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Barac M, Scaletty S, Hassett LC, Stillwell A, Croarkin PE, Chauhan M, Chesak S, Bobo WV, Athreya AP, Dyrbye LN. Wearable Technologies for Detecting Burnout and Well-Being in Health Care Professionals: Scoping Review. J Med Internet Res 2024; 26:e50253. [PMID: 38916948 PMCID: PMC11234055 DOI: 10.2196/50253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 01/01/2024] [Accepted: 03/20/2024] [Indexed: 06/26/2024] Open
Abstract
BACKGROUND The occupational burnout epidemic is a growing issue, and in the United States, up to 60% of medical students, residents, physicians, and registered nurses experience symptoms. Wearable technologies may provide an opportunity to predict the onset of burnout and other forms of distress using physiological markers. OBJECTIVE This study aims to identify physiological biomarkers of burnout, and establish what gaps are currently present in the use of wearable technologies for burnout prediction among health care professionals (HCPs). METHODS A comprehensive search of several databases was performed on June 7, 2022. No date limits were set for the search. The databases were Ovid: MEDLINE(R), Embase, Healthstar, APA PsycInfo, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Web of Science Core Collection via Clarivate Analytics, Scopus via Elsevier, EBSCOhost: Academic Search Premier, CINAHL with Full Text, and Business Source Premier. Studies observing anxiety, burnout, stress, and depression using a wearable device worn by an HCP were included, with HCP defined as medical students, residents, physicians, and nurses. Bias was assessed using the Newcastle Ottawa Quality Assessment Form for Cohort Studies. RESULTS The initial search yielded 505 papers, from which 10 (1.95%) studies were included in this review. The majority (n=9) used wrist-worn biosensors and described observational cohort studies (n=8), with a low risk of bias. While no physiological measures were reliably associated with burnout or anxiety, step count and time in bed were associated with depressive symptoms, and heart rate and heart rate variability were associated with acute stress. Studies were limited with long-term observations (eg, ≥12 months) and large sample sizes, with limited integration of wearable data with system-level information (eg, acuity) to predict burnout. Reporting standards were also insufficient, particularly in device adherence and sampling frequency used for physiological measurements. CONCLUSIONS With wearables offering promise for digital health assessments of human functioning, it is possible to see wearables as a frontier for predicting burnout. Future digital health studies exploring the utility of wearable technologies for burnout prediction should address the limitations of data standardization and strategies to improve adherence and inclusivity in study participation.
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Affiliation(s)
- Milica Barac
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Samantha Scaletty
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Leslie C Hassett
- Mayo Clinic Libraries, Mayo Clinic, Rochester, MN, United States
| | - Ashley Stillwell
- Department of Family Medicine, Mayo Clinic, Phoenix, AZ, United States
| | - Paul E Croarkin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Mohit Chauhan
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, FL, United States
| | - Sherry Chesak
- Department of Nursing, Mayo Clinic, Rochester, MN, United States
| | - William V Bobo
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, FL, United States
| | - Arjun P Athreya
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Liselotte N Dyrbye
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, United States
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Nepal S, Liu W, Pillai A, Wang W, Vojdanovski V, Huckins JF, Rogers C, Meyer ML, Campbell AT. Capturing the College Experience: A Four-Year Mobile Sensing Study of Mental Health, Resilience and Behavior of College Students during the Pandemic. PROCEEDINGS OF THE ACM ON INTERACTIVE, MOBILE, WEARABLE AND UBIQUITOUS TECHNOLOGIES 2024; 8:38. [PMID: 39086982 PMCID: PMC11290409 DOI: 10.1145/3643501] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Abstract
Understanding the dynamics of mental health among undergraduate students across the college years is of critical importance, particularly during a global pandemic. In our study, we track two cohorts of first-year students at Dartmouth College for four years, both on and off campus, creating the longest longitudinal mobile sensing study to date. Using passive sensor data, surveys, and interviews, we capture changing behaviors before, during, and after the COVID-19 pandemic subsides. Our findings reveal the pandemic's impact on students' mental health, gender based behavioral differences, impact of changing living conditions and evidence of persistent behavioral patterns as the pandemic subsides. We observe that while some behaviors return to normal, others remain elevated. Tracking over 200 undergraduate students from high school to graduation, our study provides invaluable insights into changing behaviors, resilience and mental health in college life. Conducting a long-term study with frequent phone OS updates poses significant challenges for mobile sensing apps, data completeness and compliance. Our results offer new insights for Human-Computer Interaction researchers, educators and administrators regarding college life pressures. We also detail the public release of the de-identified College Experience Study dataset used in this paper and discuss a number of open research questions that could be studied using the public dataset.
