Free-living Conditions Research Articles

Evaluation of protein requirements using the indicator amino acid oxidation method: a scoping review

BackgroundThe indicator amino acid oxidation (IAAO) method has been accepted as an approach to evaluate habitual protein requirements under free-living conditions. ObjectivesThis scoping review reports on literature that evaluated protein requirements in humans using the IAAO methods. MethodsThree databases (PubMed/Medline, Web of Science, and ProQuest) were systematically searched to identify studies that evaluated protein requirements using the IAAO method published in English until 5 June, 2023. We evaluated the study quality using previously developed criteria. We extracted the characteristics of the study design and the results of protein requirements. Two reviewers conducted both reviews and quality assessments independently; any differences among them were resolved by consensus or agreement of all team members. ResultsWe extracted 16 articles targeting children, young adults (including pregnant women, resistance training athletes, endurance-training athletes, and team sports), and older adults. In quality assessment, 14 studies were evaluated “strong,” but the remaining 2 were “moderate.” These studies were conducted in only 3 countries and did not include all sexes or life stages. The range of the estimated average protein requirements of each life stage was 1.30 g/kg body weight/d for children, 0.87 to 2.10 (0.87–0.93 for general young adults, 1.22–1.52 for pregnant women, 1.49–2.00 for resistance-trained athletes, 1.65–2.10 for endurance athletes, and 1.20–1.41 for team sports athletes) g/kg body weight/d for young adults, and 0.85 to 0.96 g/kg body weight/d for older adults. ConclusionsProtein requirements in 14 studies were higher than the current reference for each sex, life stage, and physical activity that are related to protein requirements. In the future, protein requirements of various populations including sex and life stage could be assessed using the IAAO methods worldwide.

Effects of sleep restriction on food intake and appetite under free-living conditions: A randomized crossover trial

To investigate changes in subjective psychological factors and dietary intake during sleep restriction, we carried out a randomized crossover trial with a 3-day sleep restriction condition (SR; 5 h of sleep) and control sleep condition (CS; 8 h of sleep). Days 3 and 4 involved free-living and laboratory (in the morning) conditions, respectively. Subjective psychological factors (hunger, appetite, desire for sweets and fatty foods, sleepiness, and fatigue) were assessed using a 0.0–10.0 cm visual analog scale (VAS) every hour throughout the day on day 3, and at 8:00 a.m. on day 4. Dietary intake on day 3 was assessed on the basis of the food purchased and eaten. Fasting blood samples were collected at 8:00 a.m. on day 4. Dietary intake during the ad libitum breakfast was assessed on day 4. The participants were 13 women and 11 men (mean age, 21.4 ± 1.0 years; mean body mass index, 19.8 ± 1.7 kg/m2). The areas under the curve 0–16 h after waking for hunger, desire for fatty foods, sleepiness, and fatigue were higher in the SR than CS on day 3 (P < 0.05). Energy and carbohydrate intakes from snacks (daytime and nighttime) on day 3 were higher in the SR than CS (P < 0.05) but total dietary intake on day 3 was not different between the conditions (P > 0.05). The 2-arachidonoylglycerol level was different between the conditions (P < 0.05), but was not associated with sweet taste preference, dietary intake, or the active ghrelin level on day 4 (P > 0.05). In conclusion, ratings for subjective psychological factors and energy and carbohydrate intakes from snacks increased in association with sleep restriction under free-living conditions.

Breaking prolonged sitting increases 24-h physical activity and self-perceived energy levels but does not acutely affect cognition in healthy adults.

