Biopsychosocial Response to the COVID-19 Lockdown in People with Major Depressive Disorder and Multiple Sclerosis

Smartphone digital phenotyping in mental health disorders: A review of raw sensors utilized, machine learning processing pipelines, and derived behavioral features

With increased access to digital technology, there has been a surge in the use of and interest in digital phenotyping as a tool to calculate various features from raw smart device data. However, the increased usage of digital phenotyping has created confusion. The vast number of sensors that can be utilized to collect passive data, and diverse methods utilized to convert that sensor data into features has introduced conflicting results and conclusions into the literature. Consequently, there is an identified need for standardizing how digital phenotyping data is measured and collected. This review evaluates the different sensors and methods utilized in digital phenotyping research across 112 papers, with the goal of finding the most common platforms, sensors, and methods for each behavioral measure. This should help guide future digital phenotyping research, and resolve some existing confusion in the field. Information on each study's data sensor variables were tracked and consolidated into a double-coded Codebook. Variables assessed included but were not limited to data sensors, features extracted from data sensors, statistical methods used, phone type, patient access to phones, and characteristics of patient population. This review found that most studies used Android devices (n = 67) or both Android and iPhone (n = 38) with an average duration of 14.3 weeks. The GPS sensor was also found to be the most frequently used sensor. This review underscores the need for standardization in methodological reporting, sensor utilization, and feature extraction across mental health studies.

Patient Perspectives on Healthcare Utilization During the COVID-19 Pandemic in People with Multiple Sclerosis-A Longitudinal Analysis

BACKGROUND/OBJECTIVES

The COVID-19 pandemic disrupted healthcare systems globally, altering the management of chronic conditions like multiple sclerosis (MS) and interrupting the regular monitoring and support that people with MS (pwMS) typically need. The aim of this study was to examine changes in the utilization of MS healthcare resources over various periods during the COVID-19 pandemic in 2020 and 2021, and to assess how these changes affected the perceptions of pwMSregarding their care.

METHODS

A longitudinal survey study was conducted at the MS Center at the University Hospital Dresden, Germany, involving four survey periods from April 2020 to December 2021. The study assessed the use of healthcare resources, including consultations with specialists, the use of rehabilitative therapy facilities, and unmet healthcare needs, across various phases of the pandemic, encompassing both lockdown and less restrictive periods.

RESULTS

At the onset of the pandemic in April 2020, during the first lockdown, 750 questionnaires were evaluated. While most pwMS reported consistent medical care compared with pre-pandemic levels, 19.2% had fewer general practitioner visits, and 10.6% fewer neurologist visits. During the follow-up survey periods, the use of medical care generally remained stable, although there were notable reductions reported by a subset of participants.

CONCLUSIONS

The findings suggest that medical and therapeutic care for pwMS in Germany remained largely accessible during the COVID-19 pandemic in 2020 and 2021. However, the study also reveals certain gaps in care that may be addressed by incorporating digital technologies into medical care and rehabilitation, potentially enhancing the management of healthcare during future pandemics or similar situations.

Pre- and post-COVID 19 outbreak relationship between physical activity and depressive symptoms in Spanish adults with major depressive disorder: a secondary analysis of the RADAR-MDD cohort study

Aim

To evaluate the longitudinal association of sedentary behavior, light and moderate-to-vigorous physical activity (MVPA) participation with depressive symptoms and whether their possible association changed depending on the pandemic phase.

Methods

This longitudinal study conducted secondary analysis from the Spanish cohort of the Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) study. Depressive symptoms were assessed by the Patient Health Questionnaire (PHQ-8). Sedentary behavior and physical activity were estimated via wrist-worn devices. Linear mixed models evaluated the longitudinal associations of sedentary behavior and physical activity (light and moderate-to-vigorous intensities) with depressive symptoms.

Results

In total, 95 participants (67.5% women, 53.0 [±10.5] years of age on average) were monitored pre-COVID-19 and included in the analyses. Pre-COVID-19, 73.7% of participants presented depression, and, on average, participated in 13.2 (±1.08) hours/day of sedentary behavior, 2.42 (±0.90) hours/day of light physical activity and 23.6 (±19.80) minutes/day of MVPA. Considering all the observations (from November 2019 to October 2020), an additional hour/day of sedentary behavior was longitudinally associated with higher depressive symptoms [βstd = 0.06, 95% confidence interval (CI) 0.10 to 0.47], whereas an additional hour/day in light physical activity was associated with lower depressive symptoms (βstd = -0.06, 95% CI -0.59 to -0.15). Time in MVPA was not associated with depressive symptomatology. The association of sedentary behavior and light physical activity with depressive symptoms was significant only during pre-COVID-19 and COVID-19 relaxation periods, whereas during the strictest periods of the pandemic with regards to the restrictions (lockdown and de-escalation), the association was not observed.

Conclusion

Sedentary behavior and light physical activity were longitudinally associated with depressive symptoms in participants with a history of MDD. The incorporation of light physical activity should be stimulated in adults with a history of MDD. Neither sedentary behavior nor light physical activity were associated with depressive symptoms during the most restrictive COVID-19 phases, whereas sedentary behavior (positively) and light physical activity (negatively) were associated with depressive symptoms in persons with MDD before and after the COVID-19 pandemic.

