Canonical correlation analysis for multi-omics: Application to cross-cohort analysis.

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features-referred to as canonical variables (CVs)-within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

Racial and Ethnic Differences in Self-Reported Periodontal Disease in the Multi-Ethnic Study of Atherosclerosis (MESA).

PURPOSE: Racial and ethnic disparities in periodontal disease exist in the United States. This study examined the prevalence of self-reported periodontal disease, and the extent to which racial/ethnic disparities in the reported disease were reduced or eliminated after controlling for various risk factors in a multi-ethnic study population of older adults. MATERIALS AND METHODS: Information from the baseline examination (July 2000-August 2002) of the Multi-Ethnic Study of Atherosclerosis (MESA) was used. Study participants (N = 6256) were age 45-84 years and identified themselves as either: white, black, Hispanic or Chinese. Periodontal disease was assessed by self-report; demographic and socioeconomic status (SES) indicators, biomedical risk factors and psychosocial stress factors were used as predictors of self-reported periodontal disease. RESULTS: Chinese displayed the highest prevalence of self-reported periodontal disease (39.8%), followed by blacks (32.0%) and whites (26.0%), with Hispanics displaying the lowest prevalence (17.4%). Chinese and black participants had a significantly higher prevalence of disease compared to whites that persisted after adjusting for demographic and SES indicators, biomedical risk factors and psychosocial stress factors. After such adjustment, Hispanics did not differ significantly from whites in their reporting of disease. CONCLUSION: Racial/ethnic disparities in self-reported periodontal disease persisted after adjusting for all study covariates. This study highlights the need for continued research into the determinants of racial/ethnic disparities in periodontal disease in order to better target interventions aimed at reducing the burden of disease in all segments of the U.S. population.

Evaluation of a problem-specific SBAR tool to improve after-hours nurse-physician phone communication: a randomized trial.

BACKGROUND: After-hours telephone communications are common in patient management. Patterns of communication of key information during after-hours phone calls were evaluated, and the utility of problem-specific Situation, Background, Assessment, Recommendation (SBAR) forms in improving this communication was assessed. METHODS: In a randomized trial using a simulated on-call setting, 20 nurses called physicians regarding six cases adapted from inpatient records and based on the six most common reasons for after-hours nurse-physician communication. Three of the cases were handled without the SBAR forms (control cases), and three cases were handled with the forms (SBAR cases). Two cue types of communication were evaluated: situation cues, which conveyed the patient's situation (for example, a patient is confused), and background cues, which conveyed problem-specific data indicated on the SBAR forms (for example, the patient has a low sodium level). RESULTS: Ninety-two phone calls were analyzed (43 SBAR/49 controls). Most of the nurses reported the situation cues (SBAR 88%, control 84%, p = .60) but not the background cues. There was a trend toward fewer background cues communicated in the SBAR cases (14% versus 31%, p = .08). In 14% of the cases, on average, nurses omitted information or reported wrong information regarding the background cue. Physicians asked questions that resulted in the communication of the cues in a minority of the cases when the background cues were not originally provided by the nurses (SBAR 6%, control 16%, p = .39). CONCLUSIONS: In after-hours phone communication between physicians and nurses, significant information was often not communicated and physicians did not elicit the necessary information. Simply providing an SBAR-based form did not ensure complete communication of key information.

Using the personal background preparation survey to identify health science professions students at risk for adverse academic events.

In the first predictive validity study of a diagnostic and prescriptive instrument for averting adverse academic status events (AASE) among multiple populations of diverse health science professions students, entering matriculates' personal background and preparation survey (PBPS) scores consistently significantly predicted 1st- or 2nd-year AASE. During 1st-year orientations, 441 entering matriculates in four southwestern schools from dental, medical, and nursing disciplines completed the 2004 PBPS. The following year during 1st-year orientations, 526 entering matriculates in five schools from dental, medical, nursing, and biomedical science disciplines completed the 2005 PBPS. The PBPS identifies and quantifies a student's noncognitive and cognitive academic performance risks. One standard deviation increments in PBPS risks consistently multiplied 1st- or 2nd-year AASE odds by approximately 140% (p < .05), controlling for underrepresented minority student (URMS) status and school affiliation. Odds of 2nd-year AASE for URMS one standard deviation above the 2004 PBPS mean reached 494% of odds for nonURMS at the mean. PBPS total risks, school affiliation, and URMS status together provided 70-76% correct predictions of 1st- or 2nd-year AASE. PBPS predictive validity did not differ significantly among dental, medical, nursing, or biomedical science schools, or URMS/nonURMS. PBPS sensitivity and specificity approached those for FDA-approved screening mammograms for breast cancer and PSA tests for prostate cancer. PBPS positive predictive values of 42-60% exceeded those for both. The diagnostic and prescriptive PBPS can facilitate proactive targeting of corrective interventions aimed at reducing AASE and attrition among health science education students at risk for academic difficulties.

