Skip to main content
SpringerLink
Log in

mHealth Technologies to Influence Physical Activity and Sedentary Behaviors: Behavior Change Techniques, Systematic Review and Meta-Analysis of Randomized Controlled Trials

  • Original Article
  • Published:
Annals of Behavioral Medicine

Abstract

Background

mHealth programs offer potential for practical and cost-effective delivery of interventions capable of reaching many individuals.

Purpose

To (1) compare the effectiveness of mHealth interventions to promote physical activity (PA) and reduce sedentary behavior (SB) in free-living young people and adults with a comparator exposed to usual care/minimal intervention; (2) determine whether, and to what extent, such interventions affect PA and SB levels and (3) use the taxonomy of behavior change techniques (BCTs) to describe intervention characteristics.

Methods

A systematic review and meta-analysis following PRISMA guidelines was undertaken to identify randomized controlled trials (RCTs) comparing mHealth interventions with usual or minimal care among individuals free from conditions that could limit PA. Total PA, moderate-to-vigorous intensity physical activity (MVPA), walking and SB outcomes were extracted. Intervention content was independently coded following the 93-item taxonomy of BCTs.

Results

Twenty-one RCTs (1701 participants—700 with objectively measured PA) met eligibility criteria. SB decreased more following mHealth interventions than after usual care (standardised mean difference (SMD) −0.26, 95 % confidence interval (CI) −0.53 to −0.00). Summary effects across studies were small to moderate and non-significant for total PA (SMD 0.14, 95 % CI −0.12 to 0.41); MVPA (SMD 0.37, 95 % CI −0.03 to 0.77); and walking (SMD 0.14, 95 % CI −0.01 to 0.29). BCTs were employed more frequently in intervention (mean = 6.9, range 2 to 12) than in comparator conditions (mean = 3.1, range 0 to 10). Of all BCTs, only 31 were employed in intervention conditions.

Conclusions

Current mHealth interventions have small effects on PA/SB. Technological advancements will enable more comprehensive, interactive and responsive intervention delivery. Future mHealth PA studies should ensure that all the active ingredients of the intervention are reported in sufficient detail.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Blair SN, Morris JN: Healthy hearts—and the universal benefits of being physically active: Physical activity and health. Ann Epidemiol. 2009, 19:253–256.

    Article  PubMed  Google Scholar 

  2. Arem H, Moore SC, Patel A, et al.: Leisure time physical activity and mortality: A detailed pooled analysis of the dose-response relationship. JAMA Intern Med. 2015, 175:959–967.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Hallal PC, Andersen LB, Bull FC, et al.: Global physical activity levels: Surveillance progress, pitfalls, and prospects. Lancet. 2012, 380:247–257.

    Article  PubMed  Google Scholar 

  4. Richards J, Hillsdon M, Thorogood M, Foster C: Face-to-face interventions for promoting physical activity. Cochrane Database Syst Rev. 2013, 9:CD010392.

    Google Scholar 

  5. Foster C, Richards J, Thorogood M, Hillsdon M: Remote and web 2.0 interventions for promoting physical activity. Cochrane Database Syst Rev. 2013, 9:CD010395.

    PubMed  Google Scholar 

  6. World Health Organization. mHealth: New horizons for health through mobile technologies: Second global survey on eHealth. http://www.who.int/goe/publications/goe_mhealth_web.pdf, 2011.

  7. International Telecommunication Union. Key ICT indicators for developed and developing countries and the world. http://www.itu.int/en/ITU-D/Statistics/Documents/statistics/2016/ITU_Key_2005-2016_ICT_data.xls, 2016.

  8. Pew Research Center. The smartphone difference, 2015.

  9. Bort-Roig J, Gilson ND, Puig-Ribera A, Contreras RS, Trost SG: Measuring and influencing physical activity with smartphone technology: A systematic review. Sports Med. 2014, 44:671–686.

