Exploratory study examining the at-home feasibility of a wearable tool for social-affective learning in children with autism.
Ontology highlight
ABSTRACT:
SUBMITTER: Daniels J
PROVIDER: S-EPMC6550272 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
Publications
Exploratory study examining the at-home feasibility of a wearable tool for social-affective learning in children with autism.
Daniels Jena J Schwartz Jessey N JN Voss Catalin C Haber Nick N Fazel Azar A Kline Aaron A Washington Peter P Feinstein Carl C Winograd Terry T Wall Dennis P DP
NPJ digital medicine 20180802
Although standard behavioral interventions for autism spectrum disorder (ASD) are effective therapies for social deficits, they face criticism for being time-intensive and overdependent on specialists. Earlier starting age of therapy is a strong predictor of later success, but waitlists for therapies can be 18 months long. To address these complications, we developed Superpower Glass, a machine-learning-assisted software system that runs on Google Glass and an Android smartphone, designed for us ...[more]
Similar Datasets
Project description:Little is known about outcomes of early intervention for children with autism spectrum disorder reared in bilingual homes. There are concerns that social communication deficits among children with autism spectrum disorder may reduce the developmental benefits of early intervention for children with autism spectrum disorder raised in bilingual environments. We conducted an exploratory analysis of cross-sectional and longitudinal data from a larger study to explore associations between home language environment and language ability and social skills in response to early autism spectrum disorder intervention. Participants, aged 12-26 months when recruited, were a subset of a larger 2-year, randomized intervention trial (ClinicalTrials.gov identifier: NCT00698997). Children from bilingual homes ( n = 13) began intervention with lower gesture use but otherwise demonstrated equal baseline language and social abilities as compared with age and nonverbal IQ-matched children from monolingual homes ( n = 24). Significant language growth was exhibited by children from both language groups and there was no moderating effect of home language environment. The bilingual home group demonstrated increased gesture use over the course of intervention as compared with the monolingual home group. Preliminary data revealed no basis for concerns regarding negative impact of a bilingual home environment on language or social development in young children with autism spectrum disorder.
| S-EPMC5984072 | biostudies-literature
Project description:Many of the problems associated with alcohol occur after a single drinking event (e.g. drink driving, assault). These acute alcohol problems have a huge global impact and account for a large percentage of unintentional and intentional injuries in the world. Nonetheless, alcohol research and preventive interventions rarely focus on drinking at the event-level since drinking events are complex, dynamic, and methodologically challenging to observe. This exploratory study provides an example of how event-level data may be collected, analyzed, and interpreted. The drinking behavior of twenty undergraduate students enrolled at a large Midwestern public university was observed during a single bar crawl event that is organized by students annually. Alcohol use was monitored with transdermal alcohol devices coupled with ecological momentary assessments and geospatial data. "Small N, Big Data" studies have the potential to advance health behavior theory and to guide real-time interventions. However, such studies generate large amounts of within subject data that can be challenging to analyze and present. This study examined how to visually display event-level data and also explored the relationship between some basic indicators and alcohol consumption.
