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Simon L, Levi S, Shapira S, Admon R. Stress-induced increase in heart-rate during sleep as an indicator of PTSD risk among combat soldiers. Sleep 2024:zsae183. [PMID: 39109929 DOI: 10.1093/sleep/zsae183] [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: 06/17/2024] [Indexed: 09/29/2024] Open
Abstract
STUDY OBJECTIVES Discerning the differential contribution of sleep behavior and sleep physiology to the subsequent development of posttraumatic-stress-disorder (PTSD) symptoms following military operational service among combat soldiers. METHODS Longitudinal design with three measurement time points: during basic training week (T1), during intensive stressed training week (T2), and following military operational service (T3). Participating soldiers were all from the same unit, ensuring equivalent training schedules and stress exposures. During measurement weeks soldiers completed the Depression Anxiety and Stress Scale (DASS) and the PTSD Checklist for DSM-5 (PCL-5). Sleep physiology (sleep heart-rate) and sleep behavior (duration, efficiency) were monitored continuously in natural settings during T1 and T2 weeks using wearable sensors. RESULTS Repeated measures ANOVA revealed a progressive increase in PCL-5 scores from T1 and T2 to T3, suggesting an escalation in PTSD symptom severity following operational service. Hierarchical linear regression analysis uncovered a significant relation between the change in DASS stress scores from T1 to T2 and subsequent PCL-5 scores at T3. Incorporating participants' sleep heart-rate markedly enhanced the predictive accuracy of the model, with increased sleep heart-rate from T1 to T2 emerging as a significant predictor of elevated PTSD symptoms at T3, above and beyond the contribution of DASS stress scores. Sleep behavior did not add to the accuracy of the model. CONCLUSION Findings underscore the critical role of sleep physiology, specifically elevated sleep heart-rate following stressful military training, in indicating subsequent PTSD risk following operational service among combat soldiers. These findings may contribute to PTSD prediction and prevention efforts.
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Affiliation(s)
- Lisa Simon
- School of Psychological Sciences, University of Haifa, Haifa, Israel
| | - Shlomi Levi
- School of Psychological Sciences, University of Haifa, Haifa, Israel
| | - Shachar Shapira
- Sheba Medical Center
- Department of Military Medicine, Faculty of Medicine, Hebrew University of Jerusalem, 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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2
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Fan J, Mei J, Yang Y, Lu J, Wang Q, Yang X, Chen G, Wang R, Han Y, Sheng R, Wang W, Ding F. Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model. Stress Health 2024; 40:e3386. [PMID: 38411360 DOI: 10.1002/smi.3386] [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: 02/06/2023] [Revised: 12/20/2023] [Accepted: 02/14/2024] [Indexed: 02/28/2024]
Abstract
We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self-screening in large populations. Using a Holter electrocardiogram (ECG) and a Huawei smart device, we conducted 24-h dual recordings of 159 medical workers working regular shifts. Based on photoplethysmography (PPG) and accelerometer signals acquired by the Huawei smart device, we sorted episodes of cyclic alternating pattern (CAP; unstable sleep), non-cyclic alternating pattern (NCAP; stable sleep), wakefulness, and rapid eye movement (REM) sleep based on cardiopulmonary coupling (CPC) algorithms. We further calculated the HRV indices during NCAP, CAP and REM sleep episodes using both the Holter ECG and smart-device PPG signals. We later developed a machine learning model to predict stress severity based only on the smart device data obtained from the participants along with a clinical evaluation of emotion and stress conditions. Sleep phasic HRV indices predict individual stress severity with better performance in CAP or REM sleep than in NCAP. Using the smart device data only, the optimal machine learning-based stress prediction model exhibited accuracy of 80.3 %, sensitivity 87.2 %, and 63.9 % for specificity. Sleep phasic heart rate variability can be accurately evaluated using a smart device and subsequently can be used for stress predication.