    Article  PubMed  Google Scholar 

  10. O’Reilly GA, Spruijt-Metz D: Current mHealth technologies for physical activity assessment and promotion. Am J Prev Med. 2013, 45:501–507.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Fanning J, Mullen SP, McAuley E: Increasing physical activity with mobile devices: A meta-analysis. J Med Internet Res. 2012, 14:e161.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Turner T, Spruijt-Metz D, Wen CK, Hingle MD: Prevention and treatment of pediatric obesity using mobile and wireless technologies: a systematic review. Pediatr Obes. 2015, 10:403–409.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Michie S, Richardson M, Johnston M, et al.: The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013, 46:81–95.

    Article  PubMed  Google Scholar 

  14. Taber DR, Stevens J, Murray DM, et al.: The effect of a physical activity intervention on bias in self-reported activity. Ann Epidemiol. 2009, 19:316–322.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Basterfield L, Adamson AJ, Parkinson KN, et al.: Surveillance of physical activity in the UK is flawed: Validation of the health survey for England physical activity questionnaire. Arch Dis Child. 2008, 93:1054–1058.

    Article  CAS  PubMed  Google Scholar 

  16. Garriguet D, Colley RC: A comparison of self-reported leisure-time physical activity and measured moderate-to-vigorous physical activity in adolescents and adults. Health Rep. 2014, 25:3–11.

    PubMed  Google Scholar 

  17. Skender S, Ose J, Chang-Claude J, et al.: Accelerometry and physical activity questionnaires—a systematic review. BMC Public health. 2016, 16:515.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Smith JJ, Morgan PJ, Plotnikoff RC, et al.: Smart-phone obesity prevention trial for adolescent boys in low-income communities: the ATLAS RCT. Pediatrics. 2014, 134:e723–731.

    Article  PubMed  Google Scholar 

  19. Liberati A, Altman DG, Tetzlaff J, et al.: The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration. J Clin Epidemiol. 2009, 62:e1–34.

    Article  PubMed  Google Scholar 

  20. Higgins JPT, Green S: Cochrane handbook for systematic reviews of interventions. Chichester: John Wiley & Sons, 2008.

    Book  Google Scholar 

  21. BCT Taxonomy v1 Online Training. Retrieved 11/02/2016, 2016 from http://www.webcitation.org/6fGMcajRr

  22. Higgins JPT, Deeks JJ, Altman DG: Chapter 16: special topics in statistics. In J. P. T. Higgins and S. Green (eds), Cochrane handbook for systematic reviews of interventions. Chichester: John Wiley & Sons, 2008, 481–529.

    Chapter  Google Scholar 

  23. Deeks JJ, Higgins JPT, Altman DG: Chapter 9: analysing data and undertaking meta-analyses. In J. P. T. Higgins and S. Green (eds), Cochrane handbook for systematic reviews of interventions. Chichester: John Wiley & Sons, 2008, 243–296.

    Chapter  Google Scholar 

  24. Cohen J. Statistical power analysis for the behavioral sciences: Lawrence Erlbaum Associates; 1988.

  25. Hurling R, Catt M, Boni MD, et al.: Using Internet and mobile phone technology to deliver an automated physical activity program: Randomized controlled trial. J Med Internet Res. 2007, 9:e7.

    Article  PubMed  PubMed Central  Google Scholar 

  26. King AC, Ahn DK, Oliveira BM, et al.: Promoting physical activity through hand-held computer technology. American Journal of Preventive Medicine. 2008, 34:138–142.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Shapiro JR, Bauer S, Hamer RM, et al.: Use of text messaging for monitoring sugar-sweetened beverages, physical activity, and screen time in children: a pilot study. Journal of nutrition education and behavior. 2008, 40:385–391.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Turner-McGrievy GM, Campbell MK, Tate DF, et al.: Pounds off digitally study: A randomized podcasting weight-loss intervention. Am J Prev Med. 2009, 37:263–269.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Fjeldsoe BS, Miller YD, Marshall AL: MobileMums: a randomized controlled trial of an SMS-based physical activity intervention. Ann Behav Med. 2010, 39:101–111.

    Article  PubMed  Google Scholar 

  30. Prestwich A, Perugini M, Hurling R: Can implementation intentions and text messages promote brisk walking? A randomized trial. Health Psychol. 2010, 29:40–49.

