Effect of a mobile app intervention on vegetable consumption in overweight adults: a randomized controlled trial

Sarah Mummah, Thomas N Robinson, Maya Mathur, Sarah Farzinkhou, Stephen Sutton, Christopher D Gardner, Sarah Mummah, Thomas N Robinson, Maya Mathur, Sarah Farzinkhou, Stephen Sutton, Christopher D Gardner

Abstract

Background: Mobile applications (apps) have been heralded as transformative tools to deliver behavioral health interventions at scale, but few have been tested in rigorous randomized controlled trials. We tested the effect of a mobile app to increase vegetable consumption among overweight adults attempting weight loss maintenance.

Methods: Overweight adults (n=135) aged 18-50 years with BMI=28-40 kg/m2 near Stanford, CA were recruited from an ongoing 12-month weight loss trial (parent trial) and randomly assigned to either the stand-alone, theory-based Vegethon mobile app (enabling goal setting, self-monitoring, and feedback and using "process motivators" including fun, surprise, choice, control, social comparison, and competition) or a wait-listed control condition. The primary outcome was daily vegetables servings, measured by an adapted Harvard food frequency questionnaire (FFQ) 8 weeks post-randomization. Daily vegetable servings from 24-hour dietary recalls, administered by trained, certified, and blinded interviewers 5 weeks post-randomization, was included as a secondary outcome. All analyses were conducted according to principles of intention-to-treat.

Results: Daily vegetable consumption was significantly greater in the intervention versus control condition for both measures (adjusted mean difference: 2.0 servings; 95% CI: 0.1, 3.8, p=0.04 for FFQ; and 1.0 servings; 95% CI: 0.2, 1.9; p=0.02 for 24-hour recalls). Baseline vegetable consumption was a significant moderator of intervention effects (p=0.002) in which effects increased as baseline consumption increased.

Conclusions: These results demonstrate the efficacy of a mobile app to increase vegetable consumption among overweight adults. Theory-based mobile interventions may present a low-cost, scalable, and effective approach to improving dietary behaviors and preventing associated chronic diseases.

Trial registration: ClinicalTrials.gov NCT01826591. Registered 27 March 2013.

Keywords: Behavior change; Design thinking; Diet; Digital; Mobile; Nutrition; Smartphone; User-centered design; Vegetables; mHealth.

Conflict of interest statement

Ethics approval and consent to participate

This research was approved by the Panel on Human Subjects in Medical Research (#22305) at Stanford University, Stanford, CA, USA. All participants provided written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
CONSORT flow diagram
Fig. 2
Fig. 2
Changes in daily vegetable consumption. Mean ± SE, n = 135. Measured by FFQ (baseline to 8 weeks, solid lines) and 24-h recalls (baseline to 5 weeks, dashed lines)
Fig. 3
Fig. 3
Frequency of vegetable logging among intervention condition. n = 511. Participants sorted in decreasing order of logging frequency. 1Figure excludes 17 participants who did not log

