What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data

Abstract

Background Heart rate (HR), especially at nighttime, is an important biomarker for cardiovascular health. It is known to be influenced by overall physical fitness, as well as daily life physical or psychological stressors like exercise, insufficient sleep, excess alcohol, certain foods, socialization, or air travel causing physiological arousal of the body. However, the exact mechanisms by which these stressors affect nighttime HR are unclear and may be highly idiographic (i.e. individual-specific). A single-case or “ n-of-1” observational study (N1OS) is useful in exploring such suggested effects by examining each subject's exposure to both stressors and baseline conditions, thereby characterizing suggested effects specific to that individual. Objective Our objective was to test and generate individual-specific N1OS hypotheses of the suggested effects of daily life stressors on nighttime HR. As an N1OS, this study provides conclusions for each participant, thus not requiring a representative population. Methods We studied three healthy, nonathlete individuals, collecting the data for up to four years. Additionally, we evaluated model-twin randomization (MoTR), a novel Monte Carlo method facilitating the discovery of personalized interventions on stressors in daily life. Results We found that physical activity can increase the nighttime heart rate amplitude, whereas there were no strong conclusions about its suggested effect on total sleep time. Self-reported states such as exercise, yoga, and stress were associated with increased (for the first two) and decreased (last one) average nighttime heart rate. Conclusions This study implemented the MoTR method evaluating the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way: via the N-of-1 approach. A Python implementation of MoTR is freely available.

Dejà vu: Recurrent Neural Networks for health wearables data forecast

“…The slightly more accurate model (GRU) was able to forecast the average, minimum, and maximum sleeping HR and the "time asleep" with a mean RMSE of 4.4 (± 1.4) BPM, 4.9 (± 2.6) BPM, 12.1 (± 4.0) BPM, and 5,320.0 (± 1,379.1) seconds, respectively. These values may be clinically significant, as discussed in our past research [15].…”

“…Additionally, this forecast of sleeping HR and the total time asleep should have low error rates, as small errors are significant while interpreting health data. [15] To achieve those research goals, we did three major steps during the design and evaluation of the models: S1) We elaborated on the ideal hyperparameters of RNNs for sleep trackers' data forecast. S2) We compared three approaches to train those networks (POPULATION, INDIVIDUAL, and FINE-TUNED).…”

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation

“…For such far-sighted thinking, there needs to be a new paradigm on what is acceptable and of value to all stakeholders including the patient, the sponsor, and the healthcare system. In particular, there needs to be significant investment in validation of effectiveness on the individual level, for example, N-of-1 designs [107][108][109]. This will include developing our understanding of how to incorporate contextual information into digital measures.…”