Sports Medicine

, Volume 44, Issue 5, pp 671–686 | Cite as

Measuring and Influencing Physical Activity with Smartphone Technology: A Systematic Review

  • Judit Bort-Roig
  • Nicholas D. Gilson
  • Anna Puig-Ribera
  • Ruth S. Contreras
  • Stewart G. Trost
Systematic Review

Abstract

Background

Rapid developments in technology have encouraged the use of smartphones in physical activity research, although little is known regarding their effectiveness as measurement and intervention tools.

Objective

This study systematically reviewed evidence on smartphones and their viability for measuring and influencing physical activity.

Data Sources

Research articles were identified in September 2013 by literature searches in Web of Knowledge, PubMed, PsycINFO, EBSCO, and ScienceDirect.

Study Selection

The search was restricted using the terms (physical activity OR exercise OR fitness) AND (smartphone* OR mobile phone* OR cell phone*) AND (measurement OR intervention). Reviewed articles were required to be published in international academic peer-reviewed journals, or in full text from international scientific conferences, and focused on measuring physical activity through smartphone processing data and influencing people to be more active through smartphone applications.

Study Appraisal and Synthesis Methods

Two reviewers independently performed the selection of articles and examined titles and abstracts to exclude those out of scope. Data on study characteristics, technologies used to objectively measure physical activity, strategies applied to influence activity; and the main study findings were extracted and reported.

Results

A total of 26 articles (with the first published in 2007) met inclusion criteria. All studies were conducted in highly economically advantaged countries; 12 articles focused on special populations (e.g. obese patients). Studies measured physical activity using native mobile features, and/or an external device linked to an application. Measurement accuracy ranged from 52 to 100 % (n = 10 studies). A total of 17 articles implemented and evaluated an intervention. Smartphone strategies to influence physical activity tended to be ad hoc, rather than theory-based approaches; physical activity profiles, goal setting, real-time feedback, social support networking, and online expert consultation were identified as the most useful strategies to encourage physical activity change. Only five studies assessed physical activity intervention effects; all used step counts as the outcome measure. Four studies (three pre–post and one comparative) reported physical activity increases (12–42 participants, 800–1,104 steps/day, 2 weeks–6 months), and one case-control study reported physical activity maintenance (n = 200 participants; >10,000 steps/day) over 3 months.

Limitations

Smartphone use is a relatively new field of study in physical activity research, and consequently the evidence base is emerging.

Conclusions

Few studies identified in this review considered the validity of phone-based assessment of physical activity. Those that did report on measurement properties found average-to-excellent levels of accuracy for different behaviors. The range of novel and engaging intervention strategies used by smartphones, and user perceptions on their usefulness and viability, highlights the potential such technology has for physical activity promotion. However, intervention effects reported in the extant literature are modest at best, and future studies need to utilize randomized controlled trial research designs, larger sample sizes, and longer study periods to better explore the physical activity measurement and intervention capabilities of smartphones.

