Computerized adaptive testing--ready for ambulatory monitoring?

Abstract

Background: Computerized adaptive tests (CATs) have abundant theoretical advantages over established static instruments, which could improve ambulatory monitoring of patient-reported outcomes (PROs). However, an empirical demonstration of their practical benefits is warranted. Methods: We reviewed the literature and evaluated existing data to discuss the potential of CATs for use in ambulatory monitoring outside clinical facilities. Results: Computerized adaptive tests are not being used for ambulatory monitoring, but initial results from their use in health care research allow for discussion of some issues relevant to ambulatory care. Evidence shows that CATs can capture the most relevant health outcomes as well as established static tools, with substantially decreased respondent burden. They can be more precise than static tools of similar length and can reduce floor and ceiling effects. Computerized adaptive tests can reliably measure a construct over time with different items, which yields the potential of introducing item exposure control in ambulatory monitoring. Studies have shown that CATs can be at least as valid as well-designed static tools in group comparisons, but further investigation is needed to determine whether psychometric advantages lead to increased responsiveness of CATs. Conclusions: Ambulatory monitoring of PROs demands short, yet very precise measurements, which can be repeated up to many times a day. Computerized adaptive tests may address several present shortcomings in ambulatory monitoring of PROs efficiently. However, most CAT developments have primarily focused on psychometric improvements. To use the full potential of CATs for ambulatory monitoring purposes, content must also be carefully considered.

Publication
Psychosomatic medicine, (74), 4, pp. 338 - 348
Matthias Rose
Matthias Rose
Department Head
Felix Fischer
Felix Fischer
Group Leader Psychometrics and Health Outcomes

Psychologist with a weakness for quantitative data analysis

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