Lisa J Cameron1, Kylie Wales2, Angela Casey3, Shannon Pike4, Laura Jolliffe1,5,6, Emma J Schneider7,6, Lauren J Christie8, Julie Ratcliffe9, Natasha A Lannin10,11,12. 1. Alfred Health, Melbourne, Australia. 2. School of Health Sciences, College of Health, Medicine and Wellbeing, The University of Newcastle, Callaghan, Australia. 3. School of Nursing and Midwifery, La Trobe University, Bundoora, Australia. 4. Wagga Wagga Ambulatory Rehabilitation Service, Murrumbidgee Local Health District, Wagga Wagga, Australia. 5. Department of Occupational Therapy, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Frankston, Australia. 6. Department of Neurosciences, Central Clinical School, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia. 7. Occupational Therapy Department, Alfred Health, Melbourne, Australia. 8. Allied Health Research Unit and Nursing Research Institute, St Vincent's Health Network Sydney, Darlinghurst, Australia. 9. Health and Social Care Economics Group, Caring Futures Institute, Flinders University, Adelaide, Australia. 10. School of Allied Health, Human Services and Sport (Occupational Therapy), La Trobe University, Bundoora, Australia. Natasha.Lannin@monash.edu. 11. Occupational Therapy Department, Alfred Health, Melbourne, Australia. Natasha.Lannin@monash.edu. 12. Department of Neurosciences, Central Clinical School, Monash University, 99 Commercial Road, Melbourne, VIC, 3004, Australia. Natasha.Lannin@monash.edu.
Abstract
PURPOSE: To evaluate the psychometric properties of common health-related quality-of-life instruments used post stroke and provide recommendations for research and clinical use with this diagnostic group. METHODS: A systematic review of the psychometric properties of the five most commonly used quality-of-life measurement tools (EQ-5D, SF-36, SF-6D, AQoL, SS-QOL) was conducted. Electronic searches were performed in MEDLINE, CINAHL, and EMBASE on November 27th 2019. Two authors screened papers against the inclusion criteria and where consensus was not reached, a third author was consulted. Included papers were appraised using the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) checklist and findings synthesized to make recommendations. RESULTS: A total of n = 50,908 papers were screened and n = 45 papers reporting on 40 separate evaluations of psychometric properties met inclusion criteria (EQ-5D = 19, SF-36 = 16, SF-6D = 4, AQoL = 2, SS-QOL = 4). Studies reported varied psychometric quality of instruments, and results show that psychometric properties of quality-of-life instruments for the stroke population have not been well established. The strongest evidence was identified for the use of the EQ-5D as a quality-of-life assessment for adult stroke survivors. CONCLUSIONS: This systematic evaluation of the psychometric properties of self-reported quality-of-life instruments used with adults after stroke suggests that validity across tools should not be assumed. Clinicians and researchers alike may use findings to help identify the most valid and reliable measurement instrument for understanding the impact of stroke on patient-reported quality of life.
PURPOSE: To evaluate the psychometric properties of common health-related quality-of-life instruments used post stroke and provide recommendations for research and clinical use with this diagnostic group. METHODS: A systematic review of the psychometric properties of the five most commonly used quality-of-life measurement tools (EQ-5D, SF-36, SF-6D, AQoL, SS-QOL) was conducted. Electronic searches were performed in MEDLINE, CINAHL, and EMBASE on November 27th 2019. Two authors screened papers against the inclusion criteria and where consensus was not reached, a third author was consulted. Included papers were appraised using the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) checklist and findings synthesized to make recommendations. RESULTS: A total of n = 50,908 papers were screened and n = 45 papers reporting on 40 separate evaluations of psychometric properties met inclusion criteria (EQ-5D = 19, SF-36 = 16, SF-6D = 4, AQoL = 2, SS-QOL = 4). Studies reported varied psychometric quality of instruments, and results show that psychometric properties of quality-of-life instruments for the stroke population have not been well established. The strongest evidence was identified for the use of the EQ-5D as a quality-of-life assessment for adult stroke survivors. CONCLUSIONS: This systematic evaluation of the psychometric properties of self-reported quality-of-life instruments used with adults after stroke suggests that validity across tools should not be assumed. Clinicians and researchers alike may use findings to help identify the most valid and reliable measurement instrument for understanding the impact of stroke on patient-reported quality of life.
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