@article {171,
	title = {A computer adaptive testing approach for assessing physical functioning in children and adolescents},
	journal = {Developmental Medicine and Child Neuropsychology},
	volume = {47},
	number = {2},
	year = {2005},
	note = {Haley, Stephen MNi, PengshengFragala-Pinkham, Maria ASkrinar, Alison MCorzo, DeyaniraComparative StudyResearch Support, Non-U.S. Gov{\textquoteright}tEnglandDevelopmental medicine and child neurologyDev Med Child Neurol. 2005 Feb;47(2):113-20.},
	month = {Feb},
	pages = {113-120},
	edition = {2005/02/15},
	abstract = {The purpose of this article is to demonstrate: (1) the accuracy and (2) the reduction in amount of time and effort in assessing physical functioning (self-care and mobility domains) of children and adolescents using computer-adaptive testing (CAT). A CAT algorithm selects questions directly tailored to the child{\textquoteright}s ability level, based on previous responses. Using a CAT algorithm, a simulation study was used to determine the number of items necessary to approximate the score of a full-length assessment. We built simulated CAT (5-, 10-, 15-, and 20-item versions) for self-care and mobility domains and tested their accuracy in a normative sample (n=373; 190 males, 183 females; mean age 6y 11mo [SD 4y 2m], range 4mo to 14y 11mo) and a sample of children and adolescents with Pompe disease (n=26; 21 males, 5 females; mean age 6y 1mo [SD 3y 10mo], range 5mo to 14y 10mo). Results indicated that comparable score estimates (based on computer simulations) to the full-length tests can be achieved in a 20-item CAT version for all age ranges and for normative and clinical samples. No more than 13 to 16\% of the items in the full-length tests were needed for any one administration. These results support further consideration of using CAT programs for accurate and efficient clinical assessments of physical functioning.},
	keywords = {*Computer Systems, Activities of Daily Living, Adolescent, Age Factors, Child, Child Development/*physiology, Child, Preschool, Computer Simulation, Confidence Intervals, Demography, Female, Glycogen Storage Disease Type II/physiopathology, Health Status Indicators, Humans, Infant, Infant, Newborn, Male, Motor Activity/*physiology, Outcome Assessment (Health Care)/*methods, Reproducibility of Results, Self Care, Sensitivity and Specificity},
	isbn = {0012-1622 (Print)},
	author = {Haley, S. M. and Ni, P. and Fragala-Pinkham, M. A. and Skrinar, A. M. and Corzo, D.}
}
@article {211,
	title = {Contemporary measurement techniques for rehabilitation outcomes assessment},
	journal = {Journal of Rehabilitation Medicine},
	volume = {37},
	number = {6},
	year = {2005},
	note = {1650-1977 (Print)Journal ArticleReview},
	pages = {339-345},
	abstract = {In this article, we review the limitations of traditional rehabilitation functional outcome instruments currently in use within the rehabilitation field to assess Activity and Participation domains as defined by the International Classification of Function, Disability, and Health. These include a narrow scope of functional outcomes, data incompatibility across instruments, and the precision vs feasibility dilemma. Following this, we illustrate how contemporary measurement techniques, such as item response theory methods combined with computer adaptive testing methodology, can be applied in rehabilitation to design functional outcome instruments that are comprehensive in scope, accurate, allow for compatibility across instruments, and are sensitive to clinically important change without sacrificing their feasibility. Finally, we present some of the pressing challenges that need to be overcome to provide effective dissemination and training assistance to ensure that current and future generations of rehabilitation professionals are familiar with and skilled in the application of contemporary outcomes measurement.},
	keywords = {*Disability Evaluation, Activities of Daily Living/classification, Disabled Persons/classification/*rehabilitation, Health Status Indicators, Humans, Outcome Assessment (Health Care)/*methods/standards, Recovery of Function, Research Support, N.I.H., Extramural, Research Support, U.S. Gov{\textquoteright}t, Non-P.H.S., Sensitivity and Specificity computerized adaptive testing},
	author = {Jette, A. M. and Haley, S. M.}
}
@article {50,
	title = {Measuring quality of life in chronic illness: the functional assessment of chronic illness therapy measurement system},
	journal = {Archives of Physical Medicine and Rehabilitation},
	volume = {83},
	number = {12 Suppl 2},
	year = {2002},
	note = {0003-9993Journal Article},
	month = {Dec},
	pages = {S10-7},
	abstract = {We focus on quality of life (QOL) measurement as applied to chronic illness. There are 2 major types of health-related quality of life (HRQOL) instruments-generic health status and targeted. Generic instruments offer the opportunity to compare results across patient and population cohorts, and some can provide normative or benchmark data from which to interpret results. Targeted instruments ask questions that focus more on the specific condition or treatment under study and, as a result, tend to be more responsive to clinically important changes than generic instruments. Each type of instrument has a place in the assessment of HRQOL in chronic illness, and consideration of the relative advantages and disadvantages of the 2 options best drives choice of instrument. The Functional Assessment of Chronic Illness Therapy (FACIT) system of HRQOL measurement is a hybrid of the 2 approaches. The FACIT system combines a core general measure with supplemental measures targeted toward specific diseases, conditions, or treatments. Thus, it capitalizes on the strengths of each type of measure. Recently, FACIT questionnaires were administered to a representative sample of the general population with results used to derive FACIT norms. These normative data can be used for benchmarking and to better understand changes in HRQOL that are often seen in clinical trials. Future directions in HRQOL assessment include test equating, item banking, and computerized adaptive testing.},
	keywords = {*Chronic Disease, *Quality of Life, *Rehabilitation, Adult, Comparative Study, Health Status Indicators, Humans, Psychometrics, Questionnaires, Research Support, U.S. Gov{\textquoteright}t, P.H.S., Sensitivity and Specificity},
	author = {Cella, D. and Nowinski, C. J.}
}
