Wearable Devices to Monitor and Reduce the Risk of Cardiovascular Disease: Evidence and Opportunities.

There is a growing interest in using wearable devices to improve cardiovascular risk factors and care. This review evaluates how wearable devices are used for cardiovascular disease monitoring and risk reduction. Wearables have been evaluated for detecting arrhythmias (e.g., atrial fibrillation) as well as monitoring physical activity, sleep, and blood pressure. Thus far, most interventions for risk reduction have focused on increasing physical activity. Interventions have been more successful if the use of wearable devices is combined with an engagement strategy such as incorporating principles from behavioral economics to integrate social or financial incentives. As the technology continues to evolve, wearable devices could be an important part of remote-monitoring interventions but are more likely to be effective at improving cardiovascular care if integrated into programs that use an effective behavior change strategy.

Domains of planning for future long-term care of adults with intellectual and developmental disabilities: Parent and sibling perspectives.

BACKGROUND: Research shows that adults with intellectual and developmental disabilities (IDD) increasingly outlive caregivers, who often struggle to plan for the future and have little support and knowledge surrounding long-term care planning. METHODS: The study team conducted interviews with parents and siblings of adults with IDD and performed qualitative coding using a modified grounded theory to explore domains of future planning and identify barriers and facilitators. RESULTS: Themes from the interviews revealed seven major domains of future planning that should be considered by caregivers of adults with IDD. These domains are housing, legal planning, identification of primary caregiver(s), financial planning, day-to-day care, medical management and transportation. Approaches to planning within each domain varied greatly. CONCLUSIONS: The study team dentified the domain of "identification of primary caregiver(s)" as potentially the most important step for caregivers when planning for the future, but also observed that the domains identified are significantly interrelated and should be considered together.

Association of Behavioral Phenotypes With Changes in Sleep During a Workplace Wellness Program.

PURPOSE: Examine changes in sleep duration by 3 behavioral phenotypes during a workplace wellness program with overweight and obese adults. DESIGN: Secondary analysis of a randomized clinical trial. SETTING: Remotely monitored intervention conducted across the United States. SUBJECTS: 553 participants with a body mass index ≥25. INTERVENTION: Participants were randomized to 1 of 4 study arms: control, gamification with support, gamification with collaboration, and gamification with competition to increase their physical activity. All participants were issued a wrist-worn wearable device to record their daily physical activity and sleep duration. MEASURES: The primary outcome was change in daily sleep duration from baseline during the 24 week intervention and follow-up period by study arm within behavioral phenotype class. ANALYSIS: Linear mixed effects regression. RESULTS: Participants who had a phenotype of less physically active and less social at baseline, in the gamification with collaboration arm, significantly increased their sleep duration during the intervention period (30.2 minutes [95% CI 6.9, 53.5], P = 0.01), compared to the control arm. There were no changes in sleep duration among participants who were more extroverted and motivated or participants who were less motivated and at-risk. CONCLUSIONS: Changes in sleep during a physical activity intervention varied by behavioral phenotype. Behavioral phenotypes may help to precisely identify who is likely to improve sleep duration during a physical activity intervention.

Effect of Behaviorally Designed Gamification With a Social Support Partner to Increase Mobility After Hospital Discharge: A Randomized Clinical Trial.

Importance: Hospitalization is associated with decreased mobility and functional decline. Behaviorally designed gamification can increase mobility in community settings but has not been tested among patients at risk for functional decline during a high-risk transition period after hospitalization. Objective: To test a behaviorally designed gamification intervention with a social support partner to increase patient mobility after hospital discharge. Design, Setting, and Participants: This study is a randomized clinical trial of a 12-week intervention without follow-up. Enrollment occurred from January 2018 to June 2019 at a referral hospital with a remote at-home monitoring intervention among patients living predominantly in 3 states (Pennsylvania, New Jersey, and Delaware). Participants included adult patients discharged from general medicine and oncology units to home. Data analysis was performed from October 2019 to March 2020. Interventions: All participants received a wearable device to track daily steps. The control group received feedback from the device but no other interventions. The intervention group entered into a 12-week game informed by behavioral economics to assign points and levels for achieving step goals and reinforced by a support partner who received updates on participant progress. Main Outcomes and Measures: The primary outcome was change in mean daily steps from baseline through the 12-week intervention. Secondary measures were change in functional status and urgent care utilization (ie, emergency department visits and hospital readmissions) within this period. Results: A total of 232 participants were enrolled in the study (118 randomized to control and 114 randomized to the intervention). Participants had a mean (SD) age of 40 (14) years, 141 (61%) were female, 101 (43%) were White, and 103 (44%) had an annual household income less than $50 000. Daily step counts increased from 3795 to 4652 steps (difference, 857 steps; 95% CI, 488 to 1224 steps) among intervention participants and increased from 3951 to 4499 steps (difference, 548 steps; 95% CI, 193 to 903 steps) among control participants. The change in mean daily step count from baseline was not significantly different for participants in the intervention group vs the control group (adjusted difference, 270 steps; 95% CI, -214 to 754 steps; P = .27). Among the subgroup of 76 participants with higher levels of social engagement, post hoc exploratory analyses showed a significant increase in mobility for intervention vs control (adjusted difference, 1125 steps; 95% CI, 409 to 1841 steps; P = .002). Fewer participants in this subgroup experienced functional decline (1 of 36 participants [4%] in the intervention group vs 5 of 40 participants [12%] in the control group) and hospital readmission at 30 days (3 of 36 participants [8%] in the intervention group vs 6 of 40 participants [15%] in the control group), but the differences were not statistically significant. There were no significant differences in these secondary outcomes for the overall sample. Conclusions and Relevance: Gamification with social incentives did not affect mobility or functional decline in all participants, but post hoc analysis suggests positive findings for both outcomes for patients with higher social engagement. Trial Registration: ClinicalTrials.gov Identifier: NCT03321279.

