Browsing by Author "Pai, Amruta"

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    A Computational Analysis of Meal Events Using Food Diaries and Continuous Glucose Monitors
    (2023-04-21) Pai, Amruta; Sabharwal, Ashutosh; Allen, Genevera; Patel, Ankit; Beier, Margaret; Kerr, David
    Diet self-management, through its effect on weight and glycemic control, is one of the cornerstones of Type 2 Diabetes (T2D) prevention and management. A quantitative understanding of bio-behavioral mechanisms of diet is needed to create effective diet self-management tools. Smartphone diet-tracking applications and continuous glucose monitors (CGMs) are emerging devices that enable dense sampling of an individual's diet. Research in diet analysis of app-based food diaries and CGMs have mainly focused on developing aggregate measures of nutrient intake and glucose responses. However, innovative computational analysis is required to infer actionable insights. In this thesis, we develop computational measures for various bio-behavioral aspects of diet by leveraging meal event data collected with food diaries and CGMs. First, we establish recurrent consumption measures across meal events to characterize habitual behavior in an individual's diet. We leverage a large publicly available MyFitnessPal (MFP) food diary dataset to provide novel insights on differences in habitual behavior across individuals and temporal contexts. Next, we develop calorie compensation measures to characterize self-regulatory behavior. A quantitative analysis of calorie compensation measures on the MFP dataset reveals significant meal compensation patterns and their impact on adherence to self-set calorie goals. Finally, we designed an observational study using the MFP app and CGMs to evaluate the impact of meal events on glycemic control in adults with varying hemoglobin a1c levels. We developed elevated meal event count to characterize mealtime glucose responses by exploiting its association with hemoglobin a1c. Elevated meal event count significantly affected glycemic control, suggesting its value as a novel event-driven glycemic target metric. This thesis highlights the value of using CGMs and food diaries to broaden our understanding of diet. The developed measures augment existing intake measures and could be used as a digital bio-behavioral markers to personalize diet self-management strategies.
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    Calorie Compensation Patterns Observed in App-Based Food Diaries
    (MDPI, 2023) Pai, Amruta; Sabharwal, Ashutosh
    Self-regulation of food intake is necessary for maintaining a healthy body weight. One of the characteristics of self-regulation is calorie compensation. Calorie compensation refers to adjusting the current meal’s energy content based on the energy content of the previous meal(s). Preload test studies measure a single instance of compensation in a controlled setting. The measurement of calorie compensation in free-living conditions has largely remained unexplored. This paper proposes a methodology that leverages extensive app-based observational food diary data to measure an individual’s calorie compensation profile in free-living conditions. Instead of a single compensation index followed in preload–test studies, we present the compensation profile as a distribution of days a user exhibits under-compensation, overcompensation, non-compensation, and precise compensation. We applied our methodology to the public food diary data of 1622 MyFitnessPal users. We empirically established that four weeks of food diaries were sufficient to characterize a user’s compensation profile accurately. We observed that meal compensation was more likely than day compensation. Dinner compensation had a higher likelihood than lunch compensation. Precise compensation was the least likely. Users were more likely to overcompensate for missing calories than for additional calories. The consequences of poor compensatory behavior were reflected in their adherence to their daily calorie goal. Our methodology could be applied to food diaries to discover behavioral phenotypes of poor compensatory behavior toward forming an early behavioral marker for weight gain.
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    Food Habits: Insights from Food Diaries via Computational Recurrence Measures
    (MDPI, 2022) Pai, Amruta; Sabharwal, Ashutosh; Scalable Health Labs
    Humans are creatures of habit, and hence one would expect habitual components in our diet. However, there is scant research characterizing habitual behavior in food consumption quantitatively. Longitudinal food diaries contributed by app users are a promising resource to study habitual behavior in food selection. We developed computational measures that leverage recurrence in food choices to describe the habitual component. The relative frequency and span of individual food choices are computed and used to identify recurrent choices. We proposed metrics to quantify the recurrence at both food-item and meal levels. We obtained the following insights by employing our measures on a public dataset of food diaries from MyFitnessPal users. Food-item recurrence is higher than meal recurrence. While food-item recurrence increases with the average number of food-items chosen per meal, meal recurrence decreases. Recurrence is the strongest at breakfast, weakest at dinner, and higher on weekdays than on weekends. Individuals with relatively high recurrence on weekdays also have relatively high recurrence on weekends. Our quantitatively observed trends are intuitive and aligned with common notions surrounding habitual food consumption. As a potential impact of the research, profiling habitual behaviors using the proposed recurrent consumption measures may reveal unique opportunities for accessible and sustainable dietary interventions.
