weight scale and glucometer: (1) low and waning engagement group (24/60, 40%), (2) medium engagement group (20/60, 33%), and (3) consistently high engagement group (16/60, 27%). Participants that were younger, female, nonwhite, had a low income, and with a higher baseline hemoglobin A_1c level were more likely to be in the low and waning engagement group.

Conclusions

We demonstrated how to digitally phenotype individuals’ self-monitoring behavior based on their engagement trajectory with multiple mHealth devices. Distinct self-monitoring behavior groups were identified. Individual demographic and clinical characteristics were associated with different self-monitoring behavior groups. Future research should identify methods to provide tailored support for people with T2DM to help them better monitor and manage their condition.

International Registered Report Identifier (IRRID)

RR2-10.2196/13517

Feasible but Not Yet Efficacious: A Scoping Review of Wearable Activity Monitors in Interventions Targeting Physical Activity, Sedentary Behavior, and Sleep

Purpose of Review

The present review aimed to explore the range and characteristics of interventions that utilize WAM and descriptively summarize the efficacy of these interventions.

Recent Findings

A total of 65 articles (61 studies) were included in this review. Most of the WAM-based interventions (n=58) were designed to improve physical activity (PA). Interventions targeting sedentary behavior (SB) were much less common (n=12), and even less frequent were WAM-based sleep interventions (n=3). Most studies tested the feasibility of WAM-based interventions; hence, efficacy of these interventions in improving PA, SB, and/or sleep could not be conclusively determined. Nonetheless, WAM-based interventions showed considerable potential in increasing PA and decreasing SB.

Summary

WAM-based PA interventions exhibited preliminary efficacy in increasing PA. Although not as many interventions were focused on SB, current interventions also showed potential in decreasing sedentary time. Meanwhile, more evidence is needed to determine the utility of WAM in improving sleep. Major challenges with including WAM as part of interventions are reduced engagement in using the devices over time and the rapid changes in technology resulting in devices becoming obsolete soon after completion of an efficacy trial.