Background: A number of studies have shown that the mean response to a weight loss intervention hides significant individual variation with some individuals responding well and losing large amounts of weight and others failing to respond or even gaining weight. The reason for this wide individual variation is currently the source of much speculation with some researchers implicating genetic or physiological difference and others highlighting behavioural disparities in participants as the explanation. This study aims to identify the demographic and psycho-social characteristics associated with responders and non-responders to a range of weight loss regimens.
Methods: Two complementary approaches were employed. A systematic search was performed to identify any reported associations between socio-demographic factors and weight loss outcomes noted in published reports of clinical trials employing common weight loss approaches (e.g. low carbohydrate diets). These factors were then examined in existing clinical trial datasets from studies undertaken at the Boden Institute that utilised a variety of weight loss regimens including defined calorie deficit, high protein, and Korean diet substitution, to determine whether similar associations were observed.
Results: A range of social and demographic variables were identified in the literature review to influence variations in weight loss but analysis of existing datasets was less revealing. Preliminary analyses suggests that older individuals are more likely to comply and adhere to defined calorie deficit weight loss interventions but no consistent gender differences exist in weight loss outcomes from a range of different regimens. Limited studies show that Caucasians have greater weight loss success on defined calorie deficits compared to those of other ethnicity or mixed races.
Conclusions: Current assessments have not identified demographic or simple psycho-social factors that strongly predict greater or poorer response to different weight loss regimens, but specific trends indicate the merit of further exploration in larger datasets.