Integrating Health Theories in Health and Fitness Applications for Sustained Behavior Change: Current State of the Art
Affiliation(s)
Department of Psychology, Devision of Population Health Sciences, Royal College of Surgeons in Ireland – Medical University of Bahrain, Bussaiteen, Bahrain. Dietrich College of Humanities and Social Sciences, Information Systems Program, Carnegie Mellon University in Qatar, Doha, Qatar.
Department of Psychology, Devision of Population Health Sciences, Royal College of Surgeons in Ireland – Medical University of Bahrain, Bussaiteen, Bahrain. Dietrich College of Humanities and Social Sciences, Information Systems Program, Carnegie Mellon University in Qatar, Doha, Qatar.
ABSTRACT
Two hundred million people in the US are overweight or obese mirroring a worldwide trend that is associated with high morbidity and mortality rates. Health and fitness mobile technology applications have great capacities for supporting dieters’ life- changes and could profit from and provide input of health behavior theories. Those theories have been demonstrated with massive clinical evidence to be efficient for fostering healthy life changes and weight loss. This research reviewed the 100 most popular mobile technology applications from iTunes App Store’s Health and Fitness category in respect coverage of health behavior theories’ concepts und chose 14 of those for a complete analysis. Applications provide good support for athletes’ workouts and have great potential to be extended to serve overweight users as well. Missing features could be easily implemented given the current state of technology. These developments look promising for tackling sustained weight loss in many mobile technology users.
Two hundred million people in the US are overweight or obese mirroring a worldwide trend that is associated with high morbidity and mortality rates. Health and fitness mobile technology applications have great capacities for supporting dieters’ life- changes and could profit from and provide input of health behavior theories. Those theories have been demonstrated with massive clinical evidence to be efficient for fostering healthy life changes and weight loss. This research reviewed the 100 most popular mobile technology applications from iTunes App Store’s Health and Fitness category in respect coverage of health behavior theories’ concepts und chose 14 of those for a complete analysis. Applications provide good support for athletes’ workouts and have great potential to be extended to serve overweight users as well. Missing features could be easily implemented given the current state of technology. These developments look promising for tackling sustained weight loss in many mobile technology users.
Cite this paper
Brunstein, A. , Brunstein, J. & Mansar, S. (2012). Integrating Health Theories in Health and Fitness Applications for Sustained Behavior Change: Current State of the Art. Creative Education, 3, 1-5. doi: 10.4236/ce.2012.38B001.
Brunstein, A. , Brunstein, J. & Mansar, S. (2012). Integrating Health Theories in Health and Fitness Applications for Sustained Behavior Change: Current State of the Art. Creative Education, 3, 1-5. doi: 10.4236/ce.2012.38B001.
References
[1] Ajzen, I., & Fishbein, M. (1980). Understanding atti-tudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall. [2] Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211. [3] Armitage, C. J. (2005). Can the theory of planned behavior predict the maintenance of physical activity? Health Psychology, 24, 235-245. [4] Becker, M. H., & Rosenstock, I. M. (1984). Compliance with medical advice. In: A. Steptoe and A. Matthews (Ed.), Health care and human behav-ior, pp. 135-152. London, UK: Academic Press. [5] Blumberg, S. J., Luke, J. V., Ganesh, N., Davern, M. E., Boudreaux, M. H. (2012). Wireless subs-titution: State-level estimates from the national health interview survey, 2010-2011. U.S. Department of Health and Human Services. Center of Disease Control and Prevention, National Health Statistics Report Number 61. Bethesda, DC: CDC. [6] Boulous, M. N., Wheeler, S., Tavares, C., & Jones, R. (2011). How smartphones are changing the face of mobile and participatory healthcare: An overview, with example from eCAALYX. BioMedical Engineering OnLine, 10: 24. [7] DiClemente, C. C., Prochaska, J. O., Fairhust, S. K., et al. (1991). The process of smoking cessation: An analysis of precontemplation, contemplation, and preparation stages of change. Journal of Consulting and Clinical Psychology, 59, 295-304. [8] Free, C., Phillips, G., Felix, L., Galli, L., Patel, V., & Edwards, P. (2010). The effectiveness of M-health technologies for improving health ?and health services: a systematic review protocol. BMC Research Notes 2010, 3:250. [9] Hall, K. (2012). Diet versus exercise in “the biggest loser” weight loss competition. Obesity, DOI 10.1002/oby.20065 [10] Huang, A. Barzi, F., Huxley, R. Denyer, G., Rohrlach, B., Jayne, K., & Neal, B. (2006). The effects onsaturated fat purchases of providing Internet shoppers with purchase-specificdietary advice: a randomized trial. PLoS Clinical Trials, 1:e2. [11] International Telecommunication Union (2010). Measuring the Information Society. Geneva, Switzerland: ITU. [12] IctQatar, Supreme Council of Information and Communication Technology (2009). Qatar’s ICT Landscape: Assessment of Information, Communication and Technology in Qatar. http://www.ictqatar.qa/ landscape2009/index_en.htm. Last visit 26 November 2010. [13] Mozaffarian, D., Hao, T., Rimm, E. B., Willet, W. C., & Hu, F. B. (2011). Changes in diet and Lifestyle and Long-Term Weight Gain in Women and Men. N Engl J Med, 364, 2392-2404. [14] Murray, M. & McMillan, C. (1993). Health beliefs, locus of control, emotional control and women’s cancer screening behavior. British Journal of Clinical Psychology, 32, 87-100. [15] Patrick, K., Raab, F., Adams, M. A., Dillon, L., Zabinski, M., Rock, C.L., Griswold, W.G., & Norman, G.J. (2009). A Text Mes-sage–Based Intervention for Weight Loss: Randomized Controlled Trial. J Med Internet Res, 11(1):e1. DOI 10.2196/jmir.1100. [16] Prochaska, J. O., & DiClemente, C. C. (1984). Self change processes, self efficacy and decisional balance across five stages of smoking cessa-tion. Advances in Cancer Control – 1983, pp. 131-140. New York, NY: Alan R. Liss, Inc. [17] Prochaska, J. O., Velicer, W. F., Rossie, J. S., Goldstein, M. G., Markus, B., Rakowski, W., Fiore, C., Harlow, L. L., Redding, C. A., Rosenbloom, D., & Rossi, S. R. (1994). Stages of change and decional balance for 12 problem behaviors. Health Psychology, 13, 39-46. [18] Schwarzer, R. (1992). Self-efficacy in the adoption and maintenance of health behaviors: Theoretical approaches and a new model. In: R. Schwarzer (ed.), Self-efficacy: Thought control of action. Washington, DC: Hemisphere. [19] Schwarzer, R., Luszcynska, A., & Ziegelmann, P. (2008). Social-cognitive predictors of physical exercise adherence: Three longitudinal studies in rehabilitation. Health Psychology, 27, 854-863. [20] Shaw, J. E., Sicree, & Zimmet, P. Z. (2010). Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Research and Clinical Practice, 87, 4–14. [21] Webb, T., Joseph, J., Yardley, L., & Michie, S. (2010). Using the Internet to promote health behavior change: A sys-tematic review and meta-analysis of the impact of theo-retical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res,12, 1:e4. DOI: 10.2196/jmir.1376. [22] World Health Or-ganization (2010). Global status report on noncommu-nicable diseases. Geneva, Switzerland: WHO Press.
[1] Ajzen, I., & Fishbein, M. (1980). Understanding atti-tudes and predicting social behavior. Englewood Cliffs, NJ: Prentice Hall. [2] Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211. [3] Armitage, C. J. (2005). Can the theory of planned behavior predict the maintenance of physical activity? Health Psychology, 24, 235-245. [4] Becker, M. H., & Rosenstock, I. M. (1984). Compliance with medical advice. In: A. Steptoe and A. Matthews (Ed.), Health care and human behav-ior, pp. 135-152. London, UK: Academic Press. [5] Blumberg, S. J., Luke, J. V., Ganesh, N., Davern, M. E., Boudreaux, M. H. (2012). Wireless subs-titution: State-level estimates from the national health interview survey, 2010-2011. U.S. Department of Health and Human Services. Center of Disease Control and Prevention, National Health Statistics Report Number 61. Bethesda, DC: CDC. [6] Boulous, M. N., Wheeler, S., Tavares, C., & Jones, R. (2011). How smartphones are changing the face of mobile and participatory healthcare: An overview, with example from eCAALYX. BioMedical Engineering OnLine, 10: 24. [7] DiClemente, C. C., Prochaska, J. O., Fairhust, S. K., et al. (1991). The process of smoking cessation: An analysis of precontemplation, contemplation, and preparation stages of change. Journal of Consulting and Clinical Psychology, 59, 295-304. [8] Free, C., Phillips, G., Felix, L., Galli, L., Patel, V., & Edwards, P. (2010). The effectiveness of M-health technologies for improving health ?and health services: a systematic review protocol. BMC Research Notes 2010, 3:250. [9] Hall, K. (2012). Diet versus exercise in “the biggest loser” weight loss competition. Obesity, DOI 10.1002/oby.20065 [10] Huang, A. Barzi, F., Huxley, R. Denyer, G., Rohrlach, B., Jayne, K., & Neal, B. (2006). The effects onsaturated fat purchases of providing Internet shoppers with purchase-specificdietary advice: a randomized trial. PLoS Clinical Trials, 1:e2. [11] International Telecommunication Union (2010). Measuring the Information Society. Geneva, Switzerland: ITU. [12] IctQatar, Supreme Council of Information and Communication Technology (2009). Qatar’s ICT Landscape: Assessment of Information, Communication and Technology in Qatar. http://www.ictqatar.qa/ landscape2009/index_en.htm. Last visit 26 November 2010. [13] Mozaffarian, D., Hao, T., Rimm, E. B., Willet, W. C., & Hu, F. B. (2011). Changes in diet and Lifestyle and Long-Term Weight Gain in Women and Men. N Engl J Med, 364, 2392-2404. [14] Murray, M. & McMillan, C. (1993). Health beliefs, locus of control, emotional control and women’s cancer screening behavior. British Journal of Clinical Psychology, 32, 87-100. [15] Patrick, K., Raab, F., Adams, M. A., Dillon, L., Zabinski, M., Rock, C.L., Griswold, W.G., & Norman, G.J. (2009). A Text Mes-sage–Based Intervention for Weight Loss: Randomized Controlled Trial. J Med Internet Res, 11(1):e1. DOI 10.2196/jmir.1100. [16] Prochaska, J. O., & DiClemente, C. C. (1984). Self change processes, self efficacy and decisional balance across five stages of smoking cessa-tion. Advances in Cancer Control – 1983, pp. 131-140. New York, NY: Alan R. Liss, Inc. [17] Prochaska, J. O., Velicer, W. F., Rossie, J. S., Goldstein, M. G., Markus, B., Rakowski, W., Fiore, C., Harlow, L. L., Redding, C. A., Rosenbloom, D., & Rossi, S. R. (1994). Stages of change and decional balance for 12 problem behaviors. Health Psychology, 13, 39-46. [18] Schwarzer, R. (1992). Self-efficacy in the adoption and maintenance of health behaviors: Theoretical approaches and a new model. In: R. Schwarzer (ed.), Self-efficacy: Thought control of action. Washington, DC: Hemisphere. [19] Schwarzer, R., Luszcynska, A., & Ziegelmann, P. (2008). Social-cognitive predictors of physical exercise adherence: Three longitudinal studies in rehabilitation. Health Psychology, 27, 854-863. [20] Shaw, J. E., Sicree, & Zimmet, P. Z. (2010). Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Research and Clinical Practice, 87, 4–14. [21] Webb, T., Joseph, J., Yardley, L., & Michie, S. (2010). Using the Internet to promote health behavior change: A sys-tematic review and meta-analysis of the impact of theo-retical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res,12, 1:e4. DOI: 10.2196/jmir.1376. [22] World Health Or-ganization (2010). Global status report on noncommu-nicable diseases. Geneva, Switzerland: WHO Press.