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Many patients with chronic medical conditions search the internet to obtain medical advice and health information to improve their health condition and quality of life. Diabetes is a common chronic disease that disproportionately affects different race and ethnicity groups in the United States. In the existing literature on the popularity of internet health information seeking among persons with a chronic medical condition, there are limited data on US adults living with diabetes. This study aims to examine the factors associated with internet health information seeking among US adults living with diabetes and whether there is a disparity in internet health information seeking stratified by race and ethnicity. We conducted a cross-sectional study using the Health Information National Trends Survey data from 2017 to 2020. We selected our study sample based on respondents' reports on whether they were told they had diabetes, and our primary outcome was internet health information-seeking behavior. We used 2 multivariable logistic regression models to examine the effects of sociodemographic factors and other covariates on the internet health information-seeking behavior of adults with diabetes. Jackknife replicate weights were used to provide bias-corrected variance estimates. Our study sample included 2903 adults who self-reported that they had diabetes. In total, 60.08% (1744/2903) were non-Hispanic White individuals, 46.88% (1336/2850) were men, and 64% (1812/2831) had some college or graduate education. The prevalence of internet health information seeking in this population was 64.49% (1872/2903), and the main factors associated with internet health information seeking included education level (some college vs less than high school: odds ratio [OR] 1.42, 95% CI 1.44-1.88; and college graduate or higher vs less than high school: OR 2.50, 95% CI 1.79-3.50), age (age group ≥65 years vs age group 18-44 years: OR 0.46, 95% CI 0.34-0.63), and household income level (P<.001). In addition, we found significant differences in the effects of predictors stratified by race. The findings from this study suggest that internet health information seeking is common among US adults living with diabetes. Internet health information could influence the relationship between health care providers and adults living with diabetes and improve their self-management and quality of life.

Background: The adoption of telehealth services has been a challenge in rural communities. The reasons for the slow adoption of such technology-driven services have been attributed to social norms, health care policies, and a lack of infrastructure to support the delivery of services. However, the COVID-19 pandemic–related shutdown of in-person health care services resulted in the usage of telehealth services as a necessity rather than a choice. The pandemic also fast-tracked some needed legislation to allow medical cost reimbursement for remote examination and health care services. As services return to normalcy, it is important to examine whether the usage of telehealth services during the period of a shutdown has changed any of the trends in the acceptance of telehealth as a reliable alternative to traditional in-person health care services. Objective: Our aim was to explore whether the temporary shift to telehealth services has changed the attitudes toward the usage of technology-enabled health services in rural communities. Methods: We examined the Medicaid reimbursement data for the state of Alabama from March 2019 through June 2021. Selecting the telehealth service codes, we explored the adoption rates in 3 phases of the COVID-19 shutdown: prepandemic, pandemic before the rollout of mass vaccination, and pandemic after the rollout of mass vaccination. Results: The trend in telemedicine claims had an opposite pattern to that in nontelemedicine claims across the 3 periods. The distribution of various characteristics of patients who used telemedicine (age group, gender, race, level of rurality, and service provider type) was different across the 3 periods. Claims related to behavior and mental health had the highest rates of telemedicine usage after the onset of the pandemic. The rate of telemedicine usage remained at a high level after the rollout of mass vaccination. Conclusions: The current trends indicate that adoption of telehealth services is likely to increase postpandemic and that the consumers (patients), service providers, health care establishments, insurance companies, and state and local policies have changed their attitudes toward telehealth. An increase in the use of telehealth could help local and federal governments address the shortage of health care facilities and service providers in underserved communities, and patients can get the much-needed care in a timely and effective manner.

The goal of this study is to develop and validate a machine learning approach for predicting opioid overdose risk and identifying associated risk factors among Alabama Medicaid beneficiaries during the period of 2016–2023. Using the administrative claims data, we trained three machine learning models, penalized logistic regression, random forest and gradient boosting machines, on the 2016–2018 data with 168,625 records, and test and validate the models on the 2019–2023 data with 229,212 records. A modern sampling approach, SMOTE, was incorporated into the study to deal with the imbalance in the overdose outcome. The machine learning models demonstrated strong performance assessed by C-statistics, sensitivity and specificity, precision and recall rates, etc. Several risk factors were identified including changes in prescription patterns, beneficiary’s age, and prescription denials. Differences in ROC-AUC and PR-AUC between models with and without SMOTE were modest; interpretation therefore emphasizes improvements in recall rather than overall discrimination. SMOTE-based performance metrics were averaged across 50 resampling iterations to reduce instability from single-run oversampling.

