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. 

/pdf/digital-health-tools-for-the-passive-monitoring-of-ek7l7mp8.pdf

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

Background:
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 aim to summarise the literature on remote measuring technologies, mapping methodological challenges and threats to reproducibility, and to identify leading digital signals for depression.

Methods:
Medical and computer science databases were searched between January 2007 to 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 (PROSPERO registration: 2019 CRD42019159929). A descriptive approach was taken to synthesise study methodologies. 

Results
We included 52 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 quality of reporting, limiting the interpretability of pooled results. Bivariate analyses show some consistency in statistically significant associations between depression and digital features from sleep, physical activity, location, and phone use data. Regression and classification machine learning models found predictive value of aggregated features. 

Interpretation:
Recommendations are put forward to improve aspects of generalisability and reproducibility, such as wider diversity of samples, thorough reporting methodology and the potential for reporting bias in studies with numerous features.

Funding:
National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King9s College London.

https://medrxiv.org/content/10.1101/2021.07.19.21260786v1.full.pdf

Digital Health Tools for the Passive Monitoring of Depression: A Systematic Review of Methods

Language use has been shown to correlate with depression, but large-scale validation is needed. Traditional methods like clinic studies are expensive. So, natural language processing has been employed on social media to predict depression, but limitations remain-lack of validated labels, biased user samples, and no context. Our study identified 29 topics in 3919 smartphone-collected speech recordings from 265 participants using the Whisper tool and BERTopic model. Six topics with a median PHQ-8 greater than or equal to 10 were regarded as risk topics for depression: No Expectations, Sleep, Mental Therapy, Haircut, Studying, and Coursework. To elucidate the topic emergence and associations with depression, we compared behavioral (from wearables) and linguistic characteristics across identified topics. The correlation between topic shifts and changes in depression severity over time was also investigated, indicating the importance of longitudinally monitoring language use. We also tested the BERTopic model on a similar smaller dataset (356 speech recordings from 57 participants), obtaining some consistent results. In summary, our findings demonstrate specific speech topics may indicate depression severity. The presented data-driven workflow provides a practical approach to collecting and analyzing large-scale speech data from real-world settings for digital health research.

pdf/identifying-depression-related-topics-in-smartphone-2bfa6078e9.pdf

Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model

Major Depressive Disorder (MDD) often is a recurrent and chronic disorder. We investigated the neurocognitive underpinnings of the incremental risk for poor disease course by exploring relations between enduring depression and brain functioning during regulation of negative and positive emotions using cognitive reappraisal. We used fMRI-data from the longitudinal Netherlands Study of Depression and Anxiety acquired during an emotion regulation task in 77 individuals with MDD. Task-related brain activity was related to disease load, calculated from presence and severity of depression in the preceding nine years. Additionally, we explored task related brain-connectivity. Brain functioning in individuals with MDD was further compared to 35 controls to explore overlap between load-effects and general effects related to MDD history/presence. Disease load was not associated with changes in affect or with brain activity, but with connectivity between areas essential for processing, integrating and regulating emotional information during downregulation of negative emotions. Results did not overlap with general MDD-effects. Instead, MDD was generally associated with lower parietal activity during downregulation of negative emotions. During upregulation of positive emotions, disease load was related to connectivity between limbic regions (although driven by symptomatic state), and connectivity between frontal, insular and thalamic regions was lower in MDD (vs controls). Results suggest that previous depressive load relates to brain connectivity in relevant networks during downregulation of negative emotions. These abnormalities do not overlap with disease-general abnormalities and could foster an incremental vulnerability to recurrence or chronicity of MDD. Therefore, optimizing emotion regulation is a promising therapeutic target for improving long-term MDD course. 

Functional MRI correlates of emotion regulation in Major Depressive Disorder related to depressive disease load measured over nine years

Investigating the potential role of BDNF and PRL genotypes on antidepressant response in depression patients: A prospective inception cohort study in treatment-free patients.

OBJECTIVE To investigate whether the course of depression changes when (a) follow-up duration is longer and (b) in addition to depression other mood and anxiety disorders are considered as outcome measures. DESIGN Longitudinal observational cohort study. METHOD We selected patients from the Netherlands Study of Depression and Anxiety (NESDA) who had active depression at baseline (n=903) and for whom data from the 2, 4 and/or 6 year measurements were available. Using DSM-IV diagnoses and data from the 'Life chart interview', we divided participants in one of the following four course categories: (1) recovered (no diagnosis at 2-year measurement or later), (2) recurring without chronic episodes, (3) recurring with chronic episodes or (4) consistent chronic depression since baseline. We looked at the distribution of patients over the course categories from a short, diagnostically narrow perspective (over 2 years, only looking at depression) to a long, diagnostically broad perspective (over 6 years, looking at depression, dysthymia, hypomania, mania and anxiety). RESULTS In the short, diagnostically narrow perspective, 58% of participants had recovered and 21% met the criteria for a chronic episode. In the long, diagnostically broad perspective however, only 17% had recovered while 55% had chronic episodes. CONCLUSION Monitoring patients with depression over a longer period and with broader outcome measures (depression and related psychiatric disorders belonging to the mood disorder spectrum) shows that the course of depression is unfavourable and chronic for the majority. Conceptualising depression as a defined episodic disorder underestimates the severity of the prognosis for many patients and, as a consequence, the type of care indicated.

[Complete recovery from depression is the exception rather than the rule: prognosis of depression beyond diagnostic boundaries].

OBJECTIVE: To investigate whether the course of depression changes when (a) follow-up duration is longer and (b) in addition to depression other mood and anxiety disorders are considered as outcome measures. DESIGN: Longitudinal observational cohort study. METHOD: We selected patients from the Netherlands Study of Depression and Anxiety (NESDA) who had active depression at baseline (n=903) and for whom data from the 2, 4 and/or 6 year measurements were available. Using DSM-IV diagnoses and data from the 'Life chart interview', we divided participants in one of the following four course categories: (1) recovered (no diagnosis at 2-year measurement or later), (2) recurring without chronic episodes, (3) recurring with chronic episodes or (4) consistent chronic depression since baseline. We looked at the distribution of patients over the course categories from a short, diagnostically narrow perspective (over 2 years, only looking at depression) to a long, diagnostically broad perspective (over 6 years, looking at depression, dysthymia, hypomania, mania and anxiety). RESULTS: In the short, diagnostically narrow perspective, 58% of participants had recovered and 21% met the criteria for a chronic episode. In the long, diagnostically broad perspective however, only 17% had recovered while 55% had chronic episodes. CONCLUSION: Monitoring patients with depression over a longer period and with broader outcome measures (depression and related psychiatric disorders belonging to the mood disorder spectrum) shows that the course of depression is unfavourable and chronic for the majority. Conceptualising depression as a defined episodic disorder underestimates the severity of the prognosis for many patients and, as a consequence, the type of care indicated.

Volledig herstel van depressie is eerder uitzondering dan regel

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[Complete recovery from depression is the exception rather than the rule: prognosis of depression beyond diagnostic boundaries].

Volledig herstel van depressie is eerder uitzondering dan regel