Trial reportScientific reports2026
Synergistic enhancement of athletic performance and psychological resilience through AI-driven personalized exercise regimens network.
Trial report in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
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Authors and funding
2 authors.
Funding
Abstract
The reciprocal benefits of physical exercise on physiological performance and mental well-being are well-documented, yet conventional training paradigms seldom optimize for both outcomes simultaneously. This results in a missed opportunity for synergistic adaptation, where physical gains bolster mental fortitude and vice versa. Here, we introduce and validate an AI-driven framework for generating personalized exercise regimens that explicitly co-optimize for athletic performance and psychological resilience. Our system is built on three core innovations: (1) a Dual-factor adaptation model (DFAM), a novel mathematical formalization of the coupled dynamics of physical fitness, fatigue, psychological stress, and resilience; (2) a personalized regimen optimization algorithm (PROA), a multi-objective evolutionary algorithm that navigates the trade-off space between performance and resilience to generate Pareto-optimal training schedules tailored to an individual's unique DFAM parameters; and (3) an adaptive feedback control (AFC) system that fine-tunes daily training loads in real-time based on wearable sensor data and subjective feedback. We evaluated this framework in a 12-week randomized controlled trial with 60 competitive athletes. The personalized group (PER) demonstrated markedly superior gains in a composite Athletic Performance Index (API) (+ 21.4% ± 3.8%) compared to a standardized training group (STD) (+ 12.0% ± 4.5%) and a non-training control (CTL) (+ 2.2% ± 1.5%). Concurrently, psychological resilience, quantified via the Connor-Davidson resilience scale (CD-RISC), increased significantly more in the PER group (+ 18.6% ± 2.5%) than in the STD (+ 8.3% ± 2.1%) and CTL (+ 1.1% ± 1.3%) groups (all p < 0.001). Ablation studies confirmed that both the dual-objective optimization and the adaptive feedback were critical for achieving these synergistic outcomes. Our findings suggest that an explicit, algorithmic co-optimization of physical and psychological states can significantly enhance holistic human performance, offering a promising new direction for intelligent training systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.