↓ Skip to main content

mHealth approaches to child obesity prevention: successes, unique challenges, and next directions

Overview of attention for article published in Translational Behavioral Medicine, July 2013
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
2 X users

Citations

dimensions_citation
157 Dimensions

Readers on

mendeley
365 Mendeley
Title
mHealth approaches to child obesity prevention: successes, unique challenges, and next directions
Published in
Translational Behavioral Medicine, July 2013
DOI 10.1007/s13142-013-0222-3
Pubmed ID
Authors

Eleanor B Tate, Donna Spruijt-Metz, Gillian O'Reilly, Maryalice Jordan-Marsh, Marientina Gotsis, Mary Ann Pentz, Genevieve F Dunton

Abstract

Childhood obesity continues to be a significant public health issue. mHealth systems offer state-of-the-art approaches to intervention design, delivery, and diffusion of treatment and prevention efforts. Benefits include cost effectiveness, potential for real-time data collection, feedback capability, minimized participant burden, relevance to multiple types of populations, and increased dissemination capability. However, these advantages are coupled with unique challenges. This commentary discusses challenges with using mHealth strategies for child obesity prevention, such as lack of scientific evidence base describing effectiveness of commercially available applications; relatively slower speed of technology development in academic research settings as compared with industry; data security, and patient privacy; potentially adverse consequences of increased sedentary screen time, and decreased focused attention due to technology use. Implications for researchers include development of more nuanced measures of screen time and other technology-related activities, and partnering with industry for developing healthier technologies. Implications for health practitioners include monitoring, assessing, and providing feedback to child obesity program designers about users' data transfer issues, perceived security and privacy, sedentary behavior, focused attention, and maintenance of behavior change. Implications for policy makers include regulation of claims and quality of apps (especially those aimed at children), supporting standardized data encryption and secure open architecture, and resources for research-industry partnerships that improve the look and feel of technology. Partnerships between academia and industry may promote solutions, as discussed in this commentary.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 365 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 2%
Australia 2 <1%
Spain 2 <1%
Brazil 1 <1%
United Kingdom 1 <1%
Germany 1 <1%
Sweden 1 <1%
Canada 1 <1%
Unknown 349 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 62 17%
Student > Bachelor 49 13%
Student > Ph. D. Student 46 13%
Researcher 40 11%
Other 20 5%
Other 75 21%
Unknown 73 20%
Readers by discipline Count As %
Medicine and Dentistry 69 19%
Nursing and Health Professions 38 10%
Computer Science 36 10%
Psychology 34 9%
Social Sciences 31 8%
Other 70 19%
Unknown 87 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 22 January 2018.
All research outputs
#14,431,072
of 23,577,761 outputs
Outputs from Translational Behavioral Medicine
#664
of 1,016 outputs
Outputs of similar age
#108,786
of 195,889 outputs
Outputs of similar age from Translational Behavioral Medicine
#10
of 14 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,016 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.9. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
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 195,889 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.