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Background: The evidence-based vaping cessation program, This is Quitting (TIQ), has been found to be effective in promoting abstinence among young people who use e-cigarettes. Purpose: To estimate acceptability and engagement with Discord among treatment seeking youth and young adults and assess the benefit of adding an online social platform via Discord to TIQ. Methods: Between February and March 2023, 527 TIQ participants (aged 13–24 years) were invited to join Discord with other TIQ users (TIQ Discord). Participants completed two online surveys, at baseline and 1-month post study enrollment. Descriptive statistics were used to describe acceptability and engagement with TIQ Discord. Chi-square, Fisher's exact, and t-tests were used to compare changes in confidence in ability to quit and vaping abstinence across those who joined and engaged with TIQ Discord, compared to those receiving TIQ only. Results: Among the n = 319 who were invited to TIQ Discord and provided follow-up data, 57.4 % joined. Among those who joined (n = 183), 61.7 % engaged with TIQ Discord by contributing at least one message or reaction. The mean number of contributed messages was 4.0 (median = 1, range = 1 to 51) and reactions was 0.31 (median = 0, range = 0 to 14). Engaging with TIQ Discord was positively associated with increased confidence in quitting at follow-up (p-value = 0.02), but vaping abstinence at follow-up did not differ (p-value = 0.87). Discussion: Over half of participants who were invited to TIQ Discord joined – indicating high acceptability and an uptake rate that is higher than what is typically observed for online cessation communities. Engagement was positively associated with proximal outcomes, but self-selection prevents causal attribution. Pilot study results suggest acceptability of Discord for providing digital cessation support to young people in combination with a text-message cessation intervention.

The ventromedial frontal lobes (VMR) contribute to encoding of value. Here the authors show that individuals with VMF damage have less stable, but fundamentally transitive preferences, suggesting that valuation does not solely rely on the VMF.

Across many studies, ventromedial prefrontal cortex (vmPFC) activity has been found to correlate with subjective value during value-based decision-making. Recently, however, vmPFC has also been shown to reflect a hexagonal gridlike code during navigation through physical and conceptual space, and such gridlike codes have been proposed to enable value-based choices between novel options. Here, we first show that, in theory, a hexagonal gridlike code can in some cases mimic vmPFC activity previously attributed to subjective value, raising the possibility that the subjective value correlates previously observed in vmPFC may have actually been a misconstrued gridlike signal. We then compare the two accounts empirically, using fMRI data from a large number of subjects performing an intertemporal choice task. We find clear and unambiguous evidence that subjective value is a better description of vmPFC activity in this task than a hexagonal gridlike code. In fact, we find no significant evidence at all for a hexagonal gridlike code in vmPFC activity during intertemporal choice. This result limits the generality of gridlike modulation as description of vmPFC activity. We suggest that vmPFC may flexibly switch representational schemes so as to encode the most relevant information for the current task.

BackgroundDetecting early dropout from digital interventions is crucial for developing strategies to enhance user retention and improve health-related behavioral outcomes. Bricker and colleagues proposed a single metric that accurately predicted early dropout from 4 digital tobacco cessation interventions based on log-in data in the initial week after registration. Generalization of this method to additional interventions and modalities would strengthen confidence in the approach and facilitate additional research drawing on it to increase user retention. ObjectiveThis study had two research questions (RQ): RQ1—can the study by Bricker and colleagues be replicated using data from a large-scale observational, multimodal intervention to predict early dropout? and RQ2—can first-week engagement patterns identify users at the greatest risk for early dropout, to inform development of potential “rescue” interventions? MethodsData from web users were drawn from EX, a freely available, multimodal digital intervention for tobacco cessation (N=70,265). First-week engagement was operationalized as any website page views or SMS text message responses within 1 week after registration. Early dropout was defined as having no subsequent engagement after that initial week through 1 year. First, a multivariate regression model was used to predict early dropout. Model predictors were dichotomous measures of engagement in each of the initial 6 days (days 2-7) following registration (day 1). Next, 6 univariate regression models were compared in terms of their discrimination ability to predict early dropout. The sole predictor of each model was a dichotomous measure of whether users had reengaged with the intervention by a particular day of the first week (calculated separately for each of 2-7 days). ResultsFor RQ1, the area under the receiver operating characteristic curve (AUC) of the multivariate model in predicting dropout after 1 week was 0.72 (95% CI 0.71-0.73), which was within the range of AUC metrics found in the study by Bricker and colleagues. For RQ2, the AUCs of the univariate models increased with each successive day until day 4 (0.66, 95% CI 0.65-0.67). The sensitivity of the models decreased (range 0.79-0.59) and the specificity increased (range 0.48-0.73) with each successive day. ConclusionsThis study provides independent validation of the use of first-week engagement to predict early dropout, demonstrating that the method generalizes across intervention modalities and engagement metrics. As digital intervention researchers continue to address the challenges of low engagement and early dropout, these results suggest that first-week engagement is a useful construct with predictive validity that is robust across interventions and definitions. Future research should explore the applicability and efficiency of this model to develop interventions to increase retention and improve health behavioral outcomes.