OBJECTIVE:The purpose of this study was to investigate the effectiveness of a weight maintenance program conducted over the Internet. DESIGN: Longitudinal, clinical behavioral weight loss trial with 6-month in-person behavioral obesity treatment followed by a 12-month maintenance program conducted both in-person (frequent in-person support; F-IPS, minimal in-person support; M-IPS) and over the Internet (Internet support; IS). SUBJECTS: A total of 122 healthy, overweight adults (age ¼ 48.4 AE 9.6, BMI ¼ 32.2 AE 4.5 kg=m 2 , 18 male) MEASUREMENTS: Body weight, dietary intake, energy expended in physical activity, attendance, self-monitoring, comfort with technology. RESULTS: Results (n ¼ 101) showed that weight loss did not differ by condition during treatment (8.0 AE 5 vs 11 AE 6.5 vs 9.8 AE 5.9 kg, P ¼ 0.27 for IS, M-IPS and F-IPS, respectively). The IS condition gained significantly more weight than the F-IPS group during the first 6 months of weight maintenance ( þ 2.2 AE 3.8 vs 0 AE 4 kg, P < 0.05) and sustained a significantly smaller weight loss than both in-person support groups at the 1 y follow-up ( 7 5.7 AE 5.9 vs 7 10.4 AE 9.3 vs 7 10.4 AE 6.3 kg, P < 0.05 for IS, M-IPS and F-IPS, respectively). Attendance at maintenance meetings was greater for the F-IPS than the IS condition over the 1 y maintenance program (54 vs 39%, P ¼ 0.04). Acceptability of assigned condition was higher for subjects in the F-IPS than IS condition. CONCLUSION: The results of this study suggest that Internet support does not appear to be as effective as minimal or frequent intensive in-person therapist support for facilitating the long-term maintenance of weight loss.

BACKGROUND Since the confirmation of the first patient infected with SARS-CoV-2 in Spain in January 2020, the epidemic has grown rapidly, with the greatest impact on the Madrid region. This article describes the first 2226 consecutive adult patients with COVID-19 admitted to the La Paz University Hospital in Madrid. METHODS Our cohort included all consecutively admitted patients who were hospitalized and who had a final outcome (death or discharge) in a 1286-bed hospital of Madrid (Spain) from February 25th (first case admitted) to April 19th, 2020. Data was entered manually into an electronic case report form, which was monitored prior to the analysis. RESULTS We consecutively included 2226 adult patients admitted to the hospital who either died (460) or were discharged (1766). The patients median age was 61 years; 51.8% were women. The most common comorbidity was arterial hypertension (41.3%). The most common symptoms on admission were fever (71.2%). The median time from disease onset to hospital admission was 6 days. Overall mortality was 20.7% and was higher in men (26.6% vs 15.1%). Seventy-five patients with a final outcome were transferred to the ICU (3.4%). Most patients admitted to the ICU were men, and the median age was 64 years. Baseline laboratory values on admission were consistent with an impaired immune-inflammatory profile. CONCLUSIONS We provide a description of the first large cohort of hospitalized patients with COVID-19 in Europe. Advanced age, male gender, the presence of comorbidities and abnormal laboratory values were more common among the patients with fatal outcomes.

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Background The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. MethodsIn this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Findings Three distinct phenotypes were derived in the derivation cohort (n=2667)-phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])-and reproduced in the internal validation cohort (n=1368)phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2•5% (95% CI 1•4-4•3) for patients with phenotype A, 30•5% (28•5-32•6) for patients with phenotype B, and 60•7% (53•7-67•2) for patients with phenotype C (log-rank test p<0•0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5•3% [95% CI 3•4-8•1] for phenotype A, 31•3% [28•5-34•2] for phenotype B, and 59•5% [48•8-69•3] for phenotype C; external validation cohort: 3•7% [2•0-6•4] for phenotype A, 23•7% [21•8-25•7] for phenotype B, and 51•4% [41•9-60•7] for phenotype C).Interpretation Patients admitted to hospital with COVID-19 can be classified into three...

Participants were 65 obese men and women who were randomly assigned to either weight control or weight control plus cognitive behavioral body image therapy. Both conditions showed clinically significant improvements in body image at posttreatment and 1-year follow-up. Adding body image therapy to weight control did not result in greater psychological improvements and did not result in better maintenance of body image change when participants regained weight after treatment. Weight loss and maintenance were equivalent between groups. Adding body image therapy did not improve or detract from weight loss. Although body image therapy has been shown to be effective in obese persons, it appears that a well-rounded cognitive-behavioral weight control program is effective as well.

The detection and reporting of serious adverse drug reactions (SADRs) have become important components of monitoring and evaluation activities performed in hospitals. We present the implementation of a prospective pharmacovigilance program based on automatic laboratory signals (ALSs) at a hospital. We also report the general findings after the first year of operation of the program, which involved ALSs that indicate various SADRs: agranulocytosis, aplastic anemia, liver injury, thrombocytopenia, hyponatremia, and rhabdomyolysis. The number of hospitalizations during the year was 54,525, and 1,732 patients experienced at least one ALS. The review of electronic medical records (EMRs) showed that no alternative cause (i.e., no non-SADR explanation) for the ALS was identified in 520 (30%) of the patients. After the individual ALS-patient evaluation, a total of 110 SADRs (6.35% of those identified after reviewing EMRs and 21.15% of those requiring individual patient evaluations) were identified. In other words, in order to identify a single SADR, we had to review the electronic records of approximately 16 patients and personally visit 5 patients.