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Fontes L, Machado P, Vinkemeier D, Yahaya S, Bird JJ, Ihianle IK. Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques. SENSORS (BASEL, SWITZERLAND) 2024; 24:1096. [PMID: 38400254 PMCID: PMC10892284 DOI: 10.3390/s24041096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 01/29/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024]
Abstract
Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This paper addresses the critical need for accurate stress detection, emphasising its far-reaching effects on health and social dynamics. Focusing on remote stress monitoring, it proposes an efficient deep learning approach for stress detection from facial videos. In contrast to the research on wearable devices, this paper proposes novel Hybrid Deep Learning (DL) networks for stress detection based on remote photoplethysmography (rPPG), employing (Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), 1D Convolutional Neural Network (1D-CNN)) models with hyperparameter optimisation and augmentation techniques to enhance performance. The proposed approach yields a substantial improvement in accuracy and efficiency in stress detection, achieving up to 95.83% accuracy with the UBFC-Phys dataset while maintaining excellent computational efficiency. The experimental results demonstrate the effectiveness of the proposed Hybrid DL models for rPPG-based-stress detection.
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Affiliation(s)
| | | | | | | | | | - Isibor Kennedy Ihianle
- Department of Computer Science, Nottingham Trent University, Nottingham NG1 4FQ, UK; (L.F.); (P.M.); (D.V.); (S.Y.); (J.J.B.)
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Jørgensen T, Dantoft TM, Petersen MW, Gormsen L, Winter-Jensen M, Fink P, Linneberg A, Benros ME, Eplov LF, Bjerregaard AA, Schovsbo SU, Brinth LS. Is reduced heart rate variability associated with functional somatic disorders? A cross-sectional population-based study; DanFunD. BMJ Open 2024; 14:e073909. [PMID: 38326244 PMCID: PMC10860071 DOI: 10.1136/bmjopen-2023-073909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 01/18/2024] [Indexed: 02/09/2024] Open
Abstract
OBJECTIVES It has been hypothesised that functional somatic disorders (FSD) could be initiated by sympathetic predominance in the autonomic nervous system as measured by low heart rate variability (HRV). Earlier studies on the association between HRV and FSD are small case-control studies hampered by selection bias and do not consider the great overlap between the various FSDs. The aim of the present study is to assess any associations between HRV and various FSDs and whether chronic stress confounds such an association. DESIGN A cross-sectional general population-based study. SETTING The Danish Study of Functional Somatic Disorders conducted 2013-2015 in 10 municipalities in the western part of Greater Copenhagen, Denmark. PARTICIPANTS A total of 6891 men and women aged 18-72 years were included in the analyses after exclusion of 602 persons with missing HRV data. Various delimitations of FSD (chronic fatigue, chronic widespread pain, irritable bowel and bodily distress syndrome) were identified by validated questionnaires and diagnostic interviews. HRV parameters in time and frequency domains were calculated from successive beat-to-beat heart rate (HR) data using the 'E-motion' HR monitor device during 7 min of supine rest. Chronic stress was assessed by Cohen's self-perceived stress scale. OUTCOME MEASURES Logistic regression analyses were used to calculate possible associations between the various delimitations of FSD and HRV adjusting for chronic stress. RESULTS Persons with FSD had a slightly higher mean HR and lower HRV as measured by time domain parameters, whereas associations with frequency domain parameters were not consistent. Adjusting for chronic stress attenuated associations slightly. CONCLUSION The study supports a sympathetic predominance in persons with FSD, which could not be entirely explained by chronic stress. However, it is not possible to conclude whether the association is a causal factor to or a consequence of FSD.
