An explanation for the accuracy of sensor-based measures of energy intake: Amount of food consumed matters more than dietary composition.

Understanding and intervening on eating behavior often necessitates measurement of energy intake (EI); however, commonly utilized and widely accepted methods vary in accuracy and place significant burden on users (e.g., food diaries), or are costly to implement (e.g., doubly labeled water). Thus, researchers have sought to leverage inexpensive and low-burden technologies such as wearable sensors for EI estimation. Paradoxically, one such methodology that estimates EI via smartwatch-based bite counting has demonstrated high accuracy in laboratory and free-living studies, despite only measuring the amount, not the composition, of food consumed. This secondary analysis sought to further explore this phenomenon by evaluating the degree to which EI can be explained by a sensor-based estimate of the amount consumed versus the energy density (ED) of the food consumed. Data were collected from 82 adults in free-living conditions (51.2% female, 31.7% racial and/or ethnic minority; Mage = 33.5, SD = 14.7) who wore a bite counter device on their wrist and used smartphone app to implement the Remote Food Photography Method (RFPM) to assess EI and ED for two weeks. Bite-based estimates of EI were generated via a previously validated algorithm. At a per-meal level, linear mixed effect models indicated that bite-based EI estimates accounted for 23.4% of the variance in RFPM-measured EI, while ED and presence of a beverage accounted for only 0.2% and 0.1% of the variance, respectively. For full days of intake, bite-based EI estimates and ED accounted for 41.5% and 0.2% of the variance, respectively. These results help to explain the viability of sensor-based EI estimation even in the absence of information about dietary composition.

Does maladaptive cardiovagal modulation extend to gastric modulation in women with chronic pelvic pain?

BACKGROUND: Women with chronic pelvic pain (CPP) have poor cardiovagal modulation. It is unclear whether this finding reflects a broader abnormality across many systems such as gastro-vagal modulation. AIM: To determine if maladaptive cardiovagal activity in females with CPP is accompanied by maladaptive gastric myoelectric activity. METHODS: A total of 36 health controls (HC) and 75 CPP underwent supine (10 min), then upright (tilted 70° head up; 30 min), and back to supine (10 min) positions. High-frequency heart rate variability (HF-HRV; 0.15-0.4 Hz) was measured as an index of cardiovagal activity. Cutaneous electrogastrography (EGG) assessed gastric myoelectric activity pre- and during-upright tilt. EGG measures from 16 HC and 31 CPP patients were available for analysis and included relative percentage of gastric activity within the normal (2-4 cpm) and tachygastria (4-10 cpm) ranges, plus ratio of normal/tachygastria. RESULTS: HF-HRV was lower in CPP individuals at all time points (each p < .05). CPP individuals showed lesser decrease in HF-HRV from supine to upright, and poorer HF-HRV recovery from upright back to supine (F[1, 106] = 4.62, p = .034). HC showed increase in tachygastria activity (t[15] = -2.09, p = .054) while the CPP group showed no change in tachygastria activity from pre-upright to upright (t[30] = -0.62, p = .537). CONCLUSIONS: Individuals with CPP going from supine to upright demonstrate an impairment in both tachygastria and the parallel decrement in HRV. These results support the hypothesis of a generalized blunting in the physiological modulation in CPP individuals affecting both cardiovascular and gastric systems.

The role of self-efficacy and information processing in weight loss during an mHealth behavioral intervention.

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.

A virtual experimenter does not increase placebo hypoalgesia when delivering an interactive expectancy manipulation.

Lack of standardization and unblinding threaten the research of mechanisms involved in expectancy effects on pain. We evaluated a computer-controlled virtual experimenter (VEx) to avoid these issues. Fifty-four subjects underwent a baseline-retest heat pain protocol. Between sessions, they received an expectancy manipulation (placebo or no-treatment) delivered by VEx or text-only control condition. The VEx provided standardized "social" interaction with the subjects. Pain ratings and psychological state/trait measures were recorded. We found an interaction of expectancy and delivery on pain improvement following the intervention. In the text conditions, placebo was followed by lower pain, whereas in the VEx conditions, placebo and no-treatment were followed by a comparable pain decrease. Secondary analyses indicated that this interaction was mirrored by decreases of negative mood and anxiety. Furthermore, changes in continuous pain were moderated by expectation of pain relief. However, retrospective pain ratings show an effect of expectancy but not of delivery. We conclude that we successfully applied an automated protocol for inducing expectancy effects on pain. The effect of the VEx regardless of treatment may be due to interactions of attention allocation and locus of control. This points to the diversity of expectancy mechanisms, and has implications for research and computer-based treatment applications.

