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The impact of an m-Health financial incentives program on the physical activity and diet of Australian truck drivers

Overview of attention for article published in BMC Public Health, May 2017
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Title
The impact of an m-Health financial incentives program on the physical activity and diet of Australian truck drivers
Published in
BMC Public Health, May 2017
DOI 10.1186/s12889-017-4380-y
Pubmed ID
Authors

Nicholas D. Gilson, Toby G Pavey, Olivia RL Wright, Corneel Vandelanotte, Mitch J Duncan, Sjaan Gomersall, Stewart G. Trost, Wendy J. Brown

Abstract

Chronic diseases are high in truck drivers and have been linked to work routines that promote inactivity and poor diets. This feasibility study examined the extent to which an m-Health financial incentives program facilitated physical activity and healthy dietary choices in Australian truck drivers. Nineteen men (mean [SD] age = 47.5 [9.8] years; BMI = 31.2 [4.6] kg/m(2)) completed the 20-week program, and used an activity tracker and smartphone application (Jawbone UP™) to regulate small positive changes in occupational physical activity, and fruit, vegetable, saturated fat and processed/refined sugar food/beverage choices. Measures (baseline, end-program, 2-months follow-up; April-December 2014) were accelerometer-determined proportions of work time spent physically active, and a workday dietary questionnaire. Statistical (repeated measures ANOVA) and thematic (interviews) analyses assessed program impact. Non-significant increases in the mean proportions of work time spent physically active were found at end-program and follow-up (+1%; 7 mins/day). Fruit (p = 0.023) and vegetable (p = 0.024) consumption significantly increased by one serve/day at end-program. Non-significant improvements in saturated fat (5%) and processed/refined sugar (1%) food/beverage choices were found at end-program and follow-up. Overall, 65% (n = 11) of drivers demonstrated positive changes in physical activity, and at least one dietary choice (e.g. saturated fat) at follow-up. Drivers found the financial incentives component of the program to be a less effective facilitator of change than the activity tracker and smartphone application, although this technology was easier to use for monitoring of physical activity than healthy dietary choices. Not all drivers benefitted from the program. However, positive changes for different health behaviours were observed in the majority of participants. Outcomes from this feasibility study inform future intervention development for studies with larger samples. ANZCTR12616001513404 . Registered November 2nd, 2016 (retrospectively registered).

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The data shown below were collected from the profiles of 5 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 204 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 204 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 26 13%
Student > Ph. D. Student 24 12%
Researcher 22 11%
Student > Bachelor 22 11%
Student > Doctoral Student 9 4%
Other 27 13%
Unknown 74 36%
Readers by discipline Count As %
Medicine and Dentistry 25 12%
Nursing and Health Professions 24 12%
Psychology 12 6%
Social Sciences 12 6%
Sports and Recreations 11 5%
Other 35 17%
Unknown 85 42%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 02 December 2020.
All research outputs
#13,317,733
of 22,973,051 outputs
Outputs from BMC Public Health
#9,348
of 14,963 outputs
Outputs of similar age
#155,202
of 313,770 outputs
Outputs of similar age from BMC Public Health
#171
of 242 outputs
Altmetric has tracked 22,973,051 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,963 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.9. This one is in the 36th percentile – i.e., 36% 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 313,770 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 242 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.