应用健康信念模型量化和调查计算机认知训练的期望。

Jerri D Edwards, Christine B Philllips, Melissa L O'Connor, Jennifer L O'Brien, Elizabeth M Hudak, Jody S Nicholson
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引用次数: 2

摘要

尽管计算机化的认知训练对老年人有明显的好处,但人们对训练行为的决定因素知之甚少。我们开发并测试了量表来量化对这种训练的期望,检查期望是否预测训练依从性,并探索训练期望是否从训练前到训练后发生变化。参与者(N=219)是55-96岁的健康老年人(M=75.36, SD=9.39),参加了四项研究,调查Dakim, Insight或Posit Science Brain Fitness计算机认知训练计划。工具改编自现有的健康行为量表:认知训练的自我效能、认知训练的结果预期、认知能力下降、痴呆或阿尔茨海默病的感知易感性、认知能力下降、痴呆或阿尔茨海默病的感知严重程度。参与者在基线(N=219)和训练后(N= 173)完成了量表。因子分析得到8个复合材料。依从率很高(M=81%),但没有任何复合材料预测训练依从性。总的来说,时间的影响是显著的,Wilks λ=。843, F(8,114)=2.65, p=。010,偏η 2 =。157,训练组整体效果显著,Wilks’λ=。770, F(16,228)=1.99, p=。015,偏η 2 =。123,整体显著组x时间的相互作用,Wilks的λ=。728, F(16,226)=2.44, p=。002,偏η 2 = 0.147。时间对预期心理结果和自我效能感有显著影响。训练结束后,参与者更强烈地认为训练是愉快的,并增加了他们的成就感。认知训练中自我效能的变化因项目而异,达基姆的自我效能有所提高,而更具挑战性的“大脑健康”和“洞察”参与者的自我效能则有所下降。这些新设计的量表可能对检查认知训练行为有用。然而,需要做更多的工作来了解影响老年人参加和坚持认知训练的因素。
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Applying the Health Belief Model to Quantify and Investigate Expectations for Computerized Cognitive Training.

Despite the demonstrated benefits of computerized cognitive training for older adults, little is known about the determinants of training behavior. We developed and tested scales to quantify expectations about such training, examine whether expectations predicted training adherence, and explore if training expectations changed from pre- to post-training. Participants (N=219) were healthy older adults aged 55-96 years (M=75.36, SD=9.39), enrolled in four studies investigating Dakim, Insight, or Posit Science Brain Fitness computerized cognitive training programs. Instruments were adapted from existing health behavior scales: Self Efficacy for Cognitive Training, Outcome Expectations for Cognitive Training, Perceived Susceptibility to Cognitive Decline, Dementia or Alzheimer's Disease, and Perceived Severity of Cognitive Decline, Dementia or Alzheimer's Disease. Participants completed scales at baseline (N=219) and post-training (n=173). Eight composites were derived from factor analyses. Adherence rates were high (M=81%), but none of the composites predicted training adherence. There was an overall significant effect of time, Wilks' λ=.843, F(8, 114)=2.65, p=.010, partial η 2 =.157, a significant overall effect of training group, Wilks' λ=.770, F(16, 228)=1.99, p=.015, partial η 2 =.123, and an overall significant group x time interaction, Wilks' λ=.728, F(16, 226)=2.44, p=.002, partial η 2 =.147. Significant effects of time were found for expected psychological outcomes and self-efficacy. Post-training, participants more strongly agreed that training was enjoyable and increased their sense of accomplishment. Changes in self-efficacy for cognitive training varied by program, improvingfor Dakim- and declining for the more challenging Brain Fitness- and InSight participants. These newly devised scales may be useful for examining cognitive training behaviors. However, more work is needed to understand factors that influence older adults' enrollment in and adherence to cognitive training.

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