Objective 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. Methods Caregivers of ASD (n = 144) and typically developing (TD) (n = 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. Results Prevalence of caregiver-reported sleep difficulties in ASD was approximately 70% and correlated significantly (p < 0.05) with sleep efficiency measured by actigraphy. Mean and variability of actigraphy measures like sleep efficiency and number of awakenings were related significantly (p < 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. Conclusion 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.

There is a need for measures to track symptom change in autism spectrum disorder (ASD). We conducted a validation study on a revised version of the Autism Behavior Inventory (ABI), and a short form (ABI-S). Caregivers of individuals (6–54 years) with confirmed diagnoses of ASD (N = 144) completed the ABI and other rating scales at 4 time points. Scale consistency for each domain, 3–5 day test–retest reliability, and construct validity, determined by comparison to pre-specified scales, were all good. Change in the ABI was congruent with changes in other instruments. Collectively, results suggest incipient suitability of the ABI as a measure of changes in core and associated symptoms of ASD. Trial Registration NCT02299700.

Abstract The Janssen Autism Knowledge Engine (JAKE®) collects a large number of features from five biosensors across a range of tasks. The application of data mining methods to these data may be a useful approach to enable objective discrimination between autism spectrum disorder (ASD) and typically developing (TD) participants. Following a prospective observational study using JAKE, ASD participants classified as “moderate” or “severe” based on total scores on the Social Responsiveness Scale, and TD participants were used to build models, using repeated cross-validation, to identify biosensor features contributing to diagnosis. Four different models (partial least squares, random forest, elastic net, and C5.0) were chosen to build diagnostic classifiers using the training set, and the fitted models were evaluated on the test set. Model performance on the training set, based on receiver operating characteristics (ROC), was moderate (area under ROC curve = 0.61–0.72), and model performance on the test set based on kappa statistic was between 0.40 and 0.46 across the four models. Data mining methods applied to biosensor data can lead to models that discriminate ASD from TD. This method may prove useful in creating new diagnostic tests for ASD.

Currently, no medications are approved to treat core symptoms of autism spectrum disorder (ASD). One barrier to ASD medication development is the lack of validated outcome measures able to detect symptom change. Current ASD interventions are often evaluated using retrospective caregiver reports that describe general clinical presentation but often require recall of specific behaviors weeks after they occur, potentially reducing accuracy of the ratings. My JAKE, a mobile and Web-based mobile health (mHealth) app that is part of the Janssen Autism Knowledge Engine—a dynamically updated clinical research system—was designed to help caregivers of individuals with ASD to continuously log symptoms, record treatments, and track progress, to mitigate difficulties associated with retrospective reporting. My JAKE was deployed in an exploratory, noninterventional clinical trial to evaluate its utility and acceptability to monitor clinical outcomes in ASD. Hypotheses regarding relationships among daily tracking of symptoms, behavior, and retrospective caregiver reports were tested. Caregivers of individuals with ASD aged 6 years to adults (N=144) used the My JAKE app to make daily reports on their child’s sleep quality, affect, and other self-selected specific behaviors across the 8- to 10-week observational study. The results were compared with commonly used paper-and-pencil scales acquired over a concurrent period at regular 4-week intervals. Caregiver reporting of behaviors in real time was successfully captured by My JAKE. On average, caregivers made reports 2-3 days per week across the study period. Caregivers were positive about their use of the system, with over 50% indicating that they would like to use My JAKE to track behavior outside of a clinical trial. More positive average daily reporting of overall type of day was correlated with 4 weekly reports of lower caregiver burden made at 4-week intervals (r=–0.27, P=.006, n=88) and with ASD symptoms (r=–0.42, P<.001, n=112). My JAKE reporting aligned with retrospective Web-based or paper-and-pencil scales. Use of mHealth apps, such as My JAKE, has the potential to increase the validity and accuracy of caregiver-reported outcomes and could be a useful way of identifying early changes in response to intervention. Such systems may also assist caregivers in tracking symptoms and behavior outside of a clinical trial, help with personalized goal setting, and monitoring of progress, which could collectively improve understanding of and quality of life for individuals with ASD and their families. ClinicalTrials.gov NCT02668991; https://clinicaltrials.gov/ct2/show/NCT02668991

