Influence of respiration frequency on heart rate variability parameters:A randomized cross-sectional study

BACKGROUND: Many patients visiting physiotherapists for musculoskeletal disorders face psychosocial challenges which may form a large barrier to recover. There are only a limited number of evidence based psychosocial therapies, but they are mainly based on breathing exercises. OBJECTIVE: to study which respiration frequency would lead to the highest relaxation, reflected in vagal tone derived from the heart rate variability (HRV) in healthy subjects. METHODS: A randomized controlled cross sectional study was performed. Respiration cycles of four, five, six, seven and eight breaths per minute (BPM) were delivered in randomized order for two minutes each. HRV metrics were measured during the sessions with electrocardiogram (ECG). Repeated Measures ANOVA's were performed to analyze differences between breathing frequencies. RESULTS: 100 healthy volunteers were included (40 male). Standard Deviation of inter beat intervals (SDNN) values were significantly highest at 5 BPM, whereas the Root Mean Square of Successive Differences (RMSSD) values appeared highest at 7 breaths per minute (p < 0.01). High Frequency (HF) power was lowest at 4 BPM, whereas Low Frequency (LF) power was not significantly influenced by respiration frequency. CONCLUSIONS: Breathing at a frequency of 5 to 7 breaths per minute leads to highest HRV values, but there is no single respiration ratio that maximizes all metrics. Physiotherapists may use five to seven BPM as guidance to determine ideal breathing frequencies

Ethical Considerations of Using Machine Learning for Decision Support in Occupational Health:An Example Involving Periodic Workers' Health Assessments

Purpose Computer algorithms and Machine Learning (ML) will be integrated into clinical decision support within occupational health care. This will change the interaction between health care professionals and their clients, with unknown consequences. The aim of this study was to explore ethical considerations and potential consequences of using ML based decision support tools (DSTs) in the context of occupational health. Methods We conducted an ethical deliberation. This was supported by a narrative literature review of publications about ML and DSTs in occupational health and by an assessment of the potential impact of ML-DSTs according to frameworks from medical ethics and philosophy of technology. We introduce a hypothetical clinical scenario from a workers' health assessment to reflect on biomedical ethical principles: respect for autonomy, beneficence, non-maleficence and justice. Results Respect for autonomy is affected by uncertainty about what future consequences the worker is consenting to as a result of the fluctuating nature of ML-DSTs and validity evidence used to inform the worker. A beneficent advisory process is influenced because the three elements of evidence based practice are affected through use of a ML-DST. The principle of non-maleficence is challenged by the balance between group-level benefits and individual harm, the vulnerability of the worker in the occupational context, and the possibility of function creep. Justice might be empowered when the ML-DST is valid, but profiling and discrimination are potential risks. Conclusions Implications of ethical considerations have been described for the socially responsible design of ML-DSTs. Three recommendations were provided to minimize undesirable adverse effects of the development and implementation of ML-DSTs

Predictive value of Heart Rate Variability measurements and the Brief Resilience Scale for workability and vitality

BACKGROUND: Sustainable employability is increasingly important with current socio-economic challenges. Screening for resilience could contribute to early detection of either a risk, or a protector for sustainable employability, the latter being operationalized as workability and vitality.OBJECTIVE: To study the predictive value of Heart Rate Variability (HRV) measurements and the Brief Resilience Scale (BRS) for worker self-reported workability and vitality after 2-4 years.METHODS: Prospective observational cohort study with mean follow-up period of 38 months. 1,624 workers (18-65 years old) in moderate and large companies participated. Resilience was measured by HRV (one-minute paced deep breathing protocol) and the BRS at baseline. Workability Index (WAI), and the Vitality dimension of the Utrecht Work Engagement Scale-9 (UWES-9-vitality) were the outcome measures. Backward stepwise multiple regression analysis (p &lt; 0.05) was performed to evaluate the predictive value of resilience for workability and vitality, adjusted for body mass index, age and gender.RESULTS: N = 428 workers met inclusion criteria after follow-up. The contribution of resilience, measured with the BRS, was modest but statistically significant for the prediction of vitality (R2 = 7.3%) and workability (R2 = 9.2%). HRV did not contribute to prediction of workability or vitality. Age was the only significant covariate in the WAI model.CONCLUSION: Self-reported resilience modestly predicted workability and vitality after 2-4 years. Self-reported resilience may provide early insight into the ability of workers to stay at work, although caution must be applied because explained variance was modest. HRV was not predictive.</p