Reliability of DEXA on Body Composition in Korean Athletes

PURPOSE: The purpose of this study was to assess the reliability of DEXA for measuring body composition in Korean Athletes. METHODS: Twenty-nine athletes (n=29) registered for the college athlete program voluntarily participated in the study. Participants’ height and weight were measured, and BMI (Body Mass Index) was calculated before the participants’ body composition was measured. Muscle mass (kg), lean mass (kg), bone mineral density (BMC) (g·cm-2), and total fat mass (kg) of each participant was assessed by DEXA lunar DPX-L (GE Lunar, Madison, USA) for four times within a day to examine the difference by time frames. Four trials consist of ‘early in the morning × 2 with fasting’ with 30min break between two trials, ‘after lunch × 2’ with 30 min break between the two trials. Intra-class correlation (ICC) was conducted for overall reliability (p\u3c0.05) and a repeated measure ANOVA was performed to compare the difference of each trial (p\u3c0.05). RESULTS: The mean ± SD of muscle mass, lean mass, BMC, and fat mass was 56.4 ± 4.6kg, 59.4 ± 5.0kg, 2.3 ± 0.4g·cm-2, and 9.3 ± 4.8kg respectively. Each trail (mean ± SD) of muscle mass were 56.4 ± 4.7kg, 56.1 ± 4.8kg, 56.5 ± 4.6kg, and 56.4 ± 4.7kg, respectively, lean mass were 59.4 ± 5.1kg, 59.2 ± 5.1kg, 59.5 ± 5.0kg, and 59.4 ± 5.0kg, respectively, BMC were 3.0 ± 0.4g·cm-2, 3.0 ± 0.4g·cm-2, 3.0 ± 0.4g·cm- 2, and 3.0 ± 0.4g·cm-2, respectively, and fat mass were 9.3 ± 4.9kg, 9.2 ± 4.8kg, 9.3 ± 4.9kg, and 9.3 ± 4.9kg, respectively. Reliability of the ICC test showed strong agreement on muscle mass (r=0. 994 and p\u3c0.0001), lean mass (r=0. 995 and p\u3c0.0001), BMC (r=0. 995 and p\u3c0.0001), and fat mass (r=0. 998 and p\u3c0.0001). Cronbach’s alpha were 0.99 (muscle mass), 0.99 (Lean Mass), 0.99 (BMC), and 1.00 (Fat mass). No significant difference between each trial was observed in fat mass (p\u3e0.36). However, there was a significant difference in muscle mass (p\u3c0.001), lean mass (p\u3c0.001), and BMC (p\u3c0.04). CONCLUSION: Although all of the variables showed strong agreement on overall reliability from the ICC test, the reliability for the muscle mass, lean mass, and BMC showed significant differences in different time frame

Examining the Validity of Fitbit Charge HR \u3csup\u3eTM\u3c/sup\u3e for Measuring Heart Rate in Free-Living Conditions