It is unknown whether predetermined (un)interrupted sitting within a laboratory setting will induce compensatory changes in human behaviours (energy intake and physical activity) once people return to a free-living environment. The effects of breaking up prolonged sitting on cognition are also unclear. Twenty-four (male = 13) healthy participants [age 31 ± 8 y, BMI 22.7 ± 2.3kg/m2 (mean ± SD)] completed 320min mixed-feeding trials under prolonged sitting (SIT) or with 2min walking at 6.4km/h every 20min (ACTIVE), in a randomised crossover design. Human behaviours were recorded post-trial under free-living conditions until midnight. Cognitive performance was evaluated before and immediately after SIT and ACTIVE trials. Self-perceived sensations (appetite, energy and mood) and finger prick blood glucose levels were collected at regular intervals throughout the trials. There were no differences between trials in eating behaviour and spontaneous physical activity (both, p > 0.05) in free-living conditions, resulting in greater overall total step counts [11,680 (10740,12620) versus 6049 (4845,7253) steps] and physical activity energy expenditure (PAEE) over 24-h period in ACTIVE compared to SIT (all, p < 0.05). Greater self-perceived levels of energy and lower blood glucose iAUC were found in ACTIVE trial compared to SIT trial (both, p < 0.05). No differences were found in cognitive performance between trials (all, p > 0.05). Breaking up sitting does not elicit subsequent behavioural compensation, resulting in greater 24-h step counts and PAEE in healthy adults. Breaking up sitting reduces postprandial glucose concentrations and elicits greater self-perceived energy levels, but these positive effects do not acutely translate into improved cognitive function.

Sleep restriction by sleep timing late night or early wake: The impact on physical activity and dietary intake in adults

PurposeTo date, few studies have assessed whether the timing of sleep restriction impacts physical activity and energy intake patterns. Thus, we aimed to quantify physical activity and energy intake during an early wake (EW) and late sleep (LS) period. MethodsFourteen participants who met the inclusion criteria (sleep 7–9 h/night and a BMI of <40 kg/m2) participated in 3 crossover free-living conditions: normal sleep (NS, 7–9 h), EW (2-h early wake-time), and LS (2-h late to sleep) for 4 nights. Sleep duration (via Actiwatch), energy intake (via food diaries), and physical activity (via hip accelerometry) were recorded for 4 days/4 nights throughout each condition. ResultsSleep duration was reduced in both sleep restriction conditions compared to NS (p < 0.001) with no difference between sleep restriction conditions. Daily energy intake tended to increase in the LS condition (p = 0.056) but was unchanged during EW (p = 0.56). Fat (p = 0.031) and sodium (p = 0.039) intake were increased in the LS condition only compared to NS. During the EW condition, fat (p = 0.24) and sodium (p = 0.18) intake were not altered. No changes in carbohydrate or protein intake occurred between conditions. Daily steps tended to increase in the EW condition compared to NS (p = 0.058), while steps during the LS condition were unchanged (p = 0.28), with no differences between sleep restriction conditions. ConclusionThe timing of sleep curtailment differentially influences physical activity and EI the following day, such that EW results in increased physical activity, while LS leads to poorer dietary choices.

Integrating metabolic expenditure information from wearable fitness sensors into an AI-augmented automated insulin delivery system: a randomised clinical trial

Exercise can rapidly drop glucose in people with type 1 diabetes. Ubiquitous wearable fitness sensors are not integrated into automated insulin delivery (AID) systems. We hypothesised that an AID can automate insulin adjustments using real-time wearable fitness data to reduce hypoglycaemia during exercise and free-living conditions compared with an AID not automating use of fitness data. Our study population comprised of individuals (aged 21-50 years) with type 1 diabetes from from the Harold Schnitzer Diabetes Health Center clinic at Oregon Health and Science University, OR, USA, who were enrolled into a 76 h single-centre, two-arm randomised (4-block randomisation), non-blinded crossover study to use (1) an AID that detects exercise, prompts the user, and shuts off insulin during exercise using an exercise-aware adaptive proportional derivative (exAPD) algorithm or (2) an AID that automates insulin adjustments using fitness data in real-time through an exercise-aware model predictive control (exMPC) algorithm. Both algorithms ran on iPancreas comprising commercial glucose sensors, insulin pumps, and smartwatches. Participants executed 1 week run-in on usual therapy followed by exAPD or exMPC for one 12 h primary in-clinic session involving meals, exercise, and activities of daily living, and 2 free-living out-patient days. Primary outcome was time below range (<3·9 mmol/L) during the primary in-clinic session. Secondary outcome measures included mean glucose and time in range (3·9-10 mmol/L). This trial is registered with ClinicalTrials.gov, NCT04771403. Between April 13, 2021, and Oct 3, 2022, 27 participants (18 females) were enrolled into the study. There was no significant difference between exMPC (n=24) versus exAPD (n=22) in time below range (mean [SD] 1·3% [2·9] vs 2·5% [7·0]) or time in range (63·2% [23·9] vs 59·4% [23·1]) during the primary in-clinic session. In the 2 h period after start of in-clinic exercise, exMPC had significantly lower mean glucose (7·3 [1·6] vs 8·0 [1·7] mmol/L, p=0·023) and comparable time below range (1·4% [4·2] vs 4·9% [14·4]). Across the 76 h study, both algorithms achieved clinical time in range targets (71·2% [16] and 75·5% [11]) and time below range (1·0% [1·2] and 1·3% [2·2]), significantly lower than run-in period (2·4% [2·4], p=0·0004 vs exMPC; p=0·012 vs exAPD). No adverse events occurred. AIDs can integrate exercise data from smartwatches to inform insulin dosing and limit hypoglycaemia while improving glucose outcomes. Future AID systems that integrate exercise metrics from wearable fitness sensors may help people living with type 1 diabetes exercise safely by limiting hypoglycaemia. JDRF Foundation and the Leona M and Harry B Helmsley Charitable Trust, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.

Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling

Wearable biosensors and smartphone applications can measure physiological variables over multiple days in free-living conditions. We measure food and drink ingestion, glucose dynamics, physical activity, heart rate (HR), and heart rate variability (HRV) in 25 healthy participants over 14days. We develop a Bayesian inference framework to learn personal parameters that quantify circadian rhythms and physiological responses to external stressors. Modeling the effects of ingestion events on glucose levels reveals that slower glucose decay kinetics elicit larger postprandial glucose spikes, and we uncover a circadian baseline rhythm for glucose with high amplitudes in some individuals. Physical activity and circadian rhythms explain as much as 40%-65% of the HR variance, whereas the variance explained for HRV is more heterogeneous across individuals. A more complex model incorporating activity, HR, and HRV explains up to 15% of additional glucose variability, highlighting the relevance of integrating multiple biosensors to better predict glucose dynamics.

DeepPuff: Utilizing Deep Learning for Smoking Behavior Identification in Free-living Environment.

A comprehensive assessment of cigarette smoking behavior and its effect on health requires a detailed examination of smoke exposure. We propose a CNN-LSTM-based deep learning architecture named DeepPuff to quantify Respiratory Smoke Exposure Metrics (RSEM). Smoke inhalations were detected from the breathing and hand gesture sensors of the Personal Automatic Cigarette Tracker v2 (PACT 2.0). The DeepPuff model for smoke inhalation detection was developed using data collected from 190 cigarette smoking events from 38 medium to heavy smokers and optimized for precision (avoidance of false positives). An independent dataset of 459 smoking events from 45 participants (90 smoking events in the lab and 369 smoking events in free-living conditions) was used for testing the model. The proposed model achieved a precision of 82.39% on the training and 93.80% on the testing dataset (95.88% in the lab and 93.78% in free-living). RSEM metrics were then computed from the breathing signal of each detected smoke inhalation. Results from the RSEM algorithm were compared with respiratory metrics obtained from video annotation. Smoke exposure metrics of puff duration, inhale-exhale duration, and inhalation duration were not statistically different from the ground truth generated through video annotation. The results suggest that DeepPuff may be used as a reliable means to measure respiratory smoke exposure metrics collected under free-living conditions.

Validity of activity wristbands for estimating daily physical activity in primary schoolchildren under free-living conditions: School-Fit study.

