Predicting Stress and Depressive Symptoms Using High-Resolution Smartphone Data and Sleep Behaviour in Danish Adults
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
Predicting Stress and Depressive Symptoms Using High-Resolution Smartphone Data and Sleep Behaviour in Danish Adults. / Andersen, Thea Otte; Dissing, Agnete Skovlund; Severinsen, Elin Rosenbek; Jensen, Andreas Kryger; Pham, Vi Thanh; Varga, Tibor V; Rod, Naja Hulvej.
I: Sleep, Bind 45, Nr. 6, zsac067, 2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Predicting Stress and Depressive Symptoms Using High-Resolution Smartphone Data and Sleep Behaviour in Danish Adults
AU - Andersen, Thea Otte
AU - Dissing, Agnete Skovlund
AU - Severinsen, Elin Rosenbek
AU - Jensen, Andreas Kryger
AU - Pham, Vi Thanh
AU - Varga, Tibor V
AU - Rod, Naja Hulvej
N1 - © The Author(s) 2022. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
PY - 2022
Y1 - 2022
N2 - STUDY OBJECTIVES: The early detection of mental disorders is crucial. Patterns of smartphone behaviour have been suggested to predict mental disorders. The aim of this study was to develop and compare prediction models using a novel combination of smartphone and sleep behaviour to predict early indicators of mental health problems, specifically high perceived stress and depressive symptoms.METHODS: The data material included two separate population samples nested within the SmartSleep Study. Prediction models were trained using information from 4,522 Danish adults and tested in an independent test set comprising of 1,885 adults. The prediction models utilised comprehensive information on subjective smartphone behaviour, objective night-time smartphone behaviour and self-reported sleep behaviour. Receiver operating characteristics area-under-the-curve (ROC AUC) values obtained in the test set were recorded as the performance metrics for each prediction model.RESULTS: Neither subjective nor objective smartphone behaviour was found to add additional predictive information compared to basic sociodemographic factors when forecasting perceived stress or depressive symptoms. Instead, the best performance for predicting poor mental health was found in the sleep prediction model (AUC=0.75, 95% CI: 0.72-0.78) for perceived stress and (AUC=0.83, 95%CI: 0.80-0.85) for depressive symptoms, which included self-reported information on sleep quantity, sleep quality and the use of sleep medication.CONCLUSION: Sleep behaviour is an important predictor when forecasting mental health symptoms and it outperforms novel approaches using objective and subjective smartphone behaviour.
AB - STUDY OBJECTIVES: The early detection of mental disorders is crucial. Patterns of smartphone behaviour have been suggested to predict mental disorders. The aim of this study was to develop and compare prediction models using a novel combination of smartphone and sleep behaviour to predict early indicators of mental health problems, specifically high perceived stress and depressive symptoms.METHODS: The data material included two separate population samples nested within the SmartSleep Study. Prediction models were trained using information from 4,522 Danish adults and tested in an independent test set comprising of 1,885 adults. The prediction models utilised comprehensive information on subjective smartphone behaviour, objective night-time smartphone behaviour and self-reported sleep behaviour. Receiver operating characteristics area-under-the-curve (ROC AUC) values obtained in the test set were recorded as the performance metrics for each prediction model.RESULTS: Neither subjective nor objective smartphone behaviour was found to add additional predictive information compared to basic sociodemographic factors when forecasting perceived stress or depressive symptoms. Instead, the best performance for predicting poor mental health was found in the sleep prediction model (AUC=0.75, 95% CI: 0.72-0.78) for perceived stress and (AUC=0.83, 95%CI: 0.80-0.85) for depressive symptoms, which included self-reported information on sleep quantity, sleep quality and the use of sleep medication.CONCLUSION: Sleep behaviour is an important predictor when forecasting mental health symptoms and it outperforms novel approaches using objective and subjective smartphone behaviour.
U2 - 10.1093/sleep/zsac067
DO - 10.1093/sleep/zsac067
M3 - Journal article
C2 - 35298650
VL - 45
JO - Sleep (Online)
JF - Sleep (Online)
SN - 0161-8105
IS - 6
M1 - zsac067
ER -
ID: 304745493