Evidence map›Paper›PMID 33923353›Full record

ArticleInternational journal of environmental research and public health2021

Development Issues of Healthcare Robots: Compassionate Communication for Older Adults with Dementia.

Tetsuya Tanioka, Tomoya Yokotani, Ryuichi Tanioka, Feni Betriana, Kazuyuki Matsumoto, Rozzano Locsin, Yueren Zhao, Kyoko Osaka, Misao Miyagawa, Savina Schoenhofer

Open access · goldAbstract readCase Reports
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
6.1field-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

15 citing papers in PubMed, 43 citations in OpenAlex.

  1. Article
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  3. AI in geriatric psychiatry: precision meets human experience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
    Review
  4. Socially assistive robots and meaningful work: the case of aged care.Humanities & social sciences communications · 2025
    Article
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Robots and Robotics in Nursing.Healthcare (Basel, Switzerland) · 2022
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
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

10 authors at 5 institutions in 2 countries.

Tetsuya TaniokaInstitute of Biomedical Sciences, Tokushima University, Tokushima 770-8509, Japan.
Tomoya YokotaniGraduate School of Health Sciences, Tokushima University, Tokushima 770-8509, Japan.
Ryuichi TaniokaGraduate School of Health Sciences, Tokushima University, Tokushima 770-8509, Japan.
Feni BetrianaGraduate School of Health Sciences, Tokushima University, Tokushima 770-8509, Japan.ORCID 0000-0002-8248-367X
Kazuyuki MatsumotoGraduate School of Engineering, Tokushima University, Tokushima 770-8506, Japan.
Rozzano LocsinInstitute of Biomedical Sciences, Tokushima University, Tokushima 770-8509, Japan.
Yueren ZhaoDepartment of Psychiatry, Fujita Health University, Aichi 470-1192, Japan.
Kyoko OsakaDepartment of Nursing, Nursing Course of Kochi Medical School, Kochi University, Kochi 783-8505, Japan.
Misao MiyagawaDepartment of Nursing, Faculty of Health and Welfare, Tokushima Bunri University, Tokushima 770-8514, Japan.
Savina SchoenhoferAnne Boykin Institute, Florida Atlantic University, Boca Raton, FL 33431, USA.
Tokushima University · JPFlorida Atlantic University · USFujita Health University · JPKōchi University · JPTokushima Bunri University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although progress is being made in affective computing, issues remain in enabling the effective expression of compassionate communication by healthcare robots. Identifying, describing and reconciling these concerns are important in order to provide quality contemporary healthcare for older adults with dementia. The purpose of this case study was to explore the development issues of healthcare robots in expressing compassionate communication for older adults with dementia. An exploratory descriptive case study was conducted with the Pepper robot and older adults with dementia using high-tech digital cameras to document significant communication proceedings that occurred during the activities. Data were collected in December 2020. The application program for an intentional conversation using Pepper was jointly developed by Tanioka's team and the Xing Company, allowing Pepper's words and head movements to be remotely controlled. The analysis of the results revealed four development issues, namely, (1) accurate sensing behavior for "listening" to voices appropriately and accurately interacting with subjects; (2) inefficiency in "listening" and "gaze" activities; (3) fidelity of behavioral responses; and (4) deficiency in natural language processing AI development, i.e., the ability to respond actively to situations that were not pre-programmed by the developer. Conversational engagements between the Pepper robot and patients with dementia illustrated a practical usage of technologies with artificial intelligence and natural language processing. The development issues found in this study require reconciliation in order to enhance the potential for healthcare robot engagement in compassionate communication in the care of older adults with dementia.

Indexed as

DementiaRoboticsAgedArtificial IntelligenceCommunicationDelivery of Health CareEmpathyHumansartificial intelligencegeriatric carehealthcare robotsintelligent humanoid robotsnatural language processingolder personsrobot compassion

Identifiers

PMID33923353
PMCPMC8123161
OpenAlexW3159163418

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.