The EMPOWER Occupational e-Mental Health Intervention Implementation Checklist to Foster e-Mental Health Interventions in the Workplace: Development Study.

BACKGROUND: Occupational e-mental health (OeMH) interventions significantly reduce the burden of mental health conditions. The successful implementation of OeMH interventions is influenced by many implementation strategies, barriers, and facilitators across contexts, which, however, are not systematically tracked. One of the reasons is that international consensus on documenting and reporting the implementation of OeMH interventions is lacking. There is a need for practical guidance on the key factors influencing the implementation of interventions that organizations should consider. Stakeholder consultations secure a valuable source of information about these key strategies, barriers, and facilitators that are relevant to successful implementation of OeMH interventions. OBJECTIVE: The objective of this study was to develop a brief checklist to guide the implementation of OeMH interventions. METHODS: Based on the results of a recently published systematic review, we drafted a comprehensive checklist with a wide set of strategies, barriers, and facilitators that were identified as relevant for the implementation of OeMH interventions. We then used a 2-stage stakeholder consultation process to refine the draft checklist to a brief and practical checklist comprising key implementation factors. In the first stage, stakeholders evaluated the relevance and feasibility of items on the draft checklist using a web-based survey. The list of items comprised 12 facilitators presented as statements addressing "elements that positively affect implementation" and 17 barriers presented as statements addressing "concerns toward implementation." If a strategy was deemed relevant, respondents were asked to rate it using a 4-point Likert scale ranging from "very difficult to implement" to "very easy to implement." In the second stage, stakeholders were interviewed to elaborate on the most relevant barriers and facilitators shortlisted from the first stage. The interview mostly focused on the relevance and priority of strategies and factors affecting OeMH intervention implementation. In the interview, the stakeholders' responses to the open survey's questions were further explored. The final checklist included strategies ranked as relevant and feasible and the most relevant facilitators and barriers, which were endorsed during either the survey or the interviews. RESULTS: In total, 26 stakeholders completed the web-based survey (response rate=24.8%) and 4 stakeholders participated in individual interviews. The OeMH intervention implementation checklist comprised 28 items, including 9 (32.1%) strategies, 8 (28.6%) barriers, and 11 (39.3%) facilitators. There was widespread agreement between findings from the survey and interviews, the most outstanding exception being the idea of proposing OeMH interventions as benefits for employees. CONCLUSIONS: Through our 2-stage stakeholder consultation, we developed a brief checklist that provides organizations with a guide for the implementation of OeMH interventions. Future research should empirically validate the effectiveness and usefulness of the checklist.

Diagnosis accuracy of waist-to-height ratio to predict cardiometabolic risk in children with obesity.

BACKGROUND: Waist-to-height ratio (WHtR) predicts abdominal fat and cardiometabolic risk. In children with obesity, the most adequate cut-off to predict cardiometabolic risk as well as its ability to predict risk changes over time has not been tested. Our aim was to define an appropriate WHtR cut-off to predict cardiometabolic risk in children with obesity, and to analyze its ability to predict changes in cardiometabolic risk over time. METHODS: This is an observational prospective study secondary to the OBEMAT2.0 trial. We included data from 218 participants (8-15 years) who attended baseline and final visits (12 months later). The main outcome measure was a cardiometabolic risk score derived from blood pressure, lipoproteins, and HOMA index of insulin resistance. RESULTS: The optimal cut-off to predict the cardiometabolic risk score was WHtR ≥0.55 with an area under the curve of 0.675 (95% CI: 0.589-0.760) at baseline and 0.682 (95% CI: 0.585-0.779) at the final visit. Multivariate models for repeated measures showed that changes in cardiometabolic risk were significantly associated with changes in WHtR. CONCLUSION: This study confirms the clinical utility of WHtR to predict changes in cardiometabolic risk over time in children with obesity. The most accurate cut-off to predict cardiometabolic risk in children with obesity was WHtR ≥0.55. IMPACT: In children, there is no consensus on a unique WHtR cut-off to predict cardiometabolic risk. The present work provides sufficient evidence to support the use of the 0.55 boundary. We have a large sample of children with obesity, with whom we compared the previously proposed boundaries according to cardiometabolic risk, and we found the optimal WHtR cut-off to predict it. We also analyzed if a reduction in the WHtR was associated with an improvement in their cardiometabolic profile.

