Comparison of binge-eating disorder and food addiction

Ultra-Processed Food Addiction: A Research Update

PURPOSE OF REVIEW

Detail recent advancements in the science on ultra-processed food (UPF) addiction, focusing on estimated prevalence rates and emerging health disparities; progress towards identifying biological underpinnings and behavioral mechanisms; and implications for weight management.

RECENT FINDINGS

Notable developments in the field have included: (1) estimating the global prevalence of UPF addiction at 14% of adults and 15% of youths; (2) revealing health disparities for persons of color and those with food insecurity; (3) observing altered functioning across the brain-gut-microbiome axis; (4) providing early evidence for UPF withdrawal; and (5) elucidating poorer weight management outcomes among persons with UPF addiction. The breadth of recent work on UPF addiction illustrates continued scientific and public interest in the construct and its implications for understanding and treating overeating behaviors and obesity. One pressing gap is the lack of targeted interventions for UPF addiction, which may result in more optimal clinical outcomes for this underserved population.

A three-arm randomised controlled trial of a telehealth intervention targeting improvement in addictive eating for Australian adults (the TRACE program)

There is a substantial research base for addictive eating with development of interventions. The current 3-arm RCT aimed to investigate the efficacy of the TRACE (Targeted Research for Addictive and Compulsive Eating) program to decrease addictive eating symptoms and improve mental health. Participants (18-85 yrs) endorsing ≥3 addictive eating symptoms were randomly allocated to 1) active intervention, 2) passive intervention, or 3) control group. Primary outcome was change in addictive eating symptoms 3-months post-baseline measured by the Yale Food Addiction Scale. Depression, anxiety and stress were also assessed. A total of 175 individuals were randomised. Using Linear Mixed Models, from baseline to 3-months, there was significant improvement in symptom scores in all groups with mean decrease of 4.7 (95% CI: -5.8, -3.6; p < 0.001), 3.8 (95% CI: -5.2, -2.4; p < 0.001) and 1.5 (95% CI: -2.6, -0.4; p = 0.01) respectively. Compared with the control group, participants in the active intervention were five times more likely to achieve a clinically significant change in symptom scores. There was a significant reduction in depression scores in the active and passive intervention groups, but not control group [-2.9 (95% CI: -4.5, -1.3); -2.3 (95% CI: -4.3, -0.3); 0.5 (95% CI: -1.1, 2.1), respectively]; a significant reduction in stress scores within the active group, but not passive intervention or control groups [-1.3 (95% CI: -2.2, -0.5); -1.0 (95% CI: -2.1, 0.1); 0.4 (95% CI: -0.5, 1.2), respectively]; and the reduction in anxiety scores over time was similar for all groups. A dietitian-led telehealth intervention for addictive eating in adults was more effective than a passive or control condition in reducing addictive eating scores from baseline to 6 months. Trial registration: Australia New Zealand Clinical Trial Registry ACTRN12621001079831.

Measuring Motivations to Eat Palatable Foods: Adaptation and Psychometric Properties of the Italian Version of the Palatable Eating Motives Scale (PEMS-IT)

BACKGROUND

Gaining knowledge of the various reasons behind people's consumption of highly processed foods has the potential to enhance obesity prevention initiatives and open avenues to tailor treatment approaches for obesity and binge eating at a more personalized level. This contribution aimed to test the psychometric properties and factor structure of the Palatable Eating Motives Scale (PEMS-IT) in a community sample of Italian adults.

METHODS

A confirmatory factor analysis was performed to test the factor structure of the Italian version of the PEMS (PEMS-IT) on a total of 616 respondents. Furthermore, the reliability and convergent validity analysis of the tool were evaluated.

RESULTS

The analysis confirmed the four-factor structure of PEMS-IT [(YBχ2 (164) = 537.901; p < 0.001, the CFI = 0.918, RMSEA = 0.072; 90%CI [0.065-0.078]; p(RMSEA < 0.05) < 0.001, and SRMR = 0.080] and satisfactory reliability on its subscales (Cronbach's α: 0.745-0.917). Positive correlations were also found with food addiction and binge-eating symptoms, compulsive eating behavior, and uncontrolled and emotional eating.

CONCLUSIONS

The PEMS-IT appears to be an instrument with promising psychometric properties and potential applications in clinical settings. However, it also has some limitations, and future studies could focus on improving the semantic content of the elements to increase the overall utility and precision of the instrument.