Bahrami Mohsen, Burdette Jonathan H, Laurienti Paul J, Nicklas Barbara J, Rejeski W Jack, Fanning Jason
Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Brain Behav. 2025 Jan;15(1):e70085. doi: 10.1002/brb3.70085.
The prevalence of sedentary lifestyles (SL), which includes both high volumes of extended sitting behavior and a low volume of steps accumulated across the day, among older adults continues to rise contributing to increases in associated comorbidities and the loss of independence. The social, personal, and economic burdens are enormous. In recognition of the health implications of SL, current public health physical activity guidelines now emphasize the complimentary goals of sitting less by moving more. We recently completed a 6-month weight loss (WL) study followed by 12 months of reduced contact to examine weight regain in older adults with obesity. One of the treatment conditions involved WL + a day-long movement intervention that explicitly targeted reducing sitting time and increasing steps across the day (SitLess).
The goal of the current study, using baseline fMRI and accelerometry data from 36 participants and advanced machine learning tools, was to determine if we could identify complex brain circuits underlying variability associated with changes in sitting time and daily steps during the 6-month intensive phase among participants randomized to the WL + SitLess treatment condition. Models generated from these analyses produced accuracy in predicting pre-post change in both measures that exceeded 92%, suggesting a critical role for the identified brain subnetworks in explaining variability in these outcomes in response to the intervention. The identified networks comprised regions, predominantly in the default mode and sensorimotor networks, that have been extensively linked to self-regulation and decision-making.
These results provide insights into the theoretical basis of SL for older adults and in the design of future intervention research.
久坐不动的生活方式（SL）在老年人中越来越普遍，这包括长时间坐着以及全天步数较少，这导致相关合并症增加和独立性丧失。其社会、个人和经济负担巨大。鉴于SL对健康的影响，当前的公共卫生身体活动指南现在强调通过增加活动来减少久坐这两个相辅相成的目标。我们最近完成了一项为期6个月的减肥（WL）研究，随后进行了12个月的减少接触期，以研究肥胖老年人的体重反弹情况。其中一种治疗条件包括WL + 为期一天的运动干预，该干预明确旨在减少久坐时间并增加全天步数（少坐）。
本研究的目标是，利用36名参与者的基线功能磁共振成像（fMRI）和加速度计数据以及先进的机器学习工具，确定在随机分配到WL + 少坐治疗条件的参与者的6个月强化阶段，我们是否能够识别与久坐时间和每日步数变化相关的复杂脑回路。这些分析生成的模型在预测这两项指标的前后变化时准确率超过92%，这表明所识别的脑子网在解释这些结果对干预的反应变异性方面起着关键作用。所识别的网络包括主要位于默认模式和感觉运动网络中的区域，这些区域与自我调节和决策广泛相关。
这些结果为老年人SL的理论基础以及未来干预研究的设计提供了见解。