Optical blood flow sensors (i.e. photoplethysmographic techniques) have recently been utilized in wearable activity trackers. The Fitbit Charge HRTM (FBHR) is one of the widely recognized wearable activity trackers that utilizes Fitbit’s proprietary PurePulse optical heart rate (HR) technology to automatically measure wrist-based HR. Despite its increasing popularity, however, no study to date has addressed the validity of FBHR for measuring HR in free-living conditions. PURPOSE: The purpose of this study was to examine the validity of FBHR for measuring HR using a chest strap Polar HR monitor (PHR) as a reference measure in free-living conditions. METHODS: Ten healthy college students (8 males; mean age = 26.5 ±5.4 years; mean body mass index (BMI) = 24.5 ±3.23 kg·m2) participated in the study. The participants were asked to perform normal daily activities for 8 hours in a day while wearing the PHR (model RS400) on their chest and two FBHRs on their dominant and non-dominant wrists, respectively. HR was recorded every minute and the minute-by-minute HR data from each monitor were synchronized by time of day. Pearson correlation was used to examine the linearity of average beats-per-minute (bpm) estimated from FBHRs with respect to the PHR. Mean differences in average bpm between the monitors were examined by a general linear model for repeated measures. Lastly, mean absolute percentage error (MAPE) of minute-by-minute bpm estimated from the FBHRs were calculated against the PHR. RESULTS: Average HRs (mean ±SD) for PHR, FBHR non-dominant, and FBHR dominant were 75.6 ±18.5 bpm, 72.8 ±16.7 bpm, and 73.9 ±17.06 bpm, respectively. Pearson correlation coefficients (r) between the PHR and FBHR non-dominant and dominant were r=.805 and r=.793, respectively. MAPE were 9.17 ±10.9% for FBHR non-dominant and 9.71 ± 12.4% for FBHR HR dominant. ANOVA and post-hoc analyses with Bonferroni revealed significant differences in estimating HR from FBHR non-dominant wrist (p=.001) and FBHR dominant wrist (p=.001) compared to PHR monitor. CONCLUSION: The results indicated that the wrist-oriented Fitbit Charge HRTM device does not provide an accurate measurement of HR during free-living condition in this study. However, further research is needed to validate these monitors with a larger sample with different population groups. Optical blood flow sensors (i.e. photoplethysmographic techniques) have recently been utilized in wearable activitytrackers. The Fitbit Charge HRTM (FBHR) is one of the widely recognized wearable activity trackers that utilizesFitbit’sproprietary PurePulse optical heart rate (HR) technology to automatically measure wrist-based HR. Despiteits increasing popularity, however, no study to date has addressed the validity of FBHR for measuring HR in free-living conditions. PURPOSE: The purpose of this study was to examine the validity of FBHR for measuring HRusing a chest strap Polar HR monitor (PHR) as a reference measure in free-living conditions. METHODS: Tenhealthy college students (8 males; mean age = 26.5 ±5.4 years; mean body mass index (BMI) = 24.5 ±3.23kg·m2) participated in the study. The participants were asked to perform normal daily activities for 8 hours in a daywhile wearing the PHR (model RS400) on their chest and two FBHRs on their dominant and non-dominant wrists,respectively. HR was recorded every minute and the minute-by-minute HR data from each monitor weresynchronized by time of day. Pearson correlation was used to examine the linearity of average beats-per-minute(bpm) estimated from FBHRs with respect to the PHR. Mean differences in average bpm between the monitorswere examined by a general linear model for repeated measures. Lastly, mean absolute percentage error (MAPE)of minute-by-minute bpm estimated from the FBHRs were calculated against the PHR. RESULTS: Average HRs(mean ±SD) for PHR, FBHR non-dominant, and FBHR dominant were 75.6 ±18.5 bpm, 72.8 ±16.7 bpm, and73.9 ±17.06 bpm, respectively. Pearson correlation coefficients (r) between the PHR and FBHR non-dominantand dominant were r=.805 and r=.793, respectively. MAPE were 9.17 ±10.9% for FBHR non-dominant and 9.71 ±12.4% for FBHR HR dominant. ANOVA and post-hoc analyses with Bonferroni revealed significant differences inestimating HR from FBHR non-dominant wrist (p=.001) and FBHR dominant wrist (p=.001) compared to PHRmonitor. CONCLUSION: The results indicated that the wrist-oriented Fitbit Charge HRTM device does not providean accurate measurement of HR during free-living condition in this study. However, further research is needed tovalidate these monitors with a larger sample with different population groups

Hypertensive brainstem encephalopathy involving deep supratentorial regions: does only blood pressure matter?

We report on a 42-year-old female patient who presented with high arterial blood pressure of 245/150 mmHg and hypertensive brainstem encephalopathy that involved the brainstem and extensive supratentorial deep gray and white matter. The lesions were nearly completely resolved several days after stabilization of the arterial blood pressure. Normal diffusion-weighted imaging findings and high apparent diffusion coefficient values suggested that the main pathomechanism was vasogenic edema owing to severe hypertension. On the basis of a literature review, the absolute value of blood pressure or whether the patient can control his/her blood pressure seems not to be associated with the degree of the lesions evident on magnetic resonance imaging. It remains to be determined if the acceleration rate and the duration of elevated arterial blood pressure might play a key role in the development of the hypertensive encephalopathy pattern

Validity of Optical Blood Flow Heart Rate Monitors

PURPOSE: Validate the Schoche (SC) (RhythmTM), Basis B1 Band (BB) (BASIS Science, Inc.), and Mio Alpha (MA) (Physical Enterprises, Inc.) wireless heart rate monitors. METHODS: Fifteen college students (males, n=11, age=27±5yrs; females, n=4, age=27±6yrs) participated. All participants simultaneously wore the SC on left forearm, the BB on the right wrist, the MA on the left wrist, and Polar HR strap on their chest. Participants’ resting heart rate was measured twice prior to exercise. The exercise protocol consisted of one 30-minute bout of continuous walking and running in which the treadmill speed increased every 5-minutes. The treadmill started at 2 mph and completed at 6 mph, followed by 3 minutes of cool down. HR was recorded every minute from each monitor including the Polar HR monitor as a criterion measure. RESULTS: Average HRs (means ± SD) for Polar HR, SC, MA, and BB were 113±32, 110±34, 117±32, and 111±27. A strong pearson’s correlation coefficient was observed with the SC (r =.88) and the MA (r =.75), but a weak correlation coefficient was found with the BB (r =.41), p\u3e0.01. Corresponding absolute error rates were 6.0±12.5%, 11.7±24.2%, and 18.2±21.3%. ANOVA and post hoc analyses with Bonferroni revealed nonsignificant differences between the SC, MA, and BB (p \u3e 0.05) compared to the Polar HR. CONCLUSION: The results demonstrate that the wireless wrist-oriented heart rate monitors provide an accurate measurement of HR during exercise. However, further research is needed to validate these monitors with a larger sample in different environment

How Accurate Are Wearable Activity Trackers For Measuring Steps?

