Validity of a Novel Research-Grade Physical Activity and Sleep Monitor for Continuous Remote Patient Monitoring.

In the midst of the COVID-19 pandemic, Remote Patient Monitoring technologies are highly important for clinicians and researchers. These connected-health technologies enable monitoring of patients and facilitate remote clinical trial research while reducing the potential for the spread of the novel coronavirus. There is a growing requirement for monitoring of the full 24 h spectrum of behaviours with a single research-grade sensor. This research describes a free-living and supervised protocol comparison study of the Verisense inertial measurement unit to assess physical activity and sleep parameters and compares it with the Actiwatch 2 actigraph. Fifteen adults (11 males, 23.4 ± 3.4 years and 4 females, 29 ± 12.6 years) wore both monitors for 2 consecutive days and nights in the free-living study while twelve adults (11 males, 23.4 ± 3.4 years and 1 female, 22 ± 0 years) wore both monitors for the duration of a gym-based supervised protocol study. Agreement of physical activity epoch-by-epoch data with activity classification of sedentary, light and moderate-to-vigorous activity and sleep metrics were evaluated using Spearman's rank-order correlation coefficients and Bland-Altman plots. For all activity, Verisense showed high agreement for both free-living and supervised protocol of r = 0.85 and r = 0.78, respectively. For physical activity classification, Verisense showed high agreement of sedentary activity of r = 0.72 for free-living but low agreement of r = 0.36 for supervised protocol; low agreement of light activity of r = 0.42 for free-living and negligible agreement of r = -0.04 for supervised protocol; and moderate agreement of moderate-to-vigorous activity of r = 0.52 for free-living with low agreement of r = 0.49 for supervised protocol. For sleep metrics, Verisense showed moderate agreement for sleep time and total sleep time of r = 0.66 and 0.54, respectively, but demonstrated high agreement for determination of wake time of r = 0.83. Overall, our results showed moderate-high agreement of Verisense with Actiwatch 2 for assessing epoch-by-epoch physical activity and sleep, but a lack of agreement for activity classifications. Future validation work of Verisense for activity cut-point potentially holds promise for 24 h continuous remote patient monitoring.

Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology.

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend the use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found a moderate level of evidence for seizure types without GTCS or FBTCS. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

Validation of an app-based portable spirometer in adolescents with asthma.

OBJECTIVES: Objective measurements of asthma impairment could aid teens in recognition of changes in asthma status over time. Ready access to a conventional spirometer is not realistic outside of the clinical setting. In this proof-of-concept study, we compared the performance of the VitalFlo mobile spirometer to the nSpire KoKo® sx1000 spirometer for accuracy in measuring Forced Expiratory Volume in one second (FEV1) and Forced Vital Capacity (FVC) in adolescents with asthma. METHODS: Two hundred forty pulmonary function measurements were collected from 48 adolescents with persistent asthma from the University of North Carolina's pediatric allergy and pulmonology subspecialty clinics. Participants performed spirometry with the nSpireKoKo® sx1000 spirometer and the VitalFlo spirometer during their clinic visits. 119 simulated FVC maneuvers were conducted on both devices to standardize measurements. Pearson correlations, Bland-Altman procedure, and two-sample comparison tests were performed to assess the relationship between the two spirometers. RESULTS: VitalFlo measurements were significantly highly correlated with nSpireKoKo® spirometer values for FEV1, (r2=0.721, [95% CI, 0.749 ± 0.120], P < 0.001) and moderately for FVC (r2= 0.617, [95% CI, 0.640 ± 0.130], P < 0.001) measurements. There were no statistically significant differences of the mean FEV1 (M = 0.00764, SD = 0.364, t(59)=0.16, P = 0.87) and FVC measurements (M = 0.00261, SD = 0.565, t(59)=0.036, P = 0.97.) between the VitalFlo and nSpireKoKo® systems. Both devices demonstrated significantly high correlation when comparing the automated FVC (r2 = 0.997, [95% CI, 1.00 ± 0.00974], P < 0.001) measurements. Bland-Altman plots did not demonstrate significant bias between devices for both FEV1 (0.00764 L) and FVC (0.00261 L) measurements. CONCLUSIONS: Lung function measurements from the VitalFlo mobile spirometer were comparable to a commercially-available spirometer commonly used in clinical settings. This validated app-based spirometer for home use has the potential to improve asthma self-management.

