Early predictors of weight loss in a 1‐year behavioural weight‐loss programme

James, Brittany L.; Roe, Liane S.; Loken, Eric; Rolls, Barbara J.

doi:10.1002/osp4.149

Cited by 17 publications

(19 citation statements)

References 36 publications

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“…In contrast, over 8 in 10 participants who did not respond early to treatment were unsuccessful in achieving 5% weight loss at 3 or 6 months. These findings illustrate the importance of early weight loss, which is consistent with past studies of behavioural interventions for weight loss . The GoalTracker trial is the first study to demonstrate this relation in the context of a standalone mHealth intervention, which holds promise as a less resource‐intensive and more scalable initial intervention strategy in stepped‐care approaches.…”

Section: Discussionsupporting

confidence: 84%

“…those without a counselling component) (3)(4)(5). Early weight loss is a robust predictor of clinically meaningful weight loss (6)(7)(8) and has been used as a trigger in stepped-care behavioural interventions to implement rescue efforts for individuals not responding early to obesity treatment (6,(9)(10)(11). A 12-week internet-based weight loss study found a strong positive relation of early weight loss at 1 month predicting weight loss at 3, 6 and 12 months (12).…”

Section: Introductionmentioning

confidence: 99%

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Early weight loss in a standalone mHealth intervention predicting treatment success

Patel

Hopkins

Bennett

2019

Obesity Science & Practice

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Summary Objective Early weight loss is a strong predictor of longer‐term and clinically meaningful weight loss but has not been studied in the context of mobile health (‘mHealth’) interventions. Methods GoalTracker was a randomized trial among adults (21–65 years) with overweight or obesity comparing three 12‐week standalone mHealth interventions for weight loss. All arms received a free commercial mobile app (MyFitnessPal) for daily self‐monitoring of diet and/or weight and a goal to lose 5% of weight by 3 months. Collapsing across arms, this analysis examined participants with a 1‐month weight ( n = 84), categorizing them as either early responders (≥2% weight loss at 1 month) or early non‐responders (<2% weight loss at 1 month). Results Early responders – 36% of participants – had greater per cent weight change at 3 months (−5.93% [95% confidence interval: −6.82%, −5.03%]) than early non‐responders (−1.45% [−2.15%, −0.75%]), which was sustained at 6 months (−5.91% [−7.33%, −4.48%] vs. −1.28% [−2.37%, −0.19%]; p s < 0.0001). Over half (57%) of early responders achieved ≥5% weight loss at 3 months vs. 11% of early non‐responders. At 4 weeks, self‐monitoring frequency (weight/diet) was significantly greater among early responders, which continued across 12 weeks. Conclusion Responding early to an mHealth treatment is associated with higher engagement and greater likelihood of achieving clinically meaningful weight loss.

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Section: Discussionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Early weight loss in a standalone mHealth intervention predicting treatment success

Patel

Hopkins

Bennett

2019

Obesity Science & Practice

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“…There is a substantial overlap between the variables recognized as key health lifestyle behaviours, that is dietary habits, physical activity, smoking, and drinking alcohol , and behaviours having a major impact on an individual's BMI, that is food choices and eating practices, physical activity, TV viewing, and sleep . Health lifestyle theories argue that focusing on a single or a small subset of behaviours does not sufficiently reflect the diversity of the social forces behind them .…”

Section: Introductionmentioning

confidence: 99%

“…Frequency of meals and having breakfast have both been inversely related to BMI . Time spent on food preparation might be indicative of the quality of diet as food prepared at home was shown to have a better nutritional profile than food eaten out of home . The quality of an individual's diet may also be affected by the duration of sleep , as sleep deprivation has been shown to be associated with food preferences, total energy intake, and metabolic processes .…”

Section: Introductionmentioning

confidence: 99%

Lifestyle behaviours or socioeconomic characteristics? Gender differences in covariates of BMI in Hungary

