Advertisement

International Journal of Behavioral Medicine

, Volume 24, Issue 5, pp 673–682 | Cite as

Return of the JITAI: Applying a Just-in-Time Adaptive Intervention Framework to the Development of m-Health Solutions for Addictive Behaviors

  • Stephanie P. Goldstein
  • Brittney C. Evans
  • Daniel Flack
  • Adrienne Juarascio
  • Stephanie Manasse
  • Fengqing Zhang
  • Evan M. Forman
Article

Abstract

Purpose

Lapses are strong indicators of later relapse among individuals with addictive disorders, and thus are an important intervention target. However, lapse behavior has proven resistant to change due to the complex interplay of lapse triggers that are present in everyday life. It could be possible to prevent lapses before they occur by using m-Health solutions to deliver interventions in real-time.

Method

Just-in-time adaptive intervention (JITAI) is an intervention design framework that could be delivered via mobile app to facilitate in-the-moment monitoring of triggers for lapsing, and deliver personalized coping strategies to the user to prevent lapses from occurring. An organized framework is key for successful development of a JITAI.

Results

Nahum-Shani and colleagues (2014) set forth six core elements of a JITAI and guidelines for designing each: distal outcomes, proximal outcomes, tailoring variables, decision points, decision rules, and intervention options. The primary aim of this paper is to illustrate the use of this framework as it pertains to developing a JITAI that targets lapse behavior among individuals following a weight control diet.

Conclusion

We will detail our approach to various decision points during the development phases, report on preliminary findings where applicable, identify problems that arose during development, and provide recommendations for researchers who are currently undertaking their own JITAI development efforts. Issues such as missing data, the rarity of lapses, advantages/disadvantages of machine learning, and user engagement are discussed.

Keywords

m-Health Just-in-time adaptive interventions Lapses Addictions 

Notes

Acknowledgements

The current study was funded by the Karen Miller-Kovach research grant from Weight Watchers and The Obesity Society awarded to Dr. Forman.

Compliance with Ethical Standards

Funding

The study was funded by the Karen Miller-Kovach research grant from Weight Watchers and The Obesity Society.

Conflict of Interest

Evan Forman received a research grant from Weight Watchers to support the development of DietAlert, a companion smartphone application to assist with dietary adherence.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

References

  1. 1.
    Marlatt GA, Donovan DM. Relapse prevention: Maintenance strategies in the treatment of addictive behaviors. Guilford Press; 2005.Google Scholar
  2. 2.
    McKay JR. Is there a case for extended interventions for alcohol and drug use disorders? Addiction. 2005;100(11):1594–610.CrossRefPubMedGoogle Scholar
  3. 3.
    Murphy SA. Workshop on Just In Time Adaptive Interventions. Ann Arbor: University of Michigan; 2013. https://community.isr.umich.edu/public/jitai/Workshop.aspx2013.Google Scholar
  4. 4.
    Nahum-Shani I, Hekler EB, Spruijt-Metz D. Building health behavior models to guide the development of Just-in-Time Adaptive Interventions: a pragmatic framework. Health Psychol. 2015;34(S):1209.CrossRefPubMedCentralGoogle Scholar
  5. 5.
    Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, et al. Just-in-Time Adaptive Interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med. 2016;2016:1–17.Google Scholar
  6. 6.
    Aldhaban F, editor. Exploring the adoption of Smartphone technology: Literature review. 2012 Proceedings of PICMET’12: Technology Management for Emerging Technologies; 2012: IEEE.Google Scholar
  7. 7.
    Depp CA, Mausbach B, Granholm E, Cardenas V, Ben-Zeev D, Patterson TL, et al. Mobile interventions for severe mental illness: design and preliminary data from three approaches. J Nerv Ment Dis. 2010;198(10):715.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    King AC, Ahn DK, Oliveira BM, Atienza AA, Castro CM, Gardner CD. Promoting physical activity through hand-held computer technology. Am J Prev Med. 2008;34(2):138–42.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Wei J, Hollin I, Kachnowski S. A review of the use of mobile phone text messaging in clinical and healthy behaviour interventions. J Telemed Telecare. 2011;17(1):41–8.CrossRefPubMedGoogle Scholar
  10. 10.
    Nahum-Shani I, Smith SN, Tewari A, Witkiewitz K, Collins LM, Spring B, et al. Just in time adaptive interventions (JITAIs): an organizing framework for ongoing health behavior support. Methodol Cent Tech Report. 2014;2014:14–126.Google Scholar
  11. 11.
    Hebebrand J, Albayrak Ö, Adan R, Antel J, Dieguez C, de Jong J, et al. “Eating addiction”, rather than “food addiction”, better captures addictive-like eating behavior. Neurosci Biobehav Rev. 2014;47:295–306.CrossRefPubMedGoogle Scholar
  12. 12.
    Stone AA, Shiffman S. Ecological momentary assessment (EMA) in behavioral medicine. Annals of Behavioral Medicine. 1994;16(3):199–202Google Scholar
  13. 13.
    Hekler EB, Klasnja P, Riley WT, Buman MP, Huberty J, Rivera DE, et al. Agile science: creating useful products for behavior change in the real world. Transl Behav Med. 2016;2016:1–12.Google Scholar
  14. 14.
    Franz MJ, VanWormer JJ, Crain AL, Boucher JL, Histon T, Caplan W, et al. Weight-loss outcomes: a systematic review and meta-analysis of weight-loss clinical trials with a minimum 1-year follow-up. J Am Diet Assoc. 2007;107(10):1755–67.CrossRefPubMedGoogle Scholar
  15. 15.
    Wadden TA, Butryn ML. Behavioral treatment of obesity. Endocrinol Metab Clin North Am. 2003;32(4):981–1003.CrossRefPubMedGoogle Scholar
  16. 16.
    Wadden TA, Frey DL. A multicenter evaluation of a proprietary weight loss program for the treatment of marked obesity: a five-year follow-up. Int J Eat Disord. 1997;22(2):203–12.CrossRefPubMedGoogle Scholar
  17. 17.
    Wadden TA, Sternberg JA, Letizia KA, Stunkard AJ, Foster GD. Treatment of obesity by very low calorie diet, behavior therapy, and their combination: a five-year perspective. Int J Obes. 1989;13 Suppl 2:39–46.PubMedGoogle Scholar
  18. 18.
    Jeffery RW, Epstein LH, Wilson GT, Drewnowski A, Stunkard AJ, Wing RR. Long-term maintenance of weight loss: current status. Health Psychol. 2000;19(1S):5.CrossRefPubMedGoogle Scholar
  19. 19.
    Fontaine KR, Cheskin LJ. Self-efficacy, attendance, and weight loss in obesity treatment. Addict Behav. 1997;22:567–70.CrossRefPubMedGoogle Scholar
  20. 20.
    Kramer FM, Jeffery RW, Forster JL, Snell MK. Long-term follow-up of behavioral treatment for obesity: patterns of weight regain among men and women. Int J Obes. 1989;13:123–36.PubMedGoogle Scholar
  21. 21.
    Stalonas PM, Perri MG, Kerzner AB. Do behavioral treatments of obesity last? A five-year follow-up investigation. Addict Behav. 1984;9:175–83.CrossRefPubMedGoogle Scholar
  22. 22.
    Lowe MR. Self-regulation of energy intake in the prevention and treatment of obesity: is it feasible? Obes Res. 2003;11(Suppl):44S–59.CrossRefPubMedGoogle Scholar
  23. 23.
    Wilson GT. Behavioral treatment of obesity: thirty years and counting. Adv Behav Res Ther. 1994;16(1):31–75.CrossRefGoogle Scholar
  24. 24.
    Carels RA, Douglass OM, Cacciapaglia HM, O’Brien WH. An ecological momentary assessment of relapse crises in dieting. J Consult Clin Psychol. 2004;72(2):341.CrossRefPubMedGoogle Scholar
  25. 25.
    Carels RA, Hoffman J, Collins A, Raber AC, Cacciapaglia H, O’Brien WH. Ecological momentary assessment of temptation and lapse in dieting. Eat Behav. 2002;2(4):307–21.CrossRefGoogle Scholar
  26. 26.
    McKee HC, Ntoumanis N, Taylor IM. An ecological momentary assessment of lapse occurrences in dieters. Ann Behav Med. 2014;48(3):300–10.CrossRefPubMedGoogle Scholar
  27. 27.
    Butryn ML, Phelan S, Hill JO, Wing RR. Consistent self‐monitoring of weight: a key component of successful weight loss maintenance. Obesity. 2007;15(12):3091–6.CrossRefPubMedGoogle Scholar
  28. 28.
    Burke LE, Conroy MB, Sereika SM, Elci OU, Styn MA, Acharya SD, et al. The effect of electronic self‐monitoring on weight loss and dietary intake: a randomized behavioral weight loss trial. Obesity. 2011;19(2):338–44.CrossRefPubMedGoogle Scholar
  29. 29.
    Wadden TA, West DS, Neiberg RH, Wing RR, Ryan DH, Johnson KC, et al. One‐year weight losses in the Look AHEAD study: factors associated with success. Obesity. 2009;17(4):713–22.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Grenard JL, Stacy AW, Shiffman S, Baraldi AN, MacKinnon DP, Lockhart G, et al. Sweetened drink and snacking cues in adolescents. A study using ecological momentary assessment. Appetite. 2013;67:61–73.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Thomas JG. Toward a better understanding of the development of overweight: a study of eating behavior in the natural environment using ecological momentary assessment: Drexel University; 2009.Google Scholar
  32. 32.
    Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4:1–32.CrossRefPubMedGoogle Scholar
  33. 33.
    Foreyt JP, Goodrick GK. Evidence for success of behavior modification in weight loss and control. Ann Intern Med. 1993;119:698–701.CrossRefPubMedGoogle Scholar
  34. 34.
    Witkiewitz K, Desai SA, Bowen S, Leigh BC, Kirouac M, Larimer ME. Development and evaluation of a mobile intervention for heavy drinking and smoking among college students. Psychol Addict Behav. 2014;28(3):639.CrossRefPubMedGoogle Scholar
  35. 35.
    Domingos P. A few useful things to know about machine learning. Commun ACM. 2012;55(10):78–87.CrossRefGoogle Scholar
  36. 36.
    Pereira F, Mitchell T, Botvinick M. Machine learning classifiers and fMRI: a tutorial overview. Neuroimage. 2009;45(1):S199–209.CrossRefPubMedGoogle Scholar
  37. 37.
    Goldstein SP. A Preliminary Investigation of a Personalized Risk Alert System for Weight Control Lapses: Drexel University; 2016.Google Scholar
  38. 38.
    Kotsiantis SB, Zaharakis I, Pintelas P. Supervised machine learning: A review of classification techniques. 2007.Google Scholar
  39. 39.
    Mulvaney SA, Ritterband LM, Bosslet L. Mobile intervention design in diabetes: review and recommendations. Curr Diab Rep. 2011;11(6):486–93.CrossRefPubMedGoogle Scholar
  40. 40.
    Abraham C, Michie S. A taxonomy of behavior change techniques used in interventions. Health Psychol. 2008;27(3):379.CrossRefPubMedGoogle Scholar
  41. 41.
    Tang J, Abraham C, Stamp E, Greaves C. How can weight‐loss app designers’ best engage and support users? A qualitative investigation. Br J Health Psychol. 2015;20(1):151–71.CrossRefPubMedGoogle Scholar
  42. 42.
    Collins LM, Murphy SA, Nair VN, Strecher VJ. A strategy for optimizing and evaluating behavioral interventions. Ann Behav Med. 2005;30(1):65–73.CrossRefPubMedGoogle Scholar
  43. 43.
    Whittaker R, Merry S, Dorey E, Maddison R. A development and evaluation process for mHealth interventions: examples from New Zealand. J Health Commun. 2012;17(sup1):11–21.CrossRefPubMedGoogle Scholar
  44. 44.
    Lee JH, Huber Jr J, editors. Multiple imputation with large proportions of missing data: How much is too much? United Kingdom Stata Users’ Group Meetings 2011; 2011: Stata Users Group.Google Scholar
  45. 45.
    Ziegler ML. Variable selection when confronted with missing data: University of Pittsburgh; 2006.Google Scholar
  46. 46.
    Quinlan JR. Induction of decision trees. Mach Learn. 1986;1(1):81–106.Google Scholar
  47. 47.
    Liu WZ, White AP, Thompson SG, Bramer MA. Techniques for dealing with missing values in classification. Advances in Intelligent Data Analysis Reasoning about Data. Springer; 1997. p. 527-36.Google Scholar
  48. 48.
    Guo Y, Logan HL, Glueck DH, Muller KE. Selecting a sample size for studies with repeated measures. BMC Med Res Methodol. 2013;13(1):100.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Lunardon N, Menardi G, Torelli N. ROSE: A Package for Binary Imbalanced Learning. A peer-reviewed, open-access publication of the R Foundation for Statistical Computing. 2014:79.Google Scholar
  50. 50.
    R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2015.Google Scholar
  51. 51.
    Batista GE, Prati RC, Monard MC. A study of the behavior of several methods for balancing machine learning training data. ACM Sigkdd Explorations Newsl. 2004;6(1):20–9.CrossRefGoogle Scholar
  52. 52.
    Cooney NL, Litt MD, Morse PA, Bauer LO, Gaupp L. Alcohol cue reactivity, negative-mood reactivity, and relapse in treated alcoholic men. J Abnorm Psychol. 1997;106(2):243.CrossRefPubMedGoogle Scholar
  53. 53.
    Eysenbach G. The law of attrition. J Med Internet Res. 2005;7(1):e11.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© International Society of Behavioral Medicine 2017

Authors and Affiliations

  • Stephanie P. Goldstein
    • 1
  • Brittney C. Evans
    • 1
  • Daniel Flack
    • 1
  • Adrienne Juarascio
    • 1
  • Stephanie Manasse
    • 1
  • Fengqing Zhang
    • 1
  • Evan M. Forman
    • 1
  1. 1.Department of PsychologyDrexel UniversityPhiladelphiaUSA

Personalised recommendations