Design of a randomized superiority trial of a brief couple treatment for PTSD.

Interpersonal difficulties are common among veterans with posttraumatic stress disorder (PTSD) and are associated with poorer treatment response. Treatment outcomes for PTSD, including relationship functioning, improve when partners are included and engaged in the therapy process. Cognitive-behavioral conjoint therapy for PTSD (CBCT) is a manualized 15-session intervention designed for couples in which one partner has PTSD. CBCT was developed specifically to treat PTSD, engage a partner in treatment, and improve interpersonal functioning. However, recent research suggests that an abbreviated CBCT protocol may lead to sufficient gains in PTSD and relationship functioning, and yield lower dropout rates. Likewise, many veterans report a preference for receiving psychological treatments through clinical videoteleconferencing (CVT) rather than traditional face-to-face modalities that require travel to VA clinics. This manuscript describes the development and implementation of a novel randomized controlled trial (RCT) that examines the efficacy of an abbreviated 8-session version of CBCT ("brief CBCT," or B-CBCT), and compares the efficacy of this intervention delivered via CVT to traditional in-person platforms. Veterans and their partners were randomized to receive B-CBCT in a traditional Veterans Affairs office-based setting (B-CBCT-Office), CBCT through CVT with the veteran and partner at home (B-CBCT-Home), or an in office-delivered, couple-based psychoeducation control condition (PTSD Family Education). This study is the first RCT designed to investigate the delivery of B-CBCT specifically to veterans with PTSD and their partners, as well as to examine the delivery of B-CBCT over a CVT modality; findings could increase access to care to veterans with PTSD and their partners

The use of telehealth during the coronavirus (COVID-19) pandemic in oral and maxillofacial surgery : A qualitative analysis

Peer reviewedPublisher PD

A QoE Model for Mulsemedia TV in a Smart Home Environment

The provision to the users of realistic media contents is one of the main goals of future media services. The sense of reality perceived by the user can be enhanced by adding various sensorial effects to the conventional audio-visual content, through the stimulation of the five senses stimulation (sight, hearing, touch, smell and taste), the so-called multi-sensorial media (mulsemedia). To deliver the additional effects within a smart home (SH) environment, custom devices (e.g., air conditioning, lights) providing opportune smart features, are preferred to ad-hoc devices, often deployed in a specific context such as for example in gaming consoles. In the present study, a prototype for a mulsemedia TV application, implemented in a real smart home scenario, allowed the authors to assess the user's Quality of Experience (QoE) through test measurement campaign. The impact of specific sensory effects (i.e., light, airflow, vibration) on the user experience regarding the enhancement of sense of reality, annoyance, and intensity of the effects was investigated through subjective assessment. The need for multi sensorial QoE models is an important challenge for future research in this field, considering the time and cost of subjective quality assessments. Therefore, based on the subjective assessment results, this paper instantiates and validates a parametric QoE model for multi-sensorial TV in a SH scenario which indicates the relationship between the quality of audiovisual contents and user-perceived QoE for sensory effects applications

Didactic Software for Autistic Children

In this paper we describe the aims and requirements of a project devoted to designing and developing Open Source didactic Software (SW) for children in the autism disorder spectrum, conforming to the Applied Behaviour Analysis (ABA) learning technique. In this context, participatory design with therapists and child?s parents is necessary to ensure a usable product that responds to these children?s special needs and respects education principles and constraints of the ABA methodology

Using mixed methods evaluation to assess the feasibility of online clinical training in evidence based interventions : a case study of cognitive behavioural treatment for low back pain

Background: Cognitive behavioural (CB) approaches are effective in the management of non-specific low back pain (LBP). We developed the CB Back Skills Training programme (BeST) and previously provided evidence of clinical and cost effectiveness in a large pragmatic trial. However, practice change is challenged by a lack of treatment guidance and training for clinicians. We aimed to explore the feasibility and acceptability of an online programme (iBeST) for providing training in a CB approach. Methods: This mixed methods study comprised an individually randomised controlled trial of 35 physiotherapists and an interview study of 8 physiotherapists. Participants were recruited from 8 National Health Service departments in England and allocated by a computer generated randomisation list to receive iBeST (n = 16) or a face-to-face workshop (n = 19). Knowledge (of a CB approach), clinical skills (unblinded assessment of CB skills in practice), self-efficacy (reported confidence in using new skills), attitudes (towards LBP management), and satisfaction were assessed after training. Engagement with iBeST was assessed with user analytics. Interviews explored acceptability and experiences with iBeST. Data sets were analysed independently and jointly interpreted. Results: Fifteen (94 %) participants in the iBeST group and 16 (84 %) participants in the workshop group provided data immediately after training. We observed similar scores on knowledge (MD (95 % CI): 0.97 (−1.33, 3.26)), and self-efficacy to deliver the majority of the programme (MD (95 % CI) 0.25 (−1.7; 0.7)). However, the workshop group showed greater reduction in biomedical attitudes to LBP management (MD (95 % CI): −7.43 (−10.97, −3.89)). Clinical skills were assessed in 5 (33 %) iBeST participants and 7 (38 %) workshop participants within 6 months of training and were similar between groups (MD (95 % CI): 0.17(−0.2; 0.54)). Interviews highlighted that while initially sceptical, participants found iBeST acceptable. A number of strategies were identified to enhance future versions of iBeST such as including more skills practice. Conclusions: Combined quantitative and qualitative data indicated that online training was an acceptable and promising method for providing training in an evidence based complex intervention. With future enhancement, the potential reach of this training method may facilitate evidence-based practice through large scale upskilling of the workforce

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Interactions Affording Distance Science Education

A Chapter from D. Kennepohl & L. Shaw (Eds.), Accessible Elements: Teaching Science Online and at a Distance (pp. 1-18). Edmonton: Athabasca University Pres