References

  1. 1.
    Heath GW, Parra DC, Sarmiento OL, et al. Evidence-based intervention in physical activity: lessons from around the world. Lancet. 2012;380(9838):272–81.CrossRefPubMedGoogle Scholar
  2. 2.
    Krishna S, Boren SA, Balas EA. Healthcare via cell phones: a systematic review. Telemed JE Health. 2009;15(3):231–40.CrossRefGoogle Scholar
  3. 3.
    Gilson ND, Faulkner G, Murphy M, et al. Walk@Work: an automated intervention to increase walking in university employees not achieving 10,000 daily steps. Prev Med. 2013;56(5):283–7.CrossRefPubMedGoogle Scholar
  4. 4.
    Global Mobile Statistics 2013 Home [online]. Available from URL: http://mobithibking.com/mobile-marketing-tool/latest-mobile-stats. Accessed 2013 Jun.
  5. 5.
    Vital Wave Consulting. mHealth for development: The opportunity of mobile technology for healthcare in the developing world. Washington, DC, and Berskshire, UK: UN Fundation-Vodafone Fundation Partnership, 2009.Google Scholar
  6. 6.
    International Data Corporation [online]. http://www.idc.com/getdoc.jsp?containerId=prUS24302813. Accessed 2013 Nov.
  7. 7.
    Intille SS, Lester J, Sallis JF, et al. New horizons in sensor development. Med Sci Sports Exerc 2012; 44 (Suppl. 1): 24–31S.Google Scholar
  8. 8.
    Klasnja P, Pratt W. Healthcare in the pocket: mapping the space of mobile-phone health interventions. J Biomed Inform. 2012;45(1):184–98.CrossRefPubMedCentralPubMedGoogle Scholar
  9. 9.
    Patrick K, Griswold WG, Raab F, et al. Health and the mobile phone. Am J Prev Med. 2008;35(2):177–81.CrossRefPubMedCentralPubMedGoogle Scholar
  10. 10.
    Lau PWC, Lau EY, Wong DP, et al. A systematic review of information and communication technology-based interventions for promoting physical activity behavior change in children and adolescents. J Med Internet Res 2011;13(3).Google Scholar
  11. 11.
    Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight: a systematic review. J Cardiovasc Nurs. 2013;28(4):320–9.CrossRefPubMedGoogle Scholar
  12. 12.
    Fanning J, Mullen S, McAuley E. Increasing physical activity with mobile devices: a meta-analysis. J Med Internet Res. 2012;14(6):161–9.CrossRefGoogle Scholar
  13. 13.
    Ketabdar H, Lyra M. System and methodology for using mobile phones in live remote monitoring of physical activities. In: Proceedings of the IEEE International Symposium on Technology and Society; 2010 Jun 7–9; Wollongong (NSW). IEEE publications; 2010. p. 350–6.Google Scholar
  14. 14.
    Sheng Zhong SZ, Li Wang LW, Ana M. Bernardos, et al. An accurate and adaptive pedometer integrated in mobile health application. In: Proceedings of the IET International Conference on Wireless Sensor Network; 2010 Nov 15–17; Beijing (CN). IET Conference Publications; 2010. p. 78–83.Google Scholar
  15. 15.
    Wu W, Dasgupta S, Ramirez E, et al. Classification accuracies of physical activities using smartphone motion sensors. J Med Internet Res. 2012;14(5):130.CrossRefGoogle Scholar
  16. 16.
    He Yi, Li Ye. Physical activity recognition utilizing the built-in kinematic sensors of a smartphone. Int J Distrib Sens Netw. 2013;2013:10.Google Scholar
  17. 17.
    Donaire-Gonzalez D, de Nazelle A, Seto E, et al. Comparison of physical measures using mobile phone-based CalFit and Actigraph. J Med Internet Res. 2012;15(6):e111.CrossRefGoogle Scholar
  18. 18.
    Anderson I, Maitland J, Sherwood S, et al. Shakra: Tracking and sharing daily activity levels with unaugmented mobile phones. Mob Netw Appl. 2007; 12:185-199.Google Scholar
  19. 19.
    Arsand E, Tatara N, Østengen G, et al. Mobile phone-based self-management tools for type 2 diabetes: The few touch application. J Diabetes Sci Technol. 2010;4(2):328–36.CrossRefPubMedCentralPubMedGoogle Scholar
  20. 20.
    van Dantzig S, Geleijnse G, van Halteren AT. Toward a persuasive mobile application to reduce sedentary behavior. Personal Ubiquitous Comput. 2012;17(6):1237–46.CrossRefGoogle Scholar
  21. 21.
    Tsai CC, Lee G, Raab F, et al. Usability and feasibility of PmEB: a mobile phone application for monitoring real time caloric balance. Mob Netw Appl. 2007;12(2–3):173–84.CrossRefGoogle Scholar
  22. 22.
    Gao C, Kong F, Tan J. HealthAware: Tackling obesity with health aware smart phone systems. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics; 2009 Dec 19–23; Guilin (CN). IEEE publications; 2009. p. 1549–54.Google Scholar
  23. 23.
    Fukuoka Y, Vittinghoff E, Jong SS, et al. Innovation to motivation: pilot study of a mobile phone intervention to increase physical activity among sedentary women. Prev Med. 2010;51(3–4):287–9.CrossRefPubMedCentralPubMedGoogle Scholar
  24. 24.
    Fukuoka Y, Kamitani E, Dracup K, et al. New insights into compliance with a mobile phone diary and pedometer use in sedentary women. J Phys Act Health. 2011;8(3):398–403.PubMedGoogle Scholar
  25. 25.
    Nguyen HQ, Gill DP, Wolpin S, et al. Pilot study of a cell phone-based exercise persistence intervention post-rehabilitation for COPD. Int J Chronic Obstr Pulm Dis. 2009;4:301–13.CrossRefGoogle Scholar
  26. 26.
    Toscos T, Faber A, Connelly K, et al. Encouraging physical activity in teens can technology help reduce barriers to physical activity in adolescent girls? In: Proceedings of the Second International Conference on Pervasive Computing Technologies for Healthcare; 2008 Jan 30–Feb 1; Tempere (FI). Pervasive Health. 2008; p. 218–21.Google Scholar
