Initial Response to Program, Program Participation, and Weight Reduction Among 375 MOVE! Participants, Augusta, Georgia, 2008-2010

A predictive model for medium-term weight loss response in people with type 2 diabetes engaging in behavioural weight management interventions

AIMS

To develop and evaluate prediction models for medium-term weight loss response in behavioural weight management programmes.

MATERIALS AND METHODS

We conducted three longitudinal analyses using the Action for HEalth in Diabetes (LookAHEAD) trial, Weight loss Referrals for Adults in Primary care (WRAP) trial, and routine data from the National Health Service Greater Glasgow and Clyde Weight Management Service (NHS-GGCWMS). We investigated predictors of medium-term weight loss (>5% body weight) over 3 years in NHS-GGCWMS and, separately, predictors of weight loss response in LookAHEAD over 4 years. We validated predictors in both studies using WRAP over 5 years. Predictors of interest included demographic and clinical variables, early weight change in-programme (first 4 weeks) and overall in-programme weight change.

RESULTS

In LookAHEAD and WRAP the only baseline variables consistently associated with weight loss response were female sex and older age. Of 1152 participants in NHS-GGCWMS (mean age 57.8 years, 60% female, type 2 diabetes diagnosed for a median of 5.3 years), 139 lost weight over 3 years (12%). The strongest predictor of weight loss response was early weight change (odds ratio 2.22, 95% confidence interval 1.92-2.56) per 1% weight loss. Losing 0.5% weight in the first 4 weeks predicted medium-term weight loss (sensitivity 89.9%, specificity 49.5%, negative predictive value 97.3%). Overall in-programme weight change was also associated with weight loss response over 3 years in NHS-GGCWMS and over 5 years in WRAP.

CONCLUSIONS

Not attaining a weight loss threshold of 0.5% early in weight management programmes may identify participants who would benefit from alternative interventions.

Weight Loss Trajectories and Short-Term Prediction in an Online Weight Management Program

The extent to which early weight loss in behavioral weight control interventions predicts long-term success remains unclear. In this study, we developed an algorithm aimed at classifying weight change trajectories and examined its ability to predict long-term weight loss based on weight early change. We utilized data from 667 de-identified individuals who participated in a commercial weight loss program (Instinct Health Science), comprising 69,363 weight records. Sequential polynomial regression models were employed to classify participants into distinct weight trajectory patterns based on key model parameters. Next, we applied multinomial logistic models to evaluate if early weight loss in the first 14 days and prolonged duration of participation were significantly associated with long-term weight loss patterns. The mean percentage of weight loss was 7.9 ± 5.1% over 133 ± 69 days. Our analysis revealed four main weight loss trajectory patterns: a steady decrease over time (30.6%), a decrease to a plateau with subsequent decline (15.8%), a decrease to a plateau with subsequent increase (46.9%), and no substantial decrease (6.7%). Early weight change rate and total participating duration emerged as significant factors in differentiating long-term weight loss patterns. These findings contribute to support the provision of tailored advice in the early phase of behavioral interventions for weight loss.

Initial weight loss and early intervention adherence predict long-term weight loss during the Promoting Successful Weight Loss in Primary Care in Louisiana lifestyle intervention

OBJECTIVE

This study tested whether initial weight change (WC), self-weighing, and adherence to the expected WC trajectory predict longer-term WC in an underserved primary-care population with obesity.

METHODS

Data from the intervention group (n = 452; 88% women; 74% Black; BMI 37.3 kg/m2 [SD: 4.6]) of the Promoting Successful Weight Loss in Primary Care in Louisiana trial were analyzed. Initial (2-, 4-, and 8-week) percentage WC was calculated from baseline clinic weights and daily at-home weights. Weights were considered adherent if they were on the expected WC trajectory (10% at 6 months with lower [7.5%] and upper [12.5%] bounds). Linear mixed-effects models tested whether initial WC and the number of daily and adherent weights predicted WC at 6, 12, and 24 months.

RESULTS

Percentage WC during the initial 2, 4, and 8 weeks predicted percentage WC at 6 (R2  = 0.15, R2  = 0.28, and R2  = 0.50), 12 (R2  = 0.11, R2  = 0.19, and R2  = 0.32), and 24 (R2  = 0.09, R2  = 0.11, and R2  = 0.16) months (all p < 0.01). Initial daily and adherent weights were significantly associated with WC as individual predictors, but they only marginally improved predictions beyond initial weight loss alone in multivariable models.

CONCLUSIONS

These results highlight the importance of initial WC for predicting long-term WC and show that self-weighing and adherence to the expected WC trajectory can improve WC prediction.

Deriving Weight from Big Data: A Comparison of Body Weight Measurement Cleaning Algorithms (Preprint)

Background

Patient body weight is a frequently used measure in biomedical studies, yet there are no standard methods for processing and cleaning weight data. Conflicting documentation on constructing body weight measurements presents challenges for research and program evaluation.

