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Supporting Action Planning for Sedentary Behavior Change by Visualizing Personal Mobility Patterns on Smartphone

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Published:21 May 2018Publication History

ABSTRACT

Scientific evidence has shown that long-term sedentary behaviour is detrimental to human health. Therefore, a trend appears in the field of healthy lifestyle promotion that more attention is drawn to sedentary behaviour rather than only physical activity. However, technology-based mobile health intervention tools targeting reducing sedentary behaviour are still lacking. This paper aims to explore a solution for sedentary behaviour change through supporting action planning. Action planning can not only bridge the intention-behavior gap in controlled motivation processes, but also enforce the cue-behavior association in unconscious processes. We present a smartphone-based personal mobility pattern visualization, with which we expect the users can make better action plans. The interactive visualization integrates temporal and spatial patterns of personal sedentary and walking behaviour, to provide explicit hints on when, where, and how to reduce sedentary behaviour and increase daily steps. We also present our experimental design to evaluate the visualization- based intervention tool.

References

  1. Ajzen, I. 1985. From intentions to actions: A theory of planned behavior. Action control: From cognition to behavior. (1985), 11--39.Google ScholarGoogle Scholar
  2. Belmon, L.S., Middelweerd, A., te Velde, S.J. and Brug, J. 2015. Dutch Young Adults Ratings of Behavior Change Techniques Applied in Mobile Phone Apps to Promote Physical Activity: A Cross-Sectional Survey. JMIR mHealth and uHealth. 3, 4 (Nov. 2015), e103.Google ScholarGoogle Scholar
  3. Chastin, S.F.M., Palarea-Albaladejo, J., Dontje, M.L. and Skelton, D.A. 2015. Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: A novel compositional data analysis approach. PLoS ONE. 10, 10 (2015), 1 -21.Google ScholarGoogle ScholarCross RefCross Ref
  4. De Cocker, K., De Bourdeaudhuij, I., Cardon, G. and Vandelanotte, C. 2015. Theory-driven, web-based, computer-tailored advice to reduce and interrupt sitting at work: development, feasibility and acceptability testing among employees. BMC public health. 15, 1 (Sep. 2015), 959.Google ScholarGoogle Scholar
  5. Conroy, D.E., Maher, J.P., Elavsky, S., Hyde, A.L. and Doerksen, S.E. 2013. Sedentary behavior as a daily process regulated by habits and intentions. Health psychology: official journal of the Division of Health Psychology, American Psychological Association. 32, 11 (Nov. 2013), 1149--57.Google ScholarGoogle Scholar
  6. 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. 46, 6 (2014), 649--652.Google ScholarGoogle ScholarCross RefCross Ref
  7. Crane, D., Garnett, C., Brown, J., West, R. and Michie, S. 2015. Behavior Change Techniques in Popular Alcohol Reduction Apps. Journal of Medical Internet Research. 17, 5 (May 2015), e118.Google ScholarGoogle ScholarCross RefCross Ref
  8. Dallery, J., Cassidy, R.N. and Raiff, B.R. 2013. Single-case experimental designs to evaluate novel technology-based health interventions. Journal of medical Internet research. 15, 2 (Feb. 2013), e22.Google ScholarGoogle ScholarCross RefCross Ref
  9. Dombrowski, S.U., Sniehotta, F.F., Avenell, A., Johnston, M., MacLennan, G. and Araújo-Soares, V. 2012. Identifying active ingredients in complex behavioural interventions for obese adults with obesity-related co-morbidities or additional risk factors for co-morbidities: a systematic review. Health Psychology Review. 6, 1 (Mar. 2012), 7--32.Google ScholarGoogle ScholarCross RefCross Ref
  10. Donath, L., Faude, O., Schefer, Y. and Roth, R. 2015. Repetitive daily point of choice prompts and occupational sit-stand transfers, concentration and neuromuscular performance in office workers: an RCT. International journal of environmental research and public health. 2015 Apr 20;12(4):4340--53. (2015).Google ScholarGoogle Scholar
  11. Evans, J.S.B.T. (Ed) and Frankish, K. (Ed) 2009. In two minds: Dual processes and beyond. Oxford University Press.Google ScholarGoogle Scholar
  12. Fogg, B.J. 2003. Persuasive technology: using computers to change what we think and do. Morgan Kaufmann Publishers. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Hamari, J., Koivisto, J. and Pakkanen, T. 2014. Do persuasive technologies persuade? - A review of empirical studies. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8462 LNCS, (2014), 118--136. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Henson, J., Davies, M.J., Bodicoat, D.H., Edwardson, C.L., Gill, J.M.R., Stensel, D.J., Tolfrey, K., Dunstan, D.W., Khunti, K. and Yates, T. 2015. Breaking Up Prolonged Sitting With Standing or Walking Attenuates the Postprandial Metabolic Response in Postmenopausal Women: A Randomized Acute Study. Diabetes Care. 39, 1 (2015).Google ScholarGoogle Scholar
