Download PDF
Appl Clin Inform 2016; 07(03): 850-869
DOI: 10.4338/ACI-2016-03-R-0042
Review
Schattauer GmbH

Healthcare Applications of Smart Watches

A Systematic Review
Tsung-Chien Lu
1   Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan
2   Division of Biomedical and Health Informatics, Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA, USA
,
Chia-Ming Fu
1   Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan
,
Matthew Huei-Ming Ma
1   Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan
,
Cheng-Chung Fang
1   Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan
,
Anne M. Turner
2   Division of Biomedical and Health Informatics, Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA, USA
3   Department of Health Services, School of Public Health, University of Washington, Seattle, WA, USA
› Author AffiliationsThe authors would like to thank Beryl Schulman, PhD, Jean O. Taylor, PhD, and Kristin Dew, MS for their assisting with preparation of the manuscript and critical review.
Further Information

Publication History

received: 23 March 2016

accepted: 02 August 2016

Publication Date:
19 December 2017 (online)

Also available at
   Permissions and Reprints

Summary

Objective

The aim of this systematic review is to synthesize research studies involving the use of smart watch devices for healthcare.

Materials and Methods

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was chosen as the systematic review methodology. We searched PubMed, CINAHL Plus, EMBASE, ACM, and IEEE Xplore. In order to include ongoing clinical trials, we also searched ClinicalTrials.gov. Two investigators evaluated the retrieved articles for inclusion. Discrepancies between investigators regarding article inclusion and extracted data were resolved through team discussion.

Results

356 articles were screened and 24 were selected for review. The most common publication venue was in conference proceedings (13, 54%). The majority of studies were published or presented in 2015 (19, 79%). We identified two registered clinical trials underway. A large proportion of the identified studies focused on applications involving health monitoring for the elderly (6, 25%). Five studies focused on patients with Parkinson’s disease and one on cardiac arrest. There were no studies which reported use of usability testing before implementation.

Discussion

Most of the reviewed studies focused on the chronically ill elderly. There was a lack of detailed description of user-centered design or usability testing before implementation. Based on our review, the most commonly used platform in healthcare research was that of the Android Wear. The clinical application of smart watches as assistive devices deserves further attention.

Conclusion

Smart watches are unobtrusive and easy to wear. While smart watch technology supplied with biosensors has potential to be useful in a variety of healthcare applications, rigorous research with their use in clinical settings is needed.

Citation: Lu T-C, Fu C-M, Ma M H-M, Fang C-C, Turner AM. Healthcare applications of smart watches: A systematic review.

Keywords

Other clinical informatics applications - Interfaces and usability - Healthcare - Wearable device - Smart watch
 

