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Individuals with autism spectrum disorder (ASD) have been found to view social scenes differently compared to typically developing (TD) peers, but results can vary depending on context and age. We used eye-tracking in children and adults (age 6–63) to assess allocation of visual attention in a dynamic social orientation paradigm previously used only in younger children. The ASD group (n = 94) looked less at the actor’s face compared to TD (n = 38) when they were engaged in activity (mean percentage of looking time, ASD = 30.7% vs TD = 34.9%; Cohen’s d = 0.56; p value < 0.03) or looking at a moving toy (24.5% vs 33.2%; d = 0.65; p value < 0.001). Findings indicate that there are qualitative differences in allocation of visual attention to social stimuli across ages in ASD.ClinicalTrials.gov identifier: NCT02668991.

Background: Alzheimer's Disease (AD) impairs the ability to carry out daily activities, reduces independence and quality of life and increases caregiver burden. Our understanding of functional decline has traditionally relied on reports by family and caregivers, which are subjective and vulnerable to recall bias. The Internet of Things (IoT) and wearable sensor technologies promise to provide objective, affordable, and reliable means for monitoring and understanding function. However, human factors for its acceptance are relatively unexplored. Objective: The Public Involvement (PI) activity presented in this paper aims to capture the preferences, priorities and concerns of people with AD and their caregivers for using monitoring wearables. Their feedback will drive device selection for clinical research, starting with the study of the RADAR-AD project. Method: The PI activity involved the Patient Advisory Board (PAB) of the RADAR-AD project, comprised of people with dementia across Europe and their caregivers (11 and 10, respectively). A set of four devices that optimally represent various combinations of aspects and features from the variety of currently available wearables (e.g., weight, size, comfort, battery life, screen types, water-resistance, and metrics) was presented and experienced hands-on. Afterwards, sets of cards were used to rate and rank devices and features and freely discuss preferences. Results: Overall, the PAB was willing to accept and incorporate devices into their daily lives. For the presented devices, the aspects most important to them included comfort, convenience and affordability. For devices in general, the features they prioritized were appearance/style, battery life and water resistance, followed by price, having an emergency button and a screen with metrics. The metrics valuable to them included activity levels and heart rate, followed by respiration rate, sleep quality and distance. Some concerns were the potential complexity, forgetting to charge the device, the potential stigma and data privacy. Conclusions: The PI activity explored the preferences, priorities and concerns of the PAB, a group of people with dementia and caregivers across Europe, regarding devices for monitoring function and decline, after a hands-on experience and explanation. They highlighted some expected aspects, metrics and features (e.g., comfort and convenience), but also some less expected (e.g., screen with metrics).

The most common symptom of atopic dermatitis (AD) is pruritus, which is often exacerbated at night and leads to nocturnal scratching and sleep disturbance. The quantification of nocturnal scratching provides an objective measure, which could be used as a clinical trial endpoint tracking this AD-related behavior. However, it is not clear how digital health technologies (DHTs) intended to measure scratching perform in the real-world environment of patient homes. In this study, we present the analytical validation of 2 DHTs: the GENEActiv wristband with Philips sleep and scratch algorithms (\"Philips\") and the Emerald radio frequency touchless sensor (\"Emerald\") to measure nocturnal scratching in adults with AD. Thirty-one participants (15 with moderate AD, 11 with mild AD, and 5 healthy volunteers) were enrolled in the study. Nocturnal scratching was assessed by each DHT in the study participant's home environment over a 4-week observation period. Infrared videos were recorded during sleep twice per week and manually annotated for the intended sleep window (total sleep opportunity [TSO]) and scratching events. Human annotations for sleep and scratch measures were used as a reference for comparison with DHTs (\"Reference\"). Estimated TSO was compared for each DHT to Reference using Bland-Altman analysis. Within-night agreement of DHT-predicted scratching events versus the Reference was assessed by sensitivity, precision, and F1-score. Intraclass correlation was used to compare night-level scratch summaries (scratch duration per hour of TSO and scratch frequency per hour of TSO) between each DHT and the Reference. Characterization of human-annotated scratching revealed a basal level of scratching in both healthy volunteers (13.1 seconds per hour) and in participants with AD on nights when no itch was reported (10.2 seconds per hour). The TSO window was quantified accurately with both DHTs having a mean bias compared with Reference of <30 minutes. The within-night agreement with reference to scratch detection performance resulted in F1-scores at the disease group level ranging from 0.51 to 0.68 for the Emerald DHT and 0.47 to 0.56 for the Philips DHT. The night-level agreement of nocturnal scratch duration and frequency with human raters fell mostly in the moderate-good range of intraclass correlation coefficients (0.5-0.9) in participants with AD and was not significantly lower than the level of agreement between any 2 human raters. These results support the analytical validity of both DHTs tested for continuous measurement of nocturnal scratching in individuals with AD in the home environment. Opportunities remain for improving the performance of the DHTs tested, especially in the precision of wrist-worn accelerometer scratch detection, to reach human-level performance. Additional data collection in diverse patient populations will be beneficial for practitioners intending to use or improve these tools for quantifying nocturnal scratching behavior.

