An Patient Education Framework For Designing Personalized Self-Management Interventions For Home-Based Chronic Disease Management
Bibliographic record
Abstract
Patient engagement in their care process, vis-à-vis self-management programs is an important element of the patient's longitudinal care plan, where the patient is encouraged and expected to achieve self-efficacy in the self-management of the disease through a regime of educational and behavioral modification strategies. To ensure the effectiveness of self-management programs, it is important that the proposed self-management interventions are (a) personalized to the unique needs and constraints of the patient; (b) based on sound theoretical health models; (c) based on validated health and behavior assessment tools to determine the patient's physical and behavioral dispositions; and (d) readily accessible to the patient through a ubiquitous medium, such as smart phones or the web. In this paper, we present a novel personalized self-management framework that delivers personalized health educational interventions to empower, educate and engage patients/individuals through self-observation, barrier identification, goal setting and action planning to achieve behavioral self-efficacy and self-regulation so that individuals can self-manage their condition. Our personalized self-management framework is guided by Social Cognition Theory, whereby have ensured that self-management programs for chronic disease management not just focus on changing the patient's awareness of the disease, rather they focus on enhancing the ability of the patient to make the right choices to achieve effective disease management. We present a three-stage personalized self-management framework that comprises: Stage 1: A high-level characterization of an individual with respect to a specific health outcome using validated assessment tools; Stage 2: A behavioral categorization of the individual based on his/her levels of self-efficacy, motivation and self-regulation, etc.; Stage 3: Use the personalized profile of the individual to tailor generic educational and self-management to develop a personalized self-management program that comprises personalized strategies to counter the challenges and barriers faced by the individual to achieving positive self-efficacy and self-regulation which in turn will lead to positive health outcomes. Our personalized self-management framework features (a) a novel self-management oriented individual profiling mechanism that takes into account both the health and psychosocial characteristics of an individual to generate his/her holistic profile; (b) a semantic web based knowledge model that captures the theoretical foundations of the SCT in terms of a Self-Management Program Personalization (SPP) ontology; (c) a semantic web based personalization tool that uses a logic-based execution engine that reasons over the SPP ontology, based on an individual's profile, to generate personalized self-management interventions; and (d) a mobile messaging platform to deliver the personalized self-management interventions and to monitor the patient's compliance using smart phones. We take a semantic web approach in designing the personalization approach whereby we have developed the SPP ontology to (a) model the theoretical framework of SCT in terms of SCT concepts; (b) model health assessment tools; (c) model the personalization rules that integrate the health and SCT models with the educational messages to generate a personalized self-management program. We have demonstrated the novel integration of health models, educational content and behavior change strategies to design self-management programs for cardiac risk factors, where the program is delivered through a mobile app.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".