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.

Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study.

Recent growth in digital technologies has enabled the recruitment and monitoring of large and diverse populations in remote health studies. However, the generalizability of inference drawn from remotely collected health data could be severely impacted by uneven participant engagement and attrition over the course of the study. We report findings on long-term participant retention and engagement patterns in a large multinational observational digital study for depression containing active (surveys) and passive sensor data collected via Android smartphones, and Fitbit devices from 614 participants for up to 2 years. Majority of participants (67.6%) continued to remain engaged in the study after 43 weeks. Unsupervised clustering of participants' study apps and Fitbit usage data showed 3 distinct engagement subgroups for each data stream. We found: (i) the least engaged group had the highest depression severity (4 PHQ8 points higher) across all data streams; (ii) the least engaged group (completed 4 bi-weekly surveys) took significantly longer to respond to survey notifications (3.8 h more) and were 5 years younger compared to the most engaged group (completed 20 bi-weekly surveys); and (iii) a considerable proportion (44.6%) of the participants who stopped completing surveys after 8 weeks continued to share passive Fitbit data for significantly longer (average 42 weeks). Additionally, multivariate survival models showed participants' age, ownership and brand of smartphones, and recruitment sites to be associated with retention in the study. Together these findings could inform the design of future digital health studies to enable equitable and balanced data collection from diverse populations.

Health Tracking via Mobile Apps for Depression Self-management: Qualitative Content Analysis of User Reviews.

BACKGROUND: Tracking and visualizing health data using mobile apps can be an effective self-management strategy for mental health conditions. However, little evidence is available to guide the design of mental health-tracking mechanisms. OBJECTIVE: The aim of this study was to analyze the content of user reviews of depression self-management apps to guide the design of data tracking and visualization mechanisms for future apps. METHODS: We systematically reviewed depression self-management apps on Google Play and iOS App stores. English-language reviews of eligible apps published between January 1, 2018, and December 31, 2021, were extracted from the app stores. Reviews that referenced health tracking and data visualization were included in sentiment and qualitative framework analyses. RESULTS: The search identified 130 unique apps, 26 (20%) of which were eligible for inclusion. We included 783 reviews in the framework analysis, revealing 3 themes. Impact of app-based mental health tracking described how apps increased reviewers' self-awareness and ultimately enabled condition self-management. The theme designing impactful mental health-tracking apps described reviewers' feedback and requests for app features during data reporting, review, and visualization. It also described the desire for customization and contexts that moderated reviewer preference. Finally, implementing impactful mental health-tracking apps described considerations for integrating apps into a larger health ecosystem, as well as the influence of paywalls and technical issues on mental health tracking. CONCLUSIONS: App-based mental health tracking supports depression self-management when features align with users' individual needs and goals. Heterogeneous needs and preferences raise the need for flexibility in app design, posing challenges for app developers. Further research should prioritize the features based on their importance and impact on users.

Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images.

(1) Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Reverse transcription polymerase chain reaction (RT-PCR) remains the current gold standard for detecting SARS-CoV-2 infections in nasopharyngeal swabs. In Romania, the first reported patient to have contracted COVID-19 was officially declared on 26 February 2020. (2) Methods: This study proposes a federated learning approach with pre-trained deep learning models for COVID-19 detection. Three clients were locally deployed with their own dataset. The goal of the clients was to collaborate in order to obtain a global model without sharing samples from the dataset. The algorithm we developed was connected to our internal picture archiving and communication system and, after running backwards, it encountered chest CT changes suggestive for COVID-19 in a patient investigated in our medical imaging department on the 28 January 2020. (4) Conclusions: Based on our results, we recommend using an automated AI-assisted software in order to detect COVID-19 based on the lung imaging changes as an adjuvant diagnostic method to the current gold standard (RT-PCR) in order to greatly enhance the management of these patients and also limit the spread of the disease, not only to the general population but also to healthcare professionals.

Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study.

BACKGROUND: Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data. METHODS: Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse - Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months. RESULTS: Individuals with a history of MDD (n = 623) were enrolled in the study,. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types. CONCLUSIONS: RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group.

Imaging Features of Rare Breast Lesions in Young Women.

Rare breast tumors, such as, pseudoangiomatous stromal hyperplasia, granulomatous mastitis, tubular adenoma, myofibroblastoma and xanthogranulomatous mastitis, sarcomas, neuroendocrine tumors can sometimes be misdiagnosed because of the similarities in their imagistic characteristics. The main objective of our report is to emphasize the importance of performing ultrasound-guided breast biopsies of suspect lesions in young patients. We performed an US-guided breast biopsy instead of an excisional biopsy because breast surgery has a huge psychological impact. We selected 3 atypical breast tumors in young women, with different clinical signs and symptoms, some of which similar to other breast lesions, but with rapid growth, which needed a different and multiple imaging approach.

Perceived Support for Recovery and Level of Functioning Among People With Severe Mental Illness in Central and Eastern Europe: An Observational Study.

