Szinay Dorothy, Cameron Rory A, Jones Andy, Whitty Jennifer A, Chadborn Tim, Brown Jamie, Naughton Felix
Behavioural and Implementation Science Group, School of Health Sciences, University of East Anglia, Norwich, United Kingdom.
Addiction Research Group, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, United Kingdom.
J Med Internet Res. 2025 Jan 14;27:e37083. doi: 10.2196/37083.
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.
This study sought to determine the attributes that influence smoking cessation app uptake and understand their relative importance to support future efforts to present evidence-based apps more effectively to maximize uptake.
Adult smokers from the United Kingdom were invited to participate in a discrete choice experiment. Participants made 12 choices between two hypothetical smoking cessation app alternatives, with five predefined attributes reflecting domains from the theoretical domains framework: (1) monthly price of the app (environmental resources), (2) credible source as app developer (social influence), (3) social proof as star rating (social influence), (4) app description type (beliefs about consequences), and (5) images shown (beliefs about consequences); or opting out (choosing neither app). Preferences and the relative importance of attributes were estimated using mixed logit modeling. Willingness to pay and predicted uptake of the most and least preferred app were also calculated.
A total of 337 adult smokers completed the survey (n=168, 49.8% female; mean age 35, SD 11 years). 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, representing social proof, was the strongest driver of app selection (mean preference parameter 2.27, SD 1.55; 95% CI 1.95-2.59). Participants preferred an app developed by health care-orientated trusted organization (credible source) over a hypothetical company (mean preference parameter 0.93, SD 1.23; 95% CI 0.72-1.15), with a logo and screenshots over logo only (mean preference parameter 0.39, SD 0.96; 95% CI 0.19-0.59), and with a lower monthly cost (mean preference parameter -0.38, SD 0.33; 95% CI -0.44 to -0.32). 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 US $6.96 (UK £5.49) for 4.8-star ratings, US $3.58 (UK £2.82) for 4-star ratings, and US $2.61(UK £2.06) for an app developed by a trusted organization.
On average, social proof appeared to be the most influential factor in app uptake, followed by credible source, one perceived as most likely to provide evidence-based apps. These attributes may support the selection of evidence-based apps.
如果想戒烟的吸烟者没有选择最具循证性且有效的戒烟应用程序，那么这些应用程序在支持戒烟方面的潜力就会受到限制。
本研究旨在确定影响戒烟应用程序使用情况的属性，并了解它们的相对重要性，以支持未来更有效地展示循证性应用程序的努力，从而最大限度地提高其使用率。
邀请来自英国的成年吸烟者参与一项离散选择实验。参与者在两个假设的戒烟应用程序选项之间进行12次选择，有五个预定义属性反映了理论领域框架中的各个领域：（1）应用程序的月价格（环境资源），（2）作为应用程序开发者的可靠来源（社会影响），（3）作为星级评分的社会证据（社会影响），（4）应用程序描述类型（对后果的信念），以及（5）展示的图片（对后果的信念）；或者选择退出（不选择任何应用程序）。使用混合逻辑回归模型估计属性的偏好和相对重要性。还计算了最受欢迎和最不受欢迎应用程序的支付意愿和预测使用率。
共有337名成年吸烟者完成了调查（n = 168，49.8%为女性；平均年龄35岁，标准差11岁）。在90%的选择中，参与者选择了戒烟应用程序而不是选择退出。相对于其他属性，代表社会证据的4.8星用户评分是应用程序选择的最强驱动因素（平均偏好参数2.27，标准差1.55；95%置信区间1.95 - 2.59）。参与者更喜欢由以医疗保健为导向的可信组织开发的应用程序（可靠来源），而不是假设的公司（平均偏好参数0.93，标准差1.23；95%置信区间0.72 - 1.15），有标志和截图的应用程序优于只有标志的应用程序（平均偏好参数0.39，标准差0.96；95%置信区间0.19 - 0.59），并且月成本较低（平均偏好参数 - 0.38，标准差0.33；95%置信区间 - 0.44至 - 0.32）。应用程序描述不影响偏好。最佳假设应用程序的使用率估计为93%，最差的为3%。参与者愿意为4.8星评分额外一次性支付高达6.96美元（5.49英镑），为4星评分支付3.58美元（2.82英镑），为可信组织开发的应用程序支付2.61美元（2.06英镑）。
平均而言，社会证据似乎是应用程序使用中最具影响力的因素，其次是可靠来源，可靠来源被认为最有可能提供循证性应用程序。这些属性可能有助于循证性应用程序的选择。
