Teachers often utilize automatic program repair methods to provide feedback on submitted student code using model answer code. A state-of-the-art tool is Refactory, which achieves a high repair success rate and small patch size (less code repair) by refactoring code to expand the variety of correct code samples that can be referenced. However, Refactory has two major limitations. First, it cannot fix code with syntax errors. Second, it has difficulty fixing code when there are few correct submissions. Herein we propose a new method that combines Refactory and OpenAI's GPT models to address these issues and conduct a performance measurement experiment. The experiment uses a dataset consisting of 5 programming assignment problems and almost 1,800 real-life incorrect Python program submissions from 361 students for an introductory programming course at a large public university. The proposed method improves the repair success rate by 1-21% when the set of correct code samples is sufficient and the patch size is smaller than Refactory alone in 16-45% of the cases. When there was no set of correct code samples at all (only the model answer code was used as a reference for repair), method improves the repair success rate by 1-43% and the patch size is smaller than Refactory alone in 42-68% of the cases.

Promoting subjective well-being is a crucial challenge in aging societies. In 2022, we launched a community-based intervention trial (the Chofu-Digital-Choju Movement). This initiative centered on fostering in-person and online social connections to enhance the subjective well-being of older adults. This paper describes the study design and baseline survey. This quasi-experimental study involved community-dwelling older adults aged 65–84 years in Chofu City, Tokyo, Japan. A self-administered questionnaire was distributed to 3742 residents (1681 men and 2061 women), and a baseline survey was conducted in January 2022. We assessed subjective well-being (primary outcome); psychosocial, physical, and dietary factors; and the use of information and communication technology variables (secondary outcomes) among the participants. After the intervention involving online classes, community hubs, and community events, a 2-year follow-up survey will be conducted to evaluate the effects of the intervention, comparing the intervention group (participants) with the control group (non-participants). We received 2503 questionnaires (66.9% response rate); of these, the analysis included 2343 questionnaires (62.6% valid response rate; mean age, 74.4 (standard deviation, 5.4) years; 43.7% male). The mean subjective well-being score was 7.2 (standard deviation, 1.9). This study will contribute to the development of a prototype subjective well-being strategy for older adults.

Background/Objectives: Internet use positively impacts mental health in older adults, with health literacy (HL) playing a key role. While social networks may complement individual HL, the role of neighborhood relationships in this association, particularly by gender, remains unclear. This study examined how the association between HL and Internet use among older adults was modified by neighborhood relationships. Methods: Using baseline data from the Chofu–Digital–Choju project, a cross-sectional analysis was conducted on 1955 community-dwelling adults aged 65–84 (889 men and 1066 women). HL was assessed using the Communicative and Critical Health Literacy scale and dichotomized at four points. Neighborhood relationships were categorized as high (visiting/chatting with neighbors) or low (exchanging greetings/no relationship). Gender-stratified logistic regression analyses were performed with Internet use as the dependent variable, with HL, neighborhood relationships, and their interaction as independent variables. Results: Internet user proportion was 55.6% for men and 41.8% for women. HL was positively associated with Internet use in both genders, though patterns differed. Among men, the HL–Internet use association was consistent (OR = 3.09; 95% CI: 2.25–4.24) regardless of neighborhood relationship levels. For women, this association was significantly modified (interaction OR = 0.46, 95% CI: 0.24–0.87). Women with low HL but strong neighborhood relationships showed increased odds of Internet use (OR = 2.08, 95% CI: 1.32–3.26). Conclusions: Gender-specific patterns in HL and neighborhood relationships influence Internet use among older adults. Neighborhood relationships may compensate for low HL in women, underscoring the need for gender-sensitive strategies to promote digital HL.

Background/Objective: Frailty is a growing public health challenge in Japan’s rapidly aging population, where 28.8% are aged ≥ 65. While multicomponent interventions have shown potential in preventing frailty, traditional face-to-face programs face accessibility challenges. This study evaluated the feasibility and short-term changes of an online multicomponent intervention on frailty-related factors among community-dwelling older adults. Methods: In this single-arm feasibility study, 132 participants (mean age 75.7; standard deviation 4.8 years, 65.2% women) completed a six-week online intervention combining exercise, nutrition education, cognitive activities, and the Coimagination Method to foster social connections, meeting weekly for 75 min sessions in groups of up to 10 participants. Results: The intervention demonstrated feasibility with a 96.4% retention rate and a 94.0% average participation rate. While no significant changes were observed in physical activity levels, autonomic nervous system indicators, or cognitive function, carotenoid scores and hemoglobin concentration improved significantly, with more pronounced improvements among women than men. Conclusions: This study demonstrates the high feasibility of online multicomponent interventions for older adults and suggests potential benefits for nutritional status markers, particularly among women. These findings indicate a promising and accessible approach to frailty prevention, though randomized controlled trials with longer intervention periods and direct frailty assessments are required to establish effectiveness conclusively. Study Trial registration: UMIN Clinical Trials Registry (UMIN000053089).

Programming education for beginners often employs online judges. Although this helps improve coding skills, students may not obtain sufficient educational effects if the assignment is too difficult. Instead of presenting a model answer to an assignment, this paper proposes an approach to provide students with problems that have content and answer source code similar to the assignment. The effectiveness of our approach is evaluated via an intervention experiment in a university lecture course. The improvement in the number of correct answers is statistically significant compared to the same course offered in a different year without the proposed system. Therefore, the proposed approach should aid in the understanding of an assignment and enhance the educational effect.

Background/Objectives: Although information and communication technology (ICT) offers opportunities to address challenges, evidence among frail populations is limited. We aimed to evaluate the effectiveness and feasibility of an ICT-based intervention incorporating an artificial intelligence (AI)-assisted smartphone dietary application and group communication tools to improve dietary quality and social connection among community-dwelling older adults with frailty. Methods: A non-randomized, quasi-experimental study was conducted among 29 older adults (≥65 years) in Tokyo, Japan. Participants were assigned to the intervention (n = 11) or control (n = 18) group. The 3-month intervention included weekly photo uploads of meals via an AI-based dietary application providing automated image analysis and personalized feedback, supervised by registered dietitians, along with peer communication through a group chat. The primary outcome was dietary quality. The secondary outcomes included body weight, body mass index (BMI), skin carotenoid score, and loneliness. Results: The adjusted Japanese Food Guide Spinning Top Score at 3-month follow-up was 49.0 (standard error [SE] = 2.6) and 39.5 (SE = 2.0) in the intervention and control groups, respectively. The adjusted mean difference between groups was +9.5 (95% confidence interval: 2.3 to 16.7, p = 0.01). After using analysis of covariance for adjusting for respective baseline values, age, education status, and antihypertension drug use, no statistically significant between-group differences were observed at 3-month follow-up for any secondary outcomes. Conclusions: AI-based dietary intervention and peer communication effectively improved dietary quality among older adults, highlighting the potential of such an intervention to promote healthier eating habits in this population.