The Choosing Healthy Options of Starches (CHOOS) diet and dietary intake self-monitoring for weight loss among adolescent females

Previous research has shown dietary intake self-monitoring, and culturally tailored weight loss interventions to be effective tools for weight loss. Technology can be used to tailor weight loss interventions to better suit adolescents. There is a lack of research to date on the use of personal digital assistants (PDAs) to self-monitor dietary intake among adolescents. The objective of this study was to determine the difference in dietary intake self-monitoring frequency between using a Personal Digital Assistant (PDA) or paper logs as a diet diary in obese adolescent females; and to describe differences in diet adherence, as well as changes in body size and self-efficacy to resist eating. We hypothesized dietary intake self-monitoring frequency would be greater during PDA use than during paper log use. This study was a randomized crossover trial. Participants recorded their diet for 4 weeks: 2 weeks on a PDA and 2 weeks on paper logs. Thirty-four obese females ages 12-20 were recruited for participation. Thirty were included in analyses. Participants recorded more entries/day while using the paper logs (4.10 entries/day ± 0.63) than while using the PDA (3.01 entries/day ±0.75) (p<0.001). Significantly more meals and snacks were skipped during paper log use (0.81/day ± 0.65) than during PDA use (0.23/day ± 0.22) (p=0.011). Changes in body size (BMI, weight, and waist circumference) and self-efficacy to resist eating did not differ significantly between PDA and paper log use. When compared to paper logs, participants felt the PDA was more convenient (p=0.020), looked forward to using the PDA more (p=0.008), and would rather continue using the PDA than the paper logs (p=0.020). The findings of this study indicate use of a PDA as a dietary intake self-monitoring tool among adolescents would not result in increased dietary intake self-monitoring to aid in weight loss. Use of paper logs would result in greater data returned to clinicians, though use of PDAs would likely get adolescents more excited about adhering to recommendations to record their diet. Future research should look at updated communication devices, such as cell phones and other PDAs with additional features, and the role they can play in increasing dietary intake self-monitoring among adolescents.^

BENCHMARKING AND IMPLEMENTATION OF A NEW INDEPENDENT MONTE CARLO DOSE CALCULATION QUALITY ASSURANCE AUDIT TOOL FOR CLINICAL TRIALS

Introduction Commercial treatment planning systems employ a variety of dose calculation algorithms to plan and predict the dose distributions a patient receives during external beam radiation therapy. Traditionally, the Radiological Physics Center has relied on measurements to assure that institutions participating in the National Cancer Institute sponsored clinical trials administer radiation in doses that are clinically comparable to those of other participating institutions. To complement the effort of the RPC, an independent dose calculation tool needs to be developed that will enable a generic method to determine patient dose distributions in three dimensions and to perform retrospective analysis of radiation delivered to patients who enrolled in past clinical trials. Methods A multi-source model representing output for Varian 6 MV and 10 MV photon beams was developed and evaluated. The Monte Carlo algorithm, know as the Dose Planning Method (DPM), was used to perform the dose calculations. The dose calculations were compared to measurements made in a water phantom and in anthropomorphic phantoms. Intensity modulated radiation therapy and stereotactic body radiation therapy techniques were used with the anthropomorphic phantoms. Finally, past patient treatment plans were selected and recalculated using DPM and contrasted against a commercial dose calculation algorithm. Results The multi-source model was validated for the Varian 6 MV and 10 MV photon beams. The benchmark evaluations demonstrated the ability of the model to accurately calculate dose for the Varian 6 MV and the Varian 10 MV source models. The patient calculations proved that the model was reproducible in determining dose under similar conditions described by the benchmark tests. Conclusions The dose calculation tool that relied on a multi-source model approach and used the DPM code to calculate dose was developed, validated, and benchmarked for the Varian 6 MV and 10 MV photon beams. Several patient dose distributions were contrasted against a commercial algorithm to provide a proof of principal to use as an application in monitoring clinical trial activity.

Devices in heart failure: potential methods for device-based monitoring of congestive heart failure.

Congestive heart failure has long been one of the most serious medical conditions in the United States; in fact, in the United States alone, heart failure accounts for 6.5 million days of hospitalization each year. One important goal of heart-failure therapy is to inhibit the progression of congestive heart failure through pharmacologic and device-based therapies. Therefore, there have been efforts to develop device-based therapies aimed at improving cardiac reserve and optimizing pump function to meet metabolic requirements. The course of congestive heart failure is often worsened by other conditions, including new-onset arrhythmias, ischemia and infarction, valvulopathy, decompensation, end-organ damage, and therapeutic refractoriness, that have an impact on outcomes. The onset of such conditions is sometimes heralded by subtle pathophysiologic changes, and the timely identification of these changes may promote the use of preventive measures. Consequently, device-based methods could in the future have an important role in the timely identification of the subtle pathophysiologic changes associated with congestive heart failure.

A comparative analysis of hospital -based physician monitoring systems

The purpose of the multiple case-study was to determine how hospital subsystems (such as physician monitoring and credentialing; quality assurance; risk management; and peer review) were supporting the monitoring of physicians? Three large metropolitan hospitals in Texas were studied and designated as hospitals #1, #2, and #3. Realizing that hospital subsystems are a unique entity and part of a larger system, conclusions were made on the premises of a quality control system, in relation to the tools of government (particularly the Health Care Quality Improvement Act (HCQIA)), and in relation to itself as a tool of a hospital.^ Three major analytical assessments were performed. First, the subsystems were analyzed as to their "completeness"; secondly, the subsystems were analyzed for "performance"; and thirdly, the subsystems were analyzed in reference to the interaction of completeness and performance.^ The physician credentialing and monitoring and the peer review subsystems as quality control systems were most complete, efficient, and effective in hospitals #1 and #3. The HCQIA did not seem to be an influencing factor in the completeness of the subsystem in hospital #1. The quality assurance and risk management subsystem in hospital #2 was not representative of completeness and performance and the HCQIA was not an influencing factor in the completeness of the Q.A. or R.M. systems in any hospital. The efficiency (computerization) of the physician credentialing, quality assurance and peer review subsystems in hospitals #1 and #3 seemed to contribute to their effectiveness (system-wide effect).^ The results indicated that the more complete, effective, and efficient subsystems were characterized by (1) all defined activities being met, (2) the HCQIA being an influencing factor, (3) a decentralized administrative structure, (4) computerization an important element, and (5) staff was sophisticated in subsystem operations. However, other variables were identified which deserve further research as to their effect on completeness and performance of subsystems. They include (1) medical staff affiliations, (2) system funding levels, (3) the system's administrative structure, and (4) the physician staff "cultural" characteristics. Perhaps by understanding other influencing factors, health care administrators may plan subsystems that will be compatible with legislative requirements and administrative objectives. ^