Self-care management and experiences of using telemonitoring as support when living with hypertension or heart failure: A descriptive qualitative study
Bibliographic record
Abstract
Background: The need for support in self-care at home will increase with the growing older population with chronic illness. Many people have one or more chronic illnesses and struggle with self-care activities, often supported by informal carers at home. The rapid development of telemonitoring applications in primary care calls for increased knowledge about how people with chronic illness and their informal carers experience the use of telemonitoring applications at home. Objective: This study aims to describe experiences of self-care management at home when living with hypertension or heart failure, with support from primary care through telemonitoring. Design: A descriptive qualitative approach was applied using semi-structured interviews with patients and informal carers in a pilot project on telemonitoring of chronic illness in primary care from October 2019 to June 2021. Setting: Participants were recruited from three primary care settings and one medical department at one hospital in a region in southern Sweden. Participants: = 4) were recruited. Methods: Semi-structured telephone interviews were conducted, guided by open-ended questions targeting patients' and informal carers' experiences of self-care management at home and using telemonitoring applications as support. Transcribed interviews were analyzed through qualitative content analysis. Results: 'Developing the capability to perform self-care with technology as both an intruder and an invited guest' was the unifying theme that tied together the experiences of patients with chronic illness and their informal carers. Experiences of self-care management included acquiring necessary self-care skills, expertise in managing their chronic illness, and reciprocal relationships with healthcare professionals when using telemonitoring application as support in self-care monitoring of vital parameters. However, uncertainty regarding the interpretation of symptoms and a feeling of exclusion were seen. Conclusions: Telemonitoring applications offer potential support for patients with chronic illnesses and their informal carers, enabling them to establish new routines and enhance motivation for self-care activities at home. This study emphasizes the adaptability of telemonitoring applications in meeting the unique support requirements of patients and informal carers when managing self-care at home.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".