Background Scalable weight loss maintenance (WLM) interventions for adults with obesity are lacking but vital for the health and economic benefits of weight loss to be fully realised. We examined the effectiveness and cost-effectiveness of a low-intensity technology-mediated behavioural intervention to support WLM in adults with obesity after clinically significant weight loss (≥5%) compared to standard lifestyle advice. Methods and findings The NULevel trial was an open-label randomised controlled superiority trial in 288 adults recruited April 2014 to May 2015 with weight loss of ≥5% within the previous 12 months, from a pre-weight loss BMI of ≥30 kg/m2. Participants were self-selected, and the majority self-certified previous weight loss. We used a web-based randomisation system to assign participants to either standard lifestyle advice via newsletter (control arm) or a technology-mediated low-intensity behavioural WLM programme (intervention arm). The intervention comprised a single face-to-face goal-setting meeting, self-monitoring, and remote feedback on weight, diet, and physical activity via links embedded in short message service (SMS). All participants were provided with wirelessly connected weighing scales, but only participants in the intervention arm were instructed to weigh themselves daily and told that they would receive feedback on their weight. After 12 months, we measured the primary outcome, weight (kilograms), as well as frequency of self-weighing, objective physical activity (via accelerometry), psychological variables, and cost-effectiveness. The study was powered to detect a between-group weight difference of ±2.5 kg at follow-up. Overall, 264 participants (92%) completed the trial. Mean weight gain from baseline to 12 months was 1.8 kg (95% CI 0.5–3.1) in the intervention group ( n = 131) and 1.8 kg (95% CI 0.6–3.0) in the control group ( n = 133). There was no evidence of an effect on weight at 12 months (difference in adjusted mean weight change from baseline: −0.07 [95% CI 1.7 to −1.9], p = 0.9). Intervention participants weighed themselves more frequently than control participants and were more physically active. Intervention participants reported greater satisfaction with weight outcomes, more planning for dietary and physical activity goals and for managing lapses, and greater confidence for healthy eating, weight loss, and WLM. Potential limitations, such as the use of connected weighing study in both trial arms, the absence of a measurement of energy intake, and the recruitment from one region of the United Kingdom, are discussed. Conclusions There was no difference in the WLM of participants who received the NULevel intervention compared to participants who received standard lifestyle advice via newsletter. The intervention affected some, but not all, process-related secondary outcomes of the trial. Trial regi...
Background: Many large studies have implemented wrist or thigh accelerometry to capture physical activity, but the accuracy of these measurements to infer activity energy expenditure (AEE) and consequently total energy expenditure (TEE) has not been demonstrated. The purpose of this study was to assess the validity of acceleration intensity at wrist and thigh sites as estimates of AEE and TEE under free-living conditions using a gold-standard criterion. Methods: Measurements for 193 UK adults (105 men, 88 women, aged 40-66 years, BMI 20.4-36.6 kg m −2) were collected with triaxial accelerometers worn on the dominant wrist, non-dominant wrist and thigh in free-living conditions for 9-14 days. In a subsample (50 men, 50 women) TEE was simultaneously assessed with doubly labelled water (DLW). AEE was estimated from non-dominant wrist using an established estimation model, and novel models were derived for dominant wrist and thigh in the non-DLW subsample. Agreement with both AEE and TEE from DLW was evaluated by mean bias, root mean squared error (RMSE), and Pearson correlation. Results: Mean TEE and AEE derived from DLW were 11.6 (2.3) MJ day −1 and 49.8 (16.3) kJ day −1 kg −1. Dominant and non-dominant wrist acceleration were highly correlated in free-living (r = 0.93), but less so with thigh (r = 0.73 and 0.66, respectively). Estimates of AEE were 48.6 (11.8) kJ day −1 kg −1 from dominant wrist, 48.6 (12.3) from non-dominant wrist, and 46.0 (10.1) from thigh; these agreed strongly with AEE (RMSE~12.2 kJ day −1 kg −1 , r~0.71) with small mean biases at the population level (~6%). Only the thigh estimate was statistically significantly different from the criterion. When combining these AEE estimates with estimated REE, agreement was stronger with the criterion (RMSẼ 1.0 MJ day −1 , r~0.90). Conclusions: In UK adults, acceleration measured at either wrist or thigh can be used to estimate population levels of AEE and TEE in free-living conditions with high precision.
