Search

To date, nutritional epidemiology has relied heavily on relatively weak methods including simple observational designs and substandard measurements. Despite low internal validity and other sources of bias, claims of causality are made commonly in this literature. Nutritional epidemiology investigations can be improved through greater scientific rigor and adherence to scientific reporting commensurate with research methods used. Some commentators advocate jettisoning nutritional epidemiology entirely, perhaps believing improvements are impossible. Still others support only normative refinements. But neither abolition nor minor tweaks are appropriate. Nutritional epidemiology, in its present state, offers utility, yet also needs marked, reformational renovation. Changing the status quo will require ongoing, unflinching scrutiny of research questions, practices, and reporting—and a willingness to admit that “good enough” is no longer good enough. As such, a workshop entitled “Toward more rigorous and informative nutritional epidemiology: the rational space between dismissal and defense of the status quo” was held from July 15 to August 14, 2020. This virtual symposium focused on: (1) Stronger Designs, (2) Stronger Measurement, (3) Stronger Analyses, and (4) Stronger Execution and Reporting. Participants from several leading academic institutions explored existing, evolving, and new better practices, tools, and techniques to collaboratively advance specific recommendations for strengthening nutritional epidemiology.

This article considers detecting eating in free-living humans by tracking wrist motion. We are specifically interested in the effect of secondary activities that people conduct while simultaneously eating, such as walking, watching television, or working. These secondary activities cause wrist motions that obfuscate those associated with eating, increasing the difficulty of detecting periods of eating. We collected a large dataset of 4,680 hours of wrist motion from 351 participants during free living. Participants reported secondary activities in 72% of meals. Analysis of wrist motion data revealed that the wrist was resting 12.8% of the time during self-reported meals compared to only 6.8% of the time in a cafeteria dataset, whereas walking motion was found 5.5% of the time during meals in free living compared to 0% in a cafeteria. Augmenting an eating detection classifier to include walking and resting detection improved accuracy from 74% to 77% on our free-living dataset ( t [353] = 7.86, p < 0.001). Although eating detection could be improved using more sophisticated machine learning methods or sensor modalities, all approaches would be affected by secondary activities, as they affect the labeling of data itself. Our work suggests that future work should collect detailed ground truth on secondary activities being conducted during eating, as these activities could hold insights into when an eating activity starts or stops in the absence of video-based ground truth.

The use of thermal analysis as a tool for the real-time chemistry control of carbon and silicon in ductile base iron is very important for seamless melt productivity by avoiding delays in waiting for laboratory analyses of those elements by the direct but time-consuming methods via spectrometer or combustion. With the standard market availability of four (4) different thermal analysis cup types encompassing the two binary choices of ‘round versus square’ and ‘plain versus tellurium’ in amounting to the four choices, thorough comparative testing of each cup type was conducted for gage repeatability and reproducibility (GR & R) analyses and other correlativity analyses. Major results of this testing and data analysis are presented herein along with discussion and conclusions regarding the relative merits of each of the four available cup types. Based on comparative GR&R analyses and subsequent regression analysis of the data, it is concluded that, among the four (4) thermal analysis cup types to choose from for real-time control of the carbon equivalent, carbon and silicon contents of ductile base iron, the ‘RD–PL’ (round and plain) and ‘SQ–TE’ (square and tellurium) cups perform superiorly to the ‘RD–TE’ (round and tellurium) and ‘SQ–PL’ (square and plain) cups.

This study evaluated feasibility and acceptability of adding energy balance modeling displayed on weight graphs combined with a wrist-worn bite counting sensor against a traditional online behavioral weight loss program. Adults with a BMI of 27–45 kg/m2 (83.3% women) were randomized to receive a 12-week online behavioral weight loss program with 12 weeks of continued contact (n = 9; base program), the base program plus a graph of their actual and predicted weight change based on individualized physiological parameters (n = 7), or the base program, graph, and a Bite Counter device for monitoring and limiting eating (n = 8). Participants attended weekly clinic weigh-ins plus baseline, midway (12 weeks), and study culmination (24 weeks) assessments of feasibility, acceptability, weight, and behavioral outcomes. In terms of feasibility, participants completed online lessons (M = 7.04 of 12 possible lessons, SD = 4.02) and attended weigh-ins (M = 16.81 visits, SD = 7.24). Six-month retention appears highest among nomogram participants, and weigh-in attendance and lesson completion appear highest in Bite Counter participants. Acceptability was sufficient across groups. Bite Counter use (days with ≥ 2 eating episodes) was moderate (47.8%) and comparable to other studies. Participants lost 4.6% ± 4.5 of their initial body weight at 12 weeks and 4.5% ± 5.8 at 24 weeks. All conditions increased their total physical activity minutes and use of weight control strategies (behavioral outcomes). Although all groups lost weight and the study procedures were feasible, acceptability can be improved with advances in the technology. Participants were satisfied with the online program and nomograms, and future research on engagement, adherence, and integration with other owned devices is needed. ClinicalTrials.gov Identifier: NCT02857595

In this work, we describe a new method to detect periods of eating by tracking wrist motion during everyday life. Eating uses hand-to-mouth gestures for ingestion, each of which lasts a few seconds. Previous works have detected these gestures individually and then aggregated them to identify meals. The novelty of our approach is that we analyze a much longer window (0.5–15 min) using a convolutional neural network. Longer windows can contain other gestures related to eating, such as cutting or manipulating food, preparing foods for consumption, and resting between ingestion events. The context of these other gestures can improve the detection of periods of eating. We test our methods on the public Clemson all-day dataset, which consists of 354 recordings containing 1063 eating episodes. We found that accuracy at detecting eating increased by 15% in ≥4 min windows compared to ≤15 s windows. Using a 6 min window, we detected 89% of eating episodes, with 1.7 false positives for every true positive (FP/TP). These are the best results achieved to date on this dataset.

