Skip to main content
SpringerLink
Log in

Mobile Sensors and Wearable Technology

  • Chapter
  • First Online:
Handbook Integrated Care

  • 2509 Accesses

Abstract

The Internet of Medical Things and the integration of wearables and sensors to support optimization of health through self-management and remote monitoring have dramatically accelerated over the past decade. With this gaining momentum, wearable devices to measure individuals’ physiology such as heart rate and activity levels have become highly popular, increasingly pervasive, and creating a cultural shift to help people to collect, quantify, and observe their own data relating to their behaviours in day-to-day life. With the potential to change health behaviour through these platforms, the general public has the ability to be more engaged and participatory in their own health. For healthcare providers, these devices are improving patient care through continuous objective reporting, remote monitoring and precision medicine.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abroms, L. C., Padmanabhan, N., Thaweethai, L., & Phillips, T. (2011). iPhone apps for smoking cessation: A content analysis. American Journal of Preventive Medicine, 40(3), 279–285. https://doi.org/10.1016/j.amepre.2010.10.032.

    Article  Google Scholar 

  • Alemdar, H., & Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey. Computer Networks, 54(15), 2688–2710. https://doi.org/10.1016/j.comnet.2010.05.003.

    Article  Google Scholar 

  • AliveCor Inc. (2020). AliveCor. Retrieved February 14, 2020, from https://www.alivecor.com/

  • Anliker, U., Ward, J. A, Lukowicz, P., Tröster, G., Dolveck, F., Baer, M., Vuskovic, M., et al. (2004). AMON: A wearable multiparameter medical monitoring and alert system. IEEE Transactions on Information Technology in Biomedicine, 8(4), 415–427. https://doi.org/10.1109/TITB.2004.837888

  • Appelboom, G., Camacho, E., Abraham, M. E., Bruce, S. S., Dumont, E. L., Zacharia, B. E., Connolly, E., et al. (2014). Smart wearable body sensors for patient self-assessment and monitoring. Archives of Public Health, 72(1), 28. https://doi.org/10.1186/2049-3258-72-28

  • Arsenijevic, J., Tummers, L., & Bosma, N. (2018). Adherence to E-health tools among vulnerable groups: A systematic literature review with meta-analyses. Journal of Medical Internet Research, 22(2), e11613. https://doi.org/10.2196/11613.

    Article  Google Scholar 

  • Azar, K. M. J., Lesser, L. I., Laing, B. Y., Stephens, J., Aurora, M. S., Burke, L. E., & Palaniappan, L. P. (2013). Mobile applications for weight management. American Journal of Preventive Medicine, 45(5), 583–589. https://doi.org/10.1016/j.amepre.2013.07.005.

    Article  Google Scholar 

  • Banaee, H., Ahmed, M. U., & Loutfi, A. (2013). Data mining for wearable sensors in health monitoring systems: A review of recent trends and challenges. Sensors, 13(12), 17472–17500. https://doi.org/10.3390/s131217472.

    Article  Google Scholar 

  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory (Vol. 1). Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Baquero, G. A., Banchs, J. E., Ahmed, S., Naccarelli, G. V., & Luck, J. C. (2015). Surface 12 lead electrocardiogram recordings using smart phone technology. Journal of Electrocardiology, 48(1), 1–7. https://doi.org/10.1016/j.jelectrocard.2014.09.006.

    Article  Google Scholar 

  • Bort-Roig, J., Gilson, N. D., Puig-Ribera, A., Contreras, R. S., & Trost, S. G. (2014). Measuring and influencing physical activity with smartphone technology: A systematic review. Sports Medicine, 44(5), 671–686. https://doi.org/10.1007/s40279-014-0142-5.

    Article  Google Scholar 

  • Breland, J. Y., Yeh, V. M., & Yu, J. (2013). Adherence to evidence-based guidelines among diabetes self-management apps. Translational Behavioral Medicine, 3(3), 277–286. https://doi.org/10.1007/s13142-013-0205-4.

    Article  Google Scholar 

  • Brickwood, K. J., Watson, G., O’Brien, J., & Williams, A. D. (2019). Consumer-based wearable activity trackers increase physical activity participation: Systematic review and meta-analysis. Journal of Medical Internet Research, 21(4), e11819. https://doi.org/10.2196/11819.

    Article  Google Scholar 

  • Bunn, J. A., Navalta, J. W., Fountaine, C. J., & Reece, J. D. (2017). Current state of commercial wearable technology in physical activity monitoring 2015–2017. International Journal of Exercise Science, 11(7), 503–515.

    Google Scholar 

  • Chan, A. H. Y., Stewart, A. W., Harrison, J., Camargo, C. A., Black, P. N., & Mitchell, E. A. (2015). The effect of an electronic monitoring device with audiovisual reminder function on adherence to inhaled corticosteroids and school attendance in children with asthma: A randomised controlled trial. The Lancet Respiratory Medicine, 3(3), 210–219. https://doi.org/10.1016/S2213-2600(15)00008-9.

