Author(s):

  • Gabrielle M. Turner-McGrievy
  • Chih-Hsiang Yang
  • Courtney Monroe
  • Christine Pellegrini
  • Delia Smith West 

Abstract:

Objective

The goal of this paper is to provide an overview of the emerging lower-burden mobile dietary self-monitoring approaches and provide a case study highlighting the role that habit formation (regularly logging meals) and burden played in two weight loss interventions examining three different methods of dietary self-monitoring: two lower-burden (wearable device and photo-based) and one higher-burden (standard database app).

Methods

A review of the literature of current methods for dietary self-monitoring was conducted. In addition, a case study using data from two different remotely delivered weight loss interventions is presented. Participants (= 100) were randomly assigned to one of the three mobile diet tracking methods. At 6 weeks, participants were asked seven questions on a Likert scale (1 completely disagree; 7 completely agree) assessing factors such as habit formation (e.g., remembering to use the device).

Results

Several emerging methods of dietary self-monitoring are presented. For the case study, the wearable device (5.0 ± 1.81) and photo-based app (4.0 ± 2.24) participants found it more difficult to remember to use their device than did the standard database app (2.35 ± 1.79; p < 0.001) participants, indicating that habit formation was stronger in the Standard App condition than the approaches that were aimed to be of lower burden.

Conclusions

Gaining a better understanding of the current and innovative approaches to dietary self-monitoring, as well as considering how burden and habit formation may be influencing sustained engagement could help inform future effective dietary interventions.

