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A Review of the Processes By Which School Psychologists and Counsellors Can Use Taxonomies to Evaluate Health-Related Apps

Published online by Cambridge University Press:  15 April 2018

Marko Ostojic ,
Jasmine Chung ,
Michael DiMattia ,
Brett Furlonger ,
Margherita Busacca  and
Philip Chittleborough
Marko Ostojic
Affiliation:
Department of Educational Psychology, Counselling and Inclusive Education, Faculty of Education, Monash University, Melbourne, Victoria, Australia
Jasmine Chung
Affiliation:
Department of Educational Psychology, Counselling and Inclusive Education, Faculty of Education, Monash University, Melbourne, Victoria, Australia
Michael DiMattia
Affiliation:
Department of Educational Psychology, Counselling and Inclusive Education, Faculty of Education, Monash University, Melbourne, Victoria, Australia
Brett Furlonger*
Affiliation:
Department of Educational Psychology, Counselling and Inclusive Education, Faculty of Education, Monash University, Melbourne, Victoria, Australia
Margherita Busacca
Affiliation:
Department of Educational Psychology, Counselling and Inclusive Education, Faculty of Education, Monash University, Melbourne, Victoria, Australia
Philip Chittleborough
Affiliation:
Department of Educational Psychology, Counselling and Inclusive Education, Faculty of Education, Monash University, Melbourne, Victoria, Australia
*
address for correspondence: Dr Brett Furlonger, Faculty of Education, Monash University, 57 Scenic Boulevard, Clayton VIC 3800, Australia. Email: brett.furlonger@monash.edu
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Abstract

School students are increasingly using apps for health-related purposes, either on their own or when recommended by psychologists or counsellors, as apps offer a way to assist students to change their behaviour. However, there is a growing need for psychologists and counsellors to be able to evaluate the quality and usefulness of such apps to effect behaviour change. This study was therefore undertaken to identify methods by which school psychologists and counsellors could evaluate health-related apps for clinical use or research purposes. After examining 15 studies of apps that met the inclusion criteria, it was clear that researchers used a number of taxonomies to evaluate the apps. There were seven taxonomies identified, of which five were generalisable to all health conditions, with the behaviour change technique (BCT) taxonomy being the most comprehensive, containing 13 key behaviour strategies. Despite the utility of the taxonomies to identify the amount of behaviour change content within the apps, it was difficult to determine how the behaviour change strategies were measured, thus reducing the ability to predict app effectiveness. Approaches to improving methods by which apps can be developed and evaluated are proposed.

Keywords

content-analysisappsevaluationcounsellingschoolstudents
Type
Review Article
Information
Journal of Psychologists and Counsellors in Schools , Volume 28 , Issue 2 , December 2018 , pp. 212 - 233
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Copyright
Copyright © The Author(s) 2018 

