Background: Exercise therapy is strongly recommended as a treatment for chronic nonspecific low back pain (CNSLBP). However, therapist-guided exercise therapy requires significant medical resources. Ordinary digital telerehabilitation affects efficacy due to a lack of guidance and dynamic support. Artificial intelligence (AI)–assisted interactive health promotion systems may solve these problems. Objective: We aimed to explore whether AI-assisted multimodal exercise telerehabilitation is superior to conventional telerehabilitation in the treatment of people with CNSLBP. Methods: This study was a prospective, double-arm, open-label, randomized clinical controlled trial. People with CNSLBP were randomly allocated to either the AI or video group, receiving AI-assisted multimodal exercise therapy or conventional video guidance, respectively, via a WeChat application add-in. The multimodal exercise consisted of deep core muscle, flexibility, Mackenzie, and breathing exercises. The exercises were performed for 30‐45 minutes per session, 3 times a week, for 4 weeks. Participants underwent face-to-face assessment at baseline and week 4, and web-based assessment at weeks 2 and 8. The primary outcome was the change in Numerical Rating Scale (NRS) relative to baseline at week 4. Secondary outcomes included changes in the Roland-Morris Disability Questionnaire (RMDQ), Oswestry Disability Index (ODI), Pain Castastrophizing Scale (PCS), Timed Up-and-Go (TUG) test, and thickness of the transverse abdominus (TrA) and multifidus (MF) muscles relative to baseline at week 4. Generalized estimating equation and covariance were used to examine the efficacy of the interventions. Results: A total of 38 participants (19 participants per group) were recruited. Eighteen participants in the AI group and 16 participants in the video group completed and were included in the final analysis. There was a significant difference in NRS at week 4 between the AI group and video group (most severe NRS: −3.00 vs −1.50; adjusted mean difference −1.08, 95% CI −1.68 to −0.49; P<.001; mean NRS: −2.61 vs −1.62; adjusted mean difference −0.67, 95% CI −1.19 to −0.15; P=.01). The difference in most severe NRS persisted until week 8 (−3.06 vs −1.69; adjusted mean difference −0.95, 95% CI −1.73 to −0.18; P=.02). Compared with the video group at week 4, the AI group showed significant improvement in secondary outcomes, including RMDQ, PCS, and core muscle thickness of left TrA, right TrA, left MF, and right MF. Conclusions: We showed that 4 weeks of telerehabilitation based on AI-assisted multimodal exercise has better therapeutic effects compared to conventional exercise telerehabilitation in people with CNSLBP. This study provides guidance for developing effective real-time home-based exercise therapies for people with CNSLBP, which may help reduce economic and human resource costs associated with treatment.

Background: Hypertension remains a major global health challenge, significantly increasing cardiovascular and all-cause mortality risks. While exercise therapy is effective, conventional approaches face limitations in accessibility and personalization, compromising adherence. Artificial intelligence (AI)–assisted remote rehabilitation enables real-time monitoring and personalized guidance, offering a promising alternative. Nevertheless, its clinical benefits and applicability require further systematic validation. Objective: This study aimed to evaluate the efficacy of an 8-week AI-assisted telerehabilitation program on improving exercise capacity and related health outcomes in patients with hypertension. Methods: This prospective, dual-arm, parallel, open-label, randomized controlled trial enrolled 62 patients with hypertension recruited via convenience sampling. Participants were adults aged between 18 and 75 years with a confirmed hypertension diagnosis who were excluded for severe cardiac complications, recent myocardial infarction, unstable angina, or physical disabilities preventing exercise. The participants were randomly assigned (1:1) to an intervention group that received AI-assisted remote rehabilitation plus routine health education, or a control group that received health education and conventional offline exercise guidance. The supervised exercise program included warm-up, cardiorespiratory endurance, strength resistance, balance, and flexibility training, followed by a cooldown. Sessions lasted between 30 and 50 minutes and were performed at least 3 times weekly for 8 weeks. Assessments at baseline and 8 weeks included the 6-minute walk test (6MWT), cardiopulmonary exercise testing (CPET), International Physical Activity Questionnaire (IPAQ), Short-Form Health Survey 12 (SF-12), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), exercise self-efficacy, blood pressure (BP), body weight, handgrip strength, and other health-related indicators. The primary outcome was the change in 6-minute walk distance (6MWD). Data were analyzed according to the intention-to-treat principle. Results: Throughout the 8-week intervention period, no serious adverse events related to the AI-assisted telerehabilitation intervention occurred. After 8 weeks, the intervention group demonstrated significantly greater improvements than the control group in 6-minute walk distance (6MWD; adjusted mean difference 62.77, 95% CI 26.33-99.22; P=.002), systolic BP reduction (adjusted mean difference 4.11, 95% CI 0.11-8.28; P=.046), IPAQ score (adjusted mean difference 658.96, 95% CI 159.23-1158.69; P=.011), exercise self-efficacy score (adjusted mean difference 21.71, 95% CI 13.59-29.82; P<.001), total exercise time (adjusted mean difference 98.24, 95% CI 49.39-147.08; P=.001) peak oxygen uptake (peak VO2) (adjusted mean difference 3.39, 95% CI 0.49-6.29; P=.026), and peak oxygen uptake percent predicted (peak VO2%pred) (adjusted mean difference 11.58, 95% CI 2.06-21.10; P=.021). Conclusions: Compared with conventional exercise rehabilitation, AI-assisted remote rehabilitation was found to improve exercise capacity, boost regular physical activity and exercise self-efficacy, and aid in systolic BP control among patients with hypertension. This study positioned AI-assisted rehabilitation as a scalable and effective strategy for real-world hypertension management. It further contributes actionable guidance for developing effective home-based exercise strategies tailored to populations with hypertension. Trial Registration: Chinese Clinical Trial Registry ChiCTR2300076451; https://www.chictr.org.cn/showproj.html?proj=208353

