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Chan AHY, Te Ao B, Baggott C, Cavadino A, Eikholt AA, Harwood M, Hikaka J, Gibbs D, Hudson M, Mirza F, Naeem MA, Semprini R, Chang CL, Tsang KCH, Shah SA, Jeremiah A, Abeysinghe BN, Roy R, Wall C, Wood L, Dalziel S, Pinnock H, van Boven JFM, Roop P, Harrison J. DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol. BMJ Open Respir Res 2024; 11:e002275. [PMID: 38777583 PMCID: PMC11116853 DOI: 10.1136/bmjresp-2023-002275] [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: 12/22/2023] [Accepted: 04/11/2024] [Indexed: 05/25/2024] Open
Abstract
INTRODUCTION Asthma attacks are a leading cause of morbidity and mortality but are preventable in most if detected and treated promptly. However, the changes that occur physiologically and behaviourally in the days and weeks preceding an attack are not always recognised, highlighting a potential role for technology. The aim of this study 'DIGIPREDICT' is to identify early digital markers of asthma attacks using sensors embedded in smart devices including watches and inhalers, and leverage health and environmental datasets and artificial intelligence, to develop a risk prediction model to provide an early, personalised warning of asthma attacks. METHODS AND ANALYSIS A prospective sample of 300 people, 12 years or older, with a history of a moderate or severe asthma attack in the last 12 months will be recruited in New Zealand. Each participant will be given a smart watch (to assess physiological measures such as heart and respiratory rate), peak flow meter, smart inhaler (to assess adherence and inhalation) and a cough monitoring application to use regularly over 6 months with fortnightly questionnaires on asthma control and well-being. Data on sociodemographics, asthma control, lung function, dietary intake, medical history and technology acceptance will be collected at baseline and at 6 months. Asthma attacks will be measured by self-report and confirmed with clinical records. The collected data, along with environmental data on weather and air quality, will be analysed using machine learning to develop a risk prediction model for asthma attacks. ETHICS AND DISSEMINATION Ethical approval has been obtained from the New Zealand Health and Disability Ethics Committee (2023 FULL 13541). Enrolment began in August 2023. Results will be presented at local, national and international meetings, including dissemination via community groups, and submission for publication to peer-reviewed journals. TRIAL REGISTRATION NUMBER Australian New Zealand Clinical Trials Registry ACTRN12623000764639; Australian New Zealand Clinical Trials Registry.
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Affiliation(s)
- Amy Hai Yan Chan
- School of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand
| | - Braden Te Ao
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Christina Baggott
- Department of Respiratory Medicine and Respiratory research unit, Waikato Hospital, Hamilton, New Zealand
| | - Alana Cavadino
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Amber A Eikholt
- University Medical Centre Groningen, Groningen Research Institute for Asthma and COPD, Groningen, Netherlands
- Medication Adherence Expertise Center of the northern Netherlands (MAECON), Groningen, Netherlands
| | - Matire Harwood
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Joanna Hikaka
- Te Kupenga Hauora Māori, University of Auckland, Auckland, New Zealand
| | - Dianna Gibbs
- Pinnacle Midlands Health Network, Hamilton, New Zealand
| | - Mariana Hudson
- School of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand
| | - Farhaan Mirza
- Department of IT and Software Engineering, Auckland University of Technology, Auckland, New Zealand
| | - Muhammed Asif Naeem
- Department of IT and Software Engineering, Auckland University of Technology, Auckland, New Zealand
- National University of Computer and Emerging Sciences, Islamabad, Pakistan
| | - Ruth Semprini
- Medical Research Institute of New Zealand, Wellington, New Zealand
| | - Catherina L Chang
- Department of Respiratory Medicine and Respiratory research unit, Waikato Hospital, Hamilton, New Zealand
| | - Kevin C H Tsang
- University College London, London, UK
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK
| | - Syed Ahmar Shah
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK
| | - Aron Jeremiah
- Department of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand
| | - Binu Nisal Abeysinghe
- Department of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand
| | - Rajshri Roy
- Department of Nutrition and Dietetics, University of Auckland, Auckland, New Zealand
| | - Clare Wall
- Department of Nutrition and Dietetics, University of Auckland, Auckland, New Zealand
| | - Lisa Wood
- Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, New South Wales, Australia
| | - Stuart Dalziel
- Children's Emergency Department, Starship Children's Hospital, Auckland, New Zealand
| | - Hilary Pinnock
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK
| | - Job F M van Boven
- University Medical Centre Groningen, Groningen Research Institute for Asthma and COPD, Groningen, Netherlands
- Medication Adherence Expertise Center of the northern Netherlands (MAECON), Groningen, Netherlands
| | - Partha Roop
- Department of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand
| | - Jeff Harrison
- School of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand
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Fanaroff AC, Patel MS, Chokshi N, Coratti S, Farraday D, Norton L, Rareshide C, Zhu J, Klaiman T, Szymczak JE, Russell LB, Small DS, Volpp KGM. Effect of Gamification, Financial Incentives, or Both to Increase Physical Activity Among Patients at High Risk of Cardiovascular Events: The BE ACTIVE Randomized Controlled Trial. Circulation 2024; 149:1639-1649. [PMID: 38583084 DOI: 10.1161/circulationaha.124.069531] [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: 03/04/2024] [Accepted: 03/28/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Physical activity is associated with a lower risk of major adverse cardiovascular events, but few individuals achieve guideline-recommended levels of physical activity. Strategies informed by behavioral economics increase physical activity, but their longer-term effectiveness is uncertain. We sought to determine the effect of behaviorally designed gamification, loss-framed financial incentives, or their combination on physical activity compared with attention control over 12-month intervention and 6-month postintervention follow-up periods. METHODS Between May 2019 and January 2024, participants with clinical atherosclerotic cardiovascular disease or a 10-year risk of myocardial infarction, stroke, or cardiovascular death of ≥7.5% by the Pooled Cohort equation were enrolled in a pragmatic randomized clinical trial. Participants received a wearable device to track daily steps, established a baseline, selected a step goal increase, and were randomly assigned to control (n=151), behaviorally designed gamification (n=304), loss-framed financial incentives (n=302), or gamification+financial incentives (n=305). The primary outcome of the trial was the change in mean daily steps from baseline through the 12-month intervention period. RESULTS A total of 1062 patients (mean±SD age, 67±8; 61% female; 31% non-White) were enrolled. Compared with control subjects, participants had significantly greater increases in mean daily steps from baseline during the 12-month intervention in the gamification arm (adjusted difference, 538.0 [95% CI, 186.2-889.9]; P=0.0027), financial incentives arm (adjusted difference, 491.8 [95% CI, 139.6-844.1]; P=0.0062), and gamification+financial incentives arm (adjusted difference, 868.0 [95% CI, 516.3-1219.7]; P<0.0001). During the 6-month follow-up, physical activity remained significantly greater in the gamification+financial incentives arm than in the control arm (adjusted difference, 576.2 [95% CI, 198.5-954]; P=0.0028), but it was not significantly greater in the gamification (adjusted difference, 459.8 [95% CI, 82.0-837.6]; P=0.0171) or financial incentives (adjusted difference, 327.9 [95% CI, -50.2 to 706]; P=0.09) arms after adjustment for multiple comparisons. CONCLUSIONS Behaviorally designed gamification, loss-framed financial incentives, and the combination of both increased physical activity compared with control over a 12-month intervention period, with the largest effect in gamification+financial incentives. These interventions could be a useful component of strategies to reduce cardiovascular risk in high-risk patients. REGISTRATION URL: https://clinicaltrials.gov; Unique Identifier: NCT03911141.
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Affiliation(s)
- Alexander C Fanaroff
- Department of Medicine, Perelman School of Medicine, (A.C.F., N.C., J.Z., T.K., K.G.M.V.), University of Pennsylvania, Philadelphia
- Penn Cardiovascular Outcomes, Quality, and Evaluative Research Center (A.C.F.), University of Pennsylvania, Philadelphia
- Penn Center for Health Incentives and Behavioral Economics (AC.F., S.C., D.F., L.N., C.R., J.Z., T.K., L.B.R., K.G.M.V.), University of Pennsylvania, Philadelphia
- Leonard Davis Institute of Health Economics (A.C.F., N.C., L.B.R., D.S.S., K.G.M.V.), University of Pennsylvania, Philadelphia
- Penn Center for Digital Cardiology (A.C.F., N.C.), University of Pennsylvania, Philadelphia
| | | | - Neel Chokshi
- Department of Medicine, Perelman School of Medicine, (A.C.F., N.C., J.Z., T.K., K.G.M.V.), University of Pennsylvania, Philadelphia
- Leonard Davis Institute of Health Economics (A.C.F., N.C., L.B.R., D.S.S., K.G.M.V.), University of Pennsylvania, Philadelphia
- Penn Center for Digital Cardiology (A.C.F., N.C.), University of Pennsylvania, Philadelphia
| | - Samantha Coratti
- Penn Center for Health Incentives and Behavioral Economics (AC.F., S.C., D.F., L.N., C.R., J.Z., T.K., L.B.R., K.G.M.V.), University of Pennsylvania, Philadelphia
| | - David Farraday
- Penn Center for Health Incentives and Behavioral Economics (AC.F., S.C., D.F., L.N., C.R., J.Z., T.K., L.B.R., K.G.M.V.), University of Pennsylvania, Philadelphia
| | - Laurie Norton
- Penn Center for Health Incentives and Behavioral Economics (AC.F., S.C., D.F., L.N., C.R., J.Z., T.K., L.B.R., K.G.M.V.), University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy (L.N., J.Z., L.B.R., K.G.M.V.), University of Pennsylvania, Philadelphia
| | - Charles Rareshide
- Penn Center for Health Incentives and Behavioral Economics (AC.F., S.C., D.F., L.N., C.R., J.Z., T.K., L.B.R., K.G.M.V.), University of Pennsylvania, Philadelphia
| | - Jingsan Zhu
- Department of Medicine, Perelman School of Medicine, (A.C.F., N.C., J.Z., T.K., K.G.M.V.), University of Pennsylvania, Philadelphia
- Penn Center for Health Incentives and Behavioral Economics (AC.F., S.C., D.F., L.N., C.R., J.Z., T.K., L.B.R., K.G.M.V.), University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy (L.N., J.Z., L.B.R., K.G.M.V.), University of Pennsylvania, Philadelphia
| | - Tamar Klaiman
- Department of Medicine, Perelman School of Medicine, (A.C.F., N.C., J.Z., T.K., K.G.M.V.), University of Pennsylvania, Philadelphia
- Penn Center for Health Incentives and Behavioral Economics (AC.F., S.C., D.F., L.N., C.R., J.Z., T.K., L.B.R., K.G.M.V.), University of Pennsylvania, Philadelphia
| | - Julia E Szymczak
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City (J.E.S.)
| | - Louise B Russell
- Penn Center for Health Incentives and Behavioral Economics (AC.F., S.C., D.F., L.N., C.R., J.Z., T.K., L.B.R., K.G.M.V.), University of Pennsylvania, Philadelphia
- Leonard Davis Institute of Health Economics (A.C.F., N.C., L.B.R., D.S.S., K.G.M.V.), University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy (L.N., J.Z., L.B.R., K.G.M.V.), University of Pennsylvania, Philadelphia
| | - Dylan S Small
- Leonard Davis Institute of Health Economics (A.C.F., N.C., L.B.R., D.S.S., K.G.M.V.), University of Pennsylvania, Philadelphia
- The Wharton School (D.S.S., K.G.M.V.), University of Pennsylvania, Philadelphia
| | - Kevin G M Volpp
- Department of Medicine, Perelman School of Medicine, (A.C.F., N.C., J.Z., T.K., K.G.M.V.), University of Pennsylvania, Philadelphia
- Penn Center for Health Incentives and Behavioral Economics (AC.F., S.C., D.F., L.N., C.R., J.Z., T.K., L.B.R., K.G.M.V.), University of Pennsylvania, Philadelphia
- Leonard Davis Institute of Health Economics (A.C.F., N.C., L.B.R., D.S.S., K.G.M.V.), University of Pennsylvania, Philadelphia
- Department of Medical Ethics and Health Policy (L.N., J.Z., L.B.R., K.G.M.V.), University of Pennsylvania, Philadelphia
- The Wharton School (D.S.S., K.G.M.V.), University of Pennsylvania, Philadelphia
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Buendia R, Karpefors M, Folkvaljon F, Hunter R, Sillen H, Luu L, Docherty K, Cowie MR. Wearable Sensors to Monitor Physical Activity in Heart Failure Clinical Trials: State-of-the-Art Review. J Card Fail 2024; 30:703-716. [PMID: 38452999 DOI: 10.1016/j.cardfail.2024.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/24/2024] [Accepted: 01/30/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Estimation of the effects that drugs or other interventions have on patients' symptoms and functions is crucial in heart failure trials. Traditional symptoms and functions clinical outcome assessments have important limitations. Actigraphy may help to overcome these limitations due to its objective nature and the potential for continuous recording of data. However, actigraphy is not currently accepted as clinically relevant by key stakeholders. METHODS AND RESULTS In this state-of-the-art study, the key aspects to consider when implementing actigraphy in heart failure trials are discussed. They include which actigraphy-derived measures should be considered, how to build endpoints using them, how to measure and analyze them, and how to handle the patients' and sites' logistics of integrating devices into trials. A comprehensive recommendation based on the current evidence is provided. CONCLUSION Actigraphy is technically feasible in clinical trials involving heart failure, but successful implementation and use to demonstrate clinically important differences in physical functioning with drug or other interventions require careful consideration of many design choices.
