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.

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.

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.

Deep Learning: a Promising Method for Histological Class Prediction of Breast Tumors in Mammography.

The objective of the study was to determine if the pathology depicted on a mammogram is either benign or malignant (ductal or non-ductal carcinoma) using deep learning and artificial intelligence techniques. A total of 559 patients underwent breast ultrasound, mammography, and ultrasound-guided breast biopsy. Based on the histopathological results, the patients were divided into three categories: benign, ductal carcinomas, and non-ductal carcinomas. The mammograms in the cranio-caudal view underwent pre-processing and segmentation. Given the large variability of the areola, an algorithm was used to remove it and the adjacent skin. Therefore, patients with breast lesions close to the skin were removed. The remaining breast image was resized on the Y axis to a square image and then resized to 512 × 512 pixels. A variable square of 322,622 pixels was searched inside every image to identify the lesion. Each image was rotated with no information loss. For data augmentation, each image was rotated 360 times and a crop of 227 × 227 pixels was saved, resulting in a total of 201,240 images. The reason why our images were cropped at this size is because the deep learning algorithm transfer learning used from AlexNet network has an input image size of 227 × 227. The mean accuracy was 95.8344% ± 6.3720% and mean AUC 0.9910% ± 0.0366%, computed on 100 runs of the algorithm. Based on the results, the proposed solution can be used as a non-invasive and highly accurate computer-aided system based on deep learning that can classify breast lesions based on changes identified on mammograms in the cranio-caudal view.

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.

Community mental health centres initiated by the South-Eastern Europe Stability Pact: evaluation in seven countries.

Eight community mental health care centres (initiated by the South-Eastern Europe Stability Pact) in Albania, Bosnia-Herzegovina, Croatia, Macedonia, Moldova, Montenegro and Romania were evaluated. Characteristics of patients, patient reported outcomes and patient views of care were assessed in 305 psychiatric patients. Patient characteristics varied across centres, with most patients having long term psychotic disorders. Treatment satisfaction and therapeutic relationships were rated favourably. Subjective quality of life mean scores were rather low, with higher satisfaction with health and dissatisfaction with the financial and employment situation. Being unemployed was the only factor associated with poor quality of life and lower treatment satisfaction. Most developing centres target patients with persistent psychotic disorders. Care appears highly valued by the patients. The findings encourage establishing more centres in the region and call for employment schemes for people with mental illnesses.

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.

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.

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.

Covid-19 Pandemic Impact on Breast Cancer Detection-The Major Effects Over an Early Diagnosis.

The COVID-19 pandemic has disrupted medical care systems, by decreasing patient addressability to outpatient care. The main objective of this study was to compare the patient's addressability to breast imaging techniques for diagnosis, and follow-up in the Clinical Emergency County Hospital of Craiova, Romania. We selected the mammographies performed over a period of 4 years (2018-2021) in our clinic. We divided the patients into four groups, one for each year (2018, 2019, 2020, 2021). Furtherly, we merged the data into two groups, one group for the pre-pandemic years (2018 and 2019) and one for the pandemic years (2020 and 2021). In our clinic, the number of mammographies plummeted to 0 during the month of April 2020 due to the lockdown and closure of non-urgent outpatient services in hospitals treating COVID-19 patients, and slowly creeped to 11 in the month of May and peaked to 160 in July (for the rest of the year). There was a huge difference regarding the patient's addressability to mammography immediately after the lockdown, with a 95.2% less addressability compared to the pre-pandemic period (May 2020 compared to May 2018). As an overall, by comparing both pre-pandemic years included in the study with the pandemic years, we obtained an addressability reduced with 37.3% suggesting the possible future delays in diagnosing breast tumors.

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.

Mental health system reform in Moldova: Description of the program and reflections on its implementation between 2014 and 2019.

In 2014, the Republic of Moldova started a systematic process of reforming its mental health system, implementing priority actions set out in the National Mental Health Programme. The reform entailed a service delivery re-design, instituting mechanisms for collaboration across health and social sectors, and revision of the policy framework. Outcomes of the first 4 years of the reform included: 1) the establishment of a network of mental health services in 4 pilot districts embedding mental health diagnosis, treatment and referral in primary and specialized mental healthcare; 2) creation of an enabling policy environment at the national and district level; and 3) strengthened community support and acceptance of mental health issues. Objectives of the first Phase were achieved and the reform is now in its second Phase (2018-2022). The implementation strategy in Phase 1 focused efforts on 4 pilot districts, whereas Phase 2 harnesses lessons learned from Phase 1 and facilitates local leaders and actors to scale-up the model to all 32 districts and municipalities in Moldova. Ownership over the reform process shifted from project-led in Phase 1 to national and local government-led in Phase 2. We reflect on the process and contents of the mental health reform, discuss lessons learned and implementation challenges encountered. We conclude with learning points for policymakers and researchers considering mental health reform in other countries.

Towards community-based and recovery-oriented care for severe mental disorders in Southern and Eastern Europe: aims and design of a multi-country implementation and evaluation study (RECOVER-E).

