Quality of Life and the Effects of Tailored Health Coaching in Fibromyalgia Patients
Specific Aims: To identify phenotypes of patients with fibromyalgia according to symptom clusters and to compare differences in quality of life (QOL) among different phenotypes. To examine the effects of technology-assisted and tailored health coaching in comparison to telephone support on health status, QOL, pain catastrophizing, and self-efficacy in patients with fibromyalgia. Methods: For Aims 1, the investigators will conduct a cross-sectional study and enroll 300 patients with fibromyalgia. Symptoms will be assessed using the Numerical Rating Scale, Athens Insomnia Scale, Functional Assessment of Cancer Therapy cognition scale, Beck Depression Index-II, Beck Anxiety Index, and Fatigue Severity Scale. The World Health Organization Quality of Life-Brief Form (WHOQOL-BREF) questionnaire will be used to assess participants' overall QOL. Hierarchical cluster analyses will be used to identify fibromyalgia phenotypes according to pain, physical, and psychological variables. A series of multivariate analysis of variance analyses will be used to compare the differences in WHOQOL-BREF scores among those phenotypes. For Aims 2, the investigators conduct an assessor-blind, parallel-group, randomized controlled trial and enroll 110 participants with fibromyalgia. Participants will be randomized to a health coaching group and a control group. The tailored, interactive health-coaching program will be delivered via mobile applications during a 10-week training period. The control group will receive standard education materials and weekly telephone support. The primary outcomes are the revised Fibromyalgia Impact Questionnaire and WHOQOL-BREF scores; the secondary outcomes are pain catastrophizing score and self-efficacy, which will be examined at baseline, post-training, and the 3th month follow-up. Data will be analyzed according to the intention-to-treat principle. To determine the effectiveness of health coaching on primary and secondary outcomes, differences in outcome variables will be analyzed with mixed-effects linear regression models. The between-group differences at the two posttests examined using a mixed-model will include group x time interaction. The investigators will adjust for the baseline score on the outcome variable and for demographics and comorbidities that differ significantly between the intervention and control groups at baseline.
Start: July 2019