@Article{info:doi/10.2196/22634,
author="Minaeva, Olga
and Riese, Harri{\"e}tte
and Lamers, Femke
and Antypa, Niki
and Wichers, Marieke
and Booij, Sanne H",
title="Screening for Depression in Daily Life: Development and External Validation of a Prediction Model Based on Actigraphy and Experience Sampling Method",
journal="J Med Internet Res",
year="2020",
month="Dec",
day="1",
volume="22",
number="12",
pages="e22634",
keywords="actigraphy; activity tracker; depression; experience sampling method; prediction model; screening",
abstract="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. ",
issn="1438-8871",
doi="10.2196/22634",
url="https://www.jmir.org/2020/12/e22634",
url="https://doi.org/10.2196/22634",
url="http://www.ncbi.nlm.nih.gov/pubmed/33258783"
}