Title |
The challenge of measuring intra-individual change in fatigue during cancer treatment
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Published in |
Quality of Life Research, July 2016
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DOI | 10.1007/s11136-016-1372-9 |
Pubmed ID | |
Authors |
Carol M. Moinpour, Gary W. Donaldson, Kimberly M. Davis, Arnold L. Potosky, Roxanne E. Jensen, Julie R. Gralow, Anthony L. Back, Jimmy J. Hwang, Jihye Yoon, Debra L. Bernard, Deena R. Loeffler, Nan E. Rothrock, Ron D. Hays, Bryce B. Reeve, Ashley Wilder Smith, Elizabeth A. Hahn, David Cella |
Abstract |
To evaluate how well three different patient-reported outcomes (PROs) measure individual change. Two hundred and fourteen patients (from two sites) initiating first or new chemotherapy for any stage of breast or gastrointestinal cancer participated. The 13-item FACIT Fatigue scale, a 7-item PROMIS(®) Fatigue Short Form (PROMIS 7a), and the PROMIS(®) Fatigue computer adaptive test (CAT) were administered monthly online for 6 months. Reliability of measured change was defined, under a population mixed effects model, as the ratio of estimated systematic variance in rate of change to the estimated total variance of measured individual differences in rate of change. Precision of individual measured change, the standard error of measurement of change, was given by the square root of the rate-of-change sampling variance. Linear and quadratic models were examined up to 3 and up to 6 months. A linear model for measured change showed the following by 6 and 3 months, respectively: PROMIS CAT (0.363 and 0.342); PROMIS SF (0.408 and 0.533); FACIT (0.459 and 0.473). Quadratic models offered no noteworthy improvement over linear models. Both reliability and precision results demonstrate the need to improve the measurement of intra-individual change. These results illustrate the challenge of reliably measuring individual change in fatigue with a level of confidence required for intervention. Optimizing clinically useful measurement of intra-individual differences over time continues to pose a challenge for PROs. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Comoros | 1 | 25% |
Unknown | 3 | 75% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 75% |
Scientists | 1 | 25% |
Mendeley readers
Geographical breakdown
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Unknown | 50 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Master | 13 | 26% |
Other | 7 | 14% |
Student > Ph. D. Student | 4 | 8% |
Student > Bachelor | 3 | 6% |
Researcher | 3 | 6% |
Other | 6 | 12% |
Unknown | 14 | 28% |
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Nursing and Health Professions | 11 | 22% |
Medicine and Dentistry | 7 | 14% |
Psychology | 4 | 8% |
Pharmacology, Toxicology and Pharmaceutical Science | 2 | 4% |
Decision Sciences | 2 | 4% |
Other | 7 | 14% |
Unknown | 17 | 34% |