Hostname: page-component-848d4c4894-2xdlg Total loading time: 0 Render date: 2024-06-27T08:09:12.100Z Has data issue: false hasContentIssue false
  • English
  • Français

SUGGESTIONS FOR BEHAVIOR-INTERVENTION DESIGN PRACTITIONERS: FROM BEHAVIOR CHANGE MOTIVATIONS FOR CHINESE AGED 18 TO 25

Published online by Cambridge University Press:  27 July 2021

Yuan Yin  and
Yurong Yu
Yuan Yin*
Affiliation:
Imperial College London
Yurong Yu
Affiliation:
Imperial College London
*
YIN, Yuan, Imperial College London, Dyson School of Design Engineering, United Kingdom, y.yin19@ic.ac.uk

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Using applications to change behaviors is a popular trend in recent years as mobiles are the easiest recording medium for users. However, few users can keep the behavior change for a long time. The aim of this study is to investigate motivations of keeping an application-tracked behavior change to provide effective and promote effective and targeted suggestions for application-tracked behavior intervention design practitioners and researchers. A 28-day self-report experiment and following “focus group” discussion have been conducted to detect the possible motivations. The results indicated 8 motivations which can affect maintaining behavior change: cooperation, competition, award, reminder and alarm, trust and willingness, relation with disease information and unplanned events. In addition, the results explore some motivations from negative data in applications or the cheating for good performance data behavior. At the same time, the study suggested the functions needed in future behavior change applications.

Keywords

Digital behavior changeMotivationsHuman behaviour in designUser centred designEarly design phases
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 2781 - 2790
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