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Affiliation(s)
- Subigya Nepal
- Dartmouth College, Department of Computer Science, Hanover, NH, USA
| | - Wenjun Liu
- Dartmouth College, Department of Computer Science, Hanover, NH, USA
| | - Arvind Pillai
- Dartmouth College, Department of Computer Science, Hanover, NH, USA
| | - Weichen Wang
- Dartmouth College, Department of Computer Science, Hanover, NH, USA
| | | | | | - Courtney Rogers
- Dartmouth College, Psychological and Brain Sciences, Hanover, NH, USA
| | - Meghan L Meyer
- Columbia University, Department of Psychology, New York, NY, USA
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Malgaroli M, Tseng E, Hull TD, Jennings E, Choudhury TK, Simon NM. Association of Health Care Work With Anxiety and Depression During the COVID-19 Pandemic: Structural Topic Modeling Study. JMIR AI 2023; 2:e47223. [PMID: 38875560 PMCID: PMC11041488 DOI: 10.2196/47223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 06/28/2023] [Accepted: 09/07/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Stressors for health care workers (HCWs) during the COVID-19 pandemic have been manifold, with high levels of depression and anxiety alongside gaps in care. Identifying the factors most tied to HCWs' psychological challenges is crucial to addressing HCWs' mental health needs effectively, now and for future large-scale events. OBJECTIVE In this study, we used natural language processing methods to examine deidentified psychotherapy transcripts from telemedicine treatment during the initial wave of COVID-19 in the United States. Psychotherapy was delivered by licensed therapists while HCWs were managing increased clinical demands and elevated hospitalization rates, in addition to population-level social distancing measures and infection risks. Our goal was to identify specific concerns emerging in treatment for HCWs and to compare differences with matched non-HCW patients from the general population. METHODS We conducted a case-control study with a sample of 820 HCWs and 820 non-HCW matched controls who received digitally delivered psychotherapy in 49 US states in the spring of 2020 during the first US wave of the COVID-19 pandemic. Depression was measured during the initial assessment using the Patient Health Questionnaire-9, and anxiety was measured using the General Anxiety Disorder-7 questionnaire. Structural topic models (STMs) were used to determine treatment topics from deidentified transcripts from the first 3 weeks of treatment. STM effect estimators were also used to examine topic prevalence in patients with moderate to severe anxiety and depression. RESULTS The median treatment enrollment date was April 15, 2020 (IQR March 31 to April 27, 2020) for HCWs and April 19, 2020 (IQR April 5 to April 27, 2020) for matched controls. STM analysis of deidentified transcripts identified 4 treatment topics centered on health care and 5 on mental health for HCWs. For controls, 3 STM topics on pandemic-related disruptions and 5 on mental health were identified. Several STM treatment topics were significantly associated with moderate to severe anxiety and depression, including working on the hospital unit (topic prevalence 0.035, 95% CI 0.022-0.048; P<.001), mood disturbances (prevalence 0.014, 95% CI 0.002-0.026; P=.03), and sleep disturbances (prevalence 0.016, 95% CI 0.002-0.030; P=.02). No significant associations emerged between pandemic-related topics and moderate to severe anxiety and depression for non-HCW controls. CONCLUSIONS The study provides large-scale quantitative evidence that during the initial wave of the COVID-19 pandemic, HCWs faced unique work-related challenges and stressors associated with anxiety and depression, which required dedicated treatment efforts. The study further demonstrates how natural language processing methods have the potential to surface clinically relevant markers of distress while preserving patient privacy.
