J Speight1, M D Reaney, K D Barnard. 1. AHP Research, Brunel Science Park, Brunel University, Kingston Lane, Uxbridge UB8 3PQ, UK. jane.speight@ahpresearch.com
Abstract
AIMS: Quality of life (QoL) is recognized widely as an important health outcome in diabetes, where the burden of self-management places great demands on the individual. However, the concept of QoL remains ambiguous and poorly defined. The aim of our review is to clarify the measurement of QoL in terms of conceptualization, terminology and psychometric properties, to review the instruments that have been used most frequently to assess QoL in diabetes research and make recommendations for how to select measures appropriately. METHODS: A systematic literature search was conducted to identify the ten measures most frequently used to assess QoL in diabetes research (including clinical trials) from 1995 to March 2008. RESULTS: Six thousand and eight-five abstracts were identified and screened for instrument names. Of the ten instruments most frequently used to assess 'QoL', only three actually do so [i.e. the generic World Health Organization Quality of Life (WHOQOL) and the diabetes-specific Diabetes Quality of Life (DQOL) and Audit of Diabetes-Dependent Quality of Life (ADDQoL)]. Seven instruments more accurately measure health status [Short-Form 36 (SF-36), EuroQoL 5-Dimension (EQ-5D)], treatment satisfaction [Diabetes Treatment Satisfaction Questionnaire (DTSQ)] and psychological well-being [Beck Depression Inventory (BDI), Hospital Anxiety and Depression Scale (HADS), Well-Being Questionnaire (W-BQ), Problem Areas in Diabetes (PAID)]. CONCLUSIONS: No single measure can suit every purpose or application but, when measures are selected inappropriately and data misinterpreted, any conclusions drawn are fundamentally flawed. If we value QoL as a therapeutic goal, we must ensure that the instruments we use are both valid and reliable. QoL assessment has the proven potential to identify ways in which treatments can be tailored to reduce the burden of diabetes. With careful consideration, appropriate measures can be selected and truly robust assessments undertaken successfully.
AIMS: Quality of life (QoL) is recognized widely as an important health outcome in diabetes, where the burden of self-management places great demands on the individual. However, the concept of QoL remains ambiguous and poorly defined. The aim of our review is to clarify the measurement of QoL in terms of conceptualization, terminology and psychometric properties, to review the instruments that have been used most frequently to assess QoL in diabetes research and make recommendations for how to select measures appropriately. METHODS: A systematic literature search was conducted to identify the ten measures most frequently used to assess QoL in diabetes research (including clinical trials) from 1995 to March 2008. RESULTS: Six thousand and eight-five abstracts were identified and screened for instrument names. Of the ten instruments most frequently used to assess 'QoL', only three actually do so [i.e. the generic World Health Organization Quality of Life (WHOQOL) and the diabetes-specific Diabetes Quality of Life (DQOL) and Audit of Diabetes-Dependent Quality of Life (ADDQoL)]. Seven instruments more accurately measure health status [Short-Form 36 (SF-36), EuroQoL 5-Dimension (EQ-5D)], treatment satisfaction [Diabetes Treatment Satisfaction Questionnaire (DTSQ)] and psychological well-being [Beck Depression Inventory (BDI), Hospital Anxiety and Depression Scale (HADS), Well-Being Questionnaire (W-BQ), Problem Areas in Diabetes (PAID)]. CONCLUSIONS: No single measure can suit every purpose or application but, when measures are selected inappropriately and data misinterpreted, any conclusions drawn are fundamentally flawed. If we value QoL as a therapeutic goal, we must ensure that the instruments we use are both valid and reliable. QoL assessment has the proven potential to identify ways in which treatments can be tailored to reduce the burden of diabetes. With careful consideration, appropriate measures can be selected and truly robust assessments undertaken successfully.
Authors: Liliana C Baptista; Gonçalo Dias; Nelba R Souza; Manuel T Veríssimo; Raul A Martins Journal: Qual Life Res Date: 2017-03-16 Impact factor: 4.147
Authors: Adriana Lozano-Platonoff; Jose Contreras-Ruiz; Judith Dominguez-Cherit; Andrea Cardenas-Sanchez; Valeria Alvarez-Rivero; Joel A Martínez-Regalado Journal: Int Wound J Date: 2019-11-11 Impact factor: 3.315