sEMG-controlled forearm bracelet and serious game-based rehabilitation for training manual dexterity in people with multiple sclerosis: a randomised controlled trial.

BACKGROUND: Muscle strength and dexterity impairments are common among patients with multiple sclerosis (MS) producing limitations in activities of daily living related to the upper limb (UL). This study aimed to evaluate the effectiveness of serious games specifically developed for the MYO Armband® capture sensor in improving forearm and wrist mobility, UL muscle strength, dexterity, fatigue, functionality, quality of life, satisfaction, adverse effects and compliance. METHODS: A double-blinded (allocation concealment was performed by a blinded investigator and by blinding for assessors) randomised controlled trial was conducted. The sample was randomised into two groups: an experimental group that received treatment based on UL serious games designed by the research team and controlled by the MYO Armband® gesture capture sensor, along with conventional rehabilitation and a control group that received the same conventional rehabilitation for the UL. Both groups received two 60-min sessions per week over an eight-week period. Wrist range of motion (goniometry), grip muscle strength (Jamar® dynamometer), coordination and gross UL dexterity (Box and Block Test), fatigue (Fatigue Severity Scale), functionality (ABILHAND), quality of life (Multiple Sclerosis Impact Scale-29), adverse effects (Simulator Sickness Questionnaire, SSQ), perceived workload (NASA-Task load index), satisfaction (Client Satisfaction Questionnaire-8 (CSQ-8), Satisfaction with Technology Scale, System Usability Scale (SUS) and QUEST 2.0) and compliance (attendance) were assessed in both groups pre-treatment, post-treatment and during a follow-up period of 2 weeks without receiving any treatment. RESULTS: Significant differences were observed in the experimental group compared to the control group in the assessment of forearm supination (p = .004) and grip strength (p = .004). Adverse effects were minimal (SSQ: 7/100 points) and perceived workload was low (NASA-Task Load Index: 25/100 points) in the experimental group. The MYO Armband® technology proved to be useful for the participants (SUS: 80.66/100) and the satisfaction scales received high scores (QUEST 2.0: 59.4/70 points; Satisfaction with Technology: 84.36/100 points). There were significant differences between the groups in terms of attendance percentage (p = .029). CONCLUSIONS: An experimental protocol using MYO Armband®-based serious games designed for UL rehabilitation showed improvements in active wrist range of motion and handgrip strength in patients with MS, with high satisfaction, minimal adverse effects and workload and excellent compliance. TRIAL REGISTRATION NUMBER: This randomised controlled trial has been registered at ClinicalTrials.gov Identifier: NCT04171908.

Robust Arm Impedocardiography Signal Quality Enhancement Using Recursive Signal Averaging and Multi-Stage Wavelet Denoising Methods for Long-Term Cardiac Contractility Monitoring Armbands.

Impedance cardiography (ICG) is a low-cost, non-invasive technique that enables the clinical assessment of haemodynamic parameters, such as cardiac output and stroke volume (SV). Conventional ICG recordings are taken from the patient's thorax. However, access to ICG vital signs from the upper-arm brachial artery (as an associated surrogate) can enable user-convenient wearable armband sensor devices to provide an attractive option for gathering ICG trend-based indicators of general health, which offers particular advantages in ambulatory long-term monitoring settings. This study considered the upper arm ICG and control Thorax-ICG recordings data from 15 healthy subject cases. A prefiltering stage included a third-order Savitzky-Golay finite impulse response (FIR) filter, which was applied to the raw ICG signals. Then, a multi-stage wavelet-based denoising strategy on a beat-by-beat (BbyB) basis, which was supported by a recursive signal-averaging optimal thresholding adaptation algorithm for Arm-ICG signals, was investigated for robust signal quality enhancement. The performance of the BbyB ICG denoising was evaluated for each case using a 700 ms frame centred on the heartbeat ICG pulse. This frame was extracted from a 600-beat ensemble signal-averaged ICG and was used as the noiseless signal reference vector (gold standard frame). Furthermore, in each subject case, enhanced Arm-ICG and Thorax-ICG above a threshold of correlation of 0.95 with the noiseless vector enabled the analysis of beat inclusion rate (BIR%), yielding an average of 80.9% for Arm-ICG and 100% for Thorax-ICG, and BbyB values of the ICG waveform feature metrics A, B, C and VET accuracy and precision, yielding respective error rates (ER%) of 0.83%, 11.1%, 3.99% and 5.2% for Arm-IG, and 0.41%, 3.82%, 1.66% and 1.25% for Thorax-ICG, respectively. Hence, the functional relationship between ICG metrics within and between the arm and thorax recording modes could be characterised and the linear regression (Arm-ICG vs. Thorax-ICG) trends could be analysed. Overall, it was found in this study that recursive averaging, set with a 36 ICG beats buffer size, was the best Arm-ICG BbyB denoising process, with an average of less than 3.3% in the Arm-ICG time metrics error rate. It was also found that the arm SV versus thorax SV had a linear regression coefficient of determination (R2) of 0.84.

