Brief Report: Obesogenic Behaviors of Children with Developmental Disabilities During Summer.

The 'Structured Days Hypothesis' suggests that children's obesogenic behaviors (e.g., activity, diet, sleep, and screen time) are less favorable during times when there is less-structure to a child's day (e.g., summer). To compare obesogenic behaviors of children with developmental disabilities (DD) during summer on days with differing amounts of 'structure'. Seventeen children with DD (mean age 9.8 years) attending a day camp wore a Fitbit© activity monitor on the non-dominant wrist during summer, and parents completed a survey packet, to capture obesogenic behaviors. Participants displayed improved physical activity levels, diets, and sleep timing on camp days versus other days. Providing children with DD 'structure' over summer is a potential intervention approach requiring further investigation.

Wearables in the SARS-CoV-2 Pandemic: What Are They Good for?

Recently, companies such as Apple Inc, Fitbit Inc, and Garmin Ltd have released new wearable blood oxygenation measurement technologies. Although the release of these technologies has great potential for generating health-related information, it is important to acknowledge the repercussions of consumer-targeted biometric monitoring technologies (BioMeTs), which in practice, are often used for medical decision making. BioMeTs are bodily connected digital medicine products that process data captured by mobile sensors that use algorithms to generate measures of behavioral and physiological function. These BioMeTs span both general wellness products and medical devices, and consumer-targeted BioMeTs intended for general wellness purposes are not required to undergo a standardized and transparent evaluation process for ensuring their quality and accuracy. The combination of product functionality, marketing, and the circumstances of the global SARS-CoV-2 pandemic have inevitably led to the use of consumer-targeted BioMeTs for reporting health-related measurements to drive medical decision making. In this viewpoint, we urge consumer-targeted BioMeT manufacturers to go beyond the bare minimum requirements described in US Food and Drug Administration guidance when releasing information on wellness BioMeTs. We also explore new methods and incentive systems that may result in a clearer public understanding of the performance and intended use of consumer-targeted BioMeTs.

Knowledge Discovery on IoT-Enabled mHealth Applications.

The exponential growth of the number and variety of IoT devices and applications for personal use, as well as the improvement of their quality and performance, facilitates the realization of intelligent eHealth concepts. Nowadays, it is easier than ever for individuals to monitor themselves, quantify, and log their everyday activities in order to gain insights about their body's performance and receive recommendations and incentives to improve it. Of course, in order for such systems to live up to the promise, given the treasure trove of data that is collected, machine learning techniques need to be integrated in the processing and analysis of the data. This systematic and automated quantification, logging, and analysis of personal data, using IoT and AI technologies, have given birth to the phenomenon of Quantified-Self. This work proposes a prototype decentralized Quantified-Self application, built on top of a dedicated IoT gateway that aggregates and analyzes data from multiple sources, such as biosignal sensors and wearables, and performs analytics on it.

Are Fitbits implicated in body image concerns and disordered eating in women?

OBJECTIVE: Using a daily monitoring framework, we examined the psychological consequences of Fitbit self-tracking on state body satisfaction, disordered eating (DE; i.e., binge eating and dietary restraint), levels of exercise engagement, and motivations (appearance vs. fitness/health) in adult women. A further aim within the Fitbit group was to assess whether the level of steps achieved on 1 day would be associated with the state-based outcome measures on the subsequent day. METHOD: In total, 262 participants who had never used a wearable fitness self-tracking device were allocated to a Fitbit (n = 101) or control condition (n = 161). Participants provided baseline data on sociodemographics, eating pathology, and exercise and then completed a 10-day Ecological Momentary Assessment (EMA) protocol assessing exercise amount and motives, body satisfaction, and DE symptoms via a mobile application. Those in the Fitbit condition wore a Fitbit over the entire assessment period. RESULTS: The use of a Fitbit over a 10-day period had no significant effects on exercise behavior or body satisfaction compared to a control group. However, those in the Fitbit group were more likely to exercise to reach fitness goals and less likely to engage in dietary restraint and binge-eating behavior. Among participants in the Fitbit condition, steps achieved the previous day were not predictive of exercise engagement, body satisfaction, or DE symptoms on the subsequent day. CONCLUSIONS: Our study failed to link fitness self-tracking to body dissatisfaction and DE, at least in the early stages of use. Future research directions regarding alternative pathways through which self-tracking devices may exert negative influences are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Using consumer-wearable technology for remote assessment of physiological response to stress in the naturalistic environment.

