The Impact of Digital Self-Monitoring of Weight on Improving Diabetes Clinical Outcomes: Quasi-Randomized Study.

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.

Association of Digital Engagement With Relaxation Tools and Stress Level Reduction: Retrospective Cohort Study.

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 (ß=-.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 (ß=-.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.

Blood Pressure Monitoring as a Digital Health Tool for Improving Diabetes Clinical Outcomes: Retrospective Real-world Study.

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.

The two-stage therapeutic effect of posture biofeedback training on back pain and the associated mechanism: A retrospective cohort study.

Introduction: Back pain is an extremely common symptom experienced by people of all ages and the number one cause of disability worldwide.2 Poor posture has been identified as one of the factors leading to back pain. Digital biofeedback technology demonstrates the promising therapeutic ability in pain management through posture training. One common goal of such an approach is to increase users' posture awareness with associated movement correction. However, we lack a deep understanding of the biofeedback therapeutic mechanisms and the temporal dynamics of efficacy. Objective: This study investigates the temporal dynamics of the biofeedback learning process and associated outcomes in daily life settings, testing the mechanism of the biofeedback-associated pain reduction. Methods: This retrospective real-world evidence study followed 981 users who used the UpRight posture biofeedback platform. Piecewise mixed models were used for modeling the two-stage trajectory of pain levels, perceived posture quality, and weekly training duration following an 8-week biofeedback training. Also, the mediation effect of perceived posture quality on the analgesic effect of training duration was tested using Monte Carlo simulations based on lagged effect mixed models. Results: The analysis revealed significant pain level reduction (p <.0001) and posture quality improvement (p <.0001) during the first 4 weeks of the training, maintaining similar pain levels and perceived posture quality during the next 4 weeks. In addition, weekly training duration demonstrated an increase during the first 3 weeks (p <.001) and decreased during the next 5 weeks (p <.001). Moreover, training duration predicted following-week perceived posture quality (p <.001) and in turn perceived posture quality predicted following-week pain (p <.001) (p = 0.30). Finally, perceived posture quality mediated the effect of weekly training duration on the pain levels in 2 weeks (p <.0001). Conclusion: Our findings provide a better understanding of the therapeutic dynamic during digital biofeedback intervention targeting pain, modeling the associated two-stage process. Moreover, the study sheds light on the biofeedback mechanism and may assist in developing a better therapeutic approach targeting perceived posture quality.

DO STANDARDS OF CARE MATTER? AND WHY AREN'T THEY BEING MET?.

Quality metrics have become a fact of life in medical care. In general, these metrics are based either on published standards of care or on "best practice" statistics. Some controversy surrounds the basis for metrics.

Palm glucose readings compared with fingertip readings under steady and dynamic glycemic conditions, using the OneTouch Ultra Blood Glucose Monitoring System.

OBJECTIVES: Past studies have suggested the absence of lag between palm glucose and fingertip glucose, even when glucose levels are changing rapidly. However, at any given time point, there may be differences between palm and fingertip glucose values because of glycemic instability and/or test methodology. The objectives of this study included assessing the variability in fingertip blood glucose test results between two fingers, and establishing whether the variability in blood glucose test results obtained from the palm was clinically equivalent to that observed in fingertip-to-fingertip comparisons. METHODS: This multicenter trial was conducted on patients under both steady-state glycemic conditions and after meal and exercise challenges (to promote rapidly changing glucose). Sequential capillary glucose testing, performed with the One Touch Ultra Blood Glucose Monitoring System (LifeScan, Inc., Milpitas, CA), was allocated to two of four fingertip sites and one of two palm sites in each subject using a randomized, balanced, incomplete block design. One of the fingertips was designated the reference site. Fingertip-to-fingertip variability and fingertip- to-palm variability were assessed under these steady-state and dynamic testing conditions using error grid analysis and by comparing the proportion of clinically acceptable blood glucose tests at the palm site versus the fingertip site. Clinically acceptable agreement was defined as pairs of values (fingertip to reference, or palm to reference) within 15 mg/dL when reference glucose was < or = 75 mg/dL or within 20% when reference glucose was >75 mg/dL. RESULTS: One hundred eighty-one subjects with type 1 [n = 74 (40.9%)] or type 2 [n = 107 (59.1%)] diabetes at eight clinical sites completed the study. Overall, the proportion of clinically acceptable agreement was high for both palm (95.1%) and fingertip (97.5%) testing. The mean difference between palm and fingertip clinically acceptable agreement when done by healthcare professionals was -1.3% and -4.4%, under steady-state and dynamic glycemic conditions, respectively. Error grid analysis showed >97% of all palm and fingertip measurements fell in Zone A. CONCLUSION: This study demonstrated that variability between fingertip-to-fingertip and palm-to-fingertip measurements was in the clinically acceptable range during steady-state conditions and when glucose was rapidly changing.

Rapid changes in postprandial blood glucose produce concentration differences at finger, forearm, and thigh sampling sites.

OBJECTIVE: To compare pre- and postmeal capillary blood glucose concentrations measured at the finger, forearm, and thigh in adults with diabetes. RESEARCH DESIGN AND METHODS: For phase 1, capillary blood glucose concentrations were measured at six time points (premeal and at approximately 60, 90, 120, 150, and 180 min postmeal) using a blood glucose monitoring system and technician-obtained samples collected from finger, forearm, and thigh sites of 42 adults with diabetes. The finger samples were also tested with a laboratory instrument. For phase 2, approximately 14 weeks later, the testing procedures were repeated with 38 subjects from the original study population. RESULTS: Meter finger results were accurate at all time points. Alternate sites tended to produce lower glucose readings compared to finger readings at times when glucose was increasing rapidly (60 and 90 min postmeal). Forearm-to-finger differences correlated with rates of glucose change (r = 0.56, P < 0.001), as did the thigh-to-finger differences (r = 0.52, P < 0.001). Other factors, such as subject age, BMI, diabetes type, and insulin dependence did not have a significant impact on site differences. When the testing procedures were repeated with the same subjects, the pattern of site differences was consistent, although individual results were variable. CONCLUSIONS: Changes in blood glucose immediately after a meal may be identified at finger sites before detection at forearm or thigh sites. Alternate site testing appears to be a useful option for routine self-monitoring before meals; however, patients and clinicians should recognize that results may be different from fingertip results when glucose levels are changing rapidly.