A systematic review of ecological momentary assessment studies of appetite and affect in the experience of temptations and lapses during weight loss dieting.
Mark RandleAmy L AhernEmma J BoylandPaul ChristiansenJason C G HalfordJack Stevenson-SmithCarl RobertsPublished in: Obesity reviews : an official journal of the International Association for the Study of Obesity (2023)
Dietary temptations and lapses challenge control over eating and act as barriers toward successful weight loss. These are difficult to assess in laboratory settings or with retrospective measures as they occur momentarily and driven by the current environment. A better understanding of how these experiences unfold within real-world dieting attempts could help inform strategies to increase the capacity to cope with the changes in appetitive and affective factors that surround these experiences. We performed a narrative synthesis on the empirical evidence of appetitive and affective outcomes measured using ecological momentary assessment (EMA) during dieting in individuals with obesity and their association with dietary temptations and lapses. A search of three databases (Scopus, Medline, and PsycInfo) identified 10 studies. Within-person changes in appetite and affect accompany temptations and lapses and are observable in the moments precipitating a lapse. Lapsing in response to these may be mediated through the strength of a temptation. Negative abstinence-violation effects occur following a lapse, which negatively impact self-attitudes. Engagement in coping strategies during temptations is effective for preventing lapses. These findings indicate that monitoring changes in sensations during dieting could help identify the crucial moments when coping strategies are most effective for aiding with dietary adherence.
Keyphrases
- weight loss
- bariatric surgery
- roux en y gastric bypass
- glycemic control
- gastric bypass
- mental health
- depressive symptoms
- bipolar disorder
- social support
- weight gain
- human health
- obese patients
- case control
- social media
- type diabetes
- cross sectional
- machine learning
- risk assessment
- big data
- adipose tissue
- deep learning
- artificial intelligence
- body weight