Evidence map›Paper›PMID 29650506›Full record

ArticleJMIR mHealth and uHealth2018

Evaluating the Validity of Current Mainstream Wearable Devices in Fitness Tracking Under Various Physical Activities: Comparative Study.

Junqing Xie, Dong Wen, Lizhong Liang, Yuxi Jia, Li Gao, Jianbo Lei

3 registry-linked trialsAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 121 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
121citing papers in PubMed, 8 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04379921 nacompletednot on this mapstarted 2020, after this paper: background citation

Improving Spine Surgical Care With Real-Time Objective Patient Tracking Using the Apple Watch

TypeinterventionalSponsorStanford UniversityRan2020 to 2024Enrolled255ConditionsSpine Disease, Spine Degeneration, Spinal Stenosis, SurgeryArmsApple Watch and App
NCT04568408 completednot on this mapstarted 2020, after this paper: background citation

Validation of the Quality of Sleep Data for Xiaomi Domestic Wristbands

Typeobservational_patient_registrySponsorUniversidade da CoruñaRan2020 to 2021Enrolled43ConditionsSleep DisorderArmsXiaomi MiBand 5
NCT04592796 completednot on this map

Observational Study About Sleep Quality and Its Impact on Daily Life of Nursing-home Residents, Through the Use of Wearable Technologies

TypeobservationalSponsorUniversidade da CoruñaRan2018 to 2019Enrolled21ConditionsSleep, Cognitive Impairment, Quality of Life, Age ProblemArmsXiaomi MiBand2, Socio-demographic questionnaire, EuroQol-5D-5L, Mini mental State Examination, Tinetti Scale
3 · Its place in the literature

Who cites it

121 citing papers in PubMed, 8 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Trial
  10. Trial
  11. Trial
  12. Trial
  13. Trial
  14. Trial
  15. Trial
  16. Article
  17. Demographic and Socioeconomic Factors Associated with Fitbit Ownership in the NIHInternational journal of environmental research and public health · 2026
    Article
  18. Article
  19. Article
  20. Article

61 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Junqing XieDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.ORCID http://orcid.org/0000-0002-5585-958X
Dong WenCenter for Medical Informatics, Peking University, Beijing, China.ORCID http://orcid.org/0000-0003-3549-3311
Lizhong LiangThe Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.ORCID http://orcid.org/0000-0002-5231-1930
Yuxi JiaDepartment of Medical Informatics, School of Public Health, Jilin University, Changchun, China.ORCID http://orcid.org/0000-0002-6501-3828
Li GaoSchool of Stomatology, Peking University, Beijing, China.ORCID http://orcid.org/0000-0001-8858-129X
Jianbo LeiCenter for Medical Informatics, Peking University, Beijing, China.ORCID http://orcid.org/0000-0002-1744-0235

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWearable devices have attracted much attention from the market in recent years for their fitness monitoring and other health-related metrics; however, the accuracy of fitness tracking results still plays a major role in health promotion.

objectiveThe aim of this study was to evaluate the accuracy of a host of latest wearable devices in measuring fitness-related indicators under various seminatural activities.

methodsA total of 44 healthy subjects were recruited, and each subject was asked to simultaneously wear 6 devices (Apple Watch 2, Samsung Gear S3, Jawbone Up3, Fitbit Surge, Huawei Talk Band B3, and Xiaomi Mi Band 2) and 2 smartphone apps (Dongdong and Ledongli) to measure five major health indicators (heart rate, number of steps, distance, energy consumption, and sleep duration) under various activity states (resting, walking, running, cycling, and sleeping), which were then compared with the gold standard (manual measurements of the heart rate, number of steps, distance, and sleep, and energy consumption through oxygen consumption) and calculated to determine their respective mean absolute percentage errors (MAPEs).

resultsWearable devices had a rather high measurement accuracy with respect to heart rate, number of steps, distance, and sleep duration, with a MAPE of approximately 0.10, whereas poor measurement accuracy was observed for energy consumption (calories), indicated by a MAPE of up to 0.44. The measurements varied for the same indicator measured by different fitness trackers. The variation in measurement of the number of steps was the highest (Apple Watch 2: 0.42; Dongdong: 0.01), whereas it was the lowest for heart rate (Samsung Gear S3: 0.34; Xiaomi Mi Band 2: 0.12). Measurements differed insignificantly for the same indicator measured under different states of activity; the MAPE of distance and energy measurements were in the range of 0.08 to 0.17 and 0.41 to 0.48, respectively. Overall, the Samsung Gear S3 performed the best for the measurement of heart rate under the resting state (MAPE of 0.04), whereas Dongdong performed the best for the measurement of the number of steps under the walking state (MAPE of 0.01). Fitbit Surge performed the best for distance measurement under the cycling state (MAPE of 0.04), and Huawei Talk Band B3 performed the best for energy consumption measurement under the walking state (MAPE of 0.17).

conclusionsAt present, mainstream devices are able to reliably measure heart rate, number of steps, distance, and sleep duration, which can be used as effective health evaluation indicators, but the measurement accuracy of energy consumption is still inadequate. Fitness trackers of different brands vary with regard to measurement of indicators and are all affected by the activity state, which indicates that manufacturers of fitness trackers need to improve their algorithms for different activity states.

Indexed as

data accuracyfitness trackersphysical activitywearable electronic devices

Identifiers

PMID29650506
PMCPMC5920198

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.