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Williams A, Lee H, Kamper SJ, O'Brien KM, Wiggers J, Wolfenden L, Yoong SL, Hodder RK, Robson EK, Haskins R, McAuley JH, Williams CM. Causal mechanisms of a healthy lifestyle intervention for patients with musculoskeletal pain who are overweight or obese. Clin Rehabil 2019; 33:1088-1097. [PMID: 30808203 DOI: 10.1177/0269215519831419] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PURPOSE To assess the causal mechanisms of a healthy lifestyle intervention for patients with chronic low back pain and knee osteoarthritis, who are overweight or obese. METHODS We conducted causal mediation analyses of aggregated data from two randomized controlled trials (RCTs); which included 160 patients with chronic low back pain, and 120 patients with knee osteoarthritis. The intervention consisted of brief advice and referral to a six-month telephone-based healthy lifestyle coaching service. We used causal mediation to estimate the indirect, direct and path-specific effects of hypothesized mediators including: self-reported weight, diet, physical activity, and pain beliefs. Outcomes were pain intensity, disability, and quality of life (QoL). RESULTS The intervention did not reduce weight, improve diet or physical activity or change pain beliefs, and these mediators were not associated with the outcomes. Sensitivity analyses showed that our estimates were robust to the possible effects of unknown and unmeasured confounding. CONCLUSIONS Our findings show that the intervention did not cause a meaningful change in the hypothesized mediators, and these mediators were not associated with patient-reported outcomes.
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Affiliation(s)
- Amanda Williams
- 1 School of Medicine and Public Health, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.,2 Hunter New England Population Health, Wallsend, NSW, Australia.,3 Centre for Pain, Health and Lifestyle, Newcastle, NSW, Australia
| | - Hopin Lee
- 1 School of Medicine and Public Health, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.,3 Centre for Pain, Health and Lifestyle, Newcastle, NSW, Australia.,4 Neuroscience Research Australia (NeuRA), Sydney, NSW, Australia.,5 Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Steven J Kamper
- 3 Centre for Pain, Health and Lifestyle, Newcastle, NSW, Australia.,6 School of Public Health, The University of Sydney, Camperdown, NSW, Australia
| | - Kate M O'Brien
- 1 School of Medicine and Public Health, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.,2 Hunter New England Population Health, Wallsend, NSW, Australia.,3 Centre for Pain, Health and Lifestyle, Newcastle, NSW, Australia
| | - John Wiggers
- 1 School of Medicine and Public Health, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.,2 Hunter New England Population Health, Wallsend, NSW, Australia
| | - Luke Wolfenden
- 1 School of Medicine and Public Health, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.,2 Hunter New England Population Health, Wallsend, NSW, Australia
| | - Sze L Yoong
- 1 School of Medicine and Public Health, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.,2 Hunter New England Population Health, Wallsend, NSW, Australia
| | - Rebecca K Hodder
- 1 School of Medicine and Public Health, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.,2 Hunter New England Population Health, Wallsend, NSW, Australia.,3 Centre for Pain, Health and Lifestyle, Newcastle, NSW, Australia
| | - Emma K Robson
- 1 School of Medicine and Public Health, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.,2 Hunter New England Population Health, Wallsend, NSW, Australia.,3 Centre for Pain, Health and Lifestyle, Newcastle, NSW, Australia
| | - Robin Haskins
- 7 Outpatient Services, John Hunter Hospital, Hunter New England Local Health District, Newcastle, NSW, Australia
| | - James H McAuley
- 4 Neuroscience Research Australia (NeuRA), Sydney, NSW, Australia.,8 School of Medical Sciences, Faculty of Medicine, University of NSW, Sydney, NSW, Australia
| | - Christopher M Williams
- 1 School of Medicine and Public Health, Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia.,2 Hunter New England Population Health, Wallsend, NSW, Australia.,3 Centre for Pain, Health and Lifestyle, Newcastle, NSW, Australia
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Van Cleave JH, Egleston BL, Brosch S, Wirth E, Lawson M, Sullivan-Marx EM, Naylor MD. Policy Research Challenges in Comparing Care Models for Dual-Eligible Beneficiaries. Policy Polit Nurs Pract 2017; 18:72-83. [PMID: 28735567 PMCID: PMC7133145 DOI: 10.1177/1527154417721909] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Providing affordable, high-quality care for the 10 million persons who are dual-eligible beneficiaries of Medicare and Medicaid is an ongoing health-care policy challenge in the United States. However, the workforce and the care provided to dual-eligible beneficiaries are understudied. The purpose of this article is to provide a narrative of the challenges and lessons learned from an exploratory study in the use of clinical and administrative data to compare the workforce of two care models that deliver home- and community-based services to dual-eligible beneficiaries. The research challenges that the study team encountered were as follows: (a) comparing different care models, (b) standardizing data across care models, and (c) comparing patterns of health-care utilization. The methods used to meet these challenges included expert opinion to classify data and summative content analysis to compare and count data. Using descriptive statistics, a summary comparison of the two care models suggested that the coordinated care model workforce provided significantly greater hours of care per recipient than the integrated care model workforce. This likely represented the coordinated care model's focus on providing in-home services for one recipient, whereas the integrated care model focused on providing services in a day center with group activities. The lesson learned from this exploratory study is the need for standardized quality measures across home- and community-based services agencies to determine the workforce that best meets the needs of dual-eligible beneficiaries.
