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: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
Authors:Ji YPlourde HBouzo VKilgour RDCohen TR
Link:https://pubmed.ncbi.nlm.nih.gov/32902389/
DOI:10.2196/16953
Publication:JMIR mHealth and uHealth
Keywords:3-day food diaryCanadaapplicationdiethealthy adultsimage-based dietary assessmentmHealthmobile food recordnutritionvalidity
PMID:32902389 Category: Date Added:2020-09-15
Dept Affiliation: PERFORM
1 School of Human Nutrition, McGill University, Montreal, QC, Canada.
2 Department of Health, Kinesiology, and Applied Physiology, Concordia University, Montreal, QC, Canada.
3 PERFORM Centre, Concordia University, Montreal, QC, Canada.
4 Faculty of Land and Food Systems, Food, Nutrition and Health, University of British Columbia, Vancouver, BC, Canada.

Description:

Background: Accurate dietary assessment is needed in studies that include analysis of nutritional intake. Image-based dietary assessment apps have gained in popularity for assessing diet, which may ease researcher and participant burden compared to traditional pen-to-paper methods. However, few studies report the validity of these apps for use in research. Keenoa is a smartphone image-based dietary assessment app that recognizes and identifies food items using artificial intelligence and permits real-time editing of food journals.

Objective: This study aimed to assess the relative validity of an image-based dietary assessment app - Keenoa - against a 3-day food diary (3DFD) and to test its usability in a sample of healthy Canadian adults.

Methods: We recruited 102 participants to complete two 3-day food records. For 2 weeks, on 2 non-consecutive days and 1 weekend day, in random order, participants completed a traditional pen-to-paper 3DFD and the Keenoa app. At the end of the study, participants completed the System Usability Scale. The nutrient analyses of the 3DFD and Keenoa data before (Keenoa-participant) and after they were reviewed by dietitians (Keenoa-dietitian) were analyzed using analysis of variance. Multiple tests, including the Pearson coefficient, cross-classification, kappa score, % difference, paired t test, and Bland-Altman test, were performed to analyze the validity of Keenoa (Keenoa-dietitian).

Results: The study was completed by 72 subjects. Most variables were significantly different between Keenoa-participant and Keenoa-dietitian (P<.05) except for energy, protein, carbohydrates, fiber, vitamin B1, vitamin B12, vitamin C, vitamin D, and potassium. Significant differences in total energy, protein, carbohydrates, % fat, saturated fatty acids, iron, and potassium were found between the 3DFD and Keenoa-dietitian data (P<.05). The Pearson correlation coefficients between the Keenoa-dietitian and 3DFD ranged from .04 to .51. Differences between the mean intakes assessed by the 3DFD and Keenoa-dietitian were within 10% except for vitamin D (misclassification rate=33.8%). The majority of nutrients were within an acceptable range of agreement in the Bland-Altman analysis; no agreements were seen for total energy, protein, carbohydrates, fat (%), saturated fatty acids, iron, potassium, and sodium (P<.05). According to the System Usability Scale, 34.2% of the participants preferred using Keenoa, while 9.6% preferred the 3DFD.

Conclusions: The Keenoa app provides acceptable relative validity for some nutrients compared to the 3DFD. However, the average intake of some nutrients, including energy, protein, carbohydrates, % fat, saturated fatty acids, and iron, differed from the average obtained using the 3DFD. These findings highlight the importance of verifying data entries of participants before proceeding with nutrient analysis. Overall, Keenoa showed better validity at the group level than the individual level, suggesting it can be used when focusing on the dietary intake of the general population. Further research is recommended with larger sample sizes and objective dietary assessment approaches.





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