ObjectivesDietary assessment methods not relying on self-report are needed. The Automatic Ingestion Monitor 2 (AIM-2) combines a wearable camera that captures food images with sensors that detect food intake. We compared energy intake (EI) estimates of meals derived from AIM-2 chewing sensor signals, AIM-2 images, and an internet-based diet diary, with researcher conducted weighed food records (WFR) as the gold standard.Subjects/MethodsThirty adults wore the AIM-2 for meals self-selected from a university food court on one day in mixed laboratory and free-living conditions. Daily EI was determined from a sensor regression model, manual image analysis, and a diet diary and compared with that from WFR. A posteriori analysis identified sources of error for image analysis and WFR differences.ResultsSensor-derived EI from regression modeling (R2 = 0.331) showed the closest agreement with EI from WFR, followed by diet diary estimates. EI from image analysis differed significantly from that by WFR. Bland–Altman analysis showed wide limits of agreement for all three test methods with WFR, with the sensor method overestimating at lower and underestimating at higher EI. Nutritionist error in portion size estimation and irreconcilable differences in portion size between food and nutrient databases used for WFR and image analyses were the greatest contributors to image analysis and WFR differences (44.4% and 44.8% of WFR EI, respectively).ConclusionsEstimation of daily EI from meals using sensor-derived features offers a promising alternative to overcome limitations of self-report. Image analysis may benefit from computerized analytical procedures to reduce identified sources of error.

Video observations have been widely used for providing ground truth for wearable systems for monitoring food intake in controlled laboratory conditions; however, video observation requires participants be confined to a defined space. The purpose of this analysis was to test an alternative approach for establishing activity types and food intake bouts in a relatively unconstrained environment. The accuracy of a wearable system for assessing food intake was compared with that from video observation, and inter-rater reliability of annotation was also evaluated. Forty participants were enrolled. Multiple participants were simultaneously monitored in a 4-bedroom apartment using six cameras for three days each. Participants could leave the apartment overnight and for short periods of time during the day, during which time monitoring did not take place. A wearable system (Automatic Ingestion Monitor, AIM) was used to detect and monitor participants’ food intake at a resolution of 30 s using a neural network classifier. Two different food intake detection models were tested, one trained on the data from an earlier study and the other on current study data using leave-one-out cross validation. Three trained human raters annotated the videos for major activities of daily living including eating, drinking, resting, walking, and talking. They further annotated individual bites and chewing bouts for each food intake bout. Results for inter-rater reliability showed that, for activity annotation, the raters achieved an average (±standard deviation (STD)) kappa value of 0.74 (±0.02) and for food intake annotation the average kappa (Light’s kappa) of 0.82 (±0.04). Validity results showed that AIM food intake detection matched human video-annotated food intake with a kappa of 0.77 (±0.10) and 0.78 (±0.12) for activity annotation and for food intake bout annotation, respectively. Results of one-way ANOVA suggest that there are no statistically significant differences among the average eating duration estimated from raters’ annotations and AIM predictions (p-value = 0.19). These results suggest that the AIM provides accuracy comparable to video observation and may be used to reliably detect food intake in multi-day observational studies.

PurposeMany patients are unknowingly living with chronic hyperglycemia, possibly due to low screening rates. We aimed to correlate detection of unidentified chronic hyperglycemia to practitioner reported rationale for conducting diabetes screening.MethodsPhysicians screened patients via a point-of-care A1C tests and recorded corresponding rationales. Elevated outcomes (A1C ≥ 5.7%) were correlated to recorded rationales, frequency of repeat screenings, documented diagnoses, and therapeutic actions taken as a result of elevated A1C.ResultsNearly one-half (45%) of selected patients were unknowingly living with chronic hyperglycemia, having an average A1C of 7.92% for outcomes ≥6.5%. Most commonly recorded rationales were overweight status (71%), high-risk ethnicity (58%), and age > 45 years (48%); previously recorded A1C result of ≥5.7% (χ2 16.02, p < 0.001) and hypertension diagnosis (χ2 10.37, p = 0.0013) showed statistically significant correlation with elevated A1C outcomes. A1C results ≥6.5% versus 5.7–6.5% more frequently prompted repeat screenings (77% vs 20%), ICD-10 code documentation (91% vs 28%), lifestyle modification recommendations (78% vs 35%), and drug therapy initiation (78% vs 9%).ConclusionsReported rationales were largely impacted by visual inspections of age, race, and weight, and prediabetic A1C values garnered less attention compared to higher values. Utilization of POC A1C screening followed by conformational repeat testing is a practical approach to improve diagnostic rates and initiation of care for diabetes.

