In support of an individualized approach to assessing quality of life: comparison between Patient Generated Index and standardized measures across four health conditions Journal Articles uri icon

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  • BACKGROUND: Individualized measures of health-related quality life (HRQL) have been used for decades and shown to provide unique information, but little work has been done to explain this uniqueness particularly across health conditions. AIMS: To estimate, across four health conditions, the magnitude of the association between scores derived from the Patient Generated Index (PGI) and those from fully standardized generic and disease-specific measures of the HRQL; to identify the extent to which the areas generated from the PGI are covered by the content of the fully standardized measures. METHODS: The PGI and other generic and disease-specific measures had been used in four different samples of people: stroke (n = 222), multiple sclerosis (MS; n = 185); advanced cancer (n = 173), and HIV+ (n = 690). Areas nominated on the PGI were harmonized to a standard nomenclature. Pearson correlations were estimated between PGI and other measures. RESULTS: Data from 1263 people indicated that PGI provided the lowest rating for HRQL across all health conditions. The areas nominated differed across conditions with walking/mobility: the most common for stroke (42%), work/school for MS (62%), health for HIV+ (97%), and fatigue for cancer (39%). Many of the aspects of health included in generic measures were not nominated using the PGI and vice versa. The highest correlations between the PGI and other measures were observed for people with MS, with correlations between 0.53 and 0.59; lowest correlations were observed for people with HIV and cancer, ≤0.33. DISCUSSION: The PGI scores reflect those aspects of quality of life that are important to patients in which they would most value an improvement. Heterogeneity in HRQL across health conditions is poorly discriminated using standardized measures. A "one-size-fits-all" approach to HRQL assessment may not provide the most useful representation of this important construct.


  • Mayo, Nancy E
  • Aburub, Ala’
  • Brouillette, Marie-Josée
  • Kuspinar, Ayse
  • Moriello, Carolina
  • Rodriguez, Ana Maria
  • Scott, Susan

publication date

  • March 2017