Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
BMC Public Health
Published in: BMC Public Health 1/2016

Open Access 01-12-2016 | Research article

Effectiveness of a smartphone app in increasing physical activity amongst male adults: a randomised controlled trial

Authors: Tim Harries, Parisa Eslambolchilar, Ruth Rettie, Chris Stride, Simon Walton, Hugo C. van Woerden

Published in: BMC Public Health | Issue 1/2016

Login to get access

Abstract

Background

Smartphones are ideal for promoting physical activity in those with little intrinsic motivation for exercise. This study tested three hypotheses: H1 – receipt of social feedback generates higher step-counts than receipt of no feedback; H2 – receipt of social feedback generates higher step-counts than only receiving feedback on one’s own walking; H3 – receipt of feedback on one’s own walking generates higher step-counts than no feedback (H3).

Methods

A parallel group randomised controlled trial measured the impact of feedback on steps-counts. Healthy male participants (n = 165) aged 18–40 were given phones pre-installed with an app that recorded steps continuously, without the need for user activation. Participants carried these with them as their main phones for a two-week run-in and six-week trial. Randomisation was to three groups: no feedback (control); personal feedback on step-counts; group feedback comparing step-counts against those taken by others in their group. The primary outcome measure, steps per day, was assessed using longitudinal multilevel regression analysis. Control variables included attitude to physical activity and perceived barriers to physical activity.

Results

Fifty-five participants were allocated to each group; 152 completed the study and were included in the analysis: n = 49, no feedback; n = 53, individual feedback; n = 50, individual and social feedback. The study provided support for H1 and H3 but not H2. Receipt of either form of feedback explained 7.7 % of between-subject variability in step-count (F = 6.626, p < 0.0005). Compared to the control, the expected step-count for the individual feedback group was 60 % higher (effect on log step-count = 0.474, 95 % CI = 0.166–0.782) and that for the social feedback group, 69 % higher (effect on log step-count = 0.526, 95 % CI = 0.212–0.840). The difference between the two feedback groups (individual vs social feedback) was not statistically significant.