BACKGROUND Behavioral obesity treatment (BOT) is a gold standard approach to weight loss and reduces the risk of cardiovascular disease. However, frequent lapses from the recommended diet stymie weight loss and prevent individuals from actualizing the health benefits of BOT. There is a need for innovative treatment solutions to improve adherence to the prescribed diet in BOT. OBJECTIVE The aim of this study is to optimize a smartphone-based just-in-time adaptive intervention (JITAI) that uses daily surveys to assess triggers for dietary lapses and deliver interventions when the risk of lapse is high. A microrandomized trial design will evaluate the efficacy of any interventions (ie, theory-driven or a generic alert to risk) on the proximal outcome of lapses during BOT, compare the effects of theory-driven interventions with generic risk alerts on the proximal outcome of lapse, and examine contextual moderators of interventions. METHODS Adults with overweight or obesity and cardiovascular disease risk (n=159) will participate in a 6-month web-based BOT while using the JITAI to prevent dietary lapses. Each time the JITAI detects elevated lapse risk, the participant will be randomized to no intervention, a generic risk alert, or 1 of 4 theory-driven interventions (ie, enhanced education, building self-efficacy, fostering motivation, and improving self-regulation). The primary outcome will be the occurrence of lapse in the 2.5 hours following randomization. Contextual moderators of intervention efficacy will also be explored (eg, location and time of day). The data will inform an optimized JITAI that selects the theory-driven approach most likely to prevent lapses in a given moment. RESULTS The recruitment for the microrandomized trial began on April 19, 2021, and is ongoing. CONCLUSIONS This study will optimize a JITAI for dietary lapses so that it empirically tailors the provision of evidence-based intervention to the individual and context. The finalized JITAI will be evaluated for efficacy in a future randomized controlled trial of distal health outcomes (eg, weight loss). CLINICALTRIAL ClinicalTrials.gov NCT04784585; http://clinicaltrials.gov/ct2/show/NCT04784585 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/33568

BACKGROUND: Mobile dietary self-monitoring methods allow for objective assessment of adherence to self-monitoring; however, the best way to define self-monitoring adherence is not known. OBJECTIVE: The objective was to identify the best criteria for defining adherence to dietary self-monitoring with mobile devices when predicting weight loss. DESIGN: This was a secondary data analysis from two 6-month randomized trials: Dietary Intervention to Enhance Tracking with Mobile Devices (n=42 calorie tracking app or n=39 wearable Bite Counter device) and Self-Monitoring Assessment in Real Time (n=20 kcal tracking app or n=23 photo meal app). PARTICIPANTS/SETTING: Adults (n=124; mean body mass index = 34.7±5.6) participated in one of two remotely delivered weight-loss interventions at a southeastern university between 2015 and 2017. INTERVENTION: All participants received the same behavioral weight loss information via twice-weekly podcasts. Participants were randomly assigned to a specific diet tracking method. MAIN OUTCOME MEASURES: Seven methods of tracking adherence to self-monitoring (eg, number of days tracked, and number of eating occasions tracked) were examined, as was weight loss at 6 months. STATISTICAL ANALYSES PERFORMED: Linear regression models estimated the strength of association (R(2)) between each method of tracking adherence and weight loss, adjusting for age and sex. RESULTS: Among all study completers combined (N=91), adherence defined as the overall number of days participants tracked at least two eating occasions explained the most variance in weight loss at 6 months (R(2)=0.27; P

Background This study builds on previous research that seeks to estimate kilocalorie intake through microstructural analysis of eating behaviors. As opposed to previous methods, which used a static, individual-based measure of kilocalories per bite, the new method incorporates time- and food-varying predictors. A measure of kilocalories per bite (KPB) was estimated using between- and within-subjects variables. Objective The purpose of this study was to examine the relationship between within-subjects and between-subjects predictors and KPB, and to develop a model of KPB that improves over previous models of KPB. Within-subjects predictors included time since last bite, food item enjoyment, premeal satiety, and time in meal. Between-subjects predictors included body mass index, mouth volume, and sex. Participants/setting Seventy-two participants (39 female) consumed two random meals out of five possible meal options with known weights and energy densities. There were 4,051 usable bites measured. Main outcome measures The outcome measure of the first analysis was KPB. The outcome measure of the second analysis was meal-level kilocalorie intake, with true intake compared to three estimation methods. Statistical analyses performed Multilevel modeling was used to analyze the influence of the seven predictors of KPB. The accuracy of the model was compared to previous methods of estimating KPB using a repeated-measured analysis of variance. Results All hypothesized relationships were significant, with slopes in the expected direction, except for body mass index and time in meal. In addition, the new model (with nonsignificant predictors removed) improved over earlier models of KPB. Conclusions This model offers a new direction for methods of inexpensive, accurate, and objective estimates of kilocalorie intake from bite-based measures.

