Yaguang Zheng

Abstract

Sharing research code in an open access version-controlled repository offers significant benefits for both science as a whole and for individual researchers. In this article, we focus on this practice, which is fully aligned with the NIH's Gold Standard Science (GSS) program as well as FAIR (findable, accessible, interoperable, reusable) and TRUST (transparency, responsibility, user focus, sustainability, technology) principles. Gold Standard Science supports open science by emphasizing transparency, reproducibility, and the use of best practices that enable others to verify and extend research. Pairing a research article's cited data snapshot with a versioned, environment-specific code release, deposited in a companion code repository, ensures that, upon submission to a medical journal, readers and reviewers can directly verify results. An executable and updatable companion code repository complements, rather than replaces, established research data repositories. When code underlying medical research results is made openly available, then other scientists can inspect, run, and validate analyses. These activities enhance reproducibility, which is a core aim of GSS. Shared code also facilitates collaborative innovation by allowing researchers to extend the utility of the code to new datasets and applications. For researchers, code sharing can increase visibility, credibility, and citation impact. Demonstrating transparency through shared executable and updatable code builds trust with journal readers, peer reviewers, funders, and peers. Shared code in an open access repository signals adherence to high standards of scientific integrity and attracts opportunities for collaboration. A researcher who shares code receives recognition as a leader in reproducible, trustworthy research consistent with NIH's GSS principles.

Abstract

BACKGROUND: Artificial intelligence (AI) has potential in promoting and supporting self-management in patients with chronic conditions. However, the development and application of current AI technologies to meet patients' needs and improve their performance in chronic condition self-management tasks remain poorly understood. It is crucial to gather comprehensive information to guide the development and selection of effective AI solutions tailored for self-management in patients with chronic conditions.OBJECTIVE: This scoping review aimed to provide a comprehensive overview of AI applications for chronic condition self-management based on 3 essential self-management tasks, medical, behavioral, and emotional self-management, and to identify the current developmental stages and knowledge gaps of AI applications for chronic condition self-management.METHODS: A literature review was conducted for studies published in English between January 2011 and October 2024. In total, 4 databases, including PubMed, Web of Science, CINAHL, and PsycINFO, were searched using combined terms related to self-management and AI. The inclusion criteria included studies focused on the adult population with any type of chronic condition and AI technologies supporting self-management. This review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.RESULTS: Of the 1873 articles retrieved from the search, 66 (3.5%) were eligible and included in this review. The most studied chronic condition was diabetes (20/66, 30%). Regarding self-management tasks, most studies aimed to support medical (45/66, 68%) or behavioral self-management (27/66, 41%), and fewer studies focused on emotional self-management (14/66, 21%). Conversational AI (21/66, 32%) and multiple machine learning algorithms (16/66, 24%) were the most used AI technologies. However, most AI technologies remained in the algorithm development (25/66, 38%) or early feasibility testing stages (25/66, 38%).CONCLUSIONS: A variety of AI technologies have been developed and applied in chronic condition self-management, primarily for medication, symptoms, and lifestyle self-management. Fewer AI technologies were developed for emotional self-management tasks, and most AIs remained in the early developmental stages. More research is needed to generate evidence for integrating AI into chronic condition self-management to obtain optimal health outcomes.

