Technology-based functional assessment in early childhood intervention: a pilot study

Pilot and Feasibility Studies(2018)

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摘要
Background Electronic patient-reported outcomes (e-PROs) may provide valid and feasible options for obtaining family input on their child’s functioning for care planning and outcome monitoring, but they have not been adopted into early intervention (EI). The purpose of this pilot study was to evaluate the feasibility of implementing technology-based functional assessment into EI practice and to examine child, family, service, and environmental correlates of caregiver-reported child functioning in the home. Methods In a cross-sectional design, eight individual EI providers participated in a 90-min technology-based functional assessment training to recruit participants and a 60-min semi-structured focus group post data collection. Participants completed the Young Children’s Participation and Environment Measure (YC-PEM) home section online and Pediatric Evaluation of Disability Inventory Computer Adaptive Test (PEDI-CAT) via iPad. Participants’ EI service use data were obtained from administrative records. Results A total of 37 caregivers of children between 6 and 35 months old (mean age = 19.4, SD = 7.7) enrolled, a rate of 44% (37/84) in 2.5 months. Providers suggested expanding staff training, gathering data during scheduled evaluations, and providing caregivers and providers with access to assessment summaries. Caregivers wanted their child’s participation to change in 56% of home activities. Lower caregiver education and higher EI intensity were related to less child involvement in home activities. Conclusions Implementing technology-based functional assessment is feasible with modifications, and these data can be useful for highlighting child, family, and EI service correlates of caregiver-reported child functioning that merit further study. Feasibility results informed protocol modifications related to EI provider training, timing of data collection, and management of EI service use data extraction, as preparation for a subsequent scale-up study that is underway.
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