Psychosocial stress is a major risk factor for morbidity and mortality related to a wide range of health conditions and has a significant negative impact on public health. Quantifying exposure to stress in the naturalistic environment can help to better understand its health effects and identify strategies for timely intervention. The objective of the current project was to develop and test the infrastructure and methods necessary for using wearable technology to quantify individual response to stressful situations and to determine if popular and accessible fitness trackers such as Fitbit® equipped with an optical heart rate (HR) monitor could be used to detect physiological response to psychosocial stress in everyday life. The participants in this study were University of Minnesota students (n = 18) that owned a Fitbit® tracker and had at least one upcoming examination. Continuous HR and activity measurements were obtained during a 7-day observation period containing examinations self-reported by the participants. Participants responded to six ecological momentary assessment surveys per day (~ 2 hour intervals) to indicate occurrence of stressful events. We compared HR during stressful events (e.g., exams) to baseline HR during periods indicated as non-stressful using mixed effects modeling. Our results show that HR was elevated by 8.9 beats per minute during exams and by 3.2 beats per minute during non-exam stressors. These results are consistent with prior laboratory findings and indicate that consumer wearable fitness trackers could serve as a valuable source of information on exposure to psychosocial stressors encountered in the naturalistic environment.

Implementation of ezTrack open-source pipeline for quantifying rat locomotor behavior: Comparison to commercially available software.

Animal tracking software is an important tool to record and analyze locomotor activity during behavioral assays that provides considerable advantages over traditional manual scoring approaches (e.g., counting line crosses on a grid overlay or using a stopwatch to score time spent in regions of interest). Although several options are available to researchers, tracking software is often costly or requires advanced technical knowledge to operate efficiently. In this study, a free open-source behavioral tracking pipeline called ezTrack was compared to commercially available software for assessing rat locomotor behavior and time spent in regions of interest during elevated plus maze (EPM) and open field test (OFT) assays. ezTrack produced nearly identical results to the commercial software. Overall, these results suggest that ezTrack is a cost-effective approach to quantify some aspects of behavior in these tasks.

A Walking Intervention to Increase Weekly Steps in Dialysis Patients: A Pilot Randomized Controlled Trial.

RATIONALE & OBJECTIVE: Patients receiving dialysis report very low physical activity. We implemented a pilot trial to assess the feasibility of a pedometer-based intervention to gather preliminary evidence about its impact on physical activity, symptoms, and surrogates of cardiovascular risk. STUDY DESIGN: Pilot randomized controlled trial. SETTING & PARTICIPANTS: 60 dialysis patients from San Francisco dialysis clinics. INTERVENTION: Participants were randomly assigned 1:1 to receiving pedometers with weekly step goals or usual care for 3 months. OUTCOMES: The primary outcome was step counts, measured using pedometers. Secondary outcomes included physical performance using the Short Physical Performance Battery, the Physical Function and Vitality scales of the 36-Item Short Form Health Survey, the Dialysis Symptoms Index, and the Center for Epidemiologic Studies-Depression Scale, with endothelial function as a secondary and heart rate variability as an exploratory surrogate measure of cardiovascular risk. Targeted enrollment was 50% and targeted completion was 85%. RESULTS: 49% of approached patients were enrolled, and 92% completed the study. After 3 months, patients randomly assigned to the intervention (n=30) increased their average daily steps by 2,256 (95% CI, 978-3,537) more than the 30 controls (P<0.001). Heart rate variability (standard deviation of N-N intervals) increased by 14.94 (95% CI, 0.31-33.56) millisecondsin the intervention group as compared with controls (P = 0.05). There were no statistically significant differences across intervention groups in symptoms, physical performance, or endothelial function. Participants in the intervention group reverted to baseline steps during the postintervention follow-up. LIMITATIONS: The Northern California study setting may limit generalizability. Walking does not capture the full spectrum of physical activity. CONCLUSIONS: A short-term pedometer-based intervention led to increased step counts in dialysis patients, but the increase was not sustained. Pedometer-based interventions are feasible for dialysis patients, but future studies are needed to address whether more prolonged interventions can improve physical function or symptoms. FUNDING: Supported by grants from the American Kidney Fund, National Institutes of Health-National Institute of Diabetes and Digestive and Kidney Diseases, and International Society of Nephrology. TRIAL REGISTRATION: Registered at ClinicalTrials.gov with study identifier NCT02623348.

