Telehealth parent training for sleep disturbances in young children with autism spectrum disorder: A randomized controlled trial.

BACKGROUND AND PURPOSE: Young children with autism spectrum disorder (autism) have bedtime and sleep disturbances at much higher frequency and persistency than their neurotypical counterparts. Hence, access to early, effective treatment is critical in view of the importance of sleep in early childhood. Telehealth delivery could be a means to expand access to such early treatment if efficacious. The aim of this randomized control trial (RCT) was to compare a manualized, telehealth delivered, behaviorally based sleep parent training (SPT) intervention for parents of young children with autism and sleep disturbances to a control condition, a telehealth delivered parent education program with one sleep focused session (SPE). We hypothesized that the SPT group would show more improvements on child measures of sleep outcome measures, and daytime behaviors and parent measures of stress and sense of competence. We further aimed to explore the overall feasibility of telehealth delivery of SPT and SPE. PARTICIPANTS AND METHODS: Parents of 77 young children, ages 2-7 years, with autism and co-occurring sleep disturbances were enrolled in this study. Participants were randomized to either SPT or a comparison arm that included non-sleep related parent education except for one session. Each participant was individually administered a 5 session program delivered over 10 weeks. Outcome measures, including child sleep measures, child daytime behavior and parent stress and sense of competency were collected at weeks 5 and 10 after the baseline time point. Feasibility indicators (treatment fidelity, parent adherence, and parent attendance), and safety measures were also collected. RESULTS: Of 77 randomized participants, data were available for 36 participants randomized to SPT and 38 participants randomized to SPE. The mean age was 3 years, 8 months. Results support the efficacy of this manualized SPT intervention for bedtime and sleep disturbances. Sleep outcome measures were significantly improved in the SPT group compared to SPE on the Modified Simonds & Parraga Sleep Questionnaire-Composite Sleep Index (MSPSQ - CSI) (p < 0.001) with a large effect size of 0.83 at week 10. Positive response to treatment, as determined from the Clinical Global Impression-Improvement scale (CGI-I) at week 10 was observed in 56% of SPT participants compared to 32% in SPE (p = 0.037). There were no significant group differences in either the ABC-I as measure of daytime behaviors or in parental stress. There were group differences in favor of SPT over SPE on the PSOC, a measure of parent sense of competency. Feasibility and safety were further demonstrated with telehealth delivery. CONCLUSIONS: This RCT demonstrated the efficacy of a telehealth delivered parent training intervention for bedtime and sleep disturbances in young autistic children compared to an active control condition. Further, parents in SPT reported more confidence in their parenting role than those in the SPE group, but SPT did not result in overall decreases in parental stress. Telehealth delivery allowed for a much broader reach with enrolled participants from 24 states. This study supports a telehealth approach to a manualized behavioral parent mediated intervention for sleep disturbance in young autistic children and offers an alternative to in-person delivered approaches. This telehealth delivery has the potential to improve access for families who have a young autistic child with sleep disturbances. Given the small sample size, determining predictors and moderators of treatment response was not possible and should be examined in a larger trial.

Differences among physical activity actigraphy algorithms in three chronic illness populations.

OBJECTIVES: In three chronic illness populations and in a combined sample, we assessed differences in two algorithms to determine wear time (WT%) and four algorithms to determine: Kilocalories, light physical activity (PA), moderate-to-vigorous PA (MVPA), and metabolic equivalents (METs). METHODS: Data were collected from 29 people living with HIV (PLHIV), 27 participants recovering from a cardiac event, and 15 participants with hypertension (HTN). Participants wore the ActiGraphTM wGT3X-BT for > 3 days on their hip. Analysis of variance (ANOVA) was used to assess differences among the algorithms. RESULTS: No differences were found between the two algorithms to assess WT% or among the four algorithms to assess kilocalories in each of the chronic illness populations or in the combined sample. Significant differences were found among the four algorithms for light PA (p < .001) and METs (p < .001) in each chronic illness population and in the combined sample. MVPA was significantly different among the four algorithms in the PLHIV (p = .007) and in the combined sample (p < .001), but not in the cardiac (p = .064) or HTN samples (p = .200). DISCUSSION: Our findings indicate that the choice of algorithm does make a difference in PA determination. Differences in algorithms should be considered when comparing PA across different chronic illness populations.

Large-scale informatic analysis to algorithmically identify blood biomarkers of neurological damage.

