Background: Remote data capture for blood glucose (BG) or blood pressure (BP) monitoring and the use of a supportive digital app are becoming the model in diabetes and hypertension chronic care. One of the goals in chronic condition management is to increase awareness and generate behavioral change in order to improve outcomes in diabetes and related comorbidities, such as hypertension. In addition, there is a lack of understanding of the association between BG and BP levels when using digital health tools. Objective: By applying a rigorous study framework to digital health data, this study investigated the relationship between BP monitoring and BG and BP levels, as well as a lagged association between BP and BG. We hypothesized that during the first 6 months of BP monitoring, BG and BP levels would decrease. Finally, we suggested a positive association between BP levels and the following month’s BG levels. Methods: In this retrospective, real-world case-control study, we extracted the data of 269 people with type 2 diabetes (T2D) who tracked their BG levels using the Dario digital platform for a chronic condition. We analyzed the digital data of the users who, in addition to BG, monitored their BP using the same app (BP-monitoring [BPM] group, n=137) 6 months before and after starting their BP monitoring. Propensity score matching established a control group, no blood pressure monitoring (NBPM, n=132), matched on demographic and baseline clinical measures to the BPM group. A piecewise mixed model was used for analyzing the time trajectories of BG, BP, and their lagged association. Results: Analysis revealed a significant difference in BG time trajectories associated with BP monitoring in BPM and NBPM groups (t=–2.12, P=.03). The BPM group demonstrated BG reduction improvement in the monthly average BG levels during the first 6 months (t=–3.57, P<.001), while BG did not change for the NBPM group (t=0.39, P=.70). Both groups showed similarly stable BG time trajectories (B=0.98, t=1.16, P=.25) before starting the use of the BP-monitoring system. In addition, the BPM group showed a significant reduction in systolic (t=–6.42, P<.001) and diastolic (t=–4.80, P<.001) BP during the first 6 months of BP monitoring. Finally, BG levels were positively associated with systolic (B=0.24, t=2.77, P=.001) and diastolic (B=0.30, t=2.41, P=.02) BP. Conclusions: The results of this study shed light on the association between BG and BP levels and on the role of BP self-monitoring in diabetes management. Our findings also underscore the need and provide a basis for a comprehensive approach to understanding the mechanism of BP regulation associated with BG.

Background: The management of type 2 diabetes (T2D) and obesity, particularly in the context of self-monitoring, remains a critical challenge in health care. As nearly 80% to 90% of patients with T2D have overweight or obesity, there is a compelling need for interventions that can effectively manage both conditions simultaneously. One of the goals in managing chronic conditions is to increase awareness and generate behavioral change to improve outcomes in diabetes and related comorbidities, such as overweight or obesity. There is a lack of real-life evidence to test the impact of self-monitoring of weight on glycemic outcomes and its underlying mechanisms. Objective: This study aims to assess the efficacy of digital self-monitoring of weight on blood glucose (BG) levels during diabetes management, investigating whether the weight changes may drive glucose fluctuations. Methods: In this retrospective, real-world quasi-randomized study, 50% of the individuals who regularly used the weight monitoring (WM) feature were propensity score matched with 50% of the users who did not use the weight monitoring feature (NWM) based on demographic and clinical characteristics. All the patients were diagnosed with T2D and tracked their BG levels. We analyzed monthly aggregated data 6 months before and after starting their weight monitoring. A piecewise mixed model was used for analyzing the time trajectories of BG and weight as well as exploring the disaggregation effect of between- and within-patient lagged effects of weight on BG. Results: The WM group exhibited a significant reduction in BG levels post intervention (P<.001), whereas the nonmonitoring group showed no significant changes (P=.59), and both groups showed no differences in BG pattern before the intervention (P=.59). Furthermore, the WM group achieved a meaningful decrease in BMI (P<.001). Finally, both within-patient (P<.001) and between-patient (P=.008) weight variability was positively associated with BG levels. However, 1-month lagged back BMI was not associated with BG levels (P=.36). Conclusions: This study highlights the substantial benefits of self-monitoring of weight in managing BG levels in patients with diabetes, facilitated by a digital health platform, and advocates for the integration of digital self-monitoring tools in chronic disease management. We also provide initial evidence of testing the underlying mechanisms associated with BG management, underscoring the potential role of patient empowerment.

