دورية أكاديمية

The use of commercial wrist-worn technology to track physiological outcomes in behavioral interventions.

التفاصيل البيبلوغرافية
العنوان: The use of commercial wrist-worn technology to track physiological outcomes in behavioral interventions.
المؤلفون: Artese AL; Center for the Study of Aging and Human Development., Rawat R; Division of Hematologic Malignancies and Cellular Therapy, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA., Sung AD; Division of Hematologic Malignancies and Cellular Therapy, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA.
المصدر: Current opinion in clinical nutrition and metabolic care [Curr Opin Clin Nutr Metab Care] 2023 Nov 01; Vol. 26 (6), pp. 534-540. Date of Electronic Publication: 2023 Jul 26.
نوع المنشور: Review; Journal Article; Research Support, N.I.H., Extramural
اللغة: English
بيانات الدورية: Publisher: Lippincott Williams & Wilkins Country of Publication: England NLM ID: 9804399 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1473-6519 (Electronic) Linking ISSN: 13631950 NLM ISO Abbreviation: Curr Opin Clin Nutr Metab Care Subsets: MEDLINE
أسماء مطبوعة: Publication: 1999- : London : Lippincott Williams & Wilkins
Original Publication: London ; Philadelphia : Rapid Science Publishers, c1998-
مواضيع طبية MeSH: Wrist* , Wearable Electronic Devices*, Humans ; Exercise ; Technology
مستخلص: 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.
(Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)
References: Agnihothri S, Cui L, Delasay M, Rajan B. The value of mHealth for managing chronic conditions. Healthcare Manag Sci 2020; 23:185–202.
Centers for Disease Control and Prevention. Telehealth interventions to improve chronic disease. 2020. https://www.cdc.gov/dhdsp/pubs/telehealth.htm . [Accessed 17 May 2023].
Kulkarni P, Kirkham R, McNaney R. Opportunities for smartphone sensing in E-Health research: a narrative review. Sensors 2022; 22:3893.
Statista. Number of mHealth apps available in the Google Play Store from 1st quarter 2015 to 3rd quarter 2022. 2022. https://www.statista.com/statistics/779919/health-apps-available-google-play-worldwide/ . [Accessed 18 May 2023].
Paradis S, Roussel J, Bosson JL, Kern JB. Use of smartphone health apps among patients aged 18 to 69 years in primary aare: population-based cross-sectional survey. JMIR Form Res 2022; 6:e34882.
Wearable medical devices market size, share & COVID-19 impact analysis, by product (diagnostic & patient monitoring wearable medical devices, and therapeutic wearable medical devices); by application (remote patient monitoring & home healthcare, & sports. Fortune Bus Insights. 2020. https://www.fortunebusinessinsights.com/industry-reports/wearable-medical-devices-market-101070 . [Accessed 17 May 2023].
Chandrasekaran R, Katthula V, Moustakas E. Patterns of use and key predictors for the use of wearable healthcare devices by US Adults: insights from a national survey. J Med Internet Res 2020; 22:e22443.
Thompson WR. Worldwide survey of fitness trends for 2019. ACSM's Heal Fit J 2019; 22:10–17.
Thompson WR. Worldwide survey of fitness trends for 2020. ACSM's Heal Fit J 2020; 23:10–18.
Thompson WR. Worldwide survey of fitness trends for 2021. ACSM's Heal Fit J 2021; 25:10–19.
Thompson WR. Worldwide survey of fitness trends for 2022. ACSM's Heal Fit J 2022; 26:11–20.
Thompson WR. Worldwide survey of fitness trends for 2023. ACSM's Heal Fit J 2023; 27:9–18.
Lu L, Zhang J, XXie Y, et al. Wearable health devices in healthcare: narrative systematic review. JMIR mHealth uHealth 2020; 8:e18907.
Evenson KR, Scherer E, Peter KM, et al. Historical development of accelerometry measures and methods for physical activity and sedentary behavior research worldwide: a scoping review of observational studies of adults. PLoS One 2022; 17:e0276890.
Plasqui G, Bonomi AG, Westerterp KR. Daily physical activity assessment with accelerometers: new insights and validation studies. Obes Rev 2013; 14:451–462.
Migueles JH, Molina-Garcia P, Torres-Lopez LV, et al. Equivalency of four research-grade movement sensors to assess movement behaviors and its implications for population surveillance. Sci Rep 2022; 12:5525.
ActiGraph. What's the difference among the Cut Points available in ActiLife? ActiLife (Basic). 2019. https://actigraphcorp.my.site.com/support/s/article/What-s-the-difference-among-the-Cut-Points-available-in-ActiLife . [Accessed 2 June 2023].
ActiGraph. What is the difference between the Wear Time Validation algorithms? ActiLife (Basic). 2020. https://actigraphcorp.my.site.com/support/s/article/What-is-the-difference-between-the-Wear-Time-Validation-algorithms . [Accessed 2 June 2023].
