Using Videogame Apps to Assess Gains in Adolescents' Substance Use Knowledge: New Opportunities for Evaluating Intervention Exposure and Content Mastery

Erika Montanaro, Lynn E Fiellin, Tamer Fakhouri, Tassos C Kyriakides, Lindsay R Duncan, Erika Montanaro, Lynn E Fiellin, Tamer Fakhouri, Tassos C Kyriakides, Lindsay R Duncan

Abstract

Background: Videogame interventions are becoming increasingly popular as a means to engage people in behavioral interventions; however, strategies for examining data from such interventions have not been developed.

Objective: The objective of this study was to describe how a technology-based intervention can yield meaningful, objective evidence of intervention exposure within a behavioral intervention. This study demonstrates the analysis of automatic log files, created by software from a videogame intervention, that catalog game play and, as proof of concept, the association of these data with changes in substance use knowledge as documented with standardized assessments.

Methods: We analyzed 3- and 6-month follow-up data from 166 participants enrolled in a randomized controlled trial evaluating a videogame intervention, PlayForward: Elm City Stories (PlayForward). PlayForward is a videogame developed as a risk reduction and prevention program targeting HIV risk behaviors (substance use and sex) in young minority adolescents. Log files were analyzed to extract the total amount of time spent playing the videogame intervention and the total number of game levels completed and beaten by each player.

Results: Completing and beating more of the game levels, and not total game play time, was related to higher substance use knowledge scores at the 3- (P=.001) and 6-month (P=.001) follow-ups.

Conclusions: Our findings highlight the potential contributions a videogame intervention can make to the study of health behavior change. Specifically, the use of objective data collected during game play can address challenges in traditional human-delivered behavioral interventions.

Trial registration: Clinicaltrials.gov NCT01666496; https://ichgcp.net/clinical-trials-registry/NCT01666496 (Archived by WebCite at http://www.webcitation.org/6cV9fxsOg).

Keywords: HIV; eHealth; evaluation; intervention studies; substance use; video games.

Conflict of interest statement

Conflicts of Interest: Drs Fiellin and Duncan are affiliated with KnackTime Interactive, a small commercial venture that focuses on the distribution of evidence-based videogames for risk reduction and prevention in youth and young adults. This relationship is extensively managed by Drs Fiellin and Duncan and their respective institutions.