Documentation:https://doi.org/10.1007/s41347-021-00203-9

References:
  • Alsadah, A., van Merode, T., Alshammari, R., & Kleijnen, J. (2020). A systematic literature review looking for the definition of treatment burden. Heliyon, 6(4), e03641. https://doi.org/10.1016/j.heliyon.2020.e03641Article  PubMed  PubMed Central  Google Scholar 
  • Anderson-Bill, E. S., Winett, R. A., & Wojcik, J. R. (2011). Social cognitive determinants of nutrition and physical activity among web-health users enrolling in an online intervention: the influence of social support, self-efficacy, outcome expectations, and self-regulation. Journal of Medical Internet Research13(1), e28. v13i1e28[pii].
  • Anderson, E. S., Winett, R. A., & Wojcik, J. R. (2007). Self-regulation, self-efficacy, outcome expectations, and social support: social cognitive theory and nutrition behavior. Annals of Behavioral Medicine, 34(3), 304–312.Article  Google Scholar 
  • Bagozzi, R. P., Moore, D. J., & Leone, L. (2004). Self-control and the self-regulation of dieting decisions: The role of prefactual attitudes, subjective norms, and resistance to temptation. Basic and Applied Social Psychology, 26(2–3), 199–213.Article  Google Scholar 
  • Brazier, J. E., Harper, R., Jones, N. M., O’Cathain, A., Thomas, K. J., Usherwood, T., & Westlake, L. (1992). Validating the SF-36 health survey questionnaire: New outcome measure for primary care. BMJ, 305(6846), 160–164. https://doi.org/10.1136/bmj.305.6846.160Article  PubMed  PubMed Central  Google Scholar 
  • Bruening, M., Van Woerden, I., Todd, M., Brennhofer, S., Laska, M. N., & Dunton, G. (2016). A mobile ecological momentary assessment tool (devilSPARC) for nutrition and physical activity behaviors in college students: A validation study. Journal of Medical Internet Research, 18(7), e209.Article  Google Scholar 
  • Brunken, R., Plass, J. L., & Leutner, D. (2003). Direct measurement of cognitive load in multimedia learning. Educational Psychologist, 38, 53–61. https://doi.org/10.1207/S15326985EP3801_7Article  Google Scholar 
  • Burke, L. E., Sereika, S. M., Music, E., Warziski, M., Styn, M. A., & Stone, A. (2008). Using instrumented paper diaries to document self-monitoring patterns in weight loss. Contemporary Clinical Trials, 29(2), 182–193. https://doi.org/10.1016/j.cct.2007.07.004Article  PubMed  Google Scholar 
  • Burke, L. E., Styn, M. A., Sereika, S. M., Conroy, M. B., Ye, L., Glanz, K., & Ewing, L. J. (2012). Using mHealth technology to enhance self-monitoring for weight loss: A randomized trial. American Journal of Preventive Medicine, 43(1), 20–26. https://doi.org/10.1016/j.amepre.2012.03.016Article  PubMed  PubMed Central  Google Scholar 
  • Burke, L. E., Swigart, V., Warziski Turk, M., Derro, N., & Ewing, L. J. (2009). Experiences of self-monitoring: Successes and struggles during treatment for weight loss. Qualitative Health Research, 19(6), 815–828. https://doi.org/10.1177/1049732309335395Article  PubMed  PubMed Central  Google Scholar 
  • Burke, L. E., Wang, J., & Sevick, M. A. (2011). Self-monitoring in weight loss: A systematic review of the literature. Journal of the American Dietetic Association, 111(1), 92–102. https://doi.org/10.1016/j.jada.2010.10.008Article  PubMed  PubMed Central  Google Scholar 
  • Carter, M. C., Burley, V. J., Nykjaer, C., & Cade, J. E. (2013). Adherence to a smartphone application for weight loss compared to website and paper diary: Pilot randomized controlled trial. Journal of Medical Internet Research, 15(4), e32. https://doi.org/10.2196/jmir.2283Article  PubMed  PubMed Central  Google Scholar 
  • Pew Research Center. (2019). Mobile fact sheet. https://www.pewresearch.org/internet/fact-sheet/mobile/
  • Chen, J., Cade, J. E., & Allman-Farinelli, M. (2015). The most popular smartphone apps for weight loss: A quality assessment. JMIR Mhealth Uhealth, 3(4), e104. https://doi.org/10.2196/mhealth.4334Article  PubMed  PubMed Central  Google Scholar 
  • Choe, E. K., Abdullah, S., Rabbi, M., Thomaz, E., Epstein, D. A., Cordeiro, F., & Kientz, J. A. (2017). Semi-automated tracking: A balanced approach for self-monitoring applications. IEEE Pervasive Computing, 16(1), 74–84. https://doi.org/10.1109/MPRV.2017.18Article  Google Scholar 
  • Choe, E. K., Lee, B., Kay, M., Pratt, W., & Kientz, J. A. (2015). SleepTight: Low-burden, self-monitoring technology for capturing and reflecting on sleep behaviors. Paper presented at the Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan. https://doi.org/10.1145/2750858.2804266