Personal Background Preparation Survey for early identification of nursing students at risk for attrition.

During 2004 and 2005 orientations, all 187 and 188 new matriculates, respectively, in two southwestern U.S. nursing schools completed Personal Background and Preparation Surveys (PBPS) in the first predictive validity study of a diagnostic and prescriptive instrument for averting adverse academic status events (AASE) among nursing or health science professional students. One standard deviation increases in PBPS risks (p < 0.05) multiplied odds of first-year or second-year AASE by approximately 150%, controlling for school affiliation and underrepresented minority student (URMS) status. AASE odds one standard deviation above mean were 216% to 250% those one standard deviation below mean. Odds of first-year or second-year AASE for URMS one standard deviation above the 2004 PBPS mean were 587% those for non-URMS one standard deviation below mean. The PBPS consistently and significantly facilitated early identification of nursing students at risk for AASE, enabling proactive targeting of interventions for risk amelioration and AASE or attrition prevention.

Trends in Guideline-Driven Revascularization in Diabetic Patients with Multivessel Coronary Heart Disease.

In diabetes patients with chronic ≥3 vessel disease, coronary artery bypass grafting (CABG) holds a class I recommendation in the American College of Cardiology and American Heart Association (ACC/AHA) 2011 guidelines, and this classification has not changed to date. Much of the literature has focused upon whether CABG or percutaneous coronary intervention (PCI) produces better outcomes; there is a paucity of data comparing the odds of receiving these procedures. A secondary analysis was conducted in a de-identified database comprised of 30,482 patients satisfying the entry criteria. Odds of occurrence (CABG, PCI) were determined as the binary dependent variable in period 1, (17 October 2009 through 31 December 2011), and period 2 (1 January 2013 through 16 March 2015), before and after the 2011 guidelines, while controlling for gender, ethnicity/race, and ischemic heart disease as covariates. The odds of performing CABG rather than PCI in period 2 were not statistically significantly different than in period 1 (p = 0.400). The logistic regression model chi-square statistic was statistically significant, with χ2 (7) = 308.850, p < 0.0001. The Wald statistic showed that ethnicity/race (African American, Caucasian, Hispanic and Other), gender, and heart disease contributed significantly to the prediction model with p < 0.05, but ethnicity 'Unknown' did not. The odds of CABG versus PCI in period 2 were 0.98 times those in period 1 95% confidence interval (CI) = (0.925, 1.032), statistically controlling for covariates. There was no significant rise in the odds of undergoing a CABG among this dataset of high-risk patients with diabetes and multivessel coronary heart disease. Modern practice has evolved regarding patient choice and additional variables that impact the final revascularization method employed. The degree to which odds of occurrence of procedures are a reliable surrogate for provider compliance with guidelines remains uncertain.

Interruptions in a level one trauma center: a case study.

