Tidal breathing flow profiles during sleep in wheezing children measured by impedance pneumography.

For the first time, impedance pneumography (IP) enables a continuous analysis of the tidal breathing flow volume (TBFV), overnight. We studied how corticosteroid inhalation treatments, sleep stage, and time from sleep onset modify the nocturnal TBFV profiles of children. Seventy children, 1-5 years old and with recurrent wheezing, underwent three, full-night TBFVs recordings at home, using IP. The first recorded one week before ending a 3-months inhaled corticosteroids treatment, and remaining two, 2 and 4 weeks after treatment. TBFV profiles were grouped by hour from sleep onset and estimated sleep stage. Compared with on-medication, the off-medication profiles showed lower volume at exhalation peak flow, earlier interruption of expiration, and less convex middle expiration. The differences in the first two features were significant during non-rapid eye movement (NREM), and the differences in the third were more prominent during REM after 4 h of sleep. These combinations of TBFV features, sleep phase, and sleep time potentially indicate airflow limitation in young children.

Identification of Motor Symptoms Related to Parkinson Disease Using Motion-Tracking Sensors at Home (KÄVELI): Protocol for an Observational Case-Control Study.

BACKGROUND: Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments. OBJECTIVE: The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns. METHODS: This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings. RESULTS: Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD. CONCLUSIONS: This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders. TRIAL REGISTRATION: ClinicalTrials.gov NCT03366558; https://clinicaltrials.gov/ct2/show/NCT03366558. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12808.

Tidal breathing flow volume profiles during sleep in wheezing infants measured by impedance pneumography.

Overnight analysis of tidal breathing flow volume (TBFV) loops, recorded by impedance pneumography (IP), has been successfully applied in the home monitoring of children with wheezing disorders. However, little is known on how sleep physiology modifies the relationship between TBFV profiles and wheeze. We studied such interactions in wheezing infants. Forty-three infants recruited because of recurrent lower airway symptoms were divided into three groups based on their risk of asthma: high (HR), intermediate (IR), or low (LR). Sedated patients underwent infant lung function testing including assessment of airway responsiveness to methacholine at the hospital and a full-night recording of TBFV profiles at home with IP during natural sleep. Overnight TBFV indexes were estimated from periods of higher and lower respiration variability, presumably belonging to active [rapid eye movement (REM)] and quiet [non-REM (NREM)] sleep, respectively. From 35 valid recordings, absolute time indexes showed intrasubject sleep phase differences. Peak flow relative to time and volume was lower in HR compared with LR only during REM, suggesting altered expiratory control. Indexes estimating the concavity/convexity of flow decrease during exhalation suggested limited flow during passive exhale in HR compared with IR and LR, similarly during NREM and REM. Moreover, during REM convexity was negatively correlated with maximal flow at functional residual capacity and methacholine responsiveness. We conclude that TBFV profiles determined from overnight IP recordings vary because of sleep phase and asthma risk. Physiological changes during REM, most likely decrease in respiratory muscle tone, accentuate the changes in TBFV profiles caused by airway obstruction. NEW & NOTEWORTHY Impedance pneumography was used to investigate overnight tidal breathing flow volume (TBFV) indexes and their interactions with sleep phase [rapid eye movement (REM) vs. non-REM] at home in wheezing infants. The study shows that TBFV indexes vary significantly because of sleep phase and asthma risk of the infant and that during REM the changes in TBFV indexes caused by airway obstruction are accentuated and better associated with lung function of the infant.

Identification of Parkinson's Disease Utilizing a Single Self-recorded 20-step Walking Test Acquired by Smartphone's Inertial Measurement Unit.

Parkinson's disease (PD) is a degenerative and long-term disorder of the central nervous system, which often causes motor symptoms, e.g., tremor, rigidity, and slowness. Currently, the diagnosis of PD is based on patient history and clinical examination. Technology-derived decision support systems utilizing, for example, sensor-rich smartphones can facilitate more accurate PD diagnosis. These technologies could provide less obtrusive and more comfortable remote symptom monitoring. The recent studies showed that motor symptoms of PD can reliably be detected from data gathered via smartphones. The current study utilized an open-access dataset named "mPower" to assess the feasibility of discriminating PD from non-PD by analyzing a single self-administered 20-step walking test. From this dataset, 1237 subjects (616 had PD) who were age and gender matched were selected and classified into PD and non-PD categories. Linear acceleration (ACC) and gyroscope (GYRO) were recorded by built-in sensors of smartphones. Walking bouts were extracted by thresholding signal magnitude area of the ACC signals. Features were computed from both ACC and GYRO signals and fed into a random forest classifier of size 128 trees. The classifier was evaluated deploying 100-fold cross-validation and provided an accumulated accuracy rate of 0.7 after 10k validations. The results show that PD and non-PD patients can be separated based on a single short-lasting self-administered walking test gathered by smartphones' built-in inertial measurement units.