Cross-sectional associations between personality traits and device-based measures of step count and sedentary behaviour in older age: the Lothian Birth Cohort 1936.

BACKGROUND: While the associations between personality traits and self-reported physical activity are well replicated, few studies have examined the associations between personality and device-based measures of both physical activity and sedentary behaviour. Low levels of physical activity and high levels of sedentary behaviour are known risk factors for poorer health outcomes in older age. METHODS: We used device-based measures of physical activity and sedentary behaviour recorded over 7 days in 271 79-year-old participants of the Lothian Birth Cohort 1936. Linear regression models were used to assess whether personality traits were cross-sectionally associated with step count, sedentary time, and the number of sit-to-stand transitions. Personality traits were entered one at a time, and all-together, controlling for age and sex in Model 1 and additionally for BMI and limiting long-term illness in Model 2. RESULTS: None of the associations between personality traits and measures of physical activity and sedentary behaviours remained significant after controlling for multiple-comparisons using the False Discovery Rate test (all ps > .07). CONCLUSIONS: We found no evidence that personality traits are associated with device-based measures of physical activity or sedentary behaviour in older age. More studies are needed to replicate and examine the nature of these relationships.

Comparison of On-Site Versus Remote Mobile Device Support in the Framingham Heart Study Using the Health eHeart Study for Digital Follow-up: Randomized Pilot Study Set Within an Observational Study Design.

BACKGROUND: New electronic cohort (e-Cohort) study designs provide resource-effective methods for collecting participant data. It is unclear if implementing an e-Cohort study without direct, in-person participant contact can achieve successful participation rates. OBJECTIVE: The objective of this study was to compare 2 distinct enrollment methods for setting up mobile health (mHealth) devices and to assess the ongoing adherence to device use in an e-Cohort pilot study. METHODS: We coenrolled participants from the Framingham Heart Study (FHS) into the FHS-Health eHeart (HeH) pilot study, a digital cohort with infrastructure for collecting mHealth data. FHS participants who had an email address and smartphone were randomized to our FHS-HeH pilot study into 1 of 2 study arms: remote versus on-site support. We oversampled older adults (age ≥65 years), with a target of enrolling 20% of our sample as older adults. In the remote arm, participants received an email containing a link to enrollment website and, upon enrollment, were sent 4 smartphone-connectable sensor devices. Participants in the on-site arm were invited to visit an in-person FHS facility and were provided in-person support for enrollment and connecting the devices. Device data were tracked for at least 5 months. RESULTS: Compared with the individuals who declined, individuals who consented to our pilot study (on-site, n=101; remote, n=93) were more likely to be women, highly educated, and younger. In the on-site arm, the connection and initial use of devices was ≥20% higher than the remote arm (mean percent difference was 25% [95% CI 17-35] for activity monitor, 22% [95% CI 12-32] for blood pressure cuff, 20% [95% CI 10-30] for scale, and 43% [95% CI 30-55] for electrocardiogram), with device connection rates in the on-site arm of 99%, 95%, 95%, and 84%. Once connected, continued device use over the 5-month study period was similar between the study arms. CONCLUSIONS: Our pilot study demonstrated that the deployment of mobile devices among middle-aged and older adults in the context of an on-site clinic visit was associated with higher initial rates of device use as compared with offering only remote support. Once connected, the device use was similar in both groups.

Defining Menstrual Literacy With the Aim of Evaluating Mobile Menstrual Tracking Applications.

For the estimated 75 million people in the United States who menstruate, understanding menstrual health as a critical "vital sign" is an important aspect of managing personal health. Unsurprisingly, in the past decade, menstrual tracking applications have become increasingly popular, with more than 300 applications available for download and an estimated 200 million downloads worldwide. This study had two purposes. The first was to formulate a definition for menstrual literacy-a baseline of knowledge and skills for understanding anatomical and biological facts of menstruation, caring for the menstruating body, and completing menstrual care tasks-by building on prior work about health literacy and by conducting content analysis of eight Web sites containing information about menstruation. The second was to evaluate a maximum variation sample of 17 menstrual tracking applications; here, features and functions related to the concepts about menstrual literacy identified in a content analysis were compared. These applications had insufficient support for facilitating menstrual literacy, especially for teen and perimenopausal users. The article discusses these disconnects and subsequent design opportunities for menstrual tracking applications to facilitate more robust support of menstrual literacy and overall health of people who menstruate.

