Stay in treatment: Predicting dropout from pediatric weight management study protocol.
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ABSTRACT: Introduction
Childhood obesity is a serious public health concern. Multidisciplinary pediatric weight management programs have been deemed effective. However, effectiveness of these programs is impacted by attrition, limiting health benefits to children, and inefficiently utilizing scarce resources.Methods
We have developed a model (the Outcomes Forecasting System, OFS) that isolates variables associated with attrition from pediatric weight management, with the potential to forecast participant dropout. In Aim 1, we will increase the power and precision of the OFS and then validate the model through the consistent acquisition of key patient, family, and treatment data, from three different weight management sites. In Aim 2, external validity will be established through the application of the OFS at a fourth pediatric weight management program. Aim 3 will be a pilot clinical trial, incorporating an intervention built on the results of Aims 1 and 2 and utilizing the OFS to reduce attrition.Discussion
A greater understanding of the patient, family, and disease-specific factors that predict dropout from pediatric weight management can be utilized to prevent attrition. The goal of the current study is to refine the OFS to a level of precision and efficiency to be a valuable tool to any weight management program. By identifying the most pertinent factors driving attrition across weight management sites, new avenues for treatment will be identified. This study will result in a valuable forecasting tool that will be applicable for diverse programs and populations, decrease program costs, and improve patient retention, adherence, and outcomes.Clinicaltrialsgov identifier
NCT04364282.
SUBMITTER: Berry DC
PROVIDER: S-EPMC8209185 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background and aimsThere is an urgent need for tools allowing therapists to identify patients at risk of dropout. The OQ-Analyst, an increasingly popular computer-based system, is used to track patient progress and predict dropout. However, we have been unable to find empirical documentation regarding the ability of OQ-Analyst to predict dropout. The aim of the present study was to perform the first direct test of the ability of the OQ-Analyst to predict dropout.DesignPatients were consecutively enlisted in a naturalistic, prospective, longitudinal clinical trial. As interventions based on feedback from the OQ-Analyst could alter the outcome and potentially render the prediction wrong, feedback was withheld from patients and therapists.SettingThe study was carried out during 2011-2013 in an inpatient substance use disorder clinic in Oslo, Norway.ParticipantsPatients aged 18 to 28 years who met criteria for a principal diagnosis of mental or behavioural disorder due to psychoactive substance use (ICD 10; F10.2-F19.2).MeasurementsRed signal (predictions of high risk) from the Norwegian version of the OQ-Analyst were compared with dropouts identified using patient medical records as the standard for predictive accuracy.FindingsA total of 40 patients completed 647 OQ assessments resulting in 46 red signals. There were 27 observed dropouts, only one of which followed after a red signal. Patients indicated by the OQ-Analyst as being at high risk of dropping out were no more likely to do so than those indicated as being at low risk. Random intercept logistic regression predicting dropout from a red signal was statistically nonsignificant. Bayes factor supports no association.ConclusionsThe study does not support the predictive ability of the OQ-Analyst for the present patient population. In the absence of empirical evidence of predictive ability, it may be better not to assume such ability.
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Project description:There is an urgent need to develop and evaluate weight management interventions to address childhood obesity. Recent research suggests that interventions designed for parents exclusively, which have been named parents as agents of change (PAC) approaches, have yielded positive outcomes for managing pediatric obesity. To date, no research has combined a PAC intervention approach with cognitive behavioural therapy (CBT) to examine whether these combined elements enhance intervention effectiveness. This paper describes the protocol our team is using to examine two PAC-based interventions for pediatric weight management. We hypothesize that children with obesity whose parents complete a CBT-based PAC intervention will achieve greater reductions in adiposity and improvements in cardiometabolic risk factors, lifestyle behaviours, and psychosocial outcomes than children whose parents complete a psycho-education-based PAC intervention (PEP).This study is a pragmatic, two-armed, parallel, single-blinded, superiority, randomized clinical trial. The primary objective is to examine the differential effects of a CBT-based PAC vs PEP-based PAC intervention on children's BMI z-score (primary outcome). Secondary objectives are to assess intervention-mediated changes in cardiometabolic, lifestyle, and psychosocial variables in children and parents. Both interventions are similar in frequency of contact, session duration, group facilitation, lifestyle behaviour goals, and educational content. However, the interventions differ insofar as the CBT-based intervention incorporates theory-based concepts to help parents link their thoughts, feelings, and behaviours; these cognitive activities are enabled by group leaders who possess formal training in CBT. Mothers and fathers of children (8-12?years of age; BMI ?85th percentile) are eligible to participate if they are proficient in English (written and spoken) and agree for at least one parent to attend group-based sessions on a weekly basis. Anthropometry, cardiometabolic risk factors, lifestyle behaviours, and psychosocial health of children and parents are assessed at pre-intervention, post-intervention, 6-, and 12-months follow-up.This study is designed to extend findings from earlier efficacy studies and provide data on the effect of a CBT-based PAC intervention for managing pediatric obesity in a real-world, outpatient clinical setting.ClinicalTrials.gov identifier: NCT01267097.