Low-Cost Wearable Band Sensors of Surface Electromyography for Detecting Hand Movements.

Surface electromyography (sEMG) is a non-invasive measure of electrical activity generated due to muscle contraction. In recent years, sEMG signals have been increasingly used in diverse applications such as rehabilitation, pattern recognition, and control of orthotic and prosthetic systems. This study presents the development of a versatile multi-channel sEMG low-cost wearable band system to acquire 4 signals. In this case, the signals acquired with the proposed device have been used to detect hand movements. However, the WyoFlex band could be used in some sections of the arm or the leg if the section's diameter matches the diameter of the WyoFlex band. The designed WyoFlex band was fabricated using three-dimensional (3D) printing techniques employing thermoplastic polyurethane and polylactic acid as manufacturing materials. Then, the proposed wearable electromyographic system (WES) consists of 2 WyoFlex bands, which simultaneously allow the wireless acquisition of 4 sEMG channels of each forearm. The collected sEMG can be visualized and stored for future post-processing stages using a graphical user interface designed in Node-RED. Several experimental tests were conducted to verify the performance of the WES. A dataset with sEMG collected from 15 healthy humans has been obtained as part of the presented results. In addition, a classification algorithm based on artificial neural networks has been implemented to validate the usability of the collected sEMG signals.

Monitoring Physical Activity with a Wearable Sensor in Patients with COPD during In-Hospital Pulmonary Rehabilitation Program: A Pilot Study.

Accelerometers have become a standard method of monitoring physical activity in everyday life by measuring acceleration in one, two, or three axes. These devices provide reliable and objective measurements of the duration and intensity of physical activity. We aimed to investigate whether patients undertake physical activity during non-supervised days during stationary rehabilitation and whether patients adhere to the rigor of 24 h monitoring. The second objective was to analyze the strengths and weaknesses of such kinds of sensors. The research enrolled 13 randomly selected patients, qualified for in-patient, 3 week, high-intensity, 5 times a week pulmonary rehabilitation. The SenseWear armband was used for the assessment of physical activity. Participants wore the device 24 h a day for the next 4 days (Friday-Monday). The analysis of the number of steps per day, the time spent lying as well as undertaking moderate or vigorous physical activity (>3 metabolic equivalents of task (METs)), and the energy expenditure expressed in kcal showed no statistically significant difference between the training days and the days off. It seems beneficial to use available physical activity sensors in patients with chronic obstructive pulmonary disease (COPD); measurable parameters provide feedback that may increase the patient's motivation to be active to achieve health benefits.

sEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups.

