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

Wearable Orofacial Technology and Orthodontics

Wearable technology to augment traditional approaches are increasingly being added to the arsenals of treatment providers. Wearable technology generally refers to electronic systems, devices, or sensors that are usually worn on or are in close proximity to the human body. Wearables may be stand-alone or integrated into materials that are worn on the body. What sets medical wearables apart from other systems is their ability to collect, store, and relay information regarding an individual's current body status to other devices operating on compatible networks in naturalistic settings. The last decade has witnessed a steady increase in the use of wearables specific to the orofacial region. Applications range from supplementing diagnosis, tracking treatment progress, monitoring patient compliance, and better understanding the jaw's functional and parafunctional activities. Orofacial wearable devices may be unimodal or incorporate multiple sensing modalities. The objective data collected continuously, in real time, in naturalistic settings using these orofacial wearables provide opportunities to formulate accurate and personalized treatment strategies. In the not-too-distant future, it is anticipated that information about an individual's current oral health status may provide patient-centric personalized care to prevent, diagnose, and treat oral diseases, with wearables playing a key role. In this review, we examine the progress achieved, summarize applications of orthodontic relevance and examine the future potential of orofacial wearables.

Machine learning modeling practices to support the principles of AI and ethics in nutrition research

BACKGROUND

Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failure to use careful and well-thought-out modeling processes can lead to misleading conclusions and concerns surrounding ethics and bias.

METHODS

Based on our experience as reviewers and journal editors in nutrition and obesity, we identified the most frequently omitted best practices from statistical modeling and how these same practices extend to machine learning models. We next addressed areas required for implementation of machine learning that are not included in commercial software packages.

RESULTS

Here, we provide a tutorial on best artificial intelligence and machine learning modeling practices that can reduce potential ethical problems with a checklist and guiding principles to aid nutrition researchers in developing, evaluating, and implementing artificial intelligence and machine learning models in nutrition research.

CONCLUSION

The quality of AI/ML modeling in nutrition research requires iterative and tailored processes to mitigate against potential ethical problems or to predict conclusions that are free of bias.

Predicting energy intake in adults who are dieting and exercising

BACKGROUND

When a lifestyle intervention combines caloric restriction and increased physical activity energy expenditure (PAEE), there are two components of energy balance, energy intake (EI) and physical activity energy expenditure (PAEE), that are routinely misreported and expensive to measure. Energy balance models have successfully predicted EI if PAEE is known. Estimating EI from an energy balance model when PAEE is not known remains an open question.

OBJECTIVE

The objective was to evaluate the performance of an energy balance differential equation model to predict EI in an intervention that includes both calorie restriction and increases in PAEE.

DESIGN

The Antonetti energy balance model that predicts body weight trajectories during weight loss was solved and inverted to estimate EI during weight loss. Using data from a calorie restriction study that included interventions with and without prescribed PAEE, we tested the validity of the Antonetti weight predictions against measured weight and the Antonetti EI model against measured EI using the intake-balance method at 168 days. We then evaluated the predicted EI from the model against measured EI in a study that prescribed both calorie restriction and increased PAEE.

RESULTS

Compared with measured body weight at 168 days, the mean (±SD) model error was 1.30 ± 3.58 kg. Compared with measured EI at 168 days, the mean EI (±SD) model error in the intervention that prescribed calorie restriction and did not prescribe increased PAEE, was -84.9 ± 227.4 kcal/d. In the intervention that prescribed calorie restriction combined with increased PAEE, the mean (±SD) EI model error was -155.70 ± 205.70 kcal/d.

CONCLUSION

The validity of the newly developed EI model was supported by experimental observations and can be used to determine EI during weight loss.

Combining passive eating monitoring and ecological momentary assessment to characterize dietary lapses from a lifestyle modification intervention

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.

Combining ecological momentary assessment, wrist-based eating detection, and dietary assessment to characterize dietary lapse: A multi-method study protocol

Objectives

Behavioral 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.

Method

We 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.

Results

While 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.

Conclusion

This 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.