Evidence map›Paper›PMID 35503518›Full record

ArticleJMIR aging2022

Improving a Mobile Telepresence Robot for People With Alzheimer Disease and Related Dementias: Semistructured Interviews With Stakeholders.

Marlena H Shin, Jaye McLaren, Alvin Ramsey, Jennifer L Sullivan, Lauren Moo

Open access · goldAbstract read
In one paragraph

Article in JMIR aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
7.5field-weighted citation impact, top 3% of its field
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. 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

5 authors at 4 institutions in 1 country.

Marlena H ShinCenter for Healthcare Organization and Implementation Research, Veterans Affairs Boston Healthcare System, Boston, MA, United States.ORCID https://orcid.org/0000-0001-5555-6258
Jaye McLarenNew England Geriatric Research Education and Clinical Center, Veterans Affairs Bedford Healthcare System, Bedford, MA, United States.ORCID https://orcid.org/0000-0002-5610-3864
Alvin RamseyPilltrax Systems LLC, Boston, MA, United States.ORCID https://orcid.org/0000-0002-7203-4934
Jennifer L SullivanCenter of Innovation in Long-Term Services and Supports, Veterans Affairs Providence Healthcare System, Providence, RI, United States.ORCID https://orcid.org/0000-0003-2906-2232
Lauren MooNew England Geriatric Research Education and Clinical Center, Veterans Affairs Bedford Healthcare System, Bedford, MA, United States.ORCID https://orcid.org/0000-0003-2951-8674
Brown University · USGeriatric Research Education and Clinical Center · USHarvard University · USVA Boston Healthcare System · US

Funding

VGo-Assist, A Mobile Telepresence Robot to Engage Persons with Alzheimer's Disease and Related DementiasR43AG060781 · NIA · VECNA TECHNOLOGIES, INC. · PI RAMSEY, ALVIN · 2018 to 2018
$350k
NIA NIH HHS R43 AG060781
6 · The paper itself

Abstract

backgroundBy 2050, nearly 13 million Americans will have Alzheimer disease and related dementias (ADRD), with most of those with ADRD or mild cognitive impairment (MCI) receiving home care. Mobile telepresence robots may allow persons with MCI or ADRD to remain living independently at home and ease the burden of caregiving. The goal of this study was to identify how an existing mobile telepresence robot can be enhanced to support at-home care of people with MCI or ADRD through key stakeholder input.

objectiveThe specific aims were to assess what applications should be integrated into the robot to further support the independence of individuals with MCI or ADRD and understand stakeholders' overall opinions about the robot.

methodsWe conducted in-person interviews with 21 stakeholders, including 6 people aged >50 years with MCI or ADRD living in the community, 9 family caregivers of people with MCI or ADRD, and 6 clinicians who work with the ADRD population. Interview questions about the robot focused on technology use, design and functionality, future applications to incorporate, and overall opinions. We conducted a thematic analysis of the data obtained and assessed the patterns within and across stakeholder groups using a matrix analysis technique.

resultsOverall, most stakeholders across groups felt positively about the robot's ability to support individuals with MCI or ADRD and decrease caregiver burden. Most ADRD stakeholders felt that the greatest benefits would be receiving help in emergency cases and having fewer in-person visits to the doctor's office. Caregivers and clinicians also noted that remote video communication with their family members using the robot was valuable. Adding voice commands and 1-touch lifesaving or help buttons to the robot were the top suggestions offered by the stakeholders. The 4 types of applications that were suggested included health-related alerts; reminders; smart-home-related applications; and social, entertainment, or well-being applications. Stakeholders across groups liked the robot's mobility, size, interactive connection, and communication abilities. However, stakeholders raised concerns about their physical stability and size for individuals living in smaller, cluttered spaces; screen quality for those with visual impairments; and privacy or data security.

conclusionsAlthough stakeholders generally expressed positive opinions about the robot, additional adaptations were suggested to strengthen functionality. Adding applications and making improvements to the design may help mitigate concerns and better support individuals with ADRD to live independently in the community. As the number of individuals living with ADRD in the United States increases, mobile telepresence robots are a promising way to support them and their caregivers. Engaging all 3 stakeholder groups in the development of these robots is a critical first step in ensuring that the technology matches their needs. Integrating the feedback obtained from our stakeholders and evaluating their effectiveness will be important next steps in adapting telepresence robots.

Indexed as

aging in placecaregiver supportgerontologymild cognitive impairmentmobile phonequalitative researchrobot technologysocially assistive robot

Identifiers

PMID35503518
PMCPMC9115649
OpenAlexW4225371918

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.