Surface electromyography (sEMG)-based gesture recognition systems provide the intuitive and accurate recognition of various gestures in human-computer interaction. In this study, an sEMG-based hand posture recognition algorithm was developed, considering three main problems: electrode shift, feature vectors, and posture groups. The sEMG signal was measured using an armband sensor with the electrode shift. An artificial neural network classifier was trained using 21 feature vectors for seven different posture groups. The inter-session and inter-feature Pearson correlation coefficients (PCCs) were calculated. The results indicate that the classification performance improved with the number of training sessions of the electrode shift. The number of sessions necessary for efficient training was four, and the feature vectors with a high inter-session PCC (r > 0.7) exhibited high classification accuracy. Similarities between postures in a posture group decreased the classification accuracy. Our results indicate that the classification accuracy could be improved with the addition of more electrode shift training sessions and that the PCC is useful for selecting the feature vector. Furthermore, hand posture selection was as important as feature vector selection. These findings will help in optimizing the sEMG-based pattern recognition algorithm more easily and quickly.

Objectively measured physical activity is associated with dorsolateral prefrontal cortex volume in older adults.

BACKGROUND: Epidemiological studies suggest physical activity (PA) can slow or prevent both cognitive decline and age-related atrophy in frontal and hippocampal gray matter volumes. However, much of this evidence is based on self-reported measures of PA. METHODS: PA was measured objectively with a SenseWear™ Armband to examine the cross-sectional associations between the duration of light, moderate and vigorous intensity PA with gray matter volume in the dorsolateral prefrontal cortex (DLPFC) and hippocampus in 167 (female: 43%) cognitively healthy older adults aged 73 to 78. RESULTS: The duration of objective moderate to vigorous intensity physical activity (MVPA) was associated with a greater volume of the right DLPFC (ߠ​= â€‹0.16; p â€‹= â€‹0.04). In addition, objective moderate-intensity PA alone was also associated with greater volume of the left (ߠ​= â€‹0.17; p â€‹= â€‹0.03) and right (ߠ​= â€‹0.19; p â€‹= â€‹0.01) DLPFC after controlling for covariates and adjustment for multiple comparisons. In contrast, there were no significant associations between light- or vigorous-intensity PA and gray matter volumes (all p â€‹> â€‹0.05). No associations between PA and cognitive performance were detected, and self-reported PA was not associated with any of the outcomes investigated. CONCLUSIONS: These findings suggest that an intensity-dependent relationship may exist, whereby a greater duration of MVPA, perhaps driven by moderate-intensity PA, is associated with preserved gray matter volume in frontal regions of the brain. Future research should investigate the mechanisms of this dose-effect and determine whether greater brain volumes associated with objective PA convey protective effects against cognitive decline.

Physical activity measures in patients with myalgic encephalomyelitis/chronic fatigue syndrome: correlations between peak oxygen consumption, the physical functioning scale of the SF-36 questionnaire, and the number of steps from an activity meter.

BACKGROUND: Most studies to assess effort intolerance in patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) have used questionnaires. Few studies have compared questionnaires with objective measures like an actometer or an exercise test. This study compared three measures of physical activity in ME/CFS patients: the physical functioning scale (PFS) of the SF-36, the number of steps/day (Steps) using an actometer, and the %peak VO2 of a cardiopulmonary stress test. METHODS: Female ME/CFS patients were selected from a clinical database if the three types of measurements were available, and the interval between measurements was ≤ 3 months. Data from the three measures were compared by linear regression. RESULTS: In 99 female patients the three different measures were linearly, significantly, and positively correlated (PFS vs Steps, PFS vs %peak VO2 and Steps vs %peak VO2: all P < 0.001). Subgroup analysis showed that the relations between the three measures were not different in patients with versus without fibromyalgia and with versus without a maximal exercise effort (RER ≥ 1.1). In 20 patients re-evaluated for symptom worsening, the mean of all three measures was significantly lower (P < 0.0001), strengthening the observation of the relations between them. Despite the close correlation, we observed a large variation between the three measures in individual patients. CONCLUSIONS: Given the large variation in ME/CFS patients, the use of only one type of measurement is inadequate. Integrating the three modalities may be useful for patient care by detecting overt discrepancies in activity and may inform studies that compare methods of improving exercise capacity.

