Evidence map›Paper›PMID 42652334›Full record

ArticleInternational journal of environmental research and public health2026

Systematic Co-Design of Artificial Intelligence-Enabled Innovations for Healthy Aging: A Demonstration Study Involving Socially Assistive Robots for Dementia Care.

Sajay Arthanat, Jing Wang, Dain LaRoche, Heather Fritz, Rosanne DiZazzo-Miller, Mostafa Hussein, Ola Ghattas, Moniruzzaman Akash, Momotaz Begum

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of environmental research and public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Sajay ArthanatDepartment of Occupational Therapy, University of New Hampshire, Durham, NH 03824, USA.
Jing WangSchool of Nursing, University of New Hampshire, Durham, NH 03824, USA.
Dain LaRocheDepartment of Kinesiology, College of Health and Human Services, University of New Hampshire, Durham, NH 03824, USA.
Heather FritzSchool of Occupational Therapy, Pacific Northwest University of Health Sciences, Yakima, WA 98901, USA.
Rosanne DiZazzo-MillerOccupational Therapy Program, Wayne State University, Detroit, MI 48201, USA.
Mostafa HusseinDepartment of Computer Science, University of New Hampshire, Durham, NH 03824, USA.
Ola GhattasDepartment of Computer Science, University of New Hampshire, Durham, NH 03824, USA.
Moniruzzaman AkashDepartment of Computer Science, University of New Hampshire, Durham, NH 03824, USA.ORCID 0000-0001-9736-5740
Momotaz BegumDepartment of Computer Science, University of New Hampshire, Durham, NH 03824, USA.

Funding

Effectiveness and adoption of a Smart home-based social assistive robot for care of individuals with Alzheimer's DiseaseR01AG075892 · NIA · UNIVERSITY OF NEW HAMPSHIRE · PI ARTHANAT, SAJAY, BEGUM, MOMOTAZ · 2022 to 2025
$2.6M
NIA NIH HHS 5R01AG075892-04NIA NIH HHS R01 AG075892
6 · The paper itself

Abstract

Artificial intelligence-enabled technologies offer new opportunities to support healthy aging and the long-term care needs of older adults. However, inclusive practices are paramount to ensuring that accessibility, usability, privacy, and equitable use are factored into the design and deployment of these emerging technologies. This article explores the role of co-design in health technology development and highlights the application of three methodological tools-the NIH Stage Model for Behavioral Intervention Development, the Unified Theory of Acceptance and Use of Technology, and Goal Attainment Scaling-to create a smart-home-based socially assistive robot (SAR) for the care of individuals living with Alzheimer's disease and related dementias (ADRD). Ten participants (five caregiver-care recipient dyads) from an ongoing mixed-methods pilot feasibility study trialed the SAR in their homes for 1-6 months, with the robot personalized to their daily functioning, home layout, and caregiving needs. Qualitative analysis of monthly interviews derived themes pertaining to technical design, care protocol design, training management, and complementary care. These themes, combined with goal attainment analysis, offered several insights that allowed us to iteratively scale and refine the technology tailored to ADRD care. The study offers a practical framework for future co-design efforts aimed at enhancing the adoption of AI-enabled health technologies among older adults.

Indexed as

Artificial IntelligenceDementiaHealthy AgingRoboticsAgedAged, 80 and overCaregiversFemaleHumansMalePilot ProjectsSelf-Help Devicesaging in placeAlzheimer’s disease and related dementiaco-designdementiaGoal Attainment Scalingsocially assistive robotsUnified Theory of Acceptance and Use of Technology

Identifiers

PMID42652334
PMCPMC13512601

What OpenQuestion holds

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Registered trials

None linked

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.