    Article  Google Scholar 

  • Chan, M., Estève, D., Fourniols, J.-Y., Escriba, C., & Campo, E. (2012). Smart wearable systems: Current status and future challenges. Artificial Intelligence in Medicine, 56(3), 137–156. https://doi.org/10.1016/j.artmed.2012.09.003.

    Article  Google Scholar 

  • Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., & Leung, V. C. M. (2011). Body area networks: A survey. Mobile Networks and Applications, 16(2), 171–193. https://doi.org/10.1007/s11036-010-0260-8.

    Article  Google Scholar 

  • Connelly, C. E. (1993). An empirical study of a model of self-care in chronic illness. Clinical Nurse Specialist, 7(5), 247–253. https://doi.org/10.1097/00002800-199309000-00007.

    Article  Google Scholar 

  • Cowan, L. T., Van Wagenen, S. A., Brown, B. A., Hedin, R. J., Seino-Stephan, Y., Hall, P. C., & West, J. H. (2013). Apps of steel: Are exercise apps providing consumers with realistic expectations?: A content analysis of exercise apps for presence of behavior change theory. Health Education and Behavior, 40(2), 133–139. https://doi.org/10.1177/1090198112452126.

    Article  Google Scholar 

  • Davis, R., Campbell, R., Hildon, Z., Hobbs, L., & Michie, S. (2014). Theories of behaviour and behaviour change across the social and behavioural sciences: A scoping review. Health Psychology Review, 1–36. https://doi.org/10.1080/17437199.2014.941722

  • Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity, 12(159). https://doi.org/10.1186/s12966-015-0314-1

  • Ferdman, D. J., Liberman, L., & Silver, E. S. (2015). A smartphone application to diagnose the mechanism of pediatric supraventricular tachycardia. Pediatric Cardiology, 36(7), 1452–1457. https://doi.org/10.1007/s00246-015-1185-6.

    Article  Google Scholar 

  • Ferguson, T., Rowlands, A. V., Olds, T., & Maher, C. (2015). The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: A cross-sectional study. International Journal of Behavioral Nutrition and Physical Activity, 12(1), 42. https://doi.org/10.1186/s12966-015-0201-9.

    Article  Google Scholar 

  • Fitbit Inc. (2019, November 1). Fitbit to be acquired by google. Fitbit. Retrieved February 13, 2020, from https://investor.fitbit.com/press/press-releases/press-release-details/2019/Fitbit-to-Be-Acquired-by-Google/

  • Gao, Y., Li, H., & Luo, Y. (2010). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704–1723.

    Article  Google Scholar 

  • Haberman, Z. C., Jahn, R. T., Bose, R., Tun, H., Shinbane, J. S., Doshi, R. N., Saxon, L. A., et al. (2015). Wireless smartphone ECG enables large-scale screening in diverse populations. Journal of Cardiovascular Electrophysiology, 26(5), 520–526. https://doi.org/10.1111/jce.12634

  • Haghayegh, S., Khoshnevis, S., Smolensky, M. H., Diller, K. R., & Castriotta, R. J. (2019). Accuracy of wristband fitbit models in assessing sleep: Systematic review and meta-analysis. Journal of Medical Internet Research, 21(11), e16273. https://doi.org/10.2196/16273.

    Article  Google Scholar 

  • Ho, K., Newton, L., Booth, A., & Novak Lauscher, H. (2015). Mobile digital access to a web-enhanced network (mDAWN): Assessing the feasibility of mobile health tools for self-management of type 2 diabetes. In American Medical Informatics Association 2015 Annual Symposium. San Francisco, CA.

    Google Scholar 

  • Kim, K. J., & Shin, D.-H. (2015). An acceptance model for smart watches: Implications for the adoption of future wearable technology. Internet Research, 25(4), 527–541. https://doi.org/10.1108/IntR-05-2014-0126.

    Article  Google Scholar 

  • Kirk, M. A., Amiri, M., Pirbaglou, M., & Ritvo, P. (2019). Wearable technology and physical activity behavior change in adults with chronic cardiometabolic disease: A systematic review and meta-analysis. American Journal of Health Promotion, 33(5), 778–791. https://doi.org/10.1177/0890117118816278.

    Article  Google Scholar 

  • Kompala, T., & Neinstein, A. (2019). A new era: Increasing continuous glucose monitoring use in type 2 diabetes. The American Journal of Managed Care: Evidence-Based Diabetes Management, 25(4), S123–S126.

    Google Scholar 

  • Lee, J.-M., Kim, Y., & Welk, G. J. (2014). Validity of consumer-based physical activity monitors. Medicine & Science in Sports & Exercise, 46(9), 1840–1848. https://doi.org/10.1249/MSS.0000000000000287.

    Article  Google Scholar 

  • Lewis, Z. H., Lyons, E. J., Jarvis, J. M., & Baillargeon, J. (2015). Using an electronic activity monitor system as an intervention modality: a systematic review. BMC Public Health, 15, 585. https://doi.org/10.1186/s12889-015-1947-3.

