Development of an App for Tracking Family Engagement With Early Intervention Services: Focus Groups and Pilot Evaluation Study.

BACKGROUND: Expedient access to early intervention (EI) systems has been identified as a priority for children with developmental delays, identified disabilities, and other special health care needs. Despite the mandated availability of EI, it remains challenging for families to navigate referral processes and establish appropriate services. Such challenges disproportionately affect families from traditionally underserved communities. Mobile health apps can improve clinical outcomes, increase accessibility to health services, and promote adherence to health-related interventions. Though promising, the implementation of apps within routine care is in its infancy, with limited research examining the components of what makes an effective app or how to reach families most impacted by inequities in health care delivery. OBJECTIVE: In study 1, we conducted focus groups to access a broad range of perspectives on the process of navigating the EI system, with the dual goals of identifying ways in which a patient-facing app might facilitate this process and identifying barriers to use with traditionally underrepresented and underserved groups. In study 2, focus group findings informed the development of a patient-facing app, which was subsequently tested with a pilot sample of 5 families. METHODS: In study 1, the focus groups included 29 participants from 4 shareholder groups. Targeted sampling was used to recruit participants from traditionally underrepresented groups. Focus group questions sought information about barriers families experience as they navigate the EI system, ideal features of a patient-facing app designed to track family engagement with the EI system, and potential barriers. Focus group procedures were informed by the Consolidated Framework for Implementation Research framework. In study 2, a pilot app was developed. The app was tested with a sample of 5 families of young children involved in the EI system. Families provided information on app functionality and usability. RESULTS: Qualitative analysis revealed a desire for increased communication and information about the process of accessing EI services, potential utility of an app for communication purposes, and clear recommendations for app features. Insights from focus groups were used to inform the development of the Family on Track app and related implementation supports. App features included survey customization, timing and delivery of prompts, and questions related to barriers and service satisfaction. Implementation supports include a visual guide for app installation, resources related to common family questions, and availability of study personnel to guide families through installation and provide ongoing support. Field testing provided preliminary information about app usability, including identifying future directions. CONCLUSIONS: The results of this study could support the development of a new way for the EI system to communicate and connect with families, provide families with a means to communicate satisfaction and frustration, and access the supports they need to be active participants in their child's care.

Driving Aggressively or Conservatively? Investigating the Effects of Automated Vehicle Interaction Type and Road Event on Drivers' Trust and Preferred Driving Style.

OBJECTIVE: This study aimed to investigate the impact of automated vehicle (AV) interaction mode on drivers' trust and preferred driving styles in response to pedestrian- and traffic-related road events. BACKGROUND: The rising popularity of AVs highlights the need for a deeper understanding of the factors that influence trust in AV. Trust is a crucial element, particularly because current AVs are only partially automated and may require manual takeover; miscalibrated trust could have an adverse effect on safe driver-vehicle interaction. However, before attempting to calibrate trust, it is vital to comprehend the factors that contribute to trust in automation. METHODS: Thirty-six individuals participated in the experiment. Driving scenarios incorporated adaptive SAE Level 2 AV algorithms, driven by participants' event-based trust in AVs and preferences for AV driving styles. The study measured participants' trust, preferences, and the number of takeover behaviors. RESULTS: Higher levels of trust and preference for more aggressive AV driving styles were found in response to pedestrian-related events compared to traffic-related events. Furthermore, drivers preferred the trust-based adaptive mode and had fewer takeover behaviors than the preference-based adaptive and fixed modes. Lastly, participants with higher trust in AVs favored more aggressive driving styles and made fewer takeover attempts. CONCLUSION: Adaptive AV interaction modes that depend on real-time event-based trust and event types may represent a promising approach to human-automation interaction in vehicles. APPLICATION: Findings from this study can support future driver- and situation-aware AVs that can adapt their behavior for improved driver-vehicle interaction.

The future of mobility-as-a-service: trust transfer across automated mobilities, from road to sidewalk.

