Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jul 19, 2020
Open Peer Review Period: Jul 18, 2020 - Jul 28, 2020
Date Accepted: Oct 26, 2020
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Screening for depression in daily life: Development and external validation of a prediction model based on actigraphy and experience sampling of depressive affect and behaviors
ABSTRACT
Background:
In many countries, depressed individuals often first visit primary care settings for consultation, but a considerable number of clinically depressed patients remains unidentified. Introducing additional screening tools may facilitate the diagnostic process. We aimed to examine whether Experience Sampling Method (ESM)-based measures of depressive affect and behaviors can discriminate depressed from non-depressed individuals. In addition, the added value of actigraphy-based measures was examined.
Objective:
The aim of our study was to examine whether ESM-assessed depression-related affect and behavior can discriminate between depressed and non-depressed individuals, whether actigraphy data can discriminate between depressed and non-depressed individuals on its own, and whether it has added value with respect to the use of ESM.
Methods:
We used data from two samples to develop and validate prediction models. The development dataset included 14 days of ESM and continuous actigraphy of currently depressed (n=43) and non-depressed individuals (n=82). The validation dataset included 30 days of ESM and continuous actigraphy of currently depressed (n=27) and non-depressed individuals (n=27). Backward stepwise logistic regression analyses were applied to build the prediction models. Performance of the models was assessed with goodness of fit indices, calibration curves, and discriminative ability (AUC - the area under the receiver operating characteristic curve).
Results:
In the development dataset, the discriminative ability was good for the actigraphy model (AUC=.790) and excellent for the ESM (AUC=.991) and combined-domains model (AUC=.993). In the validation dataset, the discriminative ability was reasonable for the actigraphy model (AUC=.648) and excellent for the ESM (AUC=.891) and combined-domains model (AUC=.892).
Conclusions:
ESM was a good diagnostic predictor and is easy to calculate, and it therefore holds promise for implementation in clinical practice. Actigraphy showed no added value to ESM as a diagnostic predictor, but might still be useful when ESM use is restricted.
Citation
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