BACKGROUND: The emergency department has been characterized as interrupt-driven. Government agencies and patient safety organizations recognize that interruptions contribute to medical errors. The purpose of this study was to observe, record, and contextualize activities and interruptions experienced by physicians and Registered Nurses (RNs) working in a Level One Trauma Center. DESIGN: A case study that relied on an ethnographic study design using the shadowing method. SUBJECTS: A convenience sample of physicians and RNs, each with at least 6 months of experience in the Emergency Department (ED), were asked to participate. In these kinds of detailed qualitative investigations, it is quite common to have a small sample size. Ethical approval: Approval was obtained from institutional ethic committees prior to initiating the study. Community consent was obtained from the ED staff through in-service education. SETTING: All observations were made in the trauma section of the ED of a tertiary teaching hospital. The hospital is situated in a major medical center in the Gulf Coast region of the United States of America (USA). FINDINGS: Five attending ED physicians were observed for a total of 29h, 31min. Eight RNs were shadowed for a total of 40 h, 9min. Interruptions and activities were categorized using the Hybrid Method to Categorize Interruptions and Activities (HyMCIA). Registered Nurses received slightly more interruptions per hour than physicians. People, pagers, and telephones were identified as mediums through which interruptions were delivered. The physical environment was found to contribute to interruptions in workflow because of physical design and when supplies were not available. Physicians and RNs usually returned to the original, interrupted activity more often than leaving the activity unfinished. CONCLUSION: This research provides an enhanced understanding of interruptions in workflow in the ED, the identification of work constraints, and the need to develop interventions to manage interruptions. It is crucial that interruptions be delivered in such a way that there is minimal negative impact on performance. The significance and importance of the interruption must always be weighed against the negative impact that it has on smooth, efficient workflow.

Towards a hybrid method to categorize interruptions and activities in healthcare.

OBJECTIVE: Interruptions are known to have a negative impact on activity performance. Understanding how an interruption contributes to human error is limited because there is not a standard method for analyzing and classifying interruptions. Qualitative data are typically analyzed by either a deductive or an inductive method. Both methods have limitations. In this paper, a hybrid method was developed that integrates deductive and inductive methods for the categorization of activities and interruptions recorded during an ethnographic study of physicians and registered nurses in a Level One Trauma Center. Understanding the effects of interruptions is important for designing and evaluating informatics tools in particular as well as improving healthcare quality and patient safety in general. METHOD: The hybrid method was developed using a deductive a priori classification framework with the provision of adding new categories discovered inductively in the data. The inductive process utilized line-by-line coding and constant comparison as stated in Grounded Theory. RESULTS: The categories of activities and interruptions were organized into a three-tiered hierarchy of activity. Validity and reliability of the categories were tested by categorizing a medical error case external to the study. No new categories of interruptions were identified during analysis of the medical error case. CONCLUSIONS: Findings from this study provide evidence that the hybrid model of categorization is more complete than either a deductive or an inductive method alone. The hybrid method developed in this study provides the methodical support for understanding, analyzing, and managing interruptions and workflow.

Usability of quality measures for online health information: Can commonly used technical quality criteria be reliably assessed?

PURPOSE: Many criteria have been developed to rate the quality of online health information. To effectively evaluate quality, consumers must use quality criteria that can be reliably assessed. However, few instruments have been validated for inter-rater agreement. Therefore, we assessed the degree to which two raters could reliably assess 22 popularly cited quality criteria on a sample of 42 complementary and alternative medicine Web sites. METHODS: We determined the degree of inter-rater agreement by calculating the percentage agreement, Cohen's kappa, and prevalence- and bias-adjusted kappa (PABAK). RESULTS: Our un-calibrated analysis showed poor inter-rater agreement on eight of the 22 quality criteria. Therefore, we created operational definitions for each of the criteria, decreased the number of assessment choices and defined where to look for the information. As a result 18 of the 22 quality criteria were reliably assessed (inter-rater agreement > or = 0.6). CONCLUSIONS: We conclude that even with precise definitions, some commonly used quality criteria cannot be reliably assessed. However, inter-rater agreement can be improved with precise operational definitions.

Searching for cancer-related information online: unintended retrieval of complementary and alternative medicine information.

PURPOSE: The Web is an important source of health information for consumers. Use of complementary and alternative medicine (CAM) is also increasing. Therefore, we studied the likelihood that consumers will incidentally encounter CAM information while searching the Web and the factors that influence retrieval of CAM information. METHODS: We evaluated results retrieved by 10 cancer-related searches on six common search engines. RESULTS: Of 1121 search results, 16.2% displayed CAM information. Sponsored (i.e., paid) results were more likely to display CAM information than non-sponsored results (38% versus 7.5%, p < 0.001). In Overture and Google, sponsored results accounted for 51% and 39% of results on the first page. These search engines also retrieved more CAM web pages. Search engines distinguished sponsored and non-sponsored results, but disclosure statements describing the differences were confusing. Cancer type used as the search keyword did not influence the number of CAM web pages retrieved. However, synonyms of cancer differed in their retrieval of CAM web pages (p < 0.001). Consistent with prior studies of Web search engine overlap, we found that 28% of CAM results were retrieved by two or more search engines. CONCLUSIONS: Clinicians should help consumers recognize sponsored results and encourage search engines to clearly explain sponsored results.

