MINDSET: Clinic-based decision support demonstrates longitudinal efficacy for increased epilepsy self-management adherence among Spanish speaking patients

2020 ◽  
Vol 113 ◽  
pp. 107552
Author(s):  
Ross Shegog ◽  
Charles Begley ◽  
Jenny Chong ◽  
Refugio Sepulveda ◽  
Robert Addy ◽  
...  
Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 778-P
Author(s):  
ZIYU LIU ◽  
CHAOFAN WANG ◽  
XUEYING ZHENG ◽  
SIHUI LUO ◽  
DAIZHI YANG ◽  
...  

2005 ◽  
Vol 29 (3) ◽  
pp. 225-235 ◽  
Author(s):  
Milagros C. Rosal ◽  
Barbara Olendzki ◽  
George W. Reed ◽  
Olga Gumieniak ◽  
Jeffrey Scavron ◽  
...  

2017 ◽  
Vol 40 (6) ◽  
pp. 541-554 ◽  
Author(s):  
Cheryl A. Smith-Miller ◽  
Diane C. Berry ◽  
Cass T. Miller

2018 ◽  
Vol 88 ◽  
pp. 218-226 ◽  
Author(s):  
Charles Begley ◽  
Jenny Chong ◽  
Ross Shegog ◽  
Refugio Sepulveda ◽  
Noelia Halavacs ◽  
...  

2009 ◽  
Vol 35 (5) ◽  
pp. 843-850 ◽  
Author(s):  
Amer A. Kaissi ◽  
Michael Parchman

Purpose The purpose of this article is to examine the relationship between organizational characteristics as measured by the Chronic Care Model (CCM) and patient self-management behaviors among patients with type 2 diabetes. Methods The study design was cross-sectional. The study setting included 20 primary care clinics from South Texas. The sample included approximately 30 consecutive patients that were enrolled from each clinic for a sample of 617 patients. For the data collection procedures, the CCM survey was completed by caregivers in the clinic. Self-management behaviors were obtained from patient exit surveys. For measures, the CCM consisted of 6 structural dimensions: (1) organization support, (2) community linkages, (3) self-management support, (4) decision support system, (5) delivery system design, and (6) clinical information systems. Patient self-management behavior included whether the patient reported always doing all 4 of the following behaviors as they were instructed: (1) checking blood sugars, (2) following diabetes diet, (3) exercising, and (4) taking medications. For data analyses, to account for clustering of patients within clinics, hierarchical logistic regression models were used. Results Self-management support was positively associated with medication adherence, while decision support system was positively associated with exercise and all 4 self-management behaviors. Surprisingly, community linkages were negatively associated with medication adherence, while clinical information system was negatively associated with diet and all 4 behaviors. A total score, including all dimensions, was positively associated with only exercise. Conclusions Health care providers and diabetes educators in primary care clinics should consider how organizational characteristics of the clinic might influence self-management behaviors of patients. The focus should be on better access to evidence-based information at the point of care and self-management needs and activities.


Author(s):  
Abd El Rahman Shabayek ◽  
Renato Baptista ◽  
Konstantinos Papadopoulos ◽  
Girum Demisse ◽  
Oyebade Oyedotun ◽  
...  

2017 ◽  
Author(s):  
Paul Jarle Mork ◽  
Kerstin Bach

BACKGROUND Low back pain (LBP) is a leading cause of disability worldwide. Most patients with LBP encountered in primary care settings have nonspecific LBP, that is, pain with an unknown pathoanatomical cause. Self-management in the form of physical activity and strength and flexibility exercises along with patient education constitute the core components of the management of nonspecific LBP. However, the adherence to a self-management program is challenging for most patients, especially without feedback and reinforcement. Here we outline a protocol for the design and implementation of a decision support system (DSS), selfBACK, to be used by patients themselves to promote self-management of LBP. OBJECTIVE The main objective of the selfBACK project is to improve self-management of nonspecific LBP to prevent chronicity, recurrence and pain-related disability. This is achieved by utilizing computer technology to develop personalized self-management plans based on individual patient data. METHODS The decision support is conveyed to patients via a mobile phone app in the form of advice for self-management. Case-based reasoning (CBR), a technology that utilizes knowledge about previous cases along with data about the current patient case, is used to tailor the advice to the current patient, enabling a patient-centered intervention based on what has and has not been successful in previous patient cases. The data source for the CBR system comprises initial patient data collected by a Web-based questionnaire, weekly patient reports (eg, symptom progression), and a physical activity-detecting wristband. The effectiveness of the selfBACK DSS will be evaluated in a multinational, randomized controlled trial (RCT), targeting care-seeking patients with nonspecific LBP. A process evaluation will be carried out as an integral part of the RCT to document the implementation and patient experiences with selfBACK. RESULTS The selfBACK project was launched in January 2016 and will run until the end of 2020. The final version of the selfBACK DSS will be completed in 2018. The RCT will commence in February 2019 with pain-related disability at 3 months as the primary outcome. The trial results will be reported according to the CONSORT statement and the extended CONSORT-EHEALTH checklist. Exploitation of the results will be ongoing throughout the project period based on a business plan developed by the selfBACK consortium. Tailored digital support has been proposed as a promising approach to improve self-management of chronic disease. However, tailoring self-management advice according to the needs, motivation, symptoms, and progress of individual patients is a challenging task. Here we outline a protocol for the design and implementation of a stand-alone DSS based on the CBR technology with the potential to improve self-management of nonspecific LBP. CONCLUSIONS The selfBACK project will provide learning regarding the implementation and effectiveness of an app-based DSS for patients with nonspecific LBP. REGISTERED REPORT IDENTIFIER RR1-10.2196/9379


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