Tobias Grundgeiger
ABSTRACT
Sedentary 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 functions, development, and pilot evaluation of a theoretically motivated smartphone app to combat the sedentary behavior based on human movement research and distributed prospective memory. Finally, we outline the methods of a larger evaluation of the app and discuss limitations and future extensions of the distributed prospective memory approach.
- Barbara E. Ainsworth, William L. Haskell, Stephan D. Herrmann, Nathanael Meckes, Jr David R. Bassett, Catrine Tudor-Locke, Jennifer L. Greer, Jesse W. Vezina, Melicia C. Whitt-Glover, and Arthur S. Leon. The Compendium of Physical Activities Tracking Guide. Healthy Lifestyles Research Center, College of Nursing & Health Innovation, Arizona State University. Retrieved August 22, 2014 from https://sites.google.com/ site/compendiumofphysicalactivities/Google Scholar
- Douglas L. Ballor, Motier D. Becque, and Victor L. Katch. 1987. Metabolic responses during hydraulic resistance exercise. Med. Sci. Sports Exerc. 19, 4: 363--367. Google ScholarCross Ref
- Judit Bort-Roig, Nicholas D. Gilson, Anna PuigRibera, Ruth S. Contreras, and Stewart G. Trost. 2014. Measuring and influencing physical activity with smartphone technology: A systematic review. Sports Med. 44, 5: 671--686.Google ScholarCross Ref
- Sunny Consolvo, David W. Mcdonald, Tammy Toscos, Mike Y. Chen, Jon Froehlich, Beverly Harrison, Predrag Klasnja, Anthony Lamarca, Louis Legrand, Ryan Libby, Ian Smith, and James A. Landay, 2008. Activity sensing in the wild: A field trial of ubifit garden. In Proceedings of the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '08), 17971806.http://dx.doi.org/10.1145/1357054.1357335 Google ScholarDigital Library
- Saskia Dantzig, Gijs Geleijnse, and Aart Tijmen Halteren. 2013. Toward a persuasive mobile application to reduce sedentary behavior. Personal Ubiquitous Comput. 17, 6: 1237--1246. http://dx.doi.org/10.1007/s00779-012-0588-0 Google ScholarDigital Library
- Leandro F. M. de Rezende, Maurício R. Lopes, Juan P. Rey-López, Victor K. R. Matsudo, and Olinda do Carmo Luiz. 2014. Sedentary behavior and health outcomes: An overview of systematic reviews. PLoS ONE 9, 8: e105620.Google ScholarCross Ref
- R. Key Dismukes. 2012. Prospective memory in workplace and everyday situations. Curr. Dir. Psychol. 21, 4: 215--220. Google ScholarCross Ref
- David W. Dunstan, Bronwyn A. Kingwell, Robyn Larsen, Genevieve N. Healy, Ester Cerin, Marc T. Hamilton, Jonathan E. Shaw, David A. Bertovic, Paul Z. Zimmet, and Jo Salmon. 2012. Breaking up prolonged sitting reduces postprandial glucose and insulin responses. Diabetes care 35, 976--983. Google ScholarCross Ref
- Ulf Ekelund, Jostein Steene-Johannessen, Wendy J Brown, Morten Wang Fagerland, Neville Owen, Kenneth E Powell, Adrian Bauman, I-Min Lee, Lancet Physical Activity Series, and Lancet Sedentary Behaviour Working Group. 2016. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. The Lancet 388, 10051: 1302--1310.Google Scholar
- Brian J. Fogg. 2009. A behavior model for persuasive design. In Proceedings of the 4th international Conference on Persuasive Technology ACM, 40. Google ScholarDigital Library
- Thomas Fritz, Elaine M. Huang, Gail C. Murphy, and Thomas Zimmermann. 2014. Persuasive technology in the real world: a study of long-term use of activity sensing devices for fitness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '14), 487--496. Google ScholarDigital Library
- Rúben Gouveia, Evangelos Karapanos, and Marc Hassenzahl, 2015. How do we engage with activity trackers? A longitudinal study of Habito. In Proceedings of the Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 1305--1316.Google Scholar
