Evidence map›Paper›PMID 36117570›Full record

ArticleClinical interventions in aging2022

Translation, Adaptation, and Psychometric Testing of the Almere Technology Acceptance Questionnaire (ATAQ) Among Older Adults in China.

Ying He, Qian Liu, Qiu He, Lun Li

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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.

Ying HeSchool of Medicine, Hunan Normal University, Changsha, People's Republic of China.
Qian LiuSchool of Medicine, Hunan Normal University, Changsha, People's Republic of China.
Qiu HeThe Second Xiangya Hospital of Central South University, Changsha, People's Republic of China.
Lun LiFaculty of Humanities and Social Sciences, Dalian University of Technology, Dalian, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lack of information on technology Acceptance of Social Assistant robots (SARs) limits the application of robots in the elderly care sector. Currently, no study has not reported a robot acceptance assessment tool in China. Purpose: The current study aimed to translate the Almere Technology Acceptance Questionnaire (ATAQ) from English to Mandarin Chinese, perform cross-cultural adaptations, and to evaluate its psychometric properties among elderly people in China. Methods: This study was conducted in two phases. Phase 1 comprised the context relevance evaluation, instrument translation, and cultural adaption from English to Mandarin Chinese. Phase 2 involved a quantitative cross-sectional survey of psychometric testing among 317 elderly Chinese, including reliability and evaluation of construct validity. Results: In Phase 1, the Context Relevance Index of all items was rated "3 = relevant" or "4 = totally relevant", and the Translation Validity Index of the 100% items of the ATAQ was rated 3 or 4. The ATAQ-Chinese questionnaire comprised 30 items. In Phase 2, all items of the ATAQ-Chinese questionnaire had a CR above 3 (P<0.001). Correlation coefficients of entries in the current study ranged from 0.403 to 0.763 (P<0.001). Nine factors were extracted through Exploratory Factor Analysis, and the cumulative variance contribution rate was 77.175%. Confirmatory Factor Analysis showed that the model had a good fit (χ Conclusion: The Chinese version of ATAQ has good reliability and validity, and it is an acceptable, reliable, and valid tool for determining technology acceptance of Social assistant robots in older adults.

Indexed as

TechnologyAgedChinaCross-Sectional StudiesHumansPsychometricsReproducibility of ResultsSurveys and QuestionnairesAlmere modelpsychometric propertysocial assistant robotstechnology acceptance

Identifiers

PMID36117570
PMCPMC9480597

What OpenQuestion holds

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LicenceCC BY-NC
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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.