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.
- Ajzen, I. 1985. From intentions to actions: A theory of planned behavior. Action control: From cognition to behavior. (1985), 11--39.Google Scholar
- 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 Scholar
- 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 ScholarCross Ref
- 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 Scholar
- 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 Scholar
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 Scholar
- Evans, J.S.B.T. (Ed) and Frankish, K. (Ed) 2009. In two minds: Dual processes and beyond. Oxford University Press.Google Scholar
- Fogg, B.J. 2003. Persuasive technology: using computers to change what we think and do. Morgan Kaufmann Publishers. Google ScholarDigital Library
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 Scholar
- 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 ScholarCross Ref
- 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 Scholar
- 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 Scholar
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- 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 Scholar
- 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 Scholar
Index Terms
- Supporting Action Planning for Sedentary Behavior Change by Visualizing Personal Mobility Patterns on Smartphone
Recommendations
Combating Sedentary Behavior: An App Based on a Distributed Prospective Memory Approach
CHI EA '17: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing SystemsSedentary behavior such as sitting is associated with severe health issues. We suggest that the sedentary behavior problem can be considered as a prospective memory task: remember to get enough activity within 30-minute periods. We describe the ...
Visualizing Computer-Based Activity on Ambient Displays to Reduce Sedentary Behavior at Work
OzCHI '20: Proceedings of the 32nd Australian Conference on Human-Computer InteractionWorkplace health interventions have predominantly been designed around visualizations of physical activity data in the work routine. Yet, contextual factors, such as computer-based activity, appears to be crucial to support healthier behaviors at work. ...
Sedentary Behavior-Based User Life-Log Monitoring for Wellness Services
ICOST 2016: Proceedings of the 14th International Conference on Inclusive Smart Cities and Digital Health - Volume 9677Ubiquitous computing and smart gadgets have revolutionized the self-quantification in tracking and logging activities for improving daily life and inducing healthy behavior. Life-log monitoring is the process of monitoring the daily life routines of ...
Comments