Ductile iron nodularity is of critical importance to its quality, but nodularity determination by metallographic analysis can be problematic. The widespread practice of estimating nodularity via comparator chart is highly subjective and prone to unacceptably high variation. An improvement over visual metallographic estimates is digital image analysis (IA) by which subjectivity can be greatly reduced, but the more reliable results obtained from IA are time-consuming and difficult to implement in a production environment. For this paper, the use of ultrasonic velocity testing via a standard coupon was evaluated as a possibly more reliable technique for determining front-end (real time) nodularity. Major results are presented herein along with details necessary for implementation of front-end ultrasonic nodularity determination using standard coupons as standard practice.

Dietary lapses (i.e., specific instances of nonadherence to recommended dietary goals) contribute to suboptimal weight loss outcomes during lifestyle modification programs. Passive eating monitoring could enhance lapse measurement via objective assessment of eating characteristics that could be markers for lapse (e.g., more bites consumed). The purpose of this study was to evaluate if passively-inferred eating characteristics (i.e., bites, eating duration, and eating rate), measured via wrist-worn device, could distinguish dietary lapses from non-lapse eating. Adults (n = 25) with overweight/obesity received a 24-week lifestyle modification intervention. Participants completed ecological momentary assessment (EMA; repeated smartphone surveys) biweekly to self-report on dietary lapses and non-lapse eating episodes. Participants wore a wrist device that captured continuous wrist motion. Previously-validated algorithms inferred eating episodes from wrist data, and calculated bite count, duration, and rate (seconds per bite). Mixed effects logistic regressions revealed no simple effects of bite count, duration, or eating rate on the likelihood of dietary lapse. Moderation analyses revealed that eating episodes in the evening were more likely to be lapses if they involved fewer bites (B = -0.16, p .05), were shorter (B = -0.54, p .05), or had a slower rate (B = 1.27, p .001). Statistically significant interactions between eating characteristics (Bs = -0.30 to -0.08, ps .001) revealed two distinct patterns. Eating episodes that were 1. smaller, slower, and shorter than average, or 2. larger, quicker, and longer than average were associated with increased probability of lapse. This study is the first to use objective eating monitoring to characterize dietary lapses throughout a lifestyle modification intervention. Results demonstrate the potential of sensors to identify non-adherence using only patterns of passively-sensed eating characteristics, thereby minimizing the need for self-report in future studies. CLINICAL TRIALS REGISTRY NUMBER: NCT03739151.

Many works in the field of automated dietary monitoring (ADM) have analyzed small data sets consisting of

This paper describes a novel approach of segmenting and classifying eating gestures from wrist motion using a deep learning neural network. It is inspired by the approach of fully-convolutional neural networks in the task of image segmentation. Our idea is to segment 1D gestures the same way 2D image regions are segmented, by treating each inertial measurement unit (IMU) datum like a pixel. The novelty of our approach lies in training a neural network to recognize data points that describe an eating gesture just like it would be trained to recognize pixels describing an image region. The data for this research is known as the Clemson Cafeteria Dataset. It was collected from 276 participants that ate an unscripted meal at the Harcombe Dining Hall at Clemson University. Each meal consisted of 1 - 4 courses, and 488 such recordings were used for the experiments described in this paper. Sensor readings consist of measurements taken by an accelerometer (x, y, z) and a gyroscope (yaw, pitch, roll). A total of 51,614 unique gestures associated with different activities commonly seen during a meal were identified by 18 trained raters. Our neural network classifier recognized an average of 79.7% of ‘bite’ and 84.7% of ‘drink’ gestures correctly per meal. Overall 77.7% of all gestures were recognized correctly on average per meal. This indicates that a deep learning model can successfully be used to segment eating gestures from a time series recording of IMU data using a technique similar to pixel segmentation within an image.

Self-efficacy (SE) and information processing (IP) may be important constructs to target when designing mHealth interventions for weight loss. The goal of this study was to examine the relationship between SE and IP with weight loss at six-months as part of the Dietary Interventions Examining Tracking with mobile study, a six-month randomized trial with content delivered remotely via twice-weekly podcasts. Participants were randomized to self-monitor their diet with either a mobile app (n = 42) or wearable Bite Counter device (n = 39). SE was assessed using the Weight Efficacy Life-Style Questionnaire and the IP variables assessed included user control, cognitive load, novelty, elaboration. Regression analysis examined the relationship between weight loss, SE change & IP at six months. Results indicate that elaboration was the strongest predictor of weight loss (ß =−0.423, P = 0.011) among all SE & IP variables and that for every point increase in elaboration, participants lost 0.34 kg body weight.

