Research on physical activity variability and changes of metabolic profile in patients with prediabetes using Fitbit activity trackers data

2.1Subjects

Vilnius University Hospital Santaros Klinikos (VUHSK) Family Medicine Centre patients visiting their family doctor were included in our study. The selected study population had increased fasting glucose levels (5.6 to 6.9 mmol/l), did not have skeletal or muscular diseases, and conditions that compromise patients’ ability to move. The participants had to have a smartphone with the latest operating system, such as Apple iOS 13 or later and Android OS 8.0 or later, an email address and be able to use it.

30 subjects were selected. Subjects were informed about the study, its possibilities and benefits for their health. Informed consent form has been signed.

Blood tests were performed during the first consultation: total cholesterol, triglycerides, low-density lipoproteins, high-density lipoproteins, glycated hemoglobin. We also performed body mass composition analysis with the medical bioimpedance device X-Contact 356. Also, we provided physical activity recommendations and consulted on a healthy diet.

Subjects were given Fitbit Inspire physical activity trackers (PAT) and they were asked to connect the bracelet to their smartphones through the Fitbit application. If the patient failed to perform these actions, the PAT was connected by a family doctor.

After 6 months, during a consultation at the family medicine center, the subject was asked to transfer data from his online Fitbit account about his physical activity during the study period to the principal researcher’s main computer.

During this visit, we repeated blood tests: total cholesterol, triglycerides, low-density lipoproteins, high-density lipoproteins, glycated hemoglobin, and bodyweight analysis. The collected information was analysed and discussed with patients in order to suggest lifestyle changes.

The study was approved by the Vilnius Regional Biomedical Research Ethics Committee (approval no. 2019/6-1143-634).

2.2Physical activity monitoring using Fitbit

Participants received Fitbit Inspire PAT (free of charge) during the consultation with the family doctor and agreed to allow researchers to collect data from their accounts. Subjects installed the Fitbit app on their smartphones and created their own user accounts with a personal email address. Then using a “Bluetooth” connection on the smartphones they connected their smart bracelets and synchronized with their personal Fitbit account. If the participant had difficulty with creating a personal account or connecting a bracelet, we helped him to do this and we taught him how to use the device [21]. Subjects had to periodically synchronise data with their phone throughout the study period by activating the Fitbit application on their smartphone and regularly charge their bracelet every 6–7 days.

After 6 months, during the routine visit at the family doctor’s office, we asked patients to log in to their accounts at https://accounts.fitbit.com/login. Then we used the Fitbit data export platform to download the data covering 30 days interval from start to finish of the study (Figs 1 and 2).

Figure 1.

Fitbit data extraction process.

Figure 2.

Fitbit data export platform.

Data obtained from the smart bracelets were: physical activity recording data, steps per day, distance, minutes sedentary, minutes weakly active, active, minutes fairly active, minutes very active.

2.3Poincaré plot and physical activity variability parameters

In this study, the Poincaré plot was used to analyze physical activity variability. The Poincaré plot is a two-dimensional scatterplot of paired values of time series data points, where the x-axis represents the current value at data point n⁢(xn) and the y-axis represent value at data point with a time delay of i⁢(xn+i) (Fig. 3).

Figure 3.

Example of Poincaré plot for Step counts per day.

This method is widely used to study physiological signals [22], especially in the analysis of heart rate variability [23, 24, 25]. but there are also promising applications in other areas such as measuring hand grip strength [26], assessing blood pressure variability [27], monitoring temporal blood glucose fluctuations [28, 29, 30], studying reaction time during repeated testing [31], etc.