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Affiliation(s)
- Jingjing Fan
- Department of Cardiology and Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Junhua Mei
- Department of Cardiology and Department of Neurology, The First Hospital of Wuhan City, Wuhan, China
| | - Yuan Yang
- Department of Cardiology and Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiajia Lu
- Department of Cardiology and Department of Neurology, The First Hospital of Wuhan City, Wuhan, China
| | - Quan Wang
- Department of Cardiology and Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoyun Yang
- Department of Cardiology and Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guohua Chen
- Department of Cardiology and Department of Neurology, The First Hospital of Wuhan City, Wuhan, China
| | - Runsen Wang
- Huawei Technologies Co., Ltd., Shenzhen, China
| | - Yujia Han
- Huawei Technologies Co., Ltd., Shenzhen, China
| | - Rong Sheng
- Huawei Technologies Co., Ltd., Shenzhen, China
| | - Wei Wang
- Department of Cardiology and Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fengfei Ding
- Department of Pharmacology, Shanghai Medical College, Fudan University, Shanghai, China
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3
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Yu H, Kotlyar M, Thuras P, Dufresne S, Pakhomov SV. Towards Predicting Smoking Events for Just-in-time Interventions. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2024; 2024:468-477. [PMID: 38827079 PMCID: PMC11141818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Consumer-grade heart rate (HR) sensors are widely used for tracking physical and mental health status. We explore the feasibility of using Polar H10 electrocardiogram (ECG) sensor to detect and predict cigarette smoking events in naturalistic settings with several machine learning approaches. We have collected and analyzed data for 28 participants observed over a two-week period. We found that using bidirectional long short-term memory (BiLSTM) with ECG-derived and GPS location input features yielded the highest mean accuracy of 69% for smoking event detection. For predicting smoking events, the highest accuracy of 67% was achieved using the fine-tuned LSTM approach. We also found a significant correlation between accuracy and the number of smoking events available from each participant. Our findings indicate that both detection and prediction of smoking events are feasible but require an individualized approach to training the models, particularly for prediction.
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Affiliation(s)
- Hang Yu
- University of Minnesota, Minneapolis, MN, United States
| | | | - Paul Thuras
- University of Minnesota, Minneapolis, MN, United States
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4
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Diaconașu DE, Stoleriu I, Câmpanu IA, Andrei AM, Boncu Ș, Honceriu C, Mocanu V, Juravle G. Predictors of sustained physical activity: behaviour, bodily health, and the living environment. Front Physiol 2024; 14:1213075. [PMID: 38260099 PMCID: PMC10800461 DOI: 10.3389/fphys.2023.1213075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 12/08/2023] [Indexed: 01/24/2024] Open
Abstract
This study examined the determinants of sustained physical activity. Eighty-four participants undertook a 7-weeks walking regime (i.e., a 1-h biometrically-monitored walk, at least 5 days/week), with bioelectrical impedance (BIA) and total cholesterol capillary blood measurements performed before and after programme. To investigate behavioural habit formation, 7 weeks after walking termination, all participants were interviewed and (health) re-tested. Data were modelled with an artificial neural network (ANN) cascading algorithm. Our results highlight the successful prediction of continued physical activity by considering one's physical fitness state, the environmental living context, and risk for cardiovascular disease. Importantly, those artificial neural network models also taking body mass index (BMI) and blood cholesterol as predictors excel at predicting walking continuation (i.e., predictions with 93% predictability). These results are first to highlight the type and importance of available physiological drivers in maintaining a sustained physical activity regime such as walking. They are discussed within the framework of habit formation and the nowadays health and/or wellbeing focus.