Reducing cost and time to diagnosis and treatment of obstructive sleep apnea using ambulatory sleep study: a Singapore sleep centre experience.

PURPOSE: The current gold standard for diagnosis of obstructive sleep apnea (OSA) is overnight in laboratory polysomnography (PSG). However, PSGs are expensive, labor-intensive, and have long wait times. An ambulatory sleep study device, the WatchPAT, has been shown to have high correlation for sleep indices measured compared with PSG (AASM, 2016). Use of the WatchPAT could potentially lead to shorter waiting times and earlier diagnosis of OSA (Lancet Resp Med 3:310-8, 2015). Our study aimed to investigate if WatchPAT reduces time to diagnosis and treatment of OSA in a tertiary healthcare setting. A secondary aim was to investigate the cost-benefit of an ambulatory sleep study. METHODS: All patients who underwent diagnostic sleep studies in a single tertiary institution from 2014 to 2017 were retrospectively reviewed. Baseline characteristics and time from ordering of sleep study to prescription of continuous positive airway pressure were recorded. Data were categorized into two groups by type of diagnostic sleep study, PSG, and WatchPAT. The time to treatment and cost for diagnosis of OSA were compared between groups with the Paired T test/Wilcoxon signed-rank test. RESULTS: Of 1898 patients who had diagnostic sleep studies over a 4-year period, 1660 patients (88%) underwent PSG and 238 patients (12%) underwent WatchPAT. Patients in the WatchPAT group had a shorter time to diagnosis (21 days versus 79.8 days, p < 0.001) and treatment (46.3 days versus 118.4 days, p < 0.001) compared to the PSG group. Cost-benefit calculation showed that this earlier treatment led to cost-saving of US $1179.50 per patient. CONCLUSION: An ambulatory sleep study is an option for earlier access to diagnosis and treatment of OSA with the potential of considerable cost savings.

The effect of in-lab polysomnography and home sleep polygraphy on sleep position.

PURPOSE: Little is known regarding the influence of in-laboratory polysomnography (PSG) equipment on sleep position, especially on the prevalence of supine positioning, which in many cases may lead to a more severe sleep apnea diagnosis. The aim of this study was to assess the percentage of supine sleep during an in-laboratory PSG compared to that seen during a home sleep apnea test (HSAT). METHODS: This was a retrospective cohort study comparing in-laboratory PSG and HSAT using a peripheral arterial tone (PAT) technology device. RESULTS: Of 445 PSG and 416 HSAT studies analyzed, there was no significant difference in the proportion of supine sleep time between PSG (44%) and HSAT (45%, p = 0.53). Analysis of the differences in sleep position (supine versus non-supine), analyzed by sex, BMI (≥ 30 kg/m2 versus < 30 kg/m2), and age (≥ 60 years versus < 60 years), was significant only for women, who had more supine sleep during HSAT at 61 ± 24% than during PSG at 45 ± 26% (p < 0.001). CONCLUSION: Overall there was no difference in the percentage of supine sleep when comparing in-laboratory PSG to HSAT. However, women had more supine sleep with HSAT than with PSG.

Feasibility of using "SMARTER" methodology for monitoring precipitating conditions of pediatric migraine episodes.