Wearable activity trackers have become popular for tracking individual’s daily physical activity, but little or no information is available to substantiate the validity of these devices in step counts. PURPOSE: The purpose of this study was to systemically examine the validity of newly developed wearable activity trackers for measuring steps compared to the criterion measure (hand tally) in two different conditions. METHODS: Twenty (28.2±4.8 years) healthy males (n=19) and females (n=17) participated in the study. The participants were fitted with eight wearable activity trackers while walking and running on a treadmill (speeds of 2, 2.5, 3, 3.5, 4, and 5 mph) for 3-minutes at each speed. For overground protocol, participants walked at three-self-determined speeds; gradually becoming faster (slow, normal, and fast) for one lap on an indoor track (200 meter track). The number of actual steps taken was manually tallied by researchers using a hand-tally counter. The monitors included the Basis B1 band (BB), Misfit Shine (MS), Polar Loop (PL), and Jawbone UP (UP) worn on the right wrist; the Nike+Fuelband (NF), Garmin VivoFit (GV), and Fitbit Flex (FF) worn on the left wrist; and Withings Pulse (WP) and Fitbit Zip (FZ) worn with a clip on the waist. Step counts from each monitor were compared with criterion values from manually counted steps. RESULTS: Total step counts (means ±SD) were 329.5±71.0, 267.8±89.9, 290.6±105.1, 326.2 ±73.2, 282.2±85.1, 294.3±85.8, 329.2±70.0, 322.1±75.7, 310.8±82.8, and 318.±76.7, for manual counts, NF, MS, WP, PL, FF, FZ, UP, GV, and BB, respectively. Corresponding absolute error rates (computed as the average absolute value of the individuals’ errors) were 19.8±16.4%, 18.9±12.2%, 17.4±15.8%, 11.3±13.1%, 0.7±1.4%, 4.5±7.8%, 6.6±12.6%, and 3.5±6.0%, respectively. ANOVA and Post hoc analyses with Bonferroni revealed the MS, WP, FZ, UP, GV, and BB were the devices to give non-significant differences (p\u3e .05) compared to the manual step counts, but significant differences were found with NF, PL, and FF. CONCLUSION: The results demonstrate that the waist-oriented trackers, FZ and WP, show the most accuracy in measuring steps. However, promising preliminary findings were observed with the wrist-oriented trackers, BB, UP, and GV

Validity of the iHealth-BP7 and Withings-BP800 Self Measurement Blood Pressure Monitor

PURPOSE: The purpose of this study is to validate the iHealth-BP7 and Withings-BP800 monitors according to the European Society of Hypertension (ESH) International Protocol revision 2010. METHODS: Data from 11 participants (31.6 ± 2.2 years) were initially examined according to the ESH International Protocol for the validation of BP measuring devices. Participants were asked to sit and relax for 10-15 mins with legs uncrossed, and back supported prior to the test. In all participants, sequential left arm measurements were performed by two trained observers using a mercury sphygmomanometer and one supervisor using the device. Collected data were screened according to the ESH protocol RESULTS: The mean differences between the monitor and sphygmomanometer readings were -0.55±3.75 (SBP) and 0.54±3.62 (DBP) for iHealth-BP7 and 3.18±4.37 (SBP) and - 0.35±5.42 (DBP) for Withings-BP800. The iHealth-BP7 monitor passed all of the modified requirements, however the WithingsBP800 did not meet the last phase of the modified protocol. CONCLUSION: The iHealth-BP7 monitor is recommended as a valid home BP monitoring device, however the Withings-BP800 fails to meet the ESH criteria in this study potentially due to the small sample size. Since the ESH protocol requires 33 subjects, further study with additional participants is warranted to determine validation of both devices

Validity of wearable physical activity monitors during activities of daily living