    Article  PubMed  Google Scholar 

  31. Sirriyeh R, Lawton R, Ward J: Physical activity and adolescents: An exploratory randomized controlled trial investigating the influence of affective and instrumental text messages. Br J Health Psychol. 2010, 15:825–840.

    Article  PubMed  Google Scholar 

  32. Shuger SL, Barry VW, Sui X, et al.: Electronic feedback in a diet- and physical activity-based lifestyle intervention for weight loss: A randomized controlled trial. Int J Behav Nutr Phys Act. 2011, 8:41.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Turner-McGrievy G, Tate D: Tweets, apps, and pods: Results of the 6-month mobile pounds off digitally (mobile POD) randomized weight-loss intervention among adults. J Med Internet Res. 2011, 13:e120.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Schwerdtfeger AR, Schmitz C, Warken M: Using text messages to bridge the intention-behavior gap? A pilot study on the use of text message reminders to increase objectively assessed physical activity in daily life. Frontiers in Psychology. 2012, 3:270.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Adams MA, Sallis JF, Norman GJ, et al.: An adaptive physical activity intervention for overweight adults: A randomized controlled trial. PLoS ONE [Electronic Resource]. 2013, 8:e82901.

    Google Scholar 

  36. Allen JK, Stephens J, Dennison Himmelfarb CR, Stewart KJ, Hauck S: Randomized controlled pilot study testing use of smartphone technology for obesity treatment. J Obes. 2013, 2013:151597.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Bickmore TW, Silliman RA, Nelson K, et al.: A randomized controlled trial of an automated exercise coach for older adults. J Am Geriatr Soc. 2013, 61:1676–1683.

    Article  PubMed  Google Scholar 

  38. Kim BH, Glanz K: Text messaging to motivate walking in older African Americans: A randomized controlled trial. Am J Prev Med. 2013, 44:71–75.

    Article  PubMed  Google Scholar 

  39. King AC, Hekler EB, Grieco LA, et al.: Harnessing different motivational frames via mobile phones to promote daily physical activity and reduce sedentary behavior in aging adults. PLoS ONE [Electronic Resource]. 2013, 8:e62613.

    Article  CAS  Google Scholar 

  40. Patrick K, Norman GJ, Davila EP, et al.: Outcomes of a 12-month technology-based intervention to promote weight loss in adolescents at risk for type 2 diabetes. Journal of Diabetes Science & Technology. 2013, 7:759–770.

    Article  Google Scholar 

  41. Duncan M, Vandelanotte C, Kolt GS, et al.: Effectiveness of a web- and mobile phone-based intervention to promote physical activity and healthy eating in middle-aged males: Randomized controlled trial of the ManUp study. J Med Internet Res. 2014, 16:e136.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Glynn LG, Hayes PS, Casey M, et al.: Effectiveness of a smartphone application to promote physical activity in primary care: The SMART MOVE randomised controlled trial. Br J Gen Pract. 2014, 64:e384–391.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Hebden L, Cook A, van der Ploeg HP, et al.: A mobile health intervention for weight management among young adults: A pilot randomised controlled trial. J Hum Nutr Diet. 2014, 27:322–332.

    Article  CAS  PubMed  Google Scholar 

  44. Knight E, Stuckey MI, Petrella RJ: Health promotion through primary care: Enhancing self-management with activity prescription and mHealth. Phys Sportsmed. 2014, 42:90–99.

    Article  PubMed  Google Scholar 

  45. Fassnacht DB, Ali K, Silva C, Goncalves S, Machado PP: Use of text messaging services to promote health behaviors in children. J Nutr Educ Behav. 2015, 47:75–80.

    Article  PubMed  Google Scholar 

  46. Shapiro JR, Bauer S, Hamer RM, et al.: Use of text messaging for monitoring sugar-sweetened beverages, physical activity, and screen time in children: A pilot study. J Nutr Educ Behav. 2008, 40:385–391.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Sterne JAC, Egger M, Moher D: Chapter 10: Addressing reporting biases. In J. P. T. Higgins and S. Green (eds), Cochrane handbook for systematic reviews of interventions. Chichester: John Wiley & Sons, 2008, 297–333.