    Article  Google Scholar 

  • Miyamoto, S. W., Henderson, S., Young, H. M., & Pande, A. (2016). Tracking health data is not enough: A qualitative exploration of the role of healthcare partnerships and mHealth technology to promote physical activity and to sustain behavior change. JMIR MHealth UHealth, 4(1), 1–12. https://doi.org/10.2196/mhealth.4814.

    Article  Google Scholar 

  • O’Driscoll, R., Turicchi, J., Beaulieu, K., Scott, S., Matu, J., Deighton, K., Stubbs, J., et al. (2018). How well do activity monitors estimate energy expenditure? A systematic review and meta-analysis of the validity of current technologies. British Journal of Sports Medicine, bjsports-2018–099643. https://doi.org/10.1136/bjsports-2018-099643

  • Peake, J. M., Kerr, G., & Sullivan, J. P. (2018). A critical review of consumer wearables, mobile applications, and equipment for providing biofeedback, monitoring stress, and sleep in physically active populations. Frontiers in Physiology, 9(743). https://doi.org/10.3389/fphys.2018.00743

  • Rhodes, R. E., & Yao, C. A. (2015). Models accounting for intention-behavior discordance in the physical activity domain: A user’s guide, content overview, and review of current evidence. International Journal of Behavioral Nutrition and Physical Activity, 12, 1–14. https://doi.org/10.1186/s12966-015-0168-6.

    Article  Google Scholar 

  • Soliño-Fernandez, D., Ding, A., Bayro-Kaiser, E., & Ding, E. L. (2019). Willingness to adopt wearable devices with behavioral and economic incentives by health insurance wellness programs: Results of a US cross-sectional survey with multiple consumer health vignettes. BMC Public Health, 19(1), 1649–1658. https://doi.org/10.1186/s12889-019-7920-9.

    Article  Google Scholar 

  • Stephenson, A., McDonough, S. M., Murphy, M. H., Nugent, C. D., & Mair, J. L. (2017). Using computer, mobile and wearable technology enhanced interventions to reduce sedentary behaviour: A systematic review and meta-analysis. The International Journal of Behavioral Nutrition and Physical Activity, 14(1), 105–117. https://doi.org/10.1186/s12966-017-0561-4.

    Article  Google Scholar 

  • Swan, M. (2009). Emerging patient-driven health care models: An examination of health social networks, consumer personalized medicine and quantified self-tracking. International Journal of Environmental Research and Public Health, 6(2), 492–525. https://doi.org/10.3390/ijerph6020492.

    Article  Google Scholar 

  • U.S. Food and Drug Administration. (2020a, February 10). Device classification under Section 513(f)(2)(de novo). FDA, U.S. Food and Drug Administration. Retrieved February 14, 2020, from https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/denovo.cfm?ID=DEN180044

  • U.S. Food and Drug Administration. (2020b, February 10). 510(k) Premarket Notification. FDA, U.S. Food and Drug Administration. Retrieved February 14, 2020, from https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMN/pmn.cfm?ID=K173310

  • Ware, P., Ross, H. J., Cafazzo, J. A., Boodoo, C., Munnery, M., & Seto, E. (2020). Outcomes of a heart failure telemonitoring program implemented as standard of care in an outpatient heart function clinic: Pretest-posttest pragmatic study. Journal of Medical Internet Research, 22(2), e16538. https://doi.org/10.2196/16538.

    Article  Google Scholar 

  • Welsh, J. B., & Thomas, R. (2019). Continuous glucose monitoring: An emerging standard of care. The American Journal of Managed Care: Evidence-Based Diabetes Management, 25(4), S116–S119. https://doi.org/10.1007/978-3-319-70539-2_15.

    Article  Google Scholar 

  • West, J. H., Hall, P. C., Hanson, C. L., Barnes, M. D., Giraud-Carrier, C., & Barrett, J. (2012). There’s an app for that: Content analysis of paid health and fitness apps. Journal of Medical Internet Research, 14(3), e72. https://doi.org/10.2196/jmir.1977.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Medicine, University of British Columbia, Vancouver, Canada

    Christopher A. Yao

  2. Digital Emergency Medicine, University of British Columbia, 818 West 10th Avenue (3rd Floor), Vancouver, BC, V5Z 1M9, Canada

    Kendall Ho

Authors
  1. Christopher A. Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kendall Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kendall Ho .

Editor information

Editors and Affiliations

  1. Institute for Epidemiology, Social Medicine and Health System Research, Medical School Hannover, Hannover, Germany

    Volker Amelung

  2. Leiden University Medical Center, Leiden, The Netherlands

    Viktoria Stein

  3. Faculty of Social Work, University of Calgary, Calgary, AB, Canada

    Esther Suter

  4. University of Newcastle, Gosford, NSW, Australia

    Nicholas Goodwin

  5. Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK

    Ellen Nolte

  6. Innovation Division and Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel

    Ran Balicer

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Yao, C.A., Ho, K. (2021). Mobile Sensors and Wearable Technology. In: Amelung, V., Stein, V., Suter, E., Goodwin, N., Nolte, E., Balicer, R. (eds) Handbook Integrated Care. Springer, Cham. https://doi.org/10.1007/978-3-030-69262-9_30

Download citation

Publish with us

Policies and ethics

Search

Navigation