Objective: The Janssen Autism Knowledge Engine (JAKE®) is a clinical research outcomes assessment system developed to more sensitively measure treatment outcomes and identify subpopulations in autism spectrum disorder (ASD). Here we describe JAKE and present results from its digital phenotyping (My JAKE) and biosensor (JAKE Sense) components. Methods: An observational, non-interventional, prospective study of JAKE in children and adults with ASD was conducted at nine sites in the United States. Feedback on JAKE usability was obtained from caregivers. JAKE Sense included electroencephalography, eye tracking, electrocardiography, electrodermal activity, facial affect analysis, and actigraphy. Caregivers of individuals with ASD reported behaviors using My JAKE. Results from My JAKE and JAKE Sense were compared to traditional ASD symptom measures. Results: Individuals with ASD (N = 144) and a cohort of typically developing (TD) individuals (N = 41) participated in JAKE Sense. Most caregivers reported that overall use and utility of My JAKE was "easy" (69%, 74/108) or "very easy" (74%, 80/108). My JAKE could detect differences in ASD symptoms as measured by traditional methods. The majority of biosensors included in JAKE Sense captured sizable amounts of quality data (i.e., 93-100% of eye tracker, facial affect analysis, and electrocardiogram data was of good quality), demonstrated differences between TD and ASD individuals, and correlated with ASD symptom scales. No significant safety events were reported. Conclusions: My JAKE was viewed as easy or very easy to use by caregivers participating in research outside of a clinical study. My JAKE sensitively measured a broad range of ASD symptoms. JAKE Sense biosensors were well-tolerated. JAKE functioned well when used at clinical sites previously inexperienced with some of the technologies. Lessons from the study will optimize JAKE for use in clinical trials to assess ASD interventions. Additionally, because biosensors were able to detect features differentiating TD and ASD individuals, and also were correlated with standardized symptom scales, these measures could be explored as potential biomarkers for ASD and as endpoints in future clinical studies. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02668991 identifier: NCT02668991.

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.

Eye‐tracking studies have demonstrated that individuals with autism spectrum disorder sometimes show differences in attention and gaze patterns. This includes preference for certain nonsocial objects, heightened attention to detail, and more difficulty with attention shifting and disengagement, which may be associated with restricted and repetitive behaviors. This study utilized a visual exploration task and replicates findings of reduced number of objects explored and increased fixation duration on high autism interest objects in a large sample of individuals with autism spectrum disorder (n = 129, age 6–54 years) in comparison with a typically developing group. These findings correlated with parent‐reported repetitive behaviors. Additionally, we applied recurrent quantification analysis to enable identification of new eye‐tracking features, which accounted for temporal and spatial differences in viewing patterns. These new features were found to discriminate between autism spectrum disorder and typically developing groups and were correlated with parent‐reported repetitive behaviors. Original and novel eye‐tracking features identified by recurrent quantification analysis differed in their relationships to reported behaviors and were dependent on age. Trial Registration: NCT02299700. Autism Research 2018, 11: 1554–1566. © 2018 International Society for Autism Research, Wiley Periodicals, Inc.Lay SummaryUsing eye‐tracking technology and a visual exploration task, we showed that people with autism spectrum disorder (ASD) spend more time looking at particular kinds of objects, like trains and clocks, and look at fewer objects overall than people without ASD. Where people look and the order in which they look at objects were related to the restricted and repetitive behaviors reported by parents. Eye‐tracking may be a useful addition to parent reports for measuring changes in behavior in individuals with ASD.

There are lack of validated tools for measuring change in the core and associated symptoms of Autism Spectrum Disorder (ASD). We present data from a validation study of the Autism Behavior Inventory (ABI), a 73-item online parent rating scale, which was designed to be used as an outcome measure in clinical trials for ASD.

