Zheng Zhu

In this systematic review and meta-analysis, the effects of walking interventions on sleep outcomes among middle-aged and older adults were evaluated and the potential moderating effects of exercise duration and frequency were explored. Eight randomised controlled trials involving 683 participants were included. Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), and objective outcomes included total sleep time, sleep efficiency, sleep latency, and wake after sleep onset. Walking interventions were associated with longer total sleep time (MD = 34.23 min; 95% CI: 24.72 to 43.74) and reduced wake after sleep onset (MD = - 11.58 min; 95% CI: -18.50 to - 4.66). No significant effects were observed for sleep efficiency, sleep latency, or total PSQI score (MD = - 1.41, 95% CI: -3.18 to 0.36), with substantial heterogeneity observed in the PSQI scores. Subgroup analyses suggested that sessions lasting ≥ 30 min may be associated with greater sleep-related benefits, whereas evidence regarding exercise frequency was limited. Overall, walking interventions may improve selected objective sleep outcomes, but the evidence remains limited and should be interpreted cautiously. Further high-quality studies are needed to confirm these findings and clarify the optimal intervention parameters.

Chronic pain is a significant and complex health condition characterized by persistent or recurrent pain lasting more than three months. Exercise-based rehabilitation is an effective non-pharmacological intervention, yet its underlying mechanisms have not been fully elucidated. This review systematically maps the molecular pathways of exercise-induced analgesia onto the pathophysiological cascades of chronic pain, aiming to fill a key gap in the current literature. It explores the molecular and cellular mechanisms underpinning the pathophysiology of chronic pain, indicating that the persistence of chronic pain stems from peripheral sensitization driven by inflammatory mediators and central sensitization involving glial cell activation and N-methyl-D-aspartate (NMDA) receptor-mediated neuroplasticity. Exercise can interrupt these pathological cascades through multi-system adaptations, including activation of the endogenous opioid and serotonergic systems activation and anti-inflammation. However, a significant gap remains in translating this mechanistic understanding of chronic pain into optimized, personalized exercise prescriptions, requiring future research into different exercise modalities, sex-specific responses, and the impact of comorbidities.

Background: The rapid advancements in science and technology of wrist-wearable activity devices offer considerable potential for clinical applications. Self-monitoring of physical activity (PA) with activity devices is helpful to improve the PA levels of adolescents. However, knowing the accuracy of activity devices in adolescents is necessary to identify current levels of PA and assess the effectiveness of intervention programs designed to increase PA. Objective: The study aimed to determine the validity of the 11 commercially available wrist-wearable activity devices for monitoring total steps and total 24-hour total energy expenditure (TEE) in healthy adolescents under simulated free-living conditions. Methods: Nineteen (10 male and 9 female) participants aged 14 to 18 years performed a 24-hour activity cycle in a metabolic chamber. Each participant simultaneously wore 11 commercial wrist-wearable activity devices (Mi Band 2 [XiaoMi], B2 [Huawei], Bong 2s [Meizu], Amazfit [Huamei], Flex [Fitbit], UP3 [Jawbone], Shine 2 [Misfit], GOLiFE Care-X [GoYourLife], Pulse O2 [Withings], Vivofit [Garmin], and Loop [Polar Electro]) and one research-based triaxial accelerometer (GT3X+ [ActiGraph]). Criterion measures were total EE from the metabolic chamber (mcTEE) and total steps from the GT3X+ (AGsteps). Results: Pearson correlation coefficients r for 24-hour TEE ranged from .78 (Shine 2, Amazfit) to .96 (Loop) and for steps ranged from 0.20 (GOLiFE) to 0.57 (Vivofit). Mean absolute percent error (MAPE) for TEE ranged from 5.7% (Mi Band 2) to 26.4% (Amazfit) and for steps ranged from 14.2% (Bong 2s) to 27.6% (Loop). TEE estimates from the Mi Band 2, UP3, Vivofit, and Bong 2s were equivalent to mcTEE. Total steps from the Bong 2s were equivalent to AGsteps. Conclusions: Overall, the Bong 2s had the best accuracy for estimating TEE and total steps under simulated free-living conditions. Further research is needed to examine the validity of these devices in different types of physical activities under real-world conditions.

BACKGROUND The rapid advancements in science and technology of wrist-wearable activity devices offer considerable potential for clinical applications. Self-monitoring of physical activity (PA) with activity devices is helpful to improve the PA levels of adolescents. However, knowing the accuracy of activity devices in adolescents is necessary to identify current levels of PA and assess the effectiveness of intervention programs designed to increase PA. OBJECTIVE The study aimed to determine the validity of the 11 commercially available wrist-wearable activity devices for monitoring total steps and total 24-hour total energy expenditure (TEE) in healthy adolescents under simulated free-living conditions. METHODS Nineteen (10 male and 9 female) participants aged 14 to 18 years performed a 24-hour activity cycle in a metabolic chamber. Each participant simultaneously wore 11 commercial wrist-wearable activity devices (Mi Band 2 [XiaoMi], B2 [Huawei], Bong 2s [Meizu], Amazfit [Huamei], Flex [Fitbit], UP3 [Jawbone], Shine 2 [Misfit], GOLiFE Care-X [GoYourLife], Pulse O2 [Withings], Vivofit [Garmin], and Loop [Polar Electro]) and one research-based triaxial accelerometer (GT3X+ [ActiGraph]). Criterion measures were total EE from the metabolic chamber (mcTEE) and total steps from the GT3X+ (AGsteps). RESULTS Pearson correlation coefficients r for 24-hour TEE ranged from .78 (Shine 2, Amazfit) to .96 (Loop) and for steps ranged from 0.20 (GOLiFE) to 0.57 (Vivofit). Mean absolute percent error (MAPE) for TEE ranged from 5.7% (Mi Band 2) to 26.4% (Amazfit) and for steps ranged from 14.2% (Bong 2s) to 27.6% (Loop). TEE estimates from the Mi Band 2, UP3, Vivofit, and Bong 2s were equivalent to mcTEE. Total steps from the Bong 2s were equivalent to AGsteps. CONCLUSIONS Overall, the Bong 2s had the best accuracy for estimating TEE and total steps under simulated free-living conditions. Further research is needed to examine the validity of these devices in different types of physical activities under real-world conditions.