Free C, Phillips G, Galli L, et al. The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for healthcare consumers: a systematic review. PLoS Med 2013; 10:e1001362.
Moon K, Sobolev M, Kane JM. Digital and mobile health technology in collaborative behavioral healthcare: scoping review. JMIR Ment Heal 2022; 9:e30810.
Jamieson A, Orini M, Chaturvedi N, Hughes A. A validation study of two wrist worn wearable devices for remote assessment of exercise capacity. Comput Cardiol (2010) 2022; 49:1–4.
Molina-Garcia P, Notbohm HL, Schumann M, et al. Validity of estimating the maximal oxygen consumption by consumer wearables: a systematic review with meta-analysis and expert statement of the INTERLIVE Network. Sport Med 2022; 52:1577–1597.
Chan A, Chan D, Lee H, et al. Reporting adherence, validity and physical activity measures of wearable activity trackers in medical research: a systematic review. Int J Med Inform 2022; 160:104696.
Sempionatto JR, Montiel VRV, Vargas E, et al. Wearable and mobile sensors for personalized nutrition. ACS Sensors 2021; 6:1745–1760.
Chakrabarti S, Biswas N, Jones LD, et al. Smart consumer wearables as digital diagnostic tools: a review. Diagnostics 2022; 12:2110.
Li P, van Wezel R, He F, et al. The role of wrist-worn technology in the management of Parkinson's disease in daily life: a narrative review. Front Neuroinform 2023; 17:1135300.
Fuller D, Colwell E, Low J, et al. Reliability and validity of commercially available wearable devices for measuring steps, energy expenditure, and heart rate: systematic review. JMIR mHealth uHealth 2020; 8:e18694.
Wolling F, Heimes S, Van Laerhoven K. Unity in diversity: sampling strategies in wearable photoplethysmography. IEEE Pervasive Comput 2019; 18:63–69.
Sarhaddi F, Kazemi K, Azimi I, et al. A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability. PLoS One 2022; 17:e0268361.
Alfonso C, Garcia-Gonzalez MA, Parrado E, et al. Agreement between two photoplethysmography-based wearable devices for monitoring heart rate during different physical activity situations: a new analysis methodology. Sci Rep 2022; 12:15448.
Bai Y, Tompkins C, Gell N, et al. Comprehensive comparison of Apple Watch and Fitbit monitors in a free-living setting. PLoS One 2021; 16:e0251975.
Muggeridge DJ, Hickson K, Davies AV, et al. Measurement of heart rate using the Polar OH1 and Fitbit charge 3 wearable devices in healthy adults during light, moderate, vigorous, and sprint-based exercise: validation study. JMIR mHealth uHealth 2021; 9:e25313.
Gagnon J, Khau M, Lavoie-Hudon L, et al. 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.
Nuuttila OP, Korhonen E, Laukkanen J, Kyröläinen H. Validity of the wrist-worn polar vantage v2 to measure heart rate and heart rate variability at rest. Sensors 2022; 22:137.
Støve MP, Haucke E, Nymann ML, et al. Accuracy of the wearable activity tracker Garmin Forerunner 235 for the assessment of heart rate during rest and activity. J Sports Sci 2019; 37:895–901.
Düking P, Giessing L, Frenkel MO, et al. Wrist-worn wearables for monitoring heart rate and energy expenditure while sitting or performing light-to-vigorous physical activity: validation study. JMIR mHealth uHealth 2020; 8:e16716.
Ho WTe, Yang YJ, Li TC. Accuracy of wrist-worn wearable devices for determining exercise intensity. Digit Heal 2022; 8:20552076221124393.
Martín-Escudero P, Cabanas AM, Dotor-Castilla ML, et al. Are activity wrist-worn devices accurate for determining heart rate during intense exercise? Bioengineering (Basel) 2023; 10:254.
Jachymek M, Jachymek MT, Kiedrowicz R, et al. Wristbands in home-based rehabilitation: validation of heart rate measurement. Sensors 2022; 22:60.
Zhang Y, Weaver RG, Armstrong B, et al. Validity of wrist-worn photoplethysmography devices to measure heart rate: a systematic review and meta-analysis. J Sports Sci 2020; 38:2021–2034.
Miller DJ, Sargent C, Roach GD. A validation of six wearable devices for estimating sleep, heart rate and heart rate variability in healthy adults. Sensors (Basel) 2022; 22:6317.
Pietilä J, Mehrang S, Tolonen J, et al. Evaluation of the accuracy and reliability for photoplethysmography based heart rate and beat-to-beat detection during daily activities. In EMBEC & NBC 2017: Joint Conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC). Tampere, Finland 2018; 145–148.
Hinde K, White G, Armstrong N. Wearable devices suitable for monitoring twenty four hour heart rate variability in military populations. Sensors 2021; 21:1061.