PURPOSE: To evaluate the validity of wearable activity monitors in SPT and EE under free-living environment. PURPOSE: To evaluate the validity of wearable activity monitors in SPT and EEunder free-living environment. METHODS: Thirty-nine (24.9+5.4 years) healthymales (n=26) and females (n=11) participated in this study. Total SPT and EE weremeasured by eight monitors; Nike+Fuel Band SE (NFB), Garmin VivoFit (VF), MisfitShine (MF), Fitbit Flex (FF), Jawbone UP (JU), Basis B1 (BB1), Polar Loop (PL), andSense Wear Armband Mini (SWA). The monitors were worn for at least 23 hours to beincluded in final data analysis and no PA restriction was applied. The SWA and a sleeplog were used as a criterion measure for SPT and EE, respectively. RESULTS: Total 24hours of EE (Kcal) (means±SD) were 3234.51+977, 2352.2 ±423, 2291.4±567,2679.8±752, 1955.4±251, 2950.9±864, 2724.9 ±627, 2822.1±525 for SWA, VF, JU,PL, BB1, FB, NFB, and MF, respectively. Mean absolute percent errors (MAPE) werecalculated (means±SD) 23.4%±8.0, 24.2%±8.8, 14.0% ±9.7, 28.9% ±22.0,17.5%±12.1, 16.9%±12.8, and 17.7%±15.0 for the VF, JU, PL, BB1, FB, NFB, andMF, respectively. SPT in minutes (mean±SD) were 481±83.32, 370.1+86.9,432.9±93.2, 467.7 ±51.2, 440.6±85.7, 424.6±103.3, 480.3±128.6, 436.6±35.3, and436.2±78.2 for the log, SWA, SWA laying down, VF, JU, PL, BB1, FB, and NFB,respectively. MAPE were calculated for SPT (mean±SD) 22.77% ±13.6,12.96±11.510.58% ±25.1, 11.6%±9.3, 18.2%±16.4, 14.6%±7.7, 8.7%±9.3, and13.5%±9.9 for the SWA, SWA laying down, VF, JU, PL, BB1, FB, and MF,respectively. ANOVA and post-hoc analyses with LSD indicated no significantdifferences were found with the FB, NFB, and MF in EE estimates. Additional post-hocanalyses with LSD for SPT revealed no significant difference (P\u3e.05) in all monitorsexcept SWA. CONCLUSION: The present study indicates that the FF, MS, and NFBare the most accurate wearable activity monitors when estimating EE and all monitorsprovide reasonable estimates of sleep period time, except SWA

How accurate are the wrist-based heart rate monitors during walking and running activities? Are they accurate enough?

Background Heart rate (HR) monitors are valuable devices for fitness-orientated individuals. There has been a vast influx of optical sensing blood flow monitors claiming to provide accurate HR during physical activities. These monitors are worn on the arm and wrist to detect HR with photoplethysmography (PPG) techniques. Little is known about the validity of these wearable activity trackers. Aim Validate the Scosche Rhythm (SR), Mio Alpha (MA), Fitbit Charge HR (FH), Basis Peak (BP), Microsoft Band (MB), and TomTom Runner Cardio (TT) wireless HR monitors. Methods 50 volunteers (males: n=32, age 19–43 years; females: n=18, age 19–38 years) participated. All monitors were worn simultaneously in a randomised configuration. The Polar RS400 HR chest strap was the criterion measure. A treadmill protocol of one 30 min bout of continuous walking and running at 3.2, 4.8, 6.4, 8.0, and 9.6 km/h (5 min at each protocol speed) with HR manually recorded every minute was completed. Results For group comparisons, the mean absolute percentage error values were: 3.3%, 3.6%, 4.0%, 4.6%, 4.8% and 6.2% for TT, BP, RH, MA, MB and FH, respectively. Pearson product-moment correlation coefficient (r) was observed: r=0.959 (TT), r=0.956 (MB), r=0.954 (BP), r=0.933 (FH), r=0.930 (RH) and r=0.929 (MA). Results from 95% equivalency testing showed monitors were found to be equivalent to those of the criterion HR (±10% equivalence zone: 98.15–119.96). Conclusions The results demonstrate that the wearable activity trackers provide an accurate measurement of HR during walking and running activities

High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea

The Middle East respiratory syndrome coronavirus (MERS-CoV) was exported to Korea in 2015, resulting in a threat to neighboring nations. We evaluated the possibility of using a digital surveillance system based on web searches and social media data to monitor this MERS outbreak. We collected the number of daily laboratory-confirmed MERS cases and quarantined cases from May 11, 2015 to June 26, 2015 using the Korean government MERS portal. The daily trends observed via Google search and Twitter during the same time period were also ascertained using Google Trends and Topsy. Correlations among the data were then examined using Spearman correlation analysis. We found high correlations (>0.7) between Google search and Twitter results and the number of confirmed MERS cases for the previous three days using only four simple keywords: “MERS”, “[Image: see text]” (“MERS (in Korean)”), “[Image: see text]” (“MERS symptoms (in Korean)”), and “[Image: see text]” (“MERS hospital (in Korean)”). Additionally, we found high correlations between the Google search and Twitter results and the number of quarantined cases using the above keywords. This study demonstrates the possibility of using a digital surveillance system to monitor the outbreak of MERS