This study aimed to examine changes in Healthy Eating Index-2010 (HEI-2010) scores, components, and energy intake between automated Bite Counter (Bite) and traditional diet tracking mobile app (App) groups. This was a secondary analysis of the DIET Mobile study, a 6-month weight loss intervention. Assessments were conducted at baseline, 3 and 6 months. Twenty-four-hour dietary recall data were collected. Overweight/obese adults (N = 81) were randomized to Bite or App groups. The intervention was delivered through behavioral podcasts. Participants were provided customized calorie/bite goals and used their device to track intake. We assessed changes in HEI-2010 scores from baseline to 6 months between groups. t tests, chi-square, and repeated measures ANOVA were performed. Models included time, group, and group×time interaction, controlling for no other covariates. There were no significant changes in HEI-2010 scores, components, or energy intake between groups at 3 or 6 months. This study found that both the Bite and App groups were able to reduce their energy intake and there was no difference in changes in diet quality between groups, which provides some support for using the less intensive, more automated method (Bite Counter) for long-term dietary self-monitoring. The study had a low sample size according to power calculations. Future interventions aimed at improving diet quality through mHealth technology should investigate the potential to develop a new app or modify an existing app that would allow for dietary self-monitoring that provides specific feedback on how users’ diets align with diet quality components in the HEI to improve overall diet quality.

The universal eating monitor (UEM) is a table-embedded scale used to measure grams consumed over time while a person eats. It has been used in laboratory settings to test the effects of anorectic drugs and behavior manipulations such as slowing eating, and to study relationships between demographics and body weight. However, its use requires restricted conditions on the foods consumed and behaviors allowed during eating in order to simplify analysis of the scale data. Individual bites can only be measured when the only interaction with the scale is to carefully remove a single bite of food, consume it fully, and wait a minimum amount of time before the next bite. Other interactions are prohibited such as stirring and manipulating foods, retrieving or placing napkins or utensils on the scale, and in general anything that would change the scale weight that was not related to the consumption of an individual bite. This paper describes a new algorithm that can detect and measure the weight or individual bites consumed during unrestricted eating. The algorithm works by identifying time periods when the scale weight is stable, and then, analyzing the surrounding weight changes. The series of preceding and succeeding weight changes is compared against patterns for single food bites, food mass bites, and drink bites to determine if a scale interaction is due to a bite or some other activity. The method was tested on 271 subjects, each eating a single meal in a cafeteria setting. A total of 24 101 bites were manually annotated in synchronized videos to establish ground truth as to the true, false, and missed detections of bites. Our algorithm correctly detected and weighed approximately 39% of bites with approximately one false positive (FP) per ten actual bites. The improvement compared to the UEM is approximately three times the number of true detections and a 90% reduction in the number of FPs. Finally, an analysis of bites that could not be weighed compared to those that could be weighed revealed no statistically significant difference in average weight. These results suggest that our algorithm could be used to conduct studies using a table scale outside of laboratory or clinical settings and with unrestricted eating behaviors.

Accelerometers have gained popularity in biomedical and m-health applications such as actigraphy or automated dietary monitoring due to their ease of use and their ability to characterize motion. These sensors report raw acceleration from which gravity and linear acceleration must be separated, with commercial packages reporting raw acceleration, linear acceleration or both. New researchers to the field may often be confused when to use raw acceleration or linear acceleration, especially given the susceptibility of linear acceleration to noise, and the lack of published distributions of these signals. This paper provides a short tutorial on obtaining linear acceleration estimates. Using these methods we analyze a large dataset containing 4,680 hours of wrist tracking data, the largest such dataset known to us. We learn the range of wrist motion accelerations, and quantify the expected noise in the linear acceleration signal. We explain the sources of this noise, and a filtering technique to mitigate it. For the first time, we report the range of wrist acceleration values observed during free-living, and quantify the expected range of noise in this wrist acceleration. We show that while previous work has reported average accelerations at the feet and body ranging from 0–15g during spots-like activities like walking, running or jumping, wrist acceleration in free-living subjects during daily activities is often much lower, and ranges from 0–0.2g. We show that noise in linear acceleration can range from 0–0.06g, an overlap of 70%. This suggests that in applications where the wrist acceleration is in this range of noise, linear acceleration may not provide useful features, and researchers should only rely on raw acceleration instead.

This paper evaluates the use of a sensor mounted on a finger to detect bites during food consumption. Previous works have demonstrated the capability to detect bites by tracking wrist motion. The key motion is the roll of the wrist that occurs between picking up food and delivering it to the mouth. We hypothesize that the roll axes of the wrist, hand, and finger are congruent, and that therefore the roll motion measured at the wrist can be similarly measured at the finger. We collected data for 10 meals of subjects wearing sensors on both the wrist and finger. Results indicate that the bite counting algorithm obtains similar accuracy at both locations. The practical implication is that an eating activity monitor could be embedded in the form of a ring worn on the finger, and that this form factor could work as well as a watch-like device.

