Abstract
BACKGROUND: Low Back Pain (LBP) is a common health problem worldwide. In recent years, the use of mobile applications for the treatment of various diseases has increased, due to the Corona pandemic. OBJECTIVE: The aim of this study is to investigate the extent to which artificial intelligence (AI)-assisted exercise recommendations can reduce pain and pain-related impairments in daily life for patients with LBP, compared to standard care. METHODS: To answer the research question, an 8-week app-based exercise program was conducted in the intervention group. To measure the influence of the exercise program, pain development and pain-related impairment in daily life have been evaluated. A so-called rehabilitation sports group served as the control group. The main factors for statistical analysis were factor time and group comparison. For statistical calculations, a mixed analysis of variance for pain development was conducted. A separate check for confounders was made. For pain impairment in daily life nonparametric tests with the mean of change between the time points are conducted. RESULTS: The intervention group showed a reduction in pain development of 1.4 points compared to an increase of 0.1 points in the control group on the numeric rating scale. There is a significant interaction of time and group for pain development. Regarding pain-related impairments in daily life, the intervention group has a reduction of the oswestry disability index scores by 3.8 points compared to an increase of 2.3 in the control group. The biggest differences become apparent 8 weeks after the start of treatment. The significant results have a medium to strong effect. CONCLUSION: The results shown here suggest that the use of digital AI-based exercise recommendations in patients with LBP leads to pain reduction and a reduction in pain-related impairments in daily living compared to traditional group exercise therapy.