- Pardamean, Bens
- Soeparno, Haryono
- Budiarto, Arif
- Mahesworo, Bharuno
- Baurley, James
Objectives: Recently, wearable device technology has gained more popularity in supporting a healthy lifestyle. Hence, researchers have begun to put significant efforts into studying the direct and indirect benefits of wearable devices for health and wellbeing. This paper summarizes recent studies on the use of consumer wearable devices to improve physical activity, mental health, and health consciousness. Methods: A thorough literature search was performed from several reputable data-bases, such as PubMed, Scopus, ScienceDirect, arXiv, and bioRxiv mainly using “wearable device research” as a keyword, no earlier than 2018. As a result, 25 of the most recent and relevant papers included in this review cover several topics, such as previous literature reviews (9 papers), wearable device accuracy (3 papers), self-reported data collection tools (3 papers), and wearable device intervention (10 papers). Results: All the chosen studies are discussed based on the wearable device used, complementary data, study design, and data processing method. All these previous studies indicate that wearable devices are used either to validate their benefits for general wellbeing or for more serious medical contexts, such as cardiovascular dis-orders and post-stroke treatment. Conclusions: Despite their huge potential for adoption in clinical settings, wearable device accuracy and validity remain the key challenge to be met. Some lessons learned and future projections, such as combining traditional study design with statistical and machine learning methods, are highlighted in this paper to provide a useful over-view for other researchers carrying out similar research.
1. IDC. IDC reports strong growth in the worldwide wearables market, led by holiday shipments of smartwatches, wrist bands, and ear-worn devices [Internet]. Framingham (MA): IDC Corporate; 2019 [cited at 2020 Apr 15]. Available from: https://www.idc.com/getdoc.jsp?containerId=prUS44901819.
2. Wolf G, Kelly K. What is Quantified Self? [Internet]. [place unknown]: Quantified Self; 2019 [cited at 2020 Apr 15]. Available from: https://quantifiedself.com/about/what-is-quantified-self/.
3. Swan M. The Quantified Self: fundamental disruption in big data science and biological discovery. Big Data 2013;1(2):85-99.
4. Prince JD. The Quantified Self: operationalizing the quotidien. J Electron Resour Med Libr 2014;11(2):91-9.
5. Strath SJ, Rowley TW. Wearables for promoting physical activity. Clin Chem 2018;64(1):53-63.
6. Brickwood KJ, Watson G, O’Brien J, Williams AD. Consumer-based wearable activity trackers increase physical activity participation: systematic review and meta-analysis. JMIR Mhealth Uhealth 2019;7(4):e11819.
7. Bohm B, Karwiese SD, Bohm H, Oberhoffer R. Effects of mobile health including wearable activity trackers to increase physical activity outcomes among healthy children and adolescents: systematic review. JMIR Mhealth Uhealth 2019;7(4):e8298.
8. Aroganam G, Manivannan N, Harrison D. Review on wearable technology sensors used in consumer sport applications. Sensors (Basel) 2019;19(9):1983.
9. Khakurel J, Melkas H, Porras J. Tapping into the wearable device revolution in the work environment: a systematic review. Inf Technol People 2018;31(3):791-818.
10. Taj-Eldin M, Ryan C, O’Flynn B, Galvin P. A review of wearable solutions for physiological and emotional monitoring for use by people with autism spectrum disorder and their caregivers. Sensors (Basel) 2018;18(12):4271.
11. Johansson D, Malmgren K, Alt Murphy M. Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: a mixed-methods systematic review. J Neurol 2018;265(8):1740-52.
12. Feehan LM, Geldman J, Sayre EC, Park C, Ezzat AM, Yoo JY, et al. Accuracy of Fitbit devices: systematic review and narrative syntheses of quantitative data. JMIR Mhealth Uhealth 2018;6(8):e10527.
13. Farrahi V, Niemela M, Kangas M, Korpelainen R, Jamsa T. Calibration and validation of accelerometer-based activity monitors: a systematic review of machine-learning approaches. Gait Posture 2019;68:285-99.
14. Bloss R. Wearable sensors bring new benefits to continuous medical monitoring, real time physical activity assessment, baby monitoring and industrial applications. Sens Rev 2015;35(2):141-5.
15. Nelson BW, Allen NB. Accuracy of consumer wearable heart rate measurement during an ecologically valid 24-hour period: intraindividual validation study. JMIR Mhealth Uhealth 2019;7(3):e10828.
16. Witte AK, Blankenhagel KJ, Korbel JJ, Zarnekow R. How accurate is accurate enough? An evaluation of commercial fitness trackers for individual health management. Proceedings of the Americas Conference on Information Systems (AMCIS2019); 2019 Aug 15–17. Cancun, Mexico.
17. Kwon SB, Ahn JW, Lee SM, Lee J, Lee D, Hong J, et al. Estimating maximal oxygen uptake from daily activity data measured by a watch-type fitness tracker: cross-sectional study. JMIR Mhealth Uhealth 2019;7(6):e13327.
18. Dutta-Bergman MJ. Primary sources of health information: comparisons in the domain of health attitudes, health cognitions, and health behaviors. Health Commun 2004;16(3):273-88.
