Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Mar 1, 2022
Date Accepted: Dec 2, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Eliciting preferences for the uptake of smoking cessation apps: A Discrete Choice Experiment.
ABSTRACT
Background:
If the most evidence-based and effective smoking cessation apps are not selected by smokers wanting to quit, their potential to support cessation is limited.
Objective:
This study sought to determine the attributes that influence smoking cessation app uptake and understand their relative importance, to support future efforts to design and present evidence-based apps more effectively to maximise uptake.
Methods:
Adult smokers from the UK were invited to participate in a discrete choice experiment. Participants made 12 choices between two hypothetical smoking cessation app alternatives, with five predefined attributes: 1) star rating, 2) app developer, 3) monthly price of app, 4) images shown and 5) the app’s description type; or opting out (choosing neither app). Preferences and the relative importance of attributes were estimated using mixed logit modelling. Willingness to pay (WTP) and predicted uptake of the most and least preferred app were also calculated.
Results:
A total of 337 adult smokers completed the survey (49.8% females; mean age 35, SD 11). Participants selected a smoking cessation app rather than opting out for 90% of the choices. Relative to other attributes, a 4.8 star user rating was the strongest driver of app selection (mean preference parameter 2.15; 95% confidence interval [CI] 1.90 to 2.40). Participants preferred an app developed by a healthcare-orientated trusted organisation over a hypothetical company (mean preference parameter 0.92; 95% CI 0.74 to 1.10), with a logo and screenshots over logo only (mean preference parameter 0.25, 95% CI 0.11 to 0.38), and with a lower monthly cost (mean preference parameter -0.39; 95% CI -0.45 to -0.33). App description did not influence preferences. The uptake estimate for the best hypothetical app was 93%, and for the worst 3%. Participants were willing to pay a single payment of up to an additional £9.48 for 4.8 star ratings, £3.91 for 4 star ratings, and £5.22 for app developed by a trusted organisation.
Conclusions:
On average, a high app star rating was the most important factor in determining app uptake, followed by the app being developed by a healthcare-orientated and trusted organisation, who may be most likely to provide evidence-based apps. Clinical Trial: Not applicable.
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