The use of activity wristbands to monitor and promote schoolchildren's physical activity (PA) is increasingly widespread. However, their validity has not been sufficiently studied, especially among primary schoolchildren. Consequently, the main purpose was to examine the validity of the daily steps and moderate-to-vigorous PA (MVPA) scores estimated by the activity wristbands Fitbit Ace 2, Garmin Vivofit Jr 2, and the Xiaomi Mi Band 5 in primary schoolchildren under free-living conditions. An initial sample of 67 schoolchildren (final sample = 62; 50% females), aged 9-12 years old (mean = 10.4 ± 1.0 years), participated in the present study. Each participant wore three activity wristbands (Fitbit Ace 2, Garmin Vivofit Jr 2, and Xiaomi Mi Band 5) on his/her non-dominant wrist and a research-grade accelerometer (ActiGraph wGT3X-BT) on his/her hip as the reference standard (number of steps and time in MVPA) during the waking time of one day. Results showed that the validity of the daily step scores estimated by the Garmin Vivofit Jr 2 and Xiaomi Mi Band 5 were good and acceptable (e.g., MAPE = 9.6/11.3%, and lower 95% IC of ICC = 0.87/0.73), respectively, as well as correctly classified schoolchildren as meeting or not meeting the daily 10,000/12,000-step-based recommendations, obtaining excellent/good and good/acceptable results (e.g., Garmin Vivofit Jr 2, k = 0.75/0.62; Xiaomi Mi Band 5, k = 0.73/0.53), respectively. However, the Fitbit Ace 2 did not show an acceptable validity (e.g., daily steps: MAPE = 21.1%, and lower 95% IC of ICC = 0.00; step-based recommendations: k = 0.48/0.36). None of the three activity wristbands showed an adequate validity for estimating daily MVPA (e.g., MAPE = 36.6-90.3%, and lower 95% IC of ICC = 0.00-0.41) and the validity for the MVPA-based recommendation tended to be considerably lower (e.g., k = -0.03-0.54). The activity wristband Garmin Vivofit Jr 2 obtained the best validity for monitoring primary schoolchildren's daily steps, offering a feasible alternative to the research-grade accelerometers. Furthermore, this activity wristband could be used during PA promotion programs to provide accurate feedback to primary schoolchildren to ensure their accomplishment with the PA recommendations.

Ear canal pressure sensor for food intake detection

Introduction: This paper presents a novel Ear Canal Pressure Sensor (ECPS) for objective detection of food intake, chew counting, and food image capture in both controlled and free-living conditions. The contribution of this study is threefold: 1) Development and validation of a novel wearable sensor that uses changes in ear canal pressure and the device’s acceleration as an indicator of food intake, 2) A method to identify chewing segments and count the number of chews in each eating episode, and 3) Facilitation of egocentric image capture only during eating by triggering camera from sensor detection thus reducing power consumption, privacy concerns, as well as storage and computational cost.Methods: To validate the device, data were collected from 10 volunteers in a controlled environment and three volunteers in a free-living environment. During the controlled activities, each participant wore the device for approximately 1 h, and during the free living for approximately 12 h. The food intake of the participants was not restricted in any way in both part of the experiment. Subject-independent Support Vector Machine classifiers were trained to identify periods of food intake from the features of both the pressure sensor and accelerometer, and features only from the pressure sensor.Results: Results from leave-one-out cross-validation showed an average 5 sec-epoch classification F-score of 87.6% using only pressure sensor features and 88.6% using features from both pressure sensor and accelerometer in the controlled environment. For the free-living environment, both classifiers accurately detected all eating episodes. The wearable sensor achieves 95.5% accuracy in counting the number of chews with respect to manual annotation from the videos of the eating episodes using a pressure sensor classifier in the controlled environment.Discussion: The manual review of the images found that only 3.7% of captured images belonged to the detected eating episodes, suggesting that sensor-triggered camera capture may facilitate reducing the number of captured images and power consumption of the sensor.

Development and Validation of Binary Classifiers to Predict Nocturnal Hypoglycemia in Adults With Type 1 Diabetes.