  • Chung, A. E., Skinner, A. C., Hasty, S. E., & Perrin, E. M. (2017). Tweeting to health: A novel mHealth intervention using Fitbits and Twitter to foster healthy lifestyles. Clinical Pediatrics, 56(1), 26–32.Article  Google Scholar 
  • Collins, L. M., & Kugler, K. C. (2018). Optimization of behavioral, biobehavioral, and biomedical interventions.
  • Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): New methods for more potent eHealth interventions. American Journal of Preventive Medicine, 32(5), S112–S118.Article  Google Scholar 
  • Dennison, L., Morrison, L., Conway, G., & Yardley, L. (2013). Opportunities and challenges for smartphone applications in supporting health behavior change: Qualitative study. Journal of Medical Internet Research, 15(4), e86. https://doi.org/10.2196/jmir.2583Article  PubMed  PubMed Central  Google Scholar 
  • Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design elements to gamefulness: defining “gamification”. Paper presented at the Proceedings of the 15th international academic MindTrek conference: Envisioning future media environments.
  • Dunn, C. G., Turner-McGrievy, G. M., Wilcox, S., & Hutto, B. (2019). Dietary self-monitoring through digital photography or calorie tracking app is associated with significant weight loss: The 2SMART pilot study, a six-month randomized controlled trial. Journal of the Academy of Nutrition and Dietetics, 119(9), 1525–1532.Article  Google Scholar 
  • Eveland, W. P. J., & Dunwoody, S. (2001). User control and structural isomorphism or disorientation and cognitive load?: Learning from the web versus print. Communication Research, 28(1), 48–78. https://doi.org/10.1177/009365001028001002Article  Google Scholar 
  • Eysenbach, G. (2005). The law of attrition. Journal of Medical Internet Research, 7(1), e11. https://doi.org/10.2196/jmir.7.1.e11Article  PubMed  PubMed Central  Google Scholar 
  • Ferrara, G., Kim, J., Lin, S., Hua, J., & Seto, E. (2019). A focused review of smartphone diet-tracking apps: Usability, functionality, coherence with behavior change theory, and comparative validity of nutrient intake and energy estimates. JMIR Mhealth and Uhealth, 7(5), e9232.Article  Google Scholar 
  • Gemming, L., Utter, J., & Ni Mhurchu, C. (2015). Image-assisted dietary assessment: A systematic review of the evidence. Journal of the Academy of Nutrition and Dietetics, 115(1), 64–77. https://doi.org/10.1016/j.jand.2014.09.015Article  PubMed  Google Scholar 
  • Glanz, K., Murphy, S., Moylan, J., Evensen, D., & Curb, J. D. (2006). Improving dietary self-monitoring and adherence with hand-held computers: A pilot study. The American Journal of Health Promotion 20(3), 165–170.Article  Google Scholar 
  • Gorin, A., Phelan, S., Tate, D., Sherwood, N., Jeffery, R., & Wing, R. (2005). Involving support partners in obesity treatment. Journal of Consulting and Clinical Psychology, 73(2), 341–343.Article  Google Scholar 
  • Greaney, M. L., Sprunck-Harrild, K., Bennett, G. G., Puleo, E., Haines, J., Viswanath, K. V., & Emmons, K. M. (2012). Use of email and telephone prompts to increase self-monitoring in a web-based intervention: Randomized controlled trial. Journal of Medical Internet Research, 14(4), e96. https://doi.org/10.2196/jmir.1981Article  PubMed  PubMed Central  Google Scholar 
  • Hales, S., Dunn, C., Wilcox, S., & Turner-McGrievy, G. M. (2016a). Is a picture worth a thousand words? Few evidence-based features of dietary interventions included in photo diet tracking mobile apps for weight loss. Journal of Diabetes Science Technology, 10(6), 1399–1405.Article  Google Scholar 
  • Hales, S., Turner-McGrievy, G. M., Wilcox, S., Fahim, A., Davis, R. E., Huhns, M., & Valafar, H. (2016b). Social networks for improving healthy weight loss behaviors for overweight and obese adults: A randomized clinical trial of the social pounds off digitally (social POD) mobile app. International Journal of Medical Informatics, 94, 81–90. https://doi.org/10.1016/j.ijmedinf.2016.07.003Article  PubMed  Google Scholar 
  • Harkin, B., Webb, T. L., Chang, B. P., Prestwich, A., Conner, M., Kellar, I., & Sheeran, P. (2016). Does monitoring goal progress promote goal attainment? A meta-analysis of the experimental evidence. Psychological Bulletin, 142(2), 198.Article  Google Scholar 
  • Harvey, J., Krukowski, R., Priest, J., & West, D. (2019). Log often, lose more: Electronic dietary self-monitoring for weight loss. Obesity (Silver Spring), 27(3), 380–384. https://doi.org/10.1002/oby.22382Article  Google Scholar 
