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Article: Acute Effects of Growth Hormone on the Cellular Immunologic Landscape in Pediatric Patients.

Gujral, Jasmine / Kidd, Brian A / Becker, Christine / Golden, Eddye / Lee, Hao-Chih / Kim-Schulze, Seunghee / Yau, Mabel / Dudley, Joel / Rapaport, Robert

Cureus

2024 Volume 16, Issue 4, Page(s) e57383

Abstract: Introduction: Growth hormone (GH) and the immune system have multiple bidirectional interactions. Data about the acute effects of GH on the immune system are lacking. The objective of our study was to evaluate the acute effects of GH on the immune ... ...

Abstract	Introduction: Growth hormone (GH) and the immune system have multiple bidirectional interactions. Data about the acute effects of GH on the immune system are lacking. The objective of our study was to evaluate the acute effects of GH on the immune system using time-of-flight mass cytometry. Methods: This was a prospective study of pediatric patients who were being evaluated for short stature and underwent a GH stimulation test at a tertiary care center. Blood samples for immunologic markers, i.e., complete blood count (CBC) and time of flight mass cytometry (CyTOF), were collected at baseline (T0) and over the course of three hours (T3) of the test. Differences in immune profiling in patients by timepoint (T0, T3) and GH response (growth hormone sufficient (GHS) versus growth hormone deficient (GHD)) were calculated using a two-way ANOVA test. Results: A total of 54 patients (39 boys and 15 girls) aged five to 18 years were recruited. Twenty-two participants tested GHD (peak GH <10 ng/ml). The CyTOF analysis showed a significant increase from T0 to T3 in granulocyte percentage, monocyte count, and dendritic cell (DC) count; in contrast, a significant decrease was seen in T lymphocytes (helper and cytotoxic) and IgD+ B lymphocytes. The CBC analysis supported these findings: an increase in total white blood cell count, absolute neutrophil count, and neutrophil percentage; a decrease in absolute lymphocyte count, lymphocyte percentage, absolute eosinophil count, and absolute monocyte count. No significant differences were found between CBC/CyTOF measurements and GH status at either time. Conclusions: This study provides the first high-resolution map of acute changes in the immune system with GH stimulation. This implies a key role for GH in immunomodulatory function.
Language	English
Publishing date	2024-04-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	2747273-5
ISSN	2168-8184
ISSN	2168-8184
DOI	10.7759/cureus.57383
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Article ; Online: Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study.

Hirten, Robert P / Danieletto, Matteo / Landell, Kyle / Zweig, Micol / Golden, Eddye / Pyzik, Renata / Kaur, Sparshdeep / Chang, Helena / Helmus, Drew / Sands, Bruce E / Charney, Dennis / Nadkarni, Girish / Bagiella, Emilia / Keefer, Laurie / Fayad, Zahi A

JMIR mental health

2024 Volume 11, Page(s) e55552

Abstract: Background: Heart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a ... ...