ObjectivesBehavioral obesity treatment (BOT) produces clinically significant weight loss and health benefits for many individuals with overweight/obesity. Yet, many individuals in BOT do not achieve clinically significant weight loss and/or experience weight regain. Lapses (i.e., eating that deviates from the BOT prescribed diet) could explain poor outcomes, but the behavior is understudied because it can be difficult to assess. We propose to study lapses using a multi-method approach, which allows us to identify objectively-measured characteristics of lapse behavior (e.g., eating rate, duration), examine the association between lapse and weight change, and estimate nutrition composition of lapse.MethodWe are recruiting participants (n = 40) with overweight/obesity to enroll in a 24-week BOT. Participants complete biweekly 7-day ecological momentary assessment (EMA) to self-report on eating behavior, including dietary lapses. Participants continuously wear the wrist-worn ActiGraph Link to characterize eating behavior. Participants complete 24-hour dietary recalls via structured interview at 6-week intervals to measure the composition of all food and beverages consumed.ResultsWhile data collection for this trial is still ongoing, we present data from three pilot participants who completed EMA and wore the ActiGraph to illustrate the feasibility, benefits, and challenges of this work.ConclusionThis protocol will be the first multi-method study of dietary lapses in BOT. Upon completion, this will be one of the largest published studies of passive eating detection and EMA-reported lapse. The integration of EMA and passive sensing to characterize eating provides contextually rich data that will ultimately inform a nuanced understanding of lapse behavior and enable novel interventions. Trial registration: Registered clinical trial NCT03739151; URL: https://clinicaltrials.gov/ct2/show/NCT03739151

Conclusions regarding bite count rates and body mass index (BMI) in free-living populations have primarily relied on self-report. The objective of this exploratory study was to compare the relationship between BMI and bite counts measured by a portable sensor called the Bite Counter in free-living populations and participants eating in residence. Two previously conducted studies were analyzed for relationships between BMI and sensor evaluated bite count/min, and meal duration. Participants from the first study (N = 77) wore the bite counter in a free-living environment for a continuous period of 14 days. The second study (N = 214) collected bite count/min, meal duration, and total energy intake in participants who consumed one meal in a cafeteria. Linear regression was applied to examine relationships between BMI and bite count/min. There was no significant correlation in the free-living participants average bite counts per second and BMI (R2 = 0.03, p = 0.14) and a significant negative correlation in the cafeteria participants (R 2 = 0.04, p = 0.03) with higher bite count rates observed in lean versus obese participants. There was a significant correlation between average meal duration and BMI in the free-living participants (R 2 = 0.08, p = 0.01). Total energy intake in the cafeteria participants was also significantly correlated to meal duration (R 2 = 0.31, p

This paper considers the problem of recognizing eating gestures by tracking wrist motion. Eating gestures are activities commonly undertaken during the consumption of a meal, such as sipping a drink of liquid or using utensils to cut food. Each of these gestures causes a pattern of wrist motion that can be tracked to automatically identify the activity. Previous works have studied this problem at the level of a single gesture. In this paper, we demonstrate that individual gestures have sequential dependence. To study this, three types of classifiers were built: 1) a K-nearest neighbor classifier which uses no sequential context, 2) a hidden Markov model (HMM) which captures the sequential context of subgesture motions, and 3) HMMs that model intergesture sequential dependencies. We built first-order to sixth-order HMMs to evaluate the usefulness of increasing amounts of sequential dependence to aid recognition. On a dataset of 25 meals, we found that the baseline accuracies for the KNN and the subgesture HMM classifiers were 75.8% and 84.3%, respectively. Using HMMs that model intergesture sequential dependencies, we were able to increase accuracy to up to 96.5%. These results demonstrate that sequential dependencies exist between eating gestures and that they can be exploited to improve recognition accuracy.

This paper describes a study to test the accuracy of a method that tracks wrist motion during eating to detect and count bites. The purpose was to assess its accuracy across demographic (age, gender, and ethnicity) and bite (utensil, container, hand used, and food type) variables. Data were collected in a cafeteria under normal eating conditions. A total of 271 participants ate a single meal while wearing a watch-like device to track their wrist motion. A video was simultaneously recorded of each participant and subsequently reviewed to determine the ground truth times of bites. Bite times were operationally defined as the moment when food or beverage was placed into the mouth. Food and beverage choices were not scripted or restricted. Participants were seated in groups of 2-4 and were encouraged to eat naturally. A total of 24 088 bites of 374 different food and beverage items were consumed. Overall the method for automatically detecting bites had a sensitivity of 75% with a positive predictive value of 89%. A range of 62-86% sensitivity was found across demographic variables with slower eating rates trending toward higher sensitivity. Variations in sensitivity due to food type showed a modest correlation with the total wrist motion during the bite, possibly due to an increase in head-toward-plate motion and decrease in hand-toward-mouth motion for some food types. Overall, the findings provide the largest evidence to date that the method produces a reliable automated measure of intake during unrestricted eating.

The relationship between the occurrence of simulator sickness (SS) and varying latency in a helmet-mounted display (HMD) was explored in this study. Previous work has always considered latency to be a constant. The aim of this research was to determine if a latency that varied over time would impact the experience of SS for HMD users. An object location task was used while viewing real, live video scenes via HMD. A planned comparisons approach was utilized with four experimental conditions, 2 of them having constant latency (0 ms added to system baseline, 200 ms added to system baseline) and 2 of them having sinusoidally varying latency (100 ms amplitude at 0.2 Hz frequency, and 20–100 ms varying amplitude at 0.2 Hz frequency). These conditions allowed for the assessment of the effects of constant latency vs. varying latency on the experience of SS. The results indicated that a varying latency is associated with greater experience of SS among HMD users than constant latency. Results also indicated, as has other recent research, that added constant latency on its own does not appear to be associated with the experience of higher levels of SS in an HMD.