BACKGROUND: Substantial strides have been made around the world in reforming mental health systems by shifting away from institutional care towards community-based services. Despite an extensive evidence base on what constitutes effective care for people with severe mental ill-health, many people in Europe do not have access to optimal mental health care. In an effort to consolidate previous efforts to improve community mental health care and support the complex transition from hospital-based to community-based care delivery, the RECOVER-E (LaRge-scalE implementation of COmmunity based mental health care for people with seVere and Enduring mental ill health in EuRopE) project aims to implement and evaluate multidisciplinary community mental health teams in five countries in Central and Eastern Europe. This paper provides a brief overview of the RECOVER-E project and its methods. METHODS: Five implementation sites were selected (Sofia, Bulgaria; Zagreb, Croatia; Skopje, North Macedonia; Kotor, Montenegro; Siret-Suceava, Romania) where hospital-based mental health services are available (care as usual, CAU) for patients with severe mental disorders (severe depression, bipolar disorder, schizophrenia). The intervention consists of the introduction of a new service delivery model in each site, consisting of community-based recovery-oriented care delivered by trained multidisciplinary community mental health teams (including a peer worker with lived experience of a severe mental disorder). The implementation outcomes of the teams and the effect of the team's approach on patient and service utilisation outcomes will be evaluated using a mix of research methods. The study includes five planned hybrid implementation-effectiveness trials (1 per site) with patient-level randomization (n = 180, with patients randomised to either care as usual or intervention condition). Effectiveness is evaluated using a pragmatic non-blinded design with patients randomised into two parallel groups: receiving new community-based care or receiving usual care in the form of institutional, hospital-based mental health care. Trial-based health economic evaluation will be conducted; implementation outcomes will be evaluated, with data aligned with dimensions from the RE-AIM framework. Pathways to sustaining project results will be developed through policy dialogue sessions, which will be carried out in each country and through ongoing policy engagement activities at the European level. DISCUSSION: The RECOVER-E project has been developed and conducted to demonstrate the impact of implementing an evidence-based service delivery model for people with severe mental illness in different contexts in middle-income countries in Central and Eastern Europe. It is expected that the results will contribute to the growing evidence-base on the health and economic benefits of recovery-oriented and community-based service models for health systems in transition.Trial registration Each trial was registered before participant enrolment in the clinicaltrials.gov database: Site-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).

Sustainability of Community-Based Specialized Mental Health Services in Five European Countries: Protocol for Five Randomized Controlled Trial-Based Health-Economic Evaluations Embedded in the RECOVER-E Program.

BACKGROUND: Community-based recovery-oriented mental health services for people with severe mental disorders have not been fully implemented in Bulgaria, Croatia, Macedonia, Montenegro, and Romania. The RECOVER-E project facilitates the implementation of specialized mental health care delivered by setting up services, implementing the services, and evaluating multidisciplinary community mental health teams. The outcomes of the RECOVER-E project are assessed in a trial-based outcome evaluation in each of the participating countries with a health-economic evaluation linked to these trials. OBJECTIVE: The aim of this protocol paper is to describe the methodology that will be used for the health-economic evaluation alongside the trials. METHODS: Implementation sites have been selected in each of the five countries where hospital-based mental health services are available (care as usual [CAU]) for patients with severe mental disorders (severe depression, bipolar disorder, schizophrenia, and other psychotic disorders). The newly implemented health care system will involve community-based recovery-oriented mental health care (CMHC). At each site, 180 consenting patients will be randomized to either CAU or CMHC. Patient-level outcomes are personal and social functioning and quality-adjusted life years (QALYs). Data on participants' health care use will be collected and corresponding health care costs will be computed. This enables evaluation of health care costs of CMHC as compared with CAU, and these costs can be related to patient-level outcomes (functioning and QALY gains) in health-economic evaluation. RESULTS: Data collection was started in December 2018 (Croatia), February 2019 (Montenegro), April 2019 (Romania), June 2019 (North Macedonia), and October 2019 (Bulgaria). The findings of the outcome evaluations will be reported for each of the five countries separately, and the five trials will be pooled for multilevel analysis on a combined dataset. CONCLUSIONS: The results of the health-economic evaluation of the RECOVER-E project will contribute to the growing evidence base on the health and economic benefits of recovery-oriented and community-based service models for health systems in transition. TRIAL REGISTRATION: (1) ClinicalTrials.gov NCT03922425 (Bulgaria); https://clinicaltrials.gov/ct2/show/NCT03922425 (2) ClinicalTrials.gov NCT03862209 (Croatia); https://clinicaltrials.gov/ct2/show/NCT03862209 (3) ClinicalTrials.gov NCT03892473 (Macedonia); https://clinicaltrials.gov/ct2/show/NCT03892473 (4) ClinicalTrials.gov NCT03837340 (Montenegro); https://clinicaltrials.gov/ct2/show/NCT03837340 (5) ClinicalTrials.gov NCT03884933 (Romania); https://clinicaltrials.gov/ct2/show/NCT03884933. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/17454.