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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

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He-Gui Bao

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Review Open access Aug 2026

Effects of Traditional and Technology-Based Exercise Interventions on Cognitive Function in Older Adults: A Systematic Review and Meta-Analysis

Background: As population aging accelerates, pharmacological treatments provide limited benefits for cognitive decline. Exercise and technology-assisted exercise have thus emerged as important non-pharmacological approaches for supporting cognitive health in older adults. However, comparative evidence on the relative effectiveness of different intervention modalities across cognitive outcomes remains limited. Objective: This study used a network meta-analysis to systematically compare and rank the effects of traditional and non-traditional exercise interventions on cognitive function in older adults. Methods: This systematic review and network meta-analysis was registered on PROSPERO (CRD420261278685). PubMed, Embase, the Cochrane Library, Web of Science, and Scopus were searched from inception to May 2026. Randomized controlled trials enrolling participants aged 60 years or older were included. Interventions comprised aerobic, resistance, mind–body, finger, and multicomponent exercise, as well as virtual reality-based interventions, artificial intelligence-assisted exercise, and wearable exoskeleton training. Primary outcomes included global cognition, executive function, memory, attention, and activities of daily living. A random effects network meta-analysis was applied to estimate standardized mean differences with 95 percent confidence intervals, and intervention rankings were derived using SUCRA values. Results: A total of 70 randomized controlled trials involving 5573 older adults were included. All active interventions demonstrated overall cognitive benefits compared with usual care or non-active control conditions. Mind–body exercise showed the highest probability of improving global cognitive performance. Virtual reality-based interventions were particularly effective for executive function, memory, and activities of daily living, while artificial intelligence-assisted exercise showed favorable rankings for attention outcomes. Mind–body exercise showed favorable effects on global cognition, whereas several technology-assisted interventions ranked highly for selected cognitive and functional outcomes. Conclusions: Both traditional and non non-traditional exercise interventions improve cognitive function in older adults, although their effects differ across cognitive domains. Technology-assisted approaches appear more effective for enhancing specific cognitive functions, while traditional interventions such as mind–body exercise are more advantageous for preserving overall cognitive performance. These findings highlight the potential value of stage specific and combined intervention strategies for promoting cognitive health in aging populations.

Jingzhan Ren, Xiaotong Du, Wei Wang et al. · 0 citations
Open access Aug 2026

Algorithmic fairness in AI-based fitness advice: evaluating socioeconomic bias in county-contextualized physical activity prescriptions

Background Large language models (LLMs) can significantly broaden access to physical-activity guidance. However, advice that implicitly assumes available financial resources, reliable transportation, specialized equipment, or local facilities can be difficult for individuals in resource-constrained environments to act upon. We evaluated whether such resource assumptions systematically vary across socioeconomic settings when underlying health needs remain fixed. Methods We developed a county-aware, matched-counterfactual auditing framework integrating U.S. public health and socioeconomic data, synthetic patient profiles, and structured LLM outputs. To evaluate resource burden, we constructed a transparent access-cost proxy that renders each coded component directly inspectable. We evaluated accessibility-aware prompting, output reranking, alternative weighting schemes, and a bounded three-model comparative panel (including DeepSeek) to audit and mitigate socioeconomically driven bias. Results In the full DeepSeek model panel, accessibility-aware prompting reduced the access-cost proxy gap between high- and low-socioeconomic status (SES) counties from 0.306 to 0.139. Subsequent output reranking maintained this narrowed gap while simultaneously improving coarse alignment with physical-activity volume and intensity guidelines. Sensitivity analyses using alternative weighting schemes preserved the overall comparative ordering, while the three-model comparison demonstrated model-specific variations in resource-assumption responses. Discussion This study establishes a reproducible framework that connects equity-oriented health-recommender principles to traceable output auditing and targeted bias mitigation. By offering a transparent approach to evaluating implicit resource assumptions, this work provides researchers and practitioners with an actionable foundation for downstream expert, user, and implementation validation.

He-Gui Bao, Tian-Wei Yu, Jun-Yue Wang et al. · 0 citations

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