The relationship between sleep (caregiver-reported and actigraphy-measured) and other caregiver-reported behaviors in children and adults with autism spectrum disorder (ASD) was examined, including the use of machine learning to identify sleep variables important in predicting anxiety in ASD. Caregivers of ASD ( = 144) and typically developing (TD) ( = 41) participants reported on sleep and other behaviors. ASD participants wore an actigraphy device at nighttime during an 8 or 10-week non-interventional study. Mean and variability of actigraphy measures for ASD participants in the week preceding midpoint and endpoint were calculated and compared with caregiver-reported and clinician-reported symptoms using a mixed effects model. An elastic-net model was developed to examine which sleep measures may drive prediction of anxiety. Prevalence of caregiver-reported sleep difficulties in ASD was approximately 70% and correlated significantly ( < 0.05) with sleep efficiency measured by actigraphy. Mean and variability of actigraphy measures like sleep efficiency and number of awakenings were related significantly ( < 0.05) to ASD symptom severity, hyperactivity and anxiety. In the elastic net model, caregiver-reported sleep, and variability of sleep efficiency and awakenings were amongst the important predictors of anxiety. Caregivers report problems with sleep in the majority of children and adults with ASD. Reported problems and actigraphy measures of sleep, particularly variability, are related to parent reported behaviors. Measuring variability in sleep may prove useful in understanding the relationship between sleep problems and behavior in individuals with ASD. These findings may have implications for both intervention and monitoring outcomes in ASD.

Facial expression is impaired in autism spectrum disorder (ASD), but rarely systematically studied. We focus on the ability of individuals with ASD to produce facial expressions of emotions in response to a verbal prompt. We used the Janssen Autism Knowledge Engine (JAKE ® ), including automated facial expression analysis software (FACET) to measure facial expressions in individuals with ASD (n = 144) and a typically developing (TD) comparison group (n = 41). Differences in ability to produce facial expressions were observed between ASD and TD groups, demonstrated by activation of facial action units (happy, scared, surprised, disgusted, but not angry or sad). Activation of facial action units correlated with parent-reported social communication skills. This approach has potential for diagnostic and response to intervention measures. Trial Registration NCT02299700.

Synaptic plasticity is the key to synaptic health, and aberrant synaptic plasticity, which in turn impairs the functioning of large-scale brain networks, has been associated with neurodegenerative and psychiatric disorders. The best known and most studied form of activity-dependent synaptic plasticity remains long-term potentiation (LTP), which is controlled by glutamatergic N-methyl-d-aspartate) receptors (NMDAR) and considered to be a mechanism crucial for cellular learning and memory. Over the past two decades, discrepancies have arisen in the literature regarding the contribution of NMDAR subunit assemblies in the direction of NMDAR-dependent synaptic plasticity. Here, the nonspecific NMDAR antagonist ketamine (5 and 10 mg/kg), and the selective NR2B antagonists CP-101606 and Ro 25-6981 (6 and 10 mg/kg), were administered intraperitoneally in Sprague Dawley rats to disentangle the contribution of NR2B subunit in the LTP induced at the Schaffer Collateral-CA1 synapse using the theta burst stimulation protocol (TBS). Ketamine reduced, while CP-101606 and Ro 25-6981 did not alter the LTP response. The administration of CP-101606 before TBS did not influence the effects of ketamine when administered half an hour after tetanization, suggesting a limited contribution of the NR2B subunit in the action of ketamine. This work confirms the role of NMDAR in the LTP form of synaptic plasticity, whereas specific blockade of the NR2B subunit was not sufficient to modify hippocampal LTP. Pharmacokinetics at the doses used may have contributed to the lack of effects with specific antagonists. The findings refute the role of the NR2B subunit in the plasticity mechanism of ketamine in the model.