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Affiliation(s)
| | - Brian L Egleston
- 2 Biostatistics Facility, Fox Chase Cancer, Philadelphia, PA, USA
| | | | | | | | | | - Mary D Naylor
- 4 NewCourtland Center for Transitions and Health, University of Pennsylvania School of Nursing, Philadelphia, PA, USA
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Pesola F, Williams J, Bird V, Freidl M, Le Boutillier C, Leamy M, Macpherson R, Slade M. Development and evaluation of an Individualized Outcome Measure (IOM) for randomized controlled trials in mental health. Int J Methods Psychiatr Res 2015; 24:257-65. [PMID: 26184686 PMCID: PMC6878370 DOI: 10.1002/mpr.1480] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Revised: 02/23/2015] [Accepted: 03/18/2015] [Indexed: 11/05/2022] Open
Abstract
Pre-defined, researcher-selected outcomes are routinely used as the clinical end-point in randomized controlled trials (RCTs); however, individualized approaches may be an effective way to assess outcome in mental health research. The present study describes the development and evaluation of the Individualized Outcome Measure (IOM), which is a patient-specific outcome measure to be used for RCTs of complex interventions. IOM was developed using a narrative review, expert consultation and piloting with mental health service users (n = 20). The final version of IOM comprises two components: Goal Attainment (GA) and Personalized Primary Outcome (PPO). For GA, patients identify one relevant goal at baseline and rate its attainment at follow-up. For PPO, patients choose an outcome domain related to their goal from a pre-defined list at baseline, and complete a standardized questionnaire assessing the chosen outcome domain at baseline and follow-up. A feasibility study indicated that IOM had adequate completion (89%) and acceptability (96%) rates in a clinical sample (n = 84). IOM was then evaluated in a RCT (ISRCTN02507940). GA and PPO components were associated with each other and with the trial primary outcome. The use of the PPO component of IOM as the primary outcome could be considered in future RCTs. Copyright © 2015 John Wiley & Sons, Ltd.