Kuwabara M, Harada K, Hishiki Y, Kario K. Validation of two watch-type wearable blood pressure monitors according to the ANSI/AAMI/ISO81060-2:2013 guidelines: Omron HEM-6410T-ZM and HEM-6410T-ZL. J Clin Hypertens 2019; 21:853–858.
Kario K, Shimbo D, Tomitani N, et al. The first study comparing a wearable watch-type blood pressure monitor with a conventional ambulatory blood pressure monitor on in-office and out-of-office settings. J Clin Hypertens 2019; 22:135–141.
Sayer G, Piper G, Vorovich E, et al. Continuous monitoring of blood pressure using a wrist-worn cuffless device. Am J Hypertens 2022; 35:407–413.
Falter M, Scherrenberg M, Driesen K, et al. Smartwatch-based blood pressure measurement demonstrates insufficient accuracy. Front Cardiovasc Med 2022; 9:958212.
Germini F, Noronha N, Debono VB, et al. Accuracy and acceptability of wrist-wearable activity-tracking devices: systematic review of the literature. J Med Internet Res 2022; 24:e30791.
O’Driscoll R, Turicchi J, Beaulieu K, et al. How well do activity monitors estimate energy expenditure? A systematic review and meta-analysis of the validity of current technologies. Br J Sports Med 2020; 54:332–340.
Sjoberg V, Westergren J, Monnier A, et al. Wrist-worn activity trackers in laboratory and free-living settings for patients with chronic pain: criterion validity study. JMIR mHealth uHealth 2021; 9:e24806.
Chevance G, Golaszewski NM, Tipton E, et al. Accuracy and precision of energy expenditure, heart rate, and steps measured by combined-sensing Fitbits against reference measures: systematic review and meta-analysis. JMIR mHealth uHealth 2022; 10:e35626.
Gorzelitz J, Farber C, Gangnon R, Cadmus-Bertram L. Accuracy of wearable trackers for measuring moderate- to vigorous-intensity physical activity: a systematic review and meta-analysis. J Meas Phys Behav 2020; 3:346–357.
Tedesco S, Sica M, Ancillao A, et al. Validity evaluation of the fitbit Charge2 and the Carmin Vivosmart HR+ in free-living environments in an older adult cohort. JMIR mHealth uHealth 2019; 7:e13084.
Huang JD, Wang J, Ramsey E, et al. Applying artificial intelligence to wearable sensor data to diagnose and predict cardiovascular disease: a review. Sensors (Basel) 2022; 22:8002.
Barrachina-Fernández M, Maitín AM, Sánchez-ávila C, Romero JP. Wearable technology to detect motor fluctuations in parkinson's disease patients: current state and challenges. Sensors (Basel) 2021; 21:4188.
Guo Y, Liu X, Peng S, et al. A review of wearable and unobtrusive sensing technologies for chronic disease management. Comput Biol Med 2021; 129:104163.
Ferguson T, Olds T, Curtis R, et al. Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses. Lancet Digit Heal 2022; 4:e615–e626.
Kim EH, Jenness JL, Miller AB, et al. Association of demographic and socioeconomic indicators with the use of wearable devices among children. JAMA Netw Open 2023; 6:e235681.
Western MJ, Armstrong MEG, Islam I, et al. The effectiveness of digital interventions for increasing physical activity in individuals of low socioeconomic status: a systematic review and meta-analysis. Int J Behav Nutr Phys Act 2021; 18:148.
Sieck CJ, Sheon A, Ancker JS, et al. Digital inclusion as a social determinant of health. NPJ Digit Med 2021; 4:52.
Paolillo EW, Lee SY, VandeBunte A, et al. Wearable use in an observational study among older adults: adherence, feasibility, and effects of clinicodemographic factors. Front Digit Heal 2022; 4:884208.
Zhang Z, Giordani B, Margulis A, Chen W. Efficacy and acceptability of using wearable activity trackers in older adults living in retirement communities: a mixed method study. BMC Geriatr 2022; 22:231.
Kim S, Choudhury A. Comparison of older and younger adults’ attitudes toward the adoption and use of activity trackers. JMIR mHealth uHealth 2020; 8:e18312.
Husain L, Greenhalgh T, Hughes G, et al. Desperately seeking intersectionality in digital health disparity research: narrative review to inform a richer theorization of multiple disadvantage. J Med Internet Res 2022; 24:e42358.
معلومات مُعتمدة: T32 AG000029 United States AG NIA NIH HHS
تواريخ الأحداث: Date Created: 20230731 Date Completed: 20231010 Latest Revision: 20231018
رمز التحديث: 20240628
DOI: 10.1097/MCO.0000000000000970
PMID: 37522804
قاعدة البيانات: MEDLINE
الوصف
تدمد:1473-6519
DOI:10.1097/MCO.0000000000000970