    Chapter  Google Scholar 

  48. Dishman RK, Washburn RA, Schoeller DA: Measurement of physical activity. Quest. 2001, 53:295–309.

    Article  Google Scholar 

  49. Reilly JJ, Penpraze V, Hislop J, et al.: Objective measurement of physical activity and sedentary behavior: Review with new data. Archives of Disease in Childhood. 2008, 93:614–619.

  50. Stephens J, Allen J: Mobile phone interventions to increase physical activity and reduce weight: A systematic review. Journal of Cardiovascular Nursing. 2013, 28:320–329.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Patel MS, Asch DA, Volpp KG: Wearable devices as facilitators, not drivers, of health behavior change. JAMA. 2015, 313:459–460.

    Article  CAS  PubMed  Google Scholar 

  52. Webb TL, Joseph J, Yardley L, Michie S: Using the internet to promote health behavior change: A systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res. 2010, 12:e4.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Michie S, Abraham C, Whittington C, McAteer J, Gupta S: Effective techniques in healthy eating and physical activity interventions: A meta-regression. Health Psychol. 2009, 28:690–701.

    Article  PubMed  Google Scholar 

  54. Williams SL, French DP: What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behavior—and are they the same? Health education research. 2011, 26:308–322.

    Article  CAS  PubMed  Google Scholar 

  55. Fjeldsoe B, Neuhaus M, Winkler E, Eakin E: Systematic review of maintenance of behavior change following physical activity and dietary interventions. Health Psychol. 2011, 30:99–109.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

We would like to acknowledge the authors who kindly answered our requests for additional information and shared unpublished data. AD is supported by a Foundation for Science and Technology scholarship (FCT-Portugal SFRH/BD/95762/2013). FCT had no role in experimental design, data collection, or manuscript preparation. RM was supported by a Health Research Council, Sir Charles Hercus Fellowship.

Authors’ Contributions

AD contributed to the conception, design, research, analyses, interpreted the data, and led the writing of the article. AD, EC and JR contributed to acquisition of data. RM participated in the conceptualisation of the study, data extraction and resolution of discrepancies. All authors provided feedback on the manuscript, and have read and approved the final version.

Author information

Authors and Affiliations

  1. National Institute for Health Innovation, School of Population Health, The University of Auckland, Private Bag 92019, Auckland Mail Centre, Auckland, 1142, New Zealand

    Artur Direito MSc

  2. Exercise and Health Laboratory, Faculty of Human Kinetics, Technical University of Lisbon, Lisbon, Portugal

    Eliana Carraça PhD

  3. National Institute for Health Innovation, The University of Auckland, Auckland, New Zealand

    Jonathan Rawstorn PhEd, BSc (Hons), Robyn Whittaker MBChB, PhD & Ralph Maddison PhD

Authors
  1. Artur Direito MSc
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eliana Carraça PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jonathan Rawstorn PhEd, BSc (Hons)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robyn Whittaker MBChB, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ralph Maddison PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Artur Direito MSc.

Ethics declarations

Authors’ Statement of Conflict of Interest and Adherence to Ethical Standards

Authors Direito, Carraça, Rawstorn, Whittaker, and Maddison declare that they have no conflict of interest. All procedures, including the informed consent process, were conducted in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000.

Electronic supplementary material

ESM 1

(DOCX 150 kb)

ESM 2

(DOCX 80 kb)

ESM 3

(DOCX 33 kb)

ESM 4

(XLSX 46 kb)

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Direito, A., Carraça, E., Rawstorn, J. et al. mHealth Technologies to Influence Physical Activity and Sedentary Behaviors: Behavior Change Techniques, Systematic Review and Meta-Analysis of Randomized Controlled Trials. ann. behav. med. 51, 226–239 (2017). https://doi.org/10.1007/s12160-016-9846-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12160-016-9846-0

Keywords

Search

Navigation