The identification of precision blood biomarkers which can accurately indicate damage to brain tissue could yield molecular diagnostics with the potential to improve how we detect and treat neurological pathologies. However, a majority of candidate blood biomarkers for neurological damage that are studied today are proteins which were arbitrarily proposed several decades before the advent of high-throughput omic techniques, and it is unclear whether they represent the best possible targets relative to the remainder of the human proteome. Here, we leveraged mRNA expression data generated from nearly 12,000 human specimens to algorithmically evaluate over 17,000 protein-coding genes in terms of their potential to produce blood biomarkers for neurological damage based on their expression profiles both across the body and within the brain. The circulating levels of proteins associated with the top-ranked genes were then measured in blood sampled from a diverse cohort of patients diagnosed with a variety of acute and chronic neurological disorders, including ischemic stroke, hemorrhagic stroke, traumatic brain injury, Alzheimer's disease, and multiple sclerosis, and evaluated for their diagnostic performance. Our analysis identifies several previously unexplored candidate blood biomarkers of neurological damage with possible clinical utility, many of which whose presence in blood is likely linked to specific cell-level pathologic processes. Furthermore, our findings also suggest that many frequently cited previously proposed blood biomarkers exhibit expression profiles which could limit their diagnostic efficacy.

Diagnosis of ischemic stroke using circulating levels of brain-specific proteins measured via high-sensitivity digital ELISA.

Limited lower detection ranges associated with traditional immunoassay techniques have prevented the use of brain-specific proteins as blood biomarkers of stroke in the acute phase of care, as these proteins are often only present in circulation at low concentrations. Digital ELISA is a newly developed technique with allows for quantification of proteins in biofluids with up to 1000 times greater sensitivity than conventional ELISA techniques. The purpose of this study was to determine whether the extended lower limits of detection associated with digital ELISA could enable the use of brain-specific proteins as blood biomarkers of ischemic stroke during triage. Blood was sampled from ischemic stroke patients (n = 14) at emergency department admission, as well as from neurologically normal controls matched in terms of risk factors for cardiovascular disease (n = 33). Plasma levels of two brain-specific axonal proteins, neurofilament light chain (NfL) and tau, were measured via digital ELISA, and receiver-operating characteristic analysis was used to determine their ability to discriminate between groups. Plasma levels of NfL and tau were both significantly elevated in stroke patients versus controls, and could respectively discriminate between groups with 92.9% sensitivity / 84.9% specificity, and 85.7% sensitivity / 54.6% specificity. Furthermore, adjustment of measured NfL and Tau levels according to the lower-limits of detection associated with commercially-available conventional ELISA assays resulted in a dramatic and statistically significant decrease in diagnostic performance. Collectively, our results suggest that the increased analytical sensitivity of digital ELISA could enable the use of brain-specific proteins as blood biomarkers of ischemic stroke during triage.

Application of a novel actigraphy algorithm to detect movement and sleep/wake patterns in children with autism spectrum disorder.

OBJECTIVE: Actigraphy is a non-intrusive method of recording rest/activity cycles as well as a surrogate for sleep/wake activity. Standard actigraphy analysis is limited in ascribing discrete movement events to wake status during sleep. We applied a novel algorithm to overnight actigraphy data recorded simultaneously with video polysomnography-electroencephalography (video PSG-EEG) to determine its ability to define movement and sleep/wake patterns in children with autism spectrum disorder (ASD) and age-comparable typically developing (TD) controls. METHODS: A previously published novel algorithm uses mathematical endpoints to analyze actigraphy data without assumptions about sleep/wake status, and smooths data using moving windows of increasing length. Nighttime activity level "S" events (S1-S5) determined by this algorithm (n = 273) were identified in 15 children ages 3-10 years (nine with ASD and six TD) who wore an AW2 Spectrum Actiwatch (Philips Respironics) while undergoing simultaneous video PSG-EEG. Data were analyzed to identify the time each activity level "S" event occurred, video movement events (movements captured by video and scored based on level of severity), and sleep/wake status defined by PSG-EEG. The relationships among activity level "S" events, video movement events, and sleep/wake status were analyzed statistically. RESULTS: Activity level "S" events, the presence and severity of video movement events, and sleep-wake status, were significantly associated. These associations were present in both participants with ASD and those who were typically developing. CONCLUSION: This actigraphy algorithm shows promise for detecting nighttime movements and sleep/wake status and warrants further study in larger datasets of neurotypical children and those with neurodevelopmental disorders.

Building a Research Data Repository for Chronic Condition Self-Management Using Harmonized Data.