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Affiliation(s)
- Ruben Buendia
- Data Science, Late-Stage Development, Cardiovascular, Renal and Metabolic, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
| | - Martin Karpefors
- Data Science, Late-Stage Development, Cardiovascular, Renal and Metabolic, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Folke Folkvaljon
- Patient Centered Science, BioPharmaceuticals Business, AstraZeneca, Gothenburg, Sweden
| | - Robert Hunter
- Regulatory, Late-Stage Development, Cardiovascular, Renal and Metabolic, BioPharmaceuticals R&D, AstraZeneca, Luton, UK
| | | | - Long Luu
- Digital Health R&D, AstraZeneca, Gaithersburg, MD, US
| | - Kieran Docherty
- British Heart Foundation Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
| | - Martin R Cowie
- Late-Stage Development, Cardiovascular, Renal and Metabolic, BioPharmaceuticals R&D, AstraZeneca, Boston, MA, US
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Bragada JA, Magalhães PM, São-Pedro E, Bartolomeu RF, Morais JE. Net Heart Rate for Estimating Oxygen Consumption in Active Adults. J Funct Morphol Kinesiol 2024; 9:66. [PMID: 38651424 PMCID: PMC11036223 DOI: 10.3390/jfmk9020066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 04/03/2024] [Accepted: 04/05/2024] [Indexed: 04/25/2024] Open
Abstract
The aim of this study was to verify the accuracy of predicting oxygen consumption (O2) in predominantly aerobic activities based on net heart rate (netHR), sex, and body mass index (BMI) in active adults. NetHR is the value of the difference between the resting HR (HRrest) and the average HR value obtained during a given session or period of physical activity. These activities must be continuous, submaximal, and of a stabilized intensity. The magnitude of the netHR depends mainly on the intensity of the exercise. The HR is measured in beats per minute (bpm). A total of 156 participants, 52 women and 104 men, between the ages of 18 and 81, had their netHR and net oxygen intake (netVO2) assessed. There were 79 participants in group 1 (prediction sample) (52 males and 27 females). There were 77 people in group 2 (validation sample) (52 males and 25 females). The results of the multiple linear regression showed that netVO2 (R2 = 85.2%, SEE = 3.38) could be significantly predicted by sex (p < 0.001), netHR (p < 0.001), and BMI (p < 0.001). The Bland-Altman plots satisfied the agreement requirements, and the comparison of the measured and estimated netVO2 revealed non-significant differences with a trivial effect size. We calculated the formula NetVO2 (mL/(kg·min)) = 16 + 3.67 (sex) + 0.27 (netHR) - 0.57 (BMI) to predict netVO2, where netVO2 is the amount of oxygen uptake (mL/(kg·min)) above the resting value, netHR is the heart rate (beats per minute) above the resting value measured during exercise, sex is equal to zero for women and one for men, and BMI is the body mass index. In addition, based on the knowledge of VO2, it was possible to estimate the energy expenditure from a particular training session, and to determine or prescribe the exercise intensity in MET (metabolic equivalent of task).
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Affiliation(s)
- José A. Bragada
- Department of Sport Sciences, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal; (J.A.B.); (P.M.M.); (E.S.-P.); (R.F.B.)
- Research Centre for Active Living and Wellbeing (LiveWell), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
| | - Pedro M. Magalhães
- Department of Sport Sciences, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal; (J.A.B.); (P.M.M.); (E.S.-P.); (R.F.B.)
- Research Centre for Active Living and Wellbeing (LiveWell), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
| | - Eric São-Pedro
- Department of Sport Sciences, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal; (J.A.B.); (P.M.M.); (E.S.-P.); (R.F.B.)
| | - Raul F. Bartolomeu
- Department of Sport Sciences, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal; (J.A.B.); (P.M.M.); (E.S.-P.); (R.F.B.)
- Department of Sport Sciences, Polytechnic of Guarda, 6300-559 Guarda, Portugal
- Sport Physical Activity and Health Research & Innovation Center (SPRINT), 2040-413 Rio Maior, Portugal
| | - Jorge E. Morais
- Department of Sport Sciences, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal; (J.A.B.); (P.M.M.); (E.S.-P.); (R.F.B.)
