Impact of Degree of Obesity on Sleep, Quality of Life, and Depression in Youth.

OBJECTIVE: Obese youth are more likely to report difficulties with sleep, depression, and quality of life (QOL). This study aims to characterize sleep problems, QOL, and symptoms of depression by degree of obesity. METHOD: The cross-sectional study was conducted in a specialized obesity clinic. Obese youth and their caregivers (N = 150) were evaluated with the Child Sleep Habits Questionnaire and Pediatric Quality of Life. Youth completed the Children's Depression Inventory. Regression models and correlations were calculated. RESULTS: Degree of obesity was predictive of increased sleep difficulties and decreased QOL scores. Children's Depression Inventory scores showed that children with more symptoms of depression had more sleep problems, and these were not associated with the degree of obesity. Adolescents with more difficulties sleeping also reported more symptoms of depression and lower QOL. CONCLUSIONS: Degree of obesity negatively affected QOL and sleep variables. Patients with greater sleeping difficulties reported more symptoms of depression.

Evaluation of Lean Body Weight Equation by Dual-Energy X-Ray Absorptiometry Measures.

BACKGROUND: When managing patients with disorders that require clinical intervention, a practical assessment of body habitus is valued. The Duffull-Green lean body weight (LBW) equation was derived and validated in adults across a wide body mass index (BMI) range. Whether this predictive equation will perform well in patients at BMI extremes or perform better than a widely used empiric "ideal" body weight (IBW) equation is unknown. MATERIALS AND METHODS: Calculated LBW and calculated IBW were each compared with the dual-energy x-ray absorptiometry (DXA)-derived lean body mass (LBM) by simple linear regression. A mixed model was used to determine how well the LBW equation performed over time in patients with more than 1 DXA measurement. RESULTS: At time 0, 32 patients were 18-67 years old, and all were obese (BMI: 36-65 kg/m2), while the remaining 7 had parenteral nutrition-dependent intestinal failure (BMI: 17-25 kg/m2). A subset of patients underwent bariatric surgery after time 0 (BMI at follow-up: 22-49 kg/m2). The LBW equation was a predictor of LBM ( R2 = 0.67, P < .0001), while the IBW equation was not ( R2 = 0.04, P = .25). The LBW equation remained a predictor of LBM over time ( P < .0001) without significant interaction by number of months since time 0. CONCLUSION: The Duffull-Green LBW equation successfully predicted lean body mass in a patient population with a wide range of BMIs at both a single point in time and after considerable weight loss. In the clinical setting, an equation that performs well in various disease states and body sizes is advantageous.

Association of Freestanding Children's Hospitals With Outcomes in Children With Critical Illness.

OBJECTIVES: Little is known about the relationship between freestanding children's hospitals and outcomes in children with critical illness. The purpose of this study was to evaluate the association of freestanding children's hospitals with outcomes in children with critical illness. DESIGN: Propensity score matching was performed to adjust for potential confounding variables between patients cared for in freestanding or nonfreestanding children's hospitals. We tested the sensitivity of our findings by repeating the primary analyses using inverse probability of treatment weighting method and regression adjustment using the propensity score. SETTING: Retrospective study from an existing national database, Virtual PICU Systems (LLC) database. PATIENTS: Patients less than 18 years old admitted to one of the participating PICUs in the Virtual PICU Systems, LLC database were included (2009-2014). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 538,967 patients from 140 centers were included. Of these, 323,319 patients were treated in 60 freestanding hospitals. In contrast, 215,648 patients were cared for in 80 nonfreestanding hospitals. By propensity matching, 134,656 patients were matched 1:1 in the two groups (67,328 in each group). Prior to matching, patients in the freestanding hospitals were younger, had greater comorbidities, had higher severity of illness scores, had higher incidence of cardiac arrest, had higher resource utilization, and had higher proportion of patients undergoing complex procedures such as cardiac surgery. Before matching, the outcomes including mortality were worse among the patients cared for in the freestanding hospitals (freestanding vs nonfreestanding, 2.5% vs 2.3%; p < 0.001). After matching, the majority of the study outcomes were better in freestanding hospitals (freestanding vs nonfreestanding, mortality: 2.1% vs 2.8%, p < 0.001; standardized mortality ratio: 0.77 [0.73-0.82] vs 0.99 [0.87-0.96], p < 0.001; reintubation: 3.4% vs 3.8%, p < 0.001; good neurologic outcome: 97.7% vs 97.1%, p = 0.001). CONCLUSIONS: In this large observational study, we demonstrated that ICU care provided in freestanding children's hospitals is associated with improved risk-adjusted survival chances compared to nonfreestanding children's hospitals. However, the clinical significance of this change in mortality should be interpreted with caution. It is also possible that the hospital structure may be a surrogate of other factors that may bias the results.

