Evidence map›Paper›PMID 28583905›Full record

ArticleJMIR mHealth and uHealth2017

Health App Possession Among Smartphone or Tablet Owners in Hong Kong: Population-Based Survey.

Chen Shen, Man Ping Wang, Joanna Tw Chu, Alice Wan, Kasisomayajula Viswanath, Sophia Siu Chee Chan, Tai Hing Lam

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 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
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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

7 authors.

Chen ShenSchool of Public Health, The University of Hong Kong, Hong Kong, China (Hong Kong).ORCID http://orcid.org/0000-0002-0119-4455
Man Ping WangSchool of Nursing, The University of Hong Kong, Hong Kong, China (Hong Kong).ORCID http://orcid.org/0000-0003-4000-2388
Joanna Tw ChuSchool of Public Health, The University of Hong Kong, Hong Kong, China (Hong Kong).ORCID http://orcid.org/0000-0002-5900-8141
Alice WanSchool of Public Health, The University of Hong Kong, Hong Kong, China (Hong Kong).ORCID http://orcid.org/0000-0001-5267-1269
Kasisomayajula ViswanathCenter for Community-Based Research, Dana-Farber Cancer Institute/Department of Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Cambridge, MA, United States.ORCID http://orcid.org/0000-0002-4795-5803
Sophia Siu Chee ChanSchool of Nursing, The University of Hong Kong, Hong Kong, China (Hong Kong).ORCID http://orcid.org/0000-0002-6349-3717
Tai Hing LamSchool of Public Health, The University of Hong Kong, Hong Kong, China (Hong Kong).ORCID http://orcid.org/0000-0001-5921-3033

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealth apps are increasingly used with important implications for health. Hong Kong is one of the most technologically advanced and connected cities-smartphone ownership and Internet access rates are among the highest in the world.

objectiveWe investigated the prevalence of health app possession and related sociodemographic factors and health behaviors among smartphone or tablet owners in Hong Kong.

methodsA territory-wide population-based dual (landline and mobile) telephone survey was conducted in 2016. Respondents were asked whether they had health-related apps on their smartphones or tablets and what functions were available on the apps (eg, tracking physical activity and logging health records). Logistic regression was used to calculate the adjusted odds ratio (aOR) and 95% CI of health app possession for different demographic characteristics, socioeconomic position (education, employment, and income), health behaviors (smoking, alcohol, and physical activity) and health (body mass index and chronic diseases).

resultsOf the 4129 smartphone or tablet owners (81.28%, 4129/5080 respondents), 995 (24.10%) had a health app. Tracking physical activity (67.0% of 995) and logging health records (43.0% of 995) were the most common functions of the health apps. Overall, younger age, higher education, and household income were associated with having health apps (all P<.001). Compared with physical inactivity, engaging in moderate physical activity ≥1 day/week was associated with having health apps (aOR 1.45 [95% CI 1.20-1.75] for 1-3 days/week, and aOR 1.32 [95% CI 1.07-1.62] for ≥4 days/week). Having a history of chronic diseases was associated with having health apps (aOR 1.36 [95% CI 1.11-1.68]).

conclusionsWe have shown a lower prevalence of use of information and communication technologies (ICTs) in respondents with lower education and income in the most developed Chinese city. This could be seen as a confirmation of the "Inverse information law," which suggests that those most in need have less use of services and hence receive less benefits from advancements in medicine and health related ICTs.

Indexed as

appsChinesesmartphone

Identifiers

PMID28583905
PMCPMC5476868

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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.