| S-EPMC5617212 | biostudies-literature
Project description:BackgroundDepression levels in adolescents have trended upward over the past several years. According to a 2020 survey by the National Survey on Drug Use and Health, 4.1 million US adolescents have experienced at least one major depressive episode. This number constitutes approximately 16% of adolescents aged 12 to 17 years. However, only 32.3% of adolescents received some form of specialized or nonspecialized treatment. Identifying worsening symptoms earlier using mobile and wearable sensors may lead to earlier intervention. Most studies on predicting depression using sensor-based data are geared toward the adult population. Very few studies look into predicting depression in adolescents.ObjectiveThe aim of our work was to study passively sensed data from adolescents with depression and investigate the predictive capabilities of 2 machine learning approaches to predict depression scores and change in depression levels in adolescents. This work also provided an in-depth analysis of sensor features that serve as key indicators of change in depressive symptoms and the effect of variation of data samples on model accuracy levels.MethodsThis study included 55 adolescents with symptoms of depression aged 12 to 17 years. Each participant was passively monitored through smartphone sensors and Fitbit wearable devices for 24 weeks. Passive sensors collected call, conversation, location, and heart rate information daily. Following data preprocessing, 67% (37/55) of the participants in the aggregated data set were analyzed. Weekly Patient Health Questionnaire-9 surveys answered by participants served as the ground truth. We applied regression-based approaches to predict the Patient Health Questionnaire-9 depression score and change in depression severity. These approaches were consolidated using universal and personalized modeling strategies. The universal strategies consisted of Leave One Participant Out and Leave Week X Out. The personalized strategy models were based on Accumulated Weeks and Leave One Week One User Instance Out. Linear and nonlinear machine learning algorithms were trained to model the data.ResultsWe observed that personalized approaches performed better on adolescent depression prediction compared with universal approaches. The best models were able to predict depression score and weekly change in depression level with root mean squared errors of 2.83 and 3.21, respectively, following the Accumulated Weeks personalized modeling strategy. Our feature importance investigation showed that the contribution of screen-, call-, and location-based features influenced optimal models and were predictive of adolescent depression.ConclusionsThis study provides insight into the feasibility of using passively sensed data for predicting adolescent depression. We demonstrated prediction capabilities in terms of depression score and change in depression level. The prediction results revealed that personalized models performed better on adolescents than universal approaches. Feature importance provided a better understanding of depression and sensor data. Our findings can help in the development of advanced adolescent depression predictions.
| S-EPMC9270714 | biostudies-literature
Project description:To assess the feasibility and relevance of using lymphoblastoid cell lines to study the role of noncoding RNAs in the etiology of autism, we evaluated global expression profiling of 470 mature human microRNAs from six subjects with autism compared with six matched controls. Differential expression (either higher or lower) for 9 of the 470 microRNAs was observed in our autism samples compared with controls. Potential target genes for these microRNAs were identified using computer tools, which included several autism susceptibility genes. Our preliminary results indicate microRNAs should be considered and evaluated in the etiology of autism. In addition, analysis of this class of noncoding RNAs in lymphoblastoid cells has the potential to reveal at least a subset of brain-related microRNAs implicated in autism. Subsequently, this model system should allow for detection of complex subtle changes in susceptibility genes/pathways contributing to autism.
| S-EPMC2768334 | biostudies-literature
Project description:Lay abstractBilingualism changes the way people relate to others. This is particularly interesting in the case of autism, where social interaction presents many challenges. A better understanding of the overlap between the social variations of bilingualism and autism could unveil new ways to support the social experiences of autistic people. This research aims to understand the language learning and social experiences of autistic people who speak one, two or more languages. A total of 297 autistic adults (aged between 16 and 80 years) completed an online questionnaire that included general demographic questions, social life quality self-rating questions, language history questions, and open questions about the respondents' bilingualism experience. Respondents had a wide range of language experiences: there were 89 monolingual English speakers, 98 bilinguals, 110 respondents knew three languages or more, all with a wide range of abilities in their languages. In the full group, younger respondents were more satisfied with their social life, and respondents with many languages were more satisfied with their social life than respondents with few languages. In the multilingual group, younger respondents were more satisfied with their social life, and the more skilled in their third language the more satisfied with their social life. This is the first study describing the language history and social experiences of a large group of bilingual and multilingual autistic adults. It highlights how autistic people can encounter a new language, learn it and use it in their daily life, and how their bilingualism experiences shape their social life.
| S-EPMC7549289 | biostudies-literature
Project description:IntroductionWearable sensors may be used for the assessment of behavioral manifestations of cigarette smoking under natural conditions. This paper introduces a new camera-based sensor system to monitor smoking behavior. The goals of this study were (1) identification of the best position of sensor placement on the body and (2) feasibility evaluation of the sensor as a free-living smoking-monitoring tool.MethodsA sensor system was developed with a 5MP camera that captured images every second for continuously up to 26 hours. Five on-body locations were tested for the selection of sensor placement. A feasibility study was then performed on 10 smokers to monitor full-day smoking under free-living conditions. Captured images were manually annotated to obtain behavioral metrics of smoking including smoking frequency, smoking environment, and puffs per cigarette. The smoking environment and puff counts captured by the camera were compared with self-reported smoking.ResultsA camera located on the eyeglass temple produced the maximum number of images of smoking and the minimal number of blurry or overexposed images (53.9%, 4.19%, and 0.93% of total captured, respectively). During free-living conditions, 286,245 images were captured with a mean (±standard deviation) duration of sensor wear of 647(±74) minutes/participant. Image annotation identified consumption of 5(±2.3) cigarettes/participant, 3.1(±1.1) cigarettes/participant indoors, 1.9(±0.9) cigarettes/participant outdoors, and 9.02(±2.5) puffs/cigarette. Statistical tests found significant differences between manual annotations and self-reported smoking environment or puff counts.ConclusionsA wearable camera-based sensor may facilitate objective monitoring of cigarette smoking, categorization of smoking environments, and identification of behavioral metrics of smoking in free-living conditions.ImplicationsThe proposed camera-based sensor system can be employed to examine cigarette smoking under free-living conditions. Smokers may accept this unobtrusive sensor for extended wear, as the sensor would not restrict the natural pattern of smoking or daily activities, nor would it require any active participation from a person except wearing it. Critical metrics of smoking behavior, such as the smoking environment and puff counts obtained from this sensor, may generate important information for smoking interventions.
| S-EPMC7542642 | biostudies-literature
Project description:Camouflaging refers to behavioral adaptations that individuals with autism spectrum disorder (ASD), especially females, use to mask symptoms during social situations. Compensation is a component of camouflaging in which an individual's observed behavior is considerably better than actual ability. The study explored diagnostic, sex-based, and compensatory differences using the Contextual Assessment of Social Skills (CASS). The sample included 161 youth 10:0-to-16:11 years (115 males, 46 females). T-tests were performed based on sex (female, male) or High (good ADOS + poor Theory of Mind (TOM)) compared to Low (poor ADOS + poor TOM) Compensation groups. Comparisons were examined for Social Affect (SA), Restricted Repetitive Behavior, (RRB), IQ, social behavior (Positive Affect, Overall Involvement) and communication (Vocal Expression, Gestures). Females exhibited fewer RRB t(158) = 3.05, P = 0.003, d = 0.54. For the CASS, females evidenced more Vocal Expressiveness t(157) = -2.03, P = 0.05, d = 0.35, which corroborates sex-based differences in the literature. Compensation group differences indicated the High compared to Low group showed stronger Social and Communication behaviors on the CASS for Vocal Expression t(72) = 2.56, P = 0.01, d = 0.62, and overall rapport t(72) = 2.36, P = 0.02, d = 0.56. Several differences were observed when the groups were stratified based on level of compensation, with the High compensation participants showing stronger social engagement and communication behaviors. Findings may inform efforts to understand camouflaging, compensation, and clinical practices for male and female adolescents with ASD. A more nuanced consideration of camouflaging alongside compensation models reveals subtle differences in cognition, behavior, and affect that may reflect underlying profiles of challenge and strength in youth with ASD. LAY SUMMARY: Camouflaging refers to ways individuals with autism spectrum disorder (ASD), especially females, mask symptoms. Compensation occurs when a person's observed behavior appears more typical than what would be expected based on underlying ability and symptoms. The study explored camouflaging and compensation differences in 161 youth with ASD. Findings suggest sex-based differences with females showing better vocal expression. However, several compensation differences were observed with the High compensators showing stronger social communication and rapport. A more nuanced consideration of camouflaging using compensation models reveal subtle differences in underlying challenge and strength.
| S-EPMC7986572 | biostudies-literature
Project description:During the COVID-19 pandemic, students were facing great challenges. Learning was shifted from the classroom to the home of the students. This implied that students had to complete their tasks in a more autonomous way than during regular lessons. As students' ability to handle such challenges might depend on certain cognitive and motivational prerequisites as well as individual learning conditions, the present study investigates students' cognitive competencies as well as affective-motivational factors as possible predictors of coping with this new learning situation at home. The study uses data of Starting Cohort 2 of the German National Educational Panel Study (NEPS). Data of two measurement points are analyzed: Predictors were assessed at the earlier time point, when students (N=1,452; M age=12years, 8months; 53.4% female) mostly attended seventh grade of a secondary school. They completed competence tests in reading as well as mathematics and rated affective-motivational aspects in terms of willingness to exert effort, learning enjoyment, and intrinsic motivation. One and a half years later - during the COVID-19 pandemic and the first period of school closures - the second measurement point took place. Students' parents rated the situation of learning at home with respect to students' coping with the new situation and parents' difficulties to motivate them. Regression analyses controlling for school track, students' gender, and parents' educational level and parental stress revealed that students' reading competencies and their willingness to exert effort were significant predictors of their coping with the new learning situation at home. Moreover, parents reported that boys were more difficult to motivate to learn during this time as compared to girls. Other predictors (e.g., learning enjoyment) turned out to be non-significant when entered simultaneously in the regression analyses. The results point to the importance of children's prerequisites for autonomous learning situations without structuring elements by teachers within the school context.
| S-EPMC8692791 | biostudies-literature
Project description:Research has indicated strong relationships between learners' affect and their learning. Emotions relate closely to students' well-being, learning quality, productivity, and interaction. Digital game-based learning (DGBL) has been widely recognized to be effective in enhancing learning experiences and increasing student motivation. The field of emotions in DGBL has become an active research field with accumulated literature available, which calls for a comprehensive understanding of the up-to-date literature concerning emotions in virtual DGBL among students at all educational levels. Based on 393 research articles collected from the Web of Science, this study, for the first time, explores the current advances and topics in this field. Specifically, thematic evolution analysis is conducted to explore the evolution of topics that are categorized into four different groups (i.e., games, emotions, applications, and analytical technologies) in the corpus. Social network analysis explores the co-occurrences between topics to identify their relationships. Interesting results are obtained. For example, with the integration of diverse applications (e.g., mobiles) and analytical technologies (e.g., learning analytics and affective computing), increasing types of affective states, socio-emotional factors, and digital games are investigated. Additionally, implications for future research include 1) children's anxiety/attitude and engagement in collaborative gameplay, 2) individual personalities and characteristics for personalized support, 3) emotion dynamics, 4) multimodal data use, 5) game customization, 6) balance between learners' skill levels and game challenge as well as rewards and learning anxiety.
| S-EPMC8318230 | biostudies-literature
Project description:BackgroundSubclinical (ie, threshold) social anxiety can greatly affect young people's lives, but existing solutions appear inadequate considering its rising prevalence. Wearable sensors may provide a novel way to detect social anxiety and result in new opportunities for monitoring and treatment, which would be greatly beneficial for persons with social anxiety, society, and health care services. Nevertheless, indicators such as skin temperature measured by wrist-worn sensors have not been used in prior work on physiological social anxiety detection.ObjectiveThis study aimed to investigate whether subclinical social anxiety in young adults can be detected using physiological data obtained from wearable sensors, including heart rate, skin temperature, and electrodermal activity (EDA).MethodsYoung adults (N=12) with self-reported subclinical social anxiety (measured using the widely used self-reported version of the Liebowitz Social Anxiety Scale) participated in an impromptu speech task. Physiological data were collected using an E4 Empatica wearable device. Using the preprocessed data and following a supervised machine learning approach, various classification algorithms such as Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbours (KNN) were used to develop models for 3 different contexts. Models were trained to differentiate (1) between baseline and socially anxious states, (2) among baseline, anticipation anxiety, and reactive anxiety states, and (3) social anxiety among individuals with social anxiety of differing severity. The predictive capability of the singular modalities was also explored in each of the 3 supervised learning experiments. The generalizability of the developed models was evaluated using 10-fold cross-validation as a performance index.ResultsWith modalities combined, the developed models yielded accuracies between 97.54% and 99.48% when differentiating between baseline and socially anxious states. Models trained to differentiate among baseline, anticipation anxiety, and reactive anxiety states yielded accuracies between 95.18% and 98.10%. Furthermore, the models developed to differentiate between social anxiety experienced by individuals with anxiety of differing severity scores successfully classified with accuracies between 98.86% and 99.52%. Surprisingly, EDA was identified as the most effective singular modality when differentiating between baseline and social anxiety states, whereas ST was the most effective modality when differentiating anxiety among individuals with social anxiety of differing severity.ConclusionsThe results indicate that it is possible to accurately detect social anxiety as well as distinguish between levels of severity in young adults by leveraging physiological data collected from wearable sensors.
| S-EPMC8532020 | biostudies-literature