To analyze physical activity variability, the Poincaré plot method with a time delay of one day (i=1) was applied and additional measures for selected physical activity time series accessed from patients’ Fitbit data were calculated. An average value (AVG), standard deviation (SD) and some standard Poincaré plot parameters representing short-term variability (SD1), long-term variability (SD2), the ratio of SD1 and SD2 (SD12) and area of fitting ellipse (AFE) were selected to analyze variability, as defined in other studies [22, 23, 24, 25, 26, 27, 28, 29, 30, 31]:

2.4Statistical analysis

The Shapiro-Wilk test was used for normality testing. Data was presented as mean and standard deviation (mean ± SD) or median and quartiles (median [Q1–Q3]) when appropriate. Paired t test or Wilcoxon’s matched pairs signed rank test was used for baseline and follow-up data comparison. Pearson’s or Spearman’s correlation was performed to assess association between changes of selected variables and physical activity variability parameters. Analyses were conducted using jamovi 1.6 [32] and R 4.1.0 [33], and R packages RHRV [34], corrplot [35]. The list of abbreviations used in this paper is presented in Table 1.

3.1Subjects

Mean age of study subjects was 53.8 ± 9.1 (median [Q1–Q3]: 54 [49.5–61]), there were 9 males and 21 females. After intervention, significant differences in patient weight, body composition and self-reported physical activity were observed (Table 2).

3.2Physical activity monitoring using Fitbit

In order to analyze physical activity variability, the Poincaré plots (Fig. 4) were represented and corresponding parameters were calculated for the exported Fitbit data (Table 3). Different patterns of physical activity variability were observed in different patients and different Fitbit data variables. SD1/SD2 ratio varied between 0.697 and 0.870 indicating higher long-term than short-term variability.

Figure 4.

Examples of the Poincaré plot for Fitbit parameters. Patient A – male, age 42 years and patient B – female, age 65 years.

3.3Poincaré plot and physical activity variability (PHAV) parameters

To measure association between physical activity variability (PHAV) parameters accessed from the Poincaré plot, Pearson’s and Spearman’s correlation coefficients were calculated (Fig. 5).

Figure 5.

Heatmap of Pearson’s (A) and Spearman’s correlation (B) coefficients. Non-significant associations were shown as white spaces.

Statistically significant negative correlations between changes (delta = follow-up - baseline) of lipid values and variability of Steps count, distance and minutes of very active physical activity were found, showing a decrease of lipid levels with higher long-term variability and area of an ellipse, but not short-term variability. Additionally, decreased body composition measurements were associated with minutes being fairly or very active. However, the SD1/SD2 ratio showed an opposite relationship with body composition measures indicating that more balanced long-term and short-term variability and possibly higher short-term variability could be associated with positive changes. Also, a similar association between HbA1c mmol/l and Steps and Distance SD1/SD2 ratio was observed.

3.4Vision of remote patient physical monitoring system

In previous paragraphs, we have described in detail how data were collected and analysed. However, many data operations (extraction, integration and analysis) were done manually. We have a vision to improve the proposed remote patient physical monitoring system in order the system could be used for all patients and we could remotely monitor patient health on a day-to-day basis. In this section, we will describe a vision of an integrated remote patient physical monitoring system compatible with existing SANTA-HIS and Lithuanian national eHealth framework.

VUHSK has developed hospital information system (SANTA-HIS), which integrates electronic health record, laboratory, picture archiving and communication system (PACS), registry of informed consent, staff and resource management, document management, quality management system, adverse events management system and many other systems that are necessary for effective health care services. SANTA-HIS system is integrated with Lithuanian national health information system which is called Electronic Health Services and Cooperation Infrastructure Information System (ESPBI IS). ESPBI IS was launched for patients and health care practitioners from 2015 and already has huge amounts of clinical data.

Figure 6 shows the vision of a remote patient physical monitoring system. The elements of the systems, which are shown in green, are already developed and used on a daily basis by health care practitioners.

Figure 6.

Vision of remote patient physical monitoring system.

Remote patient physical monitoring system and external medical devices integration system (shown in red) should be implemented and integrated into existing SANTA-HIS in order to automate data operations.

We strongly believe that we can benefit from the systems integrations because we will have all needed data in one place, and we can make detailed analyses and have a clear picture of the patient’s health. Systems integration will let health care practitioners to review and use patient data collected in other clinics and laboratories. Objective patient physical activity data and other parameters are essential in clinical work because it helps us know how the patient feels under real-life conditions and adheres doctor’s recommendations. We can find out why a patient can’t reach those goals and help solve the problem faster and more effectively.