- Research Centre for Active Living and Wellbeing (LiveWell), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal
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Lang AL, Bruhn RL, Fehling M, Heidenreich A, Reisdorf J, Khanyaree I, Henningsen M, Remschmidt C. Feasibility Study on Menstrual Cycles With Fitbit Device (FEMFIT): Prospective Observational Cohort Study. JMIR Mhealth Uhealth 2024; 12:e50135. [PMID: 38470472 DOI: 10.2196/50135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 11/26/2023] [Accepted: 01/24/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Despite its importance to women's reproductive health and its impact on women's daily lives, the menstrual cycle, its regulation, and its impact on health remain poorly understood. As conventional clinical trials rely on infrequent in-person assessments, digital studies with wearable devices enable the collection of longitudinal subjective and objective measures. OBJECTIVE The study aims to explore the technical feasibility of collecting combined wearable and digital questionnaire data and its potential for gaining biological insights into the menstrual cycle. METHODS This prospective observational cohort study was conducted online over 12 weeks. A total of 42 cisgender women were recruited by their local gynecologist in Berlin, Germany, and given a Fitbit Inspire 2 device and access to a study app with digital questionnaires. Statistical analysis included descriptive statistics on user behavior and retention, as well as a comparative analysis of symptoms from the digital questionnaires with metrics from the sensor devices at different phases of the menstrual cycle. RESULTS The average time spent in the study was 63.3 (SD 33.0) days with 9 of the 42 individuals dropping out within 2 weeks of the start of the study. We collected partial data from 114 ovulatory cycles, encompassing 33 participants, and obtained complete data from a total of 50 cycles. Participants reported a total of 2468 symptoms in the daily questionnaires administered during the luteal phase and menses. Despite difficulties with data completeness, the combined questionnaire and sensor data collection was technically feasible and provided interesting biological insights. We observed an increased heart rate in the mid and end luteal phase compared with menses and participants with severe premenstrual syndrome walked substantially fewer steps (average daily steps 10,283, SD 6277) during the luteal phase and menses compared with participants with no or low premenstrual syndrome (mean 11,694, SD 6458). CONCLUSIONS We demonstrate the feasibility of using an app-based approach to collect combined wearable device and questionnaire data on menstrual cycles. Dropouts in the early weeks of the study indicated that engagement efforts would need to be improved for larger studies. Despite the challenges of collecting wearable data on consecutive days, the data collected provided valuable biological insights, suggesting that the use of questionnaires in conjunction with wearable data may provide a more complete understanding of the menstrual cycle and its impact on daily life. The biological findings should motivate further research into understanding the relationship between the menstrual cycle and objective physiological measurements from sensor devices.
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Affiliation(s)
| | - Rosa-Lotta Bruhn
- Faculty of Health, University Witten Herdecke, Witten Herdecke, Germany
| | | | | | | | | | - Maike Henningsen
- Faculty of Health, University Witten Herdecke, Witten Herdecke, Germany
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Ogata H, Negishi Y, Koizumi N, Nagayama H, Kaneko M, Kiyono K, Omi N. Individually optimized estimation of energy expenditure in rescue workers using a tri-axial accelerometer and heart rate monitor. Front Physiol 2024; 15:1322881. [PMID: 38434137 PMCID: PMC10905789 DOI: 10.3389/fphys.2024.1322881] [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: 10/17/2023] [Accepted: 01/23/2024] [Indexed: 03/05/2024] Open
Abstract
Objectives: This study aimed to provide an improved energy expenditure estimation for heavy-load physical labor using accelerometer data and heart rate (HR) measured by wearables and to support food preparation and supply management for disaster relief and rescue operations as an expedition team. Methods: To achieve an individually optimized estimation for energy expenditure, a model equation parameter was determined based on the measurements of physical activity and HR during simulated rescue operations. The metabolic equivalent of task (MET), which was measured by using a tri-axial accelerometer and individual HR, was used, where two (minimum and maximum) or three (minimum, intermediate, and maximum) representative reference points were selected for each individual model fitting. In demonstrating the applicability of our approach in a realistic situation, accelerometer-based METs and HR of 30 males were measured using the tri-axial accelerometer and wearable HR during simulated rescue operations over 2 days. Results: Data sets of 27 rescue operations (age:34.2 ± 7.5 years; body mass index (BMI):22.9 ± 1.5 kg/m2) were used for the energy expenditure estimation after excluding three rescue workers due to their activity type and insufficient HR measurement. Using the combined approach with a tri-axial accelerometer and HR, the total energy expenditure increased by 143% for two points and 133% for three points, compared with the estimated total energy expenditure using only the accelerometer-based method. Conclusion: The use of wearables provided a reasonable estimation of energy expenditure for physical workers with heavy equipment. The application of our approach to disaster relief and rescue operations can provide important insights into nutrition and healthcare management.
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Affiliation(s)
- Hitomi Ogata
- Graduate School of Humanities and Social Sciences, Hiroshima University, Hiroshima, Japan
| | - Yutaro Negishi
- Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Japan
| | - Nao Koizumi
- Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Japan
| | - Hisashi Nagayama
- Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Japan
| | - Miki Kaneko
- Graduate School of Engineering Science, Osaka University, Toyonaka, Japan
| | - Ken Kiyono
- Graduate School of Engineering Science, Osaka University, Toyonaka, Japan
| | - Naomi Omi
- Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Japan
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McMahon SK, Lewis BA, Guan W, Wang Q, Hayes SM, Wyman JF, Rothman AJ. Effect of Intrapersonal and Interpersonal Behavior Change Strategies on Physical Activity Among Older Adults: A Randomized Clinical Trial. JAMA Netw Open 2024; 7:e240298. [PMID: 38421648 PMCID: PMC10905305 DOI: 10.1001/jamanetworkopen.2024.0298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 01/04/2024] [Indexed: 03/02/2024] Open
Abstract
