Keyword search (4,163 papers available)

"Application" Keyword-tagged Publications:

Title Authors PubMed ID
1 Advancements in Magnetorheological Foams: Composition, Fabrication, AI-Driven Enhancements and Emerging Applications Khodaverdi H; Sedaghati R; 40732777
ENCS
2 Web-enhanced return-to-work coordination for employees with common mental disorders: reduction of sick leave duration and relapse Corbière M; Mazaniello-Chézol M; Lecomte T; Guay S; Panaccio A; Giguère CÉ; 39966766
PSYCHOLOGY
3 Proof-of-concept testing of a mobile application-delivered mindfulness exercise for emotional eaters: RAIN delivered as a step-by-step image sequence Carrière K; Siemers N; Thapar S; Knäuper B; 39114459
HKAP
4 Advancements in Hybrid Cellulose-Based Films: Innovations and Applications in 2D Nano-Delivery Systems Ramezani G; Stiharu I; van de Ven TGM; Nerguizian V; 38667550
ENCS
5 Understanding Adolescents' Experiences With Menstrual Pain to Inform the User-Centered Design of a Mindfulness-Based App: Mixed Methods Investigation Study Gagnon MM; Brilz AR; Alberts NM; Gordon JL; Risling TL; Stinson JN; 38587886
PSYCHOLOGY
6 Call to action: equity, diversity, and inclusion in emergency medicine resident physician selection Primavesi R; Patocka C; Burcheri A; Coutin A; Elhalwi AM; Ali A; Pandya A; Gagné A; Johnston B; Thoma B; LeBlanc C; Fovet F; Gallinger J; Mohadeb J; Ragheb M; Dong S; Smith S; Oyedokun T; Newmarch T; Knight V; McColl T; 37368231
CONCORDIA
7 Hyperelastic Modeling and Validation of Hybrid-Actuated Soft Robot with Pressure-Stiffening Roshanfar M; Taki S; Sayadi A; Cecere R; Dargahi J; Hooshiar A; 37241524
ENCS
8 Human Activity Recognition with an HMM-Based Generative Model Manouchehri N; Bouguila N; 36772428
ENCS
9 Evaluation of the Diet Tracking Smartphone Application Keenoa™: A Qualitative Analysis Bouzo V; Plourde H; Beckenstein H; Cohen TR; 34582258
PERFORM
10 A historical perspective on porphyrin-based metal-organic frameworks and their applications Zhang X; Wasson MC; Shayan M; Berdichevsky EK; Ricardo-Noordberg J; Singh Z; Papazyan EK; Castro AJ; Marino P; Ajoyan Z; Chen Z; Islamoglu T; Howarth AJ; Liu Y; Majewski MB; Katz MJ; Mondloch JE; Farha OK; 33678810
CNSR
11 A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects. Khannouz M; Glatard T; 33202905
ENCS
12 Validity and Usability of a Smartphone Image-Based Dietary Assessment App Compared to 3-Day Food Diaries in Assessing Dietary Intake Among Canadian Adults: Randomized Controlled Trial Ji Y; Plourde H; Bouzo V; Kilgour RD; Cohen TR; 32902389
PERFORM
13 Augmented reality mastectomy surgical planning prototype using the HoloLens template for healthcare technology letters. Amini S, Kersten-Oertel M 32038868
PERFORM

 

Title:Evaluation of the Diet Tracking Smartphone Application Keenoa™: A Qualitative Analysis
Authors:Bouzo VPlourde HBeckenstein HCohen TR
Link:https://pubmed.ncbi.nlm.nih.gov/34582258/
DOI:10.3148/cjdpr-2021-022
Publication:Canadian journal of dietetic practice and research : a publication of Dietitians of Canada = Revue canadienne de la pratique et de la recherche en dietetique : une publication des Dietetistes du Canada
Keywords:Mobile applicationsapplications mobilesartificial intelligencediet recordsintelligence artificiellejournal alimentairenutrition assessmentévaluation nutritionnelle
PMID:34582258 Category: Date Added:2021-09-28
Dept Affiliation: PERFORM
1 School of Human Nutrition, McGill University, Montreal, QC.
2 PERFORM Centre, Concordia University, Montreal, QC.
3 Faculty of Land and Food Systems, Food, Nutrition and Health, University of British Columbia, Vancouver, BC.

Description:

Keenoa™ is a novel Canadian diet application (app) currently used by Canadian dietitians to collect diet-related data from clients. The goal of this study was to evaluate Keenoa™ based on user feedback and compare it to a conventional pen and paper method. One hundred and two participants were recruited and randomly assigned to record their diets using this application for 3 nonconsecutive days. Following this, participants were invited to complete an online "exit" survey. Seventy-two subjects responded, with 50 completing an open-ended question asking for general feedback about the app. Data were reviewed and 3 main themes emerged: strengths, challenges, and future recommendations. Strengths associated with the app consisted of picture recognition software, the additional commentary feature, and the overall pleasant data collection process. Challenges that were identified included inconsistencies with the barcode scanning features, the limited food database, time to enter food details, and software issues. Future recommendations included using a larger food database, pairing dietary intake with physical activity monitoring, and having accessible nutritional data. Despite these limitations, participants preferred using mobile apps to record diet compared with traditional written food diaries.





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