OBJECTIVE: To evaluate the feasibility in children of an intensive prospective data monitoring methodology for identifying precipitating conditions for migraine occurrence. BACKGROUND: Migraine headaches are a common pain condition in childhood and can become increasingly chronic and disabling with repeated episodes. Identifying conditions that forecast when a child's migraine is likely to occur may facilitate next-generation adaptive treatments to prevent future migraine attacks. METHODS: In this cohort study of a sample of 30 youth (ages 10-17) with migraine recruited through a pediatric headache clinic, smartphones supplemented with wearable biosensors were used over a period of 28 days to collect contextual data thought to be potentially relevant to headache occurrence. Self-reported data on headache occurrence, lifestyle, and perceptions of the environment were collected in 4 epochs per day using custom real-time reporting software. Data derived from the wearable biosensor included information on autonomic arousal and physical activity. Built-in sensors on participants' own phones also were used to indicate location and to quantify the sensory environment (e.g., ambient noise and light levels). Data fidelity was monitored to evaluate feasibility of the methods, and participant acceptability was assessed via an end-of-study survey. RESULTS: Self-report data were obtained on a mean of 88.9% (24.9/28) of assigned days (SD = 22.4%) and at a mean of 68.9% (77.2/112) of assigned moments (SD = 24.5%). Data from the wearable biosensor were obtained for a mean of 18.7 hours per day worn (SD = 2.3 hours), with participants on average wearing the sensor on 20.3 days (SD = 9.9). Fidelity of obtaining objective data from phone sensors on the sensory environment and other environmental conditions was highly variable, with these data obtainable from 5 to 22/30 (16.7%-73.3%) of participants' own phones. Most participants (63.3%-100%) responded with at least "somewhat agree" to questions about acceptability of the study methods. However, 5 to 7/30 (16.7%-23.3%) patients indicated difficulties with burden and remembering to wear the sensor. Almost all participants (29/30, 96.7%) agreed that they would want information about when a migraine might occur. CONCLUSIONS: A contemporary data sampling approach comprising ambulatory sensors and real-time reporting appears to be acceptable to most youth with migraine in this study. Reliability of acquiring some data sources from participants' own phones, however, was suboptimal. Further refining these data sampling methods may enable a novel means of predicting and preventing recurrences of migraine episodes in youth.

Clinical Relevance of Standardized Mobile Gait Tests. Reliability Analysis Between Gait Recordings at Hospital and Home in Parkinson's Disease: A Pilot Study.

BACKGROUND: Gait impairments in Parkinson's disease (PD) are quantified using inertial sensors under standardized test settings in the hospital. Recent studies focused on the assessment of free-living gait in PD. However, the clinical relevance of standardized gait tests recorded at the patient's home is unclear. OBJECTIVE: To evaluate the reliability of supervised, standardized sensor-based gait outcomes at home compared to the hospital. METHODS: Patients with PD (n = 20) were rated by a trained investigator using the Unified Parkinson Disease Rating Scale (UPDRS-III). Gait tests included a standardized 4×10 m walk test and the Timed Up and Go Test (TUG). Tests were performed in the hospital (HOSPITAL) and at patients' home (HOME), and controlled for investigator, time of the day, and medication. Statistics included reliability analysis using Intra-Class correlations and Bland-Altman plots. RESULTS: UPDRS-III and TUG were comparable between HOSPITAL and HOME. PD patients' gait at HOME was slower (gait velocity Δ= -0.07±0.11 m/s, -6.1%), strides were shorter (stride length Δ= -9.2±9.4 cm; -7.3%), and shuffling of gait was more present (maximum toe-clearance Δ= -0.7±2.5 cm; -8.8%). Particularly, narrow walkways (<85 cm) resulted in a significant reduction of gait velocity at home. Reliability analysis (HOSPITAL vs. HOME) revealed excellent ICC coefficients for UPDRS-III (0.950, p < 0.000) and gait parameters (e.g., stride length: 0.898, p < 0.000; gait velocity: 0.914, p < 0.000; stance time: 0.922, p < 0.000; stride time: 0.907, p < 0.000). CONCLUSION: This pilot study underlined the clinical relevance of gait parameters by showing excellent reliability for supervised, standardized gait tests at HOSPITAL and HOME, even though gait parameters were different between test conditions.

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals.

Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencephalography (EEG) and wireless inertial measurement units (IMU) allow the realization of experimental data recording with improved ecological validity where the subjects can be carrying out natural activities while data recording is minimally invasive. Specifically, we aim to show that EEG and IMU data fusion allows improved human activity recognition in a natural setting. We have defined an experimental protocol composed of natural sitting, standing and walking activities, and we have recruited subjects in two sites: in-house ([Formula: see text]) and out-house ([Formula: see text]) populations with different demographics. Experimental protocol data capture was carried out with validated commercial systems. Classifier model training and validation were carried out with scikit-learn open source machine learning python package. EEG features consist of the amplitude of the standard EEG frequency bands. Inertial features were the instantaneous position of the body tracked points after a moving average smoothing to remove noise. We carry out three validation processes: a 10-fold cross-validation process per experimental protocol repetition, (b) the inference of the ethograms, and (c) the transfer learning from each experimental protocol repetition to the remaining repetitions. The in-house accuracy results were lower and much more variable than the out-house sessions results. In general, random forest was the best performing classifier model. Best cross-validation results, ethogram accuracy, and transfer learning were achieved from the fusion of EEG and IMUs data. Transfer learning behaved poorly compared to classification on the same protocol repetition, but it has accuracy still greater than 0.75 on average for the out-house data sessions. Transfer leaning accuracy among repetitions of the same subject was above 0.88 on average. Ethogram prediction accuracy was above 0.96 on average. Therefore, we conclude that wireless EEG and IMUs allow for the definition of natural experimental designs with high ecological validity toward human computational neuroethology research. The fusion of both EEG and IMUs signals improves activity and ethogram recognition.