One of the biggest challenges for people with type 1 diabetes (T1D) using multiple daily injections (MDIs) is nocturnal hypoglycemia (NH). Recurrent NH can lead to serious complications; hence, prevention is of high importance. In this work, we develop and externally validate, device-agnostic Machine Learning (ML) models to provide bedtime decision support to people with T1D and minimize the risk of NH. We present the design and development of binary classifiers to predict NH (blood glucose levels occurring below 70 mg/dL). Using data collected from a 6-month study of 37 adult participants with T1D under free-living conditions, we extract daytime features from continuous glucose monitor (CGM) sensors, administered insulin, meal, and physical activity information. We use these features to train and test the performance of two ML algorithms: Random Forests (RF) and Support Vector Machines (SVMs). We further evaluate our model in an external population of 20 adults with T1D using MDI insulin therapy and wearing CGM and flash glucose monitoring sensors for two periods of eight weeks each. At population-level, SVM outperforms RF algorithm with a receiver operating characteristic-area under curve (ROC-AUC) of 79.36% (95% CI: 76.86%, 81.86%). The proposed SVM model generalizes well in an unseen population (ROC-AUC = 77.06%), as well as between the two different glucose sensors (ROC-AUC = 77.74%). Our model shows state-of-the-art performance, generalizability, and robustness in sensor devices from different manufacturers. We believe it is a potential viable approach to inform people with T1D about their risk of NH before it occurs.

Energy Expenditure, Body Composition, and Skeletal Muscle Oxidative Capacity in Patients with Myotonic Dystrophy Type 1.

Myotonic dystrophy type 1 (DM1) patients are at risk for metabolic abnormalities and commonly experience overweight and obesity. Possibly, weight issues result from lowered resting energy expenditure (EE) and impaired muscle oxidative metabolism. This study aims to assess EE, body composition, and muscle oxidative capacity in patients with DM1 compared to age-, sex- and BMI-matched controls. A prospective case control study was conducted including 15 DM1 patients and 15 matched controls. Participants underwent state-of-the-art methodologies including 24 h whole room calorimetry, doubly labeled water and accelerometer analysis under 15-days of free-living conditions, muscle biopsy, full body magnetic resonance imaging (MRI), dual-energy x-ray absorptiometry (DEXA), computed tomography (CT) upper leg, and cardiopulmonary exercise testing. Fat ratio determined by full body MRI was significantly higher in DM1 patients (56 [49-62] %) compared to healthy controls (44 [37-52] % ; p = 0.027). Resting EE did not differ between groups (1948 [1742-2146] vs (2001 [1853-2425>] kcal/24 h, respectively; p = 0.466). In contrast, total EE was 23% lower in DM1 patients (2162 [1794-2494] vs 2814 [2424-3310] kcal/24 h; p = 0.027). Also, DM1 patients had 63% less steps (3090 [2263-5063] vs 8283 [6855-11485] steps/24 h; p = 0.003) and a significantly lower VO2 peak (22 [17-24] vs 33 [26-39] mL/min/kg; p = 0.003) compared to the healthy controls. Muscle biopsy citrate synthase activity did not differ between groups (15.4 [13.3-20.0] vs 20.1 [16.6-25.8] μM/g/min, respectively; p = 0.449). Resting EE does not differ between DM1 patients and healthy, matched controls when assessed under standardized circumstances. However, under free living conditions, total EE is substantially reduced in DM1 patients due to a lower physical activity level. The sedentary lifestyle of DM1 patients seems responsible for the undesirable changes in body composition and aerobic capacity.

Near-Infrared Spectroscopy: A Free-Living Neuroscience Tool to Better Understand Diabetes and Obesity.

The human brain is the least accessible of all organs and attempts to study it in vivo rely predominantly on neuroimaging. Functional near-infrared spectroscopy (fNIRS) allows for the study of cortical neural activity in a non-invasive manner that may resemble free-living conditions. Moreover, compared to other neuroimaging tools, fNIRS is less expensive, it does not require the use of ionizing radiation, and can be applied to all study populations (patients suffering from claustrophobia, or neonates). In this narrative review, we provide an overview of the available research performed using fNIRS in patients with diabetes and obesity. The few studies conducted to date have presented controversial results regarding patients with diabetes, some reporting a greater hemodynamic response and others reporting a reduced hemodynamic response compared to the controls, with an unclear distinction between types 1 and 2. Subjects with obesity or a binge eating disorder have reduced prefrontal activation in response to inhibitory food or non-food stimuli; however, following an intervention, such as cognitive treatment, prefrontal activation is restored. Moreover, we discuss the potential of future applications of fNIRS for a better understanding of cortical neural activity in the context of metabolic disorders.