Defining Adherence to Mobile Dietary Self-Monitoring and Assessing Tracking Over Time: Tracking at Least Two Eating Occasions per Day Is Best Marker of Adherence within Two Different Mobile Health Randomized Weight Loss Interventions.

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 (R2) 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 (R2=0.27; P<0.001). Self-monitoring declined over time; all examined adherence methods had fewer than half the sample still tracking after Week 10. CONCLUSIONS: Using the total number of days at least two eating occasions are tracked using a mobile self-monitoring method may be the best way to assess self-monitoring adherence during weight loss interventions. This study shows that self-monitoring rates decline quickly and elucidates potential times for early interventions to stop the reductions in self-monitoring.

Between- and Within-Subjects Predictors of the Kilocalorie Content of Bites of Food.

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.

Bite count rates in free-living individuals: new insights from a portable sensor.

BACKGROUND: 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. METHODS: 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. RESULTS: 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 < 0.001). CONCLUSIONS: With additional novel applications of the Bite Counter, insights into free-living eating behavior may provide avenues for future interventions that are sustainable for long term application.

Byte by Bite: Use of a mobile Bite Counter and weekly behavioral challenges to promote weight loss.

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<0.05). Bite Counter use was significantly correlated with weight loss (r= -0.58, P<0.05). Future studies should examine the use of the Bite Counter and impact of behavioral challenges over a longer period of time in a controlled study.

A comparison of bite size and BMI in a cafeteria setting.

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 0.20g per point increase in BMI. Food bites and drink bites were analyzed individually, showing 0.11g/BMI and 0.23g/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 Dietary Intervention to Enhance Tracking with Mobile Devices (DIET Mobile) Study: A 6-Month Randomized Weight Loss Trial.

OBJECTIVE: To examine the use of two different mobile dietary self-monitoring methods for weight loss. METHODS: Adults with overweight (n = 81; mean BMI 34.7 ± 5.6 kg/m2 ) were randomized to self-monitor their diet with a mobile app (App, n = 42) or wearable Bite Counter device (Bite, n = 39). Both groups received the same behavioral weight loss information via twice-weekly podcasts. Weight, physical activity (International Physical Activity Questionnaire), and energy intake (two dietary recalls) were assessed at 0, 3, and 6 months. RESULTS: At 6 months, 75% of participants completed the trial. The App group lost significantly more weight (-6.8 ± 0.8 kg) than the Bite group (-3.0 ± 0.8 kg; group × time interaction: P < 0.001). Changes in energy intake (kcal/d) (-621 ± 157 App, -456 ± 167 Bite; P = 0.47) or number of days diet was tracked (90.7 ± 9.1 App, 68.4 ± 9.8 Bite; P = 0.09) did not differ between groups, but the Bite group had significant increases in physical activity metabolic equivalents (+2015.4 ± 684.6 min/wk; P = 0.02) compared to little change in the App group (-136.5 ± 630.6; P = 0.02). Total weight loss was significantly correlated with number of podcasts downloaded (r = -0.33, P < 0.01) and number of days diet was tracked (r = -0.33, P < 0.01). CONCLUSIONS: While frequency of diet tracking was similar between the App and Bite groups, there was greater weight loss observed in the App group.

Assessing the Accuracy of a Wrist Motion Tracking Method for Counting Bites Across Demographic and Food Variables.

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.

Different Disclosed Probabilities to Receive an Antiemetic Equally Decrease Subjective Symptoms in an Experimental Placebo Study: To Be or Not to Be Sure.

