Movement Path Tortuosity Predicts Compliance With Therapeutic Behavioral Prompts in Patients With Traumatic Brain Injury.

OBJECTIVE: Rehabilitation of patients with traumatic brain injury typically includes therapeutic prompts for keeping appointments and adhering to medication regimens. Level of cognitive impairment may significantly affect a traumatic brain injury victim's ability to benefit from text-based prompting. We tested the hypothesis that spatial disorientation as measured by movement path tortuosity during ambulation would be associated with poorer compliance with automated prompts by veterans actively being treated for traumatic brain injury. SETTING: Clinical polytrauma center. PARTICIPANTS: Ten (1 female) veteran patients mean age = 35.4 (SD = 12.4) years. DESIGN: Small group correlational study without random assignment. MAIN MEASURES: Fractal Dimension, a measure of movement path tortuosity derived from a GPS logging device used to record casual outdoor ambulation at the start of the study. Compliance with smart home machine-generated therapeutic prompts received during rehabilitation at the James A. Haley Veterans Administration Hospital Polytrauma Transitional Rehabilitation Program. A patient was compliant with a prompt if they transited from where the prompt was presented to the prescribed destination (both within the Polytrauma Transitional Rehabilitation Program) within 30 minutes. Noncompliance was failure to appear at the destination within the allotted time. RESULTS: Fractal dimension was significantly inversely related to overall prompt compliance (r = -0.603, n = 10, P = .032; 1-tailed). CONCLUSIONS: The findings support the hypothesis that increased spatial disorientation adversely impacts compliance with automated prompts throughout therapy. The results are consistent with previous studies linking elevated path tortuosity to cognitive impairment and increased risk for falls in assisted living facility residents.

Operations analysis of behavioral observation procedures: a taxonomy for modeling in an expert training system.

This article introduces a taxonomy based on a procedural operations analysis (Verplanck, 1996) of various method descriptions found in the behavior observation research literature. How these alternative procedures impact the recording and subsequent analysis of behavioral events on the basis of the type of time and behavior recordings made is also discussed. The taxonomy was generated as a foundation for the continuing development of an expert training system called Train-to-Code (TTC; J. M. Ray & Ray, (Behavior Research Methods 40:673-693, 2008)). Presently in its second version, TTC V2.0 is software designed for errorless training (Terrace, (Journal of the Experimental Analysis of Behavior 6:1-27, 1963)) of student accuracy and fluency in the direct observation and coding of behavioral or verbal events depicted via digital video. Two of 16 alternative procedures classified by the taxonomy are presently modeled in TTC's structural interface and functional services. These two models are presented as illustrations of how the taxonomy guides software user interface and algorithm development. The remaining 14 procedures are described in sufficient operational detail to allow similar model-oriented translation.

Train-to-Code: an adaptive expert system for training systematic observation and coding skills.

Problems in training behavioral observers to a high degree of interindividual accuracy and intraindividual stability are fundamental concerns in descriptive research, as well as in provisions of behavioral intervention services. This article presents design characteristics of and results from three formative evaluations of an adaptive computerized expert system that shapes observation and recording skills and maximizes both individual coding accuracy and stability. The system, called Train-to-Code, allows instructors or trainers to import their own video source files and to code those videos using any appropriate descriptive behavioral-coding scheme. This generates customized expert reference data that automate subsequent training on the basis of an operant response-shaping instructional design model. Successful training relies on transitions through alternative levels of prompting and feedback designed to optimize ongoing performance until stable expert-equivalent levels of interobserver accuracy are maintained without prompting or feedback.