This study examines the potential of AI-powered personal voice assistants (PVAs) in reducing loneliness and increasing social support among older adults. With the aging population rapidly expanding, innovative solutions are essential. Prior research has indicated the effectiveness of various interactive communication technologies (ICTs) in mitigating loneliness, but studies focusing on PVAs, particularly considering their modality (audio vs. video), are limited. This research aims to fill this gap by evaluating how voice assistants, in both audio and video formats, influence perceived loneliness and social support. This study examined the impact of voice assistant technology (VAT) interventions, both audio-based (A-VAT) and video-based (V-VAT), on perceived loneliness and social support among 34 older adults living alone. Over three months, participants engaged with Amazon Alexa™ PVA through daily routines for at least 30 min. Using a hybrid natural language processing framework, interactions were analyzed. The results showed reductions in loneliness (Z = −2.99, p < 0.01; pre-study loneliness mean = 1.85, SD = 0.61; post-study loneliness mean = 1.65, SD = 0.57), increases in social support post intervention (Z = −2.23, p < 0.05; pre-study social support mean = 5.44, SD = 1.05; post-study loneliness mean = 5.65, SD = 1.20), and a correlation between increased social support and loneliness reduction when the two conditions are combined (ρ = −0.39, p < 0.05). In addition, V-VAT was more effective than A-VAT in reducing loneliness (U = 85.50, p < 0.05) and increasing social support (U = 95, p < 0.05). However, no significant correlation between changes in perceived social support and changes in perceived loneliness was observed in either intervention condition (V-VAT condition: ρ = −0.24, p = 0.37; A-VAT condition: ρ = −0.46, p = 0.06). This study’s findings could significantly contribute to developing targeted interventions for improving the well-being of aging adults, addressing a critical global issue.

Aging adults are impacted by pain. Technology can assist older adults with pain self-management while allowing for independence. The current usability study explored the use of voice assistant reminders for two pain self-management tasks in aging adults. Fifteen community-dwelling older adults with chronic pain and an average age of 65 years used the voice assistant for 4 weeks. Participants had moderate scores for pain severity (mean = 4.6 [ SD = 2.3]) and pain interference (mean = 4 [ SD = 2.6]). Voice assistant usability was above average (78 of 100). Median time to set up the Google Home Assistant profile was 5 minutes ( SD = 7.5), with a median of asking for help two times. Pain self-management task reminders from the voice assistant were perceived as consistent, easy to set up, and helpful for accountability. Voice assistant reminders may be an option to help encourage a variety of pain self-management tasks in aging adults. [ Journal of Gerontological Nursing, 46 (10), 27–33.]

Background: 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. Objective: This study aimed to describe how older adults engaged with routines facilitated by a conversational AI assistant. Methods: 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. Results: 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. Conclusions: 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.

Background: Pain is difficult to manage in older adults. It has been recommended that pain management in older adults should include both nonpharmacologic and pharmacologic strategies. Unfortunately, nonadherence to pain medication is more prevalent than nonadherence to any other chronic disease treatment. Technology-based reminders have some benefit for medication adherence, but adherence behavior outcomes have mostly been verified by self-reports. Objective: We aimed to describe objective medication adherence and the latency of medication use after a voice assistant reminder prompted participants to take pain medications for chronic pain. Methods: A total of 15 older adults created a voice assistant reminder for taking scheduled pain medications. A subsample of 5 participants were randomly selected to participate in a feasibility study, in which a medication event monitoring system for pain medications was used to validate medication adherence as a health outcome. Data on the subsample’s self-assessed pain intensity, pain interference, concerns and necessity beliefs about pain medications, self-confidence in managing pain, and medication implementation adherence were analyzed. Results: In the 5 participants who used the medication event monitoring system, the overall latency between voice assistant reminder deployment and the medication event (ie, medication bottle cap opening) was 55 minutes. The absolute latency (before or after the reminder) varied among the participants. The shortest average time taken to open the cap after the reminder was 17 minutes, and the longest was 4.5 hours. Of the 168 voice assistant reminders for scheduled pain medications, 25 (14.6%) resulted in the opening of MEMS caps within 5 minutes of the reminder, and 107 (63.7%) resulted in the opening of MEMS caps within 30 minutes of the reminder. Conclusions: Voice assistant reminders may help cue patients to take scheduled medications, but the timing of medication use may vary. The timing of medication use may influence treatment effectiveness. Tracking the absolute latency time of medication use may be a helpful method for assessing medication adherence. Medication event monitoring may provide additional insight into medication implementation adherence during the implementation of mobile health interventions.

Potentially inappropriate medication (PIM) use is a serious public health problem in older adults because it may lead to adverse events. The purpose of the current study was to explore PIM use in rural, community-dwelling older adults. Participants (N = 138) underwent one-on-one medication reviews. Approximately one half (49%) of the sample used prescribed and over-the-counter (OTC) PIM. Prescribed and OTC nonsteroidal anti-inflammatory drugs (33%) and anticholinergic medications (28%) were the most frequently used PIM. Use of PIM was associated with a higher number of medications (r = 0.331, p < 0.01), more medical providers (r = 0.223, p < 0.001), and poor physical health (r = -0.193, p < 0.05). Higher number of medications increased the probability of PIM use by 85% (odds ratio: 1.8; 95% confidence interval [1.19, 2.84]). Findings highlight the importance of re-evaluating the monitoring of medications in rural, community-dwelling older adults and the need for sustainable interventions to reduce prescribing and OTC PIM use. [Journal of Gerontological Nursing, 43(9), 21-30.].