1. Background: The global COVID-19 lockdowns are unique psychological factors, requiring adjustment and coping. This illustrative, South African case study investigated the influence the COVID-19 lockdown context would have on meditation and physical activity. As these were lifestyle patterns the null hypothesis was of no change. 2. Method: The methodology consisted of quantitative and qualitative phases. The quantitative phase was an empirical, case study review of data from the author’s HeartMath and Fitbit electronic devices, as from 9 November 2019 to 31 July 2021. This consisted of coherence and achievement data from the HeartMath Inner Balance application (app), as well as distance and activity data from the Fitbit app. The qualitative phase consisted of the selection and illustration of meditation sessions documented over lockdown. 3. Results: The highly significant increases in physical activity and meditation coherence and achievement collectively indicated significant health promotion over the COVID-19 Lockdown period. Subtle energetic, experiential descriptions of meditation apprehensions resonated and supported these quantitative findings. 4. Conclusions: COVID-19 lockdown has meant transformation, humility, and reordering of priorities. The meditation descriptions amplified information from the HeartMath and Fitbit electronic devices, together co-creating conscious, embodied, relationship science for discussion, instruction, and further research.

Sleep quality has been directly linked to cognitive function, quality of life, and a variety of serious diseases across many clinical domains such as psychiatry and cardiology. Standard methods for assessing sleep involve overnight studies in hospital settings, which are uncomfortable, expensive, not representative of real sleep, and difficult to conduct on a large scale. Recently, a number of commercial digital devices have been developed that record physiological data which can act as a proxy for sleep quality in lieu of standard electroencephalogram recording equipment. Each device company makes different claims of accuracy and measures different features of sleep quality, and it is still unknown how well these devices correlate with one another and perform in a research setting. In this pilot study of 21 participants, we investigated whether outputs from four sensors, specifically FitBit, Withings Aura, Hexoskin, and Oura Ring, were related to known cognitive and psychological metrics, including the PSQI and N-back test. We found that sleep metrics extracted from these devices did not predict cognitive and psychological metrics well in our pilot data. However, we did identify certain signification associations, specifically the Oura Ring’s total sleep duration and efficiency in relation to the PSQI measure with p=0.004 and p=0.033, respectively. Additionally, correlation of various sleep features among the devices across the sleep cycle was almost uniformly low. These findings can hopefully be used to guide future sensor-based sleep research.

This study evaluates the reliability of Fitbit in assessing frailty based on motor and heart rate (HR) parameters through a validated upper extremity function (UEF) test, which involves 20 seconds of rapid elbow flexion. For motor performance, participants completed six trials of full elbow flexion using their right arm, with and without weight. Fitbit and a commercial motion sensor were worn on the right arm. For HR measurements, an ECG system was placed on the left chest alongside the Fitbit on the left wrist. Motor parameters assessing speed, flexibility, weakness, exhaustion, and HR before, during, and after UEF were measured. A total of 42 participants (age=22.022.67) were recruited. For motor parameters, excellent agreement was observed between the wearable sensor and Fitbit, except for flexibility (ICC=0.870.09). For HR parameters, ICC values showed weak agreement between ECG and Fitbit for HR increase and recovery (ICC=0.240.11), while moderate to stronger agreement was seen for mean HR during baseline, task, and post-task (ICC=0.810.13). Fitbit is a reliable tool for assessing frailty through motor parameters and provides reasonably accurate HR estimates during baseline, task, and recovery periods. However, Fitbit ability to track rapid HR changes during activity is limited.