The data for this bibliometric study was extracted from the Scoups database, by using the search chain “wearable AND health” and downloading the information of the format .RIS. For the download, it must be done in blocks of 2000 documents (corresponding to the maximum selection in Scopus). Only the aforementioned search chain was used as a search criterion and no documents between the years 2000 and 2017 were discarded. The search was made in June 2018.

The downloaded data were entered into the SciMAT software (SciMAT-v1.1.04), which can be freely downloaded ( http://sci2s.ugr.es/scimat/download.html). The data entered can be viewed with the option "Knowledge base -> Documents -> Document manager". In this case, in the first instance we find that there is a total of 6001 downloaded data. Using the Document manager view, the fields shown in an Excel spreadsheet were copied and pasted (In this case, Microsoft Office 2016 was used), and with the use of the option "Conditional Format" the repeated data were found by title. In these duplicate data by title, the documents that had the oldest year were excluded from the database compiled in SciMAT. From this search, 5696 documents were found after excluding duplicated documents.

Then, with the SciMAT software, "Group set -> Word -> Find similar word by plurals (automatic)" was used in order to limit the number of words with the same meaning between plural and singular. Finally, through "Knowledge base -> Words -> Words manager" were joined words or acronyms that had the same meaning, for example "ML" and "Machine Learning". And others that were irrelevant because they are obvious were eliminated, for example, the word "wearble" and "health" is removed from the database.

The adapted database can be found in the file attached to this document. To enter, you must have SciMAT installed, and through the "File -> Open Project" option, access the "AnalysisScimat" file.

This data was visualized with the software SciMAT, using a filtering process to avoid duplicity in similar documents and words. A growth analysis was done to see the number of articles published per year. Finally, SciMAT’s analysis tool was used to make connections between different periods and different clusters of relevant topics throughout the years.

From the documents found from Scopus, descriptions were made based on the data classified by the software SciMAT-v1.1.04 on three fundamental aspects: period of time between the years of 2000 and 2017. The journals where they were published; the authors who published under the topics of “wearable AND health”.

The SciMAT software was used for the bibliometric analysis in accordance to the indications of Cobo et al. (2012). The process has three modules. ‘Module to manage the knowledge base’. ‘Module to carry out the science mapping analysis’. And ‘Visualization module’. In the ‘Module to manage the knowledge base’ build the know base, importing files .ris, edited entities, quit duplicated documents and was defined periods. In ‘Module to carry out the science mapping analysis’, was selected periods, selected unit of analysis selections, data reduction, kind of network, network reduction, normalization, clustering, document mappers, quality measures and longitudinal analysis. Finally, in Visualization module we did visualization and interpretation. Table 1 shows the selected parameters.

Table 2 shows four columns. In the first one is the published documents year. The second corresponds to the number of documents found in that year. The third column corresponds to the percentage of articles corresponding to each year. Finally, in the fourth column, you can find the growth rate of the previous year (the difference between the publications made in a year subtracted from the previous year). It can be observed there are two periods when there is decrease in the production of articles on the subject of wearables and health. In 2001, there were 3 less articles than in 2000; however, that is not relevant considering both years concentrated less than 1% of the total production of documents in Scopus. Between 2008 and 2009 the production declined by 9 documents, but it is necessary to take into account that in both years the production was 1% of the total, with 163 documents in 2009 and 172 in 2008. This implies that, even if the production of articles declined in general, this decline was not relevant. Table 1 also shows that the bibliographic production of studies on wearables and health is increasing.

Figure 1 shows the evolution map. This map shows that in the period of 2000–2005, only two themes were relevant: ‘Telemedicine’ and ‘Bluetooth’. In the period of 2006–2010, the term ‘Telemedicine’ converges mainly to ‘Monitoring system’ and the theme ‘Bluetooth’ converges to ‘Mobile technology’. In addition, in this period, new themes appear. In the period of 2011, the themes are reduced to ‘Monitoring system’ and ‘Health care’. In the periods of 2012 and 2013, the theme of ‘Monitoring system’ is mainly maintained. In 2014, the theme ‘Monitoring system’ was diversified into ‘Device’, ‘Accelerometer’, ‘Mobile Technology’ and ‘Health care’. In the period of 2015, the themes of ‘Devices’ and ‘Mobile Technology’ are maintained, and, besides, the themes of ‘wearable antennas’, ‘Signal processing’, ‘wearable devices’ and ‘Sensor’ appear. In the period of 2016, the themes ‘Devices’, ‘mobile technology’ are maintained; ‘signal processing’ evolves into the theme of ‘Algorithms’, while ‘Wearable antennas’ evolve into ‘Body sensor network’, and the theme of ‘wearable computers’ appears. Finally, in 2017, the theme of ‘Devices’ evolves into ‘Monitoring System’ and ‘Aged’; the themes of ‘Wearable computer’ and ‘Algorithms’ are maintained.

It is interesting to observe that, in the evolutionary analysis from 2000 to 2005, the themes were not very diverse. From 2006 to 2010, several themes of interest appear, but in the periods of 2011 to 2014 they again focus on two themes only. In these periods, the predominant theme is ‘Monitoring system’ and it evolves in the periods of 2015 and 2016 into the ‘Devices’ theme, and then finally, in the period of 2017, the topics of ‘Monitoring system’ and ‘Aged’ are resumed. In the period of 2014, the theme of ‘Mobile technology’ is resumed, which is maintained until the period of 2016, and in the period of 2017 the theme ‘Monitoring system’ evolves. In the period of 2015, the theme of ‘Signal processing’ becomes stronger, which evolves in the periods of 2016 and 2017 into the theme ‘Algorithms’.

In Figure 2, the Strategic maps for the years 2015, 2016 and 2017 are shown, where the themes that emerge and those that are in motor clusters according to their centrality and their density can be seen. In the period of 2015 it is observed that the topics that in the motor cluster region ‘Signal processing’ and ‘Devices’ appear. In the period of 2016, the theme of ‘Devices’ and ‘Review’ is mainly in the motor cluster region. In this period, the theme of ‘Algorithms’ is also at the center. Finally, in the period of 2017, the themes in the motor cluster are ‘Monitoring System’ and ‘Aged’.

For this case, the two themes that are found in the motor cluster regions for the period of 2017 were taken into account. In this case, it can be observed that the theme of ‘Monitoring system’ is related to themes that present potential for studies, such as ‘Health care’, ‘Mobile technology’ and ‘Wearable devices’, among other themes. It can be observed that ‘Monitoring system’ is the center of the cluster, but that all the other themes are related to each other.

Another theme that is found in the motor cluster region is the theme of ‘Aged’, which is the center of the graph. For this case, its related topics are focused on pilot studies, of prospective and feasibility. Other themes related to this theme are ‘Devices’ and ‘Wearable electronic devices’. In this case, it can be observed that ‘Aged’ is related to the other themes, but these do not relate much to each other.