Wearable Technology to Measure the Occurrence of Self-Injury During a Functional Analysis.

To describe the prevalence of cerebral palsy (CP), subtype distribution, motor and intellectual impairment, and epilepsy in adults with CP compared with children with CP. CP subtype and impairment data from the population-based CP register of western Sweden and population data from Statistics Sweden were used to compare surviving adults (n=581; 244 females, 337 males) born between 1959 and 1978, with the same cohort as children (n=723; 307 females, 416 males), andwiththe most recent cohort, born from 2007 to 2010 (n=205; 84 females, 121 males). Prevalence of CP in adults born between 1959 and 1978 was 1.14 per 1000. The occurrence of impairments differed between CP subtypes. Motor and intellectual impairment were closely related, regardless of subtype. Subtype distribution among survivors differed significantly from the original cohorts (p=0.002), and the most recent cohort (p<0.01), tetraplegia and dyskinetic CP being less common in survivors. Severe motor impairment, intellectual disability, and epilepsy were less common among survivors than in the original cohorts (p=0.004, p=0.002, p=0.037) and the most recent cohort (p=0.004, p=0.008, p<0.01). Data on prevalence, subtype distribution, and impairments in children with CP are not applicable to adults with CP. Population-based studies of adults with CP are needed. Cerebral palsy (CP) subtypes are differently distributed in adults compared to children. The prevalence of impairments in adults with CP is related to CP subtype. Spastic tetraplegia and dyskinetic CP are less common in adults than children. Severe motor impairment, intellectual disability, and epilepsy are less common in adults.

Background: Wearable sensors are increasingly used to quantify the frequency and magnitude of head impact events in multiple sports. There is a paucity of evidence that verifies head impact events recorded by wearable sensors. Purpose: To utilize video analysis to verify head impact events recorded by wearable sensors and describe the respective frequency and magnitude. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: Thirty male (mean age, 16.6 ± 1.2 years; mean height, 1.77 ± 0.06 m; mean weight, 73.4 ± 12.2 kg) and 35 female (mean age, 16.2 ± 1.3 years; mean height, 1.66 ± 0.05 m; mean weight, 61.2 ± 6.4 kg) players volunteered to participate in this study during the 2014 and 2015 lacrosse seasons. Participants were instrumented with GForceTracker (GFT; boys) and X-Patch sensors (girls). Simultaneous game video was recorded by a trained videographer using a single camera located at the highest midfield location. One-third of the field was framed and panned to follow the ball during games. Videographic and accelerometer data were time synchronized. Head impact counts were compared with video recordings and were deemed valid if (1) the linear acceleration was ≥20g, (2) the player was identified on the field, (3) the player was in camera view, and (4) the head impact mechanism could be clearly identified. Descriptive statistics of peak linear acceleration (PLA) and peak rotational velocity (PRV) for all verified head impacts ≥20g were calculated. Results: For the boys, a total recorded 1063 impacts (2014: n = 545; 2015: n = 518) were logged by the GFT between game start and end times (mean PLA, 46 ± 31g; mean PRV, 1093 ± 661 deg/s) during 368 player-games. Of these impacts, 690 were verified via video analysis (65%; mean PLA, 48 ± 34g; mean PRV, 1242 ± 617 deg/s). The X-Patch sensors, worn by the girls, recorded a total 180 impacts during the course of the games, and 58 (2014: n = 33; 2015: n = 25) were verified via video analysis (32%; mean PLA, 39 ± 21g; mean PRV, 1664 ± 619 rad/s). Conclusion: The current data indicate that existing wearable sensor technologies may substantially overestimate head impact events. Further, while the wearable sensors always estimated a head impact location, only 48% of the impacts were a result of direct contact to the head as characterized on video. Using wearable sensors and video to verify head impacts may decrease the inclusion of false-positive impacts during game activity in the analysis.