Importance Despite guidelines that recommend physical activity (PA), little is known about which types of behavior change strategies (BCSs) effectively promote sustained increases in PA in older adults who are insufficiently active. Objective To determine whether intrapersonal BCSs (eg, goal setting) or interpersonal BCSs (eg, peer-to-peer sharing or learning) combined with the Otago Exercise Program (17 strength and balance exercises and a walking program that are learned and individually tailored, with instruction to perform 3 times per week at home or location of choice) and a wearable PA monitor help older adults sustain increases in their PA. Design, Setting, and Participants This 2 × 2 factorial randomized clinical trial (Community-Based Intervention Effects on Older Adults' Physical Activity) of community-dwelling older adults 70 years or older with PA levels below minimum national PA guidelines was conducted in urban community centers. Dates of enrollment were from November 17, 2017, to June 15, 2021, with final follow-up assessments completed on September 2, 2022. Interventions Participants were randomized to intrapersonal (eg, goal setting) BCSs, interpersonal (eg, problem-solving with peer-to-peer sharing and learning) BCSs, intrapersonal and interpersonal BCSs, or an attention control group. All interventions included a PA monitor and 8 weekly small-group meetings with discussion, practice, and instructions to implement the exercise program and relevant BCSs independently between meetings and after the intervention. Main Outcomes and Measures The primary outcome was daily minutes of objectively measured total PA (light, moderate, or vigorous intensities) averaged over 7 to 10 days, measured at baseline and after the intervention at 1 week, 6 months, and 12 months. Results Among 309 participants (mean [SD] age, 77.4 [5.0] years; 240 women [77.7%]), 305 (98.7%) completed the intervention, and 302 (97.7%) had complete data. Participants receiving PA interventions with interpersonal BCS components exhibited greater increases in total PA than did those who did not at 1 week (204 vs 177 PA minutes per day; adjusted difference, 27.1 [95% CI, 17.2-37.0]; P < .001), 6 months (195 vs 175 PA minutes per day; adjusted difference, 20.8 [95% CI, 10.0-31.6]; P < .001), and 12 months (195 vs 168 PA minutes per day; adjusted difference, 27.5 [95% CI, 16.2-38.8]; P < .001) after the intervention. Compared with participants who did not receive interventions with intrapersonal BCS components, participants who received intrapersonal BCSs exhibited no significant changes in total PA at 1 week (192 vs 190 PA minutes per day; adjusted difference, 1.8 [95% CI, -8.6 to 12.2]; P = .73), 6 months (183 vs 187 PA minutes per day; adjusted difference, -3.9 [95% CI, -15.0 to 7.1]; P = .49), or 12 months (177 vs 186 PA minutes per day; adjusted difference, -8.8 [95% CI, -20.5 to 2.9]; P = .14) after the intervention. Interactions between intrapersonal and interpersonal BCSs were not significant. Conclusions and Relevance In this randomized clinical trial, older adults with low levels of PA who received interpersonal BCSs, the exercise program, and a PA monitor exhibited significant increases in their PA for up to 12 months after the intervention. Intrapersonal BCSs elicited no significant PA changes and did not interact with interpersonal BCSs. Our findings suggest that because effects of a PA intervention on sustained increases in older adults' PA were augmented with interpersonal but not intrapersonal BCSs, approaches to disseminating and implementing the intervention should be considered. Trial Registration ClinicalTrials.gov Identifier: NCT03326141.
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Affiliation(s)
| | - Beth A. Lewis
- School of Kinesiology, University of Minnesota, Minneapolis
| | - Weihua Guan
- School of Public Health, University of Minnesota, Minneapolis
| | - Qi Wang
- School of Public Health, University of Minnesota, Minneapolis
| | | | - Jean F. Wyman
- School of Nursing, University of Minnesota, Minneapolis
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Suau Q, Bianchini E, Bellier A, Chardon M, Milane T, Hansen C, Vuillerme N. Current Knowledge about ActiGraph GT9X Link Activity Monitor Accuracy and Validity in Measuring Steps and Energy Expenditure: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:825. [PMID: 38339541 PMCID: PMC10857518 DOI: 10.3390/s24030825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/22/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024]
Abstract
Over recent decades, wearable inertial sensors have become popular means to quantify physical activity and mobility. However, research assessing measurement accuracy and precision is required, especially before using device-based measures as outcomes in trials. The GT9X Link is a recent activity monitor available from ActiGraph, recognized as a "gold standard" and previously used as a criterion measure to assess the validity of various consumer-based activity monitors. However, the validity of the ActiGraph GT9X Link is not fully elucidated. A systematic review was undertaken to synthesize the current evidence for the criterion validity of the ActiGraph GT9X Link in measuring steps and energy expenditure. This review followed the PRISMA guidelines and eight studies were included with a combined sample size of 558 participants. We found that (1) the ActiGraph GT9X Link generally underestimates steps; (2) the validity and accuracy of the device in measuring steps seem to be influenced by gait speed, device placement, filtering process, and monitoring conditions; and (3) there is a lack of evidence regarding the accuracy of step counting in free-living conditions and regarding energy expenditure estimation. Given the limited number of included studies and their heterogeneity, the present review emphasizes the need for further validation studies of the ActiGraph GT9X Link in various populations and in both controlled and free-living settings.
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Affiliation(s)
- Quentin Suau
- AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (Q.S.); (A.B.); (M.C.); (T.M.); (C.H.)
| | - Edoardo Bianchini
- AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (Q.S.); (A.B.); (M.C.); (T.M.); (C.H.)
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Sapienza University of Rome, 00189 Rome, Italy
| | - Alexandre Bellier
- AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (Q.S.); (A.B.); (M.C.); (T.M.); (C.H.)
- CHU Grenoble Alpes, Université Grenoble Alpes, Inserm CIC 1406, 38000 Grenoble, France
| | - Matthias Chardon
- AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (Q.S.); (A.B.); (M.C.); (T.M.); (C.H.)
- UNESP Human Movement Research Laboratory (MOVI-LAB), Department of Physical Education, Bauru Sao Paulo State University, Bauru 17033-360, SP, Brazil
| | - Tracy Milane
- AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (Q.S.); (A.B.); (M.C.); (T.M.); (C.H.)
| | - Clint Hansen
- AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (Q.S.); (A.B.); (M.C.); (T.M.); (C.H.)
- Department of Neurology, Kiel University, 24105 Kiel, Germany
| | - Nicolas Vuillerme
- AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (Q.S.); (A.B.); (M.C.); (T.M.); (C.H.)