Information Literacy in Food and Activity Tracking Among Parkrunners, People With Type 2 Diabetes, and People With Irritable Bowel Syndrome: Exploratory Study.

BACKGROUND: The tracking, or logging, of food intake and physical activity is increasing among people, and as a result there is increasing evidence of a link to improvement in health and well-being. Crucial to the effective and safe use of logging is a user's information literacy. OBJECTIVE: The aim of this study was to analyze food and activity tracking from an information literacy perspective. METHODS: An online survey was distributed to three communities via parkrun, diabetes.co.uk and the Irritable Bowel Syndrome Network. RESULTS: The data showed that there were clear differences in the logging practices of the members of the three different communities, as well as differences in motivations for tracking and the extent of sharing of said tracked data. Respondents showed a good understanding of the importance of information accuracy and were confident in their ability to understand tracked data, however, there were differences in the extent to which food and activity data were shared and also a lack of understanding of the potential reuse and sharing of data by third parties. CONCLUSIONS: Information literacy in this context involves developing awareness of the issues of accurate information recording, and how tracked information can be applied to support specific health goals. Developing awareness of how and when to share data, as well as of data ownership and privacy, are also important aspects of information literacy.

Validation of Four Smartwatches in Energy Expenditure and Heart Rate Assessment During Exergaming.

Objective: Validated the Apple Watch (AW), Fitbit Surge HR (FS), TomTom Multisport Cardio Watch (TT), and Microsoft Band (MB) in energy expenditure (EE), average heart rate (HR), and peak HR assessment during exergaming. Materials and Methods: Twenty-one college students participated in this study in Spring 2016. A 20-minute boxing session was completed on the Nintendo® Wii™. The AW and TT were placed on the left wrist and the FS and MB on the right. Each smartwatches' EE and HR data were compared with identical data provided by ActiGraph GT3X+-Bluetooth accelerometers and an associated ActiGraph HR strap. Results: Initial agreement was observed between the ActiGraph and: FS and TT EE (r = 0.62-0.69); AW, FS, and TT average HR (r = 0.47-0.74); and all smartwatches' peak HR (r = 0.59-0.65). However, post hoc comparisons indicated differences between the ActiGraph and: FS and TT EE measurements (P < 0.01) and MB average/peak HR measurements (P < 0.01). Low measurement bias/adequate precision observed for most smartwatches versus ActiGraph. Conclusions: Observations indicated smartwatch average/peak HR measurements as moderately valid. Smartwatch EE measurements were less valid.

Effects of Mobile Health Including Wearable Activity Trackers to Increase Physical Activity Outcomes Among Healthy Children and Adolescents: Systematic Review.

BACKGROUND: Children and adolescents do not meet the current recommendations on physical activity (PA), and as such, the health-related benefits of regular PA are not achieved. Nowadays, technology-based programs represent an appealing and promising option for children and adolescents to promote PA. OBJECTIVE: The aim of this review was to systematically evaluate the effects of mobile health (mHealth) and wearable activity trackers on PA-related outcomes in this target group. METHODS: Electronic databases such as the Cochrane Central Register of Controlled Trials, PubMed, Scopus, SPORTDiscus, and Web of Science were searched to retrieve English language articles published in peer-reviewed journals from January 2012 to June 2018. Those included were articles that contained descriptions of interventions designed to increase PA among children (aged 6 to 12 years) only, or adolescents (aged 13 to 18 years) only, or articles that include both populations, and also, articles that measured at least 1 PA-related cognitive, psychosocial, or behavioral outcome. The interventions had to be based on mHealth tools (mobile phones, smartphones, tablets, or mobile apps) or wearable activity trackers. Randomized controlled trials (RCTs) and non-RCTs, cohort studies, before-and-after studies, and cross-sectional studies were considered, but only controlled studies with a PA comparison between groups were assessed for methodological quality. RESULTS: In total, 857 articles were identified. Finally, 7 studies (5 with tools of mHealth and 2 with wearable activity trackers) met the inclusion criteria. All studies with tools of mHealth used an RCT design, and 3 were of high methodological quality. Intervention delivery ranged from 4 weeks to 12 months, whereby mainly smartphone apps were used as a tool. Intervention delivery in studies with wearable activity trackers covered a period from 22 sessions during school recess and 8 weeks. Trackers were used as an intervention and evaluation tool. No evidence was found for the effect of mHealth tools, respectively wearable activity trackers, on PA-related outcomes. CONCLUSIONS: Given the small number of studies, poor compliance with accelerometers as a measuring instrument for PA, risk of bias, missing RCTs in relation to wearable activity trackers, and the heterogeneity of intervention programs, caution is warranted regarding the comparability of the studies and their effects. There is a clear need for future studies to develop PA interventions grounded on intervention mapping with a high methodological study design for specific target groups to achieve meaningful evidence.