Autism spectrum disorder (ASD) is a developmental disability that can cause significant social, communication, and behavioral challenges. People with ASD often have problems with social, emotional, and communication skills. In addition, various challenging behavior events are a problem at the community and family levels, making life difficult for autistic people and their environment. The causes of these events can be many and depend on the person and their current condition. This includes unexpected changes in the environment of the person or sensory stimuli that are not even noticeable to typical people - but at least they are not disturbing. Monitoring the physiological changes associated with negative emotions in people with ASD can support caregivers by giving them insights into the internal emotional changes in a real-time fashion. It makes it possible to act as early as possible to prevent the formulation of behavioral problems. With the development of modern personal Information and Communication Technology (ICT) devices (e.g., smartphones, smartwatches, smart bands), wearable sensors have become ubiquitous, and their usage has become part of our everyday lives. This paper provides a protocol for a scoping review of the literature, focusing on the performance of wearable physiological sensors and associated algorithms used for detecting or predicting challenging behavior events in persons with ASD.

Over the past decades, wearable and implantable devices have demonstrated great potential for a wide range of personalized health monitoring and therapeutic applications. This special issue primarily focuses on functional and electronic materials, sensors technologies and capabilities, and the associated energy solutions for wearable and implantable devices toward healthcare applications. We have collected 17 reviews, four research articles, and one perspective, all of which are within the scope of this area and cover the topics in breadth and depth.

Learning environments such as classrooms and online systems for students with developmental or intellectual disabilities are typically dynamic, multisensory, and make use of top-down attention and working memory mechanisms to promote sense making by the student. However, the last five years have ushered a revolution in computational power, brain mapping, wearable sensors use, large scale data collection, generative artificial intelligence, and physiological signal processing techniques e.g., the 4th industrial revolution. Owing to the advent of inexpensive and highly accurate sensor technologies, generative artificial intelligence, and neurotechnologies, educators now have a new way to assess every student’s learning status, cognitive states, and promote adaption in a multi-modal and multi-dimensional way in real-time. Process data from sensors and neurotechnologies can be available for use by educators within milliseconds as opposed to minutes, hours, or days, as is the case for traditional educational data. Data from artificially intelligent systems can tracks students’ learning progressions using sensor-based data so that content adjustments and differentiation of instruction can meet a student’s needs in real-time. Keywords: Artificial Intelligence, Online Learning, Wearable Sensors, Special Education, Adaptive Learning.

Biomarkers are crucial biological indicators in medical diagnostics and therapy. However, the process of biomarker discovery and validation is hindered by a lack of standardized protocols for analytical studies, storage and sample collection. Wearable chemical sensors provide a real-time, non-invasive alternative to typical laboratory blood analysis, and are an effective tool for exploring novel biomarkers in alternative body fluids, such as sweat, saliva, tears and interstitial fluid. These devices may enable remote at-home personalized health monitoring and substantially reduce the healthcare costs. This Review introduces criteria, strategies and technologies involved in biomarker discovery using wearable chemical sensors. Electrochemical and optical detection techniques are discussed, along with the materials and system-level considerations for wearable chemical sensors. Lastly, this Review describes how the large sets of temporal data collected by wearable sensors, coupled with modern data analysis approaches, would open the door for discovering new biomarkers towards precision medicine.

Educators strive to provide services to many complex problems in our schools. Identifying assessment methodologies that serve multiple outcomes and assist with a variety of educational decisions is paramount. Functional Analysis (FA) procedures have greatly advanced our understanding of how to change problem behaviors. Such assessment procedures can be especially useful in the classroom because they focus on the environmental etiology rather than the topography of problem behaviors as a basis for the selection of treatment procedures (Mace & Roberts, 1993). Thus, FA procedures can be used to identify the function of the problem behavior (e.g., escape-motivated or attention-seeking) by examining events related to its occurrence. This information can then be used to develop a positive behavioral intervention plan that directly addresses the identified function of the behavior. (Nelson, Roberts, & Smith, 1998). ********** In general, any behavior may be maintained by positive or negative reinforcement. A FA can help determine whether a problem behavior is maintained by positive or negative reinforcement. Often problem behaviors are maintained by negative reinforcement or escape from or avoidance of aversive stimuli, which in turn increases the amount of the problem behavior. Likewise, problem behavior can also increase because it is maintained by positive reinforcement contingent upon the occurrence of the problem behavior (e.g., attention). A FA requires the direct experimental manipulation of key environmental variables hypothesized to be associated with the function of the problem behavior in order to make causal rather than descriptive statements about the function of behavior (Homer, 1994; Mace, Lalli, & Lalli 1991; Touchette, MacDonald & Langer, 1985). Despite the wealth of knowledge generated from FA research, relatively few studies have empirically demonstrated the influence of antecedent events on problematic behavior (Michael, 1982, 1993). Iwata (1994) proposed two factors that may account for the limited exploration of antecedent influences on behavior. First is the inability to describe the effects of setting in terms of behavioral mechanism. The second factor is the absence of a methodology to establish the functional relation between setting events and behavior while ruling out other potential sources of influence. Within in this context, this paper will discuss a methodology to examine the relationship between academic variables and behavior. Specifically, a FA methodology that uses Curriculum-based Assessment (CBA) to identify antecedent events that occasion classroom behaviors will be described. The Relationship between Academic Difficulty and Off-Task Behavior One of the most common reasons for referral to school support personnel is off-task behavior--students who are inattentive, distractible, and/or fail to complete assignments. Many educators believe there is a collateral relationship between the difficulty level of academic tasks and classroom behavior. More recently, researchers have been expanding FA procedures so that the relationship between academic and social behaviors in the classroom setting can be empirically examined (Lee, Sugai, & Homer, 1999; Roberts, 2001; Roberts, Marshall, Nelson & Albers, 2001). These efforts have demonstrated that the difficulty of academic materials may increase escape and/or avoidance responses of students. For example if the function of behavior were escape from a difficult task, it would be possible to conduct an FA by systematically manipulating the presentation of easy and difficult academic tasks. Conversely, if the difficulty of the academic task is reduced, one should observe a reduction in the rate or percentage of problem behaviors. Until recently, this hypothesis was not empirically validated within regular classrooms in part because a methodology for examining this relationship was not established. …