Abstract	Background: Heart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a means to remotely use and monitor this approach would allow for wider use of this technique. Advancements in wearable and digital technology present an opportunity for the widespread application of this approach. Objective: The primary aim of the study was to determine the feasibility of fully remote, self-administered short sessions of HRV-directed biofeedback in a diverse population of health care workers (HCWs). The secondary aim was to determine whether a fully remote, HRV-directed biofeedback intervention significantly alters longitudinal HRV over the intervention period, as monitored by wearable devices. The tertiary aim was to estimate the impact of this intervention on metrics of psychological well-being. Methods: To determine whether remotely implemented short sessions of HRV biofeedback can improve autonomic metrics and psychological well-being, we enrolled HCWs across 7 hospitals in New York City in the United States. They downloaded our study app, watched brief educational videos about HRV biofeedback, and used a well-studied HRV biofeedback program remotely through their smartphone. HRV biofeedback sessions were used for 5 minutes per day for 5 weeks. HCWs were then followed for 12 weeks after the intervention period. Psychological measures were obtained over the study period, and they wore an Apple Watch for at least 7 weeks to monitor the circadian features of HRV. Results: In total, 127 HCWs were enrolled in the study. Overall, only 21 (16.5%) were at least 50% compliant with the HRV biofeedback intervention, representing a small portion of the total sample. This demonstrates that this study design does not feasibly result in adequate rates of compliance with the intervention. Numerical improvement in psychological metrics was observed over the 17-week study period, although it did not reach statistical significance (all P>.05). Using a mixed effect cosinor model, the mean midline-estimating statistic of rhythm (MESOR) of the circadian pattern of the SD of the interbeat interval of normal sinus beats (SDNN), an HRV metric, was observed to increase over the first 4 weeks of the biofeedback intervention in HCWs who were at least 50% compliant. Conclusions: In conclusion, we found that using brief remote HRV biofeedback sessions and monitoring its physiological effect using wearable devices, in the manner that the study was conducted, was not feasible. This is considering the low compliance rates with the study intervention. We found that remote short sessions of HRV biofeedback demonstrate potential promise in improving autonomic nervous function and warrant further study. Wearable devices can monitor the physiological effects of psychological interventions.
MeSH term(s)	Adult ; Female ; Humans ; Male ; Middle Aged ; Biofeedback, Psychology/methods ; Biofeedback, Psychology/instrumentation ; Health Personnel ; Heart Rate/physiology ; New York City ; Prospective Studies ; Telemedicine/methods ; Telemedicine/instrumentation ; Wearable Electronic Devices
Language	English
Publishing date	2024-04-25
Publishing country	Canada
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2798262-2
ISSN	2368-7959 ; 2368-7959
ISSN (online)	2368-7959
ISSN	2368-7959
DOI	10.2196/55552
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Article ; Online: Development of the ehive Digital Health App

Robert P Hirten / Matteo Danieletto / Kyle Landell / Micol Zweig / Eddye Golden / Georgy Orlov / Jovita Rodrigues / Eugenia Alleva / Ipek Ensari / Erwin Bottinger / Girish N Nadkarni / Thomas J Fuchs / Zahi A Fayad

JMIR Research Protocols, Vol 12, p e

Protocol for a Centralized Research Platform

2023 Volume 49204

Abstract: BackgroundThe increasing use of smartphones, wearables, and connected devices has enabled the increasing application of digital technologies for research. Remote digital study platforms comprise a patient-interfacing digital application that enables ... ...

Abstract	BackgroundThe increasing use of smartphones, wearables, and connected devices has enabled the increasing application of digital technologies for research. Remote digital study platforms comprise a patient-interfacing digital application that enables multimodal data collection from a mobile app and connected sources. They offer an opportunity to recruit at scale, acquire data longitudinally at a high frequency, and engage study participants at any time of the day in any place. Few published descriptions of centralized digital research platforms provide a framework for their development. ObjectiveThis study aims to serve as a road map for those seeking to develop a centralized digital research platform. We describe the technical and functional aspects of the ehive app, the centralized digital research platform of the Hasso Plattner Institute for Digital Health at Mount Sinai Hospital, New York, New York. We then provide information about ongoing studies hosted on ehive, including usership statistics and data infrastructure. Finally, we discuss our experience with ehive in the broader context of the current landscape of digital health research platforms. MethodsThe ehive app is a multifaceted and patient-facing central digital research platform that permits the collection of e-consent for digital health studies. An overview of its development, its e-consent process, and the tools it uses for participant recruitment and retention are provided. Data integration with the platform and the infrastructure supporting its operations are discussed; furthermore, a description of its participant- and researcher-facing dashboard interfaces and the e-consent architecture is provided. ResultsThe ehive platform was launched in 2020 and has successfully hosted 8 studies, namely 6 observational studies and 2 clinical trials. Approximately 1484 participants downloaded the app across 36 states in the United States. The use of recruitment methods such as bulk messaging through the EPIC electronic health records and standard email ...
Keywords	Medicine ; R ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	020
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	JMIR Publications
Document type	Article ; Online
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Article: Development of the ehive Digital Health App: Protocol for a Centralized Research Platform.

Hirten, Robert P / Danieletto, Matteo / Landell, Kyle / Zweig, Micol / Golden, Eddye / Orlov, Georgy / Rodrigues, Jovita / Alleva, Eugenia / Ensari, Ipek / Bottinger, Erwin / Nadkarni, Girish N / Fuchs, Thomas J / Fayad, Zahi A

JMIR research protocols

2023 Volume 12, Page(s) e49204

Abstract: Background: The increasing use of smartphones, wearables, and connected devices has enabled the increasing application of digital technologies for research. Remote digital study platforms comprise a patient-interfacing digital application that enables ... ...

