Alice Theadom1, Miriam Rodrigues2, Annemarei Ranta3, Gemma Poke4, Donald Love5, Kelly Jones6, Braden Te Ao6,7, Graeme Hammond-Tooke8, Priya Parmar6,9, Gina O'Grady10, Richard Roxburgh2,11. 1. National Institute for Stroke and Applied Neurosciences, Auckland University of Technology, 90 Akoranga Dr, Northcote, 0627, New Zealand. alice.theadom@aut.ac.nz. 2. Neurology Department, Auckland City Hospital, Auckland, New Zealand. 3. Department of Medicine, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand. 4. Genetic Health Service NZ, Capital and Coast District Health Board, Wellington, New Zealand. 5. Diagnostic Genetics, LabPLUS, Auckland City Hospital, Auckland, New Zealand. 6. National Institute for Stroke and Applied Neurosciences, Auckland University of Technology, 90 Akoranga Dr, Northcote, 0627, New Zealand. 7. Population Health, Faculty of Medicine and Health Sciences, University of Auckland, Auckland, New Zealand. 8. Brain Health Research Centre, University of Otago, Dunedin, New Zealand. 9. Department of Biostatistics and Epidemiology, Faculty of Health and Environmental Studies, Auckland University of Technology, Auckland, New Zealand. 10. Paediatric Neuroservices, Starship Children's Health, Auckland District Health Board, Auckland, New Zealand. 11. School of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
Abstract
OBJECTIVES: To determine the impact of genetic muscle disorders and identify the sociodemographic, illness, and symptom factors influencing quality of life. METHODS: Adults (aged 16-90 years) with a confirmed clinical or molecular diagnosis of a genetic muscle disorder identified as part of a nationwide prevalence study were invited to complete an assessment of the impact of their condition. Quality of life was measured using the World Health Organization Quality of Life questionnaire. Impact was measured via the prevalence of symptoms and comparisons of quality of life against New Zealand norms. Multivariate regression models were used to identify the most significant predictors of quality of life domains. RESULTS: 490/596 participants completed the assessment (82.2% consent rate). Quality of life was lower than the general population on physical (t = 9.37 p < 0.0001, d = 0.54) social (t = 2.27 p = 0.02, d = 0.13) and environmental domains (t = 2.28 p = 0.02, d = 0.13), although effect sizes were small. No difference was found on the psychological domain (t = - 1.17 p = 0.24, d = 0.07). Multivariate regression models (predicting 42%-64% of the variance) revealed personal factors (younger age, being in employment and in a relationship), symptoms (lower pain, fatigue, and sleep difficulties), physical health (no need for ventilation support, fewer activity limitations and no comorbidities), and psychosocial factors (lower depression, anxiety, behavioural dyscontrol and higher self-efficacy, satisfaction with health care and social support) contributed to improved quality of life. CONCLUSIONS: A range of factors influence the quality of life in adults diagnosed with a genetic muscle disorder and some may serve as targets for multi-faceted intervention.
OBJECTIVES: To determine the impact of genetic muscle disorders and identify the sociodemographic, illness, and symptom factors influencing quality of life. METHODS: Adults (aged 16-90 years) with a confirmed clinical or molecular diagnosis of a genetic muscle disorder identified as part of a nationwide prevalence study were invited to complete an assessment of the impact of their condition. Quality of life was measured using the World Health Organization Quality of Life questionnaire. Impact was measured via the prevalence of symptoms and comparisons of quality of life against New Zealand norms. Multivariate regression models were used to identify the most significant predictors of quality of life domains. RESULTS: 490/596 participants completed the assessment (82.2% consent rate). Quality of life was lower than the general population on physical (t = 9.37 p < 0.0001, d = 0.54) social (t = 2.27 p = 0.02, d = 0.13) and environmental domains (t = 2.28 p = 0.02, d = 0.13), although effect sizes were small. No difference was found on the psychological domain (t = - 1.17 p = 0.24, d = 0.07). Multivariate regression models (predicting 42%-64% of the variance) revealed personal factors (younger age, being in employment and in a relationship), symptoms (lower pain, fatigue, and sleep difficulties), physical health (no need for ventilation support, fewer activity limitations and no comorbidities), and psychosocial factors (lower depression, anxiety, behavioural dyscontrol and higher self-efficacy, satisfaction with health care and social support) contributed to improved quality of life. CONCLUSIONS: A range of factors influence the quality of life in adults diagnosed with a genetic muscle disorder and some may serve as targets for multi-faceted intervention.
Authors: Christian U Krägeloh; D Rex Billington; Patricia Hsien-Chuan Hsu; Xuan Joanna Feng; Oleg N Medvedev; Paula Kersten; Jason Landon; Richard J Siegert Journal: PLoS One Date: 2016-11-03 Impact factor: 3.240
Authors: Matthew F Jacques; Rachel C Stockley; Gladys L Onambele-Pearson; Neil D Reeves; Georgina K Stebbings; Ellen A Dawson; Lynne Groves; Christopher I Morse Journal: Health Qual Life Outcomes Date: 2019-07-15 Impact factor: 3.186