Increasing adoption of sensor-based digital health technologies (sDHTs) in recent years has cast light on the many challenges in implementing these tools into clinical trials and patient care at scale across diverse patient populations; however, the methodological approaches taken toward sDHT usability evaluation have varied markedly. This review aims to explore the current landscape of studies reporting data related to sDHT human factors, human-centered design, and usability, to inform our concurrent work on developing an evaluation framework for sDHT usability. We conducted a scoping review of studies published between 2013 and 2023 and indexed in PubMed, in which data related to sDHT human factors, human-centered design, and usability were reported. Following a systematic screening process, we extracted the study design, participant sample, the sDHT or sDHTs used, the methods of data capture, and the types of usability-related data captured. Our literature search returned 442 papers, of which 85 papers were found to be eligible and 83 papers were available for data extraction and not under embargo. In total, 164 sDHTs were evaluated; 141 (86%) sDHTs were wearable tools while the remaining 23 (14%) sDHTs were ambient tools. The majority of studies (55/83, 66%) reported summative evaluations of final-design sDHTs. Almost all studies (82/83, 99%) captured data from targeted end users, but only 18 (22%) out of 83 studies captured data from additional users such as care partners or clinicians. User satisfaction and ease of use were evaluated for 83% (136/164) and 91% (150/164) of sDHTs, respectively; however, learnability, efficiency, and memorability were reported for only 11 (7%), 4 (2%), and 2 (1%) out of 164 sDHTs, respectively. A total of 14 (9%) out of 164 sDHTs were evaluated according to the extent to which users were able to understand the clinical data or other information presented to them (understandability) or the actions or tasks they should complete in response (actionability). Notable gaps in reporting included the absence of a sample size rationale (reported for 21/83, 25% of all studies and 17/55, 31% of summative studies) and incomplete sociodemographic descriptive data (complete age, sex/gender, and race/ethnicity reported for 14/83, 17% of studies). Based on our findings, we suggest four actionable recommendations for future studies that will help to advance the implementation of sDHTs: (1) consider an in-depth assessment of technology usability beyond user satisfaction and ease of use, (2) expand recruitment to include important user groups such as clinicians and care partners, (3) report the rationale for key study design considerations including the sample size, and (4) provide rich descriptive statistics regarding the study sample to allow a complete understanding of generalizability to other patient populations and contexts of use.

Background Reduction or differences in facial expression are a core diagnostic feature of autism spectrum disorder (ASD), yet evidence regarding the extent of this discrepancy is limited and inconsistent. Use of automated facial expression detection technology enables accurate and efficient tracking of facial expressions that has potential to identify individual response differences. Methods Children and adults with ASD ( N = 124) and typically developing (TD, N = 41) were shown short clips of “funny videos.” Using automated facial analysis software, we investigated differences between ASD and TD groups and within the ASD group in evidence of facial action unit (AU) activation related to the expression of positive facial expression, in particular, a smile. Results Individuals with ASD on average showed less evidence of facial AUs (AU12, AU6) relating to positive facial expression, compared to the TD group ( p < .05, r = − 0.17). Using Gaussian mixture model for clustering, we identified two distinct distributions within the ASD group, which were then compared to the TD group. One subgroup ( n = 35), termed “over-responsive,” expressed more intense positive facial expressions in response to the videos than the TD group ( p < .001, r = 0.31). The second subgroup ( n = 89), (“under-responsive”), displayed fewer, less intense positive facial expressions in response to videos than the TD group ( p < .001; r = − 0.36). The over-responsive subgroup differed from the under-responsive subgroup in age and caregiver-reported impulsivity ( p < .05, r = 0.21). Reduced expression in the under-responsive, but not the over-responsive group, was related to caregiver-reported social withdrawal ( p < .01, r = − 0.3). Limitations This exploratory study does not account for multiple comparisons, and future work will have to ascertain the strength and reproducibility of all results. Reduced displays of positive facial expressions do not mean individuals with ASD do not experience positive emotions. Conclusions Individuals with ASD differed from the TD group in their facial expressions of positive emotion in response to “funny videos.” Identification of subgroups based on response may help in parsing heterogeneity in ASD and enable targeting of treatment based on subtypes. Trial registration ClinicalTrials.gov , NCT02299700 . Registration date: November 24, 2014