Predicting bull behavior events in a multiple-sire pasture with video analysis, accelerometers, and classification algorithms

SummaryBackgroundBreastfeeding provides infants with nutrients required for optimal growth and development. We aimed to examine breastfeeding practices and supports that promote exclusive breastfeeding during the birth hospital stay among birthing parents with physical disabilities, sensory disabilities, intellectual or developmental disabilities, and multiple disabilities compared with those without a disability.MethodsThis population-based cohort study was done in Ontario, Canada. We accessed and analysed health administrative data from ICES and the Better Outcomes Registry & Network. We included all birthing parents aged 15–49 years who had a singleton livebirth between April 1, 2012, and March 31, 2018. The study outcomes were breastfeeding practices and supports that promoted exclusive breastfeeding during the birth hospital stay, conceptualised based on WHO–UNICEF Baby Friendly Hospital Initiative guidelines. Individuals with a physical disability, sensory disability, intellectual or developmental disability, or two or more (multiple) disabilities, identified using diagnostic algorithms, were compared with individuals without disabilities on the opportunity to initiate breastfeeding, in-hospital breastfeeding, exclusive breastfeeding at hospital discharge, skin-to-skin contact, and provision of breastfeeding assistance. Relative risks (RRs) were estimated using modified Poisson regression.FindingsOur cohort included 634 111 birthing parents, of whom 54 476 (8·6%) had a physical disability, 19 227 (3·0%) had a sensory disability, 1048 (0·2%) had an intellectual or developmental disability, 4050 (0·6%) had multiple disabilities, and 555 310 (87·6%) had no disability. Individuals with intellectual or developmental disabilities were less likely than those without a disability to have an opportunity to initiate breastfeeding (adjusted RR 0·82, 95% CI 0·76–0·88), any in-hospital breastfeeding (0·85, 0·81–0·88), exclusive breastfeeding at hospital discharge (0·73, 0·67–0·79), skin-to-skin contact (0·90, 0·87–0·94), and breastfeeding assistance (0·85, 0·79–0·91). Those with multiple disabilities were less likely to have an opportunity to initiate breastfeeding (0·93, 0·91–0·96), any in-hospital breastfeeding (0·93, 0·92–0·95), exclusive breastfeeding at hospital discharge (0·90, 0·87–0·93), skin-to-skin contact (0·93, 0·91–0·95), and breastfeeding assistance (0·95, 0·92–0·98). Differences for individuals with a physical or sensory disability only were mostly non-significant.InterpretationOur findings show disparities in breastfeeding outcomes between individuals without a disability and individuals with intellectual or developmental disabilities or multiple disabilities, but not individuals with physical or sensory disabilities. There is a need for further research on the factors that contribute to breastfeeding intentions, practices, and supports in people with intellectual or developmental disabilities and multiple disabilities, especially factors that affect breastfeeding decision making.