The Use of Wearable Activity Trackers Among Older Adults: Focus Group Study of Tracker Perceptions, Motivators, and Barriers in the Maintenance Stage of Behavior Change.

BACKGROUND: Wearable activity trackers offer the opportunity to increase physical activity through continuous monitoring. Viewing tracker use as a beneficial health behavior, we explored the factors that facilitate and hinder long-term activity tracker use, applying the transtheoretical model of behavior change with the focus on the maintenance stage and relapse. OBJECTIVE: The aim of this study was to investigate older adults' perceptions and uses of activity trackers at different points of use: from nonuse and short-term use to long-term use and abandoned use to determine the factors to maintain tracker use and prevent users from discontinuing tracker usage. METHODS: Data for the research come from 10 focus groups. Of them, 4 focus groups included participants who had never used activity trackers (n=17). These focus groups included an activity tracker trial. The other 6 focus groups (without the activity tracker trial) were conducted with short-term (n=9), long-term (n=11), and former tracker users (n=11; 2 focus groups per user type). RESULTS: The results revealed that older adults in different tracker use stages liked and wished for different tracker features, with long-term users (users in the maintenance stage) being the most diverse and sophisticated users of the technology. Long-term users had developed a habit of tracker use whereas other participants made an effort to employ various encouragement strategies to ensure behavior maintenance. Social support through collaboration was the primary motivator for long-term users to maintain activity tracker use. Short-term and former users focused on competition, and nonusers engaged in vicarious tracker use experiences. Former users, or those who relapsed by abandoning their trackers, indicated that activity tracker use was fueled by curiosity in quantifying daily physical activity rather than the desire to increase physical activity. Long-term users saw a greater range of pros in activity tracker use whereas others focused on the cons of this behavior. CONCLUSIONS: The results suggest that activity trackers may be an effective technology to encourage physical activity among older adults, especially those who have never tried it. However, initial positive response to tracker use does not guarantee tracker use maintenance. Maintenance depends on recognizing the long-term benefits of tracker use, social support, and internal motivation. Nonadoption and relapse may occur because of technology's limitations and gaining awareness of one's physical activity without changing the physical activity level itself.

Consumer-Based Wearable Activity Trackers Increase Physical Activity Participation: Systematic Review and Meta-Analysis.

BACKGROUND: The range of benefits associated with regular physical activity participation is irrefutable. Despite the well-known benefits, physical inactivity remains one of the major contributing factors to ill-health throughout industrialized countries. Traditional lifestyle interventions such as group education or telephone counseling are effective at increasing physical activity participation; however, physical activity levels tend to decline over time. Consumer-based wearable activity trackers that allow users to objectively monitor activity levels are now widely available and may offer an alternative method for assisting individuals to remain physically active. OBJECTIVE: This review aimed to determine the effects of interventions utilizing consumer-based wearable activity trackers on physical activity participation and sedentary behavior when compared with interventions that do not utilize activity tracker feedback. METHODS: A systematic review was performed searching the following databases for studies that included the use of a consumer-based wearable activity tracker to improve physical activity participation: Cochrane Controlled Register of Trials, MEDLINE, PubMed, Scopus, Web of Science, Cumulative Index of Nursing and Allied Health Literature, SPORTDiscus, and Health Technology Assessments. Controlled trials of adults comparing the use of a consumer-based wearable activity tracker with other nonactivity tracker-based interventions were included. The main outcome measures were physical activity participation and sedentary behavior. All studies were assessed for risk of bias, and the Grades of Recommendation, Assessment, Development, and Evaluation system was used to rank the quality of evidence. The guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement were followed. A random-effects meta-analysis was completed on the included outcome measures to estimate the treatment effect of interventions that included an activity tracker compared with a control group. RESULTS: There was a significant increase in daily step count (standardized mean difference [SMD] 0.24; 95% CI 0.16 to 0.33; P<.001), moderate and vigorous physical activity (SMD 0.27; 95% CI 0.15 to 0.39; P<.001), and energy expenditure (SMD 0.28; 95% CI 0.03 to 0.54; P=.03) and a nonsignificant decrease in sedentary behavior (SMD -0.20; 95% CI -0.43 to 0.03; P=.08) following the intervention versus control comparator across all studies in the meta-analyses. In general, included studies were at low risk of bias, except for performance bias. Heterogeneity varied across the included meta-analyses ranging from low (I2=3%) for daily step count through to high (I2=67%) for sedentary behavior. CONCLUSIONS: Utilizing a consumer-based wearable activity tracker as either the primary component of an intervention or as part of a broader physical activity intervention has the potential to increase physical activity participation. As the effects of physical activity interventions are often short term, the inclusion of a consumer-based wearable activity tracker may provide an effective tool to assist health professionals to provide ongoing monitoring and support.