PURPOSE: The purpose of this study was to examine whether the disclosed probability of receiving an antiemetic affects nausea. METHODS: Forty-eight healthy participants (mean [SD] age, 26.8 [5.4] years; 50% female) were exposed to 5 × 2 minutes of nauseogenic body rotations on 2 days. On day 2, participants were randomized to 3 experimental groups that were given different instructions concerning the probability of receiving an antiemetic remedy (100%, 50%, or 0% probability), whereas all received an inert substance. Subjective symptoms, behavioral (rotation tolerance) measures, and physiologic (electrogastrogram) measures of nausea were assessed and mediator and moderator analyses performed for effects of expectations and psychological characteristics on outcomes. FINDINGS: Disclosed probabilities of both 100% and 50% significantly reduced subjective symptoms of nausea in an equal manner compared with the 0% probability group from day 1 to day 2. This effect was found for neither rotation tolerance nor myoelectric gastric activity. Expectations and psychological characteristics did not affect the results found. Post hoc analyses revealed that women only seem to be susceptible to this placebo effect. IMPLICATIONS: Nausea is susceptible to placebo effects independent of the disclosed probability of receiving a drug and of explicit expectations. In line with placebo research, this effect is probably attributable to central mechanisms, and it is speculated that it could be related to the reward circuitry and social interactions.

Measuring the Consumption of Individual Solid and Liquid Bites Using a Table-Embedded Scale During Unrestricted Eating.

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.

Frequency, Not Amplitude, of Latency Affects Subjective Sickness in a Head-Mounted Display.

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.

A virtual experimenter to increase standardization for the investigation of placebo effects.

BACKGROUND: Placebo effects are mediated by expectancy, which is highly influenced by psychosocial factors of a treatment context. These factors are difficult to standardize. Furthermore, dedicated placebo research often necessitates single-blind deceptive designs where biases are easily introduced. We propose a study protocol employing a virtual experimenter - a computer program designed to deliver treatment and instructions - for the purpose of standardization and reduction of biases when investigating placebo effects. METHODS: To evaluate the virtual experimenter's efficacy in inducing placebo effects via expectancy manipulation, we suggest a partially blinded, deceptive design with a baseline/retest pain protocol (hand immersions in hot water bath). Between immersions, participants will receive an (actually inert) medication. Instructions pertaining to the medication will be delivered by one of three metaphors: The virtual experimenter, a human experimenter, and an audio/text presentation (predictor "Metaphor"). The second predictor includes falsely informing participants that the medication is an effective pain killer, or correctly informing them that it is, in fact, inert (predictor "Instruction"). Analysis will be performed with hierarchical linear modelling, with a sample size of N = 50. Results from two pilot studies are presented that indicate the viability of the pain protocol (N = 33), and of the virtual experimenter software and placebo manipulation (N = 48). DISCUSSION: It will be challenging to establish full comparability between all metaphors used for instruction delivery, and to account for participant differences in acceptance of their virtual interaction partner. Once established, the presence of placebo effects would suggest that the virtual experimenter exhibits sufficient cues to be perceived as a social agent. He could consequently provide a convenient platform to investigate effects of experimenter behavior, or other experimenter characteristics, e.g., sex, age, race/ethnicity or professional status. More general applications are possible, for example in psychological research such as bias research, or virtual reality research. Potential applications also exist for standardizing clinical research by documenting and communicating instructions used in clinical trials.

Resting blood pressure differentially predicts time course in a tonic pain experiment.

Resting blood pressure (BP) shows a negative relationship with pain sensitivity (BP-related hypoalgesia). In chronic pain conditions, this relationship is inverted. The precise mechanisms responsible for the inversion are unknown. Using a tonic pain protocol, we report findings closely resembling this inversion in healthy participants. Resting BP and state measures of anxiety and mood were assessed from 33 participants (21 female). Participants then immersed their dominant hand in painfully hot water (47 °C) for five trials of 1-min duration, with 30-s intertrial intervals. Throughout the trials, participants continually registered their pain. After a 35-min intermission, the trial sequence was repeated. A disassociation of the negative relationship of resting systolic BP (as per Trial 1) was found using hierarchical linear modeling (p < .001, R(2) = .07). The disassociation unfolds over each consecutive trial, with an increasingly positive relationship. In Sequence 2, the initially negative relationship is almost completely absent. Furthermore, the association of BP and pain was found to be moderated by anxiety, such that only persons with low anxiety exhibited BP hypoalgesia. Our findings expand the existing literature by incorporating anxiety as a moderator of BP hypoalgesia. Furthermore, the protocol emulates the changing relationship between BP and pain observed in chronic pain patients. The protocol has potential as a model for chronic pain; however, future research should determine if similar physiological systems are involved. The finding holds potential diagnostic or prognostic relevance for certain clinical pain conditions, especially those involving dysfunction of the descending modulation of pain.

Effects of Bite Count Feedback from a Wearable Device and Goal Setting on Consumption in Young Adults.