Impact of the motivational interviewing for childhood obesity treatment: The Obemat2.0 randomized clinical trial.

BACKGROUND AND OBJECTIVE: The aim was assessing a short training for healthcare providers on patient-focused counselling to treat childhood obesity in primary care, along with dietitian-led workshops and educational materials. METHODS: Randomized clustered trial conducted with paediatrician-nurse pairs (Basic Care Units [BCU]) in primary care centres from Tarragona (Spain). BCUs were randomized to intervention (MI) (motivational interview, dietitian-led education, and educational materials) or control group (SC, standard care). Participants were 8-14-year-old children with obesity, undergoing 1-11 monthly treatment visits during 1 year at primary care centres. The primary outcome was BMI z-score reduction. RESULTS: The study included 44 clusters (23 MI). Out of 303 allocated children, 201 (n = 106 MI) completed baseline, final visits, and at least one treatment visit and were included in the analysis. BMI z-score reduction was -0.27 (±0.31) in SC, versus -0.36 (±0.35) in MI (p = 0.036). Mixed models with centres as random effects showed greater reductions in BMI in MI than SC; differences were B = -0.11 (95% CI: -0.20, -0.01, p = 0.025) for BMI z-score, and B = -2.06 (95% CI: -3.89, -0.23, p = 0.028) for BMI %. No severe adverse events related to the study were notified. CONCLUSION: Training primary care professionals on motivational interviewing supported by dietitians and educational materials, enhanced the efficacy of childhood obesity therapy.

Validation of bioelectrical impedance analysis for body composition assessment in children with obesity aged 8-14y.

BACKGROUND & AIMS: The aim was to generate a predictive equation to assess body composition (BC) in children with obesity using bioimpedance (BIA), and avoid bias produced by different density levels of fat free mass (FFM) in this population. METHODS: This was a cross-sectional validation study using baseline data from a randomized intervention trial to treat childhood obesity. Participants were 8 to 14y (n = 315), underwent assessments on anthropometry and BC through Air Displacement Plethysmography (ADP), Dual X-Ray Absorptiometry and BIA. They were divided into a training (n = 249) and a testing subset (n = 66). In addition, the testing subset underwent a total body water assessment using deuterium dilution, and thus obtained results for the 4-compartment model (4C). A new equation to estimate FFM was created from the BIA outputs by comparison to a validated model of ADP adjusted by FFM density in the training subset. The equation was validated against 4C in the testing subset. As reference, the outputs from the BIA device were also compared to 4C. RESULTS: The predictive equation reduced the bias from the BIA outputs from 14.1% (95%CI: 12.7, 15.4) to 4.6% (95%CI: 3.8, 5.4) for FFM and from 18.4% (95%CI: 16.9, 19.9) to 6.4% (95% CI: 5.3, 7.4) for FM. Bland-Altman plots revealed that the new equation significantly improved the agreement with 4C; furthermore, the observed trend to increase the degree of bias with increasing FM and FFM also disappeared. CONCLUSION: The new predictive equation increases the precision of BC assessment using BIA in children with obesity.

A novel approach to assess body composition in children with obesity from density of the fat-free mass.