Significant racial/ethnic disparities in healthcare and diabetes technology use have been observed in Type 2 diabetes mellitus (T2DM), which are associated with nonengagement in diabetes self-management and out-of-range glycemia. This study aimed to assess whether there were differences in the blood glucose levels achieved by several racial/ethnic groups using the same digital tool. Study objectives were to determine whether engagement with the digital tool and blood glucose levels differ among ethnic groups, and to determine whether any differences in the in-target-glycemia are related to engagement levels. The retrospective real-world analysis followed a group of 1000 people with Type 2 diabetes who used the Dario^TM digital therapeutic platform over 12 months. Participants included in the study had a blood glucose average > 180 mg/dL (hyperglycemia, high-risk) in their first month. The differences between/within the groups’ average blood glucose level (Avg.bg) and glycemic variability were evaluated. Furthermore, three general linear models were constructed to predict the Avg.bg by the number of blood glucose measurements (Bgm) in Model 1 (with the moderator White persons (WP)/people from racial and ethnic minority groups (REM)) and by the frequency of measurements by months (F.m) within REM and WP in Model 2 and Model 3, respectively. The Avg.bg was significantly reduced in each group over a year with no differences between REM/WP users. Blood glucose measurements in Model 1 and frequency of measurements by months in Model 2 and Model 3 predicted the Avg.bg (β₁ = −0.20, p = 0.045; β₂ = −4.38, p = 0.009; β₃= −3.77, p < 0.001, respectively). Findings indicate a positive association between digital engagement and glycemia, with no differences between REM and WP participants.

Background: Stress is an emotional response caused by external triggers and is a high-prevalence global problem affecting mental and physical health. Several different digital therapeutic solutions are effective for stress management. However, there is limited understanding of the association between relaxation components and stress levels when using a digital app. Objective: This study investigated the contribution of relaxation tools to stress levels over time. We hypothesized that participation in breathing exercises and cognitive behavioral therapy–based video sessions would be associated with a reduction in stress levels. We also hypothesized a significant reduction specifically in participants’ perceived sense of burden and lack of productivity when engaged with breathing exercises and video sessions. Methods: Stress levels were evaluated in a real-world data cohort using a behavioral health app for digital intervention and monitoring change. This retrospective real-world analysis of users on a mobile platform–based treatment followed users (N=490) who started with moderate and above levels of stress and completed at least 2 stress assessments. The levels of stress were tracked throughout the first 10 weeks. A piecewise mixed effects model was applied to model the trajectories of weekly stress mean scores in 2 time segments (1-6 weeks and 6-10 weeks). Next, a simple slope analysis was used for interpreting interactions probing the moderators: breathing exercises and video sessions. Piecewise mixed-effects models were also used to model the trajectories of specific perceived stress item rates in the stress questionnaire in the 2 segments (1-6 weeks and 6-10 weeks) and whether they are moderated by the relaxation engagements. Simple slope analysis was also used here for the interpretation of the interactions. Results: Analysis revealed a significant decrease in stress symptoms (β=–.25; 95% CI –0.32 to –0.17; P<.001) during the period of 1-6 weeks of app use that was maintained during the period of 6-10 weeks. Breathing exercises significantly moderated the reduction in stress symptoms during the period of 1-6 weeks (β=–.07; 95% CI –0.13 to –0.01; P=.03), while engagement in digital video sessions did not moderate stress scores. Engagement in digital video sessions, as well as breathing exercises, significantly moderated the reduction in perceived sense of burden and lack of productivity during weeks 1-6 and remained stable during weeks 6-10 on both items. Conclusions: This study sheds light on the association between stress level reduction and specific components of engagement in a digital health app, breathing exercises, and cognitive behavioral therapy–based video sessions. Our findings provide a basis for further investigation of current and moderating factors that contribute to the personalization of digital intervention. In addition, results may aid in developing a more comprehensive understanding of how digital intervention tools work for mental health and for whom they are most effective.