BackgroundHospital stays after major surgery are shorter than ever before. Although enhanced recovery and early discharge have many benefits, some complications will now first manifest themselves in home settings. Remote patient monitoring with wearable sensors in the first days after hospital discharge may capture clinical deterioration earlier but is largely uncharted territory.ObjectiveThis study aimed to assess the technical feasibility of patients, discharged after esophagectomy, being remotely monitored at home with a wireless patch sensor and the experiences of these patients. In addition, we determined whether observing vital signs with a wireless patch sensor influences clinical decision making.MethodsIn an observational feasibility study, vital signs of patients were monitored with a wearable patch sensor (VitalPatch, VitalConnect Inc) during the first 7 days at home after esophagectomy and discharge from hospital. Vital signs trends were shared with the surgical team once a day, and they were asked to check the patient’s condition by phone each morning. Patient experiences were evaluated with a questionnaire, and technical feasibility was analyzed on a daily basis as the percentage of data loss and gap durations. In addition, the number of patients for whom a change in clinical decision was made based on the results of remote vital signs monitoring at home was assessed.ResultsPatients (N=20) completed 7 days each of home monitoring with the wearable patch sensor. Each of the patients had good recovery at home, and remotely observed vital signs trends did not alter clinical decision making. Patients appreciated that surgeons checked their vital signs daily (mean 4.4/5) and were happy to be called by the surgical team each day (mean 4.5/5). Wearability of the patch was high (mean 4.4/5), and no reports of skin irritation were mentioned. Overall data loss of vital signs measurements at home was 25%; both data loss and gap duration varied considerably among patients.ConclusionsRemote monitoring of vital signs combined with telephone support from the surgical team was feasible and well perceived by all patients. Future studies need to evaluate the impact of home monitoring on patient outcome as well as the cost-effectiveness of this new approach.