- Tobias Grundgeiger, Penelope Sanderson, and R. Key Dismukes. 2014. Prospective memory in complex sociotechnical systems. Zeitschrift für Psychologie 222, 2: 100--109. Google ScholarCross Ref
- Marc T. Hamilton, Deborah G. Hamilton, and Theodore W. Zderic. 2007. Role of low energy expenditure and sitting in obesity, metabolic syndrome, type 2 diabetes, and cardiovascular disease. Diabetes 56, 11: 2655--2667. Google ScholarCross Ref
- Qian He and Emmanuel Agu, 2014. On11: An activity recommendation application to mitigate sedentary lifestyle. In Proceedings of the 2014 Workshop on Physical Analytics2014, 3--8. Google ScholarDigital Library
- Erik Hollnagel and David D. Woods. 2005. Joint cognitive systems: Foundations of cognitive systems engineering Taylor & Francis, Boca Raton.Google Scholar
- Edwin Hutchins. 1995. How a cockpit remembers its speeds. Cogn. Sci. 19, 3: 265--288. Google ScholarCross Ref
- Predrag Klasnja, Sunny Consolvo, and Wanda Pratt. 2011. How to evaluate technologies for health behavior change in HCI research. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '11), 30633072. Google ScholarDigital Library
- Phillippa Lally, Cornelia H. M. Van Jaarsveld, Henry W. W. Potts, and Jane Wardle. 2010. How are habits formed: Modelling habit formation in the real world. Eur. J. Soc. Psychol. 40, 6: 998--1009. Google ScholarCross Ref
- James A. Levine, Sara J. Schleusner, and Michael D. Jensen. 2000. Energy expenditure of nonexercise activity. Am. J. Clin. Nutr. 72, 6: 1451--1454.Google ScholarCross Ref
- Ulman Lindenberger, Martin Lovden, Michael Schellenbach, Sue-Chen Li, and Antonio Krüger. 2008. Psychological principles of successful aging technologies: A mini-review. Gerontology 54, 1: 59--68. Google ScholarCross Ref
- Mark A. McDaniel and Gilles O. Einstein. 2000. Strategic and automatic processes in prospective memory retrieval: A multiprocess framework. App. Cogn. Psych.14, 127--144. Google ScholarCross Ref
- Laura R. Pina, Ernesto R. Ramirez, and William G. Griswold. 2012. Fitbit+: A behavior-based intervention system to reduce sedentary behavior. In 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, 175--178.Google Scholar
- James O. Prochaska and Wayne F. Velicer. 1997. The transtheoretical model of health behavior change. Am. J. Health Promot. 12, 1: 38--48. Google ScholarCross Ref
- Ian Renfree, Daniel Harrison, Paul Marshall, Katarzyna Stawarz, and Anna Cox, 2016. Don't kick the habit: The role of dependency in habit formation apps. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '16), 2932--2939. Google ScholarDigital Library
- Katarzyna Stawarz and Anna L. Cox. 2015. Designing for health behavior change: HCI research alone is not enough. In CHI'15 workshop: Crossing HCI and Health: Advancing Health and Wellness Technology Research in Home and Community Settings.Google Scholar
- Katarzyna Stawarz, Anna L. Cox, and Ann Blandford. 2015. Beyond self-tracking and reminders: Designing smartphone apps that support habit formation. In Proceedings of the SIGHCI Conference on Human Factors in Computing Systems (CHI '15), 2653--2662. Google ScholarDigital Library
Index Terms
- Combating Sedentary Behavior: An App Based on a Distributed Prospective Memory Approach
Recommendations
Supporting Action Planning for Sedentary Behavior Change by Visualizing Personal Mobility Patterns on Smartphone
PervasiveHealth '18: Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for HealthcareScientific 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 ...
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 ...
Toward a persuasive mobile application to reduce sedentary behavior
Prolonged sitting is a potential health risk, not only for people with an inactive lifestyle but also for those who meet the daily physical activity recommendations. Mobile applications that trigger people to take regular breaks from sitting seem ...
Comments