Background: Effective hypertension management, particularly through self-care strategies, remains a significant public health challenge. Despite widespread awareness, only approximately 1 in 5 adults achieves adequate blood pressure (BP) control. There is a growing need for scalable digital health interventions that enhance awareness, support behavioral change, and improve clinical outcomes. However, real-world evidence evaluating the impact of such interventions on BP levels and their underlying mechanisms is limited. Objective: This study aimed to evaluate the effectiveness of a digital intervention using data-driven nudges on monthly average BP levels. Specifically, we assessed changes in BP before and after the intervention and examined whether these changes differed compared to a control group in a high BP cohort and a normal BP cohort. Methods: In this retrospective, real-world cohort study, we analyzed two user cohorts from a digital health platform: (1) individuals with high BP readings and (2) individuals with normal BP readings. Participants who received a digital intervention were propensity score–matched to users who did not receive the intervention, based on demographic and clinical variables. Monthly average BP and the proportion of high readings were assessed 3 months before and after the intervention. A piecewise mixed-effects model was used to evaluate BP trajectories, and simple slope analysis assessed the interaction between the outcomes and the groups, as well as the moderating effect of lifestyle activities on systolic blood pressure (SBP). Results: In total, 408 users were included in the study. In the high BP cohort (n=296), the intervention group showed a significant decrease in the monthly average SBP after the intervention (B=–2.09; P <.001), while the control group showed a smaller reduction (B=–1.06; P =.007). Additionally, users reporting higher lifestyle activity levels experienced a greater reduction in SBP (B=–5.27; P <.001). In the normal BP cohort (n=112), the intervention group maintained stable BP levels after the intervention (B=–0.39; P =.27), while the control group exhibited a significant increase in BP levels (B=0.69; P =.03). Conclusions: Data-driven nudges delivered via a digital health platform were associated with improved BP outcomes among individuals with high BP levels and helped maintain BP stability among those with normal BP levels. These findings reinforce the integration of personalized digital interventions into hypertension management and highlight the potential role of positive messaging, behavioral engagement, and user empowerment in improving long-term outcomes.

BACKGROUND Modern workplaces create increasing demands on employees, often leading to stress, burnout, and reduced functional capacity. These challenges contribute to significant functionality losses, with absenteeism and presenteeism posing economic burdens. Mindfulness-based workplace interventions have been shown to improve mental health, yet their effects on specific work-related performance outcomes such as concentration difficulties, mistakes, and procrastination over time are less explored. OBJECTIVE This study aimed to evaluate self-reported workplace function changes for a 10-week period among users of a digital behavioral health program. METHODS This retrospective analysis used real-world data from users of the Dario Health behavioral health app. Participants were required to complete at least 2 workplace functionality assessments, including one at week 1 as a baseline. The assessment comprised 3 items measuring concentration difficulties, mistakes at work, and procrastination, each rated on a 0 (“not at all”) to 3 (“a lot”) scale. Users who initially scored 2 (“often”) or 3 (“a lot”) on each respective measure formed the cohorts for analysis. Longitudinal changes were examined using piecewise linear mixed-effects models. Cut points were selected empirically through sensitivity analyses comparing alternative cut points (weeks 2-7) using Akaike and Bayesian information criteria to identify the best-fitting model for each outcome. RESULTS Sensitivity analyses identified optimal cut points at week 6 for concentration difficulties and procrastination and at week 5 for mistakes at work. Among users reporting baseline concentration difficulties (n=1254), significant reductions were observed during weeks 1 to 6 (B=−0.02; P<.001), with larger reductions during weeks 6 to 10 (B=−0.08; P<.001). For mistakes at work (n=167), a significant decrease occurred during weeks 1 to 5 (B=−0.05; P=.02), followed by a more pronounced reduction during weeks 5 to 10 (B=−0.22; P<.001). Among users reporting baseline procrastination (n=1004), significant improvements were observed during weeks 1 to 6 (B=−0.02; P<.001), with greater reductions during weeks 6 to 10 (B=−0.08; P<.001). CONCLUSIONS Use of the digital behavioral health platform was associated with improvements in workplace function, particularly among users with sustained use beyond 4 weeks. The observed outcomes suggest that continued participation in such digital behavioral health programs may be linked to favorable changes in mental health and cognitive functioning over time and may support employee well-being. Long-term engagement appears essential to maximize benefits, warranting further research into sustained impacts and optimization strategies for workplace productivity.