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Affiliation(s)
- Delia Elena Diaconașu
- Sensorimotor Dynamics Laboratory, Faculty of Psychology and Educational Sciences, Alexandru Ioan Cuza University, Iasi, Romania
- Department of Social Sciences and Humanities, Institute of Interdisciplinary Research, Alexandru Ioan Cuza University, Iasi, Romania
| | - Iulian Stoleriu
- Faculty of Mathematics, Alexandru Ioan Cuza University, Iasi, Romania
| | - Ioana Andreea Câmpanu
- Sensorimotor Dynamics Laboratory, Faculty of Psychology and Educational Sciences, Alexandru Ioan Cuza University, Iasi, Romania
| | - Ana-Maria Andrei
- Sensorimotor Dynamics Laboratory, Faculty of Psychology and Educational Sciences, Alexandru Ioan Cuza University, Iasi, Romania
- Faculty of Educational Sciences, Stefan cel Mare University, Suceava, Romania
| | - Ștefan Boncu
- Faculty of Psychology and Educational Sciences, Alexandru Ioan Cuza University, Iasi, Romania
| | - Cezar Honceriu
- Faculty of Physical Education and Sports, Alexandru Ioan Cuza University, Iasi, Romania
| | - Veronica Mocanu
- Department of Morpho-Functional Sciences 2—Pathophysiology, Grigore T. Popa Medical University of Iasi, Iasi, Romania
| | - Georgiana Juravle
- Sensorimotor Dynamics Laboratory, Faculty of Psychology and Educational Sciences, Alexandru Ioan Cuza University, Iasi, Romania
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5
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Aguado-García A, Arroyo-Valerio A, Escobedo G, Bueno-Hernández N, Olguín-Rodríguez PV, Müller MF, Carrillo-Ruiz JD, Martínez-Mekler G. Opportune warning of COVID-19 in a Mexican health care worker cohort: Discrete beta distribution entropy of smartwatch physiological records. Biomed Signal Process Control 2023; 84:104975. [PMID: 37125410 PMCID: PMC10121132 DOI: 10.1016/j.bspc.2023.104975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 03/31/2023] [Accepted: 04/15/2023] [Indexed: 05/02/2023]
Abstract
We present a statistical study of heart rate, step cadence, and sleep stage registers of health care workers in the Hospital General de México "Dr. Eduardo Liceaga" (HGM), monitored continuously and non-invasively during the COVID-19 contingency from May to October 2020, using the Fitbit Charge 3® Smartwatch device. The HGM-COVID cohort consisted of 115 participants assigned to areas of COVID-19 exposure. We introduce a novel biomarker for an opportune signal for the likelihood of SARS-CoV-2 infection based on the Shannon Entropy of the Discrete Generalized Beta Distribution fit of rank ordered smartwatch registers. Our statistical test indicated infection for 94% of patients confirmed by positive polymer chain reaction (PCR+) test, 47% before the test, and 47% in coincidence. These results required innovative data preprocessing for the definition of a new biomarker index. The statistical method parameters are data-driven, confidence estimates were calibrated based on sensitivity tests using appropriately derived surrogate data as a benchmark. Our surrogate tests can also provide a benchmark for comparing results from other anomaly detection methods (ADMs). Biomarker comparison of the negative Immunoglobulin G Antibody (IgG-) subgroup with the PCR+ subgroup showed a statistically significant difference (p < 0.01, effect size = 1.44). The distribution of the uninfected population had a lower median and less dispersion than the PCR+ population. A retrospective study of our results confirmed that the biomarker index provides an early warning of the likelihood of COVID-19, even several days before the onset of symptoms or the PCR+ test request. The method can be calibrated for the analysis of different SARS-CoV-2 strains, the effect of vaccination, and previous infections. Furthermore, our biomarker screening could be implemented to provide general health profiles for other population sectors based on physiological signals from smartwatch wearable devices.