Conclusions

Always-on smartphone apps that provide step-counts can increase physical activity in young to early-middle-aged men but the provision of social feedback has no apparent incremental impact. This approach may be particularly suitable for inactive people with low levels of physical activity; it should now be tested with this population.
Literature
1.
2.
Pooley C, Tight M, Jones T, Horton D, Scheldeman G, Jopson A, et al. Understanding walking and cycling: summary of key findings and recommendations. 2012.
3.
4.
5.
MacLellan G, Baillie L, Granat M, In P, Environments ACM. The application of a physical activity and location measurement system to public health interventions to promote physical activity. Proc 2nd Int Conf Pervsive Technol Relat to Assist. 2009;09:1–8.
6.
Pate RR, Pratt M, Blair SN, Haskell WL, Macera CA, Bouchard C, et al. Physical activity and public health. A recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. JAMA. 1995;273:402–7.CrossRefPubMed
7.
Morgan AL, Tobar DA, Snyder L. Walking toward a new me: the impact of prescribed walking 10,000 steps/day on physical and psychological well-being. J Phys Act Health. 2010;7:299–307.CrossRefPubMed
8.
9.
Johansson M, Hartig T. Psychological benefits of walking: moderation by company and outdoor environment. Applied Psychology: Health and Well-Being. 2011;3(3):261–80.
10.
11.
Consolvo S, McDonald DW, Toscos T, Chen MY, Froehlich J, Harrison B, et al. Activity sensing in the wild: a field trial of ubifit garden. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM; 2008. p. 1797–806. doi:10.​1145/​1357054.​1357335.
12.
Fogg BJ. Persuasive computers: perspectives and research directions. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM Press/Addison-Wesley Publishing Co; 1998. p. 225–32. doi:10.​1145/​274644.​274677.
13.
King P, Tester J. The landscape of persuasive technologies. Commun ACM. 1999;42:31–8.CrossRef
14.
Harries T, Eslambolchilar P, Stride C, Rettie R, Walton S. Walking in the wild – Using an always-on smartphone application to increase physical activity. In: Kotzé P, Marsden G, Lindgaard G, Wesson J, Winckler M, editors. Human-computer interaction – INTERACT 2013 SE - 2. Heidelberg: Springer Berlin; 2013. p. 19–36. doi:10.​1007/​978-3-642-40498-6_​2.CrossRef
15.
16.
17.
18.
Blythe MA, Overbeeke K, Monk AF, Wright PC, editor. Funology: from usability to enjoyment. Norwell: Kluwer Academic Publishers; 2004.
19.
Fujiki Y, Kazakos K, Puri C, Pavlidis I, Starren J, Levine J. NEAT-o-games: ubiquitous activity-based gaming. CHI’07 extended abstracts on human factors in computing systems. New York: ACM; 2007. p. 2369–74. doi:10.​1145/​1240866.​1241009.
20.
Berkovsky S, Bhandari D, Kimani S, Colineau N, Paris C. Designing games to motivate physical activity. Proceedings of the 4th international conference on persuasive technology. New York: ACM; 2009. p. 37. doi:10.​1145/​1541948.​1541996. 1–37:4.
21.
Berkovsky S, Freyne J, Coombe M, Bhandari D, Baghaei N. Physical activity motivating games: you can play, mate! Proceedings of the 21st Annual Conference of the Australian Computer-Human Interaction Special Interest Group: Design: Open 24/7. New York, NY, USA: ACM; 2009. p. 273–6. doi:10.​1145/​1738826.​1738872.
22.
Hechter M, Opp K. Social norms. New York: Russell Sage Foundation; 2001.
23.
Lewis MA, Neighbors C. Social norms approaches using descriptive drinking norms education: a review of the research on personalized normative feedback. J Am Coll Health. 2006;54:213–8. doi:10.​3200/​JACH.​54.​4.​213-218
24.
Consolvo S, Everitt K, Smith I, Landay JA. Design requirements for technologies that encourage physical activity. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM; 2006. p. 457–66. doi:10.​1145/​1124772.​1124840.
25.
Baker G, Mutrie N, Lowry R. Using pedometers as motivational tools: are goals set in steps more effective than goals set in minutes for increasing walking? Int J Health Promot Educ. 2008;46(1):21–26..
26.
27.
Toscos T, Faber A, An S, Gandhi MP, In CHI. Chick clique: persuasive technology to motivate teenage girls to exercise. Ext Abstr Hum factors Comput Syst CHI EA ACM. 2006;06:1873–8.CrossRef
28.
Consolvo S, Klasnja P, McDonald DW, Avrahami D, Froehlich J, LeGrand L, et al. Flowers or a robot army? Encouraging awareness & activity with personal, mobile displays. Proceedings of the 10th International Conference on Ubiquitous Computing. New York: ACM; 2008. p. 54–63. doi:10.​1145/​1409635.​1409644.
29.
Ahtinen A, Huuskonen P, Häkkilä J. Let’s all get up and walk to the North Pole: design and evaluation of a mobile wellness application. Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries. New York: ACM; 2010. p. 3–12. doi:10.​1145/​1868914.​1868920.
30.
Fan C, Forlizzi J, Dey AK. A spark of activity: exploring informative art as visualization for physical activity. Proceedings of the 2012 ACM Conference on Ubiquitous Computing. New York: ACM; 2012. p. 81–4. doi:10.​1145/​2370216.​2370229.
31.
Thomas S, Reading J, Shephard RJ. Revision of the Physical Activity Readiness Questionnaire (PAR-Q). Canadian journal of sport sciences=Journal canadien des sciences du sport. 1992;17(4):338–45.
32.
Twisk JW. Applied longitudinal data analysis for epidemiology: a practical guide. Cambridge: Cambridge University Press; 2013.
33.
Maas CJM, Hox JJ. Sufficent sample sizes for multilevel modeling. Methodology. 2005. p. 86–92.
34.
35.
Singer JD, Willet JB. Applied longitudinal data analysis-modelling change and event occurrence. Oxford: Oxford University Press; 2003.
36.
Heck RH, Thomas SLT, Tabata LN. Multilevel and longitudinal modeling with IBM SPSS: 2nd Edition (Paperback). New York: Routledge; 2014.
37.
38.
39.
40.
Harries T, Rettie R. Waling as a social practice: dispersed walking and the organisation of everday practices. Sociology of Health & Illness. 2016;38(6):874–883.
41.
Thaler R, Sunstein C, Improving N. Nudge: improving decisions about health, wealth and happiness. London: Yale University Press; 2008.
42.
Kang M, Marshall S. Effect of pedometer-based physical activity interventions: a meta-analysis. Res Q Exerc Sport. 2009;80:648–55.CrossRefPubMed
43.
Jackson E, Howton A. Increasing walking in college students using a pedometer intervention: differences according to body mass index. J Am Coll Heal. 2008;57:159–64.CrossRef
44.
Winett R, Anderson E, Wojcik J. Guide to health: nutrition and physical activity outcomes of a group-randomized trial of an Internet-based intervention in churches. Ann Behav Med. 2007;33:251–61.CrossRefPubMed
45.
Toscos T, Faber AM, Connelly KH, Upoma AM. Encouraging physical activity in teens: can technology help reducte barriers to physical activity in adolescent girls? Proc. of Pervasive. Proc Pervasive Heal. IEEE; 2008. p. 173–84.
46.
Harries T, Rettie R, Studley M, Burchell K, Chambers S. Social norms marketing: reducing domestic electricity consumption? Eur J Mark. 2013;47:1458–75.CrossRef
47.
48.
Coughlin SS, Whitehead M, Sheats JQ, Mastromonico J, Smith S. A review of smartphone applications for promoting physical activity. 2015;2(1).
49.
Khatri A, Shastri D, Tsiamyrtzis P, Uyanik I, Akleman E, Pavlidis I. Effects of simple personalized goals on the usage of a physical activity app. Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. New York: ACM; 2016. p. 2249–56. doi:10.​1145/​2851581.​2892366.
Metadata
Title
Effectiveness of a smartphone app in increasing physical activity amongst male adults: a randomised controlled trial
Authors
Tim Harries
Parisa Eslambolchilar
Ruth Rettie
Chris Stride
Simon Walton
Hugo C. van Woerden
Publication date
01-12-2016
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2016
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-016-3593-9

Other articles of this Issue 1/2016

BMC Public Health 1/2016 Go to the issue

Study protocol

Protocol for the SEED-trial: Supported Employment and preventing Early Disability

Research article

Trends in risk factors for coronary heart disease in the Netherlands

Research article

Experiences with insecticide-treated curtains: a qualitative study in Iquitos, Peru

Research article

Population levels of sport participation: implications for sport policy

Research article

Variations in area-level disadvantage of Australian registered fitness trainers usual training locations

Research article

Use of non-emergency contraceptive pills and concoctions as emergency contraception among Nigerian University students: results of a qualitative study