Age-related differences in food-specific inhibitory control: Electrophysiological and behavioral evidence in healthy aging.
Appetite(2023)
摘要
The number of older adults in the United States is estimated to nearly double from 52 million to 95 million by 2060. Approximately 80-85% of older adults are diagnosed with a chronic health condition. Many of these chronic health conditions are influenced by diet and physical activity, suggesting improved diet and eating behaviors could improve health-related outcomes. One factor that might improve dietary habits in older adults is food-related inhibitory control. We tested whether food-related inhibitory control, as measured via behavioral data (response time, accuracy) and scalp-recorded event-related potentials (ERP; N2 and P3 components), differed between younger and older adults over age 55. Fifty-nine older adults (31 females [52.5%], Mage = 64, SDage = 7.5) and 114 younger adults (82 females [71.9%], Mage = 20.8) completed two go/no-go tasks, one inhibiting to high-calorie stimuli and one inhibiting to low-calorie stimuli, while electroencephalogram (EEG) data were recorded. Older adults had slower overall response times than younger adults, but this was not specific to either food task. There was not a significant difference in accuracy between younger and older adults, but both groups' accuracy and response times were significantly better during the high-calorie task than the low-calorie task. For both the N2 and P3 ERP components, younger adults had larger no-go ERP amplitudes than older adults, but this effect was not food-specific, reflecting overall generalized lower inhibitory control processing in older adults. P3 amplitude for the younger adults demonstrated a specific food-related effect (greater P3 amplitude for high-calorie no-go than low-calorie no-go) that was not present for older adults. Findings support previous research demonstrating age-related differences in inhibitory control though those differences may not be specific to inhibiting towards food.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要