Background Thorough dietary assessment is essential to obtain accurate food and nutrient intake data yet challenging because of the limitations of current methods. Image-based methods may decrease energy underreporting and increase the validity of self-reported dietary intake. Keenoa is an image-assisted food diary that integrates artificial intelligence food recognition. We hypothesized that Keenoa is as valid for dietary assessment as the automated self-administered 24-hour recall (ASA24)–Canada and better appreciated by users. Objective We aimed to evaluate the relative validity of Keenoa against a 24-hour validated web-based food recall platform (ASA24) in both healthy individuals and those living with diabetes. Secondary objectives were to compare the proportion of under- and overreporters between tools and to assess the user’s appreciation of the tools. Methods We used a randomized crossover design, and participants completed 4 days of Keenoa food tracking and 4 days of ASA24 food recalls. The System Usability Scale was used to assess perceived ease of use. Differences in reported intakes were analyzed using 2-tailed paired t tests or Wilcoxon signed-rank test and deattenuated correlations by Spearman coefficient. Agreement and bias were determined using the Bland-Altman test. Weighted Cohen κ was used for cross-classification analysis. Energy underreporting was defined as a ratio of reported energy intake to estimated resting energy expenditure <0.9. Results A total of 136 participants were included (mean 46.1, SD 14.6 years; 49/136, 36% men; 31/136, 22.8% with diabetes). The average reported energy intakes (kcal/d) were 2171 (SD 553) in men with Keenoa and 2118 (SD 566) in men with ASA24 (P=.38) and, in women, 1804 (SD 404) with Keenoa and 1784 (SD 389) with ASA24 (P=.61). The overall mean difference (kcal/d) was −32 (95% CI −97 to 33), with limits of agreement of −789 to 725, indicating acceptable agreement between tools without bias. Mean reported macronutrient, calcium, potassium, and folate intakes did not significantly differ between tools. Reported fiber and iron intakes were higher, and sodium intake lower, with Keenoa than ASA24. Intakes in all macronutrients (r=0.48-0.73) and micronutrients analyzed (r=0.40-0.74) were correlated (all P<.05) between tools. Weighted Cohen κ scores ranged from 0.30 to 0.52 (all P<.001). The underreporting rate was 8.8% (12/136) with both tools. Mean System Usability Scale scores were higher for Keenoa than ASA24 (77/100, 77% vs 53/100, 53%; P<.001); 74.8% (101/135) of participants preferred Keenoa. Conclusions The Keenoa app showed moderate to strong relative validity against ASA24 for energy, macronutrient, and most micronutrient intakes analyzed in healthy adults and those with diabetes. Keenoa is a new, alternative tool that may facilitate the work of dietitians and nutrition researchers. The perceived ease of use may improve food-tracking adherence over longer periods.

Background The COVID-19 pandemic and related lockdowns have impacted lifestyle behaviors, including eating habits and physical activity; yet, few studies have identified the emerging patterns of such changes and associated risk factors. Objective This study aims to identify the patterns of weight and lifestyle behavior changes, and the potential risk factors, resulting from the pandemic in Canadian adults. Methods Analyses were conducted on 1609 adults (18-89 years old; n=1450, 90.1%, women; n=1316, 81.8%, White) of the Canadian COVIDiet study baseline data (May-December 2020). Self-reported current and prepandemic weight, physical activity, smoking status, perceived eating habits, alcohol intake, and sleep quality were collected through online questionnaires. Based on these 6 indicator variables, latent class analysis (LCA) was used to identify lifestyle behavior change patterns. Associations with potential risk factors, including age, gender, ethnicity, education, income, chronic diseases, body image perception, and changes in the stress level, living situation, and work arrangement, were examined with logistic regressions. Results Participants’ mean BMI was 26.1 (SD 6.3) kg/m2. Of the 1609 participants, 980 (60.9%) had a bachelor’s degree or above. Since the pandemic, 563 (35%) had decreased income and 788 (49%) changed their work arrangement. Most participants reported unchanged weight, sleep quality, physical activity level, and smoking and alcohol consumption, yet 708 (44%) reported a perceived decrease in eating habit quality. From LCA, 2 classes of lifestyle behavior change emerged: healthy and less healthy (probability: 0.605 and 0.395, respectively; Bayesian information criterion [BIC]=15574, entropy=4.8). The healthy lifestyle behavior change group more frequently reported unchanged weight, sleep quality, smoking and alcohol intake, unchanged/improved eating habits, and increased physical activity. The less healthy lifestyle behavior change group reported significant weight gain, deteriorated eating habits and sleep quality, unchanged/increased alcohol intake and smoking, and decreased physical activity. Among risk factors, body image dissatisfaction (odds ratio [OR] 8.8, 95% CI 5.3-14.7), depression (OR 1.8, 95% CI 1.3-2.5), increased stress level (OR 3.4, 95% CI 2.0-5.8), and gender minority identity (OR 5.5, 95% CI 1.3-22.3) were associated with adopting less healthy behaviors in adjusted models. Conclusions The COVID-19 pandemic has appeared to have influenced lifestyle behaviors unfavorably in some but favorably in others. Body image perception, change in stress level, and gender identity are factors associated with behavior change patterns; whether these will sustain over time remains to be studied. Findings provide insights into developing strategies for supporting adults with poorer mental well-being in the postpandemic context and promoting healthful behaviors during future disease outbreaks. Trial Registration ClinicalTrials.gov NCT04407533; https://clinicaltrials.gov/ct2/show/NCT04407533