The goal of this study was to examine the usability and feasibility of the mobile Bite Counter (a watch-like device that detects when a user consumes food or beverage) and the impact of weekly behavioral challenges on diet and physical activity outcomes. Overweight (mean BMI 31.1±4.9 kg/m2) adults (n=12) were recruited to participate in a four-week study to test both the usability and feasibility of using the device as part of a behavioral weight loss intervention. Participants were instructed to self-monitor number of bites/day using the Bite Counter, attend weekly group sessions, and listen to weekly podcasts. Participants were given weekly challenges: use a daily bite limit goal (wk1), turn off Bite Counter when fruits/vegetables are consumed (wk2), self-monitor kilocalories vs. bites (wk3), and receive a 10 bites/day bonus for every 30 minutes of exercise (wk4). Participants lost a mean of −1.2±1.3 kg. Only the wk3 challenge produced significant differences in kcal change (wk3 1302±120 kcal/day vs. baseline 2042±302 kcal/d, P

This work describes a new dataset to improve pedometer evaluation. Prior evaluation techniques focus on regular gaits using laboratory assessment to simplify the manual counting of actual steps. Our goal is to analyze pedometer algorithms under more natural conditions that occur during daily living where gaits are frequently changing or remain regular for only brief periods of time. We video recorded 30 participants performing 3 activities: walking around a track, walking through a building, and moving around a room. Walking around a track uses a regular, consistent gait, and represents the traditional approach to pedometer evaluation. Walking through a building and around a room are activities that include varying amounts of pauses and gait changes, and represent a wider variety of normal daily activities. The ground truth time of each step was manually marked in the accelerometer signals by observing the videos. Collectively 60,853 steps were recorded and annotated. A subclass of steps called shifts were identified as those occurring at the beginning and end of regular strides, during gait changes, and during pivots changing the direction of motion. While shifts comprised only .03% of steps in the regular stride activity, they comprised 10–25% of steps in the semi-regular and unstructured activities. We believe these motions should be identified separately, as they provide different accelerometer signals, and likely result in different amounts of energy expenditure. The proposed dataset will be the first to specifically allow for pedometer algorithms to be evaluated on unstructured gaits that more closely model natural activities.

Our study investigated the relationship between BMI and bite size in a cafeteria setting. Two hundred and seventy one participants consumed one meal each. Participants were free to select any food provided by the cafeteria and could return for additional food as desired. Bite weights were measured with a table embedded scale. Data were analyzed with ANOVAs, regressions, Kolmogorov-Smirnov tests, and a repeated measures general linear model for quartile analysis. Obese participants were found to take larger bites than both normal (P=0.002) and overweight participants (p=0.017). Average bite size increased by 0.20 g per point increase in BMI. Food bites and drink bites were analyzed individually, showing 0.11 g/BMI and 0.23 g/BMI slopes, respectively. Quartiles of bites were also analyzed, and a significant interaction was found between normal and obese participants (p=0.034) such that the lower two quartiles were similar, but the upper two quartiles showed an increase in bite size for obese participants. The source of these effects could be the result of a combination of several uncontrolled factors.

The obesity epidemic has triggered a need for novel methods for measuring eating activity in free-living settings. Here, we introduce a bite-count method that has the potential to be used in long-term investigations of eating activity. The purpose of our observational study was to describe the relationship between bite count and energy intake and determine whether there are sex and body mass index group differences in kilocalories per bite in free-living human beings. From October 2011 to February 2012, 77 participants used a wrist-worn device for 2 weeks to measure bite count during 2,975 eating activities. An automated self-administered 24-hour recall was completed daily to provide kilocalorie estimates for each eating activity. Pearson's correlation indicated a moderate, positive correlation between bite count and kilocalories (r=0.44; P0.001) across all 2,975 eating activities. The average per-individual correlation was 0.53. A 2 (sex)×3 (body mass index group: normal, overweight, obese) analysis of variance indicated that men consumed 6 kcal more per bite than women on average. However, there were no body mass index group differences in kilocalories per bite. This was the longest study of a body-worn sensor for monitoring eating activity of free-living human beings to date, which highlights the strong potential for this method to be used in future, long-term investigations.

In the original article, the authors neglected to explicitly state prior to presentation of results for the as-cast water-quenched (ACWQ) plus tempered coupons from the November, 2012 (Y2012) fade campaign that all reported velocities were measured prior to tempering, as the tempering was necessary only to facilitate the sectioning of those coupons by conventional band saw for their metallographic analysis. The tempering of such ACWQ coupons at 482 °C for 48 h raises their velocities by ~ 100 m/s.

Ultrawideband signals present new opportunities for indoor position tracking due to their ability to operate in non-line-of-sight conditions. However, walls and other normal indoor infrastructure cause a systemic spatial warp in the measurements. In this paper, we describe methods to manually build a map of the measurement noise and use it in a particle filter framework to improve measurement accuracy. We tested our methods in two facilities, collecting over 5.6 million measurements. Evidence shows that the measurement noise is multimodal, location dependent, stable over time, and locally similar. Using the map in a particle filter framework, we demonstrate an average improvement in measurement accuracy of 30%.