Validity of ActivPAL CREA software detection of sitting and lying during free-living conditions

Objective. Approaches to differentiate sitting and lying are available within the default activPAL software from a single thigh-worn monitor. Dual-monitor methods use multiple monitors positioned on the thigh and torso to characterize sitting versus lying. We evaluated the validity between these two methods to measure waking, sitting, and lying time in free-living conditions. We also examined if the degree-threshold distinguishing sitting/lying for the dual-monitor (<30° and <45°) impacted results. Approach. Thirty-five young adults (24 ± 3 years, 16 females) wore an activPAL 24 h per day on their thigh and torso during free-living conditions (average: 6.8 ± 1.0 d, 239 total). Data were processed using the default activPAL software (thigh-only) or a custom MATLAB program (dual-monitor). Main results. The single-monitor recorded less lying time (59 ± 99 min d−1) and more sitting time (514 ± 203 min d−1) than the dual-monitor method regardless of 30° (lying: 85 ± 94 min d−1; sitting: 488 ± 166 min d−1) or 45° lying threshold (lying: 170 ± 142, sitting: 403 ± 164 min d−1; all, p < 0.001). The single monitor lying time was weakly correlated to the dual-monitor (30°: ρ = 0.25, 45°: ρ = 0.21; both, p < 0.001), whereas sitting was moderate-strong (30°: ρ = 0.76, 45°: ρ = 0.58; both, p < 0.001). However, the mean absolute error was 81 min d−1 (30°) and 132 min d−1 (45°) for both lying and sitting. Significance. The method of differentiating sitting/lying from a single thigh-worn activPAL records more sitting time and less lying time compared to a dual-monitor configuration (regardless of degree-threshold) that considered the position of the torso. A further refinement of algorithms or implementation of multiple-monitor methods may be needed for researchers to derive detailed sedentary positions.

Leveraging Unlabelled Data in Multiple-Instance Learning Problems for Improved Detection of Parkinsonian Tremor in Free-Living Conditions.

Data-driven approaches for remote detection of Parkinson's Disease and its motor symptoms have proliferated in recent years, owing to the potential clinical benefits of early diagnosis. The holy grail of such approaches is the free-living scenario, in which data are collected continuously and unobtrusively during every day life. However, obtaining fine-grained ground-truth and remaining unobtrusive is a contradiction and therefore, the problem is usually addressed via multiple-instance learning. Yet for large scale studies, obtaining even the necessary coarse ground-truth is not trivial, as a complete neurological evaluation is required. In contrast, large scale collection of data without any ground-truth is much easier. Nevertheless, utilizing unlabelled data in a multiple-instance setting is not straightforward, as the topic has received very little research attention. Here we try to fill this gap by introducing a new method for combining semi-supervised with multiple-instance learning. Our approach builds on the Virtual Adversarial Training principle, a state-of-the-art approach for regular semi-supervised learning, which we adapt and modify appropriately for the multiple-instance setting. We first establish the validity of the proposed approach through proof-of-concept experiments on synthetic problems generated from two well-known benchmark datasets. We then move on to the actual task of detecting PD tremor from hand acceleration signals collected in-the-wild, but in the presence of additional completely unlabelled data. We show that by leveraging the unlabelled data of 454 subjects we can achieve large performance gains (up to 9% increase in F1-score) in per-subject tremor detection for a cohort of 45 subjects with known tremor ground-truth. In doing so, we confirm the validity of our approach on a real-world problem where the need for semi-supervised and multiple-instance learning arises naturally.

P28-012-23 Testing the Validity of the Portion Size™ Smartphone App for Estimating Dietary Intake in Free-Living Conditions: A Pilot Study

P28-012-23 Testing the Validity of the Portion Size™ Smartphone App for Estimating Dietary Intake in Free-Living Conditions: A Pilot Study

Unravelling phenolic metabotypes in the frame of the COMBAT study, a randomized, controlled trial with cranberry supplementation

