This clinical trial evaluates the impact of telehealth self-management coaching sessions on quality of life in pancreatic cancer survivors and their family care givers (FCGs). Patients with pancreatic cancer experience many symptoms because of the disease and treatment, which can have a negative impact on quality of life. Patients and their families have unmet needs during treatment, including a lack of quality of life programs that offer support to patients. Supporting patients and families on managing the physical symptoms, emotional well-being, social well-being and spiritual well-being with telehealth self-management coaching sessions may help improve quality of life, manage symptoms from treatment, and support families in their role as caregivers during treatment.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
SUPPORTIVE_CARE
Masking
NONE
Enrollment
136
Attend telehealth self-management coaching sessions
Attend telehealth self-management coaching sessions
Receive standard of care
Ancillary studies
Ancillary studies
City of Hope at Arcadia
Arcadia, California, United States
NOT_YET_RECRUITINGCity of Hope Corona
Corona, California, United States
NOT_YET_RECRUITINGCity of Hope at Duarte
Duarte, California, United States
RECRUITINGCity of Hope Comprehensive Cancer Center
Duarte, California, United States
NOT_YET_RECRUITINGCity of Hope at Glendale
Glendale, California, United States
NOT_YET_RECRUITINGCity of Hope at Glendora
Glendora, California, United States
NOT_YET_RECRUITINGCity of Hope Seacliff
Huntington Beach, California, United States
NOT_YET_RECRUITINGCity of Hope at Irvine Lennar
Irvine, California, United States
NOT_YET_RECRUITINGCity of Hope at Irvine Sand Canyon
Irvine, California, United States
NOT_YET_RECRUITINGCity of Hope Antelope Valley
Lancaster, California, United States
NOT_YET_RECRUITING...and 12 more locations
Patient reported quality of life (QOL)
Patient reported quality of life will be assessed by the Functional Assessment of Cancer Therapy- Hepatobiliary (FACT-Hep) quality of life score. The primary analysis will be a treatment group comparison of the QOL at 3 months via linear regression model, with adjustment for baseline FACT-Hep score and stratification factors. Robust standard errors will be estimated via generalized estimating equations to adjust for correlation between repeated outcome measures. The dependent variables will be transformed to approximate normality as appropriate.
Time frame: At baseline and at 3 months post randomization
Enrollment rate
Feasibility of the intervention will be defined as at least 60% of eligible participants enrolling. Descriptive statistics will be used to summarize the feasibility of the intervention. Reasons for non-participation will be recorded and used to make needed modifications for improvement in future studies.
Time frame: Up to 25 months
Intervention completion rate
Feasibility of the intervention will be defined as at least 60% of participants completing ≥ 80% of the intervention (4 of 6 sessions). Descriptive statistics will be used to summarize the feasibility of the intervention. Reasons for attrition will be recorded and used to make needed modifications for improvement in future studies.
Time frame: Up to 6 months
Rate of participants completing any follow-up assessments
Feasibility of the intervention will be defined as at least 60% of participants completing any follow-up assessments after randomization. Descriptive statistics will be used to summarize the feasibility of the intervention. Reasons for attrition will be recorded and used to make needed modifications for improvement in future studies.
Time frame: Up to 6 months
Participant experience
Participant experiences with the intervention will be explored through qualitative data (structured exit interviews) from participants randomized to the intervention group and analyzed using content analysis approach. Interviews will be transcribed and data analyzed. Transcripts will be imported for the development of analytic categories, data coding, and review of coded data. Codes will be sorted into themes based on links and relationship.
Time frame: Up to 6 months
Patient reported symptom severity
Treatment group comparisons will be assessed via repeated measures linear regression models with adjustment for baseline value of the outcome including sex and age and stratification factors. Robust standard errors will be estimated via generalized estimating equations to adjust for correlation between repeated outcome measures. The dependent variables will be transformed to approximate normality as appropriate.
Time frame: At baseline and at 3 and 6 months post randomization
Patient reported psychological distress
Patient reported psychological distress will be measured using the National Comprehensive Cancer Network (NCCN) Distress Thermometer (DT). Treatment group comparisons will be assessed via repeated measures linear regression models with adjustment for baseline value of the outcome including sex and age and stratification factors. Robust standard errors will be estimated via generalized estimating equations to adjust for correlation between repeated outcome measures. The dependent variables will be transformed to approximate normality as appropriate.
Time frame: At baseline and at 3 and 6 months post randomization
Family care giver (FCG) psychological distress
FCG reported psychological distress will be measured using the NCCN DT. Treatment group comparisons will be assessed via repeated measures linear regression models with adjustment for baseline value of the outcome including sex and age and stratification factors. Robust standard errors will be estimated via generalized estimating equations to adjust for correlation between repeated outcome measures. The dependent variables will be transformed to approximate normality as appropriate.
Time frame: At baseline and at 3 and 6 months post randomization
FCG caregiving burden
FCG caregiving burden will be measured using the Montgomery Borgatta Caregiver Burden Scale. Treatment group comparisons will be assessed via repeated measures linear regression models with adjustment for baseline value of the outcome including sex and age and stratification factors. Robust standard errors will be estimated via generalized estimating equations to adjust for correlation between repeated outcome measures. The dependent variables will be transformed to approximate normality as appropriate.
Time frame: At baseline and at 3 and 6 months post randomization
FCG QOL
FCG QOL will be measured using City of Hope Quality of Life-Family questionnaire. Treatment group comparisons will be assessed via repeated measures linear regression models with adjustment for baseline value of the outcome including sex and age and stratification factors. Robust standard errors will be estimated via generalized estimating equations to adjust for correlation between repeated outcome measures. The dependent variables will be transformed to approximate normality as appropriate.
Time frame: At baseline and at 3 and 6 months post randomization
Overall survival
Time frame: From initiation of intervention to death from any cause, up to 6 months
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