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Chevance G, Golaszewski NM, Tipton E, Hekler EB, Buman M, Welk GJ, Patrick K, Godino JG. Accuracy and Precision of Energy Expenditure, Heart Rate, and Steps Measured by Combined-Sensing Fitbits Against Reference Measures: Systematic Review and Meta-analysis. JMIR Mhealth Uhealth 2022; 10:e35626. [PMID: 35416777 PMCID: PMC9047731 DOI: 10.2196/35626] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/27/2022] [Accepted: 02/10/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Although it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge. OBJECTIVE The purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits. METHODS We conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps. RESULTS A total of 52 studies were included in the systematic review. Among the 52 studies, 41 (79%) were included in the meta-analysis, representing 203 individual comparisons between Fitbit devices and a criterion measure (ie, n=117, 57.6% for heart rate; n=49, 24.1% for energy expenditure; and n=37, 18.2% for steps). Overall, most authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared with criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of -2.99 beats per minute (k comparison=74), -2.77 kcal per minute (k comparison=29), and -3.11 steps per minute (k comparison=19), respectively, of the Fitbit compared with the criterion measure (results obtained after removing the high risk of bias studies; population limit of agreements for heart rate, energy expenditure, and steps: -23.99 to 18.01, -12.75 to 7.41, and -13.07 to 6.86, respectively). CONCLUSIONS Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by the quality of the study, age of the participants, type of activities, and the model of Fitbit. The qualitative conclusions of most studies aligned with the results of the meta-analysis. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. However, the measurement of energy expenditure may be inaccurate for some research purposes.
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Affiliation(s)
| | - Natalie M Golaszewski
- Herbert Wertheim School of Public Health and Longevity Science, University of California, San Diego, La Jolla, CA, United States
- Center for Wireless & Population Health Systems, University of California, San Diego, La Jolla, CA, United States
| | - Elizabeth Tipton
- Department of Statistics, Northwestern University, Evanston, IL, United States
| | - Eric B Hekler
- Herbert Wertheim School of Public Health and Longevity Science, University of California, San Diego, La Jolla, CA, United States
- Center for Wireless & Population Health Systems, University of California, San Diego, La Jolla, CA, United States
- Exercise and Physical Activity Resource Center, University of California, San Diego, La Jolla, CA, United States
| | - Matthew Buman
- School of Nutrition & Health Promotion, Arizona State University, Phoenix, AZ, United States
| | - Gregory J Welk
- Department of Kinesiology, Iowa State University, Ames, IA, United States
| | - Kevin Patrick
- Center for Wireless & Population Health Systems, University of California, San Diego, La Jolla, CA, United States
| | - Job G Godino
- Herbert Wertheim School of Public Health and Longevity Science, University of California, San Diego, La Jolla, CA, United States
- Center for Wireless & Population Health Systems, University of California, San Diego, La Jolla, CA, United States
- Exercise and Physical Activity Resource Center, University of California, San Diego, La Jolla, CA, United States
- Laura Rodriguez Research Institute, Family Health Centers of San Diego, San Diego, CA, United States
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A Teenager Physical Fitness Evaluation Model Based on 1D-CNN with LSTM and Wearable Running PPG Recordings. BIOSENSORS 2022; 12:bios12040202. [PMID: 35448262 PMCID: PMC9032117 DOI: 10.3390/bios12040202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/26/2022] [Accepted: 03/27/2022] [Indexed: 11/17/2022]
Abstract
People attach greater importance to the physical health of teenagers because adolescence is a critical period for the healthy development of the human body. With the progress of biosensing technologies and artificial intelligence, it is feasible to apply wearable devices to continuously record teenagers’ physiological signals and make analyses based on modern advanced methods. To solve the challenge that traditional methods of monitoring teenagers’ physical fitness lack accurate computational models and in-depth data analyses, we propose a novel evaluation model for predicting the physical fitness of teenagers. First, we collected 1024 teenagers’ PPGs under the guidance of the proposed three-stage running paradigm. Next, we applied the median filter and wavelet transform to denoise the original signals and obtain HR and SpO2. Then, we used the Pearson correlation coefficient method to finalize the feature set, based on the extracted nine physical features. Finally, we built a 1D-CNN with LSTM model to classify teenagers’ physical fitness condition into four levels: excellent, good, medium, and poor, with an accuracy of 98.27% for boys’ physical fitness prediction, and 99.26% for girls’ physical fitness prediction. The experimental results provide evidence supporting the feasibility of predicting teenagers’ physical fitness levels by their running PPG recordings.
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