Digital health tools for the passive monitoring of depression: a systematic review of methods
Year: 2022
Type: review
Study design: Systematic Review
Abstract: 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.
Source: npj Digital Medicine
Language: English
Subfield: Applied Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,290
Funders: South London and Maudsley NHS Foundation Trust, Wellcome Trust, King's College Hospital NHS Foundation Trust, Scottish Government, European Federation of Pharmaceutical Industries and Associations, UK Research and Innovation, National Institute for Health and Care Research, British Heart Foundation, Department of Health and Social Care, King's College London, University College London, European Commission, Public Health Agency, Innovative Medicines Initiative, Chief Scientist Office, Scottish Government Health and Social Care Directorate, NIHR Maudsley Biomedical Research Centre, Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Health and Social Care Research and Development Division, National Institute for Health Research Applied Research Collaboration South London, Health Data Research UK
OpenAlex ID: W4221044624