%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 22
%N 12
%P e22634
%T Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method
%A Minaeva,Olga
%A Riese,Harriëtte
%A Lamers,Femke
%A Antypa,Niki
%A Wichers,Marieke
%A Booij,Sanne H
%+ Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), Department of Psychiatry, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Groningen, 9713 GZ, Netherlands, 31 50 361 2065, o.minaeva@umcg.nl
%K actigraphy
%K activity tracker
%K depression
%K experience sampling method
%K prediction model
%K screening
%D 2020
%7 1.12.2020
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: In many countries, depressed individuals often first visit primary care settings for consultation, but a considerable number of clinically depressed patients remain unidentified. Introducing additional screening tools may facilitate the diagnostic process. Objective: This study aimed to examine whether experience sampling method (ESM)-based measures of depressive affect and behaviors can discriminate depressed from nondepressed individuals. In addition, the added value of actigraphy-based measures was examined. Methods: We used data from 2 samples to develop and validate prediction models. The development data set included 14 days of ESM and continuous actigraphy of currently depressed (n=43) and nondepressed individuals (n=82). The validation data set included 30 days of ESM and continuous actigraphy of currently depressed (n=27) and nondepressed individuals (n=27). Backward stepwise logistic regression analysis was applied to build the prediction models. Performance of the models was assessed with goodness-of-fit indices, calibration curves, and discriminative ability (area under the receiver operating characteristic curve [AUC]). Results: In the development data set, the discriminative ability was good for the actigraphy model (AUC=0.790) and excellent for both the ESM (AUC=0.991) and the combined-domains model (AUC=0.993). In the validation data set, the discriminative ability was reasonable for the actigraphy model (AUC=0.648) and excellent for both the ESM (AUC=0.891) and the combined-domains model (AUC=0.892). Conclusions: ESM is a good diagnostic predictor and is easy to calculate, and it therefore holds promise for implementation in clinical practice. Actigraphy shows no added value to ESM as a diagnostic predictor but might still be useful when ESM use is restricted. 
%M 33258783
%R 10.2196/22634
%U https://www.jmir.org/2020/12/e22634
%U https://doi.org/10.2196/22634
%U http://www.ncbi.nlm.nih.gov/pubmed/33258783