Traditional methods of self-reported food intake are characterized by limitations such as underreporting, high participant burden, and high cost. With the development of automated devices to capture food images and monitor food intake, an accurate and efficient method to estimate energy intake is needed. This study aimed to develop an accurate and time efficient method for estimating energy intake from food images by defining a simple and less burdensome way of estimating energy density (ED). Four experimental methods, exchange, food score-long, food score-short, and meal, were developed to estimate ED based on nutrient composition, water content, and relative proportion of foods in images, using different approaches. Three trained nutritionists analyzed 29 food images for ED using each method. All four experimental methods were compared to the full visual method in which a nutritionist estimated the portion size of each food consumed from dietary intake images and conducted data entry and analysis software. All experimental methods overestimated ED compared to the FVM but the meal method exhibited the closest agreement, lowest variance for ED, and significantly decreased analysis time by an average of 53 s/meal (p = 0.03). The meal method was used for full-scale validation by analyzing 213 food images against weighed food records. The meal method reduced analysis time by 69% (120 s; p ≤ 0.0001) and over-estimated ED by an average of 1.56 ± 3.17 J/g (p < 0.0001) compared to the FVM and 1.67 ± 3.09 J/g (p < 0.0001) compared to the WFR. The meal method is a novel and quick approach to calculate ED from dietary intake images.
Management of chronic recurrent medical conditions (CRMC), such as migraine headaches, chronic pain and anxiety/depression, is a major challenge for modern providers. The fact that often the most effective treatments and/or preventative measures for CRMCs vary from patient to patient lends itself to a platform for self-management by patients. However, to develop such an mHealth app requires an understanding of the various applications, and barriers, to real-world use. In this pilot study with internet-based recruitment, we conducted an assessment of user satisfaction of the iMTracker iOS (iPhone) application for CRMC self-management through a self-administered survey of subjects with CRMCs. From May 15, 2019 until March 27, 2020, we recruited 135 subjects to pilot test the iMTracker application for user-selected CRMCs. The most common age group was 31-45 (48.2%), followed by under 30 (22.2%) and 46-55 (20%). There were no subjects over 75 years old completing the survey. 38.8% of subjects were college graduates, followed by 29.6% with a Masters degree, and 25.9% with some college. No subjects had not graduated from high school, and only 2 (1.5%) did not attend college after high school. 80.7% of subjects were self-identified as Caucasian, and 90.4% as not Hispanic or Latino. The most common CRMC was pain (other than headaches) in 40% of subjects, followed by mental health in 17.8% and headaches in 15.6%. 39.3% of subjects experienced the condition multiple times in a day, 40.0% experienced the condition daily, and 14.8% experienced the condition weekly, resulting in a total of 94.1% of subjects experiencing the condition at least weekly. Among the concerns about a self-management app, time demands (54.8%) and ineffectiveness (43.7%) were the most prominent, with privacy (24.4%) and data security (25.2%) also noted. In summary, we found internet-based recruitment identified primarily Caucasian population of relatively young patients with CRMCs of relatively high recurrence rate. Future work is needed to examine the use of this application in older, underrepresented minorities, and lower socioeconomic status populations.
Background Management of chronic recurrent medical conditions (CRMCs), such as migraine headaches, chronic pain, and anxiety/depression, remains a major challenge for modern providers. Our team has developed an edge-based, semiautomated mobile health (mHealth) technology called iMTracker that employs the N-of-1 trial approach to allow self-management of CRMCs. Objective This study examines the patterns of adoption, identifies CRMCs that users selected for self-application, and explores barriers to use of the iMTracker app. Methods This is a feasibility pilot study with internet-based recruitment that ran from May 15, 2019, to December 23, 2020. We recruited 180 patients to pilot test the iMTracker app for user-selected CRMCs for a 3-month period. Patients were administered surveys before and after the study. Results We found reasonable usage rates: a total of 73/103 (70.9%) patients who were not lost to follow-up reported the full 3-month use of the app. Most users chose to use the iMTracker app to self-manage chronic pain (other than headaches; 80/212, 37.7%), followed by headaches in 36/212 (17.0%) and mental health (anxiety and depression) in 27/212 (12.8%). The recurrence rate of CRMCs was at least weekly in over 93% (169/180) of patients, with 36.1% (65/180) of CRMCs recurring multiple times in a day, 41.7% (75/180) daily, and 16.1% (29/180) weekly. We found that the main barriers to use were the design and technical function of the app, but that use of the app resulted in an improvement in confidence in the efficiency and safety/privacy of this approach. Conclusions The iMTracker app provides a feasible platform for the N-of-1 trial approach to self-management of CRMCs, although internet-based recruitment provided limited follow-up, suggesting that in-person evaluation may be needed. The rate of CRMC recurrence was high enough to allow the N-of-1 trial assessment for most traits.
BACKGROUND Management of chronic recurrent medical conditions (CRMCs), such as migraine headaches, chronic pain, and anxiety/depression, remains a major challenge for modern providers. Our team has developed an edge-based, semiautomated mobile health (mHealth) technology called iMTracker that employs the N-of-1 trial approach to allow self-management of CRMCs. OBJECTIVE This study examines the patterns of adoption, identifies CRMCs that users selected for self-application, and explores barriers to use of the iMTracker app. METHODS This is a feasibility pilot study with internet-based recruitment that ran from May 15, 2019, to December 23, 2020. We recruited 180 patients to pilot test the iMTracker app for user-selected CRMCs for a 3-month period. Patients were administered surveys before and after the study. RESULTS We found reasonable usage rates: a total of 73/103 (70.9%) patients who were not lost to follow-up reported the full 3-month use of the app. Most users chose to use the iMTracker app to self-manage chronic pain (other than headaches; 80/212, 37.7%), followed by headaches in 36/212 (17.0%) and mental health (anxiety and depression) in 27/212 (12.8%). The recurrence rate of CRMCs was at least weekly in over 93% (169/180) of patients, with 36.1% (65/180) of CRMCs recurring multiple times in a day, 41.7% (75/180) daily, and 16.1% (29/180) weekly. We found that the main barriers to use were the design and technical function of the app, but that use of the app resulted in an improvement in confidence in the efficiency and safety/privacy of this approach. CONCLUSIONS The iMTracker app provides a feasible platform for the N-of-1 trial approach to self-management of CRMCs, although internet-based recruitment provided limited follow-up, suggesting that in-person evaluation may be needed. The rate of CRMC recurrence was high enough to allow the N-of-1 trial assessment for most traits.
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