Wrist-Worn Physical Activity Trackers Tend To Underestimate Steps During Walking

International Journal of Exercise Science 10(5): 764-773, 2017. The purpose of this study was to determine step-count accuracy of pedometers at different walking speeds. Ten recreationally active participants walked at five treadmill speeds (0.89, 1.11, 1.34, 1.56, and 1.79 m/s) for five minutes while wearing four wrist-worn activity trackers (Fitbit Charge HR®, Garmin Vivosmart HR®, Apple iWatch®, Jawbone UP3®) and the hip-worn Digi-Walker®. Each step was manually counted by a research technician (benchmark). Total step count at each speed was obtained for each device and compared to the benchmark using one-way MANOVA and Pearson correlation coefficient. For all five speeds, the Digi-Walker® yielded the most accurate values, averaging -0.4% difference from the benchmark counted steps, and showed the strongest correlation, r \u3e.730, p \u3c.05, at every speed. The Fitbit averaged the highest percent difference of -10.2% from the benchmark of counted steps, and underestimated steps at all speeds (p \u3c0.05). Garmin averaged a -2.7% step difference, Jawbone averaged a -5.3% step difference, and the iWatch showed a -7.9% step difference. Specifically, the Fitbit, Garmin, and Jawbone got progressively worse with increasing speed, whereas the iWatch performed the worst at the slowest and fastest speeds. All wrist-worn devices tested tended to underestimate steps. These data indicate that wrist-worn pedometers are inaccurate even with a specific designed purpose: count steps in a controlled manner. Because these devices are inaccurate in this setting, they remain highly questionable for accuracy in a real-world setting in which the definition of a “step” becomes less finite

The Risk of Bias in Validity and Reliability Studies Testing Physiological Variables using Consumer-Grade Wearable Technology: A Systematic Review and WEAR-BOT Analysis

INTRODUCTION: Wearable technology is a quickly evolving field, and new devices with new features to measure/estimate physiological variables are being released constantly. Despite their use, the validity of the devices are largely unknown to the users or researchers, and the quality of the studies that do test validity and reliability vary widely. PURPOSE: Therefore, the purpose of this systematic review was to review the current validity and reliability literature concerning consumer-grade wearable technology measurements/estimates of physiological variables during exercise. Additionally, we sought to perform risk of bias assessments utilizing the novel WEArable technology Risk of Bias and Objectivity Tool (WEAR-BOT). METHODS: This review was conducted following PRISMA guidelines, searching 3 databases: Google Scholar, Scopus, and SPORTDiscus. After screening, 46 papers were identified that met the pre-determined criteria. Then data was extracted and risk of bias assessment performed by independent researchers. Descriptive statistics, weighted averages of mean absolute percentage error (MAPE) and Pearson correlations were calculated. Sample size statistics were performed utilizing the lower 95% confidence interval of the weighted correlation average. RESULTS: Of the 46 papers reviewed, 44 performed validity testing, while 9 performed reliability. The weighted average for MAPE was 12.48% for heart rate (HR) and 30.70% for energy expenditure (EE). The weighted average for Pearson correlations was 0.737 for HR and 0.672 for EE. Risk of bias assessment of validity studies resulted in 30/44 studies being classified as having a “High Risk of Bias”, and 14/44 having “Some Risk of Bias”. None had a “Low Risk of Bias”, according to the novel WEAR-BOT. For reliability studies, 7/9 were classified as “High Risk of Bias”, 2 as “Some Risk of Bias”, and 0 as “Low Risk of Bias”. CONCLUSION: The risk of bias assessment and descriptive statistics paint a troubling picture of the overall state of validity and reliability studies. Statistical analyses, methods, and reporting vary excessively. This review and associated WEAR-BOT analysis can be used by researchers to help standardize methodology, analytics, and reporting of validation and reliability studies of consumer-grade wearable technology

The Effects of Warm-up Duration on Cycling Time Trial Performance in Trained Cyclists

