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Use of Mobile Health Applications for Health-Seeking Behavior Among US Adults

Overview of attention for article published in Journal of Medical Systems, May 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
3 X users

Citations

dimensions_citation
153 Dimensions

Readers on

mendeley
397 Mendeley
Title
Use of Mobile Health Applications for Health-Seeking Behavior Among US Adults
Published in
Journal of Medical Systems, May 2016
DOI 10.1007/s10916-016-0492-7
Pubmed ID
Authors

Soumitra S. Bhuyan, Ning Lu, Aastha Chandak, Hyunmin Kim, David Wyant, Jay Bhatt, Satish Kedia, Cyril F. Chang

Abstract

This study explores the use of mobile health applications (mHealth apps) on smartphones or tablets for health-seeking behavior among US adults. Data was obtained from cycle 4 of the 4th edition of the Health Information National Trends Survey (HINTS 4). Weighted multivariate logistic regression models examined predictors of 1) having mHealth apps, 2) usefulness of mHealth apps in achieving health behavior goals, 3) helpfulness in medical care decision-making, and 4) asking a physician new questions or seeking a second opinion. Using the Andersen Model of health services utilization, independent variables of interest were grouped under predisposing factors (age, gender, race, ethnicity, and marital status), enabling factors (education, employment, income, regular provider, health insurance, and rural/urban location of residence), and need factors (general health, confidence in their ability to take care of health, Body Mass Index, smoking status, and number of comorbidities). In a national sample of adults who had smartphones or tablets, 36 % had mHealth apps on their devices. Among those with apps, 60 % reported the usefulness of mHealth apps in achieving health behavior goals, 35 % reported their helpfulness for medical care decision-making, and 38 % reported their usefulness in asking their physicians new questions or seeking a second opinion. The multivariate models revealed that respondents were more likely to have mHealth apps if they had more education, health insurance, were confident in their ability to take good care of themselves, or had comorbidities, and were less likely to have them if they were older, had higher income, or lived in rural areas. In terms of usefulness of mHealth apps, those who were older and had higher income were less likely to report their usefulness in achieving health behavior goals. Those who were older, African American, and had confidence in their ability to take care of their health were more likely to respond that the mHealth apps were helpful in making a medical care decision and asking their physicians new questions or for a second opinion. Potentially, mHealth apps may reduce the burden on primary care, reduce costs, and improve the quality of care. However, several personal-level factors were associated with having mHealth apps and their perceived helpfulness among their users, indicating a multidimensional digital divide in the population of US adults.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 397 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Colombia 1 <1%
United States 1 <1%
Czechia 1 <1%
Unknown 393 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 58 15%
Student > Bachelor 45 11%
Student > Ph. D. Student 43 11%
Researcher 32 8%
Student > Doctoral Student 28 7%
Other 72 18%
Unknown 119 30%
Readers by discipline Count As %
Medicine and Dentistry 58 15%
Nursing and Health Professions 46 12%
Social Sciences 34 9%
Computer Science 31 8%
Psychology 27 7%
Other 69 17%
Unknown 132 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 June 2016.
All research outputs
#2,339,695
of 25,765,370 outputs
Outputs from Journal of Medical Systems
#52
of 1,287 outputs
Outputs of similar age
#36,406
of 313,334 outputs
Outputs of similar age from Journal of Medical Systems
#3
of 36 outputs
Altmetric has tracked 25,765,370 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 95% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 313,334 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.