Evidence map›Paper›PMID 31247962›Full record

ArticleInternational journal of environmental research and public health2019

Examining Consumers' Adoption of Wearable Healthcare Technology: The Role of Health Attributes.

Man Lai Cheung, Ka Yin Chau, Michael Huen Sum Lam, Gary Tse, Ka Yan Ho, Stuart W Flint, David R Broom, Ejoe Kar Ho Tso, Ka Yiu Lee

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 5 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

44 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Demographic and Socioeconomic Factors Associated with Fitbit Ownership in the NIHInternational journal of environmental research and public health · 2026
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  19. Conceptualizing Patient as an Organization With the Adoption of Digital Health.Biomedical engineering and computational biology · 2024
    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

9 authors.

Man Lai CheungDivision of Business and Management, BNU-HKBU United International College, Zhu Hai 519000, China.
Ka Yin ChauFaculty of International Tourism and Management, City University of Macau, Macau, China. gavinchau@cityu.mo.ORCID 0000-0002-0381-8401
Michael Huen Sum LamFaculty of Health and Wellbeing, Sheffield Hallam University, Sheffield S10 2BP, UK.
Gary TseLi Ka Shing Institute of Health Sciences, Chinese University of Hong Kong, Hong Kong, China.
Ka Yan HoSchool of Nursing, University of Hong Kong, Hong Kong, China.
Stuart W FlintSchool of Sport, Leeds Beckett University, Leeds LS6 3QS, UK.
David R BroomAcademy of Sport and Physical Activity, Sheffield Hallam University, Sheffield S10 2BP, UK.ORCID 0000-0002-0305-937X
Ejoe Kar Ho TsoBorneo Business School, North Borneo University College, Sabah 88400, Malaysian.
Ka Yiu LeeFaculty of Health and Wellbeing, Sheffield Hallam University, Sheffield S10 2BP, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the advancement of information technology, wearable healthcare technology has emerged as one of the promising technologies to improve the wellbeing of individuals. However, the adoption of wearable healthcare technology has lagged when compared to other well-established durable technology products, such as smartphones and tablets, because of the inadequate knowledge of the antecedents of adoption intention. The aim of this paper is to address an identified gap in the literature by empirically testing a theoretical model for examining the impact of consumers' health beliefs, health information accuracy, and the privacy protection of wearable healthcare technology on perceived usefulness. Importantly, this study also examines the influences of perceived usefulness, consumer innovativeness, and reference group influence on the adoption intention of wearable healthcare technology. The model seeks to enhance understanding of the influential factors in adopting wearable healthcare technology. Finally, suggestions for future research for the empirical investigation of the model are provided.

Indexed as

Health Knowledge, Attitudes, PracticeIntentionModels, TheoreticalConfidentialityHumansPerceptionWearable Electronic Devicesadoption intentionconsumer innovativenesshealth beliefhealth information accuracyperceived usefulnessprivacywearable healthcare technology

Identifiers

PMID31247962
PMCPMC6650855

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.