Physical Activity Trend eXtraction: A Framework for Extracting Moderate-Vigorous Physical Activity Trends From Wearable Fitness Tracker Data.

BACKGROUND: Moderate-vigorous physical activity (MVPA) offers extensive health benefits but is neglected by many. As a result, a wide body of research investigating physical activity behavior change has been conducted. As many of these studies transition from paper-based methods of MVPA data collection to fitness trackers, a series of challenges arise in extracting insights from these new data. OBJECTIVE: The objective of this research was to develop a framework for preprocessing and extracting MVPA trends from wearable fitness tracker data to support MVPA behavior change studies. METHODS: Using heart rate data collected from fitness trackers, we propose Physical Activity Trend eXtraction (PATX), a framework that imputes missing data, recalculates personalized target heart zones, and extracts MVPA trends. We tested our framework on a dataset of 123 college study participants observed across 2 academic years (18 months) using Fitbit Charge HRs. To demonstrate the value of our frameworks' output in supporting MVPA behavior change studies, we applied it to 2 case studies. RESULTS: Among the 123 participants analyzed, PATX labeled 41 participants as experiencing a significant increase in MVPA and 44 participants who experienced a significant decrease in MVPA, with significance defined as P<.05. Our first case study was consistent with previous works investigating the associations between MVPA and mental health. Whereas the second, exploring how individuals perceive their own levels of MVPA relative to their friends, led to a novel observation that individuals were less likely to notice changes in their own MVPA when close ties in their social network mimicked their changes. CONCLUSIONS: By providing meaningful and flexible outputs, PATX alleviates data concerns common with fitness trackers to support MVPA behavior change studies as they shift to more objective assessments of MVPA.

The Association Between Medication Adherence for Chronic Conditions and Digital Health Activity Tracking: Retrospective Analysis.

BACKGROUND: Chronic diseases have a widespread impact on health outcomes and costs in the United States. Heart disease and diabetes are among the biggest cost burdens on the health care system. Adherence to medication is associated with better health outcomes and lower total health care costs for individuals with these conditions, but the relationship between medication adherence and health activity behavior has not been explored extensively. OBJECTIVE: The aim of this study was to examine the relationship between medication adherence and health behaviors among a large population of insured individuals with hypertension, diabetes, and dyslipidemia. METHODS: We conducted a retrospective analysis of health status, behaviors, and medication adherence from medical and pharmacy claims and health behavior data. Adherence was measured in terms of proportion of days covered (PDC), calculated from pharmacy claims using both a fixed and variable denominator methodology. Individuals were considered adherent if their PDC was at least 0.80. We used step counts, sleep, weight, and food log data that were transmitted through devices that individuals linked. We computed metrics on the frequency of tracking and the extent to which individuals engaged in each tracking activity. Finally, we used logistic regression to model the relationship between adherent status and the activity-tracking metrics, including age and sex as fixed effects. RESULTS: We identified 117,765 cases with diabetes, 317,340 with dyslipidemia, and 673,428 with hypertension between January 1, 2015 and June 1, 2016 in available data sources. Average fixed and variable PDC for all individuals ranged from 0.673 to 0.917 for diabetes, 0.756 to 0.921 for dyslipidemia, and 0.756 to 0.929 for hypertension. A subgroup of 8553 cases also had health behavior data (eg, activity-tracker data). On the basis of these data, individuals who tracked steps, sleep, weight, or diet were significantly more likely to be adherent to medication than those who did not track any activities in both the fixed methodology (odds ratio, OR 1.33, 95% CI 1.29-1.36) and variable methodology (OR 1.37, 95% CI 1.32-1.43), with age and sex as fixed effects. Furthermore, there was a positive association between frequency of activity tracking and medication adherence. In the logistic regression model, increasing the adjusted tracking ratio by 0.5 increased the fixed adherent status OR by a factor of 1.11 (95% CI 1.06-1.16). Finally, we found a positive association between number of steps and adherent status when controlling for age and sex. CONCLUSIONS: Adopters of digital health activity trackers tend to be more adherent to hypertension, diabetes, and dyslipidemia medications, and adherence increases with tracking frequency. This suggests that there may be value in examining new ways to further promote medication adherence through programs that incentivize health tracking and leveraging insights derived from connected devices to improve health outcomes.