- LabCom Telecom4Health, Orange Labs & Université Grenoble Alpes, CNRS, Inria, Grenoble INP-UGA, 38000 Grenoble, France
- Institut Universitaire de France, 75005 Paris, France
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Colonna G, Hoye J, de Laat B, Stanley G, Ibrahimy A, Tinaz S, Morris ED. Measuring Heart Rate Accurately in Patients With Parkinson Disease During Intense Exercise: Usability Study of Fitbit Charge 4. JMIR BIOMEDICAL ENGINEERING 2023; 8:e51515. [PMID: 38875680 PMCID: PMC11041416 DOI: 10.2196/51515] [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: 08/02/2023] [Revised: 10/27/2023] [Accepted: 11/09/2023] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND Parkinson disease (PD) is the second most common neurodegenerative disease, affecting approximately 1% of the world's population. Increasing evidence suggests that aerobic physical exercise can be beneficial in mitigating both motor and nonmotor symptoms of the disease. In a recent pilot study of the role of exercise on PD, we sought to confirm exercise intensity by monitoring heart rate (HR). For this purpose, we asked participants to wear a chest strap HR monitor (Polar Electro Oy) and the Fitbit Charge 4 (Fitbit Inc) wrist-worn HR monitor as a potential proxy due to its convenience. Polar H10 has been shown to provide highly accurate R-R interval measurements. Therefore, we treated it as the gold standard in this study. It has been shown that Fitbit Charge 4 has comparable accuracy to Polar H10 in healthy participants. It has yet to be determined if the Fitbit is as accurate as Polar H10 in patients with PD during rest and exercise. OBJECTIVE This study aimed to compare Fitbit Charge 4 to Polar H10 for monitoring HR in patients with PD at rest and during an intensive exercise program. METHODS A total of 596 exercise sessions from 11 (6 male and 5 female) participants were collected simultaneously with both devices. Patients with early-stage PD (Hoehn and Yahr ≤2) were enrolled in a 6-month exercise program designed for patients with PD. They participated in 3 one-hour exercise sessions per week. They wore both Fitbit and Polar H10 during each session. Sessions included rest, warm-up, intense exercise, and cool-down periods. We calculated the bias in the HR of the Fitbit Charge 4 at rest (5 min) and during intense exercise (20 min) by comparing the mean HR during each of the periods to the respective means measured by Polar H10 (HRFitbit - HRPolar). We also measured the sensitivity and specificity of Fitbit Charge 4 to detect average HRs that exceed the threshold for intensive exercise, defined as 70% of an individual's theoretical maximum HR. Different types of correlations between the 2 devices were investigated. RESULTS The mean bias was 1.68 beats per minute (bpm) at rest and 6.29 bpm during high-intensity exercise, with an overestimation by Fitbit Charge 4 in both conditions. The mean bias of the Fitbit across both rest and intensive exercise periods was 3.98 bpm. The device's sensitivity in identifying high-intensity exercise sessions was 97.14%. The correlation between the 2 devices was nonlinear, suggesting Fitbit's tendency to saturate at high values of HR. CONCLUSIONS The performance of Fitbit Charge 4 is comparable to Polar H10 for assessing exercise intensity in a cohort of patients with PD (mean bias 3.98 bpm). The device could be considered a reasonable surrogate for more cumbersome chest-worn devices in future studies of clinical cohorts.
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Affiliation(s)
- Giulia Colonna
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - Jocelyn Hoye
- Department of Psychiatry, Yale University, New Haven, CT, United States
| | - Bart de Laat
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
- Department of Psychiatry, Yale University, New Haven, CT, United States
| | - Gelsina Stanley
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - Alaaddin Ibrahimy
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
| | - Sule Tinaz
- Department of Neurology, Yale University, New Haven, CT, United States
| | - Evan D Morris
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States
- Department of Psychiatry, Yale University, New Haven, CT, United States
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States
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Kloss EB, Givens A, Palombo L, Bernards J, Niederberger B, Bennett DW, Kelly KR. Validation of Polar Grit X Pro for Estimating Energy Expenditure during Military Field Training: A Pilot Study. J Sports Sci Med 2023; 22:658-666. [PMID: 38045749 PMCID: PMC10690511 DOI: 10.52082/jssm.2023.658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 09/27/2023] [Indexed: 12/05/2023]
Abstract
Wearables are lightweight, portable technology devices that are traditionally used to monitor physical activity and workload as well as basic physiological parameters such as heart rate. However recent advances in monitors have enabled better algorithms for estimation of caloric expenditure from heart rate for use in weight loss as well as sport performance. can be used for estimating energy expenditure and nutritional demand. Recently, the military has adopted the use of personal wearables for utilization in field studies for ecological validity of training. With popularity of use, the need for validation of these devices for caloric estimates is needed to assist in work-rest cycles. Thus the purpose of this effort was to evaluate the Polar Grit X for energy expenditure (EE) for use in military training exercises. Polar Grit X Pro watches were worn by active-duty elite male operators (N = 16; age: 31.7 ± 5.0 years, height: 180.1 ± 6.2 cm, weight: 91.7 ± 9.4 kg). Metrics were measured against indirect calorimetry of a metabolic cart and heart rate via a Polar heart rate monitor chest strap while exercising on a treadmill. Participants each performed five 10-minute bouts of running at a self-selected speed and incline to maintain a heart rate within one of five heart rate zones, as ordered and defined by Polar. Polar Grit X Pro watch had a good to excellent interrater reliability to indirect calorimetry at estimating energy expenditure (ICC = 0.8, 95% CI = 0.61-0.89, F (74,17.3) = 11.76, p < 0.0001) and a fair to good interrater reliability in estimating macronutrient partitioning (ICC = 0.49, 95% CI = 0.3-0.65, F (74,74.54) = 2.98, p < 0.0001). There is a strong relationship between energy expenditure as estimated from the Polar Grit X Pro and measured through indirect calorimetry. The Polar Grit X Pro watch is a suitable tool for estimating energy expenditure in free-living participants in a field setting and at a range of exercise intensities.
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Affiliation(s)
- Emily B Kloss
- Applied Translational Exercise and Metabolic Physiology Team, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Leidos, Inc., San Diego, CA, USA
| | - Andrea Givens
- Applied Translational Exercise and Metabolic Physiology Team, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Leidos, Inc., San Diego, CA, USA
| | - Laura Palombo
- Applied Translational Exercise and Metabolic Physiology Team, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Leidos, Inc., San Diego, CA, USA
| | - Jake Bernards
- Applied Translational Exercise and Metabolic Physiology Team, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Leidos, Inc., San Diego, CA, USA
| | - Brenda Niederberger
- Applied Translational Exercise and Metabolic Physiology Team, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
| | - Daniel W Bennett
- Applied Translational Exercise and Metabolic Physiology Team, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
- Leidos, Inc., San Diego, CA, USA
| | - Karen R Kelly
- Applied Translational Exercise and Metabolic Physiology Team, Warfighter Performance Department, Naval Health Research Center, San Diego, CA, USA
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11
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Artese AL, Rawat R, Sung AD. The use of commercial wrist-worn technology to track physiological outcomes in behavioral interventions. Curr Opin Clin Nutr Metab Care 2023; 26:534-540. [PMID: 37522804 DOI: 10.1097/mco.0000000000000970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
PURPOSE OF REVIEW The aim of this review is to provide an overview of the use of commercial wrist-worn mobile health devices to track and monitor physiological outcomes in behavioral interventions as well as discuss considerations for selecting the optimal device. RECENT FINDINGS Wearable technology can enhance intervention design and implementation. The use of wrist-worn wearables provides the opportunity for tracking physiological outcomes, thus providing a unique approach for assessment and delivery of remote interventions. Recent findings support the utility, acceptability, and benefits of commercial wrist-worn wearables in interventions, and they can be used to continuously monitor outcomes, remotely administer assessments, track adherence, and personalize interventions. Wrist-worn devices show acceptable accuracy when measuring heart rate, blood pressure, step counts, and physical activity; however, accuracy is dependent on activity type, intensity, and device brand. These factors should be considered when designing behavioral interventions that utilize wearable technology. SUMMARY With the continuous advancement in technology and frequent product upgrades, the capabilities of commercial wrist-worn devices will continue to expand, thus increasing their potential use in intervention research. Continued research is needed to examine and validate the most recent devices on the market to better inform intervention design and implementation.