Objective: To test usability and optimize the Janssen Autism Knowledge Engine (JAKE®) system's components, biosensors, and procedures used for objective measurement of core and associated symptoms of autism spectrum disorder (ASD) in clinical trials. Methods: A prospective, observational study of 29 children and adolescents with ASD using the JAKE system was conducted at three sites in the United States. This study was designed to establish the feasibility of the JAKE system and to learn practical aspects of its implementation. In addition to information collected by web and mobile components, wearable biosensor data were collected both continuously in natural settings and periodically during a battery of experimental tasks administered in laboratory settings. This study is registered at clinicaltrials.gov, NCT02299700. Results: Feedback collected throughout the study allowed future refinements to be planned for all components of the system. The Autism Behavior Inventory (ABI), a parent-reported measure of ASD core and associated symptoms, performed well. Among biosensors studied, the eye-tracker, sleep monitor, and electrocardiogram were shown to capture high quality data, whereas wireless electroencephalography was difficult to use due to its form factor. On an exit survey, the majority of parents rated their overall reaction to JAKE as positive/very positive. No significant device-related events were reported in the study. Conclusion: The results of this study, with the described changes, demonstrate that the JAKE system is a viable, useful, and safe platform for use in clinical trials of ASD, justifying larger validation and deployment studies of the optimized system.

Correlations of behavior ratings with more objective biosensor data were examined with a view to identifying potential eye-tracking biomarkers for change in autism spectrum disorder (ASD).

The goal of this presentation is to demonstrate the utility of applying data mining to biosensor data from the Janssen Autism Knowledge Engine (JAKE™) system by identifying biosensor variables, in combination or individually, that can discriminate between autism spectrum disorder (ASD) and typically developing (TD) participants.

The authentication and integrity verification of medical images is a critical and growing issue for patients in e-health services. Accurate identification of medical images and patient verification is an essential requirement to prevent error in medical diagnosis. The proposed work presents an imperceptible watermarking system to address the security issue of medical fundus images for tele-ophthalmology applications and computer aided automated diagnosis of retinal diseases.In the proposed work, patient identity is embedded in fundus image in singular value decomposition domain with adaptive quantization parameter to maintain perceptual transparency for variety of fundus images like healthy fundus or disease affected image. In the proposed method insertion of watermark in fundus image does not affect the automatic image processing diagnosis of retinal objects & pathologies which ensure uncompromised computer-based diagnosis associated with fundus image. Patient ID is correctly recovered from watermarked fundus image for integrity verification of fundus image at the diagnosis centre.The proposed watermarking system is tested in a comprehensive database of fundus images and results are convincing.results indicate that proposed watermarking method is imperceptible and it does not affect computer vision based automated diagnosis of retinal diseases.Correct recovery of patient ID from watermarked fundus image makes the proposed watermarking system applicable for authentication of fundus images for computer aided diagnosis and Tele-ophthalmology applications.

The goal of this presentation is to demonstrate the utility of the Janssen Autism Knowledge Engine (JAKE™) system, including biosensors and web and mobile phenotyping tools, in the assessment of ASD symptoms versus commonly used clinical measures.

High placebo response has been a major source of bias and is difficult to deal with in many central nervous system (CNS) clinical trials. This bias has led to a high failure rate in mood disorder trials even with known effective drugs. For cancer trials, the traditional parallel group design biases the inference on the maintenance effect of the new drug with the traditional time-to-treatment failure analysis. To minimize bias, we propose a doubly randomized delayed-start design for clinical trials with enrichment. The design consists of two periods. In the first period, patients can be randomized to receive several doses of a new drug or a control. In the second period, control patients of the first period of an enriched population can be rerandomized to receive the same or fewer doses of the new drug or to continue on the control. Depending on the clinical needs, different randomization ratios can be applied to the two periods. The essential feature is that the design is naturally adaptive because of the randomization for the second period. As a result, other aspects of the second period, such as the sample size, can be modified adaptively when an interim analysis is set up for the first period. At the end of the trial, response data from both randomizations are combined in an integrated analysis. Because of the enrichment in the second period, the design increases the probability of trial success and, in addition, reduces the required sample size. Thus, for clinical development, the design offers greater efficiency.