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.
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.
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.
Background: During a pandemic, non-pharmaceutical interventions (NPIs) play an important role in protecting oneself from infection and preventing the spread of infection to others. 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. Data from a total of 27,036 participants were analyzed. The questionnaire asked the respondents to identify their habits regarding seven well-known NPIs. Results: Compared to the region with the lowest infection rate, the odds ratios for the region with the highest infection rate were 1.24 (p<0.001) for wearing a mask in public, 1.08 (p=0.157) for washing hands after using the bathroom, 1.17 (p=0.031) for disinfecting hands with alcohol sanitizers when entering indoors, 1.54 (p<0.001) for gargling when returning home, 1.45 (p<0.001) for ventilating the room, 1.33 (p<0.001) for disinfecting or washing hands after touching frequently touched surfaces, and 1.32 (p<0.001) for carrying alcohol sanitizers when outdoors. Five of the seven NPIs showed statistically significant trends across regional infection levels, the two exceptions being wearing a mask in public and washing hands after using 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.
BACKGROUND Musculoskeletal symptoms, such as neck and shoulder pain and 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). However, current medical systems do not spare sufficient resources for non-specific musculoskeletal problems. OBJECTIVE This study aimed to evaluate the improvements in musculoskeletal symptoms after 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). METHODS We conducted a two-armed, randomized, controlled, and unblinded trial in workers with neck/shoulder stiffness and/or low back pain. We recruited participants with these symptoms through email notifications. We obtained 48 participants in the intervention group and 46 in the control group. 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 smart phone’s chatting app (LINE) for 12 weeks. The exercises could be performed within 1 minute. The control group continued with their usual care routines, which included exercising for 3 minutes at recess time provided by the company to prevent stiff shoulders and back pain. We assessed the subjective severities of the neck and shoulder pain/stiffness and low back pain in participants using a scoring scale of 1 to 5 for both the intervention and the control group at baseline and after 12 weeks of intervention using an online form. RESULTS We analyzed 47 patients in the intervention group and 40 in the control group. The participants in the intervention group showed significant improvements in the severities of the neck/shoulder pain/stiffness and low back pain compared to those in the control group (OR 12.74, P <.001). Based on the subjective assessment of the improvement of the pain/stiffness at 12 weeks, 36 (77%) participants in the intervention group and 3 (8%) in the control group had improved (improved, slightly improved) (OR 54.23, P <.001). CONCLUSIONS This study showed that the short exercises provided by the AI-assisted health program improved both neck/shoulder pain/stiffness and low back pain in 12 weeks. Digital health programs are low cost and safe and can save experts’ working hours and labor costs. Further studies are needed to identify the elements of the AI-assisted health program that worked. CLINICALTRIAL 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.
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