The HRs of two Beagles (CE1 and CE2) performing continuous exercise and two Beagles (IE1 and IE2) performing interval exercise were analyzed using a Polar H10 HR-monitor (Polar Electro Oy, Finland) throughout all exercises. After each protocol, the HR data were downloaded from the transmitters onto a computer using Polar Flow Software (Polar Electro Oy, Finland). The HR was reported as the mean values attained across the exercise stages.
Polar flow software
Manufactured by Polar Electro
Sourced in Finland
Polar Flow software is a data analysis and training tool used to monitor and track athletic performance. It provides users with information on heart rate, training load, and other fitness metrics collected from Polar devices.
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6 protocols using polar flow software
1
Continuous and Interval Exercise in Beagles
2
Monitoring Physical Activity via Wearable Devices
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Collection of data on activity was initiated immediately after the first ramp test (T0) and terminated after the last ramp test approximately 8 weeks later (T2). Each individual wore a multisensory device (Polar V800, Polar Electro Oy, Kempele, Finland) on the wrist on which they usually wore a watch for the entire study period (with the exception of short intervals for charging). When the participants were not exercising, the Polar V800 instrument without a chest-belt recorded the number of steps and intensity of daily activities automatically, with a validity comparable to that of accelerometers (Hernandez-Vicente et al., 2016 (link)). During each session of CircuitHIIT they wore a chest-belt and activated monitoring of heart rate manually. Each participant uploaded all these data for web-based storage via the Polar Flow software. This software provides the number of steps taken daily, the number of waking hours spent sedentary each day [<1.5 metabolic equivalents (MET)], and the time spent performing light (1.5–3 MET), moderate (3–6 MET) and vigorous physical activity (>6 MET) each day. For the analysis of each CircuitHIIT session, we averaged the heart rate data from the last 2 min.
3
Remote Telehealth Exercise Cardiac Rehab
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Participants randomly allocated to the telehealth delivered ExCRP will attend on-site training for the first three sessions (week 1) before continuing the exercise programme away from the university (weeks 2–6) and serve as the experimental intervention. Participants will choose their preferred mode of aerobic exercise (walking, jogging or cycling) based on exercise preference and access to equipment. To ensure standardised exercise intensity between intervention arms, participants will be provided with a target HR as well as the exercise intensity that correlates to the workload at individual VAT. Each participant will be provided with a Polar Unite fitness watch (Polar Electro Oy, Kempele, Finland) to objectively measure HR response. Participants will be required to log their training data and sessional RPE online using the Polar Flow software (Polar Electro Oy, Kempele, Finland) secured by login and password, allowing the research team to monitor adherence and training intensity weekly. Finally, participants will be required to attend a weekly audio or audio-visual meeting to discuss training adherence and compliance. MI-CBT will be incorporated into the weekly audio/audio-visual meeting for participants in the telehealth delivered ExCRP with a trained member of the research team implementing the same MI-CBT strategies as the supervised group.
4
Validating Heart Rate Monitoring for HIIT
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Participants' heart rates were recorded using a validated (Rider et al., 2019 (link)) second-to-second wrist-worn monitors (Polar A360, Polar Electro, Kempele, Finland). For each participant, age predicted HRmax was calculated using the Tanaka equation (208-0.7*age in years) (Tanaka et al., 2001 (link)). If a participant exceeded this predicted value during a HIIT session, their HRmax was recalibrated to the higher observed value (Weston et al., 2004 (link)). Following each exercise session, individual participant heart rate files were downloaded into the Polar Flow software (Polar Electro, Kempele, Finland). The data used in the analysis was the highest 1-s value from each high-intensity bout, expressed as a percentage of the individual participant's HRmax, across each attended HIIT session. Session ratings of perceived exertion (RPE) were recorded after the 2 min cool down, using the CR-10 scale (Borg, 1998 ). This scale ranges from “nothing at all” (0) to “absolute maximum” (Reed et al., 2017 (link)).
5
Data Collection and Analysis Using Polar Ignite Smartwatch
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All the data collected from the Polar Ignite smartwatches were uploaded to the FlowSync application (version 6.7.0, Polar Electro Oy, Kempele, Finland) on a Samsung Tablet for storage purposes [14 ]. The FlowSync app tracks and organizes all heart rate data (measured in 1 s averages) and all sleep data (measured in minutes). Once uploaded to FlowSync and connected to the internet, the data were automatically synced to the online Polar Flow software (Polar Electro Oy, Kempele, Finland). The Polar Flow software organized each data collection session (using the “Other Indoor” mode) in a training calendar for further analysis. Upon selection of a data collection session, the software chronologically displayed the activity of the individual wearing the smartwatch, including the duration of the session, heart rate data (1 s averages, total average, minimum, and maximum), kilocalories burned, as well as sleep length and interruptions.
6
Heart Rate Variability Analysis during Cognitive Tasks
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HRV was measured through R–R interval analysis using a heart rate monitor42 (link) positioned onto the participants’ chest (V800, Polar Electro Oy, Kempele, Finland) during the entire duration of the protocol, except during PBC sessions. To avoid HR fluctuations, the test was performed in a quiet and dimmed-light room, and participants remained still in a sitting position. Time series of R–R intervals were extracted using the Polar Flow software and analyzed with Kubios software (v. 3.3.1, 2019, Finland). The HRV analysis concentrated on data collected during the Stroop tasks performed before and after the PBC or control session.
R–R intervals during 128 s (to fit with the duration of the Stroop task) were analyzed using time-domain indices (mean HR and root mean square of the successive differences, RMSSD), frequency-domain indices (low-frequency band, LFnu; high-frequency band, HFnu, and LF/HF ratio) and nonlinear indices (standard deviations perpendicular to, SD1 in a Poincaré plot analysis), which were associated to parasympathetic tone.
R–R intervals during 128 s (to fit with the duration of the Stroop task) were analyzed using time-domain indices (mean HR and root mean square of the successive differences, RMSSD), frequency-domain indices (low-frequency band, LFnu; high-frequency band, HFnu, and LF/HF ratio) and nonlinear indices (standard deviations perpendicular to, SD1 in a Poincaré plot analysis), which were associated to parasympathetic tone.
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