19. Holzinger A, Dorner S, Fodinger M, Valdez AC, Ziefle M. Chances of increasing youth health awareness through mobile wellness applications. In: Leitner G, Hitz M, Holzinger A, editors. HCI in work and learning, life and leisure. Heidelberg: Springer; 2010. p. 71-81.
20. Grifantini K. How’s my sleep?: personal sleep trackers are gaining in popularity, but their accuracy is still open to debate. IEEE Pulse 2014;5(5):14-8.
21. Etkin J. The hidden cost of personal quantification. Journal of Consumer Research 2016;42(6):967-84.
22. Jang IY, Kim HR, Lee E, Jung HW, Park H, Cheon SH, et al. Impact of a wearable device-based walking programs in rural older adults on physical activity and health outcomes: cohort study. JMIR Mhealth Uhealth 2018;6(11):e11335.
23. Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003;35(8):1381-95.
24. Rabin R, Gudex C, Selai C, Herdman M. From translation to version management: a history and review of methods for the cultural adaptation of the EuroQol five-dimensional questionnaire. Value Health 2014;17(1):70-6.
25. Butler J, Kern ML. The PERMA-Profiler: a brief multidimensional measure of flourishing. Intl J Wellbeing 2016;6(3):1-48.
26. Stiglbauer B, Weber S, Batinic B. Does your health really benefit from using a self-tracking device? Evidence from a longitudinal randomized control trial. Comput Human Behav 2019;94:131-9.
27. Sano A, Taylor S, McHill AW, Phillips AJ, Barger LK, Klerman E, et al. Identifying objective physiological markers and modifiable behaviors for self-reported stress and mental health status using wearable sensors and mobile phones: observational study. J Med Internet Res 2018;20(6):e210.
28. Horne JA, Ostberg O. A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms. Int J Chronobiol 1976;4(2):97-110.
29. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res 1989;28(2):193-213.
30. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav 1983;24(4):385-96.
31. Ware J Jr, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care 1996;34(3):220-33.
32. John OP, Srivastava S. The Big Five trait taxonomy: history, measurement, and theoretical perspectives. In: Pervin LA, John OP, editors. Handbook of personality: theory and research. 2nd ed. New York (NY): The Guilford Press; 1999. p. 102-38.
33. Compagnat M, Batcho CS, David R, Vuillerme N, Salle JY, Daviet JC, et al. Validity of the walked distance estimated by wearable devices in stroke individuals. Sensors (Basel) 2019;19(11):2497.
34. Cheong IY, An SY, Cha WC, Rha MY, Kim ST, Chang DK, et al. Efficacy of mobile health care application and wearable device in improvement of physical performance in colorectal cancer patients undergoing chemotherapy. Clin Colorectal Cancer 2018;17(2):e353-e362.
35. Lim WK, Davila S, Teo JX, Yang C, Pua CJ, Blocker C, et al. Beyond fitness tracking: the use of consumer-grade wearable data from normal volunteers in cardiovascular and lipidomics research. PLoS Biol 2018;16(2):e2004285.
36. Buchele Harris H, Chen W. Technology-enhanced classroom activity breaks impacting children’s physical activity and fitness. J Clin Med 2018;7(7):165.
37. Reddy RK, Pooni R, Zaharieva DP, Senf B, El Youssef J, Dassau E, et al. Accuracy of wrist-worn activity monitors during common daily physical activities and types of structured exercise: evaluation study. JMIR Mhealth Uhealth 2018;6(12):e10338.
38. Vandelanotte C, Duncan MJ, Maher CA, Schoeppe S, Rebar AL, Power DA, et al. The effectiveness of a web-based computer-tailored physical activity intervention using Fitbit activity trackers: randomized trial. J Med Internet Res 2018;20(12):e11321.
39. Jones D, Crossley K, Dascombe B, Hart HF, Kemp J. Validity and reliability of the Fitbit Flex and ActiGraph GT3X+ at jogging and running speeds. Int J Sports Phys Ther 2018;13(5):860-70.
40. Redenius N, Kim Y, Byun W. Concurrent validity of the Fitbit for assessing sedentary behavior and moderate-to-vigorous physical activity. BMC Med Res Methodol 2019;19(1):29.
41. Collins JE, Yang HY, Trentadue TP, Gong Y, Losina E. Validation of the Fitbit Charge 2 compared to the Acti-Graph GT3X+ in older adults with knee osteoarthritis in free-living conditions. PLoS One 2019;14(1):e0211231.
42. Ravi D, Wong C, Lo B, Yang GZ. A deep learning approach to on-node sensor data analytics for mobile or wearable devices. IEEE J Biomed Health Inform 2017;21(1):56-64.
43. Bai J, Sun Y, Schrack JA, Crainiceanu CM, Wang MC. A two-stage model for wearable device data. Biometrics 2018;74(2):744-52.
44. de Quadros T, Lazzaretti AE, Schneider FK. A movement decomposition and machine learning-based fall detection system using wrist wearable device. IEEE Sens J 2018;18(12):5082-9.
45. Shashikumar SP, Shah AJ, Li Q, Clifford GD, Nemati S. A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology. Proceedings of 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI); 2017 Feb 16–19; Orlando, FL. p. 141-4.
46. Varatharajan R, Manogaran G, Priyan MK, Sundarasekar R. Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Cluster Comput 2018;21(1):681-90.