Identification of Users for a Smoking Cessation Mobile App: Quantitative Study

Abstract

BackgroundThe number of mobile apps that support smoking cessation is growing, indicating the potential of the mobile phone as a means to support cessation. Knowledge about the potential end users for cessation apps results in suggestions to target potential user groups in a dissemination strategy, leading to a possible increase in the satisfaction and adherence of cessation apps.ObjectiveThis study aimed to characterize potential end users for a specific mobile health (mHealth) smoking cessation app.MethodsA quantitative study was conducted among 955 Dutch smokers and ex-smokers. The respondents were primarily recruited from addiction care facilities and hospitals through Web-based media via websites and forums. The respondents were surveyed on their demographics, smoking behavior, and personal innovativeness. The intention to use and the attitude toward a cessation app were determined on a 5-point Likert scale. To study the association between the characteristics and intention to use and attitude, univariate and multivariate ordinal logistic regression analyses were performed.ResultsThe multivariate ordinal logistic regression showed that the number of previous quit attempts (odds ratio [OR] 4.1, 95% CI 2.4-7.0, and OR 3.5, 95% CI 2.0-5.9) and the score on the Fagerstrom Test of Nicotine Dependence (OR 0.8, 95% CI 0.8-0.9, and OR 0.8, 95% CI 0.8-0.9) positively correlates with the intention to use a cessation app and the attitude toward cessation apps, respectively. Personal innovativeness also positively correlates with the intention to use (OR 0.3, 95% CI 0.2-0.4) and the attitude towards (OR 0.2, 95% CI 0.1-0.4) a cessation app. No associations between demographics and the intention to use or the attitude toward using a cessation app were observed.ConclusionsThis study is among the first to show that demographic characteristics such as age and level of education are not associated with the intention to use and the attitude toward using a cessation app when characteristics related specifically to the app, such as nicotine dependency and the number of quit attempts, are present in a multivariate regression model. This study shows that the use of mHealth apps depends on characteristics related to the content of the app rather than general user characteristics.

The Association Between Willingness of Frontline Care Providers’ to Adaptively Use Telehealth Technology and Virtual Service Performance in Provider-to-Provider Communication: Quantitative Study

“…First, personal innovativeness in IT use is defined as “the willingness of an individual to try out any new information technology” [57]. This concept has been widely used in IT use studies to capture individuals’ intention to adopt technology, both generally [59] and in health care domains [60-62]. In the adaptive use context, Chow et al [63] found that personal innovativeness positively influenced adaptive IT use behavior.…”

Effectiveness of mobile applications to quit smoking: Systematic review and meta-analysis

“…These results were dependent on several factors such as age and educational level, with the age of app users being lower (OR=0.97; 95% CI: 0.96-0.98) than that of non-users, and the higher educational level related to higher app usage (OR=1.12; 95% CI: 1.01-1.24) 24 . However, in another study evaluating the characteristics of users of mobile applications to quit smoking, it was found that demographic characteristics such as age and education level were not associated with the attitude towards the use of a tobacco cessation application, but rather depended on characteristics related to the content of the application instead of the general characteristics of the users 25 .…”

Perceptions of Practitioners on Telehealth and App Use for Smoking Cessation and COPD Care—An Exploratory Study

“…Smoking cessation is beneficial to health at any age and also constitutes an important pillar of COPD treatment [ 8 , 38 ]. In a 2018 study, app characteristics were more relevant for the intention to use a cessation app than demographic characteristics such as age and level of education [ 39 ]. In clinical practice, satisfactory long-term outcomes depend on a personalized approach in the doctor-patient-app triangle, combining face-to-face counseling, pharmacotherapy, and (a) supportive app(s) with medical devices such as a carbon monoxide checker or a spirometer [ 12 ].…”