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Clinical Trials/NCT02796196
NCT02796196
Completed
Not Applicable

Effects of Prescribed Physical Activity on Sleep and Performance in Hematopoietic Cell Transplantation Patients

Stanford University1 site in 1 country75 target enrollmentMay 2016

Overview

Phase
Not Applicable
Intervention
Not specified
Conditions
Cell Transplantation
Sponsor
Stanford University
Enrollment
75
Locations
1
Primary Endpoint
ECOG performance scores
Status
Completed
Last Updated
7 years ago

Overview

Brief Summary

This research trial studies prescribed physical activity in improving sleep and physical performance in patients undergoing stem cell transplant. A wearable physical activity monitor can be used to record minutes of activity and sleep. Gathering data over time using a physical activity monitor may help doctors learn if prescribed physical activity helps improve sleep and physical performance in patients undergoing stem cell transplant.

Detailed Description

PRIMARY OBJECTIVES: I. Evaluate whether prescribed physical activity, as part of standard care, improves sleep and functional outcomes in hematopoietic cell transplantation (HCT) (stem cell transplantation) patients during a typical 30-day hospitalization period. OUTLINE: Data including demographics, type of HCT (e.g., allogeneic or autologous), preexisting physical conditions (e.g., chronic joint injury), chronic renal failure (CRF), steroid use data, Eastern Cooperative Oncology Group (ECOG) and Karnofsky performance status (KPS) scores are collected from patients' medical charts at time of enrollment. Patients are prescribed participation in a primarily self-directed physical activity program which encourages them to spend 6 hours out of bed daily and to perform 30 minutes of light-to-moderate daily aerobic activity. Patients who are able to maintain independent mobility undergo physical therapist assessment 2 times a week until hospital discharge. Patients wear a physical activity monitoring device and daily activity and sleep data are collected continuously during hospital length of stay (LOS).

Registry
clinicaltrials.gov
Start Date
May 2016
End Date
March 1, 2017
Last Updated
7 years ago
Study Type
Observational
Sex
All

Investigators

Responsible Party
Sponsor

Eligibility Criteria

Inclusion Criteria

  • Admitted to Stanford Hospital for HCT
  • Able to provide informed consent
  • Speaks language supported by interpretative services
  • Able to operate and take care of a physical activity monitor

Exclusion Criteria

  • Does not meet inclusion criteria
  • Unable to wear a physical activity monitor throughout hospital stay

Outcomes

Primary Outcomes

ECOG performance scores

Time Frame: At baseline

Will focus on trends in scores between patients with different durations of activity and sleep. Results, from both descriptive and inferential analyses, will be presented in table and/or graphical format.

KPS performance scores

Time Frame: At baseline

Will focus on trends in scores between patients with different durations of activity and sleep. Results, from both descriptive and inferential analyses, will be presented in table and/or graphical format.

Minutes of aerobic activity per day

Time Frame: Up to time of hospital discharge, or 29 days

Will use interval-censored regression to estimate distributions of activity from one day to the next. This repeated-measures analysis with a polynomial model will generate average curves for activity, each spanning average LOS (i.e., activity duration plotted on day one, day two, day three, etc.). Will also run diagnostics to verify the assumptions of a linear model (e.g., statistical independence of observations and lack of undue influence of outliers on model fit).

Minutes of sleep per day

Time Frame: Up to time of hospital discharge, or 29 days

Will use interval-censored regression to estimate distributions of sleep from one day to the next. This repeated-measures analysis with a polynomial model will generate average curves for sleep, each spanning average LOS (i.e., sleep duration plotted on day one, day two, day three, etc.). Will use multiple linear regressions to control for potential confounders in predicting sleep duration. Will also run diagnostics to verify the assumptions of a linear model (e.g., statistical independence of observations and lack of undue influence of outliers on model fit).

Study Sites (1)

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