This study provides evidence that technology-based interventions may provide a relatively low-cost addition to existing therapist-delivered interventions for children with ASD. However, sustained use of the app over the full 6-month period was a challenge for most families.
A challenge in building pervasive and smart spaces is to learn and recognize human activities of daily living (ADLs). In this paper, we address this problem and argue that in dealing with ADLs, it is beneficial to exploit both their typical duration patterns and inherent hierarchical structures. We exploit efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and apply it to the problem of learning and recognizing ADLs with complex temporal dependencies. The Coxian duration model has several advantages over existing duration parameterization using multinomial or exponential family distributions, including its denseness in the space of nonnegative distributions, low number of parameters, computational efficiency and the existence of closed-form estimation solutions. Further we combine both hierarchical and duration extensions of the hidden Markov model (HMM) to form the novel switching hidden semi-Markov model (SHSMM), and empirically compare its performance with existing models. The model can learn what an occupant normally does during the day from unsegmented training data and then perform online activity classification, segmentation and abnormality detection. Experimental results show that Coxian modeling outperforms a range of baseline models for the task of activity segmentation. We also achieve a recognition accuracy competitive to the current state-of-the-art multinomial duration model, while gaining a significant reduction in computation. Furthermore, cross-validation model selection on the number of phases K in the Coxian indicates that only a small K is required to achieve the optimal performance. Finally, our models are further tested in a more challenging setting in which the tracking is often lost and the activities considerably overlap. With a small amount of labels supplied during training in a partially supervised learning mode, our models are again able to deliver reliable performance, again with a small number of phases, making our proposed framework an attractive choice for activity modeling.
ObjectiveTo capture the clinical patterns, timing of key milestones and survival of patients presenting with amyotrophic lateral sclerosis/motor neuron disease (ALS/MND) within Australia.MethodsData were prospectively collected and were timed to normal clinical assessments. An initial registration clinical report form (CRF) and subsequent ongoing assessment CRFs were submitted with a completion CRF at the time of death.DesignProspective observational cohort study.Participants1834 patients with a diagnosis of ALS/MND were registered and followed in ALS/MND clinics between 2005 and 2015.Results5 major clinical phenotypes were determined and included ALS bulbar onset, ALS cervical onset and ALS lumbar onset, flail arm and leg and primary lateral sclerosis (PLS). Of the 1834 registered patients, 1677 (90%) could be allocated a clinical phenotype. ALS bulbar onset had a significantly lower length of survival when compared with all other clinical phenotypes (p<0.004). There were delays in the median time to diagnosis of up to 12 months for the ALS phenotypes, 18 months for the flail limb phenotypes and 19 months for PLS. Riluzole treatment was started in 78–85% of cases. The median delays in initiating riluzole therapy, from symptom onset, varied from 10 to 12 months in the ALS phenotypes and 15–18 months in the flail limb phenotypes. Percutaneous endoscopic gastrostomy was implemented in 8–36% of ALS phenotypes and 2–9% of the flail phenotypes. Non-invasive ventilation was started in 16–22% of ALS phenotypes and 21–29% of flail phenotypes.ConclusionsThe establishment of a cohort registry for ALS/MND is able to determine clinical phenotypes, survival and monitor time to key milestones in disease progression. It is intended to expand the cohort to a more population-based registry using opt-out methodology and facilitate data linkage to other national registries.
BackgroundEvidence for early intensive behavioural interventions (EIBI) by therapists as an effective treatment for children with an Autism Spectrum Disorder (ASD) is growing. High-intensity and sustained delivery of quality EIBI is expensive. The TOBY (Therapy Outcomes by You) Playpad is an App-based platform delivering EIBI to facilitate learning for young children with ASD, while enabling parents to become co-therapists. Intervention targets include increasing joint attention, imitation and communication of children with ASD. The primary aim of the study presented in this protocol is to determine the effectiveness of the TOBY App in reducing ASD symptoms when used as a complement to conventional EIBI. The secondary aim is to examine parental attributes as a result of TOBY App use.Methods and designChildren aged less than 4;3 years diagnosed with ASD and parents will be recruited into this single-blind, randomised controlled trial using a pragmatic approach. Eligible participants will be randomised to the treatment group ‘TOBY therapy + therapy as usual’ or, the control group ‘therapy as usual’ for six months. The treatment will be provided by the TOBY App and parent where a combination of learning environments such as on-iPad child only (solo), partner (with parent) and off-iPad – Natural Environment (with parent) Tasks will be implemented. Parents in the treatment group will participate in a TOBY training workshop. Treatment fidelity will be monitored via an App-based reporting system and parent diaries. The primary outcome measure is the Autism Treatment Evaluation Checklist. The secondary outcome measures involve diagnostics, functional and developmental assessments, including parent questionnaires at baseline (T0), three months (T1) and six months (T2).DiscussionThis trial will determine the effectiveness of the TOBY App as a therapeutic complement to other early interventions children with ASD receive. The trial will also determine the feasibility of a parent delivered early intervention using the iPad as an educational platform, and assess the impact of the TOBY App on parents’ self-efficacy and empowerment in an effort to reduce children’s ASD symptoms. The outcomes of this trial may have EIBI services implications for newly diagnosed children with ASD and parents.Trial registration ACTRN12614000738628 retrospectively registered on 1st of July, 2014. UTN: U1111-1158-6423.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.