Abstract

Background: Clinical use of continuous glucose monitoring (CGM) is increasing storage of CGM-related documents in electronic health records (EHR); however, the standardization of CGM storage is lacking. We aimed to evaluate the sensitivity and specificity of CGM Ambulatory Glucose Profile (AGP) classification criteria. Methods: We randomly chose 2244 (18.1%) documents from NYU Langone Health. Our document classification algorithm: (1) separated multiple-page documents into a single-page image; (2) rotated all pages into an upright orientation; (3) determined types of devices using optical character recognition; and (4) tested for the presence of particular keywords in the text. Two experts in using CGM for research and clinical practice conducted an independent manual review of 62 (2.8%) reports. We calculated sensitivity (correct classification of CGM AGP report) and specificity (correct classification of non-CGM report) by comparing the classification algorithm against manual review. Results: Among 2244 documents, 1040 (46.5%) were classified as CGM AGP reports (43.3% FreeStyle Libre and 56.7% Dexcom), 1170 (52.1%) non-CGM reports (eg, progress notes, CGM request forms, or physician letters), and 34 (1.5%) uncertain documents. The agreement for the evaluation of the documents between the two experts was 100% for sensitivity and 98.4% for specificity. When comparing the classification result between the algorithm and manual review, the sensitivity and specificity were 95.0% and 91.7%. Conclusion: Nearly half of CGM-related documents were AGP reports, which are useful for clinical practice and diabetes research; however, the remaining half are other clinical documents. Future work needs to standardize the storage of CGM-related documents in the EHR.

Abstract

Diabetes is common among nursing home residents, with approximately one in four affected, a figure expected to rise. Despite the complexity of care required, educational support for nursing home staff remains limited. This study will aim to co-design and evaluate a digital intervention to improve staff knowledge, confidence, and practices in diabetes care. The study will follow a logic model across three workstreams. Workstream 1 (WS1) will inform the model inputs through three phases: (1) a scoping review will be conducted to summarise existing diabetes education initiatives in nursing home settings; (2) approximately 20 semi-structured interviews will be carried out with nursing home staff to explore perceived barriers and supports in delivering diabetes care; and (3) a modified Delphi process involving 50-70 diverse stakeholders will be used to establish educational priorities. Workstream 2 (WS2) will involve co-designing a digital diabetes education intervention, informed by WS1 findings. Co-design participants will include nursing home staff, diabetes professionals, and people living with diabetes or their carers. Workstream 3 (WS3) will consist of a mixed-methods evaluation of the intervention. Pre- and post-intervention questionnaires will assess staff knowledge, confidence, and attitudes. The usability of the intervention will also be measured. Following implementation, focus groups with approximately 32 staff members will be conducted to explore user experiences and perceived impact on resident care. This study will address an important gap in staff education and support, aiming to improve diabetes care within nursing home settings through a digitally delivered, co-designed intervention.

Abstract

BACKGROUND: Despite evidence supporting the efficacy of culturally tailored interventions in reducing stigma, such approaches are lacking for women living with HIV/AIDS (WLWHAs) in China. We conducted this study to determine the efficacy of the culturally tailored Helping Overcome Perceived Stigma (HOPES) intervention in reducing perceived stigma among WLWHAs in China.METHODS: A single-blinded, two-arm parallel-group randomized clinical trial was conducted from 2023 to 2024 in South and Southwest China. WLWHAs from four hospitals were assigned using a WeChat-embedded randomization application to the control group (usual care) or the HOPES intervention. Data analysts remained blinded. Interventions were conducted virtually using Leave No One Behind (LNOB) platform for 3 months. The primary outcome, perceived stigma score, was assessed at baseline, immediately after the intervention and at 3 months post-intervention using 7 items from the HIV/AIDS Stigma Experience Questionnaire (HASEQ), with data analyzed through repeated measures analysis.RESULTS: Of 136 WLWHAs screened, we randomized 101 WLWHAs (50 HOPES; 51 controls). The HOPES group demonstrated a statistically significant reduction in perceived stigma scores immediately after the intervention (-3.86 points, 95 % CI: 5.34 to -2.38, P < .001) and at three months post-intervention (-5.83 points, 95 % CI: 7.20 to -4.47, P < .001) compared to the control group.CONCLUSION: The findings demonstrate HOPES' efficacy in reducing perceived stigma in WLWHA. However, the clinical significance of these changes needs further investigation. Future research should focus on defining meaningful patient-reported thresholds, assessing long-term impact, and optimizing delivery methods.