Trajectories of 12-Month Usage Patterns for Two Smoking Cessation Websites: Exploring How Users Engage Over Time

Abstract

BackgroundLittle is known about how individuals engage with electronic health (eHealth) interventions over time and whether this engagement predicts health outcomes.ObjectiveThe objectives of this study, by using the example of a specific type of eHealth intervention (ie, websites for smoking cessation), were to determine (1) distinct groups of log-in trajectories over a 12-month period, (2) their association with smoking cessation, and (3) baseline user characteristics that predict trajectory group membership.MethodsWe conducted a functional clustering analysis of 365 consecutive days of log-in data from both arms of a large (N=2637) randomized trial of 2 website interventions for smoking cessation (WebQuit and Smokefree), with a primary outcome of 30-day point prevalence smoking abstinence at 12 months. We conducted analyses for each website separately.ResultsA total of 3 distinct trajectory groups emerged for each website. For WebQuit, participants were clustered into 3 groups: 1-week users (682/1240, 55.00% of the sample), 5-week users (399/1240, 32.18%), and 52-week users (159/1240, 12.82%). Compared with the 1-week users, the 5- and 52-week users had 57% higher odds (odds ratio [OR] 1.57, 95% CI 1.13-2.17; P=.007) and 124% higher odds (OR 2.24, 95% CI 1.45-3.43; P<.001), respectively, of being abstinent at 12 months. Smokefree users were clustered into 3 groups: 1-week users (645/1309, 49.27% of the sample), 4-week users (395/1309, 30.18%), and 5-week users (269/1309, 20.55%). Compared with the 1-week users, 5-week users (but not 4-week users; P=.99) had 48% higher odds (OR 1.48, 95% CI 1.05-2.07; P=.02) of being abstinent at 12 months. In general, the WebQuit intervention had a greater number of weekly log-ins within each of the 3 trajectory groups as compared with those of the Smokefree intervention. Baseline characteristics associated with trajectory group membership varied between websites.ConclusionsPatterns of 1-, 4-, and 5-week usage of websites may be common for how people engage in eHealth interventions. The 5-week usage of either website, and 52-week usage only of WebQuit, predicted a higher odds of quitting smoking. Strategies to increase eHealth intervention engagement for 4 more weeks (ie, from 1 week to 5 weeks) could be highly cost effective.Trial RegistrationClinicalTrials.gov NCT01812278; https://www.clinicaltrials.gov/ct2/show/NCT01812278 (Archived by WebCite at http://www.webcitation.org/6yPO2OIKR)

Usability and Acceptability of Two Smartphone Apps for Smoking Cessation Among Young Adults With Serious Mental Illness: Mixed Methods Study

“…Notably, although quitSTART was designed for teens, by Visit 2 it was perceived positively based on the qualitative feedback and a high level of engagement among these young adults with SMI. Given the acceptable perceived usability at the second visit, the much higher level of engagement with quitSTART, and previous work demonstrating the importance of engagement to cessation outcomes [25][26][27], our findings suggest that quitSTART may be a reasonable choice for use among young adult smokers with SMI, particularly if support and coaching facilitate initial use of the app.…”

“…Comparison of user engagement data among studies can be challenging because of different durations of follow-up, varied measurements of app engagement (eg, app openings, days of use, and specific actions within the app), and previously demonstrated decay in the use of eHealth interventions over time [ 25 ]. Nevertheless, our QuitGuide findings appear similar to those from other studies.…”

“…Although there is increasing interest in using smartphone technology for behavioral smoking cessation interventions, early phase assessment of this technology offers crucial data in preparation for an efficacy trial [13]. Usability ("how well users can learn and use a product to achieve their goals") and acceptability (which includes perceived value, usefulness, and desirability) are important components of user experience [24], and increased user engagement is associated with improved outcomes [25][26][27]. Given that the NCI's apps are easily available and free, contain high-quality content, and provide numerous features of interest to young adults with SMI [12], we seek to evaluate the apps' potential role for cessation interventions in this population by assessing their usability and appeal among young adults with SMI.…”

Signs, things and packaging: Recovering the material agency of the cigarette packet

“…Of the more than hundred-odd smokers I have interviewed over the past decade, the vast majority describe their consumption in relation to the packet, rather than how many cigarettes they actually smoke, with the most common category being "a pack a day". Interestingly, the category of the pack-a-day-smoker has now become so naturalized that it's often used in tobacco control accounts as a measure of smoking intensity (for a recent example, see Bricker et al, 2018), with very little reflection on what this actually means. However, while 'typical' packets often contain 20 cigarettes, packs of 10, 25, 30, 40 and 50 are also widely available, depending on the country.…”

Identifying Content-Based Engagement Patterns in a Smoking Cessation Website and Associations With User Characteristics and Cessation Outcomes: A Sequence and Cluster Analysis

“… 24 A secondary data analysis found that WebQuit users categorized as “high engagers” (on the basis of a single engagement indicator—logins) saw larger increases in the acceptance of physical cravings at a 3-month follow-up (the only measured theoretical mechanism of action of the intervention) and increased chances of quit success at a 12-month follow-up compared with “low engagers.” 24 In a secondary analysis of the total duration of engagement with WebQuit, three distinct groups were identified, with 5- and 52-week users having greater odds of quit success compared with 1-week users. 25 However, we currently lack knowledge as to how users engage with WebQuit’s features over time and in what order, and whether particular temporal, content-based engagement trajectories are associated with improved cessation outcomes. Although analyses of single engagement indicators (e.g., the frequency of logins) are perhaps considered more efficient than those combining multiple indicators, the former are not suited to addressing whether engagement with particular content over time is associated with outcomes of interest.…”