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)

Background Although smartphone apps have shown promise for smoking cessation, there is a need to enhance their low engagement rates. This study evaluated the application of the growth mindset theory, which has demonstrated the potential to improve persistence in behavior change in other domains, as a means to improve engagement and cessation. Objective This study aimed to explore the feasibility, utility, and efficacy of a Web-based growth mindset intervention for addiction when used alongside a smoking cessation app. Methods Daily smokers (N=398) were all recruited on the Web and randomly assigned to receive either a cessation app alone or the app plus a Web-delivered growth mindset intervention. The primary outcome was engagement, that is, the number of log-ins to the smoking cessation app. The secondary outcome was 30-day point prevalence abstinence at 2-month follow-up collected through a Web-based survey. Results The 2-month outcome data retention rate was 91.5% (364/398). In addition, 77.9% (310/398) of the participants in the experimental arm viewed at least 1 page of their growth mindset intervention, and 21.1% (84/398) of the group viewed all the growth mindset intervention. The intention-to-treat analysis did not show statistically significant differences between the experimental and comparison arms on log-ins to the app (19.46 vs 21.61; P =.38). The experimental arm had cessation rates, which trended higher than the comparison arm (17% vs 13%; P =.10). The modified intent-to-treat analysis, including only participants who used their assigned intervention at least once (n=115 in experimental group and n=151 in the control group), showed that the experimental arm had a similar number of log-ins (32.31 vs 28.48; P =.55) but significantly higher cessation rates (21% vs 13%; P =.03) than the comparison arm. Conclusions A growth mindset intervention for addiction did not increase engagement rates, although it may increase cessation rates when used alongside a smartphone app for smoking cessation. Future research is required to refine the intervention and assess efficacy with long-term follow-up to evaluate the efficacy of the mindset intervention. Trial Registration ClinicalTrials.gov NCT03174730; https://clinicaltrials.gov/ct2/show/NCT03174730

Two well documented but still neglected blind spots of often‐used study designs limit a researcher's ability to make inferences about psychological phenomenon. First, typical designs focus on effects of conditions at the group level and are not able to assess the extent to which effects characterize each participant in the study. This blind spot can lead to erroneous (or incomplete) conclusions about the effects of manipulations both for a given participant and at the group level. Second, commonly used research designs often use a limited sample of stimuli, constraining conclusions to the particular stimuli. This blind spot can lead to non‐replication when different stimuli are used. We propose that the Highly‐Repeated Within‐Person (HRWP) approach helps mitigate these limitations. Using a study on the effects of anti‐smoking messages, we illustrate how the HRWP approach helps alert researchers when the conclusions at the group level may not apply to all (or any) participant, quantifies the heterogeneity of effects of manipulations across people, and increases confidence regarding the generalizability of the effects. We discuss how the HRWP approach may help conceptualize issues of replicability in a new light.