  • Hoyle, R. H. (2006). Personality and self-regulation: Trait and information-processing perspectives. Journal of Personality, 74(6), 1507–1526. https://doi.org/10.1111/j.1467-6494.2006.00418.xArticle  PubMed  Google Scholar 
  • Hutchesson, M. J., Tan, C. Y., Morgan, P., Callister, R., & Collins, C. (2016). Enhancement of self-monitoring in a web-based weight loss program by extra individualized feedback and reminders: Randomized trial. Journal of Medical Internet Research, 18(4), e82. https://doi.org/10.2196/jmir.4100Article  PubMed  PubMed Central  Google Scholar 
  • Johnson, D., Deterding, S., Kuhn, K. A., Staneva, A., Stoyanov, S., & Hides, L. (2016). Gamification for health and wellbeing: A systematic review of the literature. Internet Interventions, 6, 89–106. https://doi.org/10.1016/j.invent.2016.10.002Article  PubMed  PubMed Central  Google Scholar 
  • Jospe, M. R., Roy, M., Brown, R. C., Williams, S. M., Osborne, H. R., Meredith-Jones, K. A., & Taylor, R. W. (2017). The effect of different types of monitoring strategies on weight loss: A randomized controlled trial. Obesity (Silver Spring), 25(9), 1490–1498. https://doi.org/10.1002/oby.21898Article  Google Scholar 
  • Kankanhalli, A., Shin, J., & Oh, H. (2019). Mobile-based interventions for dietary behavior change and health outcomes: Scoping review. JMIR Mhealth and Uhealth, 7(1), e11312.Article  Google Scholar 
  • Koivisto, J., & Hamari, J. (2019). The rise of motivational information systems: A review of gamification research. International Journal of Information Management, 45, 191–210.Article  Google Scholar 
  • Lemstra, M., Bird, Y., Nwankwo, C., Rogers, M., & Moraros, J. (2016). Weight loss intervention adherence and factors promoting adherence: A meta-analysis. Patient Preference and Adherence, 10, 1547.Article  Google Scholar 
  • Lewis, Z. H., Swartz, M. C., & Lyons, E. J. (2016). What’s the point?: A review of reward systems implemented in gamification interventions. Games for Health Journal, 5(2), 93–99. https://doi.org/10.1089/g4h.2015.0078Article  PubMed  Google Scholar 
  • Liao, Y., & Schembre, S. (2018). Acceptability of continuous glucose monitoring in free-living healthy individuals: Implications for the use of wearable biosensors in diet and physical activity research. JMIR Mhealth Uhealth, 6(10), e11181. https://doi.org/10.2196/11181Article  PubMed  PubMed Central  Google Scholar 
  • Linde, J. A., Jeffery, R. W., French, S. A., Pronk, N. P., & Boyle, R. G. (2005). Self-weighing in weight gain prevention and weight loss trials. Annals of Behavioral Medicine, 30(3), 210–216.Article  Google Scholar 
  • Liu, S., & Willoughby, J. F. (2018). Do fitness apps need text reminders? An experiment testing goal-setting text message reminders to promote self-monitoring. Journal of Health Communication, 23(4), 379–386. https://doi.org/10.1080/10810730.2018.1455768Article  PubMed  Google Scholar 
  • Mackay, D. (1980). Helping people change: A textbook of methods. Edited by Frederick H. Kanfer and Arnold P. Goldstein. Oxford: Pergamon Press. 1980. Pp 600.£ 15.00,£ 6.30 paperback. The British Journal of Psychiatry, 137(4), 392-392.
  • Martin, C. K., Han, H., Coulon, S. M., Allen, H. R., Champagne, C. M., & Anton, S. D. (2009). A novel method to remotely measure food intake of free-living individuals in real time: The remote food photography method. British Journal of Nutrition, 101(3), 446–456. https://doi.org/10.1017/S0007114508027438Article  Google Scholar 
  • Mason, T. B., Do, B., Wang, S., & Dunton, G. F. (2019). Ecological momentary assessment of eating and dietary intake behaviors in children and adolescents: A systematic review of the literature. Appetite, 104465.
  • Maugeri, A., & Barchitta, M. (2019). A systematic review of ecological momentary assessment of diet: Implications and perspectives for nutritional epidemiology. Nutrients, 11(11), 2696.Article  Google Scholar 
  • Napolitano, M. A., Hayes, S., Bennett, G. G., Ives, A. K., & Foster, G. D. (2013). Using Facebook and text messaging to deliver a weight loss program to college students. Obesity (Silver Spring), 21(1), 25–31. https://doi.org/10.1002/oby.20232Article  Google Scholar 
  • Nour, M., Chen, J., & Allman-Farinelli, M. (2019). Young adults’ engagement with a self-monitoring app for vegetable intake and the impact of social media and gamification: feasibility study. JMIR Formative Research, 3(2), e13324.Article  Google Scholar 