The concern that most people with intellectual and developmental disabilities (IDD) are "invisible" in health surveillance has been the focus of attention for at least two decades since the publication of the Surgeon General's Closing the Gap report (Office of the Surgeon General, 2002). Surveillance refers to systematic and repeated collection, analysis, and interpretation of health related data to inform planning, implementation, and evaluation of public health practices. This concern of "invisibility" has been exacerbated by recent changes in two U.S. surveillance systems, the National Health Interview Survey (NHIS) and the Survey of Income and Program Participation (SIPP) that no longer contain questions to allow monitoring of the health of this population. "From invisible to visible to valued"—this special issue is intended to increase knowledge of researchers, policy makers, program planners, and advocates on health surveillance of people with IDD, bringing forward directions to improve the health and well-being of this population.The invited papers in this issue present ongoing efforts in the U.S. that are informed by work from other countries to improve U.S. health surveillance that, in turn, can inform policy and programs for this population. In addition to the authors, we greatly value the reviewers who generously shared their diverse expertise in strengthening the papers. The reviewers include: Coleen Boyle (National Center on Birth Defects and Developmental Disabilities [NCBDDD]), Michael H. Fox (formerly with NCBDDD), Adriane Griffen (Association of University Centers on Disabilities), Jennifer Johnson (Administration for Community Living), Donald Lollar (University of Kentucky), Margaret Nygren (American Association on Intellectual and Developmental Disabilities), David O'Hara (Westchester Institute for Human Development), Karrie Shogren (University of Kansas), and Sue Swenson (Inclusion International). Their insightful reviews enhanced the knowledge communicated by each paper in this issue. As guest editors of this special issue, we discerned several important themes emphasized throughout the papers.Havercamp & Krahn (2019) summarize the current U.S. context, identifying three foundational issues for understanding the current data conundrum on health for this population. They note the dramatic increases in community living that came about through advocacy for greater autonomy, advances in knowledge, and changing societal views on disability. These changes were reflected and advanced through a sequence of legislation. There was no corresponding process, however, for monitoring the health of the increasing number of adults with IDD who were living in their communities. The authors review models of disability that have resulted in different approaches to measurement, notably those using identification by diagnoses vs. ones based on functional limitations. Finally, they raise the "denominator" issue, and the difficulties of understanding health of adults with IDD when data are based only on those receiving developmental disability services.Krahn (2019) calls for better data to inform federal agencies' policies and programs to improve health of people with IDD. In an era of data-driven decision-making, better data are essential for fiscal projections, planning, and evaluation of programs and policies. Despite this need, the recent development of standards for disability identification in national surveillance (U.S. Department of Health & Human Services [HHS], 2011) does not allow for the identification of people with IDD, making it impossible to ascertain data specific to the IDD population. Krahn (2019) introduces the sampling issue that plagues research in this field, differentiating the "served" from the "unserved" population, with estimates that only about one-fifth of adults with IDD are known to the developmental disabilities services system in their states. She concludes by calling for improved national health surveillance that utilizes different data types while continuously asking "who is missing from this sample?" and "what implications does that have?"Efforts to improve IDD health data have benefited from ongoing collaborations. Earlier work to promote improved data was highly collaborative across the Centers for Disease Control and Prevention (CDC) and the Administration on Intellectual and Developmental Disabilities (AIDD) at the HHS, and the then National Institute on Disability and Rehabilitation Research (NIDRR) at the U.S. Department of Education (ED) (see Fox, Bonardi & Krahn, 2015; Krahn, Fox, Campbell, Ramon, & Jesien, 2010). These collaborations have expanded recently to include other agencies within HHS, including the National Center for Health Statistics (NCHS) and the Centers for Medicare and Medicaid (CMS). Havercamp and colleagues (2019) summarize the work of a national collaborative work group hosted by the Administration for Community Living that included members across a number of HHS agencies, university researchers, and national advocates (Havercamp et al., 2019). Based on an established clinical definition for intellectual disability (ID), and the definition for developmental disabilities (DD) from the Developmental Disabilities Assistance and Bill of Rights Act of 2000 (DD Act), the paper identifies priority constructs that need to be added to the National Health Interview Survey (NHIS) question set—specifically learning, independent living, and age at onset—to identify respondents with ID and DD. This core question set is intended to be a standard for use in other surveys.Havercamp and Krahn (2019) note the changing ways of looking at disability over time and the different definitions of "developmental disabilities" that are currently used. These differences are largely responsible for the variance in IDD prevalence rates reported in the literature (see Anderson, Larson, MapelLentz & Hall-Lande, 2019). Definitions differ along a number of important dimensions. First, whether the definition is based on diagnostic categories (e.g., cerebral palsy, ID, autism) or on functional limitation (e.g., limitations in mobility, thinking or remembering). A second distinction is severity level—whether the limitations are significant, or whether no mention is made of severity. Finally, the population and sampling frame determines who will have opportunity to be included in the sample and, correspondingly, to which populations the findings apply. Several papers recognize the differences between people "served" by the DD systems compared with those "unserved." Additional distinctions are beginning to be explored, such as, who is included and excluded from the Medicare system, the Medicaid systems, and private health insurer systems for interpreting findings. These definitional and sampling differences all contribute to different findings on prevalence and health status of adults with IDD.Operational definitions of ID and DD are an important consideration across the subsequent papers. Many of the authors wrestled with the differing definitions used by the programs or datasets they were drawing upon. This journal issue brings attention to these differences in operational definition without trying to bring them into a single, unifying definition. In 2003, Fujiura and Taylor noted this predicament of different operational definitions of ID and cautioned against striving for completely accurate measurement. The AAIDD definition of ID (Schalock, Borthwick-Duffy, Bradley, Buntinx, Coulter, Craig, Gomez, Lachappelle, Luckasson, Reeve, Shogren, Snell, Spreat, Tassé, Thomson, Verdugo-Alonso, Wehmeyer, & Yeager, 2010) is a commonly accepted clinical definition that requires deficits in intellectual functioning plus two areas of adaptive behavior that manifest during the developmental period. The DD Act defines DD as substantial functional limitations in at least three of seven major life activities. This results in a significant portion of people with ID not meeting criteria for DD as defined by the DD Act. Diagnostic approaches to define IDD, on the other hand, are categorical (e.g., cerebral palsy, Down syndrome, ID) and typically do not take severity of the condition into account. The state databases for DD services, Medicaid, and ID/DD registries use their own unique operational definitions to identify individuals with IDD.Havercamp and colleagues (2019) summarize the issues considered in recommending content domains for a standard set of survey items to identify adults with IDD in national surveys. They note that a significant challenge in measuring IDD is distinguishing the ability to learn or exhibit a skill from the opportunity to learn and exhibit that skill. This confounding of concepts is nowhere as present as in the inter-related concepts of "self-direction" and "self-determination."Bonardi and colleagues (2019) recognize the differences in definitions within and between states. They recommend greater consistency in how people with IDD are identified, calling on policy makers to promote greater consistency in definition that are informed by statutes applicable to all states (such as the DD Act). They further call on researchers to develop standardized methods for identifying people with IDD in large datasets, citing several recent examples of such methods.Balogh and colleagues (2019) describe examples from Canada and Australia in developing data linkage capacity across multiple administrative data sources