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Affiliation(s)
- Francesca Pesola
- King's College London, Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, Denmark Hill, London, UK
| | - Julie Williams
- King's College London, Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, Denmark Hill, London, UK
| | - Victoria Bird
- King's College London, Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, Denmark Hill, London, UK
| | - Marion Freidl
- King's College London, Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, Denmark Hill, London, UK.,Clinical Division of Social Psychiatry, Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Clair Le Boutillier
- King's College London, Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, Denmark Hill, London, UK
| | - Mary Leamy
- King's College London, Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, Denmark Hill, London, UK
| | | | - Mike Slade
- King's College London, Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, Denmark Hill, London, UK
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Peterson MJ, Thompson DK, Pieper CF, Morey MC, Kraus VB, Kraus WE, Sullivan P, Fillenbaum G, Cohen HJ. A Novel Analytic Technique to Measure Associations Between Circulating Biomarkers and Physical Performance Across the Adult Life Span. J Gerontol A Biol Sci Med Sci 2015; 71:196-202. [PMID: 25745025 DOI: 10.1093/gerona/glv007] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2014] [Accepted: 01/16/2015] [Indexed: 12/20/2022] Open
Abstract
Understanding associations between circulating biomarkers and physical performance across the adult life span could aid in better describing mechanistic pathways leading to disability. We hypothesized that high concentrations of circulating biomarkers would be associated with lower functioning across study populations representing the adult life span. The data were from four intervention and two observational studies with ages ranging 22-89 years. Biomarkers assayed included inflammatory, coagulation, and endothelial function markers. Physical performance was measured either by VO2peak (studies of young and middle-aged adults) or usual gait speed (studies of older adults). Partialled (by age, body mass index, race, and sex) and weighted common correlations were calculated between biomarkers and physical performance. Homogeneity of the associations was also assessed. Interleukin-6 (weighted r = -.22), tumor necrosis factor receptor 2 (weighted r = -.19), D-dimer (weighted r = -.16), tumor necrosis factor receptor 1 (weighted r = -.15), granulocyte colony-stimulating factor (weighted r = -.14), and tumor necrosis factor alpha (weighted r = -.10) were all significantly inversely correlated with physical performance (p < .05). All significant correlations were homogeneous across studies. In summary, we observed consistent inverse associations between six circulating biomarkers and objective measures of physical performance. These results suggest that these serum biomarkers may be broadly applicable for detection, trajectory, and treatment monitoring of physical function across the life span or possibly for midlife predictors of functionally deleterious conditions.
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Affiliation(s)
| | - Dana K Thompson
- Department of Medicine, Duke University, Durham, North Carolina
| | - Carl F Pieper
- Center for the Study of Aging and Human Development/Claude D. Pepper Older Adults Independence Center and Department of Biometry and Bioinformatics, Duke University Medical Center, Durham, North Carolina
| | | | - Virginia B Kraus
- Center for the Study of Aging and Human Development/Claude D. Pepper Older Adults Independence Center and Department of Medicine, Duke University, Durham, North Carolina
| | - William E Kraus
- Center for the Study of Aging and Human Development/Claude D. Pepper Older Adults Independence Center and Department of Medicine, Duke University, Durham, North Carolina
| | - Patrick Sullivan
- Department of Medicine, Duke University, Durham, North Carolina. Geriatric Research, Education and Clinical Center, Veteran's Affairs Medical Center, Durham, North Carolina
| | - Gerda Fillenbaum
- Center for the Study of Aging and Human Development/Claude D. Pepper Older Adults Independence Center and
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Abstract
BACKGROUND Symptom distress remains a significant health problem among older adults with cancer following surgery. Understanding factors influencing older adults' symptom distress may lead to early identification and interventions, decreasing morbidity and improving outcomes. OBJECTIVE We conducted this study to identify factors associated with symptom distress following surgery among 326 community-residing patients 65 years or older with a diagnosis of thoracic, digestive, gynecologic, and genitourinary cancers. METHODS This secondary analysis used combined subsets of data from 5 nurse-directed intervention clinical trials targeting patients after surgery at academic cancer centers in northwest and northeastern United States. Symptom distress was assessed by the Symptom Distress Scale at baseline and at 3 and 6 months. RESULTS A multivariable analysis, using generalized estimating equations, showed that symptom distress was significantly less at 3 and 6 months (3 months: P < .001, 6 months: P = .002) than at baseline while controlling for demographic, biologic, psychological, treatment, and function covariates. Thoracic cancer, comorbidities, worse mental health, and decreased function were, on average, associated with increased symptom distress (all P < .05). Participants 75 years or older reported increased symptom distress over time compared with those aged 65 to 69 years (P < .05). CONCLUSIONS Age, type of cancer, comorbidities, mental health, and function may influence older adults' symptom distress following cancer surgery. IMPLICATIONS FOR PRACTICE Older adults generally experience decreasing symptom distress after thoracic, abdominal, or pelvic cancer surgery. Symptom management over time for those with thoracic cancer, comorbidities, those with worse mental health, those with decreased function, and those 75 years or older may prevent morbidity and improve outcomes of older adults following surgery.
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