Effects of an Artificial Intelligence-Assisted Health Program on Workers With Neck/Shoulder Pain/Stiffness and Low Back Pain : Randomized Controlled Trial
©Tomomi Anan, Shigeyuki Kajiki, Hiroyuki Oka, Tomoko Fujii, Kayo Kawamata, Koji Mori, Ko Matsudaira. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 24.09.2021..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:9 |
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| Enthalten in: |
JMIR mHealth and uHealth - 9(2021), 9 vom: 24. Sept., Seite e27535 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Anan, Tomomi [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 01.11.2021 Date Revised 02.11.2021 published: Electronic Citation Status MEDLINE |
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| doi: |
10.2196/27535 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM331007398 |
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| 100 | 1 | |a Anan, Tomomi |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Effects of an Artificial Intelligence-Assisted Health Program on Workers With Neck/Shoulder Pain/Stiffness and Low Back Pain |b Randomized Controlled Trial |
| 264 | 1 | |c 2021 | |
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| 500 | |a Date Completed 01.11.2021 | ||
| 500 | |a Date Revised 02.11.2021 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a ©Tomomi Anan, Shigeyuki Kajiki, Hiroyuki Oka, Tomoko Fujii, Kayo Kawamata, Koji Mori, Ko Matsudaira. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 24.09.2021. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Randomized Controlled Trial | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a digital health | |
| 650 | 4 | |a digital intervention | |
| 650 | 4 | |a eHealth | |
| 650 | 4 | |a low back pain | |
| 650 | 4 | |a mHealth | |
| 650 | 4 | |a mobile app | |
| 650 | 4 | |a mobile phone | |
| 650 | 4 | |a musculoskeletal symptoms | |
| 650 | 4 | |a neck pain | |
| 650 | 4 | |a shoulder pain | |
| 650 | 4 | |a shoulder stiffness | |
| 700 | 1 | |a Kajiki, Shigeyuki |e verfasserin |4 aut | |
| 700 | 1 | |a Oka, Hiroyuki |e verfasserin |4 aut | |
| 700 | 1 | |a Fujii, Tomoko |e verfasserin |4 aut | |
| 700 | 1 | |a Kawamata, Kayo |e verfasserin |4 aut | |
| 700 | 1 | |a Mori, Koji |e verfasserin |4 aut | |
| 700 | 1 | |a Matsudaira, Ko |e verfasserin |4 aut | |
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