Selection of suitable maximum-heart-rate formulas for use with Karvonen formula to calculate exercise intensity

She, Jinhua; Nakamura, Hitoshi; Makino, Koji; Ohyama, Yasuhiro; Hashimoto, Hiroshi

doi:10.1007/s11633-014-0824-3

Cited by 57 publications

(34 citation statements)

References 11 publications

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“…We first cleaned the data and used linear interpolation on steps, heart rate, calories, and floors climbed to impute missing data. Following this, we developed a feature set that included intensity (Karvonen Formula) 26,27 which calculates individualized target heart rate parameters, steps entropy, which is a measure of predictability of step count, and correlation coefficient between heart rate and steps 28 . We developed the features in order to consider multiple physiological characteristics that could explain sedentary, light, moderate, and vigorous physical activity (See Table 1).…”

Section: Analysesmentioning

confidence: 99%

Using machine learning methods to predict physical activity types with Apple Watch and Fitbit data using indirect calorimetry as the criterion.

Fuller

Anaraki

Simango

et al. 2020

Preprint

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Background There is considerable promise for using commercial wearable devices for measuring physical activity at the population level. The objective of this study was to examine whether commercial wearable devices could accurately predict lying, sitting, and intensity level of other activities in a lab-based protocol. Methods We recruited a convenience sample of 49 participants (23 men and 26 women) to wear three devices, an Apple Watch Series 2, a Fitbit Charge HR2, and and iPhone 6S. Participants completed a 65-minute protocol consisting of 40 minutes of total treadmill time and 25 minutes of sitting or lying time. Indirect calorimetry was used to measure energy expenditure. The outcome variable for the study was the activity class; lying, sitting, walking self-paced, and running 3 METs, 5 METs, and 7 METs. Minute-by-minute heart rate, steps, distance, and calories from Apple Watch and Fitbit were included in four different machine learning models. Results Our dataset included 3656 and 2608 minutes of Apple Watch and Fitbit data, respectively. We tested decision trees, support vector machines, random forest, and rotation forest models. Rotation forest models had the highest classification accuracies at 82.6% for Apple Watch and 89.3% for Fitbit. Classification accuracies for Apple Watch data ranged from 72.5% for sitting to 89.0% for 7 METs . For Fitbit, accuracies varied between 86.2% for sitting to 92.6% for 7 METs . Conclusion This study demonstrated that commercial wearable devices, Apple Watch and Fitbit, were able to predict physical activity types with a reasonable accuracy. The results support the use of minute-by-minute data from Apple Watch and Fitbit combined with machine learning approaches for scalable physical activity type classification at the population level.

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Section: Analysesmentioning

confidence: 99%

Using machine learning methods to predict physical activity types with Apple Watch and Fitbit data using indirect calorimetry as the criterion.

Fuller

Anaraki

Simango

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…Most studies determine %HRR based on typical activity patterns and experimental environments, and there are very few cases that consider an actual construction site. Concerning the application of methods for determining %HRR of construction workers, it is difficult to measure HR max and HR resting [ 59 , 60 , 61 ] in advance for workers, and it is likely that %HRR will interfere with actual use as the indicators.…”

Section: Discussionmentioning

confidence: 99%

Practical Judgment of Workload Based on Physical Activity, Work Conditions, and Worker’s Age in Construction Site

Hashiguchi

Kodama

Lim

et al. 2020

Sensors

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It is important for construction companies to sustain a productive workforce without sacrificing its health and safety. This study aims to develop a practical judgement method to estimate the workload risk of individual construction workers. Based on studies, we developed a workload model comprising a hygrothermal environment, behavioral information, and the physical characteristics of workers). The construction workers’ heart rate and physical activity were measured using the data collected from a wearable device equipped with a biosensor and an acceleration sensor. This study is the first report to use worker physical activity, age, and the wet bulb globe temperature (WBGT) to determine a worker’s physical workload. The accuracy of this health risk judgment result was 89.2%, indicating that it is possible to easily judge the health risk of workers even in an environment where it is difficult to measure the subject in advance. The proposed model and its findings can aid in monitoring the health impacts of working conditions during construction activities, and thereby contribute toward determining workers’ health damage. However, the sampled construction workers are 12 workers, further studies in other working conditions are required to accumulate more evidence and assure the accuracy of the models.

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“…The experimental intervention involved 20 min of aerobic exercise on a stationary bicycle. After 5 min of walking to warm-up, the patients were asked to perform 20 min of moderate exercise on the bicycle at 60% to 80% of their target heart rate, calculated using the Karvonen formula [ 45 ]. The duration of exercise was based on previous studies, which reported that 15 to 20 min of exercise has a positive impact on cognition [ 41 , 46 ].…”

Section: Methodsmentioning

confidence: 99%

Acute Effects of Aerobic Exercise on Somatosensory-Evoked Potentials in Patients with Mild Cognitive Impairment

et al. 2020

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Mild cognitive impairment (MCI) is becoming a serious problem for developing countries as the lifespan of populations increases. Exercise is known to be clinically beneficial for MCI patients. Somatosensory-evoked potentials (SEPs) may be a potential diagnostic and prognostic marker for this population. The objective of this study was to determine the acute effects of aerobic exercise on SEPs in patients with MCI, to test whether SEPs are sensitive enough to detect improvements in early somatosensory processing. The study had a randomized parallel-group design and included 28 MCI subjects (14 in the experimental group and 14 in the control group). The experimental intervention was 20 min of aerobic exercise using a stationary bicycle. The control intervention involved 20 min of movements and stretches. Subjects were assessed before and after a single intervention session. SEPs were recorded by stimulating the median nerve of the dominant hand. Analysis of normalized SEP peak amplitudes showed that a single session of aerobic activity significantly reduced the N30 peak at the F3 channel (p = 0.03). There were no significant effects of aerobic exercise on SEP peak latencies. The results indicate that 20 min of aerobic exercise has a significant effect on the N30 SEP peak amplitude in MCI patients. The results suggest that aerobic exercise is likely to provide sensory-enriching inputs that enhance sensorimotor integration. Future studies should assess the effects of aerobic exercise on somatosensory processing in progressive stages of Alzheimer’s disease, longer exercise durations, and multiple exercise sessions.

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Selection of suitable maximum-heart-rate formulas for use with Karvonen formula to calculate exercise intensity

Cited by 57 publications

References 11 publications

Using machine learning methods to predict physical activity types with Apple Watch and Fitbit data using indirect calorimetry as the criterion.

Using machine learning methods to predict physical activity types with Apple Watch and Fitbit data using indirect calorimetry as the criterion.

Practical Judgment of Workload Based on Physical Activity, Work Conditions, and Worker’s Age in Construction Site

Acute Effects of Aerobic Exercise on Somatosensory-Evoked Potentials in Patients with Mild Cognitive Impairment

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