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Affiliation(s)
- Matteo Malgaroli
- Department of Psychiatry, Grossman School of Medicine, New York University, New York, NY, United States
| | - Emily Tseng
- Ann S Bowers College of Computing and Information Science, Cornell University, Ithaca, NY, United States
| | - Thomas D Hull
- Research and Development, Talkspace, New York, NY, United States
| | - Emma Jennings
- Department of Psychiatry, Grossman School of Medicine, New York University, New York, NY, United States
| | - Tanzeem K Choudhury
- Ann S Bowers College of Computing and Information Science, Cornell University, Ithaca, NY, United States
| | - Naomi M Simon
- Department of Psychiatry, Grossman School of Medicine, New York University, New York, NY, United States
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WANG ZHIYUAN, LARRAZABAL MARIAA, RUCKER MARK, TONER EMMAR, DANIEL KATHARINEE, KUMAR SHASHWAT, BOUKHECHBA MEHDI, TEACHMAN BETHANYA, BARNES LAURAE. Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals. PROCEEDINGS OF THE ACM ON INTERACTIVE, MOBILE, WEARABLE AND UBIQUITOUS TECHNOLOGIES 2023; 7:134. [PMID: 38737573 PMCID: PMC11087077 DOI: 10.1145/3610916] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Abstract
Mobile sensing is a ubiquitous and useful tool to make inferences about individuals' mental health based on physiology and behavior patterns. Along with sensing features directly associated with mental health, it can be valuable to detect different features of social contexts to learn about social interaction patterns over time and across different environments. This can provide insight into diverse communities' academic, work and social lives, and their social networks. We posit that passively detecting social contexts can be particularly useful for social anxiety research, as it may ultimately help identify changes in social anxiety status and patterns of social avoidance and withdrawal. To this end, we recruited a sample of highly socially anxious undergraduate students (N=46) to examine whether we could detect the presence of experimentally manipulated virtual social contexts via wristband sensors. Using a multitask machine learning pipeline, we leveraged passively sensed biobehavioral streams to detect contexts relevant to social anxiety, including (1) whether people were in a social situation, (2) size of the social group, (3) degree of social evaluation, and (4) phase of social situation (anticipating, actively experiencing, or had just participated in an experience). Results demonstrated the feasibility of detecting most virtual social contexts, with stronger predictive accuracy when detecting whether individuals were in a social situation or not and the phase of the situation, and weaker predictive accuracy when detecting the level of social evaluation. They also indicated that sensing streams are differentially important to prediction based on the context being predicted. Our findings also provide useful information regarding design elements relevant to passive context detection, including optimal sensing duration, the utility of different sensing modalities, and the need for personalization. We discuss implications of these findings for future work on context detection (e.g., just-in-time adaptive intervention development).
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Affiliation(s)
- ZHIYUAN WANG
- Department of Systems and Information Engineering, University of Virginia, USA
| | | | - MARK RUCKER
- Department of Systems and Information Engineering, University of Virginia, USA
| | - EMMA R. TONER
- Department of Psychology, University of Virginia, USA
| | | | - SHASHWAT KUMAR
- Department of Systems and Information Engineering, University of Virginia, USA
| | | | | | - LAURA E. BARNES
- Department of Systems and Information Engineering, University of Virginia, USA
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Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F. Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. J Biomed Inform 2023; 138:104278. [PMID: 36586498 DOI: 10.1016/j.jbi.2022.104278] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 12/20/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022]
Abstract
Many studies have used Digital Phenotyping of Mental Health (DPMH) to complement classic methods of mental health assessment and monitoring. This research area proposes innovative methods that perform multimodal sensing of multiple situations of interest (e.g., sleep, physical activity, mobility) to health professionals. In this paper, we present a Systematic Literature Review (SLR) to recognize, characterize and analyze the state of the art on DPMH using multimodal sensing of multiple situations of interest to professionals. We searched for studies in six digital libraries, which resulted in 1865 retrieved published papers. Next, we performed a systematic process of selecting studies based on inclusion and exclusion criteria, which selected 59 studies for the data extraction phase. First, based on the analysis of the extracted data, we describe an overview of this field, then presenting characteristics of the selected studies, the main mental health topics targeted, the physical and virtual sensors used, and the identified situations of interest. Next, we outline answers to research questions, describing the context data sources used to detect situations, the DPMH workflow used for multimodal sensing of situations, and the application of DPMH solutions in the mental health assessment and monitoring process. In addition, we recognize trends presented by DPMH studies, such as the design of solutions for high-level information recognition, association of features with mental states/disorders, classification of mental states/disorders, and prediction of mental states/disorders. We also recognize the main open issues in this research area. Based on the results of this SLR, we conclude that despite the potential and continuous evolution for using these solutions as medical decision support tools, this research area needs more work to overcome technology and methodological rigor issues to adopt proposed solutions in real clinical settings.