BACKGROUND: New technologies are emerging that may help individuals engage in healthier eating behaviors. One paradigm to test the efficacy of a technology is to determine its effect relative to environment cues that are known to cause individuals to overeat. OBJECTIVE: The purpose of this work was to independently investigate two questions: How does the presence of a technology that provides bite count feedback alter eating behavior? and, How does the presence of a technology that provides bite count feedback paired with a goal alter eating behavior? DESIGN: Two studies investigated these research questions. The first study tested the effects of a large and small plate crossed with the presence or absence of a device that provided bite count feedback on intake. The second study tested the effects of a bite count goal with bite count feedback, again crossed with plate size, on intake. Both studies used a 2×2 between-subjects design. PARTICIPANTS/SETTING: In the first study, 94 subjects (62 women aged 19.0±1.6 years with body mass index [BMI] 23.04±3.6) consumed lunch in a laboratory. The second study examined 99 subjects (56 women aged 18.5±1.5 years with BMI 22.73±2.70) under the same conditions. INTERVENTION: In both studies subjects consumed a single-course meal, using either a small or large plate. In the first study participants either wore or did not wear an automated bite counting device. In the second study all participants wore the bite counting device and were given either a low bite count goal (12 bites) or a high bite count goal (22 bites). STATISTICAL ANALYSES: Effect of plate size, feedback, and goal on consumption (grams) and number of bites taken were assessed using 2×2 analyses of variance. As adjunct measures, the effects of serving size, bite size (grams per bite), postmeal satiety, and satiety change were also assessed. RESULTS: In the first study there was a main effect of plate size on grams consumed and number of bites taken such that eating from a large plate led to greater consumption (P=0.001) and a greater number of bites (P=0.001). There was also a main effect of feedback on consumption and number of bites taken such that those who received feedback consumed less (P=0.011) and took fewer bites (P<0.001). In the second study there was a main effect of plate size on consumption such that those eating from a large plate consumed more (P=0.003) but did not take more bites. Further analysis revealed a main effect of goal on number of bites taken such that those who received the low goal took fewer bites (P<0.001) but did not consume less. CONCLUSIONS: Providing feedback on the number of bites taken from a wearable intake monitor can reduce overall intake during a single meal. Regarding the first research question, providing feedback significantly reduced intake in both plate size groups and reduced the overall number of bites taken. Regarding the second research question, participants were successful in eating to their goals. However, individuals in the low goal condition appeared to compensate for the restricted goal by taking larger bites, leading to comparable levels of consumption between the low and high goal groups. Hence, the interaction of technology with goals should be considered when introducing a health intervention.

Comparison between Human and Bite-Based Methods of Estimating Caloric Intake.

BACKGROUND: Current methods of self-monitoring kilocalorie intake outside of laboratory/clinical settings suffer from a systematic underreporting bias. Recent efforts to make kilocalorie information available have improved these methods somewhat, but it may be possible to derive an objective and more accurate measure of kilocalorie intake from bite count. OBJECTIVE: This study sought to develop and examine the accuracy of an individualized bite-based measure of kilocalorie intake and to compare that measure to participant estimates of kilocalorie intake. It was hypothesized that kilocalorie information would improve human estimates of kilocalorie intake over those with no information, but a bite-based estimate of kilocalorie intake would still outperform human estimates. PARTICIPANTS/SETTINGS: Two-hundred eighty participants were allowed to eat ad libitum in a cafeteria setting. Their bite count and kilocalorie intake were measured. After completion of the meal, participants estimated how many kilocalories they consumed, some with the aid of a menu containing kilocalorie information and some without. Using a train and test method for predictive model development, participants were randomly divided into one of two groups: one for model development (training group) and one for model validation (test group). STATISTICAL ANALYSIS: Multiple regression was used to determine whether height, weight, age, sex, and waist-to-hip ratio could predict an individual's mean kilocalories per bite for the training sample. The model was then validated with the test group, and the model-predicted kilocalorie intake was compared with human-estimated kilocalorie intake. RESULTS: Only age and sex significantly predicted mean kilocalories per bite, but all variables were retained for the test group. The bite-based measure of kilocalorie intake outperformed human estimates with and without kilocalorie information. CONCLUSIONS: Bite count might serve as an easily measured, objective proxy for kilocalorie intake. A tool that can monitor bite count may be a powerful assistant to self-monitoring.