. The purpose of this study was to assess the effects of three different warm-up condi-tions on a 5K cycling time trial (TT). Sixteen trained cyclists completed the study. At the first testing session, participants completed a maximal graded exercise test to assess maximal oxygen consumption (VO2max) and a familiarization of the TT. At three subse-quent visits, the participants completed the TT after no warm up, short warm-up of three minutes at 60% VO2max, or long warm-up of ten minutes at 60% VO2max. The warm-up was assigned in randomized order. VO2, heart rate (HR), lactate, power, and speed were assessed after the warm-up, 1K, and completion of the 5K TT. There was no dif-ference between type of warm-up for time, power, cadence, speed, VO2, HR, or lactate levels at the end of the TT. There was no significant difference between type of warm-up for time, VO2 or HR at the end of the 1K split. Warm-up length was not impactful on 5K TT performance or during the first km of the TT in trained cyclists. These results con-flict with previous evidence indicating that a warm-up in endurance events primarily improved VO2 kinetics at the onset of the exercise

Physiological and emotional influence on heart rate recovery after submaximal exercise

The purpose of this study was to assess the role of cardiovascular fitness and emotional state in heart rate recovery after submaximal exercise. Fifty recreationally active subjects (male n=19, females n= 31) completed the study. Height, weight, body composition, and waist circumference were measured, with current emotional state assessed through completion of the Profile of Mood States questionnaire, followed by the Queen’s College Step Test to estimate maximal oxygen consumption (VO2max). Heart rate recovery was determined by the difference between assessments of peak heart rate during exercise and 1 minute post-exercise. Heart rate recovery was correlated with VO2 max, body composition, body mass index, waist circumference, resting heart rate, peak heart rate and the assessed mood states. A moderate negative correlation was found between heart rate recovery and resting heart rate (r = -.307, p = .032) and was the only variable to show significance. The results of this study disagree with previous literature as only one physiologic variable had a significant relationship with heart rate recovery. This may be because the participants recruited for this study were of at least average fitness and there were no significant signs of psychological stress in study participants at the time of testing

Genetic Testing to Inform Epilepsy Treatment Management From an International Study of Clinical Practice

IMPORTANCE: It is currently unknown how often and in which ways a genetic diagnosis given to a patient with epilepsy is associated with clinical management and outcomes. OBJECTIVE: To evaluate how genetic diagnoses in patients with epilepsy are associated with clinical management and outcomes. DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective cross-sectional study of patients referred for multigene panel testing between March 18, 2016, and August 3, 2020, with outcomes reported between May and November 2020. The study setting included a commercial genetic testing laboratory and multicenter clinical practices. Patients with epilepsy, regardless of sociodemographic features, who received a pathogenic/likely pathogenic (P/LP) variant were included in the study. Case report forms were completed by all health care professionals. EXPOSURES: Genetic test results. MAIN OUTCOMES AND MEASURES: Clinical management changes after a genetic diagnosis (ie, 1 P/LP variant in autosomal dominant and X-linked diseases; 2 P/LP variants in autosomal recessive diseases) and subsequent patient outcomes as reported by health care professionals on case report forms. RESULTS: Among 418 patients, median (IQR) age at the time of testing was 4 (1-10) years, with an age range of 0 to 52 years, and 53.8% (n = 225) were female individuals. The mean (SD) time from a genetic test order to case report form completion was 595 (368) days (range, 27-1673 days). A genetic diagnosis was associated with changes in clinical management for 208 patients (49.8%) and usually (81.7% of the time) within 3 months of receiving the result. The most common clinical management changes were the addition of a new medication (78 [21.7%]), the initiation of medication (51 [14.2%]), the referral of a patient to a specialist (48 [13.4%]), vigilance for subclinical or extraneurological disease features (46 [12.8%]), and the cessation of a medication (42 [11.7%]). Among 167 patients with follow-up clinical information available (mean [SD] time, 584 [365] days), 125 (74.9%) reported positive outcomes, 108 (64.7%) reported reduction or elimination of seizures, 37 (22.2%) had decreases in the severity of other clinical signs, and 11 (6.6%) had reduced medication adverse effects. A few patients reported worsening of outcomes, including a decline in their condition (20 [12.0%]), increased seizure frequency (6 [3.6%]), and adverse medication effects (3 [1.8%]). No clinical management changes were reported for 178 patients (42.6%). CONCLUSIONS AND RELEVANCE: Results of this cross-sectional study suggest that genetic testing of individuals with epilepsy may be materially associated with clinical decision-making and improved patient outcomes