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References

Abraham, C., & Michie, S. (2008). A taxonomy of behavior change techniques used in interventions. Health Psychology, 27, 379–387. doi:10.1037/0278-6133.27.3.379Google Scholar
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. doi:10.1016/0749-5978(91)90020Google Scholar
Australian Communications and Media Authority (ACMA) (2014). Communications Report 2012–13. Sydney, Australia: Author. http://www.acma.gov.au/~/media/Research%20and%20Reporting/Publication/Comms%20Report%202012%2013/PDF/ACMA%20Communications%20report%20201213_WEB%20pdf.pdfGoogle Scholar
Azar, M.J., Lesser, L.I., Laing, B.Y., Stephens, J., Aurora, M.S., Burke, L.E., & Palaniappan, L.P. (2013). Mobile applications for weight management: Theory-based content analysis. American Journal of Preventative Medicine, 45, 583–589. doi:10.1016/j.amepre.2013.07.005Google Scholar
Azar, K., Riley, W., & Mani, M. (2015). A framework to assist health professionals in recommending high-quality apps for supporting chronic disease self-management: illustrative assessment of type 2 diabetes apps. JMIR mHealth and uHealth, 3, 4–13. e87.Google Scholar
Azevedo, A.R., de Souza, H.M., Monteiro, J.A., & Lima, A.R. (2015). Future perspectives of smartphone applications for rheumatic diseases self-management. Rheumatology International, 35, 419–431. doi:10.1007/s00296-014-3117-9Google Scholar
Bandura, A. (2002). Social cognitive theory of mass communication. In Bryant, J. & Oliver, M.B. (Eds.), Media Effects: Advances in Theory and Research (pp. 94–124). New York, NY: Routledge.Google Scholar
Bardus, M., van Beurden, S.B., Smith, J.R., & Abraham, C. (2016). A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management. International Journal of Behavioral Nutrition and Physical Activity, 13, 1–9. doi:10.1186/s12966-016-0359-9Google Scholar
Barlow, S.E. (2007). Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics, 120, S164–192.Google Scholar
Conroy, D.E., Yang, C.H., & Maher, J.P. (2014). Behavior change techniques in top-ranked mobile apps for physical activity. American Journal of Preventive Medicine, 46, 649–652. doi:10.1016/j.amepre.2014.01.010Google Scholar
Cooper, J.O., Heron, T.E., & Heward, W.L. (2007). Applied Behavior Analysis (2nd ed.). Essex, England: Pearson Education Limited.Google Scholar
Cowan, L.T., Van Wagenen, S.A., Brown, B.A., Hedin, R.J., Seino-Stephan, Y., Hall, P.C., & West, J.H. (2013). Apps of steel: Are exercise apps providing consumers with realistic expectations? A content analysis of exercise apps for presence of behavior change theory. Health Education & Behavior, 40, 133–139. doi:10.1177/1090198112452126Google Scholar
Crane, D., Garnett, C., Brown, J., West, R., & Michie, S. (2015). Behavior change techniques in popular alcohol reduction apps: Content analysis. Journal of Medical Internet Research, 17. doi:10.2196/jmir.4060Google Scholar
Direito, A., Dale, L.P., Shields, E., Dobson, R., Whittaker, R., & Maddison, R. (2014). Do physical activity and dietary smartphone applications incorporate evidence-based behaviour change techniques? BMC Public Health, 14, 646. doi:10.1186/1471-2458-14-64gGoogle Scholar
Doshi, A., Patrick, K., Sallis, J.F., & Calfas, K., (2003). Evaluation of physical activity web sites for use of behavior change theories. Annals of Behavioral Medicine, 25, 105–111. doi:10.1207/S15324796ABM2502_06Google Scholar
DPP Research Group. (2002). Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. New England Journal of Medicine, 346, 393–403. doi:10.1056/NEJMoa012512Google Scholar
DPP Research Group. (2002). The Diabetes Prevention Program (DPP): Description of lifestyle intervention. (Clinical Care/Education/Nutrition). Diabetes Care, 25, 2165–2171.Google Scholar
Fogg, B.J. (2003). Persuasive technology: Using computers to change what we think and do (interactive technologies). San Francisco, CA: Morgan Kaufmann.Google Scholar
Fogg, B.J. (2009). A behavior model for persuasive design. Retrieved from www.bjfogg.com/fbm_files/page4_1.pdfGoogle Scholar
Furlonger, B.E., & Budisa, S. (2015). Internet sites and apps available to students seeking counselling and what school counsellors should know about them. Journal of Psychologist and Counsellors in Schools, 26, 68–83.Google Scholar
Green, L., & Kreuter, M. (1991). Health promotion planning: an educational and environmental approach. Mountain View, TX: Mayfield Publishing Company.Google Scholar
Hale, K., Capra, S., & Bauer, J. (2015). A framework to assist health professionals in recommending high-quality apps for supporting chronic disease self- management: illustrative assessment of type 2 diabetes Apps. JMIR mHealth and uHealth, 3, e87. doi:10.2196/mhealth.4532Google Scholar
Higgins, J.P. (2016). Smartphone applications for patients’ health and fitness. The American Journal of Medicine, 129, 11–19. doi:10.1016/j.amjmed.2015.05.038Google Scholar
Huckvale, K., Car, M., Morrison, C., & Car, J. (2012). Apps for self-management: A systematic assessment of content and tools. BMC Medicine, 10, 144. doi:10.1186/1741-7015-10-144Google Scholar
Institute of Medicine (US) Committee on Health and Behavior Committee on Health and Behavior: Research, Practice and Policy. (2001). Health and behavior: The interplay of biological, behavioral, and societal influences. Washington, DC: National Academies Press (US). https://www.ncbi.nlm.nih.gov/pubmed/20669491Google Scholar
Jordan, J., Briggs, A., Brand, C., & Osbourne, R. (2008). Enhancing patient engagement in chronic disease self-management support initiatives in Australia: The need for an integrated approach. Medical Journal of Australia, 189, 9–13.Google Scholar
