Weighted Blanket Use to Reduce Anxiety in Oncology Patients
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age18+
SponsorOhio State University Comprehensive Cancer Center
About this trial
This clinical trial compares the effect of using weighted blankets versus regular blankets during first time infusions (e.g. chemotherapy, targeted therapy etc.) to decrease adverse side effects such as anxiety and distress in cancer (oncology) patients. Feeling safe, comforted, and grounded in the world are some of the benefits noted by individuals who use weighted blankets. Deep touch pressure (DTP) has been found to reduce symptoms of stress and anxiety and is defined as a sensation one feels when being hugged, squeezed, or held. DTP affects the nervous system by creating a calming effect which may lower stress and increase feelings of well-being. The use of weighted blankets may help to manage anxiety and distress during chemotherapy or immunotherapy infusions.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Age 18 years of age and older
About to begin either targeted or cytotoxic chemotherapy
Able to comprehend and sign a consent form
Able to read and complete surveys
Disqualifiers
Currently using a weighted blanket at home
Non-English speaking
Peripheral neuropathy
Fibromyalgia
Trial design
Design model
Parallel
Treatments tested in this trial
Best Practice
Other interventionUse a regular blanket
Supportive Care
Other interventionUse a weighted blanket
Survey Administration
Other interventionAncillary studies
Treatment groups
114 Participants
are divided into 2 treatment groupsGroup A: Arm I (weighted blanket)Experimental treatment 2 interventions
Group B: Arm II (regular blanket)Active comparator 2 interventions
Trial outcomes
Primary outcomes
1
Levels of anxiety using State Trait Anxiety Inventory (STAI) for Adults, short form
To test for the effect of the weighted blanket, will fit a mixed model with the post-level of anxiety as the dependent variable and baseline values and treatment (standard or weighted blanket) as predictors. A random effect for patient will be fit to account for the possible correlation of within-patient repeated outcome measures. The models will control for patient gender and age.
Time frame
At baseline and 30 minutes after use of either a standard or weighted blanketSecondary outcomes
1
Levels of distress using NCCN Distress Thermometer
To test for the effect of the weighted blanket, will fit a mixed model with the post-level of distress as the dependent variable and baseline values and treatment (standard or weighted blanket) as predictors. A random effect for patient will be fit to account for the possible correlation of within-patient repeated outcome measures. The models will control for patient gender and age.
Time frame
At baseline and 30 minutes after use of either a standard or weighted blanket2
Qualitative change in levels of anxiety based on the baseline and 30 min post blanket use scores using State Trait Anxiety Inventory (STAI) for Adults, short form
Will be binned to create ordinal variables. If they lead to an adequate sample size in each group, clinically relevant cut points will be used for binning. Otherwise, we will bin using outcome medians or terciles, with the choice depending on the distribution of the data collected after blanket use. The categorical representation will be modeled using generalized linear mixed modeling. We will fit ordinal logistic regression models with repeated measures. As with the linear mixed models, baseline values and treatment (standard or weighted blanket) will be predictors, with patient gender and age as control variables.
Time frame
At baseline and 30 minutes after use of either a standard or weighted blanket3
Qualitative change in levels of distress based on the baseline and 30 min post blanket use scores using NCCN Distress Thermometer
Will be binned to create ordinal variables. If they lead to an adequate sample size in each group, clinically relevant cut points will be used for binning. Otherwise, we will bin using outcome medians or terciles, with the choice depending on the distribution of the data collected after blanket use. The categorical representation will be modeled using generalized linear mixed modeling. We will fit ordinal logistic regression models with repeated measures. As with the linear mixed models, baseline values and treatment (standard or weighted blanket) will be predictors, with patient gender and age as control variables.
Time frame
At baseline and 30 minutes after use of either a standard or weighted blanketSponsors and contacts
Click on the lead sponsor to view all of their trials.
Source ClinicalTrials.gov