ArticleContemporary clinical trials communications2022
Effectiveness of daily activity record-based self-monitoring intervention for patients with chronic heart failure: A study protocol.
Article in Contemporary clinical trials communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- Patient participation in self-monitoring regarding healthcare of heart failure: an integrated systematic review.BMC primary care · 2025Pooled it
- Interventions to Enhance Interoception and Symptom Attribution in Patients with Heart Failure: A Scoping Review.Current heart failure reports · 2025Article
- Assessment Tools for Measuring Health Literacy and Digital Health Literacy in a Hospital Setting: A Scoping Review.Healthcare (Basel, Switzerland) · 2023Article
- Measuring Importance of Physical Self-Care Behavior in Patients with Heart Failure: Validation and Reliability Analysis of 14-item IPSC Scale.Clinics and practice · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The prevention of recurrent readmission among heart failure (HF) patients requires support for appropriate self-care behaviors to prevent exacerbation of HF and self-monitoring to allow for patients' early perception of physical changes during exacerbations. Such support may enable patients to seek early consultation. This study developed a self-monitoring intervention that aimed at increasing the perception of patient-unique physical sensations caused by HF, based on daily activity records of patients. Method: A parallel two-arm randomized controlled trial is being conducted with 68 HF patients early after their discharge. Participants in both groups wear a wristwatch activity tracker from time-of-discharge. Participants in the self-monitoring intervention group receive support to reflect on their actual daily activities and the associated physical sensations they experienced, based on their daily activity records. The primary outcome is participants' "Asking for Help" dimension of self-care behavior, measured using the European Heart Failure Self-Care Behavior Scale at one month follow-up after intervention. Conclusion: This study is the first trial to use an activity tracker as a tool for symptom perception among HF patients. The problem of delayed consultations during exacerbations may be resolved by assisting patients in improving their perception of their unique physical sensations associated with specific daily activities, based on their daily activity records. If the effect is clarified, it could lead to the construction of new nursing interventions for continuous disease management that aim towards re-hospitalization prevention.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.