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Temporal eating patterns: a latent class analysis approach

Overview of attention for article published in International Journal of Behavioral Nutrition and Physical Activity, January 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

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Title
Temporal eating patterns: a latent class analysis approach
Published in
International Journal of Behavioral Nutrition and Physical Activity, January 2017
DOI 10.1186/s12966-016-0459-6
Pubmed ID
Authors

Rebecca M. Leech, Anthony Worsley, Anna Timperio, Sarah A. McNaughton

Abstract

There is some evidence that large energy intakes towards the end of the day are associated with adverse health outcomes, however, studies of temporal eating patterns across the day are rare. This study examines the temporal eating patterns of Australian adults using latent class analysis (LCA), as a novel approach. Dietary data (n = 2402 men and n = 2840 women, ≥19 years) from two 24-h recalls collected during the 2011-12 Australian National Nutrition and Physical Activity Survey were analyzed. LCA was performed to identify distinct temporal eating patterns based on whether or not an eating occasion (EO) occurred within each hour of the day. F and adjusted-chi(2) tests assessed differences in sociodemographic and eating patterns (e.g., meal, snack and EO frequency) between latent classes. Three patterns, labelled "Conventional" (men: 43%, women: 41%), "Later lunch" (men: 34%, women: 34%) and "Grazing" (men: 23%, women: 25%) were identified. Men and women with a "Grazing" pattern were significantly younger (P < 0.001) and a higher proportion were from major cities (P < 0.01) and were not married (men only, P = 0.01), compared to the "Conventional" and "Later lunch" patterns. The "Grazing" pattern was also characterized by a higher EO frequency (P < 0.01) and snack frequency (P < 0.001) and consumption of a higher proportion of total energy intake from snacks but a lower proportion of total energy intake from meals (P < 0.001). This study identified three distinct temporal eating patterns in adults that varied by age, EO frequency, snack frequency and energy intake pattern. LCA is a useful approach to capture differences in EO timing across the day. Future research should examine associations between temporal eating patterns and health.

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X Demographics

The data shown below were collected from the profiles of 25 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 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 82 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 13%
Student > Master 11 13%
Student > Ph. D. Student 10 12%
Student > Bachelor 6 7%
Student > Doctoral Student 5 6%
Other 16 20%
Unknown 23 28%
Readers by discipline Count As %
Medicine and Dentistry 16 20%
Nursing and Health Professions 10 12%
Agricultural and Biological Sciences 5 6%
Biochemistry, Genetics and Molecular Biology 4 5%
Unspecified 4 5%
Other 15 18%
Unknown 28 34%
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 04 February 2017.
All research outputs
#2,039,109
of 23,835,032 outputs
Outputs from International Journal of Behavioral Nutrition and Physical Activity
#778
of 1,991 outputs
Outputs of similar age
#43,191
of 425,074 outputs
Outputs of similar age from International Journal of Behavioral Nutrition and Physical Activity
#15
of 33 outputs
Altmetric has tracked 23,835,032 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,991 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 29.0. This one has gotten more attention than average, scoring higher than 60% 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 425,074 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 89% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.