推进个性化数字疗法:整合音乐疗法、脑波娱乐方法和人工智能驱动的生物反馈。

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES
Frontiers in digital health Pub Date : 2025-02-25 eCollection Date: 2025-01-01 DOI:10.3389/fdgth.2025.1552396
Dian Jiao
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引用次数: 0

摘要

精神健康障碍和认知能力下降是全球关注的紧迫问题,增加了对针对情绪失调、记忆缺陷和神经功能障碍的非药物干预的需求。这篇综述系统地考察了三种有前途的方法——音乐疗法、脑电波引导(双耳节拍、等时音调、多感官刺激),并将它们整合到一个统一的治疗范式中。新出现的证据表明,音乐疗法通过参与大脑边缘、前额叶和奖励回路来调节情绪、减轻压力并增强认知能力。脑波携带,特别是在伽马频率范围内(30-100赫兹),促进与放松、集中和记忆有关的神经振荡模式,40赫兹的脑波携带显示出增强认知的希望,尽管个体差异。同步多感觉刺激,结合伽马频率的听觉和视觉输入,已被证明具有增强记忆和支持神经完整性的潜力,特别是在阿尔茨海默病中。然而,诸如患者反应差异、缺乏标准化和可扩展性等挑战阻碍了广泛实施。最近的研究表明,这些模式的协同应用可以通过利用互补机制优化治疗结果。为了实现这一点,人工智能驱动的生物反馈,能够实时生理评估和个性化调整,例如定制音乐复杂性,娱乐频率和多感官成分,成为一个有希望的解决方案。这种自适应模式增强了治疗的可及性和一致性,同时最大限度地提高了长期疗效。虽然还处于早期阶段,但初步证据强调了它在重塑非药物治疗策略方面的变革潜力。推进这一领域需要跨学科的研究、严格的评估和伦理数据管理,以开发创新的、以患者为中心的心理健康和认知康复解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancing personalized digital therapeutics: integrating music therapy, brainwave entrainment methods, and AI-driven biofeedback.

Mental health disorders and cognitive decline are pressing global concerns, increasing the demand for non-pharmacological interventions targeting emotional dysregulation, memory deficits, and neural dysfunction. This review systematically examines three promising methodologies-music therapy, brainwave entrainment (binaural beats, isochronic tones, multisensory stimulation), and their integration into a unified therapeutic paradigm. Emerging evidence indicates that music therapy modulates affect, reduces stress, and enhances cognition by engaging limbic, prefrontal, and reward circuits. Brainwave entrainment, particularly within the gamma frequency range (30-100 Hz), facilitates neural oscillatory patterns linked to relaxation, concentration, and memory, with 40 Hz showing promise for cognitive enhancement, albeit with individual variability. Synchronized multisensory stimulation, combining auditory and visual inputs at gamma frequencies, has demonstrated potential in enhancing memory and supporting neural integrity, particularly in Alzheimer's disease. However, challenges such as patient response variability, lack of standardization, and scalability hinder widespread implementation. Recent research suggests that a synergistic application of these modalities may optimize therapeutic outcomes by leveraging complementary mechanisms. To actualize this, AI-driven biofeedback, enabling real-time physiological assessment and individualized adjustments-such as tailoring musical complexity, entrainment frequencies, and multisensory components-emerges as a promising solution. This adaptive model enhances treatment accessibility and consistency while maximizing long-term efficacy. Although in early stages, preliminary evidence highlights its transformative potential in reshaping non-pharmacological therapeutic strategies. Advancing this field requires interdisciplinary research, rigorous evaluation, and ethical data stewardship to develop innovative, patient-centered solutions for mental health and cognitive rehabilitation.

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CiteScore
4.20
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审稿时长
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