A comparison of self-reported and device measured sedentary behaviour in adults: a systematic review and meta-analysis.

BACKGROUND: Sedentary behaviour (SB) is a risk factor for chronic disease and premature mortality. While many individual studies have examined the reliability and validity of various self-report measures for assessing SB, it is not clear, in general, how self-reported SB (e.g., questionnaires, logs, ecological momentary assessments (EMAs)) compares to device measures (e.g., accelerometers, inclinometers). OBJECTIVE: The primary objective of this systematic review was to compare self-report versus device measures of SB in adults. METHODS: Six bibliographic databases were searched to identify all studies which included a comparable self-report and device measure of SB in adults. Risk of bias within and across studies was assessed. Results were synthesized using meta-analyses. RESULTS: The review included 185 unique studies. A total of 123 studies comprising 173 comparisons and data from 55,199 participants were used to examine general criterion validity. The average mean difference was -105.19 minutes/day (95% CI: -127.21, -83.17); self-report underestimated sedentary time by ~1.74 hours/day compared to device measures. Self-reported time spent sedentary at work was ~40 minutes higher than when assessed by devices. Single item measures performed more poorly than multi-item questionnaires, EMAs and logs/diaries. On average, when compared to inclinometers, multi-item questionnaires, EMAs and logs/diaries were not significantly different, but had substantial amount of variability (up to 6 hours/day within individual studies) with approximately half over-reporting and half under-reporting. A total of 54 studies provided an assessment of reliability of a self-report measure, on average the reliability was good (ICC = 0.66). CONCLUSIONS: Evidence from this review suggests that single-item self-report measures generally underestimate sedentary time when compared to device measures. For accuracy, multi-item questionnaires, EMAs and logs/diaries with a shorter recall period should be encouraged above single item questions and longer recall periods if sedentary time is a primary outcome of study. Users should also be aware of the high degree of variability between and within tools. Studies should exert caution when comparing associations between different self-report and device measures with health outcomes. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42019118755.

Meeting the New FDA Standard for Accuracy of Self-Monitoring Blood Glucose Test Systems Intended for Home Use by Lay Users.

The OneTouch Verio Reflect blood glucose monitor (BGM) has market clearance in several countries based in part on fulfilling the lay user and system accuracy criteria described in ISO15197:2015. However, the Food and Drug Administration (FDA) does not recognize the accuracy criteria in ISO15197 as a basis for gaining regulatory clearance for these devices. The current study evaluates the BGM using the accuracy guidelines issued by the agency for self-monitoring blood glucose test systems for over-the-counter use. Glucose results were accurate vs comparator over a wide glucose range and met lay user and glucose accuracy criteria at extreme glucose values as described in the FDA guidance.Clinicaltrials.gov NCT03851549.

An Efficient Mobile-Healthcare Emergency Framework.

Mobile technologies are capable of offering individual level health care services to users. Mobile Healthcare (m-Healthcare) frameworks, which feature smartphone (SP) utilizations of ubiquitous computing made possible by applying wireless Body Sensor Networks (BSNs), have been introduced recently to provide SP clients with health condition monitoring and access to medical attention when necessary. However, in a vulnerable m-Healthcare framework, clients' personal info and sensitive data can easily be poached by intruders or any malicious party, causing serious security problems and confidentiality issues. In 2013, Lu et al. proposed a mobile-Healthcare emergency framework based on privacy-preserving opportunistic computing (SPOC), claiming that their splendid SPOC construction can opportunistically gather SP resources such as computing power and energy to handle computing-intensive Personal Health Information (PHI) with minimal privacy disclosure during an emergency. To balance between the risk of personal health information exposure and the essential PHI processing and transmission, Lu et al. presented a patient-centric privacy ingress control framework based on an attribute-based ingress control mechanism and a Privacy-Preserving Scalar Product Computation (PPSPC) technique. In spite of the ingenious design, however, Lu et al.'s framework still has some security flaws in such aspects as client anonymity and mutual authentication. In this article, we shall offer an improved version of Lu et al.'s framework with the security weaknesses mended and the computation efficiency further boosted. In addition, we shall also present an enhanced mobile-Healthcare emergency framework using Partial Discrete Logarithm (PDL) which does not only achieve flawless mutual authentication as well as client anonymity but also reduce the computation cost.

