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Digital health tools for the passive monitoring of depression a systematic review of methods.pdf (1.44 MB)

Digital health tools for the passive monitoring of depression: a systematic review of methods

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posted on 2023-06-10, 05:20 authored by Valeria De Angel, Serena Lewis, Katie White, Carolin Oetzmann, Daniel Leightley, Emanuela Oprea, Grace Lavelle, Faith Matcham, Alice Pace, David C Mohr, Richard Dobson, Matthew Hotopf
The use of digital tools to measure physiological and behavioural variables of potential relevance to mental health is a growing field sitting at the intersection between computer science, engineering, and clinical science. We summarised the literature on remote measuring technologies, mapping methodological challenges and threats to reproducibility, and identified leading digital signals for depression. Medical and computer science databases were searched between January 2007 and November 2019. Published studies linking depression and objective behavioural data obtained from smartphone and wearable device sensors in adults with unipolar depression and healthy subjects were included. A descriptive approach was taken to synthesise study methodologies. We included 51 studies and found threats to reproducibility and transparency arising from failure to provide comprehensive descriptions of recruitment strategies, sample information, feature construction and the determination and handling of missing data. The literature is characterised by small sample sizes, short follow-up duration and great variability in the quality of reporting, limiting the interpretability of pooled results. Bivariate analyses show consistency in statistically significant associations between depression and digital features from sleep, physical activity, location, and phone use data. Machine learning models found the predictive value of aggregated features. Given the pitfalls in the combined literature, these results should be taken purely as a starting point for hypothesis generation. Since this research is ultimately aimed at informing clinical practice, we recommend improvements in reporting standards including consideration of generalisability and reproducibility, such as wider diversity of samples, thorough reporting methodology and the reporting of potential bias in studies with numerous features.

History

Publication status

  • Published

File Version

  • Published version

Journal

npj Digital Medicine

ISSN

2398-6352

Publisher

Springer Science and Business Media LLC

Issue

3

Volume

5

Page range

1-14

Event location

England

Department affiliated with

  • Psychology Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2022-11-03

First Open Access (FOA) Date

2022-11-03

First Compliant Deposit (FCD) Date

2022-11-03

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