Evidence map›Paper›PMID 39865552›Full record

Trial reportJMIR formative research2025

Evaluating Older Adults' Engagement and Usability With AI-Driven Interventions: Randomized Pilot Study.

Marcia Shade, Changmin Yan, Valerie K Jones, Julie Boron

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Review
  6. Review
  7. Digital Cognitive Twins in mental health.Nature. Mental health · 2025
    Article
  8. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Marcia Shade *College of Nursing, University of Nebraska Medical Center, 985330 Nebraska Medical Center, Omaha, NE, 68198, United States, 1 4025596641.ORCID 0000-0002-1477-1565
Changmin Yan *College of Journalism and Mass Communications, University of Nebraska Lincoln, Lincoln, NE, United States.ORCID 0000-0002-4365-4595
Valerie K Jones *College of Journalism and Mass Communications, University of Nebraska Lincoln, Lincoln, NE, United States.ORCID 0000-0001-8429-5421
Julie Boron *Department of Gerontology, University of Nebraska at Omaha, Omaha, NE, United States.ORCID 0000-0003-1121-8120

Funding

Great Plains IDeA-CTR–NIH NCT05387447
6 · The paper itself

Abstract

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.

Indexed as

Artificial IntelligenceMusculoskeletal PainAgedAged, 80 and overFemaleHumansMaleMiddle AgedPersonalityPilot ProjectsagingAIAI assistantAlexaartificial intelligencechronicdigital healthdigital interventionengagementinterventionsmobile phonemusculoskeletal painolder adultspersonalitypilot trialself-managementtechnologyusabilityuser experiencevoice assistant

Identifiers

PMID39865552
PMCPMC11784632

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.