Risk Factors for Depression in Patients Undergoing Hematopoietic Cell Transplantation
Biology of blood and marrow transplantation: journal of the American Society for Blood and Marrow Transplantation (Impact Factor: 3.4). 03/2014; 20(7). DOI: 10.1016/j.bbmt.2014.03.010
Despite the prevalence and known adverse impacts of depression after hematopoietic cell transplantation (HCT), little is known about the trajectory of depression following HCT, or which pre-transplant risk factors might help predict new or worsening depression post-HCT. This secondary analysis was conducted to evaluate the relationships between pre-transplant patient-reported outcomes and demographic characteristics and post-transplant depression. 228 adult HCT patients were evaluated pre-transplant (T1) and 6 to 7 weeks post-transplant (T2), using touch-screen computers in the transplant clinic during participation in a larger trial. Measures included the Symptom Distress Scale, the EORTC QLQ-C30 for quality of life, a single-item Pain Intensity question, and the PHQ-9 for measurement of depression. At T1, rates of depression were quite low with only 6% of participants endorsing moderate or higher depression. At T2, however, 31% had moderate or higher depression. We observed a strong linear relation in PHQ-9 scores between T1 and T2 (p<.0001). T1 depression score was a significant predictor of depression scores at T2 (p=.03), as was poorer emotional function at T1 (p<.01). Results indicate that depression is common post-HCT, even for patients with low depression pre-transplant. Frequent screening for depressive symptoms at critical time points, including 6-7 weeks post-HCT, are needed in this population, followed by referrals to supportive care as appropriate.
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ABSTRACT: Effective eHealth interventions can benefit a large number of patients with content intended to support self-care and management of both chronic and acute conditions. Even though usage statistics are easily logged in most eHealth interventions, usage or exposure has rarely been reported in trials, let alone studied in relationship to effectiveness. The intent of the study was to evaluate use of a fully automated, Web-based program, the Electronic Self Report Assessment-Cancer (ESRA-C), and how delivery and total use of the intervention may have affected cancer symptom distress. Patients at two cancer centers used ESRA-C to self-report symptom and quality of life (SxQOL) issues during therapy. Participants were randomized to ESRA-C assessment only (control) or the ESRA-C intervention delivered via the Internet to patients' homes or to a tablet at the clinic. The intervention enabled participants to self-monitor SxQOL and receive self-care education and customized coaching on how to report concerns to clinicians. Overall and voluntary intervention use were defined as having ≥2 exposures, and one non-prompted exposure to the intervention, respectively. Factors associated with intervention use were explored with Fisher's exact test. Propensity score matching was used to select a sample of control participants similar to intervention participants who used the intervention. Analysis of covariance (ANCOVA) was used to compare change in Symptom Distress Scale (SDS-15) scores from pre-treatment to end-of-study by groups in the matched sample. Radiation oncology participants used the intervention, overall and voluntarily, more than medical oncology and transplant participants. Participants who were working and had more than a high school education voluntarily used the intervention more. The SDS-15 score was reduced by an estimated 1.53 points (P=.01) in the intervention group users compared to the matched control group. The intended effects of a Web-based, patient-centered intervention on cancer symptom distress were modified by intervention use frequency. Clinical and personal demographics influenced voluntary use. Clinicaltrials.gov NCT00852852; http://clinicaltrials.gov/ct2/show/NCT00852852 (Archived by WebCite at http://www.webcitation.org/6YwAfwWl7).Journal of Medical Internet Research 06/2015; 17(6):e136. DOI:10.2196/jmir.4190 · 3.43 Impact Factor
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