Biosensors to monitor MS activity.

Advances in wearable and wireless biosensing technology pave the way for a brave new world of novel multiple sclerosis (MS) outcome measures. Our current tools for examining patients date back to the 19th century and while invaluable to the neurologist invite accompaniment from these new technologies and artificial intelligence (AI) analytical methods. While the most common biosensor tool used in MS publications to date is the accelerometer, the landscape is changing quickly with multi-sensor applications, electrodermal sensors, and wireless radiofrequency waves. Some caution is warranted to ensure novel outcomes have clear clinical relevance and stand-up to the rigors of reliability, reproducibility, and precision, but the ultimate implementation of biosensing in the MS clinical setting is inevitable.

What Do People With Schizophrenia Do All Day? Ecological Momentary Assessment of Real-World Functioning in Schizophrenia.

Schizophrenia is a major cause of disability worldwide. As new treatments for functioning are tested, the need grows to demonstrate real-world functioning gains. Ecological momentary assessment (EMA) may provide a more ecologically valid measure of functioning. In this study, smartphone-based EMA was used to signal participants with schizophrenia (N = 100) and controls (N = 71) 7 times a day for 7 days to respond to brief questionnaires about social interactions and functioning behaviors. Excellent adherence was found, with both groups completing an average of 85% of surveys and only 3% of participants with schizophrenia excluded for poor adherence. Four-week test-retest reliability was high (r = .83 for total productive behaviors). Relative to controls, participants with schizophrenia reported significantly less total productive activity (d = 1.2), fewer social interactions (d = 0.3), more nonproductive behaviors (d = 1.0; watching TV, resting), and more time at home (d = 0.8). Within the schizophrenia group, participants living independently showed better functioning on EMA relative to participants in supported housing (d = 0.8) and participants engaged in vocational activities showed better functioning than individuals not engaged in vocational activities (d = 0.55). Modest correlations were found between EMA and an in-lab self-report measure of functioning activities performed in the community, but not between EMA and measures of functional capacity or potential. This study demonstrated the feasibility, sensitivity reliability, and validity of EMA methods to assess functioning in schizophrenia. EMA provides a much-needed measure of what individuals with schizophrenia are actually doing in real-world contexts. These results also suggest that there may be important disjunctions between indices of abilities and actual real-world functioning.

Continuous Real-World Gait Monitoring in Idiopathic REM Sleep Behavior Disorder.

BACKGROUND: Patients with REM sleep behavior disorder (RBD) have a high risk of developing PD, and thus can be used to study prodromal biomarkers. RBD has been associated with changes in gait; quantifying these changes using wearable technology is promising; however, most data are obtained in clinical settings precluding pragmatic application. OBJECTIVE: We aimed to investigate if wearable-based, real-world gait monitoring can detect early gait changes and discriminate individuals with RBD from controls, and explore relationships between real-world gait and clinical characteristics. METHODS: 63 individuals with RBD (66±10 years) and 34 controls recruited in the Oxford Parkinson's Disease Centre Discovery Study were assessed. Data were collected using a wearable device positioned on the lower back for 7 days. Real-world gait was quantified in terms of its Macrostructure (volume, pattern and variability (S2)) and Microstructure (14 characteristics). The value of Macro and Micro gait in discriminating RBD from controls was explored using ANCOVA and ROC analysis, and correlation analysis was performed between gait and clinical characteristics. RESULTS: Significant differences were found in discrete Micro characteristics in RBD with reduced gait velocity, variability and rhythm (p≤0.023). These characteristics significantly discriminated RBD (AUC≥0.620), with swing time as the single strongest discriminator (AUC=0.652). Longer walking bouts discriminated best between the groups for Macro and Micro outcomes (p≤0.036). CONCLUSIONS: Our results suggest that real-world gait monitoring may have utility as "risk" clinical marker in RBD participants. Real-world gait assessment is low-cost and could serve as a pragmatic screening tool to identify gait impairment in RBD.