Jarosz

2018

Obesity Science & Practice

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SummaryObjectiveLifestyle behaviours are everyday activities that result from individual's values, knowledge, and norms shaped by broader cultural and socioeconomic context. These behaviours affect body weight as well as overall health and are influenced by a number of social characteristics. The aim of this paper was to examine the net effects of lifestyle behaviours and socioeconomic factors on body mass index (BMI), and how these differed by gender.MethodsThis study used the 2009/2010 Hungarian Time Use Survey combining behavioural records, background information, and measures of self‐reported health and weight. The sample (n = 7765) was representative for the Hungarian population. Multivariate linear OLS regression models were employed to analyse the net effects of lifestyle and sociodemographic variables.ResultsDaily behaviours were associated with BMI for women, but not for men, except for smoking. Meals frequency and duration of sleep had negative effects on female BMI, whereas duration of TV viewing had a positive effect. Occupational class was associated with male BMI, but not with female. The strong negative effect of smoking was significant for both genders.ConclusionsLifestyle behaviours were linked with female BMI, with socioeconomic characteristics impacting on male BMI. These results suggest that a gender‐specific approach may be appropriate to address obesity issues in the Hungarian population.

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“…Assessment of these factors at baseline before the intervention could allow for tailored delivery of extra support for individuals who are less responsive to treatment (James et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Development of protocols to identify high hunger and low satiety phenotypes

Elfarssi¹

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Obesity has become a worldwide epidemic. Impaired appetite control is associated with weight gain. People who have difficulties in recognising their appetite sensations before and after a meal and who do not eat in response to their appetite sensations seem to be more susceptible to weight gain. These individuals may represent the ‘low satiety phenotype.’ The objective of the present study was to extend and test the work identified in the literature regarding the identification of a satiety phenotype. To identify, at baseline, individuals who struggle to lose weight on weight management programmes, due to reduced satiety, to help clinical professionals to identify those people at baseline so that they can use personalised weight loss strategies to help them. The work included studies in an acute laboratory setting and clinical studies. The first laboratory setting study was a pilot study to allow the researcher to gain expertise in the methods used to assess satiety phenotypes in people with obesity by analysing blood samples for gut hormone levels, subjective ratings of appetite response to a test meal, Three Factor Eating Questionnaire (TFEQ) subscales, food diaries and food craving. A second laboratory based study identified a satiety phenotype in individuals with normal weight by measuring energy intake in an ad libitum test meal, as well as energy intake from a 3 day food diary. Subjective ratings of appetite response, gut hormones, TFEQ and food cravings were also assessed. This study developed methods to subsequently use in a clinical community by designing a heat map, which is a visual presentation tool including independent variables, scores to help clinical professionals working in clinical settings to follow this scoring system to identify individuals who have low or high satiety prior to participation in weight management programmes. Clinical studies were carried out on individuals with obesity by determining at baseline, variables prior to participation in a weight management programme to identify those who struggle to lose weight. In the study carried out in a Tier 3 setting, fasting samples of plasma gut hormones, subjective ratings of appetite response and TFEQ subscales were used to predict subsequent weight loss and reduced satiety. In a Tier 2 setting study, food diaries, food craving and TFEQ subscales were used to identify those who may struggle to lose weight. In the acute laboratory setting, subjective ratings of appetite response were found to be the best measure to identify satiety phenotypes and this was combined with other predictive measures to build the heat maps. In the clinical studies, subjective ratings of appetite response were the best baseline measures to predict weight loss. In the Tier 3 study, subjective ratings of appetite response predicted weight loss and people with reduced satiety, as determined using the heat map tool, on average lost more body mass, BMI and waist circumference although the difference was not significant. In the Tier 2 study, carbohydrate food craving predicted subsequent weight loss. In conclusion, the novel findings in this study are the further development of protocols to identify high hunger and low satiety phenotypes. These will inform researchers and staff in clinical community settings to identify people who have a low satiety phenotype and may inform personalised treatments. However, further studies are needed with larger sample sizes to fully elucidate and validate the above findings.

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Early predictors of weight loss in a 1‐year behavioural weight‐loss programme

Cited by 17 publications

References 36 publications

Early weight loss in a standalone mHealth intervention predicting treatment success

Early weight loss in a standalone mHealth intervention predicting treatment success

Lifestyle behaviours or socioeconomic characteristics? Gender differences in covariates of BMI in Hungary

Development of protocols to identify high hunger and low satiety phenotypes

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