Understanding the Psychological Factors that Impact Hypertension: A Systematic Review
Bibliographic record
Abstract
Abstract The development and management of hypertension strongly depends on psychological elements which include depression, anxiety together with stress and psychosocial support. This review analysed psychological elements that affect hypertension development from the year 2014 to 2025. The research identified 106 studies through a systematic database search of MEDLINE, Embase, PsycINFO, Scopus and CINAHL. Due to diverse methodologies, the research employed a narrative synthesis approach. The risk of bias was assessed using the Cochrane Risk of Bias Tool, Newcastle–Ottawa Scale, Domain-Based Approach, CASP, and ROB-MR Instrument. Results indicated that both depression and anxiety increased the risk of developing hypertension and decreased adherence to treatment. In contrast, mindfulness-based interventions showed potential blood pressure lowering effects, although evidence for long term outcomes is limited. People with strong psychosocial support networks and higher levels of life satisfaction had better medication adherence and lower stress levels. However, the current evidence base shows that most studies originate from high-income countries, with low- and middle-income countries having limited evidence and most low-income settings contributing only one or two studies, with no more than four studies per country. The results support the need to integrate physical and mental health care models in the management of hypertension. To enhance understanding of the psychological aspects of hypertension, future research should include underrepresented regions and implement both longitudinal and qualitative methods.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".