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Affiliation(s)
| | - Rahul Rawat
- Division of Hematologic Malignancies and Cellular Therapy, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA
| | - Anthony D Sung
- Division of Hematologic Malignancies and Cellular Therapy, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA
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12
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Park J, Kim M, El Mistiri M, Kha R, Banerjee S, Gotzian L, Chevance G, Rivera DE, Klasnja P, Hekler E. Advancing Understanding of Just-in-Time States for Supporting Physical Activity (Project JustWalk JITAI): Protocol for a System ID Study of Just-in-Time Adaptive Interventions. JMIR Res Protoc 2023; 12:e52161. [PMID: 37751237 PMCID: PMC10565629 DOI: 10.2196/52161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 08/29/2023] [Accepted: 08/30/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND Just-in-time adaptive interventions (JITAIs) are designed to provide support when individuals are receptive and can respond beneficially to the prompt. The notion of a just-in-time (JIT) state is critical for JITAIs. To date, JIT states have been formulated either in a largely data-driven way or based on theory alone. There is a need for an approach that enables rigorous theory testing and optimization of the JIT state concept. OBJECTIVE The purpose of this system ID experiment was to investigate JIT states empirically and enable the empirical optimization of a JITAI intended to increase physical activity (steps/d). METHODS We recruited physically inactive English-speaking adults aged ≥25 years who owned smartphones. Participants wore a Fitbit Versa 3 and used the study app for 270 days. The JustWalk JITAI project uses system ID methods to study JIT states. Specifically, provision of support systematically varied across different theoretically plausible operationalizations of JIT states to enable a more rigorous and systematic study of the concept. We experimentally varied 2 intervention components: notifications delivered up to 4 times per day designed to increase a person's steps within the next 3 hours and suggested daily step goals. Notifications to walk were experimentally provided across varied operationalizations of JIT states accounting for need (ie, whether daily step goals were previously met or not), opportunity (ie, whether the next 3 h were a time window during which a person had previously walked), and receptivity (ie, a person previously walked after receiving notifications). Suggested daily step goals varied systematically within a range related to a person's baseline level of steps per day (eg, 4000) until they met clinically meaningful targets (eg, averaging 8000 steps/d as the lower threshold across a cycle). A series of system ID estimation approaches will be used to analyze the data and obtain control-oriented dynamical models to study JIT states. The estimated models from all approaches will be contrasted, with the ultimate goal of guiding rigorous, replicable, empirical formulation and study of JIT states to inform a future JITAI. RESULTS As is common in system ID, we conducted a series of simulation studies to formulate the experiment. The results of our simulation studies illustrated the plausibility of this approach for generating informative and unique data for studying JIT states. The study began enrolling participants in June 2022, with a final enrollment of 48 participants. Data collection concluded in April 2023. Upon completion of the analyses, the results of this study are expected to be submitted for publication in the fourth quarter of 2023. CONCLUSIONS This study will be the first empirical investigation of JIT states that uses system ID methods to inform the optimization of a scalable JITAI for physical activity. TRIAL REGISTRATION ClinicalTrials.gov NCT05273437; https://clinicaltrials.gov/ct2/show/NCT05273437. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/52161.
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Affiliation(s)
- Junghwan Park
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States
- Center for Wireless & Population Health Systems, Calit2's Qualcomm Institute, University of California, San Diego, La Jolla, CA, United States
- The Design Lab, University of California, San Diego, La Jolla, CA, United States
- Ministry of Health and Welfare, Korean National Government, Sejong, Republic of Korea
| | - Meelim Kim
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States
- Center for Wireless & Population Health Systems, Calit2's Qualcomm Institute, University of California, San Diego, La Jolla, CA, United States
- The Design Lab, University of California, San Diego, La Jolla, CA, United States
- Department of Preventive Medicine, College of Medicine, Yonsei University, Seoul, Republic of Korea
| | - Mohamed El Mistiri
- Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, United States
| | - Rachael Kha
- Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Sarasij Banerjee
- Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, United States
| | - Lisa Gotzian
- Lufthansa Industry Solutions, Lufthansa, Norderstedt, Germany
| | | | - Daniel E Rivera
- Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, United States
| | - Predrag Klasnja
- School of Information, University of Michigan, Ann Arbor, MI, United States
| | - Eric Hekler
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States
- Center for Wireless & Population Health Systems, Calit2's Qualcomm Institute, University of California, San Diego, La Jolla, CA, United States
- The Design Lab, University of California, San Diego, La Jolla, CA, United States
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Modde Epstein C, McCoy TP. Linking Electronic Health Records With Wearable Technology From the All of Us Research Program. J Obstet Gynecol Neonatal Nurs 2023; 52:139-149. [PMID: 36702164 DOI: 10.1016/j.jogn.2022.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 12/05/2022] [Accepted: 12/14/2022] [Indexed: 01/24/2023] Open
Abstract
OBJECTIVE To evaluate the feasibility of using electronic health records (EHRs) and wearable data to describe patterns of longitudinal change in day-level heart rate before, during, and after pregnancy and how these patterns differ by age and body mass index. DESIGN Descriptive secondary analysis feasibility study using data from the National Institutes of Health All of Us Research Program. SETTING United States. PARTICIPANTS Women (N = 89) who had a birth or length of gestation code in the EHR and at least 60 days of Fitbit heart rate data during pregnancy. METHODS We estimated pregnancy-related episodes using EHR codes. Time consisted of five 3-month periods: before pregnancy, first trimester, second trimester, third trimester, and after birth. We analyzed data using descriptive statistics and locally estimated scatterplot smoothing. RESULTS An average of 330 days (SD = 112) of Fitbit heart rate data (29,392 days) were available from participants. During pregnancy, distinct peaks in heart rate occurred during the first trimester (6% increase) and third trimester (15% increase). CONCLUSION Future researchers can examine whether longitudinal timing and patterns of heart rate from wearable devices could be leveraged to detect health problems early in pregnancy.
