Background: Diabetic retinopathy (DR) is a leading cause of blindness globally. DR has increasingly affected both individuals and health care systems as the population ages. Objective: This study aims to explore factors and identify barriers associated with nonadherence to referral recommendations among older adult participants after DR screening (DRS) during the COVID-19 pandemic. Method: This paper presents findings from a pilot study on artificial intelligence–enabled DRS conducted in two districts in Punjab, India (Moga and Mohali) during the COVID-19 pandemic. The screenings were conducted from March to June 2022 at community health center Badhani Kalan in Moga and from March to June 2021 in community settings (homes) in Block Boothgarh, Mohali. Participants were referred to the district hospital for an ophthalmological review based on artificial intelligence–enabled screening. After 1 month, the participants were contacted by telephone to assess adherence to the referral recommendations. Participants who did not adhere to the referral were then interviewed alongside health care providers to understand the barriers explaining their nonadherence. Results: We aimed to recruit 346 and 600 older adult participants from 2 sites but enrolled 390. Key challenges included health facility closures due to COVID-19, low motivation among health personnel for recruitment, incomplete nonparticipation data, and high participant workloads. Approximately 45% of the participants were male and 55% female. Most participants (62.6%) were between 60 and 69 years old, while 37.4% were 70 or older, with a mean age of 67.2 (SD 6.2) years. In total, 159 participants (40.8%) were referred, while 231 participants (59.2%) were not. Only 23 (14.5%) of those referred followed through and visited a health facility for ophthalmological review, while 136 (85.5%) did not pursue further evaluation. Our analysis revealed no significant differences in the characteristics between adherent and nonadherent participants, suggesting that demographic and health factors alone do not predict adherence behavior in patients with DR. Interviews identified limited knowledge about DR, logistical challenges, financial constraints, and attitudinal barriers as the primary challenges. Conclusions: This study, conducted during the COVID-19 pandemic, showed suboptimal adherence to referral recommendations among older adult patients due to knowledge gaps, logistical challenges, and health system issues. Quantifying and understanding adherence factors are crucial for targeted interventions addressing barriers to referral recommendations after DRS. Integrating teleophthalmology into and strengthening infrastructure for artificial intelligence–enabled diabetic retinopathy screening to enhance access and outcomes.

Objectives To assess and compare the diagnostic accuracy of non-ophthalmologist-led diabetic retinopathy screening (DRS) at health and wellness centres (HWCs) and offline artificial intelligence (AI)-assisted community-based screening, using specialist grading as the reference standard in India. Design, settings and participants Pragmatic diagnostic accuracy study in primary healthcare settings. The settings included HWCs and community-based screening sites in rural Block Boothgarh, Mohali District, Punjab, India. A total of 600 people with diabetes aged ≥30 years were enrolled across three screening models: (1) non-ophthalmologist-led DRS at the HWC, (2) AI-assisted smartphone-based DRS in the community and (3) standard referral-based care. Retinal images were captured using non-mydriatic fundus cameras and independently graded by two masked human graders; a senior retina specialist resolved any disagreements. The AI was assessed for its ability to detect diabetic retinopathy (DR) and referable diabetic retinopathy (RDR). Diagnostic performance metrics were reported. Results The non-ophthalmologist-led model demonstrated 86.4% sensitivity (95% CI 65.1% to 97.1%) and 94.3% specificity (95% CI 88.5% to 97.7%) for DR detection, with an ungradability rate of 8%. For RDR, sensitivity reached 95.8% (95% CI 78.9% to 99.9%) and specificity was 93.1% (95% CI 88.0% to 96.5%). The offline AI-assisted model achieved 93.3% sensitivity (95% CI 68.1% to 99.8%) and 85.1% specificity (95% CI 76.9% to 91.2%) for RDR, but with a higher ungradability rate (38%), mainly due to cataracts and poor image quality. Both approaches effectively identified referable cases; however, the non-ophthalmologist-led model demonstrated greater accuracy and operational feasibility. Conclusions This study demonstrates that non-ophthalmologist-led DRS at HWCs can enhance access to primary care. Offline AI-enabled screening demonstrates potential for community use but is currently limited by image quality and binary classification outputs. Integrating both approaches may strengthen DRS coverage in resource-limited settings. Clinical trials registry of India CTRI/2022/10/046283.

Introduction: Settings with insufficient human resources struggle to provide timely eye care services and information to the population. mHealth (mobile healthcare) is a promising solution; however, evidence on the effectiveness of interactive voice response (IVR) and real-time phone-based education remains scarce, despite their potential to be scalable and cost-effective. This study aims to implement the Virtual Baithak , an interactive mHealth platform, to improve eye-health literacy among older adults residing in rural India. The objectives are to (1) Develop and validate the Virtual Baithak for improving vision health and (2) Determine its effectiveness, feasibility and acceptability among the older adults. Methods and analysis: This 3-armed, parallel, randomised controlled trial of 14 months duration will enrol 381 older adults (aged 60 years and above). Participants will be blinded and randomly (computer-generated) assigned to either of the three groups based on the intervention for eye-health education they receive: both IVR and group calls moderated by a healthcare professional, only IVR and usual care. The two intervention arms will receive the information weekly over a 3-month period through the Virtual Baithak platform, which will be designed for this study using a participatory research approach to develop the content. The primary study outcomes are digital health literacy and vision health knowledge scores, measured at baseline and 14 months. The secondary outcomes include m-health technology acceptance and usage practices. A mixed-method process evaluation will be conducted to assess the intervention feasibility and implementation, including in-depth interviews with participants. The qualitative data will be thematically analysed to explore factors that promote or restrain the implementation. The inferential statistical quantitative analysis will be performed using linear mixed models. Ethics and dissemination: The study has been approved by the ‘Institute Ethics Committee,’ PGIMER, Chandigarh, India (PGI/IEC/2022/EIC000282 dated 18 February 2022). The results will be disseminated via presentations and/or publications at the national and international levels. Trial registration number: CTRI/2023/02/049383, dated 1 February 2023.