Online self-reported 24-h dietary recall systems promise increased feasibility of dietary assessment. Comparison against interviewer-led recalls established their convergent validity; however, reliability and criterion-validity information is lacking. The validity of energy intakes (EI) reported using Intake24, an online 24-h recall system, was assessed against concurrent measurement of total energy expenditure (TEE) using doubly labelled water in ninety-eight UK adults (40–65 years). Accuracy and precision of EI were assessed using correlation and Bland–Altman analysis. Test–retest reliability of energy and nutrient intakes was assessed using data from three further UK studies where participants (11–88 years) completed Intake24 at least four times; reliability was assessed using intra-class correlations (ICC). Compared with TEE, participants under-reported EI by 25 % (95 % limits of agreement −73 % to +68 %) in the first recall, 22 % (−61 % to +41 %) for average of first two, and 25 % (−60 % to +28 %) for first three recalls. Correlations between EI and TEE were 0·31 (first), 0·47 (first two) and 0·39 (first three recalls), respectively. ICC for a single recall was 0·35 for EI and ranged from 0·31 for Fe to 0·43 for non-milk extrinsic sugars (NMES). Considering pairs of recalls (first two v. third and fourth recalls), ICC was 0·52 for EI and ranged from 0·37 for fat to 0·63 for NMES. EI reported with Intake24 was moderately correlated with objectively measured TEE and underestimated on average to the same extent as seen with interviewer-led 24-h recalls and estimated weight food diaries. Online 24-h recall systems may offer low-cost, low-burden alternatives for collecting dietary information.
Dietary intake is a complex behaviour to accurately measure (1) . Twenty-four hour recalls are a popular choice for dietary surveys as they are quick to administer, do not require the participant to be literate (2) and are less burdensome to complete compared to other dietary assessment methods (3) . Technology offers the potential to make dietary assessment more convenient, intuitive and engaging for users. It also ensures consistency of coding and significantly reduces the cost as nutritional output can be generated without the need for manual coding and data entry.INTAKE24 is an online multiple pass 24hr dietary recall developed for use with 11-24 year olds in Scottish food and nutrition surveys.INTAKE24 was developed from an original prototype tool called SCRAN24 (4) . The system development was an iterative process involving four cycles of user interaction, evaluation and further development. Evaluation focussed mainly on the usability of the system (e.g. how easy it is to learn and use) and on the users experience while interacting with the system (e.g. how satisfying, enjoyable and motivating the system is to use) (5) . User evaluation was conducted with 80 participants; 20 at each stage. Researcher observation, 'think aloud' techniques, and eye tracking, were used to identify aspects of the system users found confusing. Feedback was gathered using semi-structured interviews and a system usability scale. In addition, each participant completed an interviewer led recall after completing INTAKE24 in order to identify any food and drink items missed. This fed into the development of the prompts within the system and served to gauge accuracy.In response to user feedback and observations during user testing the system interface was flattened so a single interface screen handled all aspects of the recall (e.g. free text entry, looking up foods in the database, portion size estimation). Improved search functionality and navigation around the system were also influenced through feedback from users at each stage. The time taken to complete the system reduced significantly throughout the user testing and accuracy of reported intakes improved for all nutrients except Vitamin C as can be seen in the table below.Integrating observation and post-completion interviews allowed us to obtain maximum information to feed into the design process; refining the system to have the best possible tool for use in the field.This work was funded by the Food Standards Agency, Scotland.
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