A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network.

Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Most sEMG-based hand gesture recognition, which uses machine learning as the classifier, depends on feature extraction of sEMG data. Recently, a deep leaning-based approach such as recurrent neural network (RNN) has provided a choice to automatically learn features from raw data. This paper presents a novel hand gesture prediction method by using an RNN model to learn from raw sEMG data and predict gestures. The sEMG signals of 21 short-term hand gestures of 13 subjects were recorded with a Myo armband, which is a non-intrusive, low cost, commercial portable device. At the start of the gesture, the trained model outputs an instantaneous prediction for the sEMG data. Experimental results showed that the more time steps of data that were known, the higher instantaneous prediction accuracy the proposed model gave. The predicted accuracy reached about 89.6% when the data of 40-time steps (200 ms) were used to predict hand gesture. This means that the gesture could be predicted with a delay of 200 ms after the hand starts to perform the gesture, instead of waiting for the end of the gesture.

Human-Robot Interface for Embedding Sliding Adjustable Autonomy Methods.

This work discusses a novel human-robot interface for a climbing robot for inspecting weld beads in storage tanks in the petrochemical industry. The approach aims to adapt robot autonomy in terms of the operator's experience, where a remote industrial joystick works in conjunction with an electromyographic armband as inputs. This armband is worn on the forearm and can detect gestures from the operator and rotation angles from the arm. Information from the industrial joystick and the armband are used to control the robot via a Fuzzy controller. The controller works with sliding autonomy (using as inputs data from the angular velocity of the industrial controller, electromyography reading, weld bead position in the storage tank, and rotation angles executed by the operator's arm) to generate a system capable of recognition of the operator's skill and correction of mistakes from the operator in operating time. The output from the Fuzzy controller is the level of autonomy to be used by the robot. The levels implemented are Manual (operator controls the angular and linear velocities of the robot); Shared (speeds are shared between the operator and the autonomous system); Supervisory (robot controls the angular velocity to stay in the weld bead, and the operator controls the linear velocity); Autonomous (the operator defines endpoint and the robot controls both linear and angular velocities). These autonomy levels, along with the proposed sliding autonomy, are then analyzed through robot experiments in a simulated environment, showing each of these modes' purposes. The proposed approach is evaluated in virtual industrial scenarios through real distinct operators.

An Energy-Based Method for Orientation Correction of EMG Bracelet Sensors in Hand Gesture Recognition Systems.

Hand gesture recognition (HGR) systems using electromyography (EMG) bracelet-type sensors are currently largely used over other HGR technologies. However, bracelets are susceptible to electrode rotation, causing a decrease in HGR performance. In this work, HGR systems with an algorithm for orientation correction are proposed. The proposed orientation correction method is based on the computation of the maximum energy channel using a synchronization gesture. Then, the channels of the EMG are rearranged in a new sequence which starts with the maximum energy channel. This new sequence of channels is used for both training and testing. After the EMG channels are rearranged, this signal passes through the following stages: pre-processing, feature extraction, classification, and post-processing. We implemented user-specific and user-general HGR models based on a common architecture which is robust to rotations of the EMG bracelet. Four experiments were performed, taking into account two different metrics which are the classification and recognition accuracy for both models implemented in this work, where each model was evaluated with and without rotation of the bracelet. The classification accuracy measures how well a model predicted which gesture is contained somewhere in a given EMG, whereas recognition accuracy measures how well a model predicted when it occurred, how long it lasted, and which gesture is contained in a given EMG. The results of the experiments (without and with orientation correction) executed show an increase in performance from 44.5% to 81.2% for classification and from 43.3% to 81.3% for recognition in user-general models, while in user-specific models, the results show an increase in performance from 39.8% to 94.9% for classification and from 38.8% to 94.2% for recognition. The results obtained in this work evidence that the proposed method for orientation correction makes the performance of an HGR robust to rotations of the EMG bracelet.

Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture.

