Telemedicine for obesity management among United States adults: A systematic and meta-analysis of intervention studies.

Obesity is projected to affect 86% of United States adults by 2030. Recent data show a surge to 41.9%, with the highest proportion in the 40-59 age group (44.3%). Obesity is linked to various health issues and preventable deaths. Telemedicine has emerged as a promising avenue for addressing obesity. This systematic review and meta-analysis examine the effectiveness of telemedicine interventions for managing obesity in US adults aged 40 and above. Through a thorough Preferred Reporting Items for Systematic Reviews and Meta-Analysis-guided search, 16 studies meeting inclusion criteria were identified. These studies employed diverse telemedicine technologies, including video-based and telephone sessions or a mixture of technologies. The analysis reveals a statistically significant mean difference of 0.93 in favor of telemedicine interventions for weight loss. Subgroup analysis suggests that intervention durations of 6-12 months and telephone-based sessions correlate with more substantial mean differences. This study provides valuable insights into the effectiveness of telemedicine in managing obesity, emphasizing the importance of intervention type and duration. Study limitations include variability and potential biases. Customized telemedicine strategies have the potential to combat the obesity epidemic among older adults in the United States, offering guidance to healthcare professionals aiming to reduce health risks and enhance overall well-being.

Medical diagnostic accuracy measures: an innovative approach based on the area under predictive values curves.

Positive and negative estimates are commonly used by clinicians to evaluate the likelihood of a disease stage being present based on test results. The predicted values are dependent on the prevalence of the underlying illness. However, for certain diseases or clinical conditions, the prevalence is unknown or different from one region to another or from one population to another, leading to an erroneous diagnosis. This article introduces innovative post-test diagnostic precision measures for continuous tests or biomarkers based on the combined areas under the predictive value curves for all possible prevalence values. The proposed measures do not vary as a function of the prevalence of the disease. They can be used to compare different diagnostic tests and/or biomarkers' abilities for rule-in, rule-out, and overall accuracy based on the combined areas under the predictive value curves. The relationship of the proposed measures to other diagnostic accuracy measures is discussed. We illustrate the proposed measures numerically and use a real data example on breast cancer.

Post-test diagnostic accuracy measures under tree ordering of disease classes.

The medical field commonly employs post-test measures such as predictive values and likelihood ratios to assess diagnostic accuracy. Predictive values, including positive and negative values (PPV and NPV), indicate the probability that individuals have a target health condition based on test results. On the other hand, likelihood ratios, including positive and negative ratios (LR+ and LR- respectively), compare the probability of a particular test result between the diseased and non-diseased groups. While predictive values are useful in evaluating diagnostic test accuracy in populations with varying disease prevalence, likelihood ratios provide a direct link between pre-test and post-test probabilities in specific patients. In this study, we introduce and analyze a new approach called generalized predictive values and likelihood ratios, using a tree ordering of disease classes. We evaluate the effectiveness of these methods through simulation studies and illustrate their use with real data on lung cancer.

On diagnostic accuracy measure with cut-points criterion for ordinal disease classification based on concordance and discordance.

The accuracy of a diagnostic test has always been essential in detecting disease staging. Many diagnostic tests of accuracy measures are used in binary diagnosis tests. Some measures apply to multi-stage diagnosis. Yet, there are limitations to the implementation, and the performance highly depends on the distribution of diagnostic outcomes. Another essential aspect of medical diagnostic testing using biomarkers is to find an optimal cut-point that categorizes a patient as diseased or healthy. This aspect was extended to the diseases with more than two stages. We propose a diagnostic accuracy measure and optimal cut-points selection (CD), using concordance and discordance for k-stages diseases. The CD measure uses the classification agreement and disagreement between tests outcomes and diseases stages. Simulations for power studies suggest that CD can detect the differences between the null and alternative hypotheses that other methods cannot for some scenarios. Simulation results indicate that using CD measures to select optimal cut-points can provide relatively high correct classification rates than the existing measures and more balanced accurate classification rates than the generalized Youden Index (GYI). An illustration is provided using the ANDI data to choose biomarkers for diagnosing Alzheimer's Disease (AD) and select optimal cut-points for the chosen biomarkers.