This paper describes a new method for measuring the end-to-end latency between sensing and actuation in a digital computing system. Compared to previous works, which generally measured the latency at 10–33-ms intervals or at discrete events separated by hundreds of ms, our new method measures the latency continuously at 1-ms resolution. This allows for the observation of variations in latency over sub 1-s periods, instead of relying upon averages of measurements. We have applied our method to two systems, the first using a camera for sensing and an LCD monitor for actuation, and the second using an orientation sensor for sensing and a motor for actuation. Our results show two interesting findings. First, a cyclical variation in latency can be seen based upon the relative rates of the sensor and actuator clocks and buffer times; for the components we tested, the variation was in the range of 15–50 Hz with a magnitude of 10–20 ms. Second, orientation sensor error can look like a variation in latency; for the sensor we tested, the variation was in the range of 0.5–1.0 Hz with a magnitude of 20–100 ms. Both of these findings have implications for robotics and virtual reality systems. In particular, it is possible that the variation in apparent latency caused by orientation sensor error may have some relation to simulator sickness.

Mobile health (mHealth) technologies are envisioned as self-monitoring tools of health behaviors (Kumar et al., Computer 46:28–35, 2013). They are meant to empower the individual to make sustainable behavior change that leads to better health. They are intended to be used long-term, with minimal to no supervision. This is in contrast to laboratory and clinical testing tools which are typically used short-term by physicians and researchers under strict patient constraints to resolve urgent conditions. Because of the individual-empowered focus, mHealth technologies need to meet the following design criteria: low user burden; low-cost; and long-term usability under free-living conditions. mHealth technologies present an interesting opportunity because of the high quantity of inexpensive data generated, which is far, far greater than what is typically provided by sporadic and expensive laboratory tests. In this chapter, we discuss this opportunity in the context of the development of the Bite Counter. The Bite Counter uses sensors embedded into a watch-like device to automatically track wrist motion to count bites. The device provides the user intake feedback during a meal, allowing them to self-monitor intake anywhere and anytime. The behavior change goal is to reduce intake in a way that results in healthy weight loss.

Background Interactions between frequency and amplitude of latency in head-mounted displays (HMDs) are thought to affect simulator sickness. Many studies have linked system latency to subjective sickness, but recent research has found that at least with the case of inertia-based head tracking technology, latency is not a constant; rather it varies systematically over time due to sensor errors and clock asynchronization. The purpose of this experiment was to further explore the relationship between frequency and amplitude of latency as they relate to subjective sickness experienced in an HMD. Methods In a 2 (frequency) × 2 (amplitude) design, 120 subjects were randomly assigned to 4 latency conditions. Frequency of latency was either 0.2 Hz or 1.0 Hz. Amplitude of latency was either 100 ms fixed or 20-100 ms varying. Results A main effect of frequency of latency was found. Subjects reported greater sickness in the 0.2-Hz frequency conditions (39.0 ± 27.8) compared to the 1-Hz conditions (30.3 ± 17.0). Additionally, 18 subjects withdrew their participation early in the 0.2-Hz conditions compared to 7 in the 1.0-Hz conditions. Discussion In conclusion, frequency of latency appears to play a role in the experience of sickness in HMDs in both subjective reporting of symptoms and subject performance. The current study confirms results of earlier studies, finding that real motion around a frequency of 0.2 Hz is more sickening than other frequencies. Future work should continue to parse the effects of frequency and amplitude of latency in head-tracked HMDs. Kinsella A, Mattfeld R, Muth E, Hoover A. Frequency, not amplitude, of latency affects subjective sickness in a head-mounted display. Aerosp Med Hum Perform. 2016; 87(7):604-609.

More than one third of adults in the United Statesare now classified as obese. Self monitoring of energy intakehas been shown to have a positive impact on weight loss, butexisting tools for logging eating activities are tedious to use andprone to bias which leads to noncompliance and underestimation. In this paper we describe preliminary results from our ongoingdata collection from 500 free living participants, in an effort toimprove our previous algorithm that detects periods of eating. We see a 75% accuracy in our data collected to date.

This paper considers the problems of recognizing eating gestures by tracking wrist motion. Hidden Markov models (HMMs) were developed to capture variations in motion patterns of subgroups of participants. Specifically, we examined if foreknowledge of the gender, age, and utensil used for eating could improve recognition accuracy. Improvement in accuracy was measured by comparing to a baseline HMM that was trainedon all participants. Data was collected for 276 participants eating a single meal within a cafeteria setting. A total of 44,873 gestures were manually labeled using video synchronized with the wrist motion tracking device. Results show that gender HMMs performed slightly better than the baseline, indicating that there is not much difference in wrist motion patterns during eating between females and males. Age HMMs provided a 4.3% increase in accuracy and utensil HMMs provided a 6.2% increase inaccuracy. The results suggest that contextual variables can be used for improving gesture recognition.

