Data

Mobile Diet Appetite Study 2

Queensland University of Technology
Moore, Halim ; White, Melanie ; King, Neil
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25912/RDF_1666134692051&rft.title=Mobile_Diet_Appetite_Study2&rft.identifier=10.25912/RDF_1666134692051&rft.publisher=Queensland University of Technology&rft.description=This dataset was derived from one of the investigations within a series that comprised Halim Moore's PhD thesis. Cognitive and behavioural biases have been shown to influence dietary behaviour, particularly as a proclivity for energy-dense, palatable, and processed foods. Researchers have thus considered them a potential target for intervention to promote healthier eating behaviours. Recent advances in mobile app technology make this class of intervention more scalable and easier to adhere to than conventional dietary interventions. One such example that has demonstrated the most consistent efficacy in modifying eating behaviour to date is Response Inhibition Training. However, the efficacy and utility of Response Inhibition Training (and other types of Cognitive Bias Modification) can be considered questionable due to the heterogenous samples, implementation and outcomes selected across studies. In particular, it is unclear whether Response Inhibition Training has a similar impact on liking and wanting, and whether both outcomes in response to intervention follow a similar trend. Participants completed both a single session of Response Inhibition Training and control mobile apps and were assessed on both liking and wanting for foods based on energy density. Trait measures of appetitive susceptibility, such as body mass index and food cue reactivity, were assessed in order to investigate whether trends in liking and wanting for these foods were congruent or divergent. A publicly available dataset associated with the manuscript published in Appetite, Exploring acute and non-specific effects of mobile app-based response inhibition training on food evaluation and intake (), and Study 2 (Chapter 5) in the thesis titled . &rft.creator=Moore, Halim &rft.creator=White, Melanie &rft.creator=King, Neil &rft.date=2022&rft.edition=1&rft.relation=https://eprints.qut.edu.au/235040/&rft.coverage=153.317870,-26.996845 152.668523,-26.996845 152.668523,-27.767441 153.317870,-27.767441 153.317870,-26.996845&rft_rights=© Halim Moore, 2022.&rft_rights=Creative Commons Attribution-NonCommercial 3.0 http://creativecommons.org/licenses/by-nc/4.0/&rft_subject=Food nutritional balance&rft_subject=Food sciences&rft_subject=AGRICULTURAL, VETERINARY AND FOOD SCIENCES&rft_subject=Response inhibition&rft_subject=Impulsivity&rft_subject=Reward&rft_subject=Energy density&rft_subject=Smartphone&rft_subject=Cognitive training&rft_subject=Mobile application&rft_subject=Appetite&rft_subject=Food preference&rft.type=dataset&rft.language=English Access the data

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Non-Commercial Licence view details
CC-BY-NC

Creative Commons Attribution-NonCommercial 3.0
http://creativecommons.org/licenses/by-nc/4.0/

© Halim Moore, 2022.

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Contact Information

Postal Address:
Dr. Halim Moore

halim.moore@connect.qut.edu.au

Full description

This dataset was derived from one of the investigations within a series that comprised Halim Moore's PhD thesis.

Cognitive and behavioural biases have been shown to influence dietary behaviour, particularly as a proclivity for energy-dense, palatable, and processed foods. Researchers have thus considered them a potential target for intervention to promote healthier eating behaviours. Recent advances in mobile app technology make this class of intervention more scalable and easier to adhere to than conventional dietary interventions. One such example that has demonstrated the most consistent efficacy in modifying eating behaviour to date is Response Inhibition Training. However, the efficacy and utility of Response Inhibition Training (and other types of Cognitive Bias Modification) can be considered questionable due to the heterogenous samples, implementation and outcomes selected across studies. In particular, it is unclear whether Response Inhibition Training has a similar impact on liking and wanting, and whether both outcomes in response to intervention follow a similar trend.

Participants completed both a single session of Response Inhibition Training and control mobile apps and were assessed on both liking and wanting for foods based on energy density. Trait measures of appetitive susceptibility, such as body mass index and food cue reactivity, were assessed in order to investigate whether trends in liking and wanting for these foods were congruent or divergent.

A publicly available dataset associated with the manuscript published in Appetite, "Exploring acute and non-specific effects of mobile app-based response inhibition training on food evaluation and intake" (), and Study 2 (Chapter 5) in the thesis titled "".

Data time period: 25 10 2018 to 15 03 2019

This dataset is part of a larger collection

Click to explore relationships graph

153.31787,-26.99685 152.66852,-26.99685 152.66852,-27.76744 153.31787,-27.76744 153.31787,-26.99685

152.9931965,-27.382143

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