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

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.

Differential Dynamical Pattern of Regional Homogeneity in Bipolar and Unipolar Depression: A Preliminary Resting-State fMRI Study

Background: Bipolar depression (BD) and unipolar depression (UD) are both characterized by depressive moods, which are difficult to distinguish in clinical practice. Human brain activity is time-varying and dynamic. Investigating dynamical pattern alterations of depressed brains can provide deep insights into the pathophysiological features of depression. This study aimed to explore similar and different abnormal dynamic patterns between BD and UD.Methods: Brain resting-state functional magnetic resonance imaging data were acquired from 36 patients with BD type I (BD-I), 38 patients with UD, and 42 healthy controls (HCs). Analysis of covariance was adopted to examine the differential pattern of the dynamical regional homogeneity (dReHo) temporal variability across 3 groups, with gender, age, and education level as covariates. Post-hoc analyses were employed to obtain the different dynamic characteristics between any 2 groups. We further applied the machine-learning methods to classify BD-I from UD by using the detected distinct dReHo pattern.Results: Compared with patients with UD, patients with BD-I demonstrated decreased dReHo variability in the right postcentral gyrus and right parahippocampal gyrus. By using the dReHo variability pattern of these two regions as features, we achieved the 91.89% accuracy and 0.92 area under curve in classifying BD-I from UD. Relative to HCs, patients with UD showed increased dReHo variability in the right postcentral gyrus, while there were no dReHo variability differences in patients with BD-I.Conclusions: The results of this study mainly report the differential dynamic pattern of the regional activity between BD-I and UD, particular in the mesolimbic system, and show its promising potential in assisting the diagnosis of these two depression groups.

Racemic ketamine as an alternative to electroconvulsive therapy for unipolar depression. A randomized, open-label, non-inferiority trial. (KetECT)

BACKGROUND Ketamine has emerged as a fast-acting and powerful antidepressant, but no head to head trial has been performed, Here, ketamine is compared to electroconvulsive therapy (ECT), the most effective therapy for depression. METHODS Hospitalized patients with unipolar depression were randomized (1:1) to thrice-weekly racemic ketamine (0.5 mg/kg) infusions or ECT, in a parallel, open-label, non-inferiority study. The primary outcome was remission (Montgomery Åsberg Depression Rating Scale [MADRS] score ≤10). Secondary outcomes included adverse events (AEs), time to remission and relapse. Treatment sessions (maximum of twelve) were administered until remission or maximal effect was achieved. Remitters were followed for twelve months after the final treatment session. RESULTS 186 inpatients were included and received treatment. Among patients receiving ECT 63% remitted, compared to 46% receiving ketamine infusions (p=0.026; difference 95% CI 2%, 30%). Both ketamine and ECT required a median of six treatment sessions to induce remission. Distinct adverse events (2015) were associated with each treatment. Serious and long-lasting AE, including cases of persisting amnesia, were more common with ECT, while treatment emergent AE led to more dropouts in the ketamine group. Among remitters, 70% and 63%, with 57 and 61 median days in remission, relapsed within twelve months in the ketamine and ECT group respectively (p=0.52). CONCLUSION Remission and cumulative symptom reduction following multiple racemic ketamine infusions in severely ill patients (age 18-85) in an authentic clinical setting suggest that ketamine, despite being inferior to ECT, can be a safe and valuable tool in treating unipolar depression.