as an ongoing resource in data rich environments. These methods allow researchers to extract data across databases while ensuring individuals' privacy. By establishing IDD identifiers, they are able to address a broad range of research questions about health of people with IDD through such data linkage systems. This approach has contributed to a better understanding of the prevalence of IDD, sociodemographic correlates of IDD, higher rates of chronic health conditions, much higher rates of hospitalizations for ambulatory care sensitive conditions, disparities in cancer screenings, and much higher rates of mental health conditions in both children and adults with IDD. Findings that emerge across the two countries include the higher prevalence rates from Australia where a birth registry is used, compared with data based on the served populations as occurs within the Manitoba and Ontario data linkage systems. Importantly, in both Canada and Australia, these findings are highly influential in policy and program planning. Both examples illustrate the importance of visionary leaders in establishing the data-linkage capability, observed growth and expansion over time, the need for ongoing support and funding, and the value of including persons with IDD in helping to realize the potential of data linkage.While health data for persons with IDD may be sparse, greater utilization of data that are currently available is needed. The relative dearth of information on health of persons with IDD will only be improved if health data are improved, and if there is expanded capacity among researchers to analyze data in ways that inform policy-makers to support data-informed decision-making. Current analytic expertise is concentrated among a relatively small group of researchers and centers. As interest and understanding of health determinants for people with IDD grows among policy makers, more extensive and more distributed analysis expertise is needed. For example, "super users" are analysts at the state or national level who can combine data sets across agencies and use sophisticated modeling techniques to determine how best to interpret the data.Additionally, tutorials or learning collaboratives on IDD data analyses, could support analysts to increase their own skills in a peer-learning format. For example, such a method implemented across the network of University Centers for Excellence in Developmental Disabilities (DD Act; P.L. 106–402; see https://www.aucd.org/template/index.cfm) could build interconnected capacity across the country.Health services research methods are beginning to be applied to administrative data for persons with IDD. Through a CDC-sponsored initiative, researchers are analyzing Medicaid data to gain insights into health of enrolled adults with IDD (e.g., McDermott et al., 2018; McDermott, Royer, Mann, & Armour, 2017). In this issue, Reichard, Haile, and Morris (2019) use Medicare data from enrollees who are dually eligible for Medicare and Medicaid. They document health conditions and health care utilization of Medicare Fee-for-Service beneficiaries with IDD identified through ICD 9/10 codes and compare them with beneficiaries without IDD for calendar year 2016. We believe this analysis of almost 31 million beneficiaries, with 1.56% having IDD, to be the first publication on this population through this data source. These data document substantially higher rates of having one (73%) or multiple (30.5% with 3 or more) chronic physical conditions for persons with IDD, aligning with previous survey research findings of greatly increased risk for select chronic conditions (Reichard & Stolzle, 2011; Dixon-Ibarra & Horner-Johnson, 2014). Current Medicare analyses indicate dramatically high rates of mental health conditions such as psychotic disorders (20.4%), major depressive affective disorder (28.9%), and anxiety disorder (31.5%). In concordance with analyses of Medicaid data (McDermott, et al., 2018), the authors note substantial variability across states in IDD diagnoses, likely reflecting differences in eligibility requirements for services and variability in recording of IDD codes across states and systems, raising questions of comparability and generalizability of findings across states. Undoubtedly, these issues will be explored in the near future as we anticipate increasing use of Medicaid and Medicare data sets to understand the health of the IDD population.Havercamp and colleagues (2019) and Krahn (2019) call for more attention to race/ethnicity and to data collection in the U.S. territories for a better estimate of national prevalence and more information on opportunities to promote health equity. Yet, efforts to understand how the health care barriers faced by people with disabilities are compounded by race or ethnicity have been slow to emerge and are not conducted routinely. A scoping review in 2014 found only 1 among 73 published studies where the researchers specifically framed the study design to examine barriers to health care access for people with disabilities who are also members of underserved racial or ethnic groups disability (Peterson-Besse, Walsh, Horner-Johnson, Goode, & Wheeler, 2014). Nine additional studies had other stated purposes, but included data on health care access barriers at the intersection of race/ethnicity. Recently, the National Academies of Sciences, Engineering and Medicine commissioned a paper to identify key issues in the compounding effects on health disparities at the intersection of disability and race and ethnicity (Yee et al., 2017).In their systematic review of 13 prevalence studies, Anderson and colleagues (2019) note that studies on children with IDD have disaggregated data by race, ethnicity, or other social factors; but no studies on prevalence of IDD in adults reported race and ethnicity data. Similarly, Bonardi and colleagues (2019) identified few administrative datasets that allowed disaggregation by race or ethnicity. Race and ethnicity are critically important in understanding health of persons with IDD as illustrated by Reichard and colleagues (2019) who found striking disparities across racial and ethnic groups. Wagner and colleagues (2019) remind us that innovative health promoting technologies are not equally available to groups marginalized by race, ethnicity or poverty (Wagner, Kim, & Tassé, 2019). The demographic characteristics of race and ethnicity need to be included in data collection and analyses as routinely as age and sex are now included.Collaborations across policy makers, advocates, and researchers have produced many of the advances in health surveillance of people with IDD—from the Surgeon General's report of 2002, to the initial CDC-led initiative (from 2009 to the present), to the current ACL-led summit and workgroups (from 2016-present).The two workgroup papers illustrate the value of close relationships for identifying the most pressing current problems and the possible directions for solutions. Havercamp and colleagues (2019) describe the thoughtful process for determining the core domains to assess in order to identify people with IDD in national surveys. Bonardi and colleagues (2019) highlight efforts to identify people with IDD in administrative databases across single states, territories, and multiple states, capturing rich information on health care and service utilization. They describe new developments in accessing Medicare data, and efforts to harmonize data sources available through the All Payer Claims Databases. Survey data such as the National Core Indicators and other state systems provide opportunities to build a richer picture of the health of people with IDD in each state or region.In glimpsing the future for IDD and health surveillance, Wagner and colleagues (2019) summarize some of the as-yet unrealized promises and the all-too-realized perils of electronic health records (EHR) in providing data aggregation across populations. Technology advances in other segments, like precision medicine, may promote greater inter-operability across EHR systems for persons with IDD. Their overview of wearable technologies and use of 'smart home' technologies indicates the possibility of technology monitoring that simultaneously increases personal autonomy while also promoting the health and safety of adults with IDD. These are especially promising directions given the current and predicted shortage of direct support staff.The papers in this special issue highlight the importance of improved health surveillance of people with IDD. In the two decades since the Surgeon General's call for better data (Office of the Surgeon General, 2002), we have learned a great deal about how to measure and what to measure to understand and improve the health of people with IDD. This slow but persistent progress is a testament to the leadership and collaboration across federal agencies, purposeful advocacy, and the ongoing support for targeted research undertaken by an expanding corps of committed and talented social scientists trained in the latest statistical and epidemiological methods and policy analysis. As guest editors of this special issue, we are grateful to the authors and reviewers who generously contributed their time and expertise. With them, we look forward to progress in the coming decades, that will result in people with IDD becoming fully visible and valued, their place in health data programs and policies universally recognized and secure.

