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Identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the HI-FIVES randomized clinical trial

BACKGROUND: Informal caregivers report substantial burden and depressive symptoms which predict higher rates of patient institutionalization. While caregiver education interventions may reduce caregiver distress and decrease the use of long-term institutional care, evidence is mixed. Inconsistent fi...

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Autores principales: Shepherd-Banigan, Megan, Smith, Valerie A., Lindquist, Jennifer H., Cary, Michael Paul, Miller, Katherine E. M., Chapman, Jennifer G., Van Houtven, Courtney H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7023677/
https://www.ncbi.nlm.nih.gov/pubmed/32059687
http://dx.doi.org/10.1186/s13063-020-4113-x
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author Shepherd-Banigan, Megan
Smith, Valerie A.
Lindquist, Jennifer H.
Cary, Michael Paul
Miller, Katherine E. M.
Chapman, Jennifer G.
Van Houtven, Courtney H.
author_facet Shepherd-Banigan, Megan
Smith, Valerie A.
Lindquist, Jennifer H.
Cary, Michael Paul
Miller, Katherine E. M.
Chapman, Jennifer G.
Van Houtven, Courtney H.
author_sort Shepherd-Banigan, Megan
collection PubMed
description BACKGROUND: Informal caregivers report substantial burden and depressive symptoms which predict higher rates of patient institutionalization. While caregiver education interventions may reduce caregiver distress and decrease the use of long-term institutional care, evidence is mixed. Inconsistent findings across studies may be the result of reporting average treatment effects which do not account for how effects differ by participant characteristics. We apply a machine-learning approach to randomized clinical trial (RCT) data of the Helping Invested Family Members Improve Veteran’s Experiences Study (HI-FIVES) intervention to explore how intervention effects vary by caregiver and patient characteristics. METHODS: We used model-based recursive partitioning models. Caregivers of community-residing older adult US veterans with functional or cognitive impairment at a single VA Medical Center site were randomized to receive HI-FIVES (n = 118) vs. usual care (n = 123). The outcomes included cumulative days not in the community and caregiver depressive symptoms assessed at 12 months post intervention. Potential moderating characteristics were: veteran age, caregiver age, caregiver ethnicity and race, relationship satisfaction, caregiver burden, perceived financial strain, caregiver depressive symptoms, and patient risk score. RESULTS: The effect of HI-FIVES on days not at home was moderated by caregiver burden (p < 0.001); treatment effects were higher for caregivers with a Zarit Burden Scale score ≤ 28. Caregivers with lower baseline Center for Epidemiologic Studies Depression Scale (CESD-10) scores (≤ 8) had slightly lower CESD-10 scores at follow-up (p < 0.001). CONCLUSIONS: Family caregiver education interventions may be less beneficial for highly burdened and distressed caregivers; these caregivers may require a more tailored approach that involves assessing caregiver needs and developing personalized approaches. TRIAL REGISTRATION: ClinicalTrials.gov, ID:NCT01777490. Registered on 28 January 2013.
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spelling pubmed-70236772020-02-20 Identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the HI-FIVES randomized clinical trial Shepherd-Banigan, Megan Smith, Valerie A. Lindquist, Jennifer H. Cary, Michael Paul Miller, Katherine E. M. Chapman, Jennifer G. Van Houtven, Courtney H. Trials Research BACKGROUND: Informal caregivers report substantial burden and depressive symptoms which predict higher rates of patient institutionalization. While caregiver education interventions may reduce caregiver distress and decrease the use of long-term institutional care, evidence is mixed. Inconsistent findings across studies may be the result of reporting average treatment effects which do not account for how effects differ by participant characteristics. We apply a machine-learning approach to randomized clinical trial (RCT) data of the Helping Invested Family Members Improve Veteran’s Experiences Study (HI-FIVES) intervention to explore how intervention effects vary by caregiver and patient characteristics. METHODS: We used model-based recursive partitioning models. Caregivers of community-residing older adult US veterans with functional or cognitive impairment at a single VA Medical Center site were randomized to receive HI-FIVES (n = 118) vs. usual care (n = 123). The outcomes included cumulative days not in the community and caregiver depressive symptoms assessed at 12 months post intervention. Potential moderating characteristics were: veteran age, caregiver age, caregiver ethnicity and race, relationship satisfaction, caregiver burden, perceived financial strain, caregiver depressive symptoms, and patient risk score. RESULTS: The effect of HI-FIVES on days not at home was moderated by caregiver burden (p < 0.001); treatment effects were higher for caregivers with a Zarit Burden Scale score ≤ 28. Caregivers with lower baseline Center for Epidemiologic Studies Depression Scale (CESD-10) scores (≤ 8) had slightly lower CESD-10 scores at follow-up (p < 0.001). CONCLUSIONS: Family caregiver education interventions may be less beneficial for highly burdened and distressed caregivers; these caregivers may require a more tailored approach that involves assessing caregiver needs and developing personalized approaches. TRIAL REGISTRATION: ClinicalTrials.gov, ID:NCT01777490. Registered on 28 January 2013. BioMed Central 2020-02-14 /pmc/articles/PMC7023677/ /pubmed/32059687 http://dx.doi.org/10.1186/s13063-020-4113-x Text en © The Author(s). 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Shepherd-Banigan, Megan
Smith, Valerie A.
Lindquist, Jennifer H.
Cary, Michael Paul
Miller, Katherine E. M.
Chapman, Jennifer G.
Van Houtven, Courtney H.
Identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the HI-FIVES randomized clinical trial
title Identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the HI-FIVES randomized clinical trial
title_full Identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the HI-FIVES randomized clinical trial
title_fullStr Identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the HI-FIVES randomized clinical trial
title_full_unstemmed Identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the HI-FIVES randomized clinical trial
title_short Identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the HI-FIVES randomized clinical trial
title_sort identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the hi-fives randomized clinical trial
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7023677/
https://www.ncbi.nlm.nih.gov/pubmed/32059687
http://dx.doi.org/10.1186/s13063-020-4113-x
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