Factors influencing physical activity behaviors in colorectal cancer survivors.

PURPOSE: To elucidate factors that impact intention (INT) to be active as well as actual physical activity (PA) behavior in colorectal cancer survivors (CRC-S) using the theory of planned behavior (TpB). Planning for PA was explored as a mediator of the INT-behavior relationship. Chemotherapy-induced neuropathy and fatigue were also explored. DESIGN: A cross-sectional quantitative mailed survey was used. SETTING: The study was conducted among community-dwelling adults living in Pennsylvania when diagnosed with colorectal cancer (CRC). SUBJECTS: Subjects comprised 843 CRC-S diagnosed with CRC in Pennsylvania in 2009. MEASURES: The survey included questions about planning for PA, TpB constructs, medical and social variables, and PA as measured by a modified Godin Leisure Time Questionnaire. ANALYSIS: Descriptive statistics were used to characterize the sample. A mediation analysis was used to determine if planning mediated the relationship between INT and actual PA behavior. A stepwise regression was used to determine predictors of INT and PA. RESULTS: Ninety-six CRC-S responded, with 25% meeting PA recommendations for health promotion, suggesting that CRC-S are insufficiently active. Perceived behavioral control (PBC) and social norm (SN) accounted for 43% of the variance in INT, whereas 30% of the variance in PA was explained by PBC and age. Neuropathy negatively impacted PA behavior (p = .008). Both action and coping planning partially mediated the INT-behavior relationship (ß = 20.08, p = .007; ß = 22.85, p = .001, respectively). CONCLUSION: Survivors at risk for inactivity are those with low PBC, low SN, and neuropathy, and those who are older.

A pilot study of telemedicine for post-operative urological care in children.

We conducted a retrospective study of paediatric urological surgery patients over a 12-month period. We compared patients followed up by telemedicine with those who had post-operative follow-up on site at the Arkansas Children's Hospital (ACH) in Little Rock. All pre-operative patients living in northwest Arkansas were given the opportunity to use telemedicine from a satellite clinic at Lowell, 328 km from the hospital. Of 61 patients, 10 chose telemedicine and 51 chose to be evaluated at the ACH clinic. All telemedicine visits were completed successfully, but in four cases, the video clarity of the telemedicine images was not sufficient for decision-making, and a digital photograph was sent by email to the physician at the ACH. There were no post-operative surgical complications in either patient group. In the telemedicine group, the median distance to the ACH was 330 km, and the median distance to the remote clinic was 35 km. In the on-site group, the median distance to the ACH was 293 km, which was significantly less (P=0.03). In the on-site group, the median travel time to the ACH was 174 min. If the telemedicine group had driven to the hospital, the median travel time would have been 192 min. Logistic regression showed that for every 37 km increase in distance to ACH, patients had a 111% increase in the odds of receiving telemedicine compared to receiving on-site care (OR=2.1, 95% CI: 1.0, 4.4). The pilot study supports the use of telemedicine for the post-operative evaluation of paediatric urology surgery patients and suggests that substantial travel distance and time savings can be made.