The emergence and improvement of wearable low cost sensors gives way to an improved patient rehabilitation from impairment such as those caused by accidents or stroke. We have developed a digital motion sensing system that uses accelerometers and gyroscopes worn by the patient, and enables the rehab session to be recorded visually using a high-FPS camera. Through collaboration with interns and licensed physical therapists (PTs), we conducted tests using these wearable sensors known as the inertial measurement unit (IMU) side-by-side their norm of using a static universal goniometer (UG). An Android-based portable system was demonstrated that could be used by PTs on the move. We explored the variations of the normal range of motion (ROM) in healthy uninjured individuals, following that the functional ROM is less stringent than the conventional criteria. We can show that using the system the patient can participate in developing their digital medical records, as well as in their own wellness program

Wearable inertial sensors have gradually been used as an objective technology for biomechanical assessments of both healthy and pathological movement patterns. This paper used foot-worn sensors for characterizing the spatiotemporal characteristics of walking and turning between older fallers and nonfallers. Thirty community-dwelling older fallers and 30 older nonfallers performed 10-m straight walking, turned 180° around a cone, and then walked 10-m back to the starting point. Specific algorithms were used to measure spatiotemporal gait (double support phase of the gait cycle, swing width, and minimal toe clearance) and turning parameters (turn duration and turn steps) using two foot-worn Physiolog inertial sensor system. The researchers directly exported data as reported by the system. Our findings indicated that older fallers showed 26.58% longer time (P = 0.036) and 13.21% more steps (P = 0.038) compared to nonfallers during turning. However, both groups decreased their walking velocity (both P < 0.001), increased double support (both P = 0.001), and increased the swing width (both P = 0.001) during the transition from walking to turning. The older nonfallers additionally increased toe clearance (P = 0.001). Compared with the fallers, the older nonfallers showed a larger change in the swing width (P = 0.025) and toe clearance (P = 0.025) in walking to turning. Older fallers may adopt a cautionary strategy while turning to reduce the risk of falls. Wearable sensors can provide the temporospatial characteristics of turning and reveal significant differences by fall status, indicating the potential of turning measures as possible markers for identifying those at fall risk.

