AIM: Weight loss success is highly variable among individuals. Cluster analysis contributes to future intervention development by recognising this individual variability and identifying different weight loss patterns. Identifying determinants that differentiate between these patterns would explain the source of variability. Thus, we aimed to identify weight loss patterns and their determinants in adults with overweight and obesity.
METHODS: The present study is a secondary analysis of data from the PortionControl@HOME study. The weight of 175 adults was measured at 0, 3 and 12 months and potential determinants were self-reported using validated questionnaires at 0 and 3 months. Weight loss patterns were identified based on percent weight change during Phase 1 (0-3 months) and Phase 2 (3-12 months) using cluster analysis. Determinants were assessed using multinomial logistic regression.
RESULTS: We identified three weight loss patterns: (i) low success, demonstrating low weight loss achievement, (ii) moderate success, demonstrating successful weight loss in Phase 1 followed by partial regain in Phase 2 and (iii) high success, demonstrating weight loss in Phase 1 followed by continued weight loss in Phase 2. Compared to the moderate success pattern, the low success pattern was negatively associated with power of food at baseline (i.e. the appetitive drive to consume highly palatable food) (odds ratio, OR = 0.42, 95% CI = 0.21-0.86) and change in portion control behaviour (i.e. the use of behavioural strategies to control the amount of food consumed) (OR = 0.28, 95% CI = 0.10-0.78).
CONCLUSIONS: Three weight loss patterns were identified in adults with overweight and obesity. Adults with greater power of food and increased portion control behaviour were less likely to exhibit an unsuccessful weight loss pattern.
|Number of pages||7|
|Journal||Nutrition & dietetics: the journal of the Dietitians Association of Australia|
|Early online date||7 Nov 2018|
|Publication status||Published - Apr 2020|
Bibliographical noteThis article also appears in: World Obesity Day 2020
- cluster analysis
- weight loss patterns
- weight maintenance