Cranberry (poly)phenols may have potential health benefits. Circulating (poly)phenol metabolites can act as mediators of these effects, but they are subjected to an extensive inter-individual variability. This study aimed to quantify both plasma and urine (poly)phenol metabolites following a 12-week intake of a cranberry powder in healthy older adults, and to investigate inter-individual differences by considering the existence of urinary metabotypes related to dietary (poly)phenols. Up to 13 and 67 metabolites were quantified in plasma and urine respectively. Cranberry consumption led to changes in plasma metabolites, mainly hydroxycinnamates and hippuric acid. Individual variability in urinary metabolites was assessed using different data sets and a combination of statistical models. Three phenolic metabotypes were identified, colonic metabolism being the main driver for subject clustering. Metabotypes were characterized by quali-quantitative differences in the excretion of some metabolites such as phenyl-γ-valerolactones, hydroxycinnamic acids, and phenylpropanoic acids. Metabotypes were further confirmed when applying a model only focused on flavan-3-ol colonic metabolites. 5-(3′,4′-dihydroxyphenyl)-γ-valerolactone derivatives were the most relevant metabolites for metabotyping. Metabotype allocation was well preserved after 12-week intervention. This metabotyping approach for cranberry metabolites represents an innovative step to handle the complexity of (poly)phenol metabolism in free-living conditions, deciphering the existence of metabotypes derived from the simultaneous consumption of different classes of (poly)phenols. These results will help contribute to studying the health effects of cranberries and other (poly)phenol-rich foods, mainly considering gut microbiota-driven individual differences.

Frailty detection in older adults via fractal analysis of acceleration signals from wrist-worn sensors

PurposeFrailty is a reversible multidimensional syndrome that puts older people at a high risk of adverse health outcomes. It has been proposed to emerge from the dysregulation of the complex system dynamics of physiologic control systems. We propose the analysis of the fractal complexity of hand movements as a new method to detect frailty in older adults.MethodsFRAIL scale and Fried’s phenotype scores were calculated for 1209 subjects—72.4 (5.2) y.o. 569 women—and 1279 subjects—72.6 (5.3) y.o. 604 women—in the pubicly available NHANES 2011–2014 data set, respectively. The fractal complexity of their hand movements was assessed with a detrended fluctuation analysis (DFA) of their accelerometry records and a logistic regression model for frailty detection was fit.ResultsGoodness-of-fit to a power law was excellent (R2>0.98\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{2}>0.98$$\\end{document}). The association between complexity loss and frailty level was significant, Kruskal–Wallis test (df = 2, Chisq = 27.545, p-value <0.001\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$<0.001$$\\end{document}). The AUC of the logistic classifier was moderate (AUC with complexity = 0.69 vs. AUC without complexity = 0.67).ConclusionFrailty can be characterized in this data set with the Fried phenotype. Non-dominant hand movements in free-living conditions are fractal processes regardless of age or frailty level and its complexity can be quantified with the exponent of a power law. Higher levels of complexity loss are associated with higher levels of frailty. This association is not strong enough to justify the use of complexity loss after adjusting for sex, age, and multimorbidity.

138-LB: A Retrospective Algorithm to Identify Meal Intakes from CGM Time Series

The accurate assessment of dietary behavior in clinical trials, including number and size of meal intakes, is essential to evaluate participants’ metabolic health which, in turn, might reflect an increased risk for developing diabetes. Traditional dietary assessments leverage handwritten/digital food logs, which are error-prone and time-consuming for both users and investigators. Instead, glucose timeseries unobtrusively collected by CGM could provide objective measures of meals’ consumption. Here we present a new algorithm to retrospectively identify meal intakes using CGM data only. In the Geriatric Anorexia Study (NCT04858932), 50 healthy individuals (26 females, mean±SD age: 72.26±5.02 years, BMI: 24.96±3.33 kg/m2) were monitored in free-living conditions for 2 weeks with the Abbott FreeStyle Libre Pro, and recorded meal intakes using the Renpho Smart Food Scale. After removal of days with &amp;lt;2 meals recorded, a total of 1132 meals over 365 days were available for analysis. To identify meal intakes, the proposed algorithm scans CGM data, identifies local maxima, and measures their prominence, i.e., the relative height with respect to a neighborhood window of length L. Peaks with prominence higher than Pmin are labeled as meals. Subject data was divided into a ~70% training set (n=31, 775 meals), where L and Pmin are tuned, and ~30% test set (n=12, 357 meals), where the algorithm is evaluated in terms of absolute percent error (APE) between number of recorded and detected meals. Across subjects, mean±sd daily APE is 25.1±9.70%. Similar values hold when considering only subjects with &amp;lt;3.5 meals/day (n=6, 144 meals, APE=24.5±13.2%) and ≥3.5 meals/day (n=6, 213 meals, APE=25.5±6.93%), demonstrating the reliability of the algorithm to identify the number of meals in subjects with different dietary behaviors. These results evidence the suitability of CGM for the automatic quantification of meal intakes. Further assessment of the algorithm should be performed using more controlled meal intake recordings. Disclosure N. Camerlingo: Employee; Pfizer Inc. A. Messere: None. M. Santamaria: None. C. Demanuele: None. D. Caouette: Employee; Pfizer Inc. K. C. Thomas: None. N. Shaafi kabiri: None. D. Psaltos: Employee; Pfizer Inc. F. Karahanoglu: None. S. Khan: None. I. Messina: Research Support; Pfizer Inc. M. Wicker: None. M. Kelly: Research Support; Pfizer Inc. H. Zhang: Employee; Pfizer Inc.