While trust in different types of automated vehicles has been a major focus for researchers and vehicle manufacturers, few studies have explored how people trust automated vehicles that are not cars, nor how their trust may transfer across different mobilities enabled with automation. To address this objective, a dual mobility study was designed to measure how trust in an automated vehicle with a familiar form factor-a car-compares to, and influences, trust in a novel automated vehicle-termed sidewalk mobility. A mixed-method approach involving both surveys and a semi-structured interview was used to characterize trust in these automated mobilities. Results found that the type of mobility had little to no effect on the different dimensions of trust that were studied, suggesting that trust can grow and evolve across different mobilities when the user is unfamiliar with a novel automated driving-enabled (AD-enabled) mobility. These results have important implications for the design of novel mobilities.

A Predictive Multimodal Framework to Alert Caregivers of Problem Behaviors for Children with ASD (PreMAC).

Autism Spectrum Disorder (ASD) impacts 1 in 54 children in the US. Two-thirds of children with ASD display problem behavior. If a caregiver can predict that a child is likely to engage in problem behavior, they may be able to take action to minimize that risk. Although experts in Applied Behavior Analysis can offer caregivers recognition and remediation strategies, there are limitations to the extent to which human prediction of problem behavior is possible without the assistance of technology. In this paper, we propose a machine learning-based predictive framework, PreMAC, that uses multimodal signals from precursors of problem behaviors to alert caregivers of impending problem behavior for children with ASD. A multimodal data capture platform, M2P3, was designed to collect multimodal training data for PreMAC. The development of PreMAC integrated a rapid functional analysis, the interview-informed synthesized contingency analysis (IISCA), for collection of training data. A feasibility study with seven 4 to 15-year-old children with ASD was conducted to investigate the tolerability and feasibility of the M2P3 platform and the accuracy of PreMAC. Results indicate that the M2P3 platform was well tolerated by the children and PreMAC could predict precursors of problem behaviors with high prediction accuracies.

Impacts of colored light-emitting diode illumination on the growth performance and fecal microbiota in goose.

Besides on the reproductive performance, the light also has an important effect on the growth in birds. In the present study, we for the first time investigated effects of colored light-emitting diodes (LED) on both growth performance and fecal microbiota in meat geese. We randomly selected a total of 120 geese at birth (0-day), divided them into 3 groups evenly (i.e., 40 geese each group), and then reared them under 3 colored light-emitting diodes (i.e., blue, red, and white) with the same photoperiod for 9 wk, respectively. We collected fecal samples at the experimental day 35 and 63, respectively. We observed that geese in blue light had higher body weight than those in red and white lights at the early stage of the experiment but showed lower body weight at the late stage, particularly at day 63 (P < 0.05). Interestingly, we found that the relative abundances of 3 dominant bacteria phyla, Firmicutes, Proteobacteria, and Cyanobacteria, were comparable among 3 groups at day 35, while at day 63, the blue light group had the significantly (P < 0.05) lowest and highest abundance for Firmicutes and Proteobacteria, respectively. Functional enrichment analyses revealed that the fecal microbiota in the red light group was mainly involved in metabolism at day 35, whereas at day 63, the fecal microbiota were engaged into membrane transportation and transcription. In contrast, the blue light group had more enriched pathways relevant with membrane transportation at day 63 than day 35 and had several pathways involved in metabolism at day 63 as well. Collectively, our results revealed that the light with different colors affects the growth performance of geese via the gut microbiota, which in turn influences the digestion and absorption of geese.

A Novel Multisensory Stimulation and Data Capture System (MADCAP) for Investigating Sensory Trajectories in Infancy.

Sensory processing differences, including responses to auditory, visual, and tactile stimuli, are ideal targets for early detection of neurodevelopmental risks, such as autism spectrum disorder. However, most existing studies focus on the audiovisual paradigm and ignore the sense of touch. In this paper, we present a multisensory delivery system that can deliver audio, visual, and tactile stimuli in a controlled manner and capture peripheral physiological, eye gaze, and electroencephalographic response data. The novelty of the system is the ability to provide affective touch. In particular, we have developed a tactile stimulation device that delivers tactile stimuli to infants with precisely controlled brush stroking speed and force on the skin. A usability study of 10 3-20 month-old infants was conducted to investigate the tolerability and feasibility of the system. Results have shown that the system is well tolerated by infants and all the data were collected robustly. This paper paves the way for future studies charting the sensory response trajectories in infancy.