Speech Therapy and Biofeedback
Tara McAllister’s study, Correcting Residual Errors with Spectral, ULtrasound, Traditional Speech therapy Randomized Controlled Trial (C-RESULTS RCT; McAllister & Hill), compares speech therapy with and without biofeedback technology. Data collection for a preliminary single-case experimental study has been completed and submitted for presentation at the 2020 convention of the American Speech-Language Hearing Association, and a paper preregistering the protocol for the randomized controlled trial component of the study was published in BMC Pediatrics.
McAllister, T., Preston, J. L., Hitchcock, E. R., & Hill, J. (2020). Protocol for Correcting Residual Errors with Spectral, ULtrasound, Traditional Speech therapy Randomized Controlled Trial (C-RESULTS RCT). BMC pediatrics, 20(1), 66.
Data collection for the RCT was suspended due to COVID-19, but the research team has pivoted to online data collection for a pilot study investigating the efficacy of biofeedback delivered via screen-sharing over Zoom. Telehealth is a huge topic in the speech pathology research community at the moment, and we hope to be able to make a meaningful contribution with this research, even though it represents a detour from our original plans.
A co-authored paper by Dr. McAllister,"Using Crowdsourced Listeners’ Ratings to Measure Speech Changes in Hypokinetic Dysarthria: A Proof-of-Concept Study" (Nightingale, Swartz, Ramig, & McAllister) was accepted to appear in the American Journal of Speech-Language Pathology. This paper builds on previous work conducted in collaboration with PRIISM methodologists on the subject of crowdsourcing perceptual ratings of clinical speech samples (McAllister Byun, Halpin & Szeredi 2015; McAllister Byun, Harel, Halpin, & Szeredi, 2016; Harel, Hitchcock, Szeredi, Ortiz, & McAllister Byun, 2016; Fernandez, Harel, Ipeirotis, & McAllister, 2019). With many speech scientists looking to take their research online during COVID-19, this is a timely topic; Dr. McAllister plans to share the paper along with resources to help other researchers set up their own studies using online crowdsourcing.
High-Dimensional Data Compression/Feature Extraction applied to Kinematic Data
Using rich information of kinematic and EMG data collected at the Motor Recovery Lab, we are interested in the movement patterns and how they change when the physiology is modified due to training, injury, disease and disability. Statistical models for measuring change in movement patterns after interventions is desired. Dimensionality reduction, functional data analysis and dynamic factor analysis models are being explored and developed.
The Cancer, Insulin Resistance and Lifestyle (CIRCLE) Study in the Framingham Heart Study Population
The overall purpose of the study titled is to investigate the interrelationships of physiologic, dietary and genetic factors associated with disturbances in the insulin-glucose axis in relation to combined and site-specific incidence of obesity-related cancers, by performing secondary data analyses in a large existing sample of American adults from the National Heart, Lung, and Blood Institute’s (NHLBI) Framingham Heart Study (FHS;1948-2008) consisting of ~14,000 adults 20 years or older. There is sufficient evidence in the literature to support the obesity-cancer link. Although potential biological mechanisms of obesity-related metabolic abnormalities on cancer risk have been hypothesized and confirmed by laboratory studies these relationships have not yet been fully characterized in humans, and remain unclear; this is a primary area of enquiry in the CIRCLE study.
Using NYC FITNESSGRAM data on NYC public school children, these projects examine how the school, food, and built environments at home and school impact childhood obesity.
These projects are funded by NIH:
- The Impact of the Built Environment on Body Mass Index (2016-2020)
- Impact of the Food Environment on Child Body Mass Index (2013-2017)
- How Does School Food Policy Shape Health, Fitness and Academic Outcomes among School Children? (2011-2016)
Shortening Patient-reported Outcome Measures
Patient-reported outcome measures are widely used to assess patient experiences, well-being, and treatment response in clinical trials and cohort-based observational studies. However, patients may be asked to respond to many different measures in order to provide researchers and clinicians with a wide array of information regarding their experiences. Collecting such long and cumbersome patient-reported outcome measures may burden patients, increase research costs, and potentially reduce the quality of the data collected. Nonetheless, little research has been conducted on replicable, and reproducible methods to shorten these instruments that result in shortened forms of minimal length. This work proposes the use of mixed integer programming through Optimal Test Assembly as a method to shorten patient-reported outcome measures.