Unveiling the Nexus: Depressive Symptoms and Medication Adherence in Hypertensive Patients’ Self-care: A Systematic Review
Bibliographic record
Abstract
Background: Hypertension, a significant public health concern, is frequently linked to emotional disorders like depression. Research shows a reciprocal link between depression and hypertension, potentially influencing patients' adherence to self-care routines. Objective: This systematic review aimed to examine the association between depressive symptoms and aspects of self-care, with a focus on medication adherence in individuals diagnosed with hypertension. Methods: Following PRISMA guidelines, a systematic review was conducted by searching PubMed, PsycINFO and Scopus until March 17, 2023. The included studies involved quantitative primary research conducted in English, focusing on adults (≥18 years) diagnosed with hypertension and experiencing depressive symptoms. Observational studies were assessed using the Newcastle-Ottawa Scale, and randomized controlled trials were evaluated using the revised Cochrane Risk of Bias Tool (RoB 2.0). Due to the great diversity of these studies, a narrative synthesis of the results was undertaken. Results: A total of 18 studies involving 6,131 people with hypertension, that met our eligibility criteria were ultimately included. The reported rates of depressive symptoms ranged from 4% to 43%. Of these studies, nine reported a statistically significant association, showcasing an adverse impact of depressive symptoms on medication adherence. The remaining nine did not confirm the above. Conclusion: This systematic review highlights the diverse body of research exploring depressive symptoms and medication adherence among individuals with hypertension. The review suggests a need for increased attention to self-care practices, particularly in relation to adherence to antihypertensive medication. However, it recommends the conduction of more robust longitudinal studies to comprehensively explore this relationship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".