Predicting changes in glycemic control among adults with prediabetes from activity patterns collected by wearable devices.

The use of wearables is increasing and data from these devices could improve the prediction of changes in glycemic control. We conducted a randomized trial with adults with prediabetes who were given either a waist-worn or wrist-worn wearable to track activity patterns. We collected baseline information on demographics, medical history, and laboratory testing. We tested three models that predicted changes in hemoglobin A1c that were continuous, improved glycemic control by 5% or worsened glycemic control by 5%. Consistently in all three models, prediction improved when (a) machine learning was used vs. traditional regression, with ensemble methods performing the best; (b) baseline information with wearable data was used vs. baseline information alone; and (c) wrist-worn wearables were used vs. waist-worn wearables. These findings indicate that models can accurately identify changes in glycemic control among prediabetic adults, and this could be used to better allocate resources and target interventions to prevent progression to diabetes.

Phenotyping physician practice patterns and associations with response to a nudge in the electronic health record for influenza vaccination: A quasi-experimental study.

BACKGROUND: Health systems routinely implement changes to the design of electronic health records (EHRs). Physician behavior may vary in response and methods to identify this variation could help to inform future interventions. The objective of this study was to phenotype primary care physician practice patterns and evaluate associations with response to an EHR nudge for influenza vaccination. METHODS AND FINDINGS: During the 2016-2017 influenza season, 3 primary care practices at Penn Medicine implemented an active choice intervention in the EHR that prompted medical assistants to template influenza vaccination orders for physicians to review during the visit. We used latent class analysis to identify physician phenotypes based on 9 demographic, training, and practice pattern variables, which were obtained from the EHR and publicly available sources. A quasi-experimental approach was used to evaluate response to the intervention relative to control practices over time in each of the physician phenotype groups. For each physician latent class, a generalized linear model with logit link was fit to the binary outcome of influenza vaccination at the patient visit level. The sample comprised 45,410 patients with a mean (SD) age of 58.7 (16.3) years, 67.1% were white, and 22.1% were black. The sample comprised 56 physicians with mean (SD) of 24.6 (10.2) years of experience and 53.6% were male. The model segmented physicians into groups that had higher (n = 41) and lower (n = 15) clinical workloads. Physicians in the higher clinical workload group had a mean (SD) of 818.8 (429.1) patient encounters, 11.6 (4.7) patient appointments per day, and 4.0 (1.1) days per week in clinic. Physicians in the lower clinical workload group had a mean (SD) of 343.7 (129.0) patient encounters, 8.0 (2.8) patient appointments per day, and 3.1 (1.2) days per week in clinic. Among the higher clinical workload group, the EHR nudge was associated with a significant increase in influenza vaccination (adjusted difference-in-difference in percentage points, 7.9; 95% CI, 0.4-9.0; P = .01). Among the lower clinical workload group, the EHR nudge was not associated with a significant difference in influenza vaccination rates (adjusted difference-in-difference in percentage points, -1.0; 95% CI, -5.3-5.8; P = .90). CONCLUSIONS: A model-based approach categorized physician practice patterns into higher and lower clinical workload groups. The higher clinical workload group was associated with a significant response to an EHR nudge for influenza vaccination.

Association between behavioral phenotypes and response to a physical activity intervention using gamification and social incentives: Secondary analysis of the STEP UP randomized clinical trial.

Participants often vary in their response to behavioral interventions, but methods to identify groups of participants that are more likely to respond are lacking. In this secondary analysis of a randomized clinical trial, we used baseline characteristics to group participants into distinct behavioral phenotypes and evaluated differential responses to a physical activity intervention. Latent class analysis was used to segment participants based on baseline participant data including demographics, validated measures of psychosocial variables, and physical activity behavior. The trial included 602 adults from 40 U.S. states with body mass index ≥25 who were randomized to control or one of three gamification interventions (supportive, collaborative, or competitive) to increase physical activity. Daily step counts were monitored using a wearable device for a 24-week intervention with 12 weeks of follow-up. The model segmented participants into three classes named for key defining traits: Class 1, extroverted and motivated; Class 2, less active and less social; Class 3, less motivated and at-risk. Adjusted regression models were used to test for differences in intervention response relative to control within each behavioral phenotype. In Class 1, only participants in the competitive arm increased their mean daily steps during the intervention (adjusted difference, 945; 95% CI, 352-1537; P = .002), but it was not sustained during follow-up. In Class 2, participants in all three gamification arms significantly increased their mean daily steps compared to control during the intervention (supportive arm adjusted difference 1172; 95% CI, 363-1980; P = .005; collaborative arm adjusted difference 1119; 95% CI, 319-1919; P = .006; competitive arm adjusted difference 1179; 95% CI, 400-1957; P = .003) and all three had sustained impact during follow-up. In Class 3, none of the interventions had a significant effect on physical activity. Three behavioral phenotypes were identified, each with a different response to the interventions. This approach could be used to better target behavioral interventions to participants that are more likely to respond to them.