  • O’Connor, S. G., Ke, W., Dzubur, E., Schembre, S., & Dunton, G. F. (2018). Concordance and predictors of concordance of children’s dietary intake as reported via ecological momentary assessment and 24 h recall. Public Health Nutrition, 21(6), 1019–1027.Article  Google Scholar 
  • Oulasvirta, A., Rattenbury, T., Ma, L., & Raita, E. (2012). Habits make smartphone use more pervasive. Personal and Ubiquitous Computing, 16(1), 105–114. https://doi.org/10.1007/s00779-011-0412-2Article  Google Scholar 
  • Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38, 63–71. https://doi.org/10.1207/S15326985EP3801_8Article  Google Scholar 
  • Pagoto, S., Tulu, B., Agu, E., Waring, M. E., Oleski, J. L., & Jake-Schoffman, D. E. (2018). Using the habit app for weight loss problem solving: Development and feasibility study. JMIR Mhealth Uhealth, 6(6), e145. https://doi.org/10.2196/mhealth.9801Article  PubMed  PubMed Central  Google Scholar 
  • Patel, M. L., Hopkins, C. M., Brooks, T. L., & Bennett, G. G. (2019). Comparing self-monitoring strategies for weight loss in a smartphone app: Randomized controlled trial. JMIR Mhealth Uhealth, 7(2), e12209. https://doi.org/10.2196/12209Article  PubMed  PubMed Central  Google Scholar 
  • Perrin, A., & Kumar, M. (2019). About three-in-ten US adults say they are ‘almost constantly’online. Pew Research Center.
  • Peterson, N. D., Middleton, K. R., Nackers, L. M., Medina, K. E., Milsom, V. A., & Perri, M. G. (2014). Dietary self-monitoring and long-term success with weight management. Obesity (Silver Spring), 22(9), 1962–1967. https://doi.org/10.1002/oby.20807Article  Google Scholar 
  • Poppinga, B., Heuten, W., & Boll, S. (2014). Sensor-based identification of opportune moments for triggering notifications. IEEE Pervasive Computing, 13(1), 22–29.Article  Google Scholar 
  • Riccio, M. T., Shrout, P. E., & Balcetis, E. (2019). Interpersonal pursuit of intrapersonal health goals: Social cognitive–motivational mechanisms by which social support promotes self-regulatory success. Social and Personality Psychology Compass, 13(10), e12495.Article  Google Scholar 
  • Rolstad, S., Adler, J., & Rydén, A. (2011). Response burden and questionnaire length: Is shorter better? A review and meta-analysis. Value in Health, 14(8), 1101–1108. https://doi.org/10.1016/j.jval.2011.06.003Article  PubMed  Google Scholar 
  • Samuel-Hodge, C. D., Holder-Cooper, J. C., Gizlice, Z., Davis, G., Steele, S. P., Keyserling, T. C., & Svetkey, L. P. (2017). Family partners in lifestyle support (PALS): Family-based weight loss for African American adults with type 2 diabetes. Obesity, 25(1), 45–55.Article  Google Scholar 
  • Sanders, J. P., Loveday, A., Pearson, N., Edwardson, C., Yates, T., Biddle, S. J., & Esliger, D. W. (2016). Devices for self-monitoring sedentary time or physical activity: A scoping review. Journal of Medical Internet Research, 18(5), e90.Article  Google Scholar 
  • Sarker, H., Sharmin, M., Ali, A. A., Rahman, M. M., Bari, R., Hossain, S. M., & Kumar, S. (2014). Assessing the availability of users to engage in just-in-time intervention in the natural environment. Paper presented at the Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing.
  • Schembre, S. M., Liao, Y., O’connor, S. G., Hingle, M. D., Shen, S. E., Hamoy, K. G. …, & Thomson, C. A. (2018). Mobile ecological momentary diet assessment methods for behavioral research: Systematic review. JMIR mHealth and uHealth, 6(11), e11170.Article  Google Scholar 
  • Schnoll, R., & Zimmerman, B. J. (2001). Self-regulation training enhances dietary self-efficacy and dietary fiber consumption. Journal of the American Dietetic Association, 101(9), 1006–1011.Article  Google Scholar 
  • Semper, H. M., Povey, R., & Clark-Carter, D. (2016). A systematic review of the effectiveness of smartphone applications that encourage dietary self-regulatory strategies for weight loss in overweight and obese adults. Obesity Reviews, 17(9), 895–906. https://doi.org/10.1111/obr.12428Article  PubMed  Google Scholar 
  • Shaw, R. J., Steinberg, D. M., Zullig, L. L., Bosworth, H. B., Johnson, C. M., & Davis, L. L. (2014). mHealth interventions for weight loss: A guide for achieving treatment fidelity. Journal of the American Medical Informatics Association: JAMIA, 21(6), 959–963. https://doi.org/10.1136/amiajnl-2013-002610Article  PubMed  PubMed Central  Google Scholar 
  • Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review Clinical Psychology, 4, 1–32.Article  Google Scholar 
  • Sieverdes, J. C., Treiber, F., & Jenkins, C. (2013). Improving diabetes management with mobile health technology. American Journal of the Medical Sciences, 345(4), 289–295. https://doi.org/10.1097/MAJ.0b013e3182896ceeArticle  Google Scholar 
  • Suh, H., Shahriaree, N., Hekler, E. B., & Kientz, J. A. (2016). Developing and validating the user burden scale: A tool for assessing user burden in computing systems. Paper presented at the Proceedings of the 2016 CHI conference on human factors in computing systems.
  • Tang, J., Abraham, C., Stamp, E., & Greaves, C. (2015). How can weight-loss app designers’ best engage and support users? A qualitative investigation. British Journal of Health Psychology, 20(1), 151–171. https://doi.org/10.1111/bjhp.12114Article  PubMed  Google Scholar 
  • Turner-McGrievy, G., Helander, E., Kaipainen, K., Perez-Macias, J., & Korhonen, I. (2015). The use of crowdsourcing for dietary self-monitoring: Crowdsourced ratings of food pictures are comparable to ratings by trained observers. Journal of the American Medical Informatics Association, 22(e1), e112-119.Article  Google Scholar 
  • Turner-McGrievy, G., Jake-Schoffman, D. E., Singletary, C., Wright, M., Crimarco, A., Wirth, M. D., & McGrievy, M. J. (2019a). Using commercial physical activity trackers for health promotion research: Four case studies. Health Promotion Practice, 20(3), 381–389. https://doi.org/10.1177/1524839918769559Article  PubMed  Google Scholar 
  • Turner-McGrievy, G. M., Beets, M. W., Moore, J. B., Kaczynski, A. T., Barr-Anderson, D. J., & Tate, D. F. (2013). Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program. Journal of the American Medical Informatics Association, 20(3), 513–518. https://doi.org/10.1136/amiajnl-2012-001510Article  PubMed  PubMed Central  Google Scholar 
  • Turner-McGrievy, G. M., Boutté, A., Crimarco, A., Wilcox, S., Hutto, B. E., Hoover, A., & Muth, E. R. (2017a). Byte by bite: Use of a mobile Bite Counter and weekly behavioral challenges to promote weight loss. Smart Health, 3–4, 20–26. https://doi.org/10.1016/j.smhl.2017.03.004Article  PubMed  Google Scholar 
  • Turner-McGrievy, G. M., Dunn, C. G., Wilcox, S., Boutte, A. K., Hutto, B., Hoover, A., & Muth, E. (2019b). Defining adherence to mobile dietary self-monitoring and assessing tracking over time: Tracking at least two eating occasions per day is best marker of adherence within two different mobile health randomized weight loss interventions. Journal of the Academy of Nutrition and Dietetics, 119(9), 1516–1524. https://doi.org/10.1016/j.jand.2019.03.012Article  PubMed  PubMed Central  Google Scholar 
  • Turner-McGrievy, G. M., Wilcox, S., Boutté, A., Hutto, B. E., Singletary, C., Muth, E. R., & Hoover, A. (2017b). The Dietary Intervention to Enhance Tracking with Mobile (DIET Mobile) study: A six-month randomized weight loss trial. Obesity, 25(8), 1336–1342.Article  Google Scholar 
  • Vu, T., Lin, F., Alshurafa, N., & Xu, W. (2017). Wearable food intake monitoring technologies: A comprehensive review. Computers, 6(1), 4.Article  Google Scholar 
  • Wang, J., Sereika, S. M., Chasens, E. R., Ewing, L. J., Matthews, J. T., & Burke, L. E. (2012). Effect of adherence to self-monitoring of diet and physical activity on weight loss in a technology-supported behavioral intervention. Patient Preference and Adherence, 6, 221.Article  Google Scholar 
  • Ware, J. E., Jr. (2000). SF-36 health survey update. Spine (Phila Pa 1976), 25(24), 3130-3139.
  • Watson, D. L., & Tharp, R. G. (1997). Self-directed behavior: Self-modification for personal adjustment. Pacific Grove, CA: Brooks. In: Cole Publishing Company.
  • Wen, C. K. F., Schneider, S., Stone, A. A., & Spruijt-Metz, D. (2017). Compliance with mobile ecological momentary assessment protocols in children and adolescents: A systematic review and meta-analysis. Journal of Medical Internet Research, 19(4), e132.Article  Google Scholar 
  • West, D. S., Monroe, C. M., Turner-McGrievy, G., Sundstrom, B., Larsen, C., Magradey, K., & Brandt, H. M. (2016). A technology-mediated behavioral weight gain prevention intervention for college students: Controlled, quasi-experimental study. Journal of Medical Internet Research, 18(6), e133.Article  Google Scholar 
  • Whitton, N. (2010). Game engagement theory and adult learning. Simulation & Gaming, 42(5), 596–609. https://doi.org/10.1177/1046878110378587Article  Google Scholar