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Affiliation(s)
- Torben Jørgensen
- Center for Clinical Research and Prevention, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Frederiksberg, Denmark
- Department of Public Health, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Meinertz Dantoft
- Center for Clinical Research and Prevention, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Frederiksberg, Denmark
| | - Marie Weinreich Petersen
- Research Clinic for Functional Disorders and Psychosomatics, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, University of Aarhus, Aarhus, Denmark
| | - Lise Gormsen
- Research Clinic for Functional Disorders and Psychosomatics, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, University of Aarhus, Aarhus, Denmark
| | - Matilde Winter-Jensen
- Center for Clinical Research and Prevention, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Frederiksberg, Denmark
| | - Per Fink
- Research Clinic for Functional Disorders and Psychosomatics, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, University of Aarhus, Aarhus, Denmark
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Michael Eriksen Benros
- Copenhagen Research Centre for Mental Health - CORE, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lene Falgaard Eplov
- Copenhagen Research Centre for Mental Health - CORE, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Anne Ahrendt Bjerregaard
- Center for Clinical Research and Prevention, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Frederiksberg, Denmark
| | - Signe Ulfbeck Schovsbo
- Center for Clinical Research and Prevention, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Frederiksberg, Denmark
| | - Louise Schouborg Brinth
- Department of Imaging and Radiology, Copenhagen University Hospital - North Zealand, Helsingør, Denmark
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González Ramírez ML, García Vázquez JP, Rodríguez MD, Padilla-López LA, Galindo-Aldana GM, Cuevas-González D. Wearables for Stress Management: A Scoping Review. Healthcare (Basel) 2023; 11:2369. [PMID: 37685403 PMCID: PMC10486660 DOI: 10.3390/healthcare11172369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 08/05/2023] [Accepted: 08/18/2023] [Indexed: 09/10/2023] Open
Abstract
In recent years, wearable devices have been increasingly used to monitor people's health. This has helped healthcare professionals provide timely interventions to support their patients. In this study, we investigated how wearables help people manage stress. We conducted a scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) standard to address this question. We searched studies in Scopus, IEEE Explore, and Pubmed databases. We included studies reporting user evaluations of wearable-based strategies, reporting their impact on health or usability outcomes. A total of 6259 studies were identified, of which 40 met the inclusion criteria. Based on our findings, we identified that 21 studies report using commercial wearable devices; the most common are smartwatches and smart bands. Thirty-one studies report significant stress reduction using different interventions and interaction modalities. Finally, we identified that the interventions are designed with the following aims: (1) to self-regulate during stress episodes, (2) to support self-regulation therapies for long-term goals, and (3) to provide stress awareness for prevention, consisting of people's ability to recall, recognize and understand their stress.
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Affiliation(s)
| | | | - Marcela D. Rodríguez
- Facultad de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, BC, Mexico;
| | - Luis Alfredo Padilla-López
- Laboratorio de Psicofisiología, Facultad de Ciencias Humanas, Universidad Autónoma de Baja California, Mexicali 21720, BC, Mexico;
| | - Gilberto Manuel Galindo-Aldana
- Laboratorio de Neurociencia y Cognición, Facultad de Ingeniería y Negocios, Universidad Autonónoma de Baja California, Mexicali 21725, BC, Mexico;
| | - Daniel Cuevas-González
- Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21280, BC, Mexico;
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Robinson T, Condell J, Ramsey E, Leavey G. Self-Management of Subclinical Common Mental Health Disorders (Anxiety, Depression and Sleep Disorders) Using Wearable Devices. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20032636. [PMID: 36768002 PMCID: PMC9916237 DOI: 10.3390/ijerph20032636] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/21/2023] [Accepted: 01/28/2023] [Indexed: 05/05/2023]
Abstract
RATIONALE Common mental health disorders (CMD) (anxiety, depression, and sleep disorders) are among the leading causes of disease burden globally. The economic burden associated with such disorders is estimated at $2.4 trillion as of 2010 and is expected to reach $16 trillion by 2030. The UK has observed a 21-fold increase in the economic burden associated with CMD over the past decade. The recent COVID-19 pandemic was a catalyst for adopting technologies for mental health support and services, thereby increasing the reception of personal health data and wearables. Wearables hold considerable promise to empower users concerning the management of subclinical common mental health disorders. However, there are significant challenges to adopting wearables as a tool for the self-management of the symptoms of common mental health disorders. AIMS This review aims to evaluate the potential utility of wearables for the self-management of sub-clinical anxiety and depressive mental health disorders. Furthermore, we seek to understand the potential of wearables to reduce the burden on the healthcare system. METHODOLOGY a systematic review of research papers was conducted, focusing on wearable devices for the self-management of CMD released between 2018-2022, focusing primarily on mental health management using technology. RESULTS We screened 445 papers and analysed the reports from 12 wearable devices concerning their device type, year, biometrics used, and machine learning algorithm deployed. Electrodermal activity (EDA/GSR/SC/Skin Temperature), physical activity, and heart rate (HR) are the most common biometrics with nine, six and six reference counts, respectively. Additionally, while smartwatches have greater penetration and integration within the marketplace, fitness trackers have the most significant public value benefit of £513.9 M, likely due to greater retention.
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Affiliation(s)
- Tony Robinson
- School of Computing, Engineering, and Intelligent Systems, Ulster University, Magee Campus, Derry/Londonderry BT48 7JL, UK
- Correspondence:
| | - Joan Condell
- School of Computing, Engineering, and Intelligent Systems, Ulster University, Magee Campus, Derry/Londonderry BT48 7JL, UK
| | - Elaine Ramsey
- Department of Global Business and Enterprise, Ulster University, Magee Campus, Derry/Londonderry BT48 7JL, UK
| | - Gerard Leavey
- The Bamford Centre for Mental Health and Wellbeing, School of Psychology, Ulster University, Coleraine Campus, Cromore Rd., Coleraine BT52 1SA, UK
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Clay I, Cormack F, Fedor S, Foschini L, Gentile G, van Hoof C, Kumar P, Lipsmeier F, Sano A, Smarr B, Vandendriessche B, De Luca V. Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint. J Med Internet Res 2022; 24:e35951. [PMID: 35617003 PMCID: PMC9185357 DOI: 10.2196/35951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/14/2022] [Accepted: 04/25/2022] [Indexed: 11/18/2022] Open
Abstract
The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.