BACKGROUND: Building nursing research data repositories with the goal of comparing and synthesizing results across numerous studies and public sharing of data is still in early stages of development. OBJECTIVES: We describe the process of using common data elements (CDEs) to build a data repository for research addressing self-management of chronic conditions. Issues in the development of CDEs, lessons learned in the creation of a combined data set across seven studies of different chronic condition populations, and recommendations for creating and sharing harmonized nursing research data sets are provided. METHODS: In 2014, at initiation of a National Institutes of Health-funded Centers of Excellence in Self-Management Research, our center investigators defined a set of CDEs for use in future center-funded pilot studies consisting of populations having different chronic conditions with the intent to combine the study data sets. Over the next 4 years, center investigators were provided with standardized codebooks and data collection protocols for applying the CDEs and data storage. Data from seven pilot studies were subsequently combined. RESULTS: Although each pilot study was small-with sample sizes ranging from 18 to 31 participants-our combined data set of 179 participants provides us with a sample size sufficient to conduct analyses that could not be done with the individual small samples alone. The research data repository addressing self-management of chronic conditions will soon be available for public sharing. DISCUSSION: Our experience demonstrates that, with careful, upfront planning and ongoing vigilant oversight, CDEs can be applied across studies consisting of different chronic condition populations to combine data sets to create research data repositories for public sharing.

Use of high-sensitivity digital ELISA improves the diagnostic performance of circulating brain-specific proteins for detection of traumatic brain injury during triage.

Background: Historically, limited sensitivity associated with traditional immunoassay methods has prevented the use of brain-specific proteins as blood biomarkers of traumatic brain injury (TBI) during triage, as these proteins exhibit low circulating concentrations. Digital ELISA is a newly-developed technique that is up to 1000 times more sensitive than conventional ELISA methods. The purpose of this study was to determine whether the use of digital ELISA over conventional ELISA improves the performance of brain-specific proteins as blood biomarkers of TBI during triage.Methods: Blood was sampled from TBI patients (n = 13) at emergency department admission, as well as from neurologically normal controls (n = 72). Serum levels of two brain-specific proteins, neurofilament light chain (NfL) and Tau, were measured via digital ELISA. Estimated conventional ELISA measures were generated by adjusting values according to the lower limits of detection achievable with commercially available conventional ELISA assays, and receiver operating characteristic (ROC) analysis was used to compare the diagnostic performance of digital ELISA measures to estimated conventional ELISA measures in terms of their ability to discriminate between TBI patients and controls.Results: Used in combination, digital ELISA measures of NfL and Tau could discriminate between groups with 100% sensitivity and 91.7% specificity. Estimated conventional ELISA measures could only discriminate between groups with 7.7% sensitivity and 94.4% specificity. This difference in diagnostic performance was statistically significant when comparing areas under ROC curves.Conclusions: The use of digital ELISA over conventional ELISA methods improves the diagnostic performance of circulating brain-specific proteins for detection of TBI during triage.

Neuro biomarker levels measured with high-sensitivity digital ELISA differ between serum and plasma.

Aim: Digital ELISA-based assays for blood biomarkers of neurological disease are on the verge of clinical use. Here, we aimed to determine whether different preanalytical blood processing techniques influence results. Materials & methods: Concentrations of neurofilament light chain (NfL), Tau and amyloid beta (Aß) were measured in human plasma and serum specimens using digital ELISA and compared between blood products. Measured levels of NfL were highly equivalant between serum and plasma in all analyses, however, measured levels of Tau and Aß were consistently lower in serum relative to plasma. Conclusion: Tau and Aß are likely lost during clotting in serum preparations, and should be assayed in plasma to get an accurate measure of circulating levels.

Characterization of Brain Signatures to Add Precision to Self-Management Health Information Interventions.

BACKGROUND: Although many of the proposed mediating processes of self-management interventions are operationally defined as cognitive processes (e.g., acquiring and using information, self-efficacy, motivation, and decision-making), little is known about their underlying brain mechanisms. Brain biomarkers of how people process health information may be an important characteristic on which to individualize health information to optimize self-management of chronic conditions. OBJECTIVES: We describe a program of research addressing the identification of brain biomarkers that differentially predict responses to two types of health information (analytic focused and emotion focused) designed to support optimal self-management of chronic conditions. METHODS: We pooled data from two pilot studies (N = 52) that included functional magnetic resonance imaging during a specially designed, ecologically valid protocol to examine brain activation (task differentiation) associated with two large-scale neural networks-the Analytic Network and the Empathy Network-and the ventral medial prefrontal cortex while individuals responded to different types of health information (analytic and emotional). RESULTS: Findings indicate that analytic information and emotional information are processed differently in the brain, and the magnitude of this differentiation in response to type of information varies from person to person. Activation in the a priori regions identified in response to both analytic and emotion information was confirmed. The feasibility of obtaining brain imaging data from persons with chronic conditions also is demonstrated. DISCUSSION: An understanding of brain signatures related to information processing has potential to assist in the design of more individualized, effective self-management interventions.