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IntroductionThe UK Government developed the Change4Life Food Scanner app to provide families with engaging feedback on the nutritional content of packaged foods. There is a lack of research exploring the cost-effectiveness of dietary health promotion apps.MethodsThrough stakeholder engagement, a conceptual model was developed, outlining the pathway by which the Food Scanner app leads to proximal and distal outcomes. The conceptual model informed the development of a pilot randomized controlled trial which investigated the feasibility and acceptability of evaluating clinical outcomes in children and economic effectiveness of the Food Scanner app through a cost-consequence analysis. Parents of 4–11 years-olds (n = 126) were randomized into an app exposure condition (n = 62), or no intervention control (n = 64). Parent-reported Child Health Utility 9 Dimension (CHU9D) outcomes were collected alongside child healthcare resource use and associated costs, school absenteeism and parent productivity losses at baseline and 3 months follow up. Results for the CHU9D were converted into utility scores based on UK adult preference weights. Sensitivity analysis accounted for outliers and multiple imputation methods were adopted for the handling of missing data.Results64 participants (51%) completed the study (intervention: n = 29; control: n = 35). There was a mean reduction in quality adjusted life years between groups over the trial period of –0.004 (SD = 0.024, 95% CI: –0.005; 0.012). There was a mean reduction in healthcare costs of –£30.77 (SD = 230.97; 95% CI: –£113.80; £52.26) and a mean reduction in workplace productivity losses of –£64.24 (SD = 241.66, 95% CI: –£147.54; £19.07) within the intervention arm, compared to the control arm, over the data collection period. Similar findings were apparent after multiple imputation.DiscussionModest mean differences between study arms may have been due to the exploration of distal outcomes over a short follow-up period. The study was also disrupted due to the coronavirus pandemic, which may have confounded healthcare resource data. Although measures adopted were deemed feasible, the study highlighted difficulties in obtaining data on app development and maintenance costs, as well as the importance of economic modeling to predict long-term outcomes that may not be reliably captured over the short-term.Clinical trial registrationhttps://osf.io/, identifier 62hzt. |