Middelweerd, A., Mollee, J.S., van der Wal, C.N., Brug, J., & Te Velde, S.J. (2014). Apps to promote physical activity among adults: A review and content analysis. International Journal of Behavioral Nutrition & Physical Activity, 11, 97. doi:10.1186/s12966-014-0097-9Google Scholar
Michie, S., Ashford, S., Sniehotta, F.F., Dombrowski, S.U., Bishop, A., & French, D.P. (2011). A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: The CALO-RE taxonomy. Psychology & Health, 26, 1479–1498. doi:10.1080/08870446.2010.540664Google Scholar
Michie, S., Richardson, M., Johnston, M., Abraham, C., Francis, J., Hardeman, W., Eccles, M.P., Cane, J., & 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, 81–95. doi:10.1007/s12160-013-9486-6Google Scholar
Morrissey, E.C., Corbett, T.K., Walsh, J.C., & Molloy, G.J. (2016). Behavior change techniques in apps for medication adherence: A content analysis. American Journal of Preventive Medicine, 50, 143–146. doi:10.1016/j.amepre.2015.09.034Google Scholar
Nundy, S., Mishra, A., Hogan, P., Lee, S.M., Solomon, M.C., & Peek, M.E. (2014). How do mobile phone diabetes programs drive behavior change? Evidence from a mixed methods observational cohort study. Diabetes Education, 40, 806–819. doi:10.1177/0145721714551992.Google Scholar
Pagoto, S., Schneider, K., Jojic, M., DeBiasse, M., & Mann, D. (2013). Evidence-based strategies in weight-loss mobile apps. American Journal of Preventive Medicine, 45, 576–582. doi:10.1016/j.amepre.2013.04.025Google Scholar
Patrick, K., Griswold, W.G., Raab, F., & Intille, S.S. (2008). Health and the mobile phone. American Journal of Preventative Medicine, 35, 177–181. doi:10.1016/j.amepre.2008.05.001Google Scholar
Payne, H.E., Wilkinson, J., West, J.H., & Bernhardt, J.M. (2016). A content analysis of precede-proceed constructs in stress management mobile apps. mHealth, 2 (5). doi:10.3978/j.issn.2306-9740.2016.02.02Google Scholar
Prochaska, J. O., & DiClemente, C.C. (2005). The transtheoretical approach. In Norcross, J.C. & Goldfried, M.R. (Eds.), Handbook of psychotherapy integration (2nd ed., pp. 147–171). New York, NY: Oxford University Press.Google Scholar
Reynoldson, C., Stones, C., Allsop, M., Gardner, P., Bennett, M.I., Closs, S.J., Jones, R., & Knapp, P. (2014). Assessing the quality and usability of smartphone apps for pain self-management. Pain Medicine, 15, 898–909. doi:10.1111/pme.12327Google Scholar
Rosenstock, M. (1974). Historical origins of the health belief model. Health Education Monographs, 2, 328–335. doi: 10.1177/109019817400200403.Google Scholar
Sheehy, S., Cohen, G.R., & Owen, K. (2014). Self-management of diabetes in children and young adults using technology and smartphone applications. Current Diabetes Reviews, 10, 298–301.Google Scholar
Schoffman, D.E., Turner-McGrievy, G., Jones, S.J., & Wilcox, S. (2013). Mobile apps for pediatric obesity prevention and treatment, healthy eating, and physical activity promotion: Just fun and games? Translational Behavioral Medicine, 3, 320–325. doi:10.1007/s13142-013-0206-3Google Scholar
Stoyanov, S.R., Hides, L., Kavanagh, D.J., Zelenko, O., Tjondrongoro, D., & Mani, M. (2015). Mobile app rating scale: A new tool for assessing the quality of mobile apps. JMIR Mhealth Uhealth, 3, e27. doi:10.2196/mhealth.3422.Google Scholar
Sims, G. (2015). Google Play Store vs the Apple App Store: by the numbers (2015). Android Authority. Retrieved from http://www.androidauthority.com/google-play-store-vs-the-apple-app-store-601836/Google Scholar
Stoyanov, S.R., Hides, L., Kavanagh, D.J., Zelenko, O., Tjondronegoro, D., & Mani, M. (2015). Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR mHealth and uHealth, 3, e27. doi:10.2196/mhealth.3422Google Scholar
Sutton, E.F., & Redman, L.M. (2016). Smartphone applications to aid weight loss and management: Current perspectives. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 9, 213–216.Google Scholar
Ubhi, H.K., Kotz, D., Michie, S., van Schayck, O.C.P., Sheard, D., Selladurai, A., & West, R. (2016). Comparative analysis of smoking cessation smartphone applications available in 2012 versus 2014. Addictive Behaviors, 58, 175–181. doi:10.1016/j.addbeh.2016.02.026Google Scholar
Ubhi, H.K., Michie, S., Kotz, D., van Schayck, O.C.P., Selladurai, A., & West, R. (2015). Characterising smoking cessation smartphone applications in terms of behaviour change techniques, engagement and ease-of-use features. Translational Behavioral Medicine, 6, 410–417. doi:10.1007/s13142-015-0352-xGoogle Scholar
West, J.H., Hall, P.C., Arredondo, V., Berrett, B., Guerra, B., Farrell, J. (2013). Health behavior theories in diet apps. Journal of Consumer Health on the Internet, 17, 10–24. doi:10.1080/15398285.2013.756343Google Scholar
West, J.H., Hall, P.C., Hanson, C.L., Barnes, M.D., Giraud-Carrier, C., & Barrett, J. (2012). There's an app for that: Content analysis of paid health and fitness apps. Journal of Medical Internet Research, 14, doi:10.2196/jmir.1977.Google Scholar
Whitehead, L., & Seaton, P (2016). The effectiveness of self-management mobile phone and tablet apps in long-term condition management: A systematic review. Journal of Medical Internet Research, 18, http://ro.ecu.edu.au/ecuworkspost2013/2098Google Scholar
Whittemore, R. (2011). A systematic review of the translational research on the Diabetes Prevention Program. Translational Behavioral Medicine, 1, 480–491. doi:10.1007/s13142-011-0062-yGoogle Scholar
Wood, C.E., Richardson, M., Johnston, M., Abraham, C., Francis, J., Hardeman, W., & Michie, S. (2015). Applying the behaviour change technique (BCT) taxonomy v1: A study of coder training. Translational Behavioral Medicine, 5, 134–148. doi:10.1007/s13142-014-0290-zGoogle 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, 48, 452–455. doi:10.1016/j.amepre.2014.10.010Google Scholar