One Drop Improves Productivity for Workers With Type 2 Diabetes: One Drop for Workers With Type 2 Diabetes.

OBJECTIVE: Diabetes research on work productivity has been largely cross-sectional and retrospective, with only one known randomized controlled trial (RCT) published, to our knowledge. Secondary analysis of the Fit-One RCT tested the effect of One Drop's digital health program on workplace productivity outcomes, absenteeism, and presenteeism, for employees and specifically for older workers with type 2 diabetes. METHODS: Analysis of the 3-month Fit-One trial data from employees who have type 2 diabetes explored productivity using logistic analyses and generalized estimating equations. RESULTS: Treatment and control group comparisons showed that workers ( N = 125) using One Drop see direct benefits to workplace productivity, which leads to productivity savings for employers. CONCLUSION: This was the first RCT to demonstrate that a mobile health application for managing type 2 diabetes can positively affect productivity at work.

Effects of a Novel Blood Glucose Forecasting Feature on Glycemic Management and Logging in Adults With Type 2 Diabetes Using One Drop: Retrospective Cohort Study.

BACKGROUND: Personalized feedback is an effective behavior change technique frequently incorporated into mobile health (mHealth) apps. Innovations in data science create opportunities for leveraging the wealth of user data accumulated by mHealth apps to generate personalized health forecasts. One Drop's digital program is one of the first to implement blood glucose forecasts for people with type 2 diabetes. The impact of these forecasts on behavior and glycemic management has not been evaluated to date. OBJECTIVE: This study sought to evaluate the impact of exposure to blood glucose forecasts on blood glucose logging behavior, average blood glucose, and percentage of glucose points in range. METHODS: This retrospective cohort study examined people with type 2 diabetes who first began using One Drop to record their blood glucose between 2019 and 2021. Cohorts included those who received blood glucose forecasts and those who did not receive forecasts. The cohorts were compared to evaluate the effect of exposure to blood glucose forecasts on logging activity, average glucose, and percentage of glucose readings in range, after controlling for potential confounding factors. Data were analyzed using analysis of covariance (ANCOVA) and regression analyses. RESULTS: Data from a total of 1411 One Drop users with type 2 diabetes and elevated baseline glucose were analyzed. Participants (60.6% male, 795/1311; mean age 50.2 years, SD 11.8) had diabetes for 7.1 years on average (SD 7.9). After controlling for potential confounding factors, blood glucose forecasts were associated with more frequent blood glucose logging (P=.004), lower average blood glucose (P<.001), and a higher percentage of readings in range (P=.03) after 12 weeks. Blood glucose logging partially mediated the relationship between exposure to forecasts and average glucose. CONCLUSIONS: Individuals who received blood glucose forecasts had significantly lower average glucose, with a greater amount of glucose measurements in a healthy range after 12 weeks compared to those who did not receive forecasts. Glucose logging was identified as a partial mediator of the relationship between forecast exposure and week-12 average glucose, highlighting a potential mechanism through which glucose forecasts exert their effect. When administered as a part of a comprehensive mHealth program, blood glucose forecasts may significantly improve glycemic management among people living with type 2 diabetes.

Reducing Treatment Burden Among People With Chronic Conditions Using Machine Learning: Viewpoint.

The COVID-19 pandemic has illuminated multiple challenges within the health care system and is unique to those living with chronic conditions. Recent advances in digital health technologies (eHealth) present opportunities to improve quality of care, self-management, and decision-making support to reduce treatment burden and the risk of chronic condition management burnout. There are limited available eHealth models that can adequately describe how this can be carried out. In this paper, we define treatment burden and the related risk of affective burnout; assess how an eHealth enhanced Chronic Care Model can help prioritize digital health solutions; and describe an emerging machine learning model as one example aimed to alleviate treatment burden and burnout risk. We propose that eHealth-driven machine learning models can be a disruptive change to optimally support persons living with chronic conditions.