Annina Seiler1, Vanessa Klaas2, Gerhard Tröster2, Christopher P Fagundes1,3. 1. Department of Psychology, Rice University, Houston, TX, USA. 2. Wearable Computing Laboratory, Department of Information Technology and Electrical Engineering, ETH Zurich, Zürich, Switzerland. 3. Department of Symptom Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Abstract
OBJECTIVES: To (1) evaluate existing eHealth/mHealth interventions developed to help manage cancer-related fatigue (CRF); and (2) summarize the best available evidence on their effectiveness. METHODS: A comprehensive literature search of PubMed, MEDLINE, EMBASE, and the Cochrane Library up to November 2016 was conducted. Study outcomes were extracted, tabulated, and summarized. Random effects meta-analyses were conducted for the primary outcome (fatigue), and the secondary outcomes quality of life and depression, yielding pooled effect sizes (r), and 95% confidence intervals (CI). RESULTS: For eHealth interventions, our search of published papers identified 9 completed studies and 6 protocols for funded projects underway. No studies were identified for mHealth interventions that met our inclusion criteria. A meta-analysis of the 9 completed eHealth studies revealed a statistically significant beneficial effect of eHealth interventions on CRF (r = .27, 95% CI [.1109 - .4218], P < 0.01). Therapist-guided eHealth interventions were more efficacious then self-guided interventions (r = .58, 95% CI: [.3136 - .5985, P < 0.001). Small to moderate therapeutic effects were also observed for HRQoL (r = .17, 95% CI [.0384 - .3085], P < 0.05) and depression (r = .24, 95% CI [.1431 - .3334], P < 0.001). CONCLUSIONS: eHealth interventions appear to be effective for managing fatigue in cancer survivors with CRF. Continuous development of eHealth interventions for the treatment of CRF in cancer survivors and their testing in long-term, large-scale efficacy outcome studies is encouraged. The degree to which mHealth interventions can change CRF in cancer survivors need to be assessed systematically and empirically.
OBJECTIVES: To (1) evaluate existing eHealth/mHealth interventions developed to help manage cancer-related fatigue (CRF); and (2) summarize the best available evidence on their effectiveness. METHODS: A comprehensive literature search of PubMed, MEDLINE, EMBASE, and the Cochrane Library up to November 2016 was conducted. Study outcomes were extracted, tabulated, and summarized. Random effects meta-analyses were conducted for the primary outcome (fatigue), and the secondary outcomes quality of life and depression, yielding pooled effect sizes (r), and 95% confidence intervals (CI). RESULTS: For eHealth interventions, our search of published papers identified 9 completed studies and 6 protocols for funded projects underway. No studies were identified for mHealth interventions that met our inclusion criteria. A meta-analysis of the 9 completed eHealth studies revealed a statistically significant beneficial effect of eHealth interventions on CRF (r = .27, 95% CI [.1109 - .4218], P < 0.01). Therapist-guided eHealth interventions were more efficacious then self-guided interventions (r = .58, 95% CI: [.3136 - .5985, P < 0.001). Small to moderate therapeutic effects were also observed for HRQoL (r = .17, 95% CI [.0384 - .3085], P < 0.05) and depression (r = .24, 95% CI [.1431 - .3334], P < 0.001). CONCLUSIONS: eHealth interventions appear to be effective for managing fatigue in cancer survivors with CRF. Continuous development of eHealth interventions for the treatment of CRF in cancer survivors and their testing in long-term, large-scale efficacy outcome studies is encouraged. The degree to which mHealth interventions can change CRF in cancer survivors need to be assessed systematically and empirically.
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