Fontenelle Dumans Canetti, Elisa

Orr, Rob Marc

Schram, Ben

Tomes, Colin

English

Objective: Police tactical group (PTG) occupational requirements are known to expose officers to high levels of physical, mental, and emotional stressors. As such, selection courses must ensure candidates are not only physically, but mentally and emotionally suitable. Assessing personnel for suitability requires a holistic approach; wearable technologies may provide organizational decision makers additional objective information to optimize candidate selection. Heart Rate Variability (HRV), one metric obtainable from wearable technology, has been increasingly utilized as a stress biosignal, but the value of HRV data in the context of specialist police selection remains limited.Hypothesis: HRV analysis will effectively identify higher performing candidates attending PTG selection?Methods: This study was a prospective cross-sectional study of three male PTG candidates completing a highly demanding selection course. HR and HRV were measured from 1300 – 1800 on a single day of tactical maneuver training. HRV was analysed as follows: R to R interval (RRI) length, root-mean-square of successive RRI differences (RMSSD), percentage of adjacent RR intervals varying by at least 50ms (pRR50), nonlinear short-term variability (SD1), and nonlinear long-term variability (SD2).Data: Measures of central tendency were generated; the maximum, minimum, mean, and standard deviation were reported for each HRV measure and visualized with a box plot.Results: Data from one participant appeared to skew the mean results. The maximum values for mean RRI (Max: 709ms, Min: 580ms, Mean: 632.67±67.68ms), RMSSD (Max value: 42.3ms, Min value: 18.8ms, Mean: 26.8±13.43ms), pRR50 (Max value: 12.47%, Min value: 1.98%, Mean: 5.76±5.82%), SD1 (Max value: 29.9ms, Min value: 13.3ms, Mean: 18.93±9.50ms), and SD2 (Max value: 61.1ms, Min value: 37.5ms, Mean: 49.87±11.84ms) all occurred in the same participant. That participant also held the lowest maximum HR (138bpm), lowest mean HR (85bpm) and was the highest performer as rated by unit leadership.Conclusions: Given the potential discriminatory capacity of HRV in this context, HRV may be a valuable objective metric to support PTG candidate selection by providing objective measurements of holistic stress response.<br/