Mobile phone-based clinical guidance for rural health providers in India.

There are few tried and tested mobile technology applications to enhance and standardize the quality of health care by frontline rural health providers in low-resource settings. We developed a media-rich, mobile phone-based clinical guidance system for management of fevers, diarrhoeas and respiratory problems by rural health providers. Using a randomized control design, we field tested this application with 16 rural health providers and 128 patients at two rural/tribal sites in Tamil Nadu, Southern India. Protocol compliance for both groups, phone usability, acceptability and patient feedback for the experimental group were evaluated. Linear mixed-model analyses showed statistically significant improvements in protocol compliance in the experimental group. Usability and acceptability among patients and rural health providers were very high. Our results indicate that mobile phone-based, media-rich procedural guidance applications have significant potential for achieving consistently standardized quality of care by diverse frontline rural health providers, with patient acceptance.

One-hour lecture/demo produces mastery level back safety transfer skills self-efficacy in randomized comparisons among hospital nurses.

More than 1/3 of back injuries among nurses result from patient transfers. So, a rigorous between-subjects quasi-experimental methodology having broad clinical applicability was developed to evaluate the effects of a one-hour didactic back safety and patient transfer training lecture/demonstration upon 42 recently hired hospital nurses. The design implemented independent random samples including independent control groups, though practical constraints required all 42 to participate in the training. Training effects were assessed for each of three back safety transfer skill self-efficacy measures and their composite total self-efficacy score. Four monthly training sessions, each serving different nurses, achieved significant and substantial impacts on both composite total back safety procedure self-efficacy (where effect sizes ranged from approximately 0.8 to 1.8 standard deviations) and on each individual back safety transfer skill self-efficacy measure. After training, 85% to 100% of the nurses rated their back safety procedure self-efficacy within three points of the maximum composite total of 30 on the self-efficacy scale. Results may help improve nursing services and identify training strategies to reduce healthcare worker back injuries.

Assessing the benefit of a personalized EHR-generated informed consent in a dental school setting.

Informed consents are routinely used as an important source of information to help patients make appropriate clinical decisions. However, current standard consent forms may not accomplish their intended purpose due to the variety of patient literacy and experiences and, in the dental school setting, the developing competence of students. The purpose of this pilot study was to test the efficacy of a personalized informed consent generated through an electronic health record (EHR) at one dental school and its role in patient decision making. In the study, a set of informed consents, or SmartConsents, were developed for specific diagnoses and procedures, enhanced with graphics, and delivered through the school's EHR. Fifty patients were recruited in the school's Urgent Care Clinic and divided evenly into two groups: one (control) receiving the standard consent, with the second receiving a SmartConsent. Following treatment, patients were assessed based on demographics, decisional conflict, satisfaction, health literacy, and knowledge. Overall, there were no significant differences in main effects between SmartConsent and standard informed consents for decisional conflict and satisfaction, although significant differences were identified during secondary analysis of satisfaction, gender, and ethnicity. The study also demonstrated the potential for consents to aid the student provider in better communicating with patients.

A benchmark comparison of deterministic and probabilistic methods for defining manual review datasets in duplicate records reconciliation.

INTRODUCTION: Clinical databases require accurate entity resolution (ER). One approach is to use algorithms that assign questionable cases to manual review. Few studies have compared the performance of common algorithms for such a task. Furthermore, previous work has been limited by a lack of objective methods for setting algorithm parameters. We compared the performance of common ER algorithms: using algorithmic optimization, rather than manual parameter tuning, and on two-threshold classification (match/manual review/non-match) as well as single-threshold (match/non-match). METHODS: We manually reviewed 20,000 randomly selected, potential duplicate record-pairs to identify matches (10,000 training set, 10,000 test set). We evaluated the probabilistic expectation maximization, simple deterministic and fuzzy inference engine (FIE) algorithms. We used particle swarm to optimize algorithm parameters for a single and for two thresholds. We ran 10 iterations of optimization using the training set and report averaged performance against the test set. RESULTS: The overall estimated duplicate rate was 6%. FIE and simple deterministic algorithms allowed a lower manual review set compared to the probabilistic method (FIE 1.9%, simple deterministic 2.5%, probabilistic 3.6%; p<0.001). For a single threshold, the simple deterministic algorithm performed better than the probabilistic method (positive predictive value 0.956 vs 0.887, sensitivity 0.985 vs 0.887, p<0.001). ER with FIE classifies 98.1% of record-pairs correctly (1/10,000 error rate), assigning the remainder to manual review. CONCLUSIONS: Optimized deterministic algorithms outperform the probabilistic method. There is a strong case for considering optimized deterministic methods for ER.

