No abstract

Background Self-monitoring of behavior (namely, diet and physical activity) and physiology (namely, glucose) has been shown to be effective in type 2 diabetes (T2D) and prediabetes prevention. By combining self-monitoring technologies, the acute physiological consequences of behaviors could be shown, prompting greater consideration to physical activity levels today, which impact the risk of developing diabetes years or decades later. However, until recently, commercially available technologies have not been able to show individuals the health benefits of being physically active. Objective The objective of this study was to examine the usage, feasibility, and acceptability of behavioral and physiological self-monitoring technologies in individuals at risk of developing T2D. Methods A total of 45 adults aged ≥40 years and at moderate to high risk of T2D were recruited to take part in a 3-arm feasibility trial. Each participant was provided with a behavioral (Fitbit Charge 2) and physiological (FreeStyle Libre flash glucose monitor) monitor for 6 weeks, masked according to group allocation. Participants were allocated to glucose feedback (4 weeks) followed by glucose and physical activity (biobehavioral) feedback (2 weeks; group 1), physical activity feedback (4 weeks) followed by biobehavioral feedback (2 weeks; group 2), or biobehavioral feedback (6 weeks; group 3). Participant usage (including time spent on the apps and number of glucose scans) was the primary outcome. Secondary outcomes were the feasibility (including recruitment and number of sensor displacements) and acceptability (including monitor wear time) of the intervention. Semistructured qualitative interviews were conducted at the 6-week follow-up appointment. Results For usage, time spent on the Fitbit and FreeStyle Libre apps declined over the 6 weeks for all groups. Of the FreeStyle Libre sensor scans conducted by participants, 17% (1798/10,582) recorded rising or falling trends in glucose, and 24% (13/45) of participants changed ≥1 of the physical activity goals. For feasibility, 49% (22/45) of participants completed the study using the minimum number of FreeStyle Libre sensors, and a total of 41 sensors were declared faulty or displaced. For acceptability, participants wore the Fitbit for 40.1 (SD 3.2) days, and 20% (9/45) of participants and 53% (24/45) of participants were prompted by email to charge or sync the Fitbit, respectively. Interviews unearthed participant perceptions on the study design by suggesting refinements to the eligibility criteria and highlighting important issues about the usability, wearability, and features of the technologies. Conclusions Individuals at risk of developing T2D engaged with wearable digital health technologies providing behavioral and physiological feedback. Modifications are required to both the study and to commercially available technologies to maximize the chances of sustained usage and behavior change. The study and intervention were feasible to conduct and acceptable to most participants. Trial Registration International Standard Randomized Controlled Trial Number (ISRCTN) 17545949; isrctn.com/ISRCTN17545949

Background The constructs and interdependency of physical behaviors are not well described and the complexity of physical activity (PA) data analysis remains unexplored in COPD. This study examined the interrelationships of 24-hour physical behaviors and investigated their associations with participant characteristics for individuals with mild–moderate airflow obstruction and healthy control subjects. Patients and methods Vigorous PA (VPA), moderate-to-vigorous PA (MVPA), light PA (LPA), stationary time (ST), average movement intensity (vector magnitude counts per minute), and sleep duration for 109 individuals with COPD and 135 healthy controls were obtained by wrist-worn accelerometry. Principal components analysis (PCA) examined interrelationships of physical behaviors to identify distinct behavioral constructs. Using the PCA component loadings, linear regressions examined associations with participant (+, positive correlation; -, negative correlation), and were compared between COPD and healthy control groups. Results For both groups PCA revealed ST, LPA, and average movement intensity as distinct behavioral constructs to MVPA and VPA, labeled “low-intensity movement” and “high-intensity movement,” respectively. Sleep was also found to be its own distinct behavioral construct. Results from linear regressions supported the identification of distinct behavioral constructs from PCA. In COPD, low-intensity movement was associated with limitations with mobility (−), daily activities (−), health status (+), and body mass index (BMI) (−) independent of high-intensity movement and sleep. High-intensity movement was associated with age (−) and self-care limitations (−) independent of low-intensity movement and sleep. Sleep was associated with gender (0= female, 1= male; [−]), lung function (−), and percentage body fat (+) independent of low-intensity and high-intensity movement. Conclusion Distinct behavioral constructs comprising the 24-hour day were identified as “low-intensity movement,” “high-intensity movement,” and “sleep” with each construct independently associated with different participant characteristics. Future research should determine whether modifying these behaviors improves health outcomes in COPD.

Albert Einstein taught us that “everything is relative.” People’s experience of physical activity (PA) is no different, with “relativism” particularly pertinent to the perception of intensity. Markers of absolute and relative intensities of PA have different but complimentary utilities, with absolute intensity considered best for PA guideline adherence and relative intensity for personalized exercise prescription. Under the paradigm of exercise and PA as medicine, our Technical Note proposes a method of synchronizing accelerometry with the incremental shuttle walking test to facilitate description of the intensity of the free-living PA profile in absolute and relative terms. Our approach is able to generate and distinguish “can do” or “cannot do” (based on exercise capacity) and “does do” or “does not do” (based on relative intensity PA) classifications in a chronic respiratory disease population, facilitating the selection of potential appropriate individually tailored interventions. By synchronizing direct assessments of exercise capacity and PA, clearer insights into the intensity of PA performed during everyday life can be gleaned. We believe the next steps are as follows: (1) to determine the feasibility and effectiveness of using relative and absolute intensities in combination to personalize the approach, (2) to determine its sensitivity to change following interventions (eg, exercise-based rehabilitation), and (3) to explore the use of this approach in healthier populations and in other long-term conditions.