The importance of estimating human movement has increased in the field of human motion capture. HTC VIVE is a popular device that provides a convenient way of capturing human motions using several sensors. Recently, the motion of only users' hands has been captured, thereby greatly reducing the range of motion captured. This paper proposes a framework to estimate single-arm orientations using soft sensors mainly by combining a Bi-long short-term memory (Bi-LSTM) and two-layer LSTM. Positions of the two hands are measured using an HTC VIVE set, and the orientations of a single arm, including its corresponding upper arm and forearm, are estimated using the proposed framework based on the estimated positions of the two hands. Given that the proposed framework is meant for a single arm, if orientations of two arms are required to be estimated, the estimations are performed twice. To obtain the ground truth of the orientations of single-arm movements, two Myo gesture-control sensory armbands are employed on the single arm: one for the upper arm and the other for the forearm. The proposed framework analyzed the contextual features of consecutive sensory arm movements, which provides an efficient way to improve the accuracy of arm movement estimation. In comparison with the ground truth, the proposed method estimated the arm movements using a dynamic time warping distance, which was the average of 73.90% less than that of a conventional Bayesian framework. The distinct feature of our proposed framework is that the number of sensors attached to end-users is reduced. Additionally, with the use of our framework, the arm orientations can be estimated with any soft sensor, and good accuracy of the estimations can be ensured. Another contribution is the suggestion of the combination of the Bi-LSTM and two-layer LSTM.

Machine-Learning-Based Muscle Control of a 3D-Printed Bionic Arm.

In this paper, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated.

Evolution of the Greater Manchester Nutrition and Hydration Programme.

Older adults in the community are at risk of malnutrition and dehydration. The present article aims to outline an intervention and a population-health approach to raise awareness of the importance of good nutrition and hydration in later life. This was addressed by developing strong partnership working, governance frameworks and local steering committees. Through the Greater Manchester Nutrition and Hydration Programme, 39 500 older people have been asked about appetite and weight loss and/or used the PaperWeight Armband to date. A total of 5586 people from this population were found to be at risk of malnutrition. All were provided resources, advice and signposting to address this issue. The gross fiscal return on investment over a 5-year period was 3.2-fold and the net present budget impact was £800 000. The long-term cashable fiscal return on investment was estimated at 2.69. This very promising approach has potential to enable older adults to extend their healthy life span and deliver cost savings to the health and care system.

Sensor-based machine learning for workflow detection and as key to detect expert level in laparoscopic suturing and knot-tying.

INTRODUCTION: The most common way of assessing surgical performance is by expert raters to view a surgical task and rate a trainee's performance. However, there is huge potential for automated skill assessment and workflow analysis using modern technology. The aim of the present study was to evaluate machine learning (ML) algorithms using the data of a Myo armband as a sensor device for skills level assessment and phase detection in laparoscopic training. MATERIALS AND METHODS: Participants of three experience levels in laparoscopy performed a suturing and knot tying task on silicon models. Experts rated performance using Objective Structured Assessment of Surgical Skills (OSATS). Participants wore Myo armbands (Thalmic Labs™, Ontario, Canada) to record acceleration, angular velocity, orientation, and Euler orientation. ML algorithms (decision forest, neural networks, boosted decision tree) were compared for skill level assessment and phase detection. RESULTS: 28 participants (8 beginner, 10 intermediate, 10 expert) were included, and 99 knots were available for analysis. A neural network regression model had the lowest mean absolute error in predicting OSATS score (3.7 ± 0.6 points, r2 = 0.03 ± 0.81; OSATS min.-max.: 4-37 points). An ensemble of binary-class neural networks yielded the highest accuracy in predicting skill level (beginners: 82.2% correctly identified, intermediate: 3.0%, experts: 79.5%) whereas standard statistical analysis failed to discriminate between skill levels. Phase detection on raw data showed the best results with a multi-class decision jungle (average 16% correctly identified), but improved to 43% average accuracy with two-class boosted decision trees after Dynamic time warping (DTW) application. CONCLUSION: Modern machine learning algorithms aid in interpreting complex surgical motion data, even when standard analysis fails. Dynamic time warping offers the potential to process and compare surgical motion data in order to allow automated surgical workflow detection. However, further research is needed to interpret and standardize available data and improve sensor accuracy.