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    HRVCam: Measuring Heart Rate Variability With A Camera
    (2018-12-03) Pai, Amruta; Sabharwal, Ashutosh
    The inter-beat-interval (time period of the cardiac cycle) changes slightly for every heartbeat; this variation is measured as Heart Rate Variability (HRV). HRV is presumed to occur due to interactions between the parasympathetic and sympathetic nervous system. Therefore, it is sometimes used as an indicator of the stress level of an individual. HRV also reveals some clinical information about cardiac health. Currently, HRV is accurately measured using contact devices such as a pulse oximeter. However, recent research in the eld of non-contact imaging Photoplethysmography (IPPG) has made it possible to extract vital sign measurements from the video recording of any exposed skin surface, such as a person's face. Extracting HRV using a camera holds a lot of promise as it opens up the applications of stress, engagement monitoring during interviews and online education. It can also be used as a non-invasive monitoring solution for measuring autonomous nervous system function for diabetic patients who are at high risk for developing diabetic autonomic neuropathy. The current signal processing methods for extracting HRV using peak detection perform well for contact-based systems but have poor performance for the IPPG signals. The main reason for this poor performance is the fact that current methods are sensitive to large noise sources which are often present in IPPG data. Further, current methods are not robust to motion artifacts that are common in IPPG systems. We developed a new algorithm, HRVCam, for robustly extracting HRV even in low SNR such as is common with IPPG recordings. HRVCam combines spatial combination and frequency demodulation to obtain HRV from the instantaneous frequency of the IPPG signal. HRVCam outperforms other current methods of HRV estimation. Ground truth data was obtained from FDA-approved pulse oximeter for validation purposes. HRVCam improves the accuracy by 25% for light skin tones and by 60% for darker skin tones. HRVCam also allows us to measure HRV during high motion scenarios such as talking with an error of 25%. In such scenarios state-of-the-art approaches perform rather poorly.
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    Temporal changes in bio-behavioral and glycemic outcomes following a produce prescription program among predominantly Hispanic/Latino adults with or at risk of type 2 diabetes
    (Elsevier, 2023) Sato Imuro, Sandra Emi; Sabharwal, Ashutosh; Conneely, Casey; Glantz, Namino; Bevier, Wendy; Barua, Souptik; Pai, Amruta; Larez, Arianna; Kerr, David
    In the United States (U.S.), consumption of fresh vegetables and fruits is below recommended levels. Enhancing access to nutritious food through food prescriptions has been recognized as a promising approach to combat diet-related illnesses. However, the effectiveness of this strategy at a large scale remains untested, particularly in marginalized communities where food insecurity rates and the prevalence of health conditions such as type 2 diabetes (T2D) are higher compared to the background population. This study evaluated the impact of a produce prescription program for predominantly Hispanic/Latino adults living with or at risk of T2D. A total of 303 participants enrolled in a 3-month observational cohort received 21 medically prescribed portions/week of fresh produce. A subgroup of 189 participants used continuous glucose monitoring (CGM) to assess the relationship between CGM profile changes and HbA1c level changes. For 247 participants completing the study (76% female, 84% Hispanic/Latino, 32% with T2D, age 56·6 ± 11·9 years), there was a reduction in weight (−1·1 [-1·6 to −0·6] lbs., p < 0.001), waist circumference (−0·4 [-1·0 to 0·6] cm, p = 0·007) and systolic blood pressure (SBP) for participants with baseline SBP >120 mmHg (−4·2 [-6·8 to −1·8] mmHg, p = 0·001). For participants with an HbA1c ≥ 7·0% at baseline, HbA1c fell significantly (−0·5 [-0·9 to −0·1] %, p = 0·01). There were also improvements in food security (p < 0·0001), self-reported ratings of sleep, mood, pain (all p < 0·001), and measures of depression (p < 0·0001), anxiety (p = 0·045), and stress (p = 0·002) (DASS-21). There was significant correlation (r = 0·8, p = 0·001) between HbA1c change and the change in average glucose for participants with worsening HbA1c, but not for participants with an improvement in HbA1c. In conclusion, medical prescription of fresh produce is associated with significant improvements in cardio-metabolic and psycho-social risk factors for Hispanic/Latino adults with or at risk of T2D.