Abstract	Background: The increasing use of smartphones, wearables, and connected devices has enabled the increasing application of digital technologies for research. Remote digital study platforms comprise a patient-interfacing digital application that enables multimodal data collection from a mobile app and connected sources. They offer an opportunity to recruit at scale, acquire data longitudinally at a high frequency, and engage study participants at any time of the day in any place. Few published descriptions of centralized digital research platforms provide a framework for their development. Objective: This study aims to serve as a road map for those seeking to develop a centralized digital research platform. We describe the technical and functional aspects of the ehive app, the centralized digital research platform of the Hasso Plattner Institute for Digital Health at Mount Sinai Hospital, New York, New York. We then provide information about ongoing studies hosted on ehive, including usership statistics and data infrastructure. Finally, we discuss our experience with ehive in the broader context of the current landscape of digital health research platforms. Methods: The ehive app is a multifaceted and patient-facing central digital research platform that permits the collection of e-consent for digital health studies. An overview of its development, its e-consent process, and the tools it uses for participant recruitment and retention are provided. Data integration with the platform and the infrastructure supporting its operations are discussed; furthermore, a description of its participant- and researcher-facing dashboard interfaces and the e-consent architecture is provided. Results: The ehive platform was launched in 2020 and has successfully hosted 8 studies, namely 6 observational studies and 2 clinical trials. Approximately 1484 participants downloaded the app across 36 states in the United States. The use of recruitment methods such as bulk messaging through the EPIC electronic health records and standard email portals enables broad recruitment. Light-touch engagement methods, used in an automated fashion through the platform, maintain high degrees of engagement and retention. The ehive platform demonstrates the successful deployment of a central digital research platform that can be modified across study designs. Conclusions: Centralized digital research platforms such as ehive provide a novel tool that allows investigators to expand their research beyond their institution, engage in large-scale longitudinal studies, and combine multimodal data streams. The ehive platform serves as a model for groups seeking to develop similar digital health research programs. International registered report identifier (irrid): DERR1-10.2196/49204.
Language	English
Publishing date	2023-11-16
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2719222-2
ISSN	1929-0748
ISSN	1929-0748
DOI	10.2196/49204
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Article ; Online: A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort.

Hirten, Robert P / Suprun, Maria / Danieletto, Matteo / Zweig, Micol / Golden, Eddye / Pyzik, Renata / Kaur, Sparshdeep / Helmus, Drew / Biello, Anthony / Landell, Kyle / Rodrigues, Jovita / Bottinger, Erwin P / Keefer, Laurie / Charney, Dennis / Nadkarni, Girish N / Suarez-Farinas, Mayte / Fayad, Zahi A

JAMIA open

2023 Volume 6, Issue 2, Page(s) ooad029

Abstract: Objective: To assess whether an individual's degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device.: Materials and methods: Data were analyzed in this secondary analysis of the ... ...

Abstract	Objective: To assess whether an individual's degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device. Materials and methods: Data were analyzed in this secondary analysis of the Warrior Watch Study dataset, a prospective cohort of healthcare workers enrolled across 7 hospitals in New York City. Subjects wore an Apple Watch for the duration of their participation. Surveys were collected measuring resilience, optimism, and emotional support at baseline. Results: We evaluated data from 329 subjects (mean age 37.4 years, 37.1% male). Across all testing sets, gradient-boosting machines (GBM) and extreme gradient-boosting models performed best for high- versus low-resilience prediction, stratified on a median Connor-Davidson Resilience Scale-2 score of 6 (interquartile range = 5-7), with an AUC of 0.60. When predicting resilience as a continuous variable, multivariate linear models had a correlation of 0.24 ( Discussion: In a Conclusions: These findings support the further assessment of psychological characteristics from passively collected wearable data in dedicated studies.
Language	English
Publishing date	2023-05-02
Publishing country	United States
Document type	Journal Article
ISSN	2574-2531
ISSN (online)	2574-2531
DOI	10.1093/jamiaopen/ooad029
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Article ; Online: Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers.