The resting metabolic rate in women with polycystic ovary syndrome and its relation to the hormonal milieu, insulin metabolism, and body fat distribution: a cohort study.

PURPOSE: To evaluate possible alterations of a major determinant of energy expenditure, the resting metabolic rate (RMR), in women with polycystic ovary syndrome (PCOS) compared with age-BMI similar controls. To assess whether the hormonal milieu, the body fat distribution and the insulin metabolism may affect energy consumption in these patients. METHODS: This is a monocentric observational prospective cohort study, including 109 Caucasian PCOS subjects and 31 healthy control women. (Median age PCOS 26.0 ± 9.2 years, controls 25.5 ± 8.5 years; median BMI-body mass index PCOS 26.4 ± 9.4 kg/m2, controls 27.2 ± 12.8 kg/m2). RMR was evaluated by the SenseWear Armband (SWA), a reliable and validated metabolic holter, never previously used in the PCOS population to this purpose. Hormonal assessment, insulin metabolism evaluated by HOMA-IR and OGTT, anthropometric features (BMI and WHR) were also assessed. RESULTS: Median RMR resulted similar in PCOS and control women: 1520.0 ± 248.00 kcal/day vs 1464.0 ± 332.70 kcal/day (p = 0.472), even after adjusting for BMI, fat distribution, insulin metabolism parameters. RMR resulted significantly correlated with BMI, WHR, estradiol levels, SHBG, total cholesterol, triglycerides, basal glycaemia, basal insulinemia, AUC insulin 240', and HOMA. In the subgroup of patients with WHR > 0.85, PCOS women showed a significantly lower RMR compared with controls. CONCLUSIONS: The higher prevalence of obesity, which negatively influences the reproductive and general health of PCOS women, could be related to factors other than an intrinsic alteration of the RMR. Further studies are needed to clarify the possible role of the visceral fat in modulating the energy balance in PCOS. TRIAL REGISTRATION NUMBER: clinicaltrials.gov Identifier NCT03132545.

Longitudinal analysis of physical activity, sedentary behaviour and anthropometric measures from ages 6 to 11 years.

BACKGROUND/OBJECTIVES: The aim of this study was to examine the effect of physical activity (PA) and sedentary behaviour (SB) on body mass index (BMI) and fat mass index (FMI) in children over the course of five years and identify potential bi-directional associations. SUBJECTS/METHODS: Data were drawn from the EU Childhood Obesity Project (CHOP). PA and SB were measured with the SenseWear Armband 2 at the ages of 6 (T1), 8 (T2) and 11 (T3) years. Height and weight were measured and BMI was calculated at each time point, resulting in 1254 complete observations from 600 children. Bio impedance analysis was used to measure body fat mass and eventually calculate FMI. To examine the longitudinal association between PA/SB and BMI/FMI as well as to account for repeated measure on these children, mixed model analysis was employed. RESULTS: Higher levels of total PA and moderate-to-vigorous PA (MVPA) were associated with lower BMI and FMI and higher SB with higher BMI and FMI over the five year period. When looking at the age dependent effects, negative associations of MVPA (ßMVPA x age: - 0.05, 95% confidence interval (CI): - 0.09 - -0.01, p = 0.007) and positive associations of SB (ßSB x age: 0.04, 95% CI: 0.02-0.06, p < 0.001) increased with each year of age. In a model combining these two effects, only SB x age interaction remained significant (ßSB x age: 0.04, 95% CI: 0.03-0.06, p = 0.01). No significant interaction between MVPA and SB could be discerned. Light Physical activity showed no significant associations with BMI or FMI. When reversing outcome and predictor; higher BMI or FMI showed a negative association with MVPA and a positive association with SB, but no age dependency. CONCLUSIONS: More time per day in SB was associated with a higher BMI over the course of five years, whereas higher MVPA had an inverse effect. In a combined model, only effects of higher SB remained significant, emphasizing the importance of SB in obesity prevention. Present bidirectional associations, where lower body size was associated with higher PA and lower SB, indicated the need for an integrated approach of activity and weight control for obesity prevention. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT00338689 . Registered: June 19, 2006 (retrospectively registered).