Acceptability and Feasibility of Implementing Accelorometry-Based Activity Monitors and a Linked Web Portal in an Exercise Referral Scheme: Feasibility Randomized Controlled Trial.

BACKGROUND: Exercise referral schemes (ERSs) are recommended for patients with health conditions or risk factors. Evidence points to the initial effectiveness and cost-effectiveness of such schemes for increasing physical activity, but effects often diminish over time. Techniques such as goal setting, self-monitoring, and personalized feedback may support motivation for physical activity and maintenance of effects. Wearable technologies could provide an opportunity to integrate motivational techniques into exercise schemes. However, little is known about acceptability to exercise referral populations or implementation feasibility within exercise referral services. OBJECTIVE: To determine the feasibility and acceptability of implementing an activity-monitoring device within the Welsh National ERS to inform a decision on whether and how to proceed to an effectiveness trial. METHODS: We conducted a feasability randomized controlled trial with embedded mixed-methods process evaluation and an exploratory economic analysis. Adults (N=156) were randomized to intervention (plus usual practice; n=88) or usual practice only (n=68). Usual practice was a 16-week structured exercise program. The intervention group additionally received an accelerometry-based activity monitor (MyWellnessKey) and associated Web platform (MyWellnessCloud). The primary outcomes were predefined progression criteria assessing acceptability and feasibility of the intervention and proposed evaluation. Postal questionnaires were completed at baseline (time 0:T0), 16 weeks (T1), and 12 months after T0 (T2). Routine data were accessed at the same time-points. A subsample of intervention participants and scheme staff were interviewed following the initiation of intervention delivery and at T2. RESULTS: Participants were on average aged 56.6 (SD 16.3) years and mostly female (101/156, 64.7%) and white (150/156, 96.2%). Only 2 of 5 progression criteria were met; recruitment and randomization methods were acceptable to participants, and contamination was low. However, recruitment and retention rates (11.3% and 67.3%, respectively) fell substantially short of target criteria (20% and 80%, respectively), and disproportionally recruited from the least deprived quintile. Only 57.4% of intervention participants reported receipt of the intervention (below the 80% progression threshold). Less than half reported the intervention to be acceptable at T2. Participant and staff interviews revealed barriers to intervention delivery and engagement related to the device design as well as context-specific technological challenges, all of which made it difficult to integrate the technology into the service. Routinely collected health economic measures had substantial missing data, suggesting that other methods for collecting these should be used in future. CONCLUSIONS: To our knowledge, this is the first study to evaluate short- and long-term feasibility and acceptability of integrating wearable technologies into community-based ERSs. The findings highlight device- and context-specific barriers to doing this in routine practice, with typical exercise referral populations. Key criteria for progression to a full-scale evaluation were not met. TRIAL REGISTRATION: ISRCTN Registry ISRCTN85785652; http://www.isrctn.com/ISRCTN85785652.

Physical Activity Surveillance Through Smartphone Apps and Wearable Trackers: Examining the UK Potential for Nationally Representative Sampling.