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Affiliation(s)
- Ieuan Clay
- Digital Medicine Society, Boston, MA, United States
| | | | | | | | | | | | | | | | - Akane Sano
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States
| | - Benjamin Smarr
- Department of Bioengineering and Halicioglu Data Science Institute, University of California, San Diego, San Diego, CA, United States
| | | | - Valeria De Luca
- Novartis Institutes for Biomedical Research, Basel, Switzerland
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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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Chaudhry B, Islam A, Matthieu M. Towards Designs of Workplace Stress Management Mobile Apps for Frontline Health Workers during COVID-19 and Beyond: A Qualitative Study. JMIR Form Res 2021; 6:e30640. [PMID: 34806985 PMCID: PMC8789255 DOI: 10.2196/30640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 09/15/2021] [Accepted: 09/27/2021] [Indexed: 01/30/2023] Open
Abstract
Background In recent years, mobile apps have been developed to prevent burnout, promote anxiety management, and provide health education to workers in various workplace settings. However, there remains a paucity of such apps for frontline health workers (FHWs), even though FHWs are the most susceptible to stress due to the nature of their jobs. Objective The goal of this study was to provide suggestions for designing stress management apps to address workplace stressors of FHWs based on the understanding of their needs from FHWs’ own perspectives and theories of stress. Methods A mixed methods qualitative study was conducted. Using a variety of search strings, we first collected 41 relevant web-based news articles published between December 2019 and May 2020 through the Google search engine. We then conducted a cross-sectional survey with 20 FHWs. Two researchers independently conducted qualitative analysis of all the collected data using a deductive followed by an inductive approach. Results Prevailing uncertainty and fear of contracting the infection was causing stress among FHWs. Moral injury associated with seeing patients die from lack of care and lack of experience in handling various circumstances were other sources of stress. FHWs mentioned 4 coping strategies. Quick coping strategies such as walking away from stressful situations, entertainment, and exercise were the most common ways to mitigate the impact of stress at work. Peer support and counseling services were other popular methods. Building resilience and driving oneself forward using internal motivation were also meaningful ways of overcoming stressful situations. Time constraints and limited management support prevented FHWs from engaging in stress management activities. Conclusions Our study identified stressors, coping strategies, and challenges with applying coping strategies that can guide the design of stress management apps for FHWs. Given that the pandemic is ongoing and health care crises continue, FHWs remain a vulnerable population in need of attention.
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Affiliation(s)
- Beenish Chaudhry
- University of Louisiana at Lafayette, 104 E. University Circle, Lafayette, US
| | - Ashraful Islam
- University of Louisiana at Lafayette, 104 E. University Circle, Lafayette, US
| | - Monica Matthieu
- Saint Louis University, 3500 Lindell Blvd., Tegeler Hall, 3rd floor, Saint Louis, US
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Hickey BA, Chalmers T, Newton P, Lin CT, Sibbritt D, McLachlan CS, Clifton-Bligh R, Morley J, Lal S. Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review. SENSORS 2021; 21:s21103461. [PMID: 34065620 PMCID: PMC8156923 DOI: 10.3390/s21103461] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 12/21/2022]
Abstract
Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.
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Affiliation(s)
- Blake Anthony Hickey
- Neuroscience Research Unit, School of Life Sciences, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia; (B.A.H.); (T.C.)
| | - Taryn Chalmers
- Neuroscience Research Unit, School of Life Sciences, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia; (B.A.H.); (T.C.)
| | - Phillip Newton
- School of Nursing and Midwifery, Western Sydney University, Penrith, NSW 2747, Australia;
| | - Chin-Teng Lin
- Australian AI Institute, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia;
| | - David Sibbritt
- School of Public Health, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia;
| | - Craig S. McLachlan
- Centre for Healthy Futures, Torrens University, Sydney, NSW 2009, Australia;
| | - Roderick Clifton-Bligh
- Kolling Institute for Medical Research, Royal North Shore Hospital, St Leonards, NSW 2064, Australia;
| | - John Morley
- School of Medicine, Western Sydney University, Penrith, NSW 2747, Australia;
| | - Sara Lal
- Neuroscience Research Unit, School of Life Sciences, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia; (B.A.H.); (T.C.)
- Correspondence: ; Tel.: +612-9514-1592
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