While the construct of food addiction has been controversial, there is growing evidence that certain foods can activate biobehavioral and neurological mechanisms consistent with addiction to other substances. Despite increased evidence and acceptance of certain foods as addictive substances amongst the scientific community, there is a paucity of interventions available that are uniquely suited for the treatment of this condition. Further, many of the addiction and disordered eating treatment models currently utilized for food addiction are seemingly at odds, with the former often recommending complete abstinence from trigger foods and the latter promoting intake of all foods in moderation. The Food Addiction Clinical Treatment (FACT) manual was created as an alternative using an empirically supported harm-reduction model specifically targeted to treat the addiction and disordered eating features of food addiction. The purpose of the current article is to expose readers to the key tenets of the FACT manual, demonstrate the feasibility of this intervention with a sample of participants with severe food addiction, and discuss future directions for the treatment of food addiction. Positive outcomes from this intervention provide preliminary evidence for the efficacy of FACT for the treatment of food addiction with minimal negative adverse effects. Future research using randomized control trials and longer follow-up is needed to validate the FACT manual as an empirically supported treatment for food addiction.

The sentinel event theory provides a stepwise approach for building models to understand how negative events can spark health behaviour change. This study tested a preliminary model using the sentinel events method in a sample (N = 300) of smokers who sought care for acute cardiac symptoms. Patients completed measures on: smoking-related causal attribution, perceived severity of the acute illness event, illness-related fear and intentions to quit smoking. Patients were followed up one week after the health event and a seven-day timeline follow back was completed to determine abstinence from tobacco. Structural equation models were performed using average predictor scale scores at baseline, as well as three different time anchors for ratings of illness severity and illness-related fear. Quit intentions, actual illness severity and age were the consistent, positive and independent predictors of seven-day point prevalence abstinence. Additional research on the influences of perceptions and emotional reactions is warranted.

The latest recommendations for building dynamic health behavior theories emphasize that cognitions, emotions, and behaviors – and the nature of their inter-relationships – can change over time. This paper describes the development and psychometric validation of four scales created to measure smoking-related causal attributions, perceived illness severity, event-related emotions, and intention to quit smoking among patients experiencing acute cardiac symptoms. After completing qualitative work with a sample of 50 cardiac patients, we administered the scales to 300 patients presenting to the emergency department for cardiac-related symptoms. Factor analyses, alpha coefficients, ANOVAs, and Pearson correlation coefficients were used to establish the scales' reliability and validity. Factor analyses revealed a stable factor structures for each of the four constructs. The scales were internally consistent, with the majority having an alpha of >0.80 (range: 0.57–0.89). Mean differences in ratings of the perceived illness severity and event-related emotions were noted across the three time anchors. Significant increases in intention to quit at the time of enrollment, compared to retrospective ratings of intention to quit before the event, provide preliminary support for the sensitivity of this measure to the motivating impact of the event. Finally, smoking-related causal attributions, perceived illness severity, and event-related emotions correlated in the expected directions with intention to quit smoking, providing preliminary support for construct validity.

Objective: This study examined how smoking-related causal attributions, perceived illness severity, and event-related emotions relate to both intentions to quit and subsequent smoking behavior after an acute medical problem (sentinel event). Methods: Three hundred and seventy-five patients were enrolled from 10 emergency departments (EDs) across the USA and followed for six months. Two saturated, manifest structural equation models were performed: one predicting quit attempts and the other predicting seven-day point prevalence abstinence at 14 days, three months, and six months after the index ED visit. Stage of change was regressed onto each of the other predictor variables (causal attribution, perceived illness severity, event-related emotions) and covariates, and tobacco cessation outcomes were regressed on all of the predictor variables and covariates. Results: Non-White race, baseline stage of change, and an interaction between causal attribution and event-related fear were the strongest predictors of quit attempt. In contrast, abstinence at six months was most strongly predicted by baseline stage of change and nicotine dependence. Conclusion: Predictors of smoking behavior after an acute medical illness are complex and dynamic. The relations vary depending on the outcome examined (quit attempts vs. abstinence), differ based on the time that has progressed since the event, and include significant interactions.