References

    1. Oyebode O, Gordon-Dseagu V, Walker A, Mindell JS. Fruit and vegetable consumption and all-cause, cancer and CVD mortality: analysis of health survey for England data. J Epidemiol Community Health. 2014;68(9):856–862. doi: 10.1136/jech-2013-203500.
    1. Dauchet L, Amouyel P, Dallongeville J. Fruit and vegetable consumption and risk of stroke: a meta-analysis of cohort studies. Neurology. 2005;65(8):1193–1197. doi: 10.1212/01.wnl.0000180600.09719.53.
    1. Wang X, Ouyang Y, Liu J, et al. Fruit and vegetable consumption and mortality from all causes, cardiovascular disease, and cancer: systematic review and dose-response meta-analysis of prospective cohort studies. BMJ (Clinical research ed) 2014;349:g4490.
    1. U.S. Department of Agriculture and U.S. Department of Health and Human Services. Dietary guidelines for Americans, 2010. (accessed September 19 2015).
    1. Casagrande SS, Wang Y, Anderson C, Gary TL. Have Americans increased their fruit and vegetable intake? The trends between 1988 and 2002. Am J Prev Med. 2007;32(4):257–263. doi: 10.1016/j.amepre.2006.12.002.
    1. Thomson CA, Ravia J. A systematic review of behavioral interventions to promote intake of fruit and vegetables. J Am Diet Assoc. 2011;111(10):1523–1535. doi: 10.1016/j.jada.2011.07.013.
    1. Cobiac LJ, Vos T, Veerman JL. Cost-effectiveness of interventions to promote fruit and vegetable consumption. PLoS One. 2010;5(11):e14148. doi: 10.1371/journal.pone.0014148.
    1. Pomerleau J, Lock K, Knai C, McKee M. Interventions designed to increase adult fruit and vegetable intake can be effective: a systematic review of the literature. J Nutr. 2005;135(10):2486–2495.
    1. Klasnja P, Pratt W. Healthcare in the pocket: mapping the space of mobile-phone health interventions. J Biomed Inform. 2012;45(1):184–198. doi: 10.1016/j.jbi.2011.08.017.
    1. Stephens J, Allen JK, Dennison Himmelfarb CR. “smart” coaching to promote physical activity, diet change, and cardiovascular health. The Journal of cardiovascular nursing. 2011;26(4):282–284. doi: 10.1097/JCN.0b013e31821ddd76.
    1. Azar KMJ, Lesser LI, Laing BY, et al. Mobile applications for weight management: theory-based content analysis. Am J Prev Med. 2013;45(5):583–589. doi: 10.1016/j.amepre.2013.07.005.
    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? Translational behavioral medicine. 2011;1(1):53–71. doi: 10.1007/s13142-011-0021-7.
    1. Pagoto S, Schneider K, Jojic M, DeBiasse M, Mann D. Evidence-based strategies in weight-loss mobile apps. Am J Prev Med. 2013;45(5):576–582. doi: 10.1016/j.amepre.2013.04.025.
    1. Payne HE, Lister C, West JH, Bernhardt JM. Behavioral functionality of mobile apps in health interventions: a systematic review of the literature. JMIR mHealth and uHealth. 2015;3(1):e20. doi: 10.2196/mhealth.3335.
    1. Nollen NL, Mayo MS, Carlson SE, Rapoff MA, Goggin KJ, Ellerbeck EF. Mobile technology for obesity prevention: a randomized pilot study in racial- and ethnic-minority girls. Am J Prev Med. 2014;46(4):404–408. doi: 10.1016/j.amepre.2013.12.011.
    1. Breton E, Fuemmeler B, Abroms L. Weight loss—there is an app for that! But does it adhere to evidence-informed practices? Translational behavioral medicine. 2011;1(4):523–529. doi: 10.1007/s13142-011-0076-5.
    1. Helander E, Kaipainen K, Korhonen I, Wansink B. Factors related to sustained use of a free mobile app for dietary self-monitoring with photography and peer feedback: retrospective cohort study. J Med Internet Res. 2014;16(4):e109. doi: 10.2196/jmir.3084.
    1. Mummah S, Mathur M, King AC, Gardner CD, Sutton S. Mobile technology for vegetable consumption: a randomized controlled pilot study in overweight adults. JMIR Mhealth Uhealth. (in press)
    1. Mummah SA, King AC, Gardner CD, Sutton S. Iterative development of Vegethon: a theory-based mobile app intervention to increase vegetable consumption. The international journal of behavioral nutrition and physical activity. 2016;13:90. doi: 10.1186/s12966-016-0400-z.
    1. Robinson TN, et al. Stealth interventions for obesity prevention and control: motvating behavior change. In: Dubé L, Bechara A, Dagher A, et al., editors. Obesity Prevention: The Role of Brain and Society on Individual Behavior. New York: Elsevier Inc; 2010. pp. 319–327.
    1. Robinson TN. Save the world, prevent obesity: piggybacking on existing social and ideological movements. Obesity. 2010;18(S1):S17–S22. doi: 10.1038/oby.2009.427.
    1. Bandura A. Social foundations of thought and action. Englewood Cliffs, NJ: Prentice-Hall; 1986.
    1. Willet WC. Nutritional epidemiology. New York: Oxford University Press; 1990.
    1. Resnicow K, Odom E, Wang T, et al. Validation of three food frequency questionnaires and 24-hour recalls with serum carotenoid levels in a sample of African-American adults. Am J Epidemiol. 2000;152(11):1072–1080. doi: 10.1093/aje/152.11.1072.
    1. Harvard School of Public Health. Nurses Health Study II Questionnaire. 2003. (accessed September 19 2015).
    1. Park SK, Tucker KL, O'Neill MS, et al. Fruit, vegetable, and fish consumption and heart rate variability: the veterans administration normative aging study. Am J Clin Nutr. 2009;89(3):778–786. doi: 10.3945/ajcn.2008.26849.
    1. Liu S, Manson JE, Lee IM, et al. Fruit and vegetable intake and risk of cardiovascular disease: the Women's health study. Am J Clin Nutr. 2000;72(4):922–928.
    1. Feskanich D, Sielaff BH, Chong K, Buzzard IM. Computerized collection and analysis of dietary intake information. Comput Methods Prog Biomed. 1989;30(1):47–57. doi: 10.1016/0169-2607(89)90122-3.
    1. Johnson RK, Driscoll P, Goran MI. Comparison of multiple-pass 24-hour recall estimates of energy intake with total energy expenditure determined by the doubly labeled water method in young children. J Am Diet Assoc. 1996;96(11):1140–1144. doi: 10.1016/S0002-8223(96)00293-3.
    1. King AC, Hekler EB, Grieco LA, et al. Harnessing different motivational frames via mobile phones to promote daily physical activity and reduce sedentary behavior in aging adults. PLoS One. 2013;8(4):e62613. doi: 10.1371/journal.pone.0062613.
    1. Cohen J. A power primer. Psychol Bull. 1992;112(1):155–159. doi: 10.1037/0033-2909.112.1.155.
    1. Kraemer HC, Wilson GT, Fairburn CG, Agras WS. Mediators and moderators of treatment effects in randomized clinical trials. Arch Gen Psychiatry. 2002;59(10):877–883. doi: 10.1001/archpsyc.59.10.877.
    1. Ioannidis JP. Contradicted and initially stronger effects in highly cited clinical research. JAMA. 2005;294(2):218–228. doi: 10.1001/jama.294.2.218.
    1. Mytton OT, Nnoaham K, Eyles H, Scarborough P, Ni MC. Systematic review and meta-analysis of the effect of increased vegetable and fruit consumption on body weight and energy intake. BMC Public Health. 2014;14:886. doi: 10.1186/1471-2458-14-886.
    1. Norman GJ, Kolodziejczyk JK, Adams MA, Patrick K, Marshall SJ. Fruit and vegetable intake and eating behaviors mediate the effect of a randomized text-message based weight loss program. Prev Med. 2013;56(1):3–7. doi: 10.1016/j.ypmed.2012.10.012.
    1. Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH. The disease burden associated with overweight and obesity. JAMA. 1999;282(16):1523–1529. doi: 10.1001/jama.282.16.1523.

Source: PubMed

3
Abonnere