References

    1. Eysenbach G. What is e-health? J Med Internet Res. 2001;3(2):E20. doi: 10.2196/jmir.3.2.e20.
    1. Lehto T, Oinas-Kukkonen H. Persuasive features in web-based alcohol and smoking interventions: A systematic review of the literature. J Med Internet Res. 2011;13(3):e46. doi: 10.2196/jmir.1559.
    1. Noar SM, Black HG, Pierce LB. Efficacy of computer technology-based HIV prevention interventions: A meta-analysis. AIDS. 2009 Jan 2;23(1):107–15. doi: 10.1097/QAD.0b013e32831c5500.
    1. Hieftje K, Edelman EJ, Camenga DR, Fiellin LE. Electronic media-based health interventions promoting behavior change in youth: A systematic review. JAMA Pediatr. 2013 Jun;167(6):574–80. doi: 10.1001/jamapediatrics.2013.1095.
    1. Fiellin LE, Hieftje KD, Duncan LR. Videogames, here for good. Pediatrics. 2014 Nov;134(5):849–51. doi: 10.1542/peds.2014-0941.
    1. Ruggiero L, Moadsiri A, Quinn LT, Riley BB, Danielson KK, Monahan C, Bangs VA, Gerber BS. Diabetes island: Preliminary impact of a virtual world self-care educational intervention for African Americans with type 2 diabetes. JMIR Serious Games. 2014;2(2):e10. doi: 10.2196/games.3260.
    1. Kato PM, Cole SW, Bradlyn AS, Pollock BH. A video game improves behavioral outcomes in adolescents and young adults with cancer: A randomized trial. Pediatrics. 2008 Aug;122(2):e305–17. doi: 10.1542/peds.2007-3134.
    1. Hieftje K, Duncan LR, Fiellin LE. Novel methods to collect meaningful data from adolescents for the development of health interventions. Health Promot Pract. 2014 Sep;15(5):714–22. doi: 10.1177/1524839914521211.
    1. Glanz K, Bishop DB. The role of behavioral science theory in development and implementation of public health interventions. Annu Rev Public Health. 2010;31:399–418. doi: 10.1146/annurev.publhealth.012809.103604.
    1. Noar SM. Behavioral interventions to reduce HIV-related sexual risk behavior: Review and synthesis of meta-analytic evidence. AIDS Behav. 2008 May;12(3):335–53. doi: 10.1007/s10461-007-9313-9.
    1. Norman GJ, Zabinski MF, Adams MA, Rosenberg DE, Yaroch AL, Atienza AA. A review of eHealth interventions for physical activity and dietary behavior change. Am J Prev Med. 2007 Oct;33(4):336–345. doi: 10.1016/j.amepre.2007.05.007.
    1. Riley WT, Rivera DE, Atienza AA, Nilsen W, Allison SM, Mermelstein R. Health behavior models in the age of mobile interventions: Are our theories up to the task? Transl Behav Med. 2011 Mar;1(1):53–71. doi: 10.1007/s13142-011-0021-7.
    1. Strecher V. Internet methods for delivering behavioral and health-related interventions (eHealth) Annu Rev Clin Psychol. 2007;3:53–76. doi: 10.1146/annurev.clinpsy.3.022806.091428.
    1. Neuhauser L, Kreps GL. Rethinking communication in the e-health era. J Health Psychol. 2003 Jan;8(1):7–23. doi: 10.1177/1359105303008001426.
    1. Barak A, Klein B, Proudfoot JG. Defining internet-supported therapeutic interventions. Ann Behav Med. 2009 Aug;38(1):4–17. doi: 10.1007/s12160-009-9130-7.
    1. Johnston L, O’Malley P, Bachman J, Schulenberg J. Monitoring the Future National Survey Results on Drug Use, 1975–2011. Volume I: Secondary School Students. Ann Arbor, MI: University of Michigan; 2012.
    1. Weinhardt LS, Carey MP. Does alcohol lead to sexual risk behavior? Findings from event-level research. Annu Rev Sex Res. 2000;11:125–57.
    1. Anderson BJ, Stein MD. A behavioral decision model testing the association of marijuana use and sexual risk in young adult women. AIDS Behav. 2011 May;15(4):875–84. doi: 10.1007/s10461-010-9694-z.
    1. Eaton D, Kann L, Kinchen S, Shanklin S, Flint KH, Hawkins J, Centers for Disease Control and Prevention Youth risk behavior surveillance—United States, 2011. MMWR Morb Mortal Wkly Rep. 2012;61(4):1–162.
    1. Bandura A. Health promotion from the perspective of social cognitive theory. Psychol Health. 1998;13(4):623–649.
    1. Rothman AJ, Salovey P. Shaping perceptions to motivate healthy behavior: The role of message framing. Psychol Bull. 1997 Jan;121(1):3–19.
    1. Kahneman D, Tversky A. Prospect theory: An analysis of decision under risk. Econometrica. 1979;263:291.
    1. Fisher JD, Fisher WA, Misovich SJ, Kimble DL, Malloy TE. Changing AIDS risk behavior: Effects of an intervention emphasizing AIDS risk reduction information, motivation, and behavioral skills in a college student population. Health Psychol. 1996 Mar;15(2):114–23.
    1. Duncan LR, Hieftje KD, Culyba S, Fiellin LE. Game playbooks: Tools to guide multidisciplinary teams in developing videogame-based behavior change interventions. Transl Behav Med. 2014 Mar;4(1):108–16. doi: 10.1007/s13142-013-0246-8.
    1. Hieftje K, Rosenthal MS, Camenga DR, Edelman EJ, Fiellin LE. A qualitative study to inform the development of a video game for adolescent HIV prevention. Games Health J. 2012 Aug 10;1(4):294–298. doi: 10.1089/g4h.2012.0025.
    1. Jemmott JB, Jemmott LS, Fong GT. Efficacy of a theory-based abstinence-only intervention over 24 months: A randomized controlled trial with young adolescents. Arch Pediatr Adolesc Med. 2010 Feb;164(2):152–9. doi: 10.1001/archpediatrics.2009.267.
    1. Kelly JA, St Lawrence JS, Hood HV, Brasfield TL. An objective test of AIDS risk behavior knowledge: scale development, validation, and norms. J Behav Ther Exp Psychiatry. 1989 Sep;20(3):227–34.
    1. Jemmott L, Jemmott J, McCaffree K. Making a Difference!: An Abstinence Approach to HIV/STD and Teen Pregnancy Prevention. New York: Select Media, Inc; 2002. pp. 1–263.
    1. Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: A systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res. 2010;12(1):e4. doi: 10.2196/jmir.1376.
    1. Peters E, Klein W, Kaufman A, Meilleur L, Dixon A. More is not always better: intuitions about effective public policy can lead to unintended consequences. Soc Issues Policy Rev. 2013 Jan 1;7(1):114–148. doi: 10.1111/j.1751-2409.2012.01045.x.
    1. Zhu SH, Stretch V, Balabanis M, Rosbrook B, Sadler G, Pierce JP. Telephone counseling for smoking cessation: Effects of single-session and multiple-session interventions. J Consult Clin Psychol. 1996 Feb;64(1):202–11.
    1. Jacobson NS, Dobson KS, Truax PA, Addis ME, Koerner K, Gollan JK, Gortner E, Prince SE. A component analysis of cognitive-behavioral treatment for depression. J Consult Clin Psychol. 1996 Apr;64(2):295–304.
    1. Borkovec TD, Newman MG, Pincus AL, Lytle R. A component analysis of cognitive-behavioral therapy for generalized anxiety disorder and the role of interpersonal problems. J Consult Clin Psychol. 2002 Apr;70(2):288–98.

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe