Dataset Information

Study protocol for an evaluation of ASDetect - a Mobile application for the early detection of autism.

ABSTRACT: BACKGROUND:Autism Spectrum Conditions (ASC) can be reliably diagnosed by 24?months of age. However, despite the well-known benefits of early intervention, there is still a research-practice gap in the timely identification of ASC, particularly in low-resourced settings. The Social Attention and Communication Surveillance (SACS) tool, which assesses behavioural markers of autism between 12 to 24?months of age, has been implemented in Maternal and Child Health (MCH) settings, with excellent psychometric properties. ASDetect is a free mobile application based on the SACS, which is designed to meet the need for an effective, evidence-based tool for parents, to learn about children's early social-communication development and assess their child's 'likelihood' for ASC. STUDY AIMS:The primary aim of this study is to evaluate the psychometric properties of ASDetect in the early detection of children with ASC. A secondary aim is to assess ASDetect's acceptability and parental user experience with the application. METHODS:Families are recruited to download the application and participate in the study via social media, health professionals (e.g., MCH nurses, paediatricians) and word of mouth. All participating caregivers complete a demographic questionnaire, survey regarding their user experience, and the Social Responsiveness Scale-2 (SRS-2), an autism screening questionnaire; they are also invited to participate in focus groups. Children identified at 'high likelihood' for ASC based on the ASDetect results, the SRS-2 or parental and/or professional concerns undergo a formal, gold-standard, diagnostic assessment. Receiver Operating Characteristic analyses will be used to assess psychometric properties of ASDetect. Thematic analyses will be used to explore themes arising in the focus groups to provide insights regarding user experiences with the app. Multiple regression analyses will be carried out to determine the extent to which demographic factors, parental stress and beliefs on health surveillance and child results on ASDetect are associated with the parental user-experience of the application. DISCUSSION:With a strong evidence-base and global access, ASDetect has the potential to empower parents by providing them with knowledge of their child's social-communication development, validating and reassuring any parental concerns, and supporting them in communicating with other health professionals, ultimately enhancing child and family outcomes and well-being.

SUBMITTER: Barbaro J

PROVIDER: S-EPMC6969425 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Study protocol for an evaluation of ASDetect - a Mobile application for the early detection of autism.

Barbaro Josephine J Yaari Maya M

BMC pediatrics 20200118 1

<h4>Background</h4>Autism Spectrum Conditions (ASC) can be reliably diagnosed by 24 months of age. However, despite the well-known benefits of early intervention, there is still a research-practice gap in the timely identification of ASC, particularly in low-resourced settings. The Social Attention and Communication Surveillance (SACS) tool, which assesses behavioural markers of autism between 12 to 24 months of age, has been implemented in Maternal and Child Health (MCH) settings, with excellen ...[more]

PMID: 31952489

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Project description:BackgroundDetection and management of neglected tropical diseases such as cutaneous leishmaniasis present unmet challenges stemming from their prevalence in remote, rural, resource constrained areas having limited access to health services. These challenges are frequently compounded by armed conflict or illicit extractive industries. The use of mobile health technologies has shown promise in such settings, yet data on outcomes in the field remain scarce.MethodsWe adapted a validated prediction rule for the presumptive diagnosis of CL to create a mobile application for use by community health volunteers. We used human-centered design practices and agile development for app iteration. We tested the application in three rural areas where cutaneous leishmaniasis is endemic and an urban setting where patients seek medical attention in the municipality of Tumaco, Colombia. The application was assessed for usability, sensitivity and inter-rater reliability (kappa) when used by community health volunteers (CHV), health workers and a general practitioner, study physician.ResultsThe application was readily used and understood. Among 122 screened cases with cutaneous ulcers, sensitivity to detect parasitologically proven CL was >95%. The proportion of participants with parasitologically confirmed CL was high (88%), precluding evaluation of specificity, and driving a high level of crude agreement between the app and parasitological diagnosis. The chance-adjusted agreement (kappa) varied across the components of the risk score. Time to diagnosis was reduced significantly, from 8 to 4 weeks on average when CHV conducted active case detection using the application, compared to passive case detection by health facility-based personnel.ConclusionsTranslating a validated prediction rule to a mHealth technology has shown the potential to improve the capacity of community health workers and healthcare personnel to provide opportune care, and access to health services for underserved populations. These findings support the use of mHealth tools for NTD research and healthcare.

Project description:BackgroundDespite the known benefits of early, specialized intervention for toddlers with Autism Spectrum Disorder (ASD), access to such intervention remains limited. This pragmatic trial examines a novel healthcare delivery model (Screen-Refer-Treat [SRT]), which capitalizes upon existing health care and early intervention (EI) infrastructure to increase community capacity for ASD detection and treatment before age 3, when it is likely to have the greatest impact. This model comprises three components: (1) universal use of Stage 1 ASD screening by primary care providers (PCPs) at 18-month well-child visits (i.e., Screen); (2) immediate referral of positive screens to a community-based EI program (i.e., Refer); and (3) provision of an inexpensive, evidence-based ASD-specialized treatment by EI providers, after verifying ASD risk with a Stage 2 screen (i.e., Treat). This paper describes our research design and the initial successes, challenges, and adaptations made during the early implementation phase.Method/designA stepped-wedge cluster RCT was used to implement the SRT model sequentially in four diverse Washington State counties ("clusters"). Counties are randomly assigned to the time of receipt of the SRT intervention, which comprises training workshops and technical assistance focused on the use of evidence-based ASD screening and intervention tools. Separate cohorts of families with toddlers (16-35 months old) with and without ASD concerns are recruited before and after the SRT intervention from participating PCP practices and EI programs. PCPs and EI providers complete measures on their screening, referral, and intervention practices before and after the SRT intervention. Each family cohort completes surveys about their well-being, parenting efficacy, health care satisfaction, and toddler's social-communicative behaviors.ConclusionThis trial is the first of its kind to work simultaneously with two service delivery systems with the goal of improving early detection and treatment for ASD. Our approach was successful in attaining buy-in from PCPs and EI providers, building and maintaining partnerships with providers, and achieving high levels of retention and survey completion. Fostering provider engagement and problem-solving issues together as partners were integral to overcoming the main challenges. Numerous lessons have been learned thus far, which have applicability for implementation researchers in ASD and those in other fields.Trial registrationThe registration number for this trial is NCT02409303 and it was posted on ClinicalTrials.gov on April 6, 2015.

Project description:BackgroundThe standard approaches to diagnosing autism spectrum disorder (ASD) evaluate between 20 and 100 behaviors and take several hours to complete. This has in part contributed to long wait times for a diagnosis and subsequent delays in access to therapy. We hypothesize that the use of machine learning analysis on home video can speed the diagnosis without compromising accuracy. We have analyzed item-level records from 2 standard diagnostic instruments to construct machine learning classifiers optimized for sparsity, interpretability, and accuracy. In the present study, we prospectively test whether the features from these optimized models can be extracted by blinded nonexpert raters from 3-minute home videos of children with and without ASD to arrive at a rapid and accurate machine learning autism classification.Methods and findingsWe created a mobile web portal for video raters to assess 30 behavioral features (e.g., eye contact, social smile) that are used by 8 independent machine learning models for identifying ASD, each with >94% accuracy in cross-validation testing and subsequent independent validation from previous work. We then collected 116 short home videos of children with autism (mean age = 4 years 10 months, SD = 2 years 3 months) and 46 videos of typically developing children (mean age = 2 years 11 months, SD = 1 year 2 months). Three raters blind to the diagnosis independently measured each of the 30 features from the 8 models, with a median time to completion of 4 minutes. Although several models (consisting of alternating decision trees, support vector machine [SVM], logistic regression (LR), radial kernel, and linear SVM) performed well, a sparse 5-feature LR classifier (LR5) yielded the highest accuracy (area under the curve [AUC]: 92% [95% CI 88%-97%]) across all ages tested. We used a prospectively collected independent validation set of 66 videos (33 ASD and 33 non-ASD) and 3 independent rater measurements to validate the outcome, achieving lower but comparable accuracy (AUC: 89% [95% CI 81%-95%]). Finally, we applied LR to the 162-video-feature matrix to construct an 8-feature model, which achieved 0.93 AUC (95% CI 0.90-0.97) on the held-out test set and 0.86 on the validation set of 66 videos. Validation on children with an existing diagnosis limited the ability to generalize the performance to undiagnosed populations.ConclusionsThese results support the hypothesis that feature tagging of home videos for machine learning classification of autism can yield accurate outcomes in short time frames, using mobile devices. Further work will be needed to confirm that this approach can accelerate autism diagnosis at scale.