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O'Sullivan G, Whelan B, Gallagher N, Doyle P, Smyth S, Murphy K, Dröes RM, Devane D, Casey D. Challenges of using a Fitbit smart wearable among people with dementia. Int J Geriatr Psychiatry 2023; 38:e5898. [PMID: 36814072 DOI: 10.1002/gps.5898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 02/19/2023] [Indexed: 02/24/2023]
Abstract
OBJECTIVES Limited research on using smart wearables such as Fitbit devices among people with dementia has shown favourable outcomes. The aim of this study was to explore the acceptability and feasibility of using a Fitbit Charge 3 among people with dementia, living in the community, who took part in the physical exercise component of the Comprehensive REsilience-building psychoSocial intervenTion pilot study. METHODS A mixed methods study was conducted; Quantitative data relating to wear rates for the Fitbit were recorded and qualitative data were collected by group and individual interviews with the people with dementia and their caregiver about their experience of wearing/using the Fitbit in the study. RESULTS Nine people with dementia and their caregiver completed the intervention. Only one participant wore the Fitbit consistently. Supporting set-up and use of the devices was time consuming and caregiver involvement was essential for day-to-day support: none of the people with dementia owned a smartphone. Few of them engaged with the Fitbit features, primarily only using it to check the time and only a minority wanted to keep the device beyond the intervention. DISCUSSION When designing a study using smart wearables such as a Fitbit among people with dementia, consideration should be given to the following: the possible burden on caregivers supporting the use of the device; a lack of familiarity with this technology in the target population; dealing with missing data, and the involvement of the researcher in setting up and supporting use of the device.
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Affiliation(s)
- Grace O'Sullivan
- School of Nursing and Midwifery, University of Galway, Áras Moyola, Galway, Ireland
| | - Barbara Whelan
- School of Nursing and Midwifery, University of Galway, Áras Moyola, Galway, Ireland
| | - Niamh Gallagher
- School of Nursing and Midwifery, University of Galway, Áras Moyola, Galway, Ireland
| | - Priscilla Doyle
- School of Nursing and Midwifery, University of Galway, Áras Moyola, Galway, Ireland
| | - Siobhán Smyth
- School of Nursing and Midwifery, University of Galway, Áras Moyola, Galway, Ireland
| | - Kathleen Murphy
- School of Nursing and Midwifery, University of Galway, Áras Moyola, Galway, Ireland
| | - Rose-Marie Dröes
- Department of Psychiatry, VU University Medical Center, Amsterdam, The Netherlands
| | - Declan Devane
- School of Nursing and Midwifery, University of Galway, Áras Moyola, Galway, Ireland
| | - Dympna Casey
- School of Nursing and Midwifery, University of Galway, Áras Moyola, Galway, Ireland
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Martín-Escudero P, Cabanas AM, Dotor-Castilla ML, Galindo-Canales M, Miguel-Tobal F, Fernández-Pérez C, Fuentes-Ferrer M, Giannetti R. Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise? Bioengineering (Basel) 2023; 10:bioengineering10020254. [PMID: 36829748 PMCID: PMC9952291 DOI: 10.3390/bioengineering10020254] [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: 12/08/2022] [Revised: 01/28/2023] [Accepted: 01/30/2023] [Indexed: 02/17/2023] Open
Abstract
The market for wrist-worn devices is growing at previously unheard-of speeds. A consequence of their fast commercialization is a lack of adequate studies testing their accuracy on varied populations and pursuits. To provide an understanding of wearable sensors for sports medicine, the present study examined heart rate (HR) measurements of four popular wrist-worn devices, the (Fitbit Charge (FB), Apple Watch (AW), Tomtom runner Cardio (TT), and Samsung G2 (G2)), and compared them with gold standard measurements derived by continuous electrocardiogram examination (ECG). Eight athletes participated in a comparative study undergoing maximal stress testing on a cycle ergometer or a treadmill. We analyzed 1,286 simultaneous HR data pairs between the tested devices and the ECG. The four devices were reasonably accurate at the lowest activity level. However, at higher levels of exercise intensity the FB and G2 tended to underestimate HR values during intense physical effort, while the TT and AW devices were fairly reliable. Our results suggest that HR estimations should be considered cautiously at specific intensities. Indeed, an effective intervention is required to register accurate HR readings at high-intensity levels (above 150 bpm). It is important to consider that even though none of these devices are certified or sold as medical or safety devices, researchers must nonetheless evaluate wrist-worn wearable technology in order to fully understand how HR affects psychological and physical health, especially under conditions of more intense exercise.
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Affiliation(s)
- Pilar Martín-Escudero
- Professional Medical School of Physical Education and Sport, Faculty of Medicine, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Ana María Cabanas
- Departamento de Física, FACI, Universidad de Tarapacá, Arica 1010069, Chile
- Correspondence:
| | | | - Mercedes Galindo-Canales
- Professional Medical School of Physical Education and Sport, Faculty of Medicine, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Francisco Miguel-Tobal
- Professional Medical School of Physical Education and Sport, Faculty of Medicine, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Cristina Fernández-Pérez
- Servicio de Medicina Preventiva Complejo Hospitalario de Santiago de Compostela, Instituto de Investigación Sanitaria de Santiago, 15706 Santiago de Compostela, Spain
| | - Manuel Fuentes-Ferrer
- Unidad de Investigación, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
| | - Romano Giannetti
- IIT, Institute of Technology Research, Universidad Pontificia Comillas, 28015 Madrid, Spain
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