Community-wide physical activity intervention based on the Japanese physical activity guidelines for adults: A non-randomized controlled trial.

Very few community intervention studies that promote physical activity (PA) using guidelines and its dissemination and implementation have been conducted. Consequently, we evaluated the effectiveness of a community-wide intervention (CWI) of PA with adults based on the Japanese guidelines for promoting PA. This was a non-randomized controlled trial, with four administrative districts in Fujisawa city assigned to the intervention group and nine to the control group. The CWI, conducted from 2013 to 2015, comprised information dissemination, education, and community support. The primary outcome was change in PA participation. Secondary outcomes were CWI awareness and PA guideline knowledge. Outcomes were assessed using questionnaires distributed to two independent, random samples of 3000 community-based adults (aged ≥20years). Two separate samples-1230 adults at baseline and 1393 at the two-year follow-up-responded to the survey. The median time spent in PA did not differ between intervention and control groups after adjusting for potential confounders (adjusted difference between groups=-0.02min/day [95% confidence interval (CI): -0.11, 0.10]). However, intervention group participants were more aware of the CWI (33.8%) than were control group participants (25.2%) at the two-year follow-up (odds ratio=1.44 [95% CI: 1.06, 1.95]). A significant difference was also observed in participants' PA guideline knowledge (adjusted difference between groups=0.82% [95% CI: 0.33, 1.31]). Although significant differences in awareness and knowledge were observed between groups, this CWI did not change PA levels over two years. Future studies should investigate the long-term effects of CWIs beyond two years. TRIAL REGISTRATION NUMBER: UMIN-CTR UMIN000018389.

Using the Natural Language Processing System Medical Named Entity Recognition-Japanese to Analyze Pharmaceutical Care Records: Natural Language Processing Analysis.

BACKGROUND: Large language models have propelled recent advances in artificial intelligence technology, facilitating the extraction of medical information from unstructured data such as medical records. Although named entity recognition (NER) is used to extract data from physicians' records, it has yet to be widely applied to pharmaceutical care records. OBJECTIVE: In this study, we aimed to investigate the feasibility of automatic extraction of the information regarding patients' diseases and symptoms from pharmaceutical care records. The verification was performed using Medical Named Entity Recognition-Japanese (MedNER-J), a Japanese disease-extraction system designed for physicians' records. METHODS: MedNER-J was applied to subjective, objective, assessment, and plan data from the care records of 49 patients who received cefazolin sodium injection at Keio University Hospital between April 2018 and March 2019. The performance of MedNER-J was evaluated in terms of precision, recall, and F1-score. RESULTS: The F1-scores of NER for subjective, objective, assessment, and plan data were 0.46, 0.70, 0.76, and 0.35, respectively. In NER and positive-negative classification, the F1-scores were 0.28, 0.39, 0.64, and 0.077, respectively. The F1-scores of NER for objective (0.70) and assessment data (0.76) were higher than those for subjective and plan data, which supported the superiority of NER performance for objective and assessment data. This might be because objective and assessment data contained many technical terms, similar to the training data for MedNER-J. Meanwhile, the F1-score of NER and positive-negative classification was high for assessment data alone (F1-score=0.64), which was attributed to the similarity of its description format and contents to those of the training data. CONCLUSIONS: MedNER-J successfully read pharmaceutical care records and showed the best performance for assessment data. However, challenges remain in analyzing records other than assessment data. Therefore, it will be necessary to reinforce the training data for subjective data in order to apply the system to pharmaceutical care records.