Background: Many people with severe mental illness experience limitations in personal and social functioning. Care delivered in a person's community that addresses needs and preferences and focuses on clinical and personal recovery can contribute to addressing the adverse impacts of severe mental illness. In Central and Eastern Europe, mental health care systems are transitioning from institutional-based care toward community-based care. The aim of this study is to document the level of functioning and perceived support for recovery in a large population of service users with severe mental illness in Central and Eastern Europe, and to explore associations between perceived support for recovery and the degree of functional limitations. Methods: The implementation of community mental health teams was conducted in five mental health centers in five countries in Central and Eastern Europe. The present study is based on trial data at baseline among service users across the five centers. Baseline data included sociodemographic, the World Health Organization Disability Assessment Schedule (WHODAS 2.0) for functional limitations, and the Recovery Support (INSPIRE) tool for perceived staff support toward recovery. We hypothesized that service users reporting higher levels of perceived support for their recovery would indicate lower levels of functional limitation. Results: Across all centers, the greatest functional limitations were related to participation in society (43.8%), followed by daily life activities (33.3%), and in education or work (35.6%). Service users (N = 931) indicated that they were satisfied overall with the support received from their mental health care provider for their social recovery (72.5%) and that they valued their relationship with their providers (80.3%). Service users who perceived the support they received from their provider as valuable (b = -0.10, p = 0.001) and who reported to have a meaningful relationship with them (b = -0.13, p = 0.003) had a lower degree of functional limitation. Conclusion: As hypothesized, the higher the degree of perceived mental health support from providers, the lower the score in functional limitations. The introduction of the community-based care services that increase contact with service users and consider needs and which incorporate recovery-oriented principles, may improve clinical recovery and functional outcomes of service users with severe mental illness.

Experiences of healthcare staff providing community-based mental healthcare as a multidisciplinary community mental health team in Central and Eastern Europe findings from the RECOVER-E project: an observational intervention study.

BACKGROUND: Community Mental Health Teams (CMHTs) deliver healthcare that supports the recovery of people with mental illness. The aim of this paper was to explore to what extent team members of five CMHTs newly implemented in five countries perceived that they had introduced aspects of the recovery-oriented, strength-based approach into care after a training week on recovery-oriented practice. In addition, it evaluated what the team members' perceptions on their care roles and their level of confidence with this role were. METHOD: An observational intervention study using a quantitative survey that was administered among 52 health professionals (21 Nurses, 13 Psychiatrists, 9 Psychologists, 8 Social Workers) and 14 peer workers including the Recovery Self-Assessment Tool Provider Version (RSA-P), the Team Member Self-Assessment Tool (TMSA), and demographic questions was conducted. The measures were self-reported. Descriptive statistics were used to calculate the means and standard deviations for continuous variables and frequencies and percentages for categorical variables (TMSA tool and demographic data). The standard technique to calculate scale scores for each subscale of the RSA-P was used. Bivariate linear regression analyses were applied to explore the impact of predictors on the subscales of the RSA-P. Predictors with significant effects were included in multiple regression models. RESULT: The RSA-P showed that all teams had the perception that they provide recovery-oriented practice to a moderately high degree after a training week on recovery-oriented care (mean scores between 3.85-4.46). Health professionals with fewer years of professional experience perceived more frequently that they operated in a recovery-oriented way (p = 0.036, B = - 0.268). Nurses and peer workers did not feel confident or responsible to fulfil specific roles. CONCLUSION: The findings suggest that a one-week training session on community-based practices and collaborative teamwork may enhance recovery-oriented practice, but the role of nurses and peer workers needs further attention. TRIAL REGISTRATION: Each trial was registered before participant enrolment in the clinicaltrials.gov database: Croatia, Zagreb (Trial Reg. No. NCT03862209 ); Montenegro, Kotor (Trial Reg. No. NCT03837340 ); Romania, Suceava (Trial Reg. No. NCT03884933 ); Macedonia, Skopje (Trial Reg. No. NCT03892473 ); Bulgaria, Sofia (Trial Reg. No. NCT03922425 ).

Investigating the impact of COVID-19 lockdown on adults with a recent history of recurrent major depressive disorder: a multi-Centre study using remote measurement technology.

BACKGROUND: The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes a clinical illness Covid-19, has had a major impact on mental health globally. Those diagnosed with major depressive disorder (MDD) may be negatively impacted by the global pandemic due to social isolation, feelings of loneliness or lack of access to care. This study seeks to assess the impact of the 1st lockdown - pre-, during and post - in adults with a recent history of MDD across multiple centres. METHODS: This study is a secondary analysis of an on-going cohort study, RADAR-MDD project, a multi-centre study examining the use of remote measurement technology (RMT) in monitoring MDD. Self-reported questionnaire and passive data streams were analysed from participants who had joined the project prior to 1st December 2019 and had completed Patient Health and Self-esteem Questionnaires during the pandemic (n = 252). We used mixed models for repeated measures to estimate trajectories of depressive symptoms, self-esteem, and sleep duration. RESULTS: In our sample of 252 participants, 48% (n = 121) had clinically relevant depressive symptoms shortly before the pandemic. For the sample as a whole, we found no evidence that depressive symptoms or self-esteem changed between pre-, during- and post-lockdown. However, we found evidence that mean sleep duration (in minutes) decreased significantly between during- and post- lockdown (- 12.16; 95% CI - 18.39 to - 5.92; p <  0.001). We also found that those experiencing clinically relevant depressive symptoms shortly before the pandemic showed a decrease in depressive symptoms, self-esteem and sleep duration between pre- and during- lockdown (interaction p = 0.047, p = 0.045 and p <  0.001, respectively) as compared to those who were not. CONCLUSIONS: We identified changes in depressive symptoms and sleep duration over the course of lockdown, some of which varied according to whether participants were experiencing clinically relevant depressive symptoms shortly prior to the pandemic. However, the results of this study suggest that those with MDD do not experience a significant worsening in symptoms during the first months of the Covid - 19 pandemic.