Can commercially available wearable EEG devices be used for diagnostic purposes? An explorative pilot study.

Electroencephalography (EEG) is a core element in the diagnosis of epilepsy syndromes and can help to monitor antiseizure treatment. Mobile EEG (mEEG) devices are increasingly available on the consumer market and may offer easier access to EEG recordings especially in rural or resource-poor areas. The usefulness of consumer-grade devices for clinical purposes is still underinvestigated. Here, we compared EEG traces of a commercially available mEEG device (Emotiv EPOC) to a simultaneously recorded clinical video EEG (vEEG). Twenty-two adult patients (11 female, mean age 40.2 years) undergoing noninvasive vEEG monitoring for clinical purposes were prospectively enrolled. The EEG recordings were evaluated by 10 independent raters with unmodifiable view settings. The individual evaluations were compared with respect to the presence of abnormal EEG findings (regional slowing, epileptiform potentials, seizure pattern). Video EEG yielded a sensitivity of 56% and specificity of 88% for abnormal EEG findings, whereas mEEG reached 39% and 85%, respectively. Interrater reliability coefficients were better in vEEG as compared to mEEG (ϰ = 0.50 vs. 0.30), corresponding to a moderate and fair agreement. Intrarater reliability between mEEG and vEEG evaluations of simultaneous recordings of a given participant was moderate (ϰ = 0.48). Given the limitations of our exploratory pilot study, our results suggest that vEEG is superior to mEEG, but that mEEG can be helpful for diagnostic purposes. We present the first quantitative comparison of simultaneously acquired clinical and mobile consumer-grade EEG for a clinical use-case.

Rationale and protocol for the Assessment of Impact of Real-time Continuous Glucose Monitoring on people presenting with severe Hypoglycaemia (AIR-CGM) study.

BACKGROUND: Severe hypoglycaemia carries a significant risk of morbidity and mortality for people with type 1 diabetes. Economic costs are also high, estimated at approximately £13 million annually in England, UK. Continuous glucose monitoring (CGM) has been shown to reduce hypoglycaemia and associated fear, improve overall glycaemia and quality of life, and is cost-effective. Despite effective pathways in place with high levels of resource utilization, it has been reported there are low levels of follow-up, therapy change and specialist intervention after severe hypoglycaemia. This study is designed to assess the impact of providing real-time CGM to people with type 1 diabetes, who have had a recent episode of severe hypoglycaemia (within 72 h), compared to standard care. METHODS/DESIGN: Fifty-five participants with type 1 diabetes and a recent episode of severe hypoglycaemia, who are CGM naïve, will be recruited to the study. Participants will be randomised to CGM or standard care. The primary outcome is percentage time spent in hypoglycaemia (< 3.0 mmol/L, 55 mg/dL). Secondary outcomes include other measures of hypoglycaemia, time in euglycaemia, overall glucose status and patient reported qualitative measures. DISCUSSION: This study assesses the impact of providing continuous glucose monitoring at the outset in individuals at highest risk of hypoglycaemia. Changing demand means that novel approaches need to be taken to healthcare provision. This study has the potential to shape future national standards. TRIAL REGISTRATION: NCT03748433 , November 2018 (UK).

How to Annotate Freezing of Gait from Video: A Standardized Method Using Open-Source Software.

Visually scoring freezing of gait (FOG) from video is increasingly recognized as the gold-standard for assessing FOG severity in Parkinson's disease. Surprisingly, no guidelines exist on how to visually score FOG. Here, I present a free template that can be implemented in open-source software to annotate FOG from video. I provide a user guide on how to implement the template and standardize the scoring of FOG and the percentage of time spent with FOG (% FOG). It is hoped that by disseminating this method investigators will be better able to employ % FOG as an outcome in their studies and therapeutic trials.

Clinical Relevance of Home Monitoring of Vital Signs and Blood Glucose Levels: A Narrative Review.