  15. Kaptein, M., Markopoulos, P., De Ruyter, B. and Aarts, E. 2015. Personalizing persuasive technologies: Explicit and implicit personalization using persuasion profiles. International Journal of Human Computer Studies. 77, (2015), 38--51. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Maher, J.P. and Conroy, D.E. 2015. Habit Strength Moderates the Effects of Daily Action Planning Prompts on Physical Activity but Not Sedentary Behavior. Journal of Sport and Exercise Psychology. 37, 1 (2015), 97--107.Google ScholarGoogle ScholarCross RefCross Ref
  17. Michie, S., Richardson, M., Johnston, M., Abraham, C., Francis, J., Hardeman, W., Eccles, M.P., Cane, J. and Wood, C.E. 2013. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions. Annals of Behavioral Medicine. 46, 1 (2013), 81--95.Google ScholarGoogle ScholarCross RefCross Ref
  18. Norton, M.I., Mochon, D. and Ariely, D. 2012. The IKEA effect: When labor leads to love. Journal of Consumer Psychology. 22, 3 (2012), 453--460.Google ScholarGoogle ScholarCross RefCross Ref
  19. Oinas-kukkonen, H. and Harjumaa, M. 2009. Persuasive Systems Design: Key Issues, Process Model, and System Features Persuasive Systems Design: Key Issues, Process Model, and System Features. Communications of the Association for Information Systems. 24, 1 (2009).Google ScholarGoogle Scholar
  20. Owen, N., Sugiyama, T., Eakin, E.E., Gardiner, P.A., Tremblay, M.S. and Sallis, J.F. 2011. Adults' Sedentary Behavior. American Journal of Preventive Medicine. 41, 2 (Aug. 2011), 189--196.Google ScholarGoogle Scholar
  21. Puig-Ribera, A., Bort-Roig, J. and González-Suárez, A. 2015. Patterns of impact resulting from a "sit less, move more"web-based program in sedentary office employees. PloS one. (2015).Google ScholarGoogle Scholar
  22. Pulsford, R.M., Blackwell, J., Hillsdon, M. and Kos, K. 2016. Intermittent walking, but not standing, improves postprandial insulin and glucose relative to sustained sitting: A randomised cross-over study in inactive middle-aged men. Journal of science and medicine in sport. (Aug. 2016).Google ScholarGoogle Scholar
  23. Rabbi, M., Aung, M.H., Zhang, M. and Choudhury, T. 2015. MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones. Ubicomp '15, September 7-11, 2015, Osaka, Japan. (2015), 707--718. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Schwarzer, R. 2008. Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors. Applied Psychology. 57, 1 (2008), 1--29.Google ScholarGoogle ScholarCross RefCross Ref
  25. Stephenson, A., McDonough, S.M., Murphy, M.H., Nugent, C.D. and Mair, J.L. 2017. Using computer, mobile and wearable technology enhanced interventions to reduce sedentary behaviour: a systematic review and meta-analysis. International Journal of Behavioral Nutrition and Physical Activity. 14, 1 (Dec. 2017), 105.Google ScholarGoogle ScholarCross RefCross Ref
  26. Sullivan, A.N. and Lachman, M.E. 2016. Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity. Frontiers in public health. 4, (2016), 289.Google ScholarGoogle Scholar
  27. Sutton, S. 2008. How does the Health Action Process Approach (HAPA) bridge the intention-behavior gap? An examination of the model's causal structure. Applied Psychology. 57, 1 (2008), 66--74.Google ScholarGoogle ScholarCross RefCross Ref
  28. Taylor, W.C., Paxton, R.J., Shegog, R., Coan, S.P., Dubin, A., Page, T.F. and Rempel, D.M. 2016. Impact of Booster Breaks and Computer Prompts on Physical Activity and Sedentary Behavior Among Desk-Based Workers: A Cluster-Randomized Controlled Trial. Preventing chronic disease. 13, (Nov. 2016), E155.Google ScholarGoogle Scholar
  29. Tong, X., Gromala, D., Bartram, L. and Carpendale, S. 2015. Evaluating the Effectiveness of Three Physical Activity Visualizations -- How People Perform vs. Perceive. In Personal Visualization: Exploring Data in Everyday Life. 1, (2015).Google ScholarGoogle Scholar
  30. Wang, Y., Duan, L., Mueller, J., Butscher, S. and Reiterer, H. 2016. "Fingerprints": Detecting meaningful moments for mobile health intervention. Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct, MobileHCI 2016 (2016). Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Wang, Y., Fadhil, A., Lange, J.-P. and Reiterer, H. 2017. Towards a Holistic Approach to Designing Theory-based Mobile Health Interventions. arXiv.org. (2017). DOI: arXiv:1712.02548v1.Google ScholarGoogle Scholar
  32. Wang, Y., Pfeil, U. and Reiterer, H. 2016. Supporting self-assembly: The IKEA effect on mobile health persuasive technology. MMHealth 2016 - Proceedings of the 2016 ACM Workshop on Multimedia for Personal Health and Health Care, co-located with ACM Multimedia 2016 (2016). Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Wang, Y., Sommer, B., Schreiber, F. and Reiterer, H. 2018. Clustering with Temporal Constraints on Spatio-Temporal Data of Human Mobility. arXiv.org. (2018). DOI: arXiv:1807.00546v1.Google ScholarGoogle Scholar
  34. Wang, Y., Wu, L., Lange, J.-P., Fadhil, A. and Reiterer, H. 2017. Persuasive Technology in Reducing Prolonged Sedentary Behavior at Work: A Systematic Review. Smart Health. (2018).Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Other conferences
      PervasiveHealth '18: Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare
      May 2018
      413 pages
      ISBN:9781450364508
      DOI:10.1145/3240925

      Copyright © 2018 ACM

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      Publication History

      • Published: 21 May 2018

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