BACKGROUND The COVID-19 pandemic and related lockdowns has impacted lifestyle behaviours including eating habits and physical activity; yet few studies identified emerging patterns of such changes and associated risk factors. OBJECTIVE The objective was to identify patterns of weight and lifestyle behaviour change, and potential risk factors, resulting from the pandemic in Canadian adults. METHODS Analyses were conducted on 1,609 adults (18-89 y; 90.1% women; 81.8% White) of the Canadian COVIDiet study baseline data (May-Dec 2020). Self-reported current and pre-pandemic weight, physical activity, smoking status, perceived eating habits, alcohol intake and sleep quality were collected by online questionnaires. Based on these 6 indicator variables, latent class analysis (LCA) was used to identify lifestyle behaviour change patterns. Associations with potential risk factors including age, gender, ethnicity, education, income, chronic diseases, body image perception, and changes in stress level, living situation and work arrangement were examined with logistic regressions. RESULTS From LCA, 2 classes of lifestyle behaviour change emerged; “healthy” and “less healthy” (probability: 0.605 and 0.395; BIC=15574.3, entropy=4.8). “Healthy” class participants more frequently reported unchanged weight, sleep quality, smoking and alcohol intake, unchanged/improved eating habits and increased physical activity. The “less healthy” class reported significant weight gain, deteriorated eating habits and sleep quality, unchanged/increased alcohol intake and smoking, and decreased physical activity. Among risk factors, body image dissatisfaction [OR=8.8, 95%CI(5.3-14.7)], depression [OR=1.8, 95%CI(1.3, 2.5)], increased stress level [OR=3.4, 95%CI(2.0, 5.8)] and of gender minority identity [OR=5.5, 95% CI (1.3-22.3)] were associated with adopting “less healthy” behaviours in adjusted models. CONCLUSIONS The COVID-19 pandemic appeared to have influenced lifestyle behaviours unfavorably in some, but favorably in others. Body image perception and change in stress level may have modulated these changes; whether these will sustain overtime remains to be studied. CLINICALTRIAL ClinicalTrials.gov Identifier: NCT04407533

BACKGROUND Thorough dietary assessment is essential to obtain accurate food and nutrient intake data, yet challenging due to limitations of current methods. Image-based methods may decrease underreporting and increase validity of self-reported dietary intake. We hypothesized that Keenoa is as valid for dietary assessment as the Automated Self-Assessment (ASA) 24-Canada and better appreciated by users. OBJECTIVE to evaluate the relative validity of Keenoa against a 24-hour validated web-based food recall platform (ASA24) in both healthy individuals and those living with diabetes. Secondary objectives were to compare the proportion of under and over-reporters between tools, and to assess the user’s appreciation of the tools. METHODS Using a randomized crossover design, participants completed 4 days of Keenoa food tracking and 4 days of ASA24 food recalls. The System Usability Scale (SUS) assessed perceived ease of use. Differences in reported intakes were analyzed using paired t-tests or Wilcoxon signed-rank test and deattenuated correlations, by Spearman’s coefficient. Agreement and bias were determined using Bland-Altman’s test. Weighted Cohen’s kappa was used for cross-classification analysis. Underreporting was defined as a ratio of reported energy intake:estimated resting energy expenditure <0.9. RESULTS One hundred and thirty-six participants were included (46.1 ± 14.6 years; 36% men; 23% with diabetes). Mean (± SD) reported energy intakes (kcal/d) were, in men, 2171 ± 553 with Keenoa and 2118 ± 566 with ASA24 (P=.38), and in women, 1804 ± 404 with Keenoa and 1784 ± 389 with ASA 24 (P=0.61). The overall mean difference (kcal/d) was -32 (95%CI: -97 to 33), limit of agreement of -789 to 725, indicating acceptable agreement between tools, without bias. Mean reported macronutrient, calcium, potassium, and folate intakes did not significantly differ between tools. Reported fiber and iron intakes were higher, and sodium intake lower, with Keenoa than ASA24. Intakes in all macronutrients (r=0.48 to 0.73) and micronutrients analyzed (r=0.40 to 0.74) correlated (all P<.05) between tools. Weighted Cohen’s kappa scores ranged from 0.30-0.52 (all P<.001). Under-reporting rate was of 8.8% with both tools. Mean SUS scores were higher for Keenoa than ASA24 (77 vs. 53/100, P<.001); 75% of participants preferred Keenoa. CONCLUSIONS The Keenoa application showed moderate to strong relative validity against ASA24 for energy, macronutrient, and most micronutrient intakes analyzed in healthy adults and those with diabetes. Keenoa is a new, alternative tool that may facilitate the work of dietitians and nutrition researchers. The perceived ease of use may improve food tracking adherence over longer periods. CLINICALTRIAL This study was registered on the Dietary Assessment Calibration/Validation (DACV) Register from the National Cancer Institute (NIH).

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