Evidence map›Paper›PMID 36003315›Full record

ArticleDigital health

What drives the use of wearable healthcare devices? A cross-country comparison between the US and Korea.

Jong-Youn Rha, Youngwon Nam, Sun Young Ahn, Jihye Kim, Youchung Chang, Jisu Jang, Keiko Kurita, Jin-Young Park, Kunsun Eom, Hyunseok Moon and 4 more

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Inequalities in Exclusively Mobile Interventions Targeting Weight-Related Behaviors: Systematic Review of Observational Studies.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026
    Pooled it
  2. Article
  3. 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

14 authors.

Jong-Youn RhaDepartment of Consumer Science, Seoul National University, Seoul, Gwanak-gu, Korea.
Youngwon NamDepartment of Consumer Science, Seoul National University, Seoul, Gwanak-gu, Korea.ORCID https://orcid.org/0000-0001-7630-5896
Sun Young AhnSchool of Business and Leadership, University of Puget Sound, Tacoma, WA, USA.
Jihye KimResearch Institute of Human Ecology, Seoul National University, Seoul, Gwanak-gu, Korea.
Youchung ChangDepartment of Consumer Science, Seoul National University, Seoul, Gwanak-gu, Korea.
Jisu JangDepartment of Consumer Science, Seoul National University, Seoul, Gwanak-gu, Korea.
Keiko KuritaSamsung Research America, Mountain View, CA, USA.
Jin-Young ParkAdvanced Sensor Lab, Device Research Center, SAIT, Samsung Electronics, Suwon-si, Gyeonggi, Korea.
Kunsun EomAdvanced Sensor Lab, Device Research Center, SAIT, Samsung Electronics, Suwon-si, Gyeonggi, Korea.
Hyunseok MoonAdvanced Sensor Lab, Device Research Center, SAIT, Samsung Electronics, Suwon-si, Gyeonggi, Korea.
Myoung Hoon JungAdvanced Sensor Lab, Device Research Center, SAIT, Samsung Electronics, Suwon-si, Gyeonggi, Korea.
Yoon Jae KimAdvanced Sensor Lab, Device Research Center, SAIT, Samsung Electronics, Suwon-si, Gyeonggi, Korea.
Jeong-Eun HwangAdvanced Sensor Lab, Device Research Center, SAIT, Samsung Electronics, Suwon-si, Gyeonggi, Korea.
HyucK ChooAdvanced Sensor Lab, Device Research Center, SAIT, Samsung Electronics, Suwon-si, Gyeonggi, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Given the rapid growth of the wearable healthcare device market, we examined the associations among health-related and technology-related characteristics of using wearable healthcare devices and demonstrated how the associations differ between the US and Korean users. Methods: Online self-administered surveys were conducted with 4098 participants (3035 in the US and 1063 in Korea) who were recruited through two online survey service providers based on quota sampling. The primary outcome was the use of wearable healthcare devices. Seven health-related, two technology-related, and five socio-demographic factors were included as explanatory variables. Binary logistic regression analyses and a Chow test were conducted. Results: The health-related characteristics that were significantly associated with using wearable healthcare devices included disease-related worries (β = 0.11**), health information seeking (β = 0.26***), physical activity (β = 0.62***), and health-related expenditures ($50-$199, β = 0.38***; $200 or more, β = 0.56***). Hedonic (β = 0.33***), social (β = 0.31***), and cognitive innovativeness (β = 0.14*) also exhibited positive relationships. Younger, higher earner, and individuals with a child were more likely to use wearable healthcare devices. However, for Korean users, several associations disappeared including health information seeking, hedonic and social innovativeness, age, and household income. Conclusions: Key drivers of using wearable healthcare devices include greater concern about a specific illness, active engagement in health-promoting behaviors, and hedonic and social motivation to adopt new technologies. However, more country-specific considerations are needed in future studies to identify the main benefits for target markets.

Indexed as

cross-country studyhealth promotionsmartwatchtechnology innovativenessWearable healthcare device

Identifiers

PMID36003315
PMCPMC9393678

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.