TY  - JOUR
AU  - Minaeva, Olga
AU  - Riese, Harriëtte
AU  - Lamers, Femke
AU  - Antypa, Niki
AU  - Wichers, Marieke
AU  - Booij, Sanne H
PY  - 2020
DA  - 2020/12/1
TI  - Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method
JO  - J Med Internet Res
SP  - e22634
VL  - 22
IS  - 12
KW  - actigraphy
KW  - activity tracker
KW  - depression
KW  - experience sampling method
KW  - prediction model
KW  - screening
AB  - 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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2020/12/e22634
UR  - https://doi.org/10.2196/22634
UR  - http://www.ncbi.nlm.nih.gov/pubmed/33258783
DO  - 10.2196/22634
ID  - info:doi/10.2196/22634
ER  -