Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study

Abstract

Background In 2017, an estimated 17.3 million adults in the United States experienced at least one major depressive episode, with 35% of them not receiving any treatment. Underdiagnosis of depression has been attributed to many reasons, including stigma surrounding mental health, limited access to medical care, and barriers due to cost. Objective This study aimed to determine if low-burden personal health solutions, leveraging person-generated health data (PGHD), could represent a possible way to increase engagement and improve outcomes. Methods Here, we present the development of PSYCHE-D (Prediction of Severity Change-Depression), a predictive model developed using PGHD from more than 4000 individuals, which forecasts the long-term increase in depression severity. PSYCHE-D uses a 2-phase approach. The first phase supplements self-reports with intermediate generated labels, and the second phase predicts changing status over a 3-month period, up to 2 months in advance. The 2 phases are implemented as a single pipeline in order to eliminate data leakage and ensure results are generalizable. Results PSYCHE-D is composed of 2 Light Gradient Boosting Machine (LightGBM) algorithm–based classifiers that use a range of PGHD input features, including objective activity and sleep, self-reported changes in lifestyle and medication, and generated intermediate observations of depression status. The approach generalizes to previously unseen participants to detect an increase in depression severity over a 3-month interval, with a sensitivity of 55.4% and a specificity of 65.3%, nearly tripling sensitivity while maintaining specificity when compared with a random model. Conclusions These results demonstrate that low-burden PGHD can be the basis of accurate and timely warnings that an individual’s mental health may be deteriorating. We hope this work will serve as a basis for improved engagement and treatment of individuals experiencing depression.

Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review

“…Year of publication, n (%) [27,30,38,48,52,59,63,64,69,81] 10 ( 13) 2022 [19][20][21]23,25,28,41,45,49,54,61,62,68,73,74,77,78] 17 (25) 2021 [22,29,31,33,40,43,44,53,57,60,66,70,71,76,79] 15 (22) 2020 [26,32,34,42,46,47,51,56,…”

“…Type of publication, n (%) [19,21,23,[25][26][27][28][29][30]34,[38][39][40][41][42][43][44][45][46][48][49][50][51][52][53][54][56][57][58][59][60][61][64][65][66][69][70][71][73][74][75][77][78][79]81,82,84,86,87] 49 (71) Journal article [20,22,24,…”

“…Target condition [19,20,[23][24][25][26][27][28]30,32,[34][35][36][37][38]42,43,[46][47][48][49][50][51][52][53] 12) Wired [81] 1 (1) ANT+ (ANT Wireless) a The number of studies does not add up, as 1 (1%) study has both commercial and noncommercial wearable devices. b The number of studies does not add up, as several studies have used >1 wearable device.…”

“…AI category [19,20,23,25,26,31,33,34,37,39,40,42,46,[49][50][51][52][53][54][55][56][57][58][59][60][61][63][64][65][66][67][69][70][71]73,[75][76][77][78][79][80][81]83,84,86,87] 46 (67) ML a [24,29,32,44,47,62,82...…”

“…Ground truth assessment e [19,20,32,[34][35][36]42,43,47,48,62,65,[68][69][70][71]86] 17 (25) MADRS f [23,24,27,28,30,38,52,53,59,60,73,77,83 [27,29,33,37,40,41,45,46,[49][50][51]54,57,58,63,64,66,69,75,76,78,…”