Association between COVID-19 infection rates by region and implementation of non-pharmaceutical interventions: a cross-sectional study in Japan.

BACKGROUND: During a pandemic, non-pharmaceutical interventions (NPIs) play an important role in protecting oneself and others from infection. There are large regional differences in COVID-19 infection rates in Japan. We hypothesized that the local infection incidence may affect adherence to individual NPIs. METHODS: This cross-sectional study was conducted online among full-time workers in Japan in December 2020. The questionnaire asked the respondents to identify their habits regarding seven common NPIs (wearing masks, washing hands after the bathroom, disinfecting hands when entering indoors, gargling when returning home, ventilating the room, disinfecting or washing hands after touching frequently touched surfaces, carrying alcohol sanitizers when outdoors). RESULTS: A total of 27 036 participants were analyzed. Compared with the region with the lowest infection rate, five of the seven NPIs showed statistically significant trends across regional infection levels, the two exceptions being wearing masks and washing hands after the bathroom. Multivariate adjustment did not change these trends. CONCLUSIONS: This study found that NPIs were more prevalent in regions with higher incidence rates of COVID-19 in Japanese workers. The findings suggest that the implementation of NPIs was influenced not only by personal attributes but also by contextual effects of the local infection level.

Changes in Presenteeism Six Months After Returning from Sick Leave Due to Mental Illness.

This study examines how psychological distress (measured by the K10 screening test) and presenteeism (measured by the quality and quantity method) change in the six months after returning to work from having taken a sick leave because of a mental illness. In a manufacturing company with approximately 2,600 employees, 23 employees returned to work after experiencing mental illness between April 2015 and March 2016, and all 23 agreed to participate in the study. We analyzed 18 cases for which we had sufficient data. Two of the employees were absent from work in the sixth month. We performed multilevel analysis for K10 and presenteeism over time on the 16 without recurrence. A significant decreasing trend was observed for both K10 and presenteeism. Eleven of the 16 employees were consistently below the K10 cutoff value of 10 for six months, and 5 had zero presenteeism in the sixth month, whereas 6 employees showed improvement in presenteeism that stopped midway through the study. An occupational physician judged that the employees could work normally with presenteeism of zero. After returning to work, it is important to monitor not only psychiatric symptoms but also presenteeism.

Effects of an Artificial Intelligence-Assisted Health Program on Workers With Neck/Shoulder Pain/Stiffness and Low Back Pain: Randomized Controlled Trial.

BACKGROUND: Musculoskeletal symptoms such as neck and shoulder pain/stiffness and low back pain are common health problems in the working population. They are the leading causes of presenteeism (employees being physically present at work but unable to be fully engaged). Recently, digital interventions have begun to be used to manage health but their effectiveness has not yet been fully verified, and adherence to such programs is always a problem. OBJECTIVE: This study aimed to evaluate the improvements in musculoskeletal symptoms in workers with neck/shoulder stiffness/pain and low back pain after the use of an exercise-based artificial intelligence (AI)-assisted interactive health promotion system that operates through a mobile messaging app (the AI-assisted health program). We expected that this program would support participants' adherence to exercises. METHODS: We conducted a two-armed, randomized, controlled, and unblinded trial in workers with either neck/shoulder stiffness/pain or low back pain or both. We recruited participants with these symptoms through email notifications. The intervention group received the AI-assisted health program, in which the chatbot sent messages to users with the exercise instructions at a fixed time every day through the smartphone's chatting app (LINE) for 12 weeks. The program was fully automated. The control group continued with their usual care routines. We assessed the subjective severity of the neck and shoulder pain/stiffness and low back pain of the participants by using a scoring scale of 1 to 5 for both the intervention group and the control group at baseline and after 12 weeks of intervention by using a web-based form. We used a logistic regression model to calculate the odds ratios (ORs) of the intervention group to achieve to reduce pain scores with those of the control group, and the ORs of the subjective assessment of the improvement of the symptoms compared to the intervention and control groups, which were performed using Stata software (version 16, StataCorp LLC). RESULTS: We analyzed 48 participants in the intervention group and 46 participants in the control group. The adherence rate was 92% (44/48) during the intervention. The participants in the intervention group showed significant improvements in the severity of the neck/shoulder pain/stiffness and low back pain compared to those in the control group (OR 6.36, 95% CI 2.57-15.73; P<.001). Based on the subjective assessment of the improvement of the pain/stiffness at 12 weeks, 36 (75%) out of 48 participants in the intervention group and 3 (7%) out of 46 participants in the control group showed improvements (improved, slightly improved) (OR 43.00, 95% CI 11.25-164.28; P<.001). CONCLUSIONS: This study shows that the short exercises provided by the AI-assisted health program improved both neck/shoulder pain/stiffness and low back pain in 12 weeks. Further studies are needed to identify the elements contributing to the successful outcome of the AI-assisted health program. TRIAL REGISTRATION: University hospital Medical Information Network-Clinical Trials Registry (UMIN-CTR) 000033894; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000038307.

Emerging Occupational Health Needs at a Semiconductor Factory Following the 2016 Kumamoto Earthquakes: Evaluation of Effectiveness and Necessary Improvements of List of Postdisaster Occupational Health Needs.

OBJECTIVES: We sought to share our experiences with occupational health efforts at a manufacturing site after a large earthquake event, and to evaluate the effectiveness of our "List of Occupational Health Needs Following Disasters" (Needs List). METHODS: We implemented occupational health efforts following the 2016 Kumamoto earthquakes in Japan, with reference to the Needs List and a related manual, and analyzed the needs that arose. RESULTS: The instruments used enabled occupational health personnel to predict what needs would arise and facilitate their countermeasures. Among such needs that arose, 87.7% were in the Needs List, but seven undocumented needs were also observed. CONCLUSION: We confirmed that the Needs List could predict as many occupational health needs as we had expected at the disaster, though it is necessary to improve them by reflecting on our experiences.