Objective. To adapt a classroom assessment technique (CAT) from an anthropology course to a diabetes module in a clinical pharmacy skills laboratory and to determine student knowledge retention from baseline. Design. Diabetes item stems, focused on module objectives, replaced anthropology terms. Answer choices, coded to Bloom’s Taxonomy, were expanded to include higher-order thinking. Students completed the online 5-item probe 4 times: prelaboratory lecture, postlaboratory, and at 6 months and 12 months after laboratory. Statistical analyses utilized a single factor, repeated measures design using rank transformations of means with a Mann-Whitney-Wilcoxon test. Assessment. The CAT revealed a significant increase in knowledge from prelaboratory compared to all postlaboratory measurements (p<0.0001). Significant knowledge retention was maintained with basic terms, but declined with complex terms between 6 and 12 months. Conclusion. The anthropology assessment tool was effectively adapted using Bloom’s Taxonomy as a guide and, when used repeatedly, demonstrated knowledge retention. Minimal time was devoted to application of the probe making it an easily adaptable CAT.

Evidence suggests that eHealth tools adoption is associated with better health outcomes among various populations. The patterns and factors influencing eHealth adoption among the US Medicaid population remain obscure. The objective of this study is to explore patterns of eHealth tools adoption among the Medicaid population and examine factors associated with eHealth adoption. Data from the Health Information National Trends Survey from 2017 to 2019 were used to estimate the patterns of eHealth tools adoption among Medicaid and non-Medicaid populations. The effects of Medicaid insurance status and other influencing factors were assessed with logistic regression models. Compared with the non-Medicaid population, the Medicaid beneficiaries had significantly lower eHealth tools adoption rates for health information management (11.2% to 17.5% less) and mobile health for self-regulation (0.8% to 9.7% less). Conversely, the Medicaid population had significantly higher adoption rates for using social media for health information than their counterpart (8% higher in 2018, P=.01; 10.1% higher in 2019, P=.01). Internet access diversity, education, and cardiovascular diseases were positively associated with health information management and mobile health for self-regulation among the Medicaid population. Internet access diversity is the only factor significantly associated with social media adoption for acquisition of health information (OR 1.98, 95% CI 1.26-3.11). Our results suggest digital disparities in eHealth tools adoption between the Medicaid and non-Medicaid populations. Future research should investigate behavioral correlates and develop interventions to improve eHealth adoption and use among underserved communities.

: This exploratory study sought to elicit information from rural Baptist leaders about their interest in HIV prevention activities within their congregation and other influences in their human deficiency virus (HIV) prevention activities based on their geographical residence (urban vs rural). : This study utilized both qualitative (in-depth interviews, N = 8) and quantitative (written survey, N = 56) methodologies (mixed method) in order to obtain pertinent information. A ministerial liaison was hired to assist in recruitment of participants within a statewide Baptist conference. Written surveys were distributed at a statewide meeting. : The majority of participants (N = 50) in this study (89.3%) were receptive to conducting HIV/AIDS prevention activities within their congregations. The study also revealed rural/urban differences, including: interest in HIV/AIDS prevention, direct experiences with infected persons, or whether churches have a health-related ministry. Positive influences of HIV/AIDS prevention in rural church leaders included either the participant or their spouse being in a health-related occupation, migratory patterns from larger metropolitan areas in other areas of the country to the rural south, and whether the church has a health-related ministry. : Findings from this study are significant for a variety of reasons, including use of faith-based models for HIV/ AIDS capacity building and use of potential influencers on HIV/AIDS prevention in African Americans in the rural Deep South, where the epidemic is growing fastest. Future implications of this study might include expansion of faith-based models to include other denominations and health care providers as well of use of positive influencers to develop future HIV/AIDS intervention strategies.