Shade Marcia, Yan Changmin, Jones Valerie K, Boron Julie
College of Nursing, University of Nebraska Medical Center, 985330 Nebraska Medical Center, Omaha, NE, 68198, United States, 1 4025596641.
College of Journalism and Mass Communications, University of Nebraska Lincoln, Lincoln, NE, United States.
JMIR Form Res. 2025 Jan 24;9:e64763. doi: 10.2196/64763.
Technologies that serve as assistants are growing more popular for entertainment and aiding in daily tasks. Artificial intelligence (AI) in these technologies could also be helpful to deliver interventions that assist older adults with symptoms or self-management. Personality traits may play a role in how older adults engage with AI technologies. To ensure the best intervention delivery, we must understand older adults' engagement with and usability of AI-driven technologies.
This study aimed to describe how older adults engaged with routines facilitated by a conversational AI assistant.
A randomized pilot trial was conducted for 12-weeks in adults aged 60 years or older, self-reported living alone, and having chronic musculoskeletal pain. Participants (N=50) were randomly assigned to 1 of 2 intervention groups (standard vs enhanced) to engage with routines delivered by the AI assistant Alexa (Amazon). Participants were encouraged to interact with prescribed routines twice daily (morning and evening) and as needed. Data were collected and analyzed on routine engagement characteristics and perceived usability of the AI assistant. An analysis of the participants' personality traits was conducted to describe how personality may impact engagement and usability of AI technologies as interventions.
The participants had a mean age of 79 years, with moderate to high levels of comfort and trust in technology, and were predominately White (48/50, 96%) and women (44/50, 88%). In both intervention groups, morning routines (n=62, 74%) were initiated more frequently than evening routines (n=52, 62%; z=-2.81, P=.005). Older adult participants in the enhanced group self-reported routine usability as good (mean 74.50, SD 11.90), and those in the standard group reported lower but acceptable usability scores (mean 66.29, SD 6.94). Higher extraversion personality trait scores predicted higher rates of routine initiation throughout the whole day and morning in both groups (standard day: B=0.47, P=.004; enhanced day: B=0.44, P=.045; standard morning: B=0.50, P=.03; enhanced morning: B=0.53, P=.02). Higher agreeableness (standard: B=0.50, P=.02; enhanced B=0.46, P=.002) and higher conscientiousness (standard: B=0.33, P=.04; enhanced: B=0.38, P=.006) personality trait scores predicted better usability scores in both groups.
he prescribed interactive routines delivered by an AI assistant were feasible to use as interventions with older adults. Engagement and usability by older adults may be influenced by personality traits such as extraversion, agreeableness, and conscientiousness. While integrating AI-driven interventions into health care, it is important to consider these factors to promote positive outcomes.
作为辅助工具的技术在娱乐和协助日常任务方面越来越受欢迎。这些技术中的人工智能（AI）也可能有助于提供干预措施，以帮助老年人缓解症状或进行自我管理。人格特质可能在老年人与人工智能技术的互动方式中发挥作用。为了确保提供最佳的干预措施，我们必须了解老年人对人工智能驱动技术的参与度和可用性。
本研究旨在描述老年人如何参与由对话式人工智能助手促成的日常活动。
对60岁及以上、自我报告独居且患有慢性肌肉骨骼疼痛的成年人进行了为期12周的随机试点试验。参与者（N = 50）被随机分配到2个干预组中的1组（标准组与增强组），以参与由人工智能助手Alexa（亚马逊）提供的日常活动。鼓励参与者每天早晚各与规定的日常活动互动一次，并根据需要进行互动。收集并分析了关于日常参与特征和人工智能助手的感知可用性的数据。对参与者的人格特质进行了分析，以描述人格如何影响作为干预措施的人工智能技术的参与度和可用性。
参与者的平均年龄为79岁，对技术有中度到高度的舒适度和信任度，主要为白人（48/50，96%）和女性（44/50，88%）。在两个干预组中，早晨的日常活动（n = 62，74%）比晚上的日常活动（n = 52，62%）启动得更频繁（z = -2.81，P = .005）。增强组中的老年参与者自我报告日常可用性良好（平均74.50，标准差11.90），标准组中的参与者报告的可用性得分较低但可以接受（平均66.29，标准差6.94）。在两组中，较高的外向性人格特质得分预测全天和早晨的日常活动启动率更高（标准组白天：B = 0.47，P = .004；增强组白天：B = 0.44，P = .045；标准组早晨：B = 0.50，P = .03；增强组早晨：B = 0.53，P = .02）。较高的宜人性（标准组：B = 0.50，P = .02；增强组：B = 0.46，P = .002）和较高的尽责性（标准组：B = 0.33，P = .04；增强组：B = 0.38，P = .006）人格特质得分预测两组的可用性得分更高。
人工智能助手提供的规定互动式日常活动作为对老年人的干预措施是可行的。老年人的参与度和可用性可能受到外向性、宜人性和尽责性等人格特质的影响。在将人工智能驱动的干预措施整合到医疗保健中时，考虑这些因素以促进积极结果很重要。