BACKGROUND: Smartphones and wearable activity trackers present opportunities for large-scale physical activity (PA) surveillance that overcome some limitations of questionnaires or researcher-administered devices. However, it remains unknown whether current users of such technologies are representative of the UK population. OBJECTIVE: The objective of this study was to investigate potential sociodemographic biases in individuals using, or with the potential to use, smartphone apps or wearable activity trackers for PA surveillance in the United Kingdom. METHODS: We used data of adults (aged ≥16 years) from two nationally representative surveys. Using the UK-wide 2018 Ofcom Technology Tracker (unweighted N=3688), we derived mutually adjusted odds ratios (ORs; 95% CI) of personal use or household ownership of a smartwatch or fitness tracker and personal use of a smartphone by age, sex, social grade, activity- or work-limiting disability, urban or rural, and home nation. Using the 2016 Health Survey for England (unweighted N=4539), we derived mutually adjusted ORs of the use of wearable trackers or websites or smartphone apps for weight management. The explanatory variables were age, sex, PA, deprivation, and body mass index (BMI). Furthermore, we stratified these analyses by BMI, as these questions were asked in the context of weight management. RESULTS: Smartphone use was the most prevalent of all technology outcomes, with 79.01% (weighted 2085/2639) of the Technology Tracker sample responding affirmatively. All other outcomes were <30% prevalent. Age ≥65 years was the strongest inverse correlate of all outcomes (eg, OR 0.03, 95% CI 0.02-0.05 for smartphone use compared with those aged 16-44 years). In addition, lower social grade and activity- or work-limiting disability were inversely associated with all Technology Tracker outcomes. Physical inactivity and male sex were inversely associated with both outcomes assessed in the Health Survey for England; higher levels of deprivation were only inversely associated with websites or phone apps used for weight management. The conclusions did not differ meaningfully in the BMI-stratified analyses, except for deprivation that showed stronger inverse associations with website or phone app use in the obese. CONCLUSIONS: The sole use of PA data from wearable trackers or smartphone apps for UK national surveillance is premature, as those using these technologies are more active, younger, and more affluent than those who do not.

User Models for Personalized Physical Activity Interventions: Scoping Review.

BACKGROUND: Fitness devices have spurred the development of apps that aim to motivate users, through interventions, to increase their physical activity (PA). Personalization in the interventions is essential as the target users are diverse with respect to their activity levels, requirements, preferences, and behavior. OBJECTIVE: This review aimed to (1) identify different kinds of personalization in interventions for promoting PA among any type of user group, (2) identify user models used for providing personalization, and (3) identify gaps in the current literature and suggest future research directions. METHODS: A scoping review was undertaken by searching the databases PsycINFO, PubMed, Scopus, and Web of Science. The main inclusion criteria were (1) studies that aimed to promote PA; (2) studies that had personalization, with the intention of promoting PA through technology-based interventions; and (3) studies that described user models for personalization. RESULTS: The literature search resulted in 49 eligible studies. Of these, 67% (33/49) studies focused solely on increasing PA, whereas the remaining studies had other objectives, such as maintaining healthy lifestyle (8 studies), weight loss management (6 studies), and rehabilitation (2 studies). The reviewed studies provide personalization in 6 categories: goal recommendation, activity recommendation, fitness partner recommendation, educational content, motivational content, and intervention timing. With respect to the mode of generation, interventions were found to be semiautomated or automatic. Of these, the automatic interventions were either knowledge-based or data-driven or both. User models in the studies were constructed with parameters from 5 categories: PA profile, demographics, medical data, behavior change technique (BCT) parameters, and contextual information. Only 27 of the eligible studies evaluated the interventions for improvement in PA, and 16 of these concluded that the interventions to increase PA are more effective when they are personalized. CONCLUSIONS: This review investigates personalization in the form of recommendations or feedback for increasing PA. On the basis of the review and gaps identified, research directions for improving the efficacy of personalized interventions are proposed. First, data-driven prediction techniques can facilitate effective personalization. Second, use of BCTs in automated interventions, and in combination with PA guidelines, are yet to be explored, and preliminary studies in this direction are promising. Third, systems with automated interventions also need to be suitably adapted to serve specific needs of patients with clinical conditions. Fourth, previous user models focus on single metric evaluations of PA instead of a potentially more effective, holistic, and multidimensional view. Fifth, with the widespread adoption of activity monitoring devices and mobile phones, personalized and dynamic user models can be created using available user data, including users' social profile. Finally, the long-term effects of such interventions as well as the technology medium used for the interventions need to be evaluated rigorously.