  • References

  • 1 Patel MS, Asch DA, Volpp KG. Wearable devices as facilitators, not drivers, of health behavior change. JAMA 2015; 313: 459-460.
    CrossrefPubMedGoogle Scholar
  • 2 CCS Insight. Wearables Market to Be Worth $25 Billion by 2019. [cited 2016 May 17]. Available from http://www.ccsinsight.com/press/company-news/2332-wearables-market-to-be-worth-25-billion-by-2019-reveals-ccs-insight
    PubMedGoogle Scholar
  • 3 Lukowicz P, Kirstein T, Tröster G. Wearable systems for health care applications. Methods Inf Med 2004; 43: 232-238.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 4 Ajami S, Teimouri F. Features and application of wearable biosensors in medical care. J Res Med Sci 2015; 20: 1208-1215.
    CrossrefPubMedGoogle Scholar
  • 5 Schroetter J. The Future of Wearable Computing in Healthcare. 2014. Aug [cited 2015 Dec 3]. Available from http://www.mdtmag.com/blog/2014/01/future-wearable-computing-healthcare
    Google Scholar
  • 6 Poon CC, Zhang YT. Perspectives on high technologies for low-cost healthcare. IEEE Eng Med Biol Mag 2008; 27: 42-47.
    CrossrefPubMedGoogle Scholar
  • 7 Zheng YL, Ding XR, Poon CC, Lo BP, Zhang H, Zhou XL, Yang GZ, Zhao N, Zhang YT. Unobtrusive sensing and wearable devices for health informatics. IEEE Trans Biomed Eng 2014; 61: 1538-1554.
    CrossrefPubMedGoogle Scholar
  • 8 O’Donnell B. Smartwatches: The New Smartphones Jr.?. 2015 Apr [cited 2015 Dec 3]. Available from http://www.usatoday.com/story/tech/2015/04/02/smartwatches-the-new-smartphones/70825490/
    PubMedGoogle Scholar
  • 9 Scher DL. Will the Apple Watch Revolutionize Healthcare?. Medscape Business of Medicine. 2015 June [cited 2015 Dec 3]. Available from: http://www.medscape.com/viewarticle/845762
    PubMedGoogle Scholar
  • 10 Moher D, Liberati A, Tetzlaff J, Altman DG. PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 2009; 339: b2535.
    CrossrefPubMedGoogle Scholar
  • 11 Casilari E, Oviedo-Jiménez MA. Automatic Fall Detection System Based on the Combined Use of a Smartphone and a Smartwatch. PLoS One 2015; 10: e0140929.
    CrossrefPubMedGoogle Scholar
  • 12 Mortazavi B, Nemati E, VanderWall K, Flores-Rodriguez HG, Cai JY, Lucier J, Naeim A, Sarrafzadeh M. Can Smartwatches Replace Smartphones for Posture Tracking?. Sensors (Basel) 2015; 15: 26783-26800.
    CrossrefPubMedGoogle Scholar
  • 13 Patterson AL, Mudigoudar B, Fulton S, McGregor A, Poppel KV, Wheless MC, Brooks L, Wheless JW. SmartWatch by SmartMonitor: Assessment of Seizure Detection Efficacy for Various Seizure Types in Children, a Large Prospective Single-Center Study. Pediatr Neurol 2015; 53: 309-311.
    CrossrefPubMedGoogle Scholar
  • 14 Kalantarian H, Sarrafzadeh M. Audio-based detection and evaluation of eating behavior using the smartwatch platform. Comput Biol Med 2015; 65: 1-9.
    CrossrefPubMedGoogle Scholar
  • 15 Fardoun HM, Mashat AA, Ramirez JCastillo. Recognition of familiar people with a mobile cloud architecture for Alzheimer patients. Disabil Rehabil 2015; 1-5.
    PubMedGoogle Scholar
  • 16 Carlson JD, Mittek M, Parkison SA, Sathler P, Bayne D, Psota ET, Perez LC, Bonasera SJ. Smart watch RSSI localization and refinement for behavioral classification using laser-SLAM for mapping and fingerprinting. Conf Proc IEEE Eng Med Biol Soc 2014; 2014: 2173-2176.
    PubMedGoogle Scholar
  • 17 Wile DJ, Ranawaya R, Kiss ZH. Smart watch accelerometry for analysis and diagnosis of tremor. J Neurosci Methods 2014; 230: 1-4.
    CrossrefPubMedGoogle Scholar
  • 18 Sailer F, Pobiruchin M, Wiesner M. An Approach to Improve Medication Adherence by Smart Watches. In the 26th Medical Informatics Europe Conference: Digital Healthcare Empowering Europeans: Proceedings of MIE 2015; 2015. May 27-29 Madrid, Spain.:
    Google Scholar
  • 19 Gazit E, BernadElazari H, Moore ST, Cho C, Kubota K, Vincent L, Cohen S, Reitblat L, Fixler N, Mirelman A, Giladi N, Hausdorff JM. Assessment of Parkinsonian motor symptoms using a continuously worn smartwatch: Preliminary experience. Mov Disord 2015; 30 (Suppl. 01) S272.
    PubMedGoogle Scholar
  • 20 Ahanathapillai V, Amor JD, James CJ. Assistive technology to monitor activity, health and wellbeing in old age: The wrist wearable unit in the USEFIL project. Technol Disabil 2015; 27: 17-29.
    CrossrefPubMedGoogle Scholar
  • 21 Gruenerbl A, Prikl G, Monger E, Gobbi M, Lukowicz P. Smart-watch life saver: Smart-watch interactivefeedback system for improving bystander CPR. In The 19th International Symposium on Wearable Computers (ISWC 2015); 2015 Sep 7–11; Osaka, Japan. New York: ACM; 2015
