Keyword search (4,163 papers available)

"Bouguila N" Authored Publications:

Title Authors PubMed ID
1 Neural topic modeling on hyperspheres: Spherical representation learning with von Mises-Fisher mixtures Guo D; Luo Z; Bouguila N; Fan W; 41791177
ENCS
2 Disentangled representation learning for multi-view clustering via von Mises-Fisher hyperspherical embedding Li Z; Luo Z; Bouguila N; Su W; Fan W; 40664160
ENCS
3 Clustering and Interpretability of Residential Electricity Demand Profiles Kallel S; Amayri M; Bouguila N; 40218540
ENCS
4 SAVE: Self-Attention on Visual Embedding for Zero-Shot Generic Object Counting Zgaren A; Bouachir W; Bouguila N; 39997554
ENCS
5 Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings Guo J; Fan W; Amayri M; Bouguila N; 39662201
ENCS
6 FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images Paul S; Patterson Z; Bouguila N; 38535151
ENCS
7 Perceptions of self-monitoring dietary intake according to a plate-based approach: A qualitative study Kheirmandparizi M; Gouin JP; Bouchaud CC; Kebbe M; Bergeron C; Madani Civi R; Rhodes RE; Farnesi BC; Bouguila N; Conklin AI; Lear SA; Cohen TR; 38015899
PERFORM
8 Unsupervised Mixture Models on the Edge for Smart Energy Consumption Segmentation with Feature Saliency Al-Bazzaz H; Azam M; Amayri M; Bouguila N; 37837127
ENCS
9 Data-Weighted Multivariate Generalized Gaussian Mixture Model: Application to Point Cloud Robust Registration Ge B; Najar F; Bouguila N; 37754943
ENCS
10 Human Activity Recognition with an HMM-Based Generative Model Manouchehri N; Bouguila N; 36772428
ENCS
11 Cross-collection latent Beta-Liouville allocation model training with privacy protection and applications Luo Z; Amayri M; Fan W; Bouguila N; 36685642
ENCS
12 Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning Bouhamed O; Amayri M; Bouguila N; 35590880
ENCS
13 Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures Bourouis S; Pawar Y; Bouguila N; 35009726
ENCS
14 Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images Bourouis S; Alharbi A; Bouguila N; 34460578
ENCS

 

Title:Perceptions of self-monitoring dietary intake according to a plate-based approach: A qualitative study
Authors:Kheirmandparizi MGouin JPBouchaud CCKebbe MBergeron CMadani Civi RRhodes REFarnesi BCBouguila NConklin AILear SACohen TR
Link:https://pubmed.ncbi.nlm.nih.gov/38015899/
DOI:10.1371/journal.pone.0294652
Publication:PloS one
Keywords:
PMID:38015899 Category: Date Added:2023-11-28
Dept Affiliation: PERFORM
1 Faculty of Land and Food Systems, Food, Nutrition and Health, the University of British Columbia, Vancouver, British Columbia, Canada.
2 PERFORM Centre, Concordia University, Montreal, Quebec, Canada.
3 Department of Psychology, Concordia University, Montreal, Quebec, Canada.
4 Faculty of Kinesiology, University of New Brunswick, Fredericton, New Brunswick, Canada.
5 School of Exercise Science, Physical & Health Education, University of Victoria, Victoria, British Columbia, Canada.
6 Division of Adolescent Medicine, Montreal Children's Hospital, Westmount, Quebec, Canada.
7 Concordia Institute for Information Systems Engineering, Engineering, Computer Science and Visual Arts Integrated Complex, Concordia University, Montreal, Quebec, Canada.
8 Faculty of Pharmaceutical Sciences, the University of British Columbia, Vancouver, British Columbia, Canada.
9 Faculty of Health Sciences, Burnaby and Division of Cardiology, Providence Health Care, Simon Fraser University, Vancouver, BC, Canada.

Description:

Dietary self-monitoring is a behaviour change technique used to help elicit and sustain dietary changes over time. Current dietary self-monitoring tools focus primarily on itemizing foods and counting calories, which can be complex, time-intensive, and dependent on health literacy. Further, there are no dietary self-monitoring tools that conform to the plate-based approach of the 2019 Canada Food Guide (CFG), wherein the recommended proportions of three food groups are visually represented on a plate without specifying daily servings or portion sizes. This paper explored the perceptions of end-users (i.e., general public) and Registered Dietitians of iCANPlateTM-a dietary self-monitoring mobile application resembling the CFG. Qualitative data were collected through virtual focus groups. Focus group questions were based on the Capability, Opportunity, Motivation-Behaviour (COM-B) theoretical framework to explore perceptions of using the CFG and currently available dietary self-monitoring tools. The prototype iCANPlateTM (version 0.1) was presented to gain feedback on perceived barriers and facilitators of its use. Focus group discussions were audio recorded and verbatim transcribed. Trained researchers used thematic analysis to code and analyze the transcripts independently. Seven focus groups were conducted with Registered Dietitians (n = 44) and nine focus groups with members from the general public (n = 52). During the focus groups, participants mainly discussed the capabilities and opportunities required to use the current iteration of iCANPlateTM. Participants liked the simplicity of the application and its capacity to foster self-awareness of dietary behaviours rather than weight control or calorie counting. However, concerns were raised regarding iCANPlateTM's potential to improve adherence to dietary self-monitoring due to specific characteristics (i.e., insufficient classifications, difficulty in conceptualizing proportions, and lack of inclusivity). Overall, participants liked the simplicity of iCANPlateTM and its ability to promote self-awareness of dietary intakes, primarily through visual representation of foods on a plate as opposed to reliance on numerical values or serving sizes, were benefits of using the app. Findings from this study will be used to further develop the app with the goal of increasing adherence to plate-based dietary approaches.





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