Cardiac telerehabilitation is a method that uses digital technologies to deliver cardiac rehabilitation from a distance. It has been shown to have benefits to improve patients' disease outcomes and quality of life, and further reduce readmission and adverse cardiac events. The outbreak of the coronavirus pandemic has brought considerable new challenges to cardiac rehabilitation, which foster cardiac telerehabilitation to be broadly applied. This transformation is associated with some difficulties that urgently need some innovations to search for the right path. Artificial intelligence, which has a high level of data mining and interpretation, may provide a potential solution. This review evaluates the current application and limitations of artificial intelligence in cardiac telerehabilitation and offers prospects.
AimsTo describe the experiences and perceptions of acute myocardial infarction (AMI) patients with a prolonged decision‐making phase of treatment‐seeking.BackgroundPrevious attempts to reduce the treatment‐seeking time of AMI have been less than optimal. Due to the coronavirus disease 2019 (COVID‐19) pandemic, the situation of prehospital delay is possibly worse. Decisions to seek treatment are influenced by multiple factors and need individualised interventions. Understanding patients' external and internal experiences and psychological perceptions is essential.DesignMeta‐synthesis.Data sourcesWe searched PubMed, Embase, Cochrane Library, Web of Science, Scopus and four Chinese databases from inception to April 2022.MethodsWe screened the retrieved articles with predetermined inclusion and exclusion criteria, and reviewed articles using Thomas and Harden's (BMC Medical Research Methodology, 2008 8, 45) qualitative thematic synthesis approach. The Joanna Briggs Institute critical appraisal tool for qualitative research was used to assess the quality of studies.ResultsTwenty‐one studies were included, identifying four themes and nine sub‐themes. The four primary themes were difficulty recognising and attributing symptoms, attempt to act, unwillingness to change and self‐sacrifice.ConclusionDeciding to seek treatment is a complex social and psychological process, which needs comprehensive interventions considering personal and sociocultural factors and factors related to the COVID‐19 pandemic.Implications for the profession and/or patient careDetails of interventions for decisions to seek treatment in AMI patients need to be further designed and evaluated.ImpactResults would help healthcare professionals to implement individualised management of decision‐making of treatment‐seeking among AMI patients, and improve medical records of patients' prehospital experiences.Reporting MethodThe Preferred Reporting Items for Systematic Reviews 2020 checklist was used to report the findings.Patient or Public ContributionTwo AMI patients contributed to the data synthesis by giving simple feedback about the final themes.
Purpose A comprehensive health history contributes to identifying the most appropriate interventions and care priorities. However, history-taking is challenging to learn and develop for most nursing students. Chatbot was suggested by students to be used in history-taking training. Still, there is a lack of clarity regarding the needs of nursing students in these programs. This study aimed to explore nursing students’ needs and essential components of chatbot-based history-taking instruction program. Methods This was a qualitative study. Four focus groups, with a total of 22 nursing students, were recruited. Colaizzi's phenomenological methodology was used to analyze the qualitative data generated from the focus group discussions. Results Three main themes and 12 subthemes emerged. The main themes included limitations of clinical practice for history-taking, perceptions of chatbot used in history-taking instruction programs, and the need for history-taking instruction programs using chatbot. Students had limitations in clinical practice for history-taking. When developing chatbot-based history-taking instruction programs, the development should reflect students’ needs, including feedback from the chatbot system, diverse clinical situations, chances to practice nontechnical skills, a form of chatbot (i.e., humanoid robots or cyborgs), the role of teachers (i.e., sharing experience and providing advice) and training before the clinical practice. Conclusion Nursing students had limitations in clinical practice for history-taking and high expectations for chatbot-based history-taking instruction programs.
Aims We sought to explore the latent classifications of psychosocial adaptation in young and middle-aged patients with acute myocardial infarction (AMI) and analyze the characteristics of different profiles of AMI patients. Methods and results A cross-sectional study was performed in 438 Chinese young and middle-aged patients with AMI. The investigation time was 1 month after discharge. Three different self-report instruments were distributed to the participants, including the Psychosocial Adjustment to Illness Scale, the Perceived Stress Scale, and the Social Support Rating Scale. The 7 dimensions of the Psychosocial Adjustment to Illness Scale was then used to perform a latent profile analysis. All participants signed informed consent forms in accordance with the ethical principles of the Declaration of Helsinki. Finally, a total of 411 young and middle-aged AMI patients were enrolled. Three distinct profiles were identified, including the “well-adapted group” (44.8%), “highlight in psychological burdens group” (25.5%), and “poorly adapted group” (29.7%). The influencing factors included stress perception, social support, occupational type, and marital status (p < 0.05). Conclusion The psychosocial adaptation of young and middle-aged AMI patients can be divided into 3 profiles. Clinical nurses can carry out individualized psychological interventions according to the characteristics of patients in different potential profiles to improve the psychosocial adaptation of patients and the prognosis of their disease.
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