    Google Scholar
  • 22 Porzi L, Messelodi S, Modena CM, Ricci EA. smart watch-based gesture recognition system for assisting people with visual impairments. In Proceedings of the 3rd ACM international workshop on Interactive multimedia on mobile & portable devices, 19–24; 2013 Oct pp. 21–25; Barcelona, Spain. New York: ACM; 2013
    Google Scholar
  • 23 Mielke M, Brück R. A Pilot Study about the Smartwatch as Assistive Device for Deaf People. In Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility, pp. 301–2; 2015 Oct 26–28; Lisbon, Portugal. New York: ACM; 2015
    Google Scholar
  • 24 Dubey H, Goldberg JC, Abtahi M, Mahler L, Mankodiya K. EchoWear: Smartwatch Technology for Voice and Speech Treatments of Patients with Parkinson’s Disease. In Proceedings of the conference on Wireless Health; 2015 Oct 14–16; Bethesda, MD, USA. New York: ACM; 2015
    Google Scholar
  • 25 Lee S, Kim Y, Ahn D, Ha R, Lee K, Cha H. Non-obstructive Room-level Locating System in Home Environments using Activity Fingerprints from Smartwatch. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp.939–50; 2015 Sep 7–11; Osaka, Japan. New York: ACM; 2015
    Google Scholar
  • 26 Thomaz E, Essa I, Abowd GD. A Practical Approach for Recognizing Eating Moments with Wrist- Mounted Inertial Sensing. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp.1029–40; 2015 Sep 7–11; Osaka, Japan. New York: ACM; 2015
    Google Scholar
  • 27 Maglogiannis I, Spyroglou G, Panagopoulos C, Mazonaki M. Mobile reminder system for furthering patient adherence utilizing commodity smartwatch and Android devices. In 2014 EAI 4th International Conference on Wireless Mobile Communication and Healthcare (Mobihealth), pp. 124–7; 2014 Nov 3–5; Athens, Greece. IEEE Xplore; 2014
    Google Scholar
  • 28 Sen S, Subbaraju V, Misra A, Balan RK, Lee Y. The case for smartwatch-based diet monitoring. In 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp.585–90; 2015 Mar 23–27; St. Louis, Missouri, USA. IEEE Xplore; 2015
    Google Scholar
  • 29 Sanders TH, Clements MA. Multimodal monitoring for neurological disorders. In 2014 40th Annual Northeast on Bioengineering Conference; 2014 Apr 25–27; Boston, Mass., USA. IEEE Xplore; 2014
    Google Scholar
  • 30 Kalantarian H, Alshurafa N, Nemati E, Le T, Sarrafzadeh M. A Smartwatch-Based Medication Adherence System. In 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks; 2015 Jun 9–12; Cambridge, MA, USA. IEEE Xplore; 2015
    Google Scholar
  • 31 Jovanov E. Preliminary Analysis of the Use of Smartwatches for Longitudinal Health Monitoring. In 2015 37th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society; 2015 Aug 25–29; Milan, Italy. IEEE Xplore; 2015
    Google Scholar
  • 32 Ye X, Chen G, Cao Y. Automatic Eating Detection Using Head-Mount and Wrist-Worn Accelerometers. In 2015 17th International Conference on E-health Networking, Application & Services (HealthCom). 2015 Oct 14–17; Boston, MA, USA. IEEE Xplore; 2015
    Google Scholar
  • 33 Steins D. The Effect of Activity Feedback Enabled by Smart Watches During In-patient Stroke Rehabilitation. 2015 Oct [cited 2015 Dec 3]; Available from: https://clinicaltrials.gov/ct2/show/NCT02587585
    PubMedGoogle Scholar
  • 34 Faber MJ. RealPD Trial: Development of Clinical Prognostic Models for Parkinson’s Disease. 2015 Aug [cited 2015 Dec 3]; Available from: https://clinicaltrials.gov/ct2/show/NCT02474329
    PubMedGoogle Scholar
  • 35 Apple Inc. Developing for watchOS. [cited 2015 Dec 3]; Available from: https://developer.apple.com/watchos/
    PubMedGoogle Scholar
  • 36 Android Official Blog. Cellular support comes to Android Wear. [cited 2015 Dec 3]; Available from: http://officialandroid.blogspot.tw/2015/11/cellular-support-comes-to-android-wear.html
    PubMedGoogle Scholar
  • 37 Comstock J. Eight ways the Microsoft Kinect will change healthcare. [cited 2016 May 20]; Available from: http://mobihealthnews.com/25281/eight-ways-the-microsoft-kinect-will-change-healthcare
    PubMedGoogle Scholar
  • 38 Álvarez M, Bosch J, Martínez A, Macías F, Valdéz P. 3D sensors, the new paradigm for assessing movement disorders. In the MDS 17th International Congress of Parkinson’s Disease and Movement Disorders: Movement Disorder; 2013 June 16-20; Sydney, Australia.
    PubMedGoogle Scholar
