- Data note
- Open Access
Dataset of fitness trackers and smartwatches to measuring physical activity in research
BMC Research Notes volume 15, Article number: 258 (2022)
Accelerometer-based wrist-worn fitness trackers and smartwatches (wearables) appeared on the consumer market in 2011. Many wearable devices have been released since. The objective of this data paper is to describe a dataset of 423 wearables released before July 2017.
We identified wearables and extracted information from six online and offline databases. We also visited websites for all identified companies/brands to identify additional wearables, as well as obtained additional information for each identified device. Twelve attributes were collected: wearable name, company/brand name, release year, country of origin, whether the wearable was crowd funded, form factor (fitness tracker or smartwatch), and sensors supported. Support for the following sensors were mapped: accelerometer, magnetometer, gyroscope, altimeter or barometer, global-positioning-system, and optical pulse sensor (i.e., photoplethysmograph). The search was conducted between May 15th and July 1st, 2017. The included data gives an overview of most in-scope wearables released before July 2017 and allows researchers to conduct additional analysis not performed in the related article. Further insights can be achieved by complementing this list with wearable models released after July 2017.
Accelerometer-based wrist-worn fitness trackers and smartwatches (wearables) appeared on the consumer market in 2011 . Many wearable devices have been released since. In addition to being used for personal physical activity tracking, these wearables are also increasingly being used in research as tools to collect health data [2,3,4]. These wearables are especially designed for long-term usage and thus facilitates long-term recording with low participant burden.
The objective of this data paper is to describe a dataset containing most in-scope wearables (n = 423) released before July 2017. In a study published in 2018, where this data set was used, we showed how sensor support changed over time, and that (in addition to accelerometer) optical pulse sensor (i.e., photoplethysmograph [PPG]) and global positioning system (GPS) were the two most common sensors .
The dataset contains information on 423 wrist-worn consumer-based wearables, produced by 132 different companies. The dataset is stored at DataverseNO . A ReadMe-file (Table 1, Data file 1) describes the content of the dataset. The data are stored in two files, containing the same information using different formats, where “data.csv” (Table 1, Dataset 1) is a comma-separated file and “data.txt” (Table 1, dataset 2) is a tab-separated file. See Table 1.
Data were collected in two steps. In step one, we identified wearables and extracted information from six online and offline databases: The Queen’s University’s Wearable Device Inventory [6, 7], The Vandrico Wearables database , GsmArena.com , Wearables.com , SpecBucket.com , and PrisGuide.no . In step two, we visited websites for all companies/brands identified in step one to identify additional wearables and extract additional information for each identified wearable.
Conflicting information from the databases was resolved by visiting brand websites. If no website could be found, or the website did not contain the needed information, we used Google search and other online resources (e.g., Wikipedia).
We collected 12 variables for each wearable. Six variables were related to sensor support and six variables were background attributes (i.e., meta data). The six sensor variables are: (1) accelerometer, (2) magnetometer, (3) gyroscope, (4) altimeter or barometer, (5) GPS, and (6) PPG.
The six background attributes are: (1) wearable name, (2) company/brand name, (3) release year, (4) country of origin, (5) whether the wearable was crowd funded, and (6) form factor (fitness tracker or smartwatch). An overview of each variable with a short description is available in the 00_ReadMe-file (Table 1, Data file 1).
The search was conducted between May 15th and July 1st, 2017. Inclusion criteria were: (1) wrist-worn, (2) consumer-based, (3) equipped with accelerometer for physical activity monitoring, and (4) capable of transferring data to connected smartphone.
Despite a thorough search of wearables, the list is likely not complete. During the search we only used English or Norwegian sources, or sources that could be translated using Google Translate. It is therefore possible that among the wearables we did not identify, a predominance of non-western countries is represented. We used multiple sources to map wearable capabilities, sometimes with conflicting information. We visited official vendor websites to identify the correct information, but these were not always available, and some misclassifications may thus exist.
The data set contains variables released between 2011 and 2017, as the search was conducted in 2017. We invite other researchers to update this list with newer models released after 2017, using this data set as a starting point.
Availability of data and materials
The data described in this Data note can be freely and openly accessed on DataverseNO under https://doi.org/10.18710/6ZWC9Z. Please see Table 1 and references  for details and links to the data.
Global positioning system
Henriksen A, Haugen Mikalsen M, Woldaregay AZ, Muzny M, Hartvigsen G, Hopstock LA, Grimsgaard S. Using fitness trackers and smartwatches to measure physical activity in research: analysis of consumer wrist-worn wearables. J Med Internet Res. 2018;20(3):e110. https://doi.org/10.2196/jmir.9157.
Phillips SM, Cadmus-Bertram L, Rosenberg D, Buman MP, Lynch BM. Wearable technology and physical activity in chronic disease: opportunities and challenges. Am J Prev Med. 2018;54(1):144–50. https://doi.org/10.1016/j.amepre.2017.08.015.
Shin G, Jarrahi MH, Fei Y, Karami A, Gafinowitz N, Byun A, Lu X. Wearable activity trackers, accuracy, adoption, acceptance and health impact: a systematic literature review. J Biomed Inform. 2019;93: 103153. https://doi.org/10.1016/j.jbi.2019.103153.
Wright SP, Hall Brown TS, Collier SR, Sandberg K. How consumer physical activity monitors could transform human physiology research. Am J Physiol Regul Integr Comp Physiol. 2017;312(3):R358–67. https://doi.org/10.1152/ajpregu.00349.2016.
Henriksen A, Woldaregay AZ, Miroslav M, Gunnar H, Hopstock LA, Grimsgaard S, Replication data for Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research. DataverseNO, 2021 https://doi.org/10.18710/6ZWC9Z.
Richardson S, Mackinnon D. Left to their own Devices? Privacy Implications of Wearable Technology in Canadian Workplaces. 2017.
Richardson SM. Debra. Wearable Device Inventory, Queen's University. 2019; https://www.sscqueens.org/resources/wearable-device-inventory-2017. Accessed 2019 05–05.
Vandrico. The wearables database. 2017; http://vandrico.com/wearables (No longer available). Accessed cited 2017 07–12.
GsmArena. GsmArena. 2017; http://www.gsmarena.com/results.php3?sFormFactors=8. Accessed cited 2017 04–01.
Wearables. Wearables.com. 2017. http://www.wearables.com/devices. Accessed 2017 07–12.
Specbucket. Smart Watches. 2017; https://smartwatches.specbucket.com. Accessed 2017 07–12.
Prisguiden. Prisguiden - Prissammenligning - Den enkleste starten på et godt kjøp. 2021; https://prisguiden.no. Accessed 2017 10–19.
This work was supported by UiT The Arctic University of Norway thematic priority Grant “Personalized medicine for public health”.
Open access funding provided by UiT The Arctic University of Norway (incl University Hospital of North Norway). This work was supported by UiT The Arctic University of Norway thematic priority Grant “Personalized medicine for public health”.
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Henriksen, A., Woldaregay, A.Z., Muzny, M. et al. Dataset of fitness trackers and smartwatches to measuring physical activity in research. BMC Res Notes 15, 258 (2022). https://doi.org/10.1186/s13104-022-06146-5
- Motor activity
- Activity tracker
- Smart watch
- Heart rate