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Affiliation(s)
- Alejandro Aguado-García
- Dirección de Investigación, Hospital General de México, Ciudad de México 06720, Mexico
- Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca 62210, Mexico
| | | | - Galileo Escobedo
- Dirección de Investigación, Hospital General de México, Ciudad de México 06720, Mexico
| | | | - P V Olguín-Rodríguez
- Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Mexico
| | - Markus F Müller
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, Mexico
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
- Centro Internacional de Ciencias A.C., Cuernavaca 62131, Mexico
| | - José Damián Carrillo-Ruiz
- Dirección de Investigación, Hospital General de México, Ciudad de México 06720, Mexico
- Universidad Anáhuac, Estado de México, 52786, Mexico
| | - Gustavo Martínez-Mekler
- Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca 62210, Mexico
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
- Centro Internacional de Ciencias A.C., Cuernavaca 62131, Mexico
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6
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Billeci L, Sanmartin C, Tonacci A, Taglieri I, Bachi L, Ferroni G, Braceschi GP, Odello L, Venturi F. Wearable Sensors to Evaluate Autonomic Response to Olfactory Stimulation: The Influence of Short, Intensive Sensory Training. BIOSENSORS 2023; 13:bios13040478. [PMID: 37185553 PMCID: PMC10136665 DOI: 10.3390/bios13040478] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 03/27/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023]
Abstract
In the last few decades, while the sensory evaluation of edible products has been leveraged to make strategic decisions about many domains, the traditional descriptive analysis performed by a skilled sensory panel has been seen to be too complex and time-consuming for the industry needs, making it largely unsustainable in most cases. In this context, the study of the effectiveness of different methods for sensory training on panel performances represents a new trend in research activity. With this purpose, wearable sensors are applied to study physiological signals (ECG and skin conductance) concerned with the emotions in a cohort of volunteers undergoing a short, two-day (16 h) sensory training period related to wine tasting. The results were compared with a previous study based on a conventional three-month (65 h) period of sensory training. According to what was previously reported for long panel training, it was seen that even short, intensive sensory training modulated the ANS activity toward a less sympathetically mediated response as soon as odorous compounds become familiar. A large-scale application of shorter formative courses in this domain appears possible without reducing the effectiveness of the training, thus leading to money saving for academia and scientific societies, and challenging dropout rates that might affect longer courses.
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Affiliation(s)
- Lucia Billeci
- Institute of Clinical Physiology, National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy
| | - Chiara Sanmartin
- Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 80, 56124 Pisa, Italy
- Interdepartmental Research Centre "Nutraceuticals and Food for Health", University of Pisa, Via del Borghetto 80, 56124 Pisa, Italy
| | - Alessandro Tonacci
- Institute of Clinical Physiology, National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy
| | - Isabella Taglieri
- Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 80, 56124 Pisa, Italy
| | - Lorenzo Bachi
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, 56127 Pisa, Italy
| | - Giuseppe Ferroni
- Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 80, 56124 Pisa, Italy
| | | | - Luigi Odello
- Centro Studi Assaggiatori Società Cooperativa, Galleria V. Veneto, 9, 25128 Brescia, Italy
| | - Francesca Venturi
- Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 80, 56124 Pisa, Italy
- Interdepartmental Research Centre "Nutraceuticals and Food for Health", University of Pisa, Via del Borghetto 80, 56124 Pisa, Italy
- Interdepartmental Centre for Complex Systems Studies, University of Pisa, Largo Bruno Pontecorvo, 2, 56126 Pisa, Italy
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7
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Yu H, Kotlyar M, Dufresne S, Thuras P, Pakhomov S. Feasibility of Using an Armband Optical Heart Rate Sensor in Naturalistic Environment. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2023; 28:43-54. [PMID: 36540963 PMCID: PMC9830591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Consumer-grade heart rate (HR) sensors including chest straps, wrist-worn watches and rings have become very popular in recent years for tracking individual physiological state, training for sports and even measuring stress levels and emotional changes. While the majority of these consumer sensors are not medical devices, they can still offer insights for consumers and researchers if used correctly taking into account their limitations. Multiple previous studies have been done using a large variety of consumer sensors including Polar® devices, Apple® watches, and Fitbit® wrist bands. The vast majority of prior studies have been done in laboratory settings where collecting data is relatively straightforward. However, using consumer sensors in naturalistic settings that present significant challenges, including noise artefacts and missing data, has not been as extensively investigated. Additionally, the majority of prior studies focused on wrist-worn optical HR sensors. Arm-worn sensors have not been extensively investigated either. In the present study, we validate HR measurements obtained with an arm-worn optical sensor (Polar OH1) against those obtained with a chest-strap electrical sensor (Polar H10) from 16 participants over a 2-week study period in naturalistic settings. We also investigated the impact of physical activity measured with 3-D accelerometers embedded in the H10 chest strap and OH1 armband sensors on the agreement between the two sensors. Overall, we find that the arm-worn optical Polar OH1 sensor provides a good estimate of HR (Pearson r = 0.90, p <0.01). Filtering the signal that corresponds to physical activity further improves the HR estimates but only slightly (Pearson r = 0.91, p <0.01). Based on these preliminary findings, we conclude that the arm-worn Polar OH1 sensor provides usable HR measurements in daily living conditions, with some caveats discussed in the paper.