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Project description:BackgroundThe prevalence and consequences of obesity among children and adolescents remain a leading global public health concern, and evidence-based, multidisciplinary lifestyle interventions are the cornerstone of treatment. Mobile electronic devices are widely used across socioeconomic categories and may provide a means of extending the reach and efficiency of health care interventions.ObjectiveWe aimed to synthesize the evidence regarding mobile health (mHealth) for the treatment of childhood overweight and obesity to map the breadth and nature of the literature in this field and describe the characteristics of published studies.MethodsWe conducted a systematic scoping review in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews, by searching nine academic databases in addition to gray literature for studies describing acceptability, usability, feasibility, effectiveness, adherence, or cost-effectiveness of interventions assessing mHealth for childhood obesity treatment. We also hand searched the reference lists of relevant articles. Studies aimed at the prevention of overweight or obesity were excluded, as were studies in which mHealth was not the primary mode of treatment delivery for at least one study arm or was not independently assessed. A random portion of all abstracts and full texts was double screened by a second reviewer to ensure consistency. Data were charted according to study characteristics, including design, participants, intervention content, behavior change theory (BCT) underpinning the study, mode of delivery, and outcomes measured.ResultsWe identified 42 eligible studies assessing acceptability (n=7), usability (n=2), feasibility or pilot studies (n=15), treatment effect (n=17), and fidelity (n=1). Change in BMI z-scores or percentiles was most commonly measured, among a variety of dietary, physical activity, psychological, and usability or acceptability measures. SMS, mobile apps, and wearable devices made up the majority of mobile interventions, and 69% (29/42) of the studies specified a BCT used.ConclusionsPediatric weight management using mHealth is an emerging field, with most work to date aimed at developing and piloting such interventions. Few large trials are published, and these are heterogeneous in nature and rarely reported according to the Consolidated Standards of Reporting Trials for eHealth guidelines. There is an evidence gap in the cost-effectiveness analyses of such studies.
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Project description:ObjectiveTo examine changes in prevalence of overeating behaviors in a comparative effectiveness study of two pediatric weight management interventions.MethodsFour-hundred and seven children, ages 6-12 years, with a BMI ≥ 85th percentile were enrolled in a comparative effectiveness trial of two pediatric weight management interventions. Prevalence of "sneaking, hiding or hoarding food", and 'eating in the absence of hunger' was evaluated at baseline and 12 months. Statistical methods included McNemar's test and longitudinal logistic regression.ResultsPrevalence of "sneak, hide, or hoard food" significantly decreased in all participants from 29.1% to 20.7% at 12 months. The prevalence of "eating in the absence of hunger" decreased in all participants from 46.7% to 22.4% at 12 months. Use of SNAP benefits, free/reduced meals at school, parental stress, housing, and food insecurity at baseline were associated with an increased likelihood of endorsing overeating behaviors at 12 months. Conversely, those who engaged in at least one session of the pediatric weight management intervention were significantly less likely to endorse "eating in the absence of hunger" at 12 months.ConclusionsParticipation in pediatric weight management interventions improves the prevalence of overeating behaviors and is associated with participant engagement and social determinants of health, specifically food security status. Efforts to engage populations impacted by food insecurity and other social determinants of health risk factors will be critical for success of weight management interventions.Clinical trial registrationThis trial has been registered at ClinicalTrials.gov (identifier: NCT03012126).
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