Errors in after-hours phone consultations: a simulation study.

BACKGROUND: After-hours out-of-hospital phone consultations require physicians to make decisions based on information provided by a nurse over the phone. METHODS: We conducted a simulation study to evaluate physicians' actions following communication of key information. 22 nurses were asked to call physicians with six cases based on the six most common reasons for after-hours phone calls. We evaluated physicians' actions following the communication of key clinical information: A situation cue described a patient's problem (eg, confusion). A background cue described a specific clinical finding regarding the cause of the problem (eg, patient's sodium is low). For each cue we defined a list of indicators, based on the medical literature, to ascertain whether physicians acted upon the provided information (which was defined as addressing at least one of the indicators). RESULTS: A total of 108 phone consultations (containing 88 situation and 93 background cues) were analysed. Situation cues were communicated in 90% (79/88) of the calls and background cues in 33% (31/93). Physician acted upon the provided information in 57% (45/79) and 48% (15/31) of the communicated situation and background cues, respectively. When the background cues were not communicated, physicians asked questions expected to elicit the cue in 12% of the cases. Responding to the situation cue was associated with longer conversations and active inquiry by the physician. CONCLUSIONS: After-hours phone calls are error prone. Both nurse communication and physician decision-making are problematic. Efforts to improve patient safety in this setting must address both communication and decision-making.

Optimized dual threshold entity resolution for electronic health record databases--training set size and active learning.

Clinical databases may contain several records for a single patient. Multiple general entity-resolution algorithms have been developed to identify such duplicate records. To achieve optimal accuracy, algorithm parameters must be tuned to a particular dataset. The purpose of this study was to determine the required training set size for probabilistic, deterministic and Fuzzy Inference Engine (FIE) algorithms with parameters optimized using the particle swarm approach. Each algorithm classified potential duplicates into: definite match, non-match and indeterminate (i.e., requires manual review). Training sets size ranged from 2,000-10,000 randomly selected record-pairs. We also evaluated marginal uncertainty sampling for active learning. Optimization reduced manual review size (Deterministic 11.6% vs. 2.5%; FIE 49.6% vs. 1.9%; and Probabilistic 10.5% vs. 3.5%). FIE classified 98.1% of the records correctly (precision=1.0). Best performance required training on all 10,000 randomly-selected record-pairs. Active learning achieved comparable results with 3,000 records. Automated optimization is effective and targeted sampling can reduce the required training set size.

Data analysis: evaluation of nanoscale contrast agent enhanced CT scan to differentiate between benign and malignant lung cancer in mouse model.

Proposed is a method for statistical analysis for a small sample size, repeated measure experiment with nesting factors. In the original experiment the Student t-test was used for analysis. Using the same data, we modeled the experiment into two groups of mice with benign and malignant primary lung tumors. 4 tumor nodules were selected from each mouse (N= 36). The dependent variables are the volume, diameter, and signal attenuation measured using computed tomography (CT). The measurements are made before injecting the contrast and at 0, 72, and 168 hours after injection. The contrast agent enhances tumor nodule volume and volume differences between benign and malignant tumor nodules measured across time (p < 0.05). The signal attenuation measured across time differentiates between benign and malignant groups (p < 0.05). There is significant correlation between rate of change of volume and diameter of tumor. The advantages of this statistical method are discussed.

Predictors of student success in graduate biomedical informatics training: introductory course and program success.

