Dataset Information

Pilot randomized controlled trial Protocol: Life context-informed pre-visit planning to improve care plans for primary care patients with multiple chronic conditions including diabetes.

ABSTRACT:

Background

Multimorbidity is common, and care is impacted by patient life context. Effective, efficient interventions to improve patient-centered outcomes such as perceived treatment burden are limited. There is a need for interventions that integrate patient contextual information into primary care encounters to improve such outcomes. Patient life context is a multitude of factors that influence a patient's life and healthcare, including social determinants of health and broader elements such as family and work demands.

Methods

This pilot randomized controlled trial (RCT) protocol will compare standard pre-visit planning to context-informed pre-visit planning that incorporates the patient's life context, for patients with diabetes plus other chronic comorbid conditions. Primary outcomes include measures of trial protocol and intervention feasibility and acceptability: physician study and visit perceived burden, patient satisfaction, and patient, physician and staff experience with the trial. Additional measurements of intervention impact include: initial estimates of effect size on patient treatment burden and other patient-oriented outcomes, change in glycemic control, and other intermediate medical outcomes.

Discussion

This intervention is novel as it collects patient life context information using a direct person-to-person approach, allows physicians to review the information prior to patient arrival at the clinic and, where appropriate, incorporate it when negotiating treatment plans, and is longitudinal, summarizing evolving contextual information over time. This pilot RCT has the potential to demonstrate trial protocol and intervention feasibility and acceptability, and estimate effect size on patient and provider outcomes, to inform for a future, definitive RCT.Trial Registration: This trial was registered at ClinicalTrials.gov prior to patient enrollment: NCT04568382.

SUBMITTER: Magnan E

PROVIDER: S-EPMC8647221 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundmHealth tablet-based interventions are increasingly being studied and deployed in various health care settings, yet little knowledge exists regarding patient uptake and acceptance or how patient demographics influence these important implementation metrics.ObjectiveTo determine which factors influence the uptake and successful completion of an mHealth tablet questionnaire by analyzing its implementation in a primary care setting.MethodsWe prospectively studied a patient-facing electronic touch-tablet asthma questionnaire deployed as part of the Electronic Asthma Management System. We describe tablet uptake and completion rates and corresponding predictor models for these behaviors.ResultsThe tablet was offered to and accepted by patients in 891/1715 (52.0%) visits. Patients refused the tablet in 33.0% (439/1330) visits in which it was successfully offered. Patients aged older than 65 years of age (odds ratio [OR] 2.30, 95% CI 1.33-3.95) and with concurrent chronic obstructive pulmonary disease (OR 2.22, 95% CI 1.05-4.67) were more likely to refuse the tablet, and those on an asthma medication (OR 0.55, 95% CI 0.30-0.99) were less likely to refuse it. Once accepted, the questionnaire was completed in 784/891 (88.0%) instances, with those on an asthma medication (OR 0.53, 95% CI 0.32-0.88) being less likely to leave it incomplete.ConclusionsOlder age predicted initial tablet refusal but not tablet questionnaire completion, suggesting that perceptions of mHealth among older adults may negatively impact uptake, independent of usability. The influence of being on an asthma medication suggests that disease severity may also mediate mHealth acceptance. Although use of mHealth questionnaires is growing rapidly across health care settings and diseases, few studies describe their real-world acceptance and its predictors. Our results should be complemented by qualitative methods to identify barriers and enablers to uptake and may inform technological and implementation strategies to drive successful usage.