Background: Alzheimer's disease (AD) pathology is increasingly linked to metabolic disturbances induced by high‐fat diets (HFD), with both autophagy‐lysosomal pathway (ALP) dysfunction and activation of the NLRP3 inflammasome emerging as key pathological mechanisms. This study introduces a novel approach using electroacupuncture (EA) to mitigate HFD‐accelerated AD pathology, focusing on the regulatory role of transcription factors TFEB and TFE3 in ALP and their potential modulation by EA.Methods: 3xTg‐AD mice were fed either an HFD or a standard diet for 16 weeks, followed by EA treatment at the ST36 acupoint. AAV‐mediated hippocampal knockdown of TFEB and TFE3 was performed using shRNA constructs. Cognitive performance was evaluated with the Morris water maze (MWM) and Y‐maze tests. Molecular analyses, including immunoblotting and immunohistochemistry, were conducted to examine the impact of EA on NLRP3 inflammasome activation, ALP dynamics, and the involvement of TFEB and TFE3 in modulating these pathways within the brain.Results: HFD feeding significantly exacerbated Tau pathology and NLRP3 inflammasome activation in 3xTg‐AD mice, leading to pronounced cognitive deficits. Notably, EA‐ST36 intervention reversed these pathological effects, enhancing memory function and reducing both Tau aggregation and NLRP3 inflammasome activation. Mechanistic insights revealed that EA‐ST36 activated TFEB and TFE3 by modulating the MTOR/ULK1 signaling pathway, thereby enhancing autophagic clearance of NLRP3 inflammasome components and Tau aggregates.Conclusion: This study proposes EA‐ST36 as a promising therapeutic strategy for counteracting HFD‐induced AD pathology by activating TFEB/TFE3‐driven autophagic pathways, thereby enhancing the clearance of toxic protein aggregates and inflammasome components. These findings advance our understanding of EA's molecular mechanisms in AD therapy, offering a novel, nonpharmacological approach for mitigating diet‐induced exacerbation of neurodegenerative processes.

Background: Hypertension increases the risk of cognitive impairment and related dementia, causing impaired executive function and unusual gait parameters. However, the mechanism of neural function illustrating this is unclear. Our research aimed to explore the differences of cerebral cortex activation, gait parameters, and working memory performance between healthy older adults (HA) and older hypertensive (HT) patients when performing cognitive and walking tasks.Method: A total of 36 subjects, including 12 healthy older adults and 24 older hypertensive patients were asked to perform series conditions including single cognitive task (SC), single walking task (SW), and dual‐task (DT), wearing functional near‐infrared spectroscopy (fNIRS) equipment and Intelligent Device for Energy Expenditure and Activity equipment to record cortical hemodynamic reactions and various gait parameters.Results: The left somatosensory cortex (L‐S1) and bilateral supplementary motor area (SMA) showed higher cortical activation (p < .05) than HA when HT performed DT. The intragroup comparison showed that HT had higher cortical activation (p < .05) when performing DT as SW. The cognitive performance of HT was significantly worse (p < .05) than HA when executing SC. The activation of the L‐S1, L‐M1, and bilateral SMA in HT were significantly higher during SW (p < .05).Conclusion: Hypertension can lead to cognitive impairment in the elderly, including executive function and walking function decline. As a result of these functional declines, elderly patients with hypertension are unable to efficiently allocate brain resources to support more difficult cognitive interference tasks and need to meet more complex task demands by activating more brain regions.

Background: Previous evidence suggests a link between gut microbiota and chronic pain, but the causal relationship is not yet fully understood.Methods: We categorized gut microbiota based on phylum, class, order, family, and genus levels and gathered pain-related information from the UKB and FinnGen GWAS project. Then, we conducted MR analysis to explore the potential causal relationship between gut microbiota and chronic pain at 12 specific locations.Results: We have discovered a direct connection between genetic susceptibility in the gut microbiota (gut metabolites) and pain experienced at 12 specific locations. Notably, Serotonin (5-HT) and Glycine were found to be associated with a higher risk of pain in the extremities. On the other hand, certain microbial families and orders were found to have a protective effect against migraines. Specifically, the family Bifidobacteriaceae (IVW, FDR p = 0.013) was associated with a lower risk of migraines. Furthermore, the genus Oxalobacter (IVW, FDR p = 0.044) was found to be linked to an increased risk of low back pain. Importantly, these associations remained significant even after applying the Benjamini-Hochberg correction test. Our analysis did not find any heterogeneity in the data (p > 0.05), as confirmed by the Cochrane’s Q-test. Additionally, both the MR-Egger and MR-PRESSO tests indicated no significant evidence of horizontal pleiotropy (p > 0.05).Conclusion: Our MR analysis demonstrated a causal relationship between the gut microbiota and pain, highlighting its potential significance in advancing our understanding of the underlying mechanisms and clinical implications of microbiota-mediated pain.