Objective

In this study, we aim to describe and compare methods for extracting and cleaning weight data from electronic health record databases to develop guidelines for standardized approaches that promote reproducibility.

Methods

We conducted a systematic review of studies published from 2008 to 2018 that used Veterans Health Administration electronic health record weight data and documented the algorithms for constructing patient weight. We applied these algorithms to a cohort of veterans with at least one primary care visit in 2016. The resulting weight measures were compared at the patient and site levels.

Results

We identified 496 studies and included 62 (12.5%) that used weight as an outcome. Approximately 48% (27/62) included a replicable algorithm. Algorithms varied from cutoffs of implausible weights to complex models using measures within patients over time. We found differences in the number of weight values after applying the algorithms (71,961/1,175,995, 6.12% to 1,175,177/1,175,995, 99.93% of raw data) but little difference in average weights across methods (93.3, SD 21.0 kg to 94.8, SD 21.8 kg). The percentage of patients with at least 5% weight loss over 1 year ranged from 9.37% (4933/52,642) to 13.99% (3355/23,987).

Conclusions

Contrasting algorithms provide similar results and, in some cases, the results are not different from using raw, unprocessed data despite algorithm complexity. Studies using point estimates of weight may benefit from a simple cleaning rule based on cutoffs of implausible values; however, research questions involving weight trajectories and other, more complex scenarios may benefit from a more nuanced algorithm that considers all available weight data.

Construction and Use of Body Weight Measures from Administrative Data in a Large National Health System: A Systematic Review

OBJECTIVE

Administrative data are increasingly used in research and evaluation yet lack standardized guidelines for constructing measures using these data. Body weight measures from administrative data serve critical functions of monitoring patient health, evaluating interventions, and informing research. This study aimed to describe the algorithms used by researchers to construct and use weight measures.

METHODS

A structured, systematic literature review of studies that constructed body weight measures from the Veterans Health Administration was conducted. Key information regarding time frames and time windows of data collection, measure calculations, data cleaning, treatment of missing and outlier weight values, and validation processes was collected.

RESULTS

We identified 39 studies out of 492 nonduplicated records for inclusion. Studies parameterized weight outcomes as change in weight from baseline to follow-up (62%), weight trajectory over time (21%), proportion of participants meeting weight threshold (46%), or multiple methods (28%). Most (90%) reported total time in follow-up and number of time points. Fewer reported time windows (54%), outlier values (51%), missing values (34%), or validation strategies (15%).

CONCLUSIONS

A high variability in the operationalization of weight measures was found. Improving methods to construct clinical measures will support transparency and replicability in approaches, guide interpretation of findings, and facilitate comparisons across studies.

Weight Reduction Goal Achievement Among Veterans With Mental Health Diagnoses

BACKGROUND: Despite the use of weight management programs among veterans, the impact of mental health diagnoses on weight reduction goal achievement is unknown. AIMS: We aimed to describe the prevalence and association of mental health diagnoses with a 5% weight reduction goal achievement. METHODS: Logistic regression was used to describe the association between mental health diagnoses and weight reduction goal achievement at 6, 12, and 24 months among 402 veterans enrolled in a weight management program. RESULTS: More than 43% of veterans had a mental health diagnoses, with depressive disorders, posttraumatic stress disorder (PTSD), and substance use disorders being the most prevalent. At all three times, simply having a mental health diagnosis was not associated with weight reduction goal achievement. Specific diagnoses were associated with a greater likelihood of achieving weight reduction goals at 12 months (PTSD and Drug Use Disorder) and 24 months (Anxiety Disorder and Other Mental Health Diagnosis). CONCLUSION: The findings suggest that unhealthy weight is quite common for individuals with mental health diagnoses; however, weight reduction goal achievement may be equally likely for those with and without mental health diagnoses. The prevalence of mental health diagnoses among veterans seeking weight reduction suggests that psychiatric nurses should be aware of this common comorbidity.

Initial Weight Loss Response as an Indicator for Providing Early Rescue Efforts to Improve Long-term Treatment Outcomes

PURPOSE OF REVIEW

There is a large variability in response to behavioral weight loss (WL) programs. Reducing rates of obesity and diabetes may require more individuals to achieve clinically significant WL post-treatment. Given that WL within the first 1-2 months of a WL program is associated with long-term WL, it may be possible to improve treatment outcomes by identifying and providing additional intervention to those with poor initial success (i.e., "early non-responders"). We review the current literature regarding early non-response to WL programs and discuss how adaptive interventions can be leveraged as a strategy to "rescue" early non-responders.

RECENT FINDINGS

Preliminary findings suggest that adaptive interventions, specifically stepped care approaches, offer promise for improving outcomes among early non-responders. Future studies need to determine the optimal time point and threshold for intervening and the type of early intervention to employ. Clinicians and researchers should consider the discussed factors when making treatment decisions.