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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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Urbanin G, Meira W, Serpa A, Costa DDS, Baldaçara L, da Silva AP, Guatimosim R, Lacerda AM, Oliveira EA, Braule A, Romano-Silva MA, da Silva AG, Malloy-Diniz L, Pappa G, Miranda DM. Social determinants in self-protective behavior: association rule mining study (Preprint). JMIR Public Health Surveill 2021; 8:e34020. [PMID: 35704360 PMCID: PMC9202654 DOI: 10.2196/34020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 03/14/2022] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
Background Human behavior is crucial in health outcomes. Particularly, individual behavior is a determinant of the success of measures to overcome critical conditions, such as a pandemic. In addition to intrinsic public health challenges associated with COVID-19, in many countries, some individuals decided not to get vaccinated, streets were crowded, parties were happening, and businesses struggling to survive were partially open, despite lockdown or stay-at-home instructions. These behaviors contrast with the instructions for potential benefits associated with social distancing, use of masks, and vaccination to manage collective and individual risks. Objective Considering that human behavior is a result of individuals' social and economic conditions, we investigated the social and working characteristics associated with reports of appropriate protective behavior in Brazil. Methods We analyzed data from a large web survey of individuals reporting their behavior during the pandemic. We selected 3 common self-care measures: use of protective masks, distancing by at least 1 m when out of the house, and handwashing or use of alcohol, combined with assessment of the social context of respondents. We measured the frequency of the use of these self-protective measures. Using a frequent pattern–mining perspective, we generated association rules from a set of answers to questions that co-occur with at least a given frequency, identifying the pattern of characteristics of the groups divided according to protective behavior reports. Results The rationale was to identify a pool of working and social characteristics that might have better adhesion to behaviors and self-care measures, showing these are more socially determined than previously thought. We identified common patterns of socioeconomic and working determinants of compliance with protective self-care measures. Data mining showed that social determinants might be important to shape behavior in different stages of the pandemic. Conclusions Identification of context determinants might be helpful to identify unexpected facilitators and constraints to fully follow public policies. The context of diseases contributes to psychological and physical health outcomes, and context understanding might change the approach to a disease. Hidden social determinants might change protective behavior, and social determinants of protective behavior related to COVID-19 are related to work and economic conditions. Trial Registration Not applicable.
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Affiliation(s)
- Gabriel Urbanin
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Wagner Meira
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Alexandre Serpa
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Danielle de Souza Costa
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Leonardo Baldaçara
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Associação Brasileira de Psiquiatria, Brasilia, Brazil
| | - Ana Paula da Silva
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Rafaela Guatimosim
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Anísio Mendes Lacerda
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Andre Braule
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Marco Aurélio Romano-Silva
- Centro de Tecnologia em Medicina Molecular, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Antônio Geraldo da Silva
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Associação Brasileira de Psiquiatria, Brasilia, Brazil
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal
| | - Leandro Malloy-Diniz
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Associação Brasileira de Psiquiatria, Brasilia, Brazil
- Centro de Tecnologia em Medicina Molecular, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Gisele Pappa
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Débora Marques Miranda
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Departamento de Pediatria, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Centro de Tecnologia em Medicina Molecular, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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