Physical Activity in Cardiac Implantable Electronic Device Recipients During the COVID-19 Pandemic

Fatigue and physical activity in post-CABG patients: the parallel mediation model of kinesiophobia and self-efficacy : a prospective multicentre and cross-sectional study in China

OBJECTIVES

The objective of this study was to investigate how kinesiophobia and self-efficacy explain the relationship between fatigue and physical activity (PA) in post-coronary artery bypass grafting (post-CABG) patients over the age of 45.

DESIGN

A prospective multicentre and cross-sectional study.

SETTING

The study was conducted in four public tertiary hospitals in China.

PARTICIPANTS

A total of 1278 patients who underwent CABG surgery were selected from the case pool, with their surgeries occurring between 3 and 19 months prior to selection. Out of 1038 patients who met the inclusion criteria and were invited to participate in the study, 759 patients agreed to participate and complete the questionnaire. Ultimately, 376 questionnaires were deemed eligible and included in the analysis.

PRIMARY AND SECONDARY OUTCOME MEASURES

The questionnaire included the following scales: the Chinese version of the Multidimensional Fatigue Inventory (MFI-20), the Tampa Scale for Kinesiophobia Heart (TSK-SV Heart), the Cardiac Exercise Self-Efficacy Instrument (CESEI) and the International Physical Activity Questionnaire-Long (IPAQ-L). A serial mediation model was used to test whether the association between fatigue and PA was mediated by kinesiophobia and self-efficacy, in the overall sample and subsamples defined by age.

RESULTS

The results confirmed that fatigue was directly (95% CI (-5.73 to -3.02)) associated with PA. Higher kinesiophobia (95% CI (-0.16 to -0.05)) or lower PA self-efficacy (95% CI (-0.11 to -0.02)) were parallel pathways through which higher fatigue impediment reduced PA levels. In both subgroups, the street pathways of kinesiophobia and self-efficacy were altered. In the age, 45-60 years group, kinesiophobia (Boot 95% CI (-0.19 to-0.05)) was a mediator of fatigue on PA levels, while in the 61-75 years age group, self-efficacy (Boot 95% CI (-0.17 to -0.04)) was a mediator of fatigue on PA levels.

CONCLUSIONS

A clear relationship between fatigue and PA was mediated by both kinesiophobia and self-efficacy. Furthermore, our findings highlight the importance of adapting the intervention according to the age of the patients, mainly by reducing patients' kinesiophobia in patients aged 45-60 years and increasing patients' self-efficacy in patients aged 61-75 years. It may be possible to improve PA levels in post-CABG patients over 45 years of age by eliminating kinesiophobia and increasing self-efficacy.

Implantable cardiac monitors: artificial intelligence and signal processing reduce remote ECG review workload and preserve arrhythmia detection sensitivity

Introduction

Implantable cardiac monitors (ICMs) provide long-term arrhythmia monitoring, but high rates of false detections increase the review burden. The new "SmartECG" algorithm filters false detections. Using large real-world data sets, we aimed to quantify the reduction in workload and any loss in sensitivity from this new algorithm.

Methods

Patients with a BioMonitor IIIm and any device indication were included from three clinical projects. All subcutaneous ECGs (sECGs) transmitted via remote monitoring were classified by the algorithm as "true" or "false." We quantified the relative reduction in workload assuming "false" sECGs were ignored. The remote monitoring workload from five hospitals with established remote monitoring routines was evaluated. Loss in sensitivity was estimated by testing a sample of 2000 sECGs against a clinical board of three physicians.

Results

Of our population of 368 patients, 42% had an indication for syncope or pre-syncope and 31% for cryptogenic stroke. Within 418.5 patient-years of follow-up, 143,096 remote monitoring transmissions contained 61,517 sECGs. SmartECG filtered 42.8% of all sECGs as "false," reducing the number per patient-year from 147 to 84. In five hospitals, nine trained reviewers inspected on average 105 sECGs per working hour. This results in an annual working time per patient of 83 min without SmartECG, and 48 min with SmartECG. The loss of sensitivity is estimated as 2.6%. In the majority of cases where true arrhythmias were rejected, SmartECG classified the same type of arrhythmia as "true" before or within 3 days of the falsely rejected sECG.

Conclusion

SmartECG increases efficiency in long-term arrhythmia monitoring using ICMs. The reduction of workload by SmartECG is meaningful and the risk of missing a relevant arrhythmia due to incorrect filtering by the algorithm is limited.