Wearables with photoplethysmography (PPG) sensors are being increasingly used in clinical research as a non-invasive, inexpensive method for remote monitoring of physiological health. Ensuring the accuracy and reliability of PPG-derived measurements is critical, as inaccuracies can impact research findings and clinical decisions. This paper systematically compares heart rate (HR) and heart rate variability (HRV) measures from PPG against an electrocardiogram (ECG) monitor in free-living settings. Two devices with PPG and one device with an ECG sensor were worn by 25 healthy volunteers for 10 days. PPG-derived HR and HRV showed reasonable accuracy and reliability, particularly during sleep, with mean absolute error < 1 beat for HR and 6–15 ms for HRV. The relative error of HRV estimated from PPG varied with activity type and was higher than during the resting state by 14–51%. The accuracy of HR/HRV was impacted by the proportion of usable data, body posture, and epoch length. The multi-scale peak and trough detection algorithm demonstrated superior performance in detecting beats from PPG signals, with an F1 score of 89% during sleep. The study demonstrates the trade-offs of utilizing PPG measurements for remote monitoring in daily life and identifies optimal use conditions by recommending enhancements.

Video electroencephalography recordings, routinely used in epilepsy monitoring units, are the gold standard for monitoring epileptic seizures. However, monitoring is also needed in the day-to-day lives of people with epilepsy, where video electroencephalography is not feasible. Wearables could fill this gap by providing patients with an accurate log of their seizures. Although there are already systems available that provide promising results for the detection of tonic-clonic seizures (TCSs), research in this area is often limited to detection from 1 biosignal modality or only during the night when the patient is in bed. The aim of this study is to provide evidence that supervised machine learning can detect TCSs from multimodal data in a new data set during daytime and nighttime. An extensive data set of biosignals from a multimodal watch worn by people with epilepsy was recorded during their stay in the epilepsy monitoring unit at 2 European clinical sites. From a larger data set of 243 enrolled participants, those who had data recorded during TCSs were selected, amounting to 10 participants with 21 TCSs. Accelerometry and electrodermal activity recorded by the wearable device were used for analysis, and seizure manifestation was annotated in detail by clinical experts. Ten accelerometry and 3 electrodermal activity features were calculated for sliding windows of variable size across the data. A gradient tree boosting algorithm was used for seizure detection, and the optimal parameter combination was determined in a leave-one-participant-out cross-validation on a training set of 10 seizures from 8 participants. The model was then evaluated on an out-of-sample test set of 11 seizures from the remaining 2 participants. To assess specificity, we additionally analyzed data from up to 29 participants without TCSs during the model evaluation. In the leave-one-participant-out cross-validation, the model optimized for sensitivity could detect all 10 seizures with a false alarm rate of 0.46 per day in 17.3 days of data. In a test set of 11 out-of-sample TCSs, amounting to 8.3 days of data, the model could detect 10 seizures and produced no false positives. Increasing the test set to include data from 28 more participants without additional TCSs resulted in a false alarm rate of 0.19 per day in 78 days of wearable data. We show that a gradient tree boosting machine can robustly detect TCSs from multimodal wearable data in an original data set and that even with very limited training data, supervised machine learning can achieve a high sensitivity and low false-positive rate. This methodology may offer a promising way to approach wearable-based nonconvulsive seizure detection.