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Bond UniversityResearch RepositoryIncorporation of heart rate variability into police tactical group small unit tactics selectionTomes, Colin; Fontenelle Dumans Canetti, Elisa; Orr, Rob Marc; Schram, BenLicence:Free to readLink to output in Bond University research repository.Recommended citation(APA):Tomes, C., Fontenelle Dumans Canetti, E., Orr, R. M., & Schram, B. (2023). Incorporation of heart ratevariability into police tactical group small unit tactics selection. Poster session presented at AmericanPhysiological Summit 2023, California, United States.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.For more information, or if you believe that this document breaches copyright, please contact the Bond University research repositorycoordinator.Download date: 18 Jul 2023Printing:Incorporation of heart rate variability into police tactical group small unit tactics selectionColin Tomes1,2,3, Elisa Canetti2, Robin Orr2, Ben Schram21. Bond Institute of Health and Sport, Bond University, Gold Coast, QLD, 4229   2. Tactical Research Unit, Bond University, Gold Coast, QLD, 4229 3. Dept of Rehabilitation Science, Moravian University, Bethlehem, PA, USA 18018Background & Purpose• Law enforcement is known to be a physically, mentally, and emotionally demanding profession (Andrew et. al., 2017)• Police Tactical Group (PTG) personnel are subject to additional demands that may include search and rescue, counterterrorism, explosive ordnance disposal, and active shooter response (Robinson, et. al., 2022)• For these reasons, selection for PTG service is physically and technically challenging (Maupin et. al., 2017)• Individuals must be assessed holistically• Heart Rate Variability (HRV) may provide quantitative measures of holistic stress and load, providing unit leadership additional information on candidate suitability (Shaffer et. al., 2017)• Therefore, The purpose of this study was to measure HRV relative to performance in a cohort of PTG candidates DiscussionConclusion & RecommendationsReferencesContact: colin.tomes@student.bond.edu.au.Methods• The maximum pRR50 value was above the 6.25% threshold posited as a cardiovascular disease risk indictor (Mietus et. al., 2002).• both the mean and minimum were below.• The levels of agreement between the HRV values observed in this study and previous specialist police candidate studies are also of note.• Even this tentative finding is of potential interest for determining the presence of excessive stress in the present personnelFigure 1 (Above Left). Example ECG with a pRR50 interval identified.Figure 2 (Above Right). Example Poincaré Plot identifying nonlinear HRV (SD1 & SD2)Figure 7 (Above left). RMSSD HRV, with max, min, and mean valuesFigure 8 (Above Right). Nonlinear HRV, with max, min, and mean values of SD1 and SD2Figure 9 (Below Left). pRR50% HRV, with max, min, and mean values• HRV may be a valuable metric for quantifying load holistically in tactical police training• The individual with the highest rated performance by the directional staff also demonstrated the most optimal HRV values.• HRV can be collected and measured in instances where leadership can immediately utilise the dataFigure 3 (Above Left). Room clearance training in a dedicated training structure (US Army).Figure 4 (Above Right). Four-person room clearance procedure schematic (US Army).Figure 10. (Right) Reference List QR codeFigure 11 (Left). tru.bond.edu.auMetric Units Definition Application InterpretationRMSSD ms Root-mean square of successive differencesPrimary measurement for short term PNS assessmentValues generally increase with cardiovascular fitnessDecreasing trends in time-domain components over repeated measurements may indicate failure to adapt or overstresspRR50 % Percentage of adjacent intervals that differ by more than 50msAccepted cardiovascular disease risk assessment in epidemiological literatureSD1 ms Standard deviation with the respect to the y-axis on Poincare plotNonlinear short term detrended assessment Surrogate for time-domain measures when recordings contain noise or other flawsSD2 ms Standard deviation with respect to the x-axis on Poincare plotNonlinear long term detrended assessmentSurrogate for LF power when recordings contain noise or other flawsMethods• This study was a prospective cross-sectional study of three male PTG candidates (n = 3)• 3-lead seated ECGs were recorded for 5 hours during the training session, between 13:00 and 18:00.• Participants were supplied with body worn Equivital™EQ02+LifeMonitor (ADInstruments, Sydney, Australia)• Participants were introduced to a room clearance training structure comprised of residential and commercial specification rooms and hallways.• Scaled room clearance training scenarios were performed (Figure 3)• Role players engaged candidates as active shooters.• Duration of the selection events ranged from seconds to clear a single room to several minutes for multiple room clearances.• Participants were assessed on movement technique, tactical proficiency and transition from primary to second weapons (Figure 4).• Verbal and non-verbal communication was also assessed, as was safety and overall outcome of each scenario.• HRV (Table 1) was analysed via the following metrics:• Heart rate descriptives were also explored: min heart rate, max heart rate, and mean heart rate during the recording window.• Frequency-domain HRV: natural log (Ln) High, Low, and Very Low • Root-mean square of successive RR differences (RMSSD)• Percentage of adjacent R to R wave (RR) ECG intervals varying by at least 50ms (pRR50)• Nonlinear short-term (SD1) and long-term (SD2) variability• Statistical analysis consisted of maximum, minimum, and mean for each HRV measure and visual box plot analysis (Figs. 5-9)Results• All three candidates successfully completed the selection procedures• Only one participant was approved to progress beyond this stage of selection• Data from that participant appeared to consistently skew results• Maximum values for mean RRI, RMSSD, pRR50, SD1, and SD2 were all derived from a single participant.• That same participant also held the lowest maximum heart rate and lowest mean heart rate.Table 1. HRV Interpretation Guide13.318.9329.937.561.650.03Nonlinear HRVHRV (Milliseconds)010203040506070SD1 (ms)SD2 (ms)1039685138168151628068Heart Rate CharacteristicsHR (bpm)020406080100120140160180Mean HR (bpm)Max HR (bpm)Min HR (bpm)18.842.326.8RMSSDHRV (ms)051015202530354045ResultsFigure 5 (Above Left). Heart Rate Characteristics, with max, min, and mean valuesFigure 6 (Above Right). Ln Frequency Domain HRV, with max, min, and mean values1.9812.475.76pRR50%HRV (Percentage)024681012145.0378.3886.576 6.7148.9527.6714.5685.8075.137Frequency Domain HRVLn(ms2)012345678910LnHFLnLFLnVLF

Incorporation of heart rate variability into police tactical group small unit tactics selection

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Incorporation of heart rate variability into police tactical group small unit tactics selection

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