OBJECTIVE: To predict student performance in an introductory graduate-level biomedical informatics course from application data. DESIGN: A predictive model built through retrospective review of student records using hierarchical binary logistic regression with half of the sample held back for cross-validation. The model was also validated against student data from a similar course at a second institution. MEASUREMENTS: Earning an A grade (Mastery) or a C grade (Failure) in an introductory informatics course. RESULTS: The authors analyzed 129 student records at the University of Texas School of Health Information Sciences at Houston (SHIS) and 106 at Oregon Health and Science University Department of Medical Informatics and Clinical Epidemiology (DMICE). In the SHIS cross-validation sample, the Graduate Record Exam verbal score (GRE-V) correctly predicted Mastery in 69.4%. Undergraduate grade point average (UGPA) and underrepresented minority status (URMS) predicted 81.6% of Failures. At DMICE, GRE-V, UGPA, and prior graduate degree significantly correlated with Mastery. Only GRE-V was a significant independent predictor of Mastery at both institutions. There were too few URMS students and Failures at DMICE to analyze. Course Mastery strongly predicted program performance defined as final cumulative GPA at SHIS (n=19, r=0.634, r2=0.40, p=0.0036) and DMICE (n=106, r=0.603, r2=0.36, p<0.001). CONCLUSIONS: The authors identified predictors of performance in an introductory informatics course including GRE-V, UGPA and URMS. Course performance was a very strong predictor of overall program performance. Findings may be useful for selecting students for admission and identifying students at risk for Failure as early as possible.

Screening for obstructive sleep apnea on the internet: randomized trial.

BACKGROUND: Obstructive sleep apnea is underdiagnosed. We conducted a pilot randomized controlled trial of an online intervention to promote obstructive sleep apnea screening among members of an Internet weight-loss community. METHODS: Members of an Internet weight-loss community who have never been diagnosed with obstructive sleep apnea or discussed the condition with their healthcare provider were randomized to intervention (online risk assessment+feedback) or control. The primary outcome was discussing obstructive sleep apnea with a healthcare provider at 12 weeks. RESULTS: Of 4700 members who were sent e-mail study announcements, 168 (97% were female, age 39.5 years [standard deviation 11.7], body mass index 30.3 [standard deviation 7.8]) were randomized to intervention (n=84) or control (n=84). Of 82 intervention subjects who completed the risk assessment, 50 (61%) were low risk and 32 (39%) were high risk for obstructive sleep apnea. Intervention subjects were more likely than control subjects to discuss obstructive sleep apnea with their healthcare provider within 12 weeks (11% [9/84] vs 2% [2/84]; P=.02; relative risk=4.50; 95% confidence interval, 1.002-20.21). The number needed to treat was 12. High-risk intervention subjects were more likely than control subjects to discuss obstructive sleep apnea with their healthcare provider (19% [6/32] vs 2% [2/84]; P=.004; relative risk=7.88; 95% confidence interval, 1.68-37.02). One high-risk intervention subject started treatment for obstructive sleep apnea. CONCLUSION: An online screening intervention is feasible and likely effective in encouraging members of an Internet weight-loss community to discuss obstructive sleep apnea with their healthcare provider.

Explanations for unsuccessful weight loss among bariatric surgery candidates.

BACKGROUND: Our objective was to analyze subjective explanations for unsuccessful weight loss among bariatric surgery candidates. METHODS: This was a retrospective analysis of 909 bariatric surgery candidates (78.2% female, average body mass index [BMI] 47.3) at a university center from 2001 to April 2007 who answered an open-ended question about why they were unable to lose weight. We generated a coding scheme for answers to the question and established inter-rater reliability of the coding process. Associations with demographic parameters and initial BMI were tested. RESULTS: The most common categories of answers were nonspecific explanations related to diet (25.3%), physical activity (21.0%), or motivation (19.7%), followed by diet-related motivation (12.7%) and medical conditions or medications affecting physical activity (12.7%). Categories related to time, financial cost, social support, physical environment, and knowledge occurred in less than 4% each. Men were more likely than women to cite a medical condition or medication affecting physical activity (19.2% vs 10.8%, P = 0.002, odds ratio [OR] = 1.96, 95% confidence interval [CI] = 1.28-2.99) but less likely to cite diet-related motivation (7.1% vs 14.2%, P = 0.008, OR = 0.46, 95% CI = 0.26-0.82). CONCLUSIONS: Our findings suggest that addressing diet, physical activity, and motivation in a comprehensive approach would meet the stated needs of obese patients. Raising patient awareness of under-recognized barriers to weight loss, such as the physical environment and lack of social support, should also be considered. Lastly, anticipating gender-specific attributions may facilitate tailoring of interventions.