  • 39 Galna B, Barry G, Jackson D, Mhiripiri D, Olivier P, Rochester L. Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson’s disease. Gait Posture 2014; 39: 1062-1068.
    PubMedGoogle Scholar
  • 40 Ťupa O, Procházka A, Vyšata O, Schätz M, Mareš J, Vališ M, Mařík V. Motion tracking and gait feature estimation for recognising Parkinson’s disease using MS Kinect. Biomed Eng Online 2015; 14: 97.
    CrossrefPubMedGoogle Scholar
  • 41 Torres R, Huerta M, Clotet R, González R, Sagbay G, Erazo M, Pirrone J. Diagnosis of the corporal movement in Parkinson’s Disease using Kinect Sensors. In the World Congress on Medical Physics and Biomedical Engineering: IFMBE Proceedings; 2015 June 7-12, Toronto, Canada.
    PubMedGoogle Scholar
  • 42 Elgendi M, Picon F, Magnenat-Thalmann N, Abbott D. Arm movement speed assessment via a Kinect camera: A preliminary study in healthy subjects. Biomed Eng Online 2014; 13: 88.
    CrossrefPubMedGoogle Scholar
  • 43 Vincent GK, Velkoff VA. The next four decades, the older population in the United States: 2010 to 2050, Current Population Reports. Washington, DC: U.S. Census Bureau; 2010: 25-1138.
    Google Scholar
  • 44 Healthy aging; helping people to live long and productive lives and enjoy a good quality of life. At a glance 2011. Atlanta, GA: Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Adult and Community Health. [Internet]. [cited 2015 Dec 3]; Available from: http://stacks.cdc.gov/view/cdc/6114
    PubMedGoogle Scholar
  • 45 Report to Congress: Aging services technology study. Washington, DC: U.S. Department of Health and Human Services, Assistant Secretary for Planning and Evaluation, Office of Disability, Aging and Long-Term Care Policy; 2012 Jun. [cited 2015 Dec 3]; Available from: https://aspe.hhs.gov/basic-report/reportcongress-aging-services-technology-study
    PubMedGoogle Scholar
  • 46 Rakhman AZ, Kurnianingsihi, Nugrohoi LE. Widyawani. u-FASt: Ubiquitous fall detection and alert system for elderly people in smart home environment. in 2014 Electrical Engineering and Informatics (MICEEI) on Makassar International Conference, pp.136–140; 2014 Nov 26–30; Makassar, Indonesia. IEEE Xplore; 2014
    Google Scholar
  • 47 Pannurat N, Thiemjarus S, Nantajeewarawat E. Automatic fall monitoring: a review. Sensors (Basel) 2014; 14: 12900-36.
    CrossrefPubMedGoogle Scholar
  • 48 Chaudhuri S, Oudejans D, Thompson HJ, Demiris G. Real-World Accuracy and Use of a Wearable Fall Detection Device by Older Adults. J Am Geriatr Soc 2015; 63: 2415-2416.
    CrossrefPubMedGoogle Scholar
  • 49 Rubin J, Chisnell D. Handbook of usability testing : how to plan, design, and conduct effective tests. 2nd ed. Indianapolis: Wiley Publishing; 2008
    Google Scholar
  • 50 Quesenbery W. Usable Accessibility: Making Web Sites Work Well for People with Disabilities. 2009 Feb. [cited 2015 Dec 3]; Available from: http://www.uxmatters.com/mt/archives/2009/02/usable-accessibility-making-web-sites-work-well-for-people-with-disabilities.php
    PubMedGoogle Scholar
  • 51 Meaney PA, Bobrow BJ, Mancini ME, Christenson J, de Caen AR, Bhanji F, Abella BS, Kleinman ME, Edelson DP, Berg RA, Aufderheide TP, Menon V, Leary M. CPR Quality Summit Investigators, the American Heart Association Emergency Cardiovascular Care Committee, and the Council on Cardiopulmonary, Critical Care, Perioperative and Resuscitation. Cardiopulmonary resuscitation quality: [corrected] improving cardiac resuscitation outcomes both inside and outside the hospital: a consensus statement from the American Heart Association. Circulation 2013; 128: 417-435.
    CrossrefPubMedGoogle Scholar
  • 52 Jain K. Machine Learning basics for a newbie. 2015 Jun [cited 2016 Jun 13]. Available from http://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/
    PubMedGoogle Scholar
  • 53 Simon HA. The Sciences of the Artificial. Cambridge, MA: MIT press; 1969
    Google Scholar
  • 54 Langley P. Machine learning as an experimental science. Mach Learn 1988; 03: 5-8.
    PubMedGoogle Scholar
  • 55 Langley P, Kibler D. The experimental study of machine learning. NASA Ames Research Center, Moffett Field, CA. 1991 Unpublished Report. [cited 2016 May 17]. Available from http://cll.stanford.edu/∼langley/papers/exp
    PubMedGoogle Scholar
  • 56 Alias AR, Alias MS, Shamsuddin IZ, Ahmad RAR, Abdullah SNHS. Measure the Ability and Limitation of Gyroscope, Acceleration and Gyro-accelaration for Stabilized Platform. In the16th FIRA RoboWorld Congress: FIRA 2013 Proceedings; 2013 Aug 24–29; Kuala Lumpur, Malaysia.
    Google Scholar