Evaluation of a smartphone food diary application using objectively measured energy expenditure.

BACKGROUND: Dietary assessment methods are limited in their ability to adequately measure food and beverage consumption. Smartphone applications may provide a novel method of dietary assessment to capture real-time food intake and the contextual factors surrounding eating occasions. The aim of this study is to evaluate the capability of a Smartphone meal diary app ("FoodNow") to measure food intake using a validated objective method for assessing energy expenditure among young adults. METHODS: Participants (18-30 years) used FoodNow over four non-consecutive days recording all eating occasions through a combination of written text, and/or optional images and voice recordings. A series of contextual questions were also completed. Participants wore the validated SenseWear Armband (BodyMedia Inc, USA) during the same period to measure free-living energy expenditure. Intra-class correlation coefficients (ICC) estimated the reliability of FoodNow to measure estimated energy intake compared to measured energy expenditure. RESULTS: Ninety participants (71 female, 19 male; mean age = 24.9 ± 4.1 years) were recruited to use the FoodNow app to record their eating occasions. Thirteen were excluded as they did not meet minimum requirements for number of reporting days (n = 3) or SenseWear Armband wear time (5 days of 11 h), while 21 participants were excluded after being identified as mis-reporters (Huang method). Among the remaining sample (n = 56), reliability between estimated energy intake and measured energy expenditure was high (ICC, 95% CI: 0.75, 0.61-0.84). CONCLUSIONS: FoodNow is a suitable method for capturing estimated energy intake data from young adults. Despite wide levels of agreement at the individual level (-3709 kJ to 2056 kJ), at the group level, FoodNow appears to have potential as a dietary assessment tool. This new dietary assessment method will offer an alternative and novel method of dietary assessment which is capable of collecting both estimated energy intake and contextual factors surrounding eating occasions. Information collected may be used to inform future public health messages or research interventions.

Bias in food intake reporting in children and adolescents with type 1 diabetes: the role of body size, age and gender.

BACKGROUND: An assessment of total daily energy intake is helpful in planning the overall treatment of children with type 1 diabetes (T1D). However, energy intake misreporting may hinder nutritional intervention. AIMS: To assess the plausibility of energy intake reporting and the potential role of gender, body mass index (BMI) z-score (z-BMI), disease duration and insulin requirement in energy intake misreporting in a sample of children and adolescents with T1D. METHODS: The study included 58 children and adolescents aged 8-16 yr with T1D. Anthropometry, blood pressure and glycated hemoglobin (HbA1c) were measured. Subjects were instructed to wear a SenseWear Pro Armband (SWA) for 3 consecutive days, including a weekend day and to fill out with their parents a weighed dietary record for the same days. Predicted energy expenditure (pEE) was calculated by age and gender specific equations, including gender, age, weight, height and physical activity level (assessed by SWA). The percent reported energy intake (rEI)/pEE ratio was used as an estimate of the plausibility of dietary reporting. RESULTS: Misreporting of food intake, especially under-reporting, was common in children and adolescents with T1D: more than one-third of participants were classified as under-reporters and 10% as over-reporters. Age, z-BMI and male gender were associated with the risk of under-reporting (model R2 = 0.5). Waist circumference was negatively associated with the risk of over-reporting (model R2 = 0.25). CONCLUSIONS: Children and adolescents with T1D frequently under-report their food intake. Age, gender and z-BMI contribute to identify potential under-reporters.

BMI and recommended levels of physical activity in school children.

