Utility of Two iPhone Device Apps in Assessing Heart Rate at Rest and During Activity

Heart rate (HR) is a critical physiological variable used for prescribing exercise, assessing fitness level and tracking fitness improvements. Electrocardiography (ECG) stands as the criterion measure of HR. While recent development of HR-detecting mobile device applications (apps) has made evaluating HR more convenient; their degree of accuracy is unknown. Therefore, the purpose of this current study was to examine the accuracy and reliability of two-iPhone applications to detect HR at rest and during low-intensity exercise conditions. Eighteen female and 22 male subjects (26 + 9.5 yrs) were prepped for simultaneous detection of HR via three methods: ECG and two HR-detecting apps. App 1, a camera-based app called Azumio Instant Heart Rate (CAM), was used by placement of a finger over the camera lens of the mobile device. App 2, a microphone-based app called Heart Monitor by Bluespark, was employed via placement of an external microphone over the radial pulse. The participants underwent a series of 5-minute stages: seated rest followed by cycle then treadmill walking at low intensities. HR was recorded concurrently, at several time intervals from the three methods once a steady-state HR was reached. The means of the three devices were compared via ANOVA with the significance level set, a priori, at 0.05. Correlation analysis was employed to investigate relationships between the apps and ECG. No statistical difference was found between the CAM and ECG HR (p \u3e 0.05) during the resting and cycle stages. However, during the treadmill phase, there was a significant difference (p = 0.018) between CAM and ECG. Nevertheless, there was a significant (p \u3c 0.05), positive correlation between CAM and ECG under the resting, cycle and treadmill conditions (r = .966, r = .984, r = .877, respectively). Significant differences (p \u3c 0.05) were found for each condition when comparing ECG and MIC HR. Data also revealed poor correlations (p \u3e 0.05; r between -.004 and -.136) between MIC and ECG. The utility of CAM and MIC-based apps to detect HR remains in question as evidence appears to indicate exercise mode and app specificity. Caution should be shown when using these devices. The CAM-based app may accurately detect HR during resting and seated cycling but not during treadmill activity. The MIC-based app is not recommended for use in any condition. Of note, statistical significance may not mitigate usefulness when considering the accuracy of palpation. Additional research is necessary

Exercise Knowledge, Exercise Beliefs, Physical Activity Engagement and Physical Function in Older Adults

Historically, recall surveys have been used to gather information about exercise knowledge and beliefs (EKB) and physical activity (PA). There seems to be a disconnect between what people know and believe about exercise and the choices they make about engaging in exercise. Advancements in the capacity to capture verifiable PA data have greatly improved with application of accelerometers. The ability to objectively verify PA makes reexamining the relationship between EKB and both PA and physical function (PF) worthwhile. The aim of this investigation was to revisit the relationship between EKB and PA and PF in older adults using recall surveys and accelerometry. Fourteen older adults (8 females; 6 males; M age 69.5 ± 9.4) underwent a single, 75-minute session consisting of questionnaires to gather information about EKB and PA (CHAMPS) and PF tasks (examples: chair sit and reach and 400m aerobic walk) to assess function. The EKB questionnaires were comprised of subsets, such as knowledge about aerobic exercise, beliefs about flexibility exercise, and beliefs about resistance training. Subset scores were compared to PF outcomes in that specific area. A total knowledge score was summed from the subset scores. Based upon age and sex specific norms, PF tasks were scored as below average, average, or above average. A 7-day period of objectively-determined PA was recorded by the ActivPal accelerometer. Partial correlations controlling for age were run on the variables described below. Neither CHAMPS PA nor total steps (accelerometry) correlated with the total EKB score (p \u3e 0.05). The flexibility subset score rating correlated negatively with the chair sit and reach rating (r = -.554, p = 0.049). The aerobic knowledge and beliefs subset score trended towards significance when correlated with the 400 meter walk task (r = -.518, p = 0.070). The resistance training subset score did not correlate with either the arm curl or the chair stand task (p \u3e 0.05). Previous research employing recall surveys has shown that EKB do not predict activity engagement. The present research (with accelerometry) supports this assertion which suggests the lack of relationship is not connected to survey collection recall bias. Subset EKB in flexibility and aerobic exercise correlated or trended towards (respectively) functional outcomes in those areas. It is interesting to speculate that the convenience of engaging in flexibility and aerobic exercise makes acting upon knowledge and beliefs easier when compared with resistance training

Benign Conduction Abnormalities in Response to Acute, Moderately-High, Simulated Altitude Exposure

Acclimatization to altitude can improve endurance performance above levels achieved solely by training at sea level. There is natural limitation in the applicability of employing terrestrial altitude training – namely proximity. A simple, non-cumbersome method of simulating altitude is desirable to many types of endurance athletes. The Alto2Lab (Pharma Pacific Inc.), consisting of primarily a breathing tube and silo stack, has shown some potential in this role. There is a lack of evidence regarding whether simulated altitude exposure triggers abnormal cardiovascular responses. The aim of this study was to provide initial evidence of cardiac changes associated with usage patterns that follow distributor guidelines. Twenty-five participants (mean age 29 ± 10.7; 16 males; 9 females) volunteered for the study. Subjects underwent a baseline ECG recording followed by ECG recording during sham (4-5 mins), hypoxia (~6 mins), and recovery (3-4 mins) phases. The sham phase consisted of subjects breathing normoxive air through a foam-filled silo system. The sham stack mimicked the look and feel of the silo system used to produce hypoxia with the difference being a single, soda lime-filled silo. A recovery phase followed hypoxia. Pulse oximetry (SpO2) was used to assess oxygen saturation. Cochran’s Q was employed to test the frequencies of responses across the phases. An independent, blinded, experienced clinician (DK) analyzed the recordings. Two subjects were removed from the final analysis (inability to finish the protocol, baseline right bundle branch block). All subjects demonstrated an increase in heart rate (mean = +16.8 ± 8.0) during the hypoxia (mean oxygen saturation = 82 ± 4.1%) phase. No ECG ischemic changes were seen across any of the phases. Benign conduction abnormalities (sinus arrhythmia = 9; junctional rhythms = 4) occurred with some regularity during hypoxia. These abnormalities occurred with less frequency during the sham and recovery phases. It is possible that an altered breathing pattern or an inadequate washout period between phases might account for these findings. Overall, there was no significant relationship between the heart response and phase (p = .375). While the Alto2Lab did not produce any ECG changes indicative of an ischemic response, the present study used a small sample of healthy, recreationally-active participants. A larger study employing patients among higher risk categories would provide data that is not currently present in the literature and to which this trial cannot speak