*3.2. Study Design*

We conducted a retrospective study to investigate the impact of shelter-in-place measures on human behavior, resting heart rate, sleep, and activity patterns in the context of telemedical services. We enrolled five adult volunteers of different age groups and backgrounds (three working participants and two octogenarians) who were smartwatch users and self-isolating at home during the Covid-19 pandemic. Eligible volunteers were those who met all of the following inclusion criteria for the study: 18 years of age or older, signed informed consent, stayed at home, and self-isolated during the lockdown period, and were Fitbit Versa users in the previous year. The last criterion was mandatory, as it was the only way to conduct a retrospective comparative analysis. Volunteers with dementia, stroke, and those who were not self-isolated at home were not eligible. The study protocol conformed to the guidelines set forth by the Declaration of Helsinki.

The self-isolate interval began on 10 March 2020 and ended on 30 April 2020 while the overall investigated interval began on 22 January 2019 and ended on 30 April 2020 (464 days).

To avoid bias in measurements among volunteers, we chose Fitbit Versa smartwatches, which allow for continuous recording of parameters: quality and duration of sleep, resting heart rate and the number of steps during the day. This choice was dictated by literature data which showed that the Fitbit Versa was 10 times more used in research projects and registered in ClinicalTrials than other brands [42].

Cardiovascular and Pre-Frailty Risk was assessed using a developed 4-point scale. The rationale for the selection of elements for the Cardiovascular and Pre-Frailty Risk Assessment is primarily the selection of available parameters measured with smartwatches and relating them to their suitability and usefulness for the elderly population, along with an appropriate justification of the selected parameters based on available literature data, cardiological (ESC) and geriatric guidelines and clinical experience. This scale could also be adapted in qualification for surgical procedures of the older adults. According to the ESC Guidelines (2016) [12], age is the dominant cardiovascular risk factor, but it should not be considered in isolation from other factors. Interestingly, Sergi et al. (2015) conducted a study with 1567 participants aged 65 to 96, which showed that pre-frailty is independently associated with a higher risk of developing cardiovascular disease in older adults [43]. The use of smartwatches to assess pre-frailty and cardiovascular risk is still unknown. We are the first to report the benefits of this type of measurement, especially among older adults. In our artificial intelligence-based approach to our Cardiovascular and Pre-Frailty Risk Assessment method, we mapped low physical activity (the component of frailty syndrome and cardiovascular risk) to fewer steps using the developed Python and Matlab scripts.

Aging is associated with a decreased ability not only to initiate but also to maintain sleep [44]. Deterioration of sleep in the older adults also correlates with deterioration of health and well-being. Sleep disturbances are very common in older patients with delirium. Delirium can be characterized by changes in the sleep-wake cycle and may be the first sign of deterioration in health, such as from infection [45–47].

In the Clinical point of view section, we emphasized the role of the resting heart rate as an important predictor of cardiovascular risk. Moreover, a recent study by Toosizadeh et al. (2021) suggested that measuring the dynamics of heart rate in response to daily activities

could be a significant marker in screening for the frailty syndrome in older adults [48]. Our Cardiovascular and Pre-Frailty Risk Assessment method is a non-linear scale developed based on clinical practice. Reaching a high-risk state should be alarming and should prompt appropriate corrective action as it can still be reversible i.e., lifestyle change.

For validation we collected qualitative and narrative data from respondents using a proprietary questionnaire to verify the data collected by Fitbit Versa. The author's qualitative questionnaire covering sociodemographic status, self-assessment of changes in behavior, mood, sleep, physical activity, and daily activities was conducted by researchers using videoconferencing at the end of the shelter-in-place.
