JMIR Publications

ABSTRACT

Background:

Research studies are establishing the use of smartphone-sensing to measure mental well-being. Smartphone sensor information captures behavioral patterns and its analysis helps reveal well-being changes. Depression in diabetes goes highly under-diagnosed and under-reported. The co-morbidity has been associated with increased mortality and worse clinical outcomes; including poor glycemic control and poor self-management. Clinical only intervention has been found to have very modest effect on diabetes management among people with depression. Smartphone technologies could play a significant role in complementing co-morbid care.

Objective:

The study aimed to analyse the association between smartphone-sensing parameters and symptoms of depression and to explore an approach to risk-stratify people with diabetes.

Methods:

A cross-sectional observational study (Project SHADO- Analyzing Social and Health Attributes through Daily Digital Observation) was conducted on 47 participants with diabetes. The study smartphone-sensing app passively collected data regarding activity, mobility, sleep and communication from each participant. Self-reported symptoms of depression using validated Patient Health Questionnaire-9 (PHQ-9) was collected once every 2 weeks from all participants. A descriptive analysis was performed to understand the representation of the participants. A univariate analysis was performed on each derived sensing variable to compare behavioral changes between depression states- those with self-reported major depression (PHQ-9 > 9) and those with none (PHQ-9 <= 9). A classification predictive modeling, using supervised machine-learning methods, was explored using derived sensing variables as input to construct and compare classifiers that could risk-stratify people with diabetes based on symptoms of depression.

Results:

A noticeably high prevalence of self-reported depression (30 out of 47 participants, 63%) was found among the participants. Between depression states, a significant difference was found for average activity rates (day time) among participant-day instances with symptoms of major depression (mean=16.06, SD=14.90) and those with none (mean=18.79, SD=16.72); P= .005. For average number of people called (calls made and received), a significant difference was found between participant-day instances with symptoms of major depression (mean=5.08, SD=3.83) and those with none (mean=8.59, SD=7.05); P < .001. These results suggest that participants with diabetes and symptoms of major depression exhibited lower activity through the day and maintained contact with fewer people. Using all the derived sensing variables, the XGBoost (Extreme Gradient Boosting) machine-learning classifier provided the best performance with an average cross-validation accuracy of 79.07% (95% CI: 74%, 84%) and test accuracy of 81.05% to classify symptoms of depression.

Conclusions:

Participants with diabetes and self-reported symptoms of major depression were observed to show lower levels of social contact and lower activity levels during the day. While findings must be reproduced in a broader randomized controlled study, the study shows promise in use of predictive modeling for early detection of symptoms of depression in people with diabetes using smartphone-sensing information.