Project description:BackgroundAccurate measurement of dietary intake is vital for providing nutrition interventions and understanding the complex role of diet in health. Traditional dietary assessment methods are very resource intensive and burdensome to participants. Technology may help mitigate these limitations and improve dietary data capture.ObjectiveOur objective was to evaluate the accuracy of a novel mobile application (PIQNIQ) in capturing dietary intake by self-report. Our secondary objective was to assess whether food capture using PIQNIQ was comparable with an interviewer-assisted 24-h recall (24HR).MethodsThis study was a single-center randomized clinical trial enrolling 132 adults aged 18 to 65 y from the general population. Under a provided-food protocol with 3 menus designed to include a variety of foods, participants were randomly assigned to 1 of 3 food capture methods: simultaneous entry using PIQNIQ, photo-assisted recall using PIQNIQ, and 24HR. Primary outcomes were energy and nutrient content (calories, total fat, carbohydrates, protein, added sugars, calcium, dietary fiber, folate, iron, magnesium, potassium, saturated fat, sodium, and vitamins A, C, D, and E) captured by the 3 methods.ResultsThe majority of nutrients reported were within 30% of consumed intake in all 3 food capture methods (n = 129 completers). Reported intake was highly (>30%) overestimated for added sugars in both PIQNIQ groups and underestimated for calcium in the photo-assisted recall group only (P < 0.001 for all). However, in general, both PIQNIQ methods had similar levels of accuracy and were comparable to the 24HR except in their overestimation (>30%) of added sugars and total fat (P < 0.001 for both).ConclusionsOur results suggest that intuitive, technology-based methods of dietary data capture are well suited to modern users and, with proper execution, can provide data that are comparable to data obtained with traditional methods. This trial was registered at clinicaltrials.gov as NCT03578458.

Project description:INTRODUCTION:Individuals with autism spectrum disorder (ASD) have difficulties in communication and social interaction resulting from atypical perceptual and cognitive information processing, leading to an accumulation of anxiety. Extreme overloading experienced internally may not be externally visible. Identifying stressful situations at an early stage may avoid socially problematic behaviour from occurring, such as self-injurious behaviour. Activation of the autonomous nervous system (ANS) is involved in the response to anxiety, which can be measured through heart rate variability and skin conductance with the use of portable devices, non-intrusively and pain-free. Thus, developing innovative analysis of signal perception and reaction is necessary, mainly for non-communicative individuals with autism. METHODS AND ANALYSIS:The protocol will take place in real life (home and social environments). We aim to associate modifications of the ANS with external events that will be recorded in a synchronous manner through a specific design (spy glasses with video/audio recording). Four phases will be carried out on ASD participants and aged-matched controls: (1) 24-hour baseline pre-experiment (physical activity, sleep), (2) 2 h in a real life situation, (3) 30 min in a quiet environment, interrupted by a few seconds of stressful sound, (4) an interview to record feelings about events triggering anxiety. ASD and control participants will be together for phases 2 and 3, revealing different physiological responses to the same situations, and thus identifying potentially problematic events. The novelty will be to apply time-series analyses (which led to several Nobel Prizes in quantitative finance) on ANS series (heart rate, heart rate variability, skin conductance) and wrist motion. ETHICS AND DISSEMINATION:Ethical approval has been obtained from Ethics Committee of Clermont-Ferrand (South-East I), France (2014-A00611-46). Trial findings will be disseminated via open-access peer-reviewed publications, conferences, clinical networks, public lectures and our websites. TRIAL REGISTRATION NUMBER:ClinicalTrials identifier NCT02275455.

Dataset Information

Study protocol for an evaluation of ASDetect - a Mobile application for the early detection of autism.

Publications

Study protocol for an evaluation of ASDetect - a Mobile application for the early detection of autism.

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