The transportome of the endophyte Serendipita indica in free life and symbiosis with Arabidopsis and its expression in moderate salinity.

Serendipita indica is an endophytic root symbiont fungus that enhances the growth of various plants under different stress conditions, including salinity. Here, the functional characterization of two fungal Na+/H+ antiporters, SiNHA1 and SiNHX1 has been carried out to study their putative role in saline tolerance. Although their gene expression does not respond specifically to saline conditions, they could contribute, together with the previously characterized Na+ efflux systems SiENA1 and SiENA5, to relieve Na+ from the S. indica cytosol under this stressed condition. In parallel, an in-silico study has been carried out to define its complete transportome. To further investigate the repertoire of transporters expressed in free-living cells of S. indica and during plant infection under saline conditions, a comprehensive RNA-seq approach was taken. Interestingly, SiENA5 was the only gene significantly induced under free-living conditions in response to moderate salinity at all the tested time points, revealing that it is one of the main salt-responsive genes of S. indica. In addition, the symbiosis with Arabidopsis thaliana also induced SiENA5 gene expression, but significant changes were only detected after long periods of infection, indicating that the association with the plant somehow buffers and protects the fungus against the external stress. Moreover, the significant and strongest induction of the homologous gene SiENA1 occurred during symbiosis, regardless the exposure to salinity. The obtained results suggest a novel and relevant role of these two proteins during the establishment and maintenance of fungus-plant interaction.

Wearable derived cardiovascular responses to stressors in free-living conditions.

Stress contributes to the progression of many diseases. Despite stress' contribution towards disease, few methods for continuously measuring stress exist. We investigated if continuously measured cardiovascular signals from a wearable device can be used as markers of stress. Using wearable technology (WHOOP Inc, Boston, MA) that continuously measures and calculates heart rate (HR) and heart rate variability (root-mean-square of successive differences; HRV), we assessed duration and magnitude of deviations in HR and HRV around the time of a run (from 23665 runs) or high-stress work (from 8928 high-stress work events) in free-living conditions. HR and HRV were assessed only when participants were motionless (HRmotionless). Runs were grouped into light, moderate, and vigorous runs to determine dose response relationships. When examining HRmotionless and HRV throughout the day, we found that these metrics display circadian rhythms; therefore, we normalized HRmotionless and HRV measures for each participant relative to the time of day. Relative to the period within 30 minutes leading up to a run, HRmotionless is elevated for up to 180-210 minutes following a moderate or vigorous run (P<0.05) and is unchanged or reduced following a light run. HRV is reduced for at least 300 minutes following a moderate or vigorous run (P<0.05) and is unchanged during a light run. Relative to the period within 30 minutes leading up to high-stress work, HRmotionless is elevated during and for up to 30 minutes following high-stress work. HRV tends to be lower during high-stress work (P = 0.06) and is significantly lower 90-300 minutes after the end of the activity (P<0.05). These results demonstrate that wearables can quantify stressful events, which may be used to provide feedback to help individuals manage stress.