References

Conroy, D. E., Yang, C.-H. and Maher, J.P. (2014), “Behavior change techniques in top-ranked mobile apps for physical activity”, American Journal of Preventive Medicine, Vol. 46 No. 6, pp.649–652. https://doi.org/10.1016/j.amepre.2014.01.010CrossRefGoogle Scholar
Chiasson, K., Terras, K., and Smart, K. (2015), “Faculty perceptions of moving a face-to-face course to online instruction”, Journal of College Teaching and Learning (TLC), Vol.12 No.3, pp.321–340. https://doi.org/10.19030/tlc.v12i3.9315CrossRefGoogle Scholar
Elaheebocus, S. M. R. A., Weal, M., Morrison, L. and Yardley, L. (2018), “Peer-based social media features in behavior change interventions: systematic review”, Journal of Medical Internet Research, Vol.20 No.2, pp. e20. https://doi.org/10.2196/jmir.8342CrossRefGoogle ScholarPubMed
Gibbs, A. (1997). “Focus groups”. Social research update, Vol.19 No.8, pp.1–8.Google Scholar
Hermsen, S., Frost, J., Renes, R. J. and Kerkhof, P. (2016), “Using feedback through digital technology to disrupt and change habitual behavior: A critical review of current literature”, Computers in Human Behavior, Vol. 57, pp. 61–74. https://doi.org/10.1016/j.chb.2015.12.023CrossRefGoogle Scholar
Lathia, N., Pejovic, V., Rachuri, K. K., Mascolo, C., Musolesi, M. and Rentfrow, P. J. (2013), “Smartphones for large-scale behavior change interventions”, IEEE Pervasive Computing, Vol.12 No. 3, pp.66–73. https://doi.org/10.1109/mprv.2013.56CrossRefGoogle Scholar
Lorig, K. R., Ritter, P. L., Laurent, D. D., Plant, K. J. A. C. and Rheumatology, R. (2008), “The internet-based arthritis self-management program: A one-year randomized trial for patients with arthritis or fibromyalgia”, Arthritis & Rheumatism, Vol.59 No.7, pp. 1009–1017. https://doi.org/10.1002/art.23817CrossRefGoogle ScholarPubMed
Lupton, D. (2016), The quantified self, John Wiley and Sons.Google Scholar
Marshall, A. L., Leslie, E. R., Bauman, A. E., Marcus, B. H. and Owen, N. (2003), “Print versus website physical activity programs: a randomized trial”, American Journal of Preventive Medicine, Vol.25 No. 2, pp.88–94. https://doi.org/10.1016/s0749-3797(03)00111-9CrossRefGoogle ScholarPubMed
McGloin, A. F. and Eslami, S. (2015), “Digital and social media opportunities for dietary behaviour change”, Proceedings of the Nutrition Society, Vol.74 No. 2, pp. 139–148. https://doi.org/10.1017/s0029665114001505CrossRefGoogle ScholarPubMed
Michie, S., Yardley, L., West, R., Patrick, K. and Greaves, F. (2017), “Developing and evaluating digital interventions to promote behavior change in health and health care: recommendations resulting from an international workshop”, Journal of Medical Internet Research, Vol.19 No.6, pp. e232. https://doi.org/10.2196/jmir.7126CrossRefGoogle ScholarPubMed
Mohr, D., Cuijpers, P. and Lehman, K. (2011), “Supportive accountability: a model for providing human support to enhance adherence to eHealth interventions”, Journal of Medical Internet Research, Vol. 13 No.1, pp. e30. https://doi.org/10.2196/jmir.1602CrossRefGoogle Scholar
Morrison, L. G. (2015), “Theory-based strategies for enhancing the impact and usage of digital health behaviour change interventions: a review”, DIGITAL HEALTH, Vol.1, pp. 2055207615595335. https://doi.org/10.1177/2055207615595335CrossRefGoogle ScholarPubMed
Morrison, L. G., Yardley, L., Powell, J., Michie, S. (2012), “What design features are used in effective e-health interventions? A review using techniques from critical interpretive synthesis”, Telemedicine and e-Health, Vol.18 No.2, pp. 137–144. https://doi.org/10.1089/tmj.2011.0062CrossRefGoogle Scholar
Norem, J. K., Cantor, N. (1986), “Defensive pessimism: Harnessing anxiety as motivation”, Journal of Personality and Social Psychology, Vol.51 No.6, pp.1208. https://doi.org/10.1037/0022-3514.51.6.1208CrossRefGoogle ScholarPubMed
Park, A., Nitzke, S., Kritsch, K., Kattelmann, K., White, A., Boeckner, L., Lohse, B., Hoerr, S., Greene, G., Zhang, Z. (2008), “Internet-based interventions have potential to affect short-term mediators and indicators of dietary behavior of young adults”, Journal of Nutrition Education and Behavior, Vol.40 No.5, pp. 288–297. https://doi.org/10.1016/j.jneb.2008.02.001CrossRefGoogle ScholarPubMed
Patrick, K., Hekler, E. B., Estrin, D., Mohr, D. C., Riper, H., Crane, D., Godino, J. and Riley, W. T. (2016), “The pace of technologic change: implications for digital health behavior intervention research”, American Journal of Preventive Medicine,Vol.51 No. 5, pp. 816–824. https://doi.org/10.1016/j.amepre.2016.05.001CrossRefGoogle ScholarPubMed
Patten, C. A., Rock, E., Meis, T. M., Decker, P. A., Colligan, R. C., Pingree, S., Dornelas, E. A., Offord, K. P., Boberg, E. W. and Gustafson, D. H. (2007), “Frequency and type of use of a home-based, Internet intervention for adolescent smoking cessation”, Journal of Adolescent Health, Vol.41 No. 5, pp. 437–443. https://doi.org/10.1016/j.jadohealth.2007.05.016CrossRefGoogle ScholarPubMed
Piccione, A. (1978), The effects of success-contingent versus engagement-contingent rewards on multiple indices of intrinsic motivation, Doctoral dissertation, Bowling Green State University.Google Scholar
Rooksby, J., Rost, M., Morrison, A., and Chalmers, M. (2014), “Personal tracking as lived informatics”, In Proceedings of the SIGCHI conference on human factors in computing systems, pp. 1163–1172. https://doi.org/10.1145/2556288.2557039CrossRefGoogle Scholar
Roberts, K., Dowell, A., and Nie, J. B. (2019), “Attempting rigour and replicability in thematic analysis of qualitative research data; a case study of codebook development”, BMC medical research methodology, Vol.19 No.66, pp.1–8. https://doi.org/10.1186/s12874-019-0707-yCrossRefGoogle Scholar
Ryan, R. M.,and Deci, E. L. (2000), “Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being”, American psychologist, Vol.55 No.1, pp.68–78. https://dx.doi.org/10.1037110003-066X.55.1.68CrossRefGoogle ScholarPubMed
Schueller, Stephen M., Muñoz, Ricardo F., and Mohr, David C.. (2013), “Realizing the potential of behavioral intervention technologies.” Current Directions in Psychological Science, Vol.22 No.6, pp. 478–483. https://doi.org/10.1177/0963721413495872CrossRefGoogle Scholar
Spittaels, H., De Bourdeaudhuij, I., Brug, J., and Vandelanotte, C. (2007), “Effectiveness of an online computer-tailored physical activity intervention in a real-life setting”, Health education research, Vol.22 No.3, pp. 385–396. https://doi.org/10.1093/her/cyl096CrossRefGoogle ScholarPubMed
Sritakaew, N., O'Brien, A. P., and Hoffman, K. (2017), “Nursing the family of teenage mothers in Thailand: Under pressure and the lack of support”, Australian Nursing and Midwifery Journal, Vol.24 No.11,pp. 39.Google Scholar
Williams, K. (2015), “An anxious alliance”, In Proceedings of The Fifth Decennial Aarhus Conference on Critical Alternatives, pp. 121–131. https://doi.org/10.7146/aahcc.v1i1.21146CrossRefGoogle Scholar
Yang, C. H., Maher, J. P., & Conroy, D. E. (2015), “Implementation of behavior change techniques in mobile applications for physical activity”, American journal of preventive medicine, Vol.48 No.4, pp. 452–455. https://doi.org/10.1016/j.amepre.2014.10.010CrossRefGoogle Scholar
Yardley, L., Douglas, E., Anthierens, S., Tonkin-Crine, S., O'Reilly, G., Stuart, B., Geraghty, A. W., Arden-Close, E., van der Velden, A. W. and Goosens, H. (2013), Evaluation of a web-based intervention to reduce antibiotic prescribing for LRTI in six European countries: quantitative process analysis of the GRACE/INTRO randomised controlled trial, Implementation Science, Vol. 8 No.134. https://doi.org/10.1186/1748-5908-8-134CrossRefGoogle ScholarPubMed
Yardley, L., Morrison, L., Bradbury, K. and Muller, I. (2015), “The person-based approach to intervention development: application to digital health-related behavior change interventions”, Journal of medical Internet research, Vol.17 No.1, pp. e30. https://doi.org/10.2196/jmir.4055CrossRefGoogle ScholarPubMed
Yardley, L., Spring, B. J., Riper, H., Morrison, L. G., Crane, D. H., Curtis, K., Merchant, G. C., Naughton, F. and Blandford, A. (2016), “Understanding and promoting effective engagement with digital behavior change interventions”, American journal of preventive medicine, Vol.51 No. 5, pp. 833–842. https://doi.org/10.1016/j.amepre.2016.06.015Google ScholarPubMed