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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.

Publication ,  Journal Article
Shepherd-Banigan, M; Smith, VA; Lindquist, JH; Cary, MP; Miller, KEM; Chapman, JG; Van Houtven, CH
Published in: Trials
February 14, 2020

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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Published In

Trials

DOI

EISSN

1745-6215

Publication Date

February 14, 2020

Volume

21

Issue

1

Start / End Page

189

Location

England

Related Subject Headings

  • Veterans
  • Treatment Outcome
  • Stress, Psychological
  • Randomized Controlled Trials as Topic
  • Quality of Life
  • Middle Aged
  • Male
  • Machine Learning
  • Long-Term Care
  • Institutionalization
 

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Shepherd-Banigan, M., Smith, V. A., Lindquist, J. H., Cary, M. P., Miller, K. E. M., Chapman, J. G., & Van Houtven, C. H. (2020). 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. Trials, 21(1), 189. https://doi.org/10.1186/s13063-020-4113-x
Shepherd-Banigan, Megan, Valerie A. Smith, Jennifer H. Lindquist, Michael Paul Cary, Katherine E. M. Miller, Jennifer G. Chapman, and Courtney H. Van Houtven. “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.Trials 21, no. 1 (February 14, 2020): 189. https://doi.org/10.1186/s13063-020-4113-x.
Journal cover image

Published In

Trials

DOI

EISSN

1745-6215

Publication Date

February 14, 2020

Volume

21

Issue

1

Start / End Page

189

Location

England

Related Subject Headings

  • Veterans
  • Treatment Outcome
  • Stress, Psychological
  • Randomized Controlled Trials as Topic
  • Quality of Life
  • Middle Aged
  • Male
  • Machine Learning
  • Long-Term Care
  • Institutionalization