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1.
Clin Obes ; 5(1): 38-41, 2015 Feb.
Article in English | MEDLINE | ID: mdl-25530148

ABSTRACT

BACKGROUND: Researchers and participants' expectations can influence treatment response. Less is known about the effects of researchers' expectations on the accuracy of data collection in the context of a weight loss trial. METHODS: Student raters (N = 58; age = 20.1 ± 2.3 years) were recruited to weigh individuals who they thought were completing a 12-month weight loss trial, although these 'participants' were actually standardized patients (SPs) playing these roles. Prior to data collection, student raters were provided information suggesting that the tested treatment had been effective. Each student rater received a list of 9-10 'participants' to weigh. While the list identified each person as 'treatment' or 'control', this assignment was at random, which allowed us to examine the effects of non-blinding and expectancy manipulation on weight measurement accuracy. We hypothesized that raters would record the weights of 'treatment participants' as lower than those of 'control participants'. RESULTS: Contrary to our hypothesis, raters recorded weights that were 0.293 kg heavier when weighing 'treatment' vs. 'control' SPs, although this difference was not significant (P = 0.175). CONCLUSIONS: This pilot study found no evidence that manipulating expectancies about treatment efficacy or not blinding raters biased measurements. Future work should examine other biases which may be created by not blinding research staff who implement weight loss trials as well as the participants in those trials.


Subject(s)
Data Collection/methods , Weight Loss , Weight Reduction Programs , Double-Blind Method , Female , Humans , Male , Pilot Projects , Reproducibility of Results , Research Subjects , Selection Bias
2.
Int J Obes (Lond) ; 39(8): 1181-7, 2015 Aug.
Article in English | MEDLINE | ID: mdl-25323965

ABSTRACT

BACKGROUND: Public health and clinical interventions for obesity in free-living adults may be diminished by individual compensation for the intervention. Approaches to predict weight outcomes do not account for all mechanisms of compensation, so they are not well suited to predict outcomes in free-living adults. Our objective was to quantify the range of compensation in energy intake or expenditure observed in human randomized controlled trials (RCTs). METHODS: We searched multiple databases (PubMed, CINAHL, SCOPUS, Cochrane, ProQuest, PsycInfo) up to 1 August 2012 for RCTs evaluating the effect of dietary and/or physical activity interventions on body weight/composition. INCLUSION CRITERIA: subjects per treatment arm ≥5; ≥1 week intervention; a reported outcome of body weight/body composition; the intervention was either a prescribed amount of over- or underfeeding and/or supervised or monitored physical activity was prescribed; ≥80% compliance; and an objective method was used to verify compliance with the intervention (for example, observation and electronic monitoring). Data were independently extracted and analyzed by multiple reviewers with consensus reached by discussion. We compared observed weight change with predicted weight change using two models that predict weight change accounting only for metabolic compensation. FINDINGS: Twenty-eight studies met inclusion criteria. Overfeeding studies indicate 96% less weight gain than expected if no compensation occurred. Dietary restriction and exercise studies may result in up to 12-44% and 55-64% less weight loss than expected, respectively, under an assumption of no behavioral compensation. INTERPRETATION: Compensation is substantial even in high-compliance conditions, resulting in far less weight change than would be expected. The simple algorithm we report allows for more realistic predictions of intervention effects in free-living populations by accounting for the significant compensation that occurs.


Subject(s)
Energy Intake/physiology , Energy Metabolism/physiology , Obesity/prevention & control , Public Health , Weight Loss/physiology , Adult , Algorithms , Humans , Predictive Value of Tests , Randomized Controlled Trials as Topic
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