Malignant Solid Neoplasm Clinical Trial
Official title:
Weighted Blanket Use in Oncology Patients to Reduce Anxiety
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.
Status | Recruiting |
Enrollment | 114 |
Est. completion date | December 31, 2025 |
Est. primary completion date | December 31, 2024 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: - 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 - Alert and oriented Exclusion Criteria: - Currently using a weighted blanket at home - Non-English speaking - Peripheral neuropathy - Fibromyalgia - Open pressure ulcer - Recent surgical flap - Claustrophobic - Weight 45 kg or less |
Country | Name | City | State |
---|---|---|---|
United States | Ohio State University Comprehensive Cancer Center | Columbus | Ohio |
Lead Sponsor | Collaborator |
---|---|
Ohio State University Comprehensive Cancer Center |
United States,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | 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. | At baseline and 30 minutes after use of either a standard or weighted blanket | |
Secondary | 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. | At baseline and 30 minutes after use of either a standard or weighted blanket | |
Secondary | 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. | At baseline and 30 minutes after use of either a standard or weighted blanket | |
Secondary | 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. | At baseline and 30 minutes after use of either a standard or weighted blanket |
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