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: Pain is a dynamic experience that varies over time, but it remains unknown whether trajectories of pain are associated with subsequent cognitive decline. The purpose of this study was to identify distinct trajectories of pain presence and activity-limiting pain and investigate their longitudinal associations with the rate of subsequent cognitive decline in older adults. Methods: A total of 5685 participants from the English Longitudinal Study of Ageing (ELSA) and 7619 participants from the Health and Retirement Study (HRS) were included. Pain presence trajectories were identified over eight years in the ELSA and 10 years in the HRS, while trajectories of activity-limiting pain were identified over 10 years in the HRS. We utilised linear mixed-effects models to investigate the long-term relationship between pain trajectories and the rate of cognitive decline across various domains, including memory, orientation, executive function and global cognition. Results: Three pain presence trajectories were identified. Moderate-increasing and high-stable groups exhibited steeper declines in global cognition than the low-stable group. Furthermore, individuals in the moderate-increasing group experienced a more rapid decline in executive function, while the high-stable group showed a faster decline in orientation function. Two trajectories of activity-limiting pain were identified, with the moderate-increasing group experiencing a faster decline in orientation function and global cognition. Conclusions: The trajectories of both pain presence and activity-limiting pain are linked to the rate of subsequent cognitive decline among older people. Interventions for specific pain trajectories might help to delay the decline rate of cognition in specific domains.

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.

Pre-frailty is a transitional stage between health and frailty. Previous studies have demonstrated that individuals with pre-frailty experience declines in cognitive and gait performances compared with healthy individuals. However, the basic neural mechanism underlying this needs to be clarified. In this cross-sectional study, twenty-one healthy older adults and fifteen with pre-frailty underwent three conditions, including a single cognitive task (SC), single walking task (SW), and dual-task (DT), while cortical hemodynamic reactions were measured using functional near-infrared spectroscopy (fNIRS). The prefrail group (PG) showed a significantly lower activation of the left dorsolateral prefrontal cortex (L-DLPFC) than the healthy group (HG) when performing SC (p < 0.05). The PG showed a significantly lower Timed Up and Go test and step speed than the HG during SW (p < 0.05). The coefficient of variation (CV) of the step length of the PG was significantly higher than that of the HG when performing DT (p < 0.05). No significant correlation in cerebral cortex activation and gait parameters in the HG when performing SW and DT was noted (p > 0.05). Participants of the PG with a higher oxygenated area in the left anterior prefrontal cortex (L-APFC) had a lower step frequency during SW (r = −0.533, p = 0.041), and so did the following indicators of the PG during DT: L-APFC and step speed (r = −0.557, p = 0.031); right anterior prefrontal cortex and step speed (r = −0.610, p = 0.016); left motor cortex and step speed (r = −0.674, p = 0.006); step frequency (r = −0.656, p = 0.008); and step length (r = −0.535, p = 0.040). The negative correlations between the cerebral cortex and gait parameters of the PG indicated a neural compensatory effect of pre-frailty. Therefore, older adults with pre-frailty promote prefrontal activation to compensate for the impaired sensorimotor systems.