BACKGROUND: Physical activity (PA) and its health benefits are a continuous point of discussion. Recommendations for children's daily PA vary between guidelines. To better define the amount of PA necessary to prevent overweight and obesity in children, further research is needed. The present study investigates children's compliance to physical activity guidelines (PAGs) and the association between objectively measured PA and body mass index (BMI). METHODS: Participating children were 11 years old (n = 419) and part of the European CHOP trial, which was conducted in Germany, Belgium, Poland, Spain, Italy. At least 2 days of PA measurements were collected from each child using a SenseWear™ armband. BMI was calculated from children's height and weight. Thresholds of min·day-1 in PA needed to differentiate between normal and excess weight (overweight/obesity) were determined with Receiver Operator Characteristics (ROC) analysis. Additionally, adjusted linear and logistic regressions models were calculated for group differences and effects of a 5, 15 and 60 min·day-1 increases in PA on BMI. RESULTS: Median time spent in total PA was 462 min·day-1 (25th percentile; 75th percentile: 389; 534) and 75 min·day-1 (41; 115) in moderate to vigorous PA (MVPA). Girls spent 36 min·day-1 less in MVPA than boys and overweight/obese children 24 min·day-1 less than normal weight children (linear regression, p < 0.001). 63.2% of the children met PAGs of 60 min·day-1 in MVPA. The optimal threshold for min·day-1 in MVPA determined with ROC analysis was 46 min·day-1. Comparing 5, 15 and 60 min·day-1 increases in PA revealed that an additional 15 min·day-1 of vigorous PA had the same effect as 60 min·day-1 of MVPA. Sedentary time and light PA showed contrary associations to one another, with light PA being negatively and sedentary time being positively associated with excessive weight. CONCLUSIONS: Current PAGs are met by 2/3 of children and seem appropriate to prevent excess weight in children. An official recommendation of daily 15-20 min of vigorous PA and further reduction of sedentary time could help to fight youth overweight and thus be of potential public health importance. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT00338689 . Registered: June 19, 2006 (retrospectively registered).

Plasma kisspeptin and ghrelin levels are independently correlated with physical activity in patients with anorexia nervosa.

While physical hyperactivity represents a frequent symptom of anorexia nervosa and may have a deleterious impact on the course of the disease, the underlying mechanisms are poorly understood. Since several food intake-regulatory hormones affect physical activity, the aim of the study was to investigate the association of physical activity with novel candidate hormones (kisspeptin, ghrelin, oxyntomodulin, orexin-A, FGF-21, R-spondin-1) possibly involved in patients with anorexia nervosa. Associations with psychometric parameters and body composition were also assessed. We included 38 female anorexia nervosa inpatients (body mass index, BMI, mean ± SD: 14.8 ± 1.7 kg/m2). Physical activity was evaluated using portable armband devices, body composition by bioelectrical impedance analysis. Blood withdrawal (hormones measured by ELISA) and psychometric assessment of depressiveness (PHQ-9), anxiety (GAD-7), perceived stress (PSQ-20) and disordered eating (EDI-2) were performed at the same time. Patients displayed a broad spectrum of physical activity (2479-26,047 steps/day) which showed a negative correlation with kisspeptin (r = -0.41, p = 0.01) and a positive association with ghrelin (r = 0.42, p = 0.01). The negative correlation with oxyntomodulin (r = -0.37, p = 0.03) was lost after consideration of potential confounders by regression analysis. No correlations were observed between physical activity and orexin-A, FGF-21 and R-spondin-1 (p > 0.05). Kisspeptin was positively correlated with BMI and body fat mass and negatively associated with the interpersonal distrust subscale of the EDI-2 (p < 0.01). Depressiveness, anxiety, and perceived stress did not correlate with kisspeptin or any other of the investigated hormones (p > 0.05). In conclusion, kisspeptin is inversely and ghrelin positively associated with physical activity as measured by daily step counts in anorexia nervosa patients suggesting an implication of these peptide hormones in the regulation of physical activity in anorexia nervosa.