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Affiliation(s)
- Hang Yu
- University of Minnesota, Minneapolis, MN 55108, USA,
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8
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Mavragani A, Khau M, Lavoie-Hudon L, Vachon F, Drapeau V, Tremblay S. Comparing a Fitbit Wearable to an Electrocardiogram Gold Standard as a Measure of Heart Rate Under Psychological Stress: A Validation Study. JMIR Form Res 2022; 6:e37885. [PMID: 36542432 PMCID: PMC9813817 DOI: 10.2196/37885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 10/28/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Wearable devices collect physiological and behavioral data that have the potential to identify individuals at risk of declining mental health and well-being. Past research has mainly focused on assessing the accuracy and the agreement of heart rate (HR) measurement of wearables under different physical exercise conditions. However, the capacity of wearables to sense physiological changes, assessed by increasing HR, caused by a stressful event has not been thoroughly studied. OBJECTIVE This study followed 3 objectives: (1) to test the ability of a wearable device (Fitbit Versa 2) to sense an increase in HR upon induction of psychological stress in the laboratory; (2) to assess the accuracy of the wearable device to capture short-term HR variations caused by psychological stress compared to a gold-standard electrocardiogram (ECG) measure (Biopac); and (3) to quantify the degree of agreement between the wearable device and the gold-standard ECG measure across different experimental conditions. METHODS Participants underwent the Trier Social Stress Test protocol, which consists of an oral phase, an arithmetic stress phase, an anticipation phase, and 2 relaxation phases (at the beginning and the end). During the stress protocol, the participants wore a Fitbit Versa 2 and were also connected to a Biopac. A mixed-effect modeling approach was used (1) to assess the effect of experimental conditions on HR, (2) to estimate several metrics of accuracy, and (3) to assess the agreement: the Bland-Altman limits of agreement (LoA), the concordance correlation coefficient, the coverage probability, the total deviation index, and the coefficient of an individual agreement. Mean absolute error and mean absolute percent error were calculated as accuracy indices. RESULTS A total of 34 university students were recruited for this study (64% of participants were female with a mean age of 26.8 years, SD 8.3). Overall, the results showed significant HR variations across experimental phases. Post hoc tests revealed significant pairwise differences for all phases. Accuracy analyses revealed acceptable accuracy according to the analyzed metrics of accuracy for the Fitbit Versa 2 to capture the short-term variations in psychological stress levels. However, poor indices of agreement between the Fitbit Versa 2 and the Biopac were found. CONCLUSIONS Overall, the results support the use of the Fitbit Versa 2 to capture short-term stress variations. The Fitbit device showed acceptable levels of accuracy but poor agreement with an ECG gold standard. Greater inaccuracy and smaller agreement were found for stressful experimental conditions that induced a higher HR. Fitbit devices can be used in research to measure HR variations caused by stress, although they cannot replace an ECG instrument when precision is of utmost importance.