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Affiliation(s)
- Ivan Moura
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil.
| | - Ariel Teles
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil; Federal Institute of Maranhão, Brazil
| | - Davi Viana
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Jean Marques
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Luciano Coutinho
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Francisco Silva
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
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Adler DA, Wang F, Mohr DC, Choudhury T. Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies. PLoS One 2022; 17:e0266516. [PMID: 35476787 PMCID: PMC9045602 DOI: 10.1371/journal.pone.0266516] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 03/23/2022] [Indexed: 11/19/2022] Open
Abstract
Mobile sensing data processed using machine learning models can passively and remotely assess mental health symptoms from the context of patients' lives. Prior work has trained models using data from single longitudinal studies, collected from demographically homogeneous populations, over short time periods, using a single data collection platform or mobile application. The generalizability of model performance across studies has not been assessed. This study presents a first analysis to understand if models trained using combined longitudinal study data to predict mental health symptoms generalize across current publicly available data. We combined data from the CrossCheck (individuals living with schizophrenia) and StudentLife (university students) studies. In addition to assessing generalizability, we explored if personalizing models to align mobile sensing data, and oversampling less-represented severe symptoms, improved model performance. Leave-one-subject-out cross-validation (LOSO-CV) results were reported. Two symptoms (sleep quality and stress) had similar question-response structures across studies and were used as outcomes to explore cross-dataset prediction. Models trained with combined data were more likely to be predictive (significant improvement over predicting training data mean) than models trained with single-study data. Expected model performance improved if the distance between training and validation feature distributions decreased using combined versus single-study data. Personalization aligned each LOSO-CV participant with training data, but only improved predicting CrossCheck stress. Oversampling significantly improved severe symptom classification sensitivity and positive predictive value, but decreased model specificity. Taken together, these results show that machine learning models trained on combined longitudinal study data may generalize across heterogeneous datasets. We encourage researchers to disseminate collected de-identified mobile sensing and mental health symptom data, and further standardize data types collected across studies to enable better assessment of model generalizability.
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Affiliation(s)
- Daniel A. Adler
- Department of Information Science, Cornell Tech, New York, New York, United States of America
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States of America
| | - David C. Mohr
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Tanzeem Choudhury
- Department of Information Science, Cornell Tech, New York, New York, United States of America
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7
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Magal N, Rab SL, Goldstein P, Simon L, Jiryis T, Admon R. Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors. CHRONIC STRESS (THOUSAND OAKS, CALIF.) 2022; 6:24705470221100987. [PMID: 35911618 PMCID: PMC9329827 DOI: 10.1177/24705470221100987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 04/29/2022] [Indexed: 12/22/2022]
Abstract
Background Chronic stress is a highly prevalent condition that may stem from different
sources and can substantially impact physiology and behavior, potentially
leading to impaired mental and physical health. Multiple physiological and
behavioral lifestyle features can now be recorded unobtrusively in
daily-life using wearable sensors. The aim of the current study was to
identify a distinct set of physiological and behavioral lifestyle features
that are associated with elevated levels of chronic stress across different
stress sources. Methods For that, 140 healthy female participants completed the Trier inventory for
chronic stress (TICS) before wearing the Fitbit Charge3 sensor for seven
consecutive days while maintaining their daily routine. Physiological and
lifestyle features that were extracted from sensor data, alongside
demographic features, were used to predict high versus low chronic stress
with support vector machine classifiers, applying out-of-sample model
testing. Results The model achieved 79% classification accuracy for chronic stress from a
social tension source. A mixture of physiological (resting heart-rate,
heart-rate circadian characteristics), lifestyle (steps count, sleep onset
and sleep regularity) and non-sensor demographic features (smoking status)
contributed to this classification. Conclusion As wearable technologies continue to rapidly evolve, integration of
daily-life indicators could improve our understanding of chronic stress and
its impact of physiology and behavior.
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Affiliation(s)
- Noa Magal
- School of Psychological Sciences, University of Haifa, Haifa, Israel
| | - Sharona L Rab
- School of Psychological Sciences, University of Haifa, Haifa, Israel
| | | | - Lisa Simon
- School of Psychological Sciences, University of Haifa, Haifa, Israel
| | - Talita Jiryis
- School of Psychological Sciences, University of Haifa, Haifa, Israel
| | - Roee Admon
- School of Psychological Sciences, University of Haifa, Haifa, Israel.,The Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Haifa, Israel
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