Hirten, Robert P / Tomalin, Lewis / Danieletto, Matteo / Golden, Eddye / Zweig, Micol / Kaur, Sparshdeep / Helmus, Drew / Biello, Anthony / Pyzik, Renata / Bottinger, Erwin P / Keefer, Laurie / Charney, Dennis / Nadkarni, Girish N / Suarez-Farinas, Mayte / Fayad, Zahi A

JAMIA open

2022 Volume 5, Issue 2, Page(s) ooac041

Abstract: Objective: To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices.: Materials and methods: Health care workers from 7 hospitals were enrolled and prospectively followed ...

Abstract	Objective: To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices. Materials and methods: Health care workers from 7 hospitals were enrolled and prospectively followed in a multicenter observational study. Subjects downloaded a custom smart phone app and wore Apple Watches for the duration of the study period. Daily surveys related to symptoms and the diagnosis of Coronavirus Disease 2019 were answered in the app. Results: We enrolled 407 participants with 49 (12%) having a positive nasal SARS-CoV-2 polymerase chain reaction test during follow-up. We examined 5 machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable validation performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC) = 86.4% (confidence interval [CI] 84-89%). The model was calibrated to value sensitivity over specificity, achieving an average sensitivity of 82% (CI ±∼4%) and specificity of 77% (CI ±∼1%). The most important predictors included parameters describing the circadian heart rate variability mean (MESOR) and peak-timing (acrophase), and age. Discussion: We show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV-2 infection. Conclusion: Applying machine learning models to the passively collected physiological metrics from wearable devices may improve SARS-CoV-2 screening methods and infection tracking.
Language	English
Publishing date	2022-05-18
Publishing country	United States
Document type	Journal Article
ISSN	2574-2531
ISSN (online)	2574-2531
DOI	10.1093/jamiaopen/ooac041
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Article ; Online: StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials.

Konigorski, Stefan / Wernicke, Sarah / Slosarek, Tamara / Zenner, Alexander M / Strelow, Nils / Ruether, Darius F / Henschel, Florian / Manaswini, Manisha / Pottbäcker, Fabian / Edelman, Jonathan A / Owoyele, Babajide / Danieletto, Matteo / Golden, Eddye / Zweig, Micol / Nadkarni, Girish N / Böttinger, Erwin

Journal of medical Internet research

2022 Volume 24, Issue 7, Page(s) e35884

Abstract: N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully ... ...

Abstract	N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice.
MeSH term(s)	Humans ; Mobile Applications ; Research Design
Language	English
Publishing date	2022-07-05
Publishing country	Canada
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1439-4456
ISSN (online)	1438-8871
ISSN	1439-4456
DOI	10.2196/35884
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Article ; Online: A Resilience-Building App to Support the Mental Health of Health Care Workers in the COVID-19 Era: Design Process, Distribution, and Evaluation.

Golden, Eddye A / Zweig, Micol / Danieletto, Matteo / Landell, Kyle / Nadkarni, Girish / Bottinger, Erwin / Katz, Lindsay / Somarriba, Ricardo / Sharma, Vansh / Katz, Craig L / Marin, Deborah B / DePierro, Jonathan / Charney, Dennis S

JMIR formative research

2021 Volume 5, Issue 5, Page(s) e26590

Abstract: Background: The COVID-19 pandemic has resulted in increased strain on health care systems and negative psychological effects on health care workers (HCWs). This is anticipated to result in long-term negative mental health effects on the population, with ...