From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression

BACKGROUND

Smartphone-based digital phenotyping enables potentially clinically relevant information to be collected as individuals go about their day. This could improve monitoring and interventions for people with Major Depressive Disorder (MDD). The aim of this systematic review was to investigate current digital phenotyping features and methods used in MDD.

METHODS

We searched PubMed, PsycINFO, Embase, Scopus and Web of Science (10/11/2023) for articles including: (1) MDD population, (2) smartphone-based features, (3) validated ratings. Risk of bias was assessed using several sources. Studies were compared within analysis goals (correlating features with depression, predicting symptom severity, diagnosis, mood state/episode, other). Twenty-four studies (9801 participants) were included.

RESULTS

Studies achieved moderate performance. Common themes included challenges from complex and missing data (leading to a risk of bias), and a lack of external validation.

DISCUSSION

Studies made progress towards relating digital phenotypes to clinical variables, often focusing on time-averaged features. Methods investigating temporal dynamics more directly may be beneficial for patient monitoring. European Research Council consolidator grant: 101001118, Prospero: CRD42022346264, Open Science Framework: https://osf.io/s7ay4.

Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

BACKGROUND

Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features.

OBJECTIVE

We aimed to address these 3 challenges to inform future work in stratified analyses.

METHODS

Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model.

RESULTS

We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression.

CONCLUSIONS

This work contributes to our understanding of how these mobile health-derived features are associated with depression symptom severity to inform future work in stratified analyses.

Impact of COVID-19 pandemic on major depressive disorder in acute psychiatric inpatients

Introduction

The spread of the coronavirus disease 2019 (COVID-19) pandemic and the subsequent restrictions significantly affected mental health, especially major depressive disorder (MDD) whose incidence increased by 27.6% in 2020, after the COVID-19 outbreak. Few studies focused on the impact of the pandemic on the clinical characteristics of outpatients with MDD and even fewer on inpatients admitted for a major depressive episode (MDE). We aimed to compare the characteristics of MDD of two groups of patients admitted for an MDE before and after the pandemic outbreak and to investigate which variables are significantly related to post-lockdown hospitalizations.

Methods

This retrospective study included 314 patients with MDD hospitalized from January 2018 to December 2021 for an MDE (DSM-5) before (n = 154) and after (n = 160) the Italian lockdown (9th of March 2020). We compared patients' sociodemographic and clinical characteristics. The characteristics significantly different between the two groups were included in a logistic regression to identify the factors more strictly associated with post-lockdown hospitalizations.

Results

During post-lockdown hospitalization, we found a higher rate of severe MDE (33 patients, 21.4%, in the pre-lockdown and 55 patients, 34.4%, in the post), MDE with psychotic features (3 patients, 2.0%, in the pre-lockdown and 11 patients, 6.9%, in the post-lockdown), and suicidal ideation (42, 27.3%, in the pre-lockdown and 67, 41.9%, in the post-lockdown), with a lower proportion of patients followed by psychiatric services before admission (106 patients, 68.8%, in the pre-lockdown and 90 patients, 56.3%, in the post-lockdown) and a higher percentage of them in treatment with psychotherapy (18 patients, 11.7% in the pre-lockdown and 32, 20.0%, in the post-lockdown) and more frequent increase of the antidepressant dosage (16 patients, 10.4% in the pre-lockdown and 32 patients, 20.0% in the post-lockdown) and adoption of augmentation strategies (13 patients, 8.4%, in the pre-lockdown and 26 patients, 16.3%, in the post-lockdown) to treat the MDE. In the regression model, post-lockdown hospitalizations were significantly associated with suicidal ideation (OR = 1.86; p = 0.016) and psychotic features (OR = 4.41; p = 0.029) at admission, the increase in the antidepressant daily dose (OR = 2.45; p = 0.009), and the employment of an augmentation therapy (OR = 2.25; p = 0.029).

Discussion

These results showed an association between the COVID-19 pandemic and the occurrence of MDE with more severe clinical features. This might be true also for future calamities, suggesting that in these emergency contexts, patients with MDD would require more attention, resources, and intense treatments with a specific focus on suicide prevention.

The Impact of COVID-19 Lockdown on Adults with Major Depressive Disorder from Catalonia: A Decentralized Longitudinal Study

The present study analyzes the effects of each containment phase of the first COVID-19 wave on depression levels in a cohort of 121 adults with a history of major depressive disorder (MDD) from Catalonia recruited from 1 November 2019, to 16 October 2020. This analysis is part of the Remote Assessment of Disease and Relapse-MDD (RADAR-MDD) study. Depression was evaluated with the Patient Health Questionnaire-8 (PHQ-8), and anxiety was evaluated with the Generalized Anxiety Disorder-7 (GAD-7). Depression's levels were explored across the phases (pre-lockdown, lockdown, and four post-lockdown phases) according to the restrictions of Spanish/Catalan governments. Then, a mixed model was fitted to estimate how depression varied over the phases. A significant rise in depression severity was found during the lockdown and phase 0 (early post-lockdown), compared with the pre-lockdown. Those with low pre-lockdown depression experienced an increase in depression severity during the "new normality", while those with high pre-lockdown depression decreased compared with the pre-lockdown. These findings suggest that COVID-19 restrictions affected the depression level depending on their pre-lockdown depression severity. Individuals with low levels of depression are more reactive to external stimuli than those with more severe depression, so the lockdown may have worse detrimental effects on them.