BACKGROUND & AIMS: Assessment of Fat Mass (FM) and fat-free mass (FFM) using Air-displacement plethysmography (ADP) technique assumes constant density of FFM (DFFM) by age and sex. It has been recently shown that DFFM further varies according to body mass index (BMI), meaning that ADP body composition assessments of children with obesity could be biased if DFFM is assumed to be constant. The aim of this study was to validate the use of the calculations of DFFM (rather than constant density of the FFM) to improve accuracy of body composition assessment in children with obesity. METHODS: cross-sectional validation study in 66 children with obesity (aged 8-14 years) where ADP assessments of body composition assuming constant density (FFMBODPOD and FMBODPOD) were compared to those where DFFM was adjusted in relation to BMI (FFMadjusted and FMadjusted), and both compared to the gold standard reference, the 4-component model (FFM4C and FM4C). RESULTS: FFMBODPOD was overestimated by 1.50 kg (95%CI -0.68 kg, 3.63 kg) while FFMadjusted was 0.71 kg (-1.08 kg, 2.51 kg) (percentage differences compared to FFM4C were 4.9% (±2.9%) and 2.8% (±2.1%), respectively (p < 0.001)). Consistently, FM was underestimated by both methods, representing a mean difference between methods of 4.0% (±2.9%) and 6.8% (±3.8%), respectively, when compared to the reference method. The agreement and reliability of body composition assessments were improved when adjusted using calculations (adjusted models) rather than assuming constant DFFM. CONCLUSIONS: The use of constant values for fat-free mass properties may increase bias when assessing body composition (FM and FFM) in children with obesity by two-component techniques such as ADP. Using adjusted corrections as proposed in the present work may reduce the bias by half.

Associations of age and body mass index with hydration and density of fat-free mass from 4 to 22 years.

BACKGROUND: Most body composition techniques assume constant properties of fat free mass (FFM) (hydration and density) regardless of nutritional status, which may lead to biased values. AIM: To evaluate the interactive associations of age and body mass index (BMI) with hydration and density of FFM. METHODS: Data from subjects aged between 4 and 22 years old from several studies conducted in London, UK were assessed. Hydration (HFFM) and density (DFFM) of FFM obtained from the four-component model in 936 and 905 individuals, respectively, were assessed. BMI was converted in to z-scores, and categorised into five groups using z-score cut-offs (thin, normal weight, overweight, obese, and severely obese). Linear regression models for HFFM and DFFM were developed using age, sex, and BMI group as predictors. RESULTS: Nearly 30% of the variability in HFFM was explained by models including age and BMI groups, showing increasing HFFM values in heavier BMI groups. On the other hand, ∼40% of variability in DFFM was explained by age, sex, and BMI groups, with DFFM values decreasing in association with higher BMI group. CONCLUSION: Nutritional status should be considered when assessing body composition using two-component methods, and reference data for HFFM and DFFM is needed for higher BMI groups to avoid bias. Further research is needed to explain intra-individual variability in FFM properties.

The Obemat2.0 Study: A Clinical Trial of a Motivational Intervention for Childhood Obesity Treatment.

The primary aim of the Obemat2.0 trial was to evaluate the efficacy of a multicomponent motivational program for the treatment of childhood obesity, coordinated between primary care and hospital specialized services, compared to the usual intervention performed in primary care. This was a cluster randomized clinical trial conducted in Spain, with two intervention arms: motivational intervention group vs. usual care group (as control), including 167 participants in each. The motivational intervention consisted of motivational interviewing, educational materials, use of an eHealth physical activity monitor and three group-based sessions. The primary outcome was body mass index (BMI) z score increments before and after the 12 (+3) months of intervention. Secondary outcomes (pre-post intervention) were: adherence to treatment, waist circumference (cm), fat mass index (z score), fat free mass index (z score), total body water (kg), bone mineral density (z score), blood lipids profile, glucose metabolism, and psychosocial problems. Other assessments (pre and post-intervention) were: sociodemographic information, physical activity, sedentary activity, neuropsychological testing, perception of body image, quality of the diet, food frequency consumption and foods available at home. The results of this clinical trial could open a window of opportunity to support professionals at the primary care to treat childhood obesity. The clinicaltrials.gov identifier was NCT02889406.