Abstract	Background: The COVID-19 pandemic has resulted in increased strain on health care systems and negative psychological effects on health care workers (HCWs). This is anticipated to result in long-term negative mental health effects on the population, with HCWs representing a particularly vulnerable group. The scope of the COVID-19 pandemic necessitates the development of a scalable mental health platform to provide services to large numbers of at-risk or affected individuals. The Mount Sinai Health System in New York City was at the epicenter of the pandemic in the United States. Objective: The Center for Stress, Resilience, and Personal Growth (CSRPG) was created to address the current and anticipated psychological impact of the pandemic on the HCWs in the health system. The mission of the Center is to support the resilience and mental health of employees through educational offerings, outreach, and clinical care. Our aim was to build a mobile app to support the newly founded Center in its mission. Methods: We built the app as a standalone digital platform that hosts a suite of tools that users can interact with on a daily basis. With consideration for the Center's aims, we determined the overall vision, initiatives, and goals for the Wellness Hub app, followed by specific milestone tasks and deliverables for development. We defined the app's primary features based on the mental health assessment and needs of HCWs. Feature definition was informed by the results of a resilience survey widely distributed to Mount Sinai HCWs and by the resources offered at CSRPG, including workshop content. Results: We launched our app over the course of two phases, the first phase being a "soft" launch and the second being a broader launch to all of Mount Sinai. Of the 231 HCWs who downloaded the app, 173 (74.9%) completed our baseline assessment of all mental health screeners in the app. Results from the baseline assessment show that more than half of the users demonstrate a need for support in at least one psychological area. As of 3 months after the Phase 2 launch, approximately 55% of users re-entered the app after their first opening to explore additional features, with an average of 4 app openings per person. Conclusions: To address the mental health needs of HCWs during the COVID-19 pandemic, the Wellness Hub app was built and deployed throughout the Mount Sinai Health System. To our knowledge, this is the first resilience app of its kind. The Wellness Hub app is a promising proof of concept, with room to grow, for those who wish to build a secure mobile health app to support their employees, communities, or others in managing and improving mental and physical well-being. It is a novel tool offering mental health support broadly.
Language	English
Publishing date	2021-05-05
Publishing country	Canada
Document type	Journal Article
ISSN	2561-326X
ISSN (online)	2561-326X
DOI	10.2196/26590
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Article ; Online: Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study.

Journal of medical Internet research

2021 Volume 23, Issue 2, Page(s) e26107

Abstract: Background: Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification.: Objective: We performed an evaluation of HRV collected by ...

Abstract	Background: Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. Objective: We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. Methods: Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. Results: Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19-related symptom compared to all other symptom-free days (P=.01). Conclusions: Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19-related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.
MeSH term(s)	Adult ; COVID-19/diagnosis ; COVID-19/physiopathology ; COVID-19/virology ; COVID-19 Testing/methods ; Circadian Rhythm/physiology ; Female ; Health Personnel ; Heart Rate/physiology ; Humans ; Male ; SARS-CoV-2/genetics ; SARS-CoV-2/isolation & purification ; Wearable Electronic Devices
Language	English
Publishing date	2021-02-22
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1439-4456
ISSN (online)	1438-8871
ISSN	1439-4456
DOI	10.2196/26107
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Article ; Online: A Resilience-Building App to Support the Mental Health of Health Care Workers in the COVID-19 Era

JMIR Formative Research, Vol 5, Iss 5, p e

Design Process, Distribution, and Evaluation

2021 Volume 26590

Abstract: BackgroundThe COVID-19 pandemic has resulted in increased strain on health care systems and negative psychological effects on health care workers (HCWs). This is anticipated to result in long-term negative mental health effects on the population, with ... ...

Abstract	BackgroundThe COVID-19 pandemic has resulted in increased strain on health care systems and negative psychological effects on health care workers (HCWs). This is anticipated to result in long-term negative mental health effects on the population, with HCWs representing a particularly vulnerable group. The scope of the COVID-19 pandemic necessitates the development of a scalable mental health platform to provide services to large numbers of at-risk or affected individuals. The Mount Sinai Health System in New York City was at the epicenter of the pandemic in the United States. ObjectiveThe Center for Stress, Resilience, and Personal Growth (CSRPG) was created to address the current and anticipated psychological impact of the pandemic on the HCWs in the health system. The mission of the Center is to support the resilience and mental health of employees through educational offerings, outreach, and clinical care. Our aim was to build a mobile app to support the newly founded Center in its mission. MethodsWe built the app as a standalone digital platform that hosts a suite of tools that users can interact with on a daily basis. With consideration for the Center’s aims, we determined the overall vision, initiatives, and goals for the Wellness Hub app, followed by specific milestone tasks and deliverables for development. We defined the app’s primary features based on the mental health assessment and needs of HCWs. Feature definition was informed by the results of a resilience survey widely distributed to Mount Sinai HCWs and by the resources offered at CSRPG, including workshop content. ResultsWe launched our app over the course of two phases, the first phase being a “soft” launch and the second being a broader launch to all of Mount Sinai. Of the 231 HCWs who downloaded the app, 173 (74.9%) completed our baseline assessment of all mental health screeners in the app. Results from the baseline assessment show that more than half of the users demonstrate a need for support in at least one psychological area. As of 3 ...
Keywords	Medicine ; R
Subject code	360
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	JMIR Publications
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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