  27. 27.
    Schiel R, Kaps A, Bieber G, et al. Identification of determinants for weight reduction in overweight and obese children and adolescents. J Telemed Telecare. 2010;16(7):368–73.CrossRefPubMedGoogle Scholar
  28. 28.
    Schiel R, Thomas A, Kaps A, et al. An innovative telemedical support system to measure physical activity in children and adolescents with type 1 diabetes mellitus. Exp Clin Endocrinol Diabetes. 2011;119(9):565–8.CrossRefPubMedGoogle Scholar
  29. 29.
    Mattila J, Ding H, Mattila E, et al. Mobile tools for home-based cardiac rehabilitation based on heart rate and movement activity analysis. In: Proceedings of theAnnual International Conference of the IEEE Engineering in Medicine and Biology Society; 2009 Sept 3–6; Minneapolis (MN). IEEE publications; 2009. p. 6448–52.Google Scholar
  30. 30.
    Mattila E, Parkka J, Hermersdorf M, et al. Mobile diary for wellness management—results on usage and usability in two user studies. IEEE Trans Inform Technol Biomed. 2008; 2(4).Google Scholar
  31. 31.
    Mattila E, Lappalainen R, Pärkkä J, et al. Use of a mobile phone diary for observing weight management and related behaviours. J Telemed Telecare. 2010;16(5):260–4.CrossRefPubMedGoogle Scholar
  32. 32.
    Lee W, Chae YM, Kim S, et al. Evaluation of a mobile phone-based diet game for weight control. J Telemed Telecare. 2010;16(5):270–5.CrossRefPubMedGoogle Scholar
  33. 33.
    Stuckey M, Russell-Minda E, Read E, et al. Diabetes and technology for increased activity (DaTA) study: Results of a remote monitoring intervention for prevention of metabolic syndrome. J Diabetes Sci Technol. 2011;5(4):928–35.CrossRefPubMedCentralPubMedGoogle Scholar
  34. 34.
    Varnfield M, Karunanithi MK, Särelä A, et al. Uptake of a technology-assisted home-care cardiac rehabilitation program. Med J Aust. 2011;194(4):S15–9.PubMedGoogle Scholar
  35. 35.
    Khalil A, Glal S. StepUp: A step counter mobile application to promote healthy lifestyle. In: Proceedings of the International Conference on the current Trends in Information Technology; 2009 Dec 15–16; Dubai (UAE). IEEE publications; 2009. p. 208–12.Google Scholar
  36. 36.
    Lee MH, Kim J, Jee SH, et al. Integrated solution for physical activity monitoring based on mobile phone and PC. Healthc Inform Res. 2011;17(1):76–86.CrossRefPubMedCentralPubMedGoogle Scholar
  37. 37.
    Kirwan M, Duncan MJ, Vandelanotte C, et al. Using smartphone technology to monitor physical activity in the 10,000 steps program: a matched case-control trial. J Med Internet Res 2012;14 (2).Google Scholar
  38. 38.
    Fukuoka Y, Lindgren T, Jong S. Qualitative exploration of the acceptability of a mobile phone and pedometer-based physical activity program in a diverse sample of sedentary women. Public Health Nurs. 2012;29(3):232–40.CrossRefPubMedGoogle Scholar
  39. 39.
    The World Bank. Countries and Economies [online]. http://data.worldbank.org/country. Accessed 2013 Jun.
  40. 40.
    Yach D, Hawkes C, Gould CL, et al. The global burden of chronic diseases: Overcoming impediments to prevention and control. JAMA. 2004;291(21):2616–22.CrossRefPubMedGoogle Scholar
  41. 41.
    Rice RE, Katz JE. Comparing internet and mobile phone usage: Digital divides of usage, adoption, and dropouts. Telecommun Policy. 2003;27(8–9):597–623.CrossRefGoogle Scholar
  42. 42.
    Pratt M, Sarmiento OL, Montes F, et al. The implications of megatrends in information and communication technology and transportation for changes in global physical activity. Lancet. 2012;380(9838):282–93.CrossRefPubMedGoogle Scholar
  43. 43.
    Intille SS, Albinali F, Mota S, et al. Design of a wearable physical activity monitoring system using mobile phones and accelerometers. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology and Society; 2011 Aug 30–Sept 3; Boston (MA). IEEE Conference Publications. 2011; p. 3636–3639.Google Scholar
  44. 44.
    Walters DL, Sarela A, Fairfull A, et al. A mobile phone-based care model for outpatient cardiac rehabilitation: the care assessment platform (CAP). BMC Cardiovasc Disord. 2010;10:5.CrossRefPubMedCentralPubMedGoogle Scholar
  45. 45.
    Glynn LG, Hayes PS, Casey M, et al. SMART MOVE—a smartphone-based intervention to promote physical activity in primary care: study protocol for a randomized controlled trial. Trials. 2013;14:157.CrossRefPubMedCentralPubMedGoogle Scholar
  46. 46.
    Kirwan M, Duncan MJ, Vandelanotte C, et al. Design, development, and formative evaluation of a smartphone application for recording and monitoring physical activity levels: The 10,000 steps “iStepLog”. Health Educ Behav. 2013;40(2):140–51.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Judit Bort-Roig
    • 1
    • 4
  • Nicholas D. Gilson
    • 2
  • Anna Puig-Ribera
    • 1
  • Ruth S. Contreras
    • 3
  • Stewart G. Trost
    • 2
  1. 1.Grup de Recerca en Esport i Activitat FísicaUniversitat de VicBarcelonaSpain
  2. 2.School of Human Movement StudiesUniversity of QueenslandBrisbaneAustralia
  3. 3.Grup de Recerca Interaccions DigitalsUniversitat de VicBrisbaneSpain
  4. 4.Centre d’Estudis Sanitaris i Socials, Carrer de la Sagrada Família, 7Universitat de VicBarcelona, CataloniaSpain

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