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Affiliation(s)
| | - Michelle Khau
- Faculty of Social Sciences, Laval University, Québec, QC, Canada
| | | | - François Vachon
- School of Psychology, Faculty of Social Sciences, Laval University, Québec, QC, Canada
| | - Vicky Drapeau
- Quebec Heart and Lung Institute Research Center, Department of Physical Education, Faculty of Educational Sciences, Centre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Laval University, Québec, QC, Canada
| | - Sébastien Tremblay
- School of Psychology, Faculty of Social Sciences, Laval University, Québec, QC, Canada
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9
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Manning JB, Blandford A, Edbrooke-Childs J. Digital Companion Choice to Support Teachers' Stress Self-management: Systematic Approach Through Taxonomy Creation. JMIR Form Res 2022; 6:e32312. [PMID: 35171106 PMCID: PMC8892277 DOI: 10.2196/32312] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 11/02/2021] [Accepted: 11/02/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND There are thousands of digital companions designed for emotional well-being and stress, including websites, wearables, and smartphone apps. Although public evaluation frameworks and ratings exist, they do not facilitate digital companion choice based on contextual or individual information, such as occupation or personal management strategies. OBJECTIVE The aim of this study is to establish a process for creating a taxonomy to support systematic choice of digital companions for teachers' stress self-management. METHODS We used a 4-step study design. In step 1, we identified the dimension of stress self-management and strategic classifications. In step 2, we identified the dimension of the digital techniques and conceptual descriptions. In step 3, we created 6 criteria for the inclusion of digital companions. In step 4, we used the taxonomy framework created by steps 1 and 2 and populated it with digital companions for stress self-management, as identified in step 3. RESULTS First, in the dimension of stress self-management, we identified four classes of strategies: educational, physiological, cognitive, and social. Second, in the digital techniques dimension, we derived four conceptual descriptions for the digital companions' mechanisms of action: fostering reflection, suggesting treatment, peer-to-peer support, and entertainment. Third, we created six criteria for digital companion inclusion in the taxonomy: suitability, availability, evaluation, security, validity, and cost. Using the taxonomy framework and criteria, we populated it with digital companions for stress management ahead of presentation to teachers in a stress study workshop. CONCLUSIONS The elements of our approach can be generalized as principles for the creation of taxonomies for other occupations or conditions. Taxonomies such as this could be a valuable resource for individuals to understand which digital companion could be of help in their personal context.
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Affiliation(s)
- Julia B Manning
- Interaction Centre, Department of Computer Science, University College London, London, United Kingdom
| | - Ann Blandford
- Institute of Healthcare Engineering, University College London, London, United Kingdom
| | - Julian Edbrooke-Childs
- Evidence-based Practice Unit, University College London and Anna Freud Centre, London, United Kingdom
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10
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Huhn S, Axt M, Gunga HC, Maggioni MA, Munga S, Obor D, Sié A, Boudo V, Bunker A, Sauerborn R, Bärnighausen T, Barteit S. The Impact of Wearable Technologies in Health Research: Scoping Review. JMIR Mhealth Uhealth 2022; 10:e34384. [PMID: 35076409 PMCID: PMC8826148 DOI: 10.2196/34384] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/23/2021] [Accepted: 12/17/2021] [Indexed: 12/23/2022] Open
Abstract
Background Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research. Objective In this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research. Methods We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >€500 (US $570), or obtrusive smart clothing. Results We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <€200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations—wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%). Conclusions Wearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.
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Affiliation(s)
- Sophie Huhn
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Miriam Axt
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Hanns-Christian Gunga
- Charité - Universitätsmedizin Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environment, Berlin, Germany
| | - Martina Anna Maggioni
- Charité - Universitätsmedizin Berlin, Institute of Physiology, Center for Space Medicine and Extreme Environment, Berlin, Germany.,Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milano, Italy
| | | | - David Obor
- Kenya Medical Research Institute, Kisumu, Kenya
| | - Ali Sié
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany.,Centre de Recherche en Santé Nouna, Nouna, Burkina Faso
| | | | - Aditi Bunker
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Rainer Sauerborn
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Till Bärnighausen
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany.,Harvard Center for Population and Development Studies, Cambridge, MA, United States.,Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - Sandra Barteit
- Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
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11
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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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12
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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: 51] [Impact Index Per Article: 17.0] [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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