Aaron P Turner1, Danielle S Roubinov2, David C Atkins3, Jodie K Haselkorn4. 1. Veterans Affairs Puget Sound Healthcare System, Seattle, WA, USA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA. Electronic address: aaron.turner@va.gov. 2. Veterans Affairs Puget Sound Healthcare System, Seattle, WA, USA; Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA. 3. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA. 4. Veterans Affairs Puget Sound Healthcare System, Seattle, WA, USA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA; Department of Epidemiology, University of Washington, Seattle, WA, USA.
Abstract
BACKGROUND: Poor medication adherence exerts a substantial negative impact on the health and well-being of individuals with multiple sclerosis (MS). Improving adherence rates requires a proactive approach of frequent and ongoing monitoring; however, this can be difficult to achieve within traditional, reactive health care systems that generally emphasize acute care services. Telephone-based home monitoring may circumvent these barriers and facilitate optimal care coordination and management for individuals with MS and other chronic illnesses. OBJECTIVE: The current study evaluated the utility of a one-item, telephone-administered measure of adherence expectations as a prospective predictor of medication adherence across a six month period among individuals with MS. METHODS: As part of a longitudinal study, Veterans with MS (N = 89) who were receiving medical services through the Veterans Health Administration completed monthly telephone-based interviews for six months. RESULTS: Using mixed model regression analyses, adherence expectations predicted adherence after adjusting for demographic, illness-related, and psychosocial factors (B = -5.54, p < .01). CONCLUSIONS: Brief, telephone-based assessments of adherence expectations may represent an easy and efficient method for monitoring medication use among individuals with MS. The results offer an efficient method to detect and provide support for individuals who may benefit from interventions to promote medication adherence.
BACKGROUND: Poor medication adherence exerts a substantial negative impact on the health and well-being of individuals with multiple sclerosis (MS). Improving adherence rates requires a proactive approach of frequent and ongoing monitoring; however, this can be difficult to achieve within traditional, reactive health care systems that generally emphasize acute care services. Telephone-based home monitoring may circumvent these barriers and facilitate optimal care coordination and management for individuals with MS and other chronic illnesses. OBJECTIVE: The current study evaluated the utility of a one-item, telephone-administered measure of adherence expectations as a prospective predictor of medication adherence across a six month period among individuals with MS. METHODS: As part of a longitudinal study, Veterans with MS (N = 89) who were receiving medical services through the Veterans Health Administration completed monthly telephone-based interviews for six months. RESULTS: Using mixed model regression analyses, adherence expectations predicted adherence after adjusting for demographic, illness-related, and psychosocial factors (B = -5.54, p < .01). CONCLUSIONS: Brief, telephone-based assessments of adherence expectations may represent an easy and efficient method for monitoring medication use among individuals with MS. The results offer an efficient method to detect and provide support for individuals who may benefit from interventions to promote medication adherence.
Authors: Aliza Ben-Zacharia; Meagan Adamson; Allison Boyd; Paula Hardeman; Jennifer Smrtka; Bryan Walker; Tracy Walker Journal: Int J MS Care Date: 2018 Nov-Dec
Authors: Roger J Hart; Thomas D'Hooghe; Eline A F Dancet; Ramón Aurell; Bruno Lunenfeld; Raoul Orvieto; Antonio Pellicer; Nikolaos P Polyzos; Wenjing Zheng Journal: Reprod Sci Date: 2021-11-15 Impact factor: 2.924