Individuals with autism spectrum disorder (ASD) are at a greater risk for challenging behavior than individuals with other developmental disabilities. An essential step in the treatment of these behaviors is the identification of the function of the behavior. In the current study, data were collected from a large database, in which supervising clinicians from a community-based behavioral health agency recorded the topography and function(s) of behaviors treated as a part of an individual’s behavior intervention plan. In a sample of 3216 individuals (mean age = 10.67, SD = 4.61) with ASD, the frequency of the most common challenging behaviors and the identified function of the behavior were examined. Stereotypy was the most commonly reported topography, followed by noncompliance and aggression. Overall, escape was the most commonly reported function of behavior. To further evaluate how clinicians operationally define these behaviors, a part-of-speech text analysis was conducted and found a high degree of overlap in the operational definitions of challenging behavior (i.e., aggression, disruption and tantrum; noncompliance and tantrum; obsessive behavior and stereotypy; self-injurious behavior and aggression). These data are discussed in further detail.

BackgroundAlthough it is well-established that osteoarthritis (OA) impairs daily-life gait, objective gait assessments are not part of routine clinical evaluation. Wearable inertial sensors provide an easily accessible and fast way to routinely evaluate gait quality in clinical settings. However, during these assessments, more complex and meaningful aspects of daily-life gait, including turning, dual-task performance, and upper body motion, are often overlooked. The aim of this study was therefore to investigate turning, dual-task performance, and upper body motion in individuals with knee or hip OA in addition to more commonly assessed spatiotemporal gait parameters using wearable sensors.MethodsGait was compared between individuals with unilateral knee (n = 25) or hip OA (n = 26) scheduled for joint replacement, and healthy controls (n = 27). For 2 min, participants walked back and forth along a 6-m trajectory making 180° turns, with and without a secondary cognitive task. Gait parameters were collected using 4 inertial measurement units on the feet and trunk. To test if dual-task gait, turning, and upper body motion had added value above spatiotemporal parameters, a factor analysis was conducted. Effect sizes were computed as standardized mean difference between OA groups and healthy controls to identify parameters from these gait domains that were sensitive to knee or hip OA.ResultsFour independent domains of gait were obtained: speed-spatial, speed-temporal, dual-task cost, and upper body motion. Turning parameters constituted a gait domain together with cadence. From the domains that were obtained, stride length (speed-spatial) and cadence (speed-temporal) had the strongest effect sizes for both knee and hip OA. Upper body motion (lumbar sagittal range of motion), showed a strong effect size when comparing hip OA with healthy controls. Parameters reflecting dual-task cost were not sensitive to knee or hip OA.ConclusionsBesides more commonly reported spatiotemporal parameters, only upper body motion provided non-redundant and sensitive parameters representing gait adaptations in individuals with hip OA. Turning parameters were sensitive to knee and hip OA, but were not independent from speed-related gait parameters. Dual-task parameters had limited additional value for evaluating gait in knee and hip OA, although dual-task cost constituted a separate gait domain. Future steps should include testing responsiveness of these gait domains to interventions aiming to improve mobility.