OBJECTIVES: We sought to assess the presence and reporting quality of peer-reviewed literature concerning the accuracy, precision, and reliability of home monitoring technologies for vital signs and glucose determinations in older adult populations. METHODS: A narrative literature review was undertaken searching the databases Medline, Embase, and Compendex. Peer-reviewed publications with keywords related to vital signs, monitoring devices and technologies, independent living, and older adults were searched. Publications between the years 2012 and 2018 were included. Two reviewers independently conducted title and abstract screening, and four reviewers independently undertook full-text screening and data extraction with all disagreements resolved through discussion and consensus. RESULTS: Two hundred nine articles were included. Our review showed limited assessment and low-quality reporting of evidence concerning the accuracy, precision, and reliability of home monitoring technologies. Of 209 articles describing a relevant device, only 45 percent (n = 95) provided a citation or some evidence to support their validation claim. Of forty-eight articles that described the use of a comparator device, 65 percent (n = 31) used low-quality statistical methods, 23 percent (n = 11) used moderate-quality statistical methods, and only 12 percent (n = 6) used high-quality statistical methods. CONCLUSIONS: Our review found that current validity claims were based on low-quality assessments that do not provide the necessary confidence needed by clinicians for medical decision-making purposes. This narrative review highlights the need for standardized health technology reporting to increase health practitioner confidence in these devices, support the appropriate adoption of such devices within the healthcare system, and improve health outcomes.

Stressing the accuracy: Wrist-worn wearable sensor validation over different conditions.

Wearable sensors are promising instruments for conducting both laboratory and ambulatory research in psychophysiology. However, scholars should be aware of their measurement error and the conditions in which accuracy is achieved. This study aimed to assess the accuracy of a wearable sensor designed for research purposes, the E4 wristband (Empatica, Milan, Italy), in measuring heart rate (HR), heart rate variability (HRV), and skin conductance (SC) over five laboratory conditions widely used in stress reactivity research (seated rest, paced breathing, orthostatic, Stroop, speech task) and two ecological conditions (slow walking, keyboard typing). Forty healthy participants concurrently wore the wristband and two gold standard measurement systems (i.e., electrocardiography and finger SC sensor). The wristband accuracy was determined by evaluating the signal quality and the correlations with and the Bland-Altman plots against gold standard-derived measurements. Moreover, exploratory analyses were performed to assess predictors of measurement error. Mean HR measures showed the best accuracy over all conditions. HRV measures showed satisfactory accuracy in seated rest, paced breathing, and recovery conditions but not in dynamic conditions, including speaking. Accuracy was diminished by wrist movements, cognitive and emotional stress, nonstationarity, and larger wrist circumferences. Wrist SC measures showed neither correlation nor visual resemblance with finger SC signal, suggesting that the two sites may reflect different phenomena. Future studies are needed to assess the responsivity of wrist SC to emotional and cognitive stress. Limitations and implications for laboratory and ambulatory research are discussed.

Accelerometry for remote monitoring of physical activity in amyotrophic lateral sclerosis: a longitudinal cohort study.

BACKGROUND: The extensive heterogeneity between patients with amyotrophic lateral sclerosis (ALS) complicates the quantification of disease progression. In this study, we determine the value of remote, accelerometer-based monitoring of physical activity in patients with ALS. METHODS: This longitudinal cohort study was conducted in a home-based setting; all study materials were sent by mail. Patients wore the ActiGraph during waking hours for 7 days every 2-3 months and provided information regarding their daily functioning (ALSFRS-R). We defined four accelerometer-based endpoints that either reflect the average daily activity or quantify the patient's physical capacity. RESULTS: A total of 42 patients participated; the total valid monitoring period was 9288 h with a 93.0% adherence rate. At baseline, patients were active 27.9% (range 11.6-52.4%) of their time; this declined by 0.64% (95% 0.43-0.86, p < 0.001) per month. Accelerometer-based endpoints were strongly associated with the ALSFRS-R (r 0.78, 95% CI 0.63-0.92, p < 0.001), but showed less variability over time than the ALSFRS-R (coefficient of variation 0.64-0.81 vs. 1.06, respectively). Accelerometer-based endpoints could reduce sample size by 30.3% for 12-month trials and 44.6% for 18-month trials; for trials lasting less than 9 months, the ALSFRS-R resulted in smaller sample sizes. CONCLUSION: Accelerometry is an objective method for quantifying disease progression, which could obtain real-world insights in the patient's physical functioning and may personalize the delivery of care. In addition, remote monitoring provides patients with the opportunity to participate in clinical trials from home, paving the way to a patient-centric clinical trial model.