The Effects of Deep Learning-Based Back Pain Management on Pain, Disability, Respiratory Muscle Strength, and Quality of Life in Patients with Low Back Pain

• 발행기관: 한국보건기초의학회
• 저자명: 박찬희 차영주
• 간행물 정보: 『한국보건기초의학회지』제17권 제1호, 63~74쪽, 전체 12쪽
• 주제분류: 의약학 > 면역학
• 파일형태: PDF
• 발행일자: 2024.06.30

국문 초록

Low back pain (LBP) impairment has a wide-ranging impact on both biological and psychosocial aspects. A variety of conventional LBP management strategies have been used broadly to improve in patients with LBP. This study designed to compare the therapeutic effects of artificial intelligence–based self-back management with application physical therapy (APT) and conventional physical therapy (CPT) on low back pain intensity, respiratory function, limited functional disability, and quality of life in 100 participants with LBP. A convenience sample of 100 participants (mean age 35.5 ± 8.8 years; 20 females) was participated and underwent either APT or CPT for 30 min per sessions, three times a week over a 4-week period. Statistical analyses comprised analysis of variance (ANOVA) to determine intervention-related changes in these outcome measurements. ANOVA revealed a positive effect on therapeutic outcome measures after 8 weeks of both APT and CPT. In conclusion, APT was a successful intervention for clinical outcome variables (Oswestry disability index, Roland-Morris disability questionnaire, Numeric pain rating scale, maximum respiratory pressure, and quality of life. This research compared the effects of APT and CPT in participants with LBP. We demonstrated that APT could afford superior improvements in pain, disabilioty and respiratory strength as well as quality of life than CPT in participants with low back pain.

목차

INTRODUCTION

METHODS

RESULTS

DISCUSSION

CONCLUSIONS

REFERENCES

키워드

Low back pain Deep learning Smartphone application