Background Smartphone photography and crowdsourcing feedback could reduce participant burden for dietary self-monitoring. Objectives To assess if untrained individuals can accurately crowdsource diet quality ratings of food photos using the Traffic Light Diet (TLD) approach. Methods Participants were recruited via Amazon Mechanical Turk and read a one-page description on the TLD. The study examined the participant accuracy score (total number of correctly categorized foods as red, yellow, or green per person), the food accuracy score (accuracy by which each food was categorized), and if the accuracy of ratings increased when more users were included in the crowdsourcing. For each of a range of possible crowd sizes ( n = 15, n = 30, etc.), 10,000 bootstrap samples were drawn and a 95% confidence interval (CI) for accuracy constructed using the 2.5th and 97.5th percentiles. Results Participants ( n = 75; body mass index 28.0 ± 7.5; age 36 ± 11; 59% attempting weight loss) rated 10 foods as red, yellow, or green. Raters demonstrated high red/yellow/green accuracy (>75%) examining all foods. Mean accuracy score per participant was 77.6 ± 14.0%. Individual photos were rated accurately the majority of the time (range = 50%–100%). There was little variation in the 95% CI for each of the five different crowd sizes, indicating that large numbers of individuals may not be needed to accurately crowdsource foods. Conclusions Nutrition-novice users can be trained easily to rate foods using the TLD. Since feedback from crowdsourcing relies on the agreement of the majority, this method holds promise as a low-burden approach to providing diet-quality feedback.

With two-thirds of the U.S. population being either overweight or obese, accurate tools for self-monitoring eating behaviors are needed for both research and personal purposes. To increase user compliance, these tools should be easy to use and impose a minimal burden on the user. The present study compares user preferences and compliance with the Bite Counter, a wrist-worn device designed to monitor eating behavior by counting bites, to the Internet-based Automated Self-Administered 24-hour dietary recall (ASA24). Eighty-three participants used the Bite Counter and the ASA24 for two weeks. At the end of the two-week period, participants were asked to rate their preference, compliance, and the ease of use for the two tools. 76% of the participants preferred the Bite Counter to the ASA24. Participants consistently used both tools. The Bite Counter was significantly easier to use, according to subjective ratings. These results indicate that users may be more likely to use the Bite Counter consistently than 24-hour dietary recalls.

Slow eating may be associated with reduced energy intake. A device that counts bites can provide bite-rate feedback to the user. The purpose of this study was to explore the bite counter's utility for slowing bite-rate and reducing energy intake. The study was a within-participants design with three conditions. From February to April 2009, university students (N=30) ate three meals in the laboratory: a baseline meal without feedback (Baseline), a meal during which participants received bite-rate feedback (Feedback), and a meal during which participants followed a 50% slower bite-rate target (Slow Bite-Rate). Kilocalories of food consumed, ratings of satiation and food-liking, and milliliters of water consumed were statistically compared across conditions using repeated-measures analyses of variance. Overall, participants ate 70 kcal fewer during the Slow Bite-Rate condition compared with the Feedback condition. In addition, when baseline energy consumption was added post hoc as a grouping variable, participants who ate more than 400 kcal at baseline (n=11) ate 164 kcal fewer during the Slow Bite-Rate condition compared to Baseline, and 142 kcal fewer in the Feedback condition compared with Baseline. However, the Slow Bite-Rate condition did not significantly affect participants who ate fewer than 400 kcal at baseline (n=19). Therefore, it seems that slowing bite-rate with the bite counter may be most effective for individuals who consume larger amounts of food. Future research should explore more foods and drinks, more diverse groups of individuals, potential moderating variables, and additional applications of the bite counter.

Physiological compliance (PC) refers to the correlation between physiological measures of team members over time. The goals of this study were to examine ways of measuring PC in heart rate variability (HRV) data and the relationship between PC and team performance. Teams were tasked with entering both real and simulated rooms and “shooting” individuals with a weapon and identifying individuals without a weapon. The linear correlation and directional agreement PC methods were shown to be the most sensitive to differences in performance, with greater PC being associated with better performance. The correlation method when applied to a measure of respiratory sinus arrhythmia (RSA) revealed a significant difference between high and low performers ( t [8] = −2.31, p = 0.03) and the directional agreement applied to inter-beat-intervals and RSA revealed trend-level differences ( t [4.62] = −1.86, p = 0.06 and t [8] = −1.68, p = 0.07). These results suggest that PC may have merit for predicting team performance.

This paper combines data from two experiments that evaluated the effectiveness of different virtual environments (VEs) for training the task of building clearing. 112 subjects were divided into 28 teams across two experiments. Each study consisted of 3 phases: lecture, team training, and testing in a real-world shoothouse. There were 6 training conditions: pc-based VE, helmet mounted display-based VE, real-world shoothouse, game console, single room real-world, and game console + single room real-world. The real-world shoothouse condition was the “gold-standard” against which test performances in other conditions were compared. An ANOVA was performed to compare test performance. There was a marginally significant main effect of performance across conditions, F(5, 22)=2.26, pp2=0.34. The game console/single room real-world condition performed similarly to the “gold standard” and better than the next highest performing condition. These data provide some evidence that VE training could be utilized to augment real-world training rather than supplant it.