DOI: 10.5061/dryad.s1rn8pkd6
title: Data from: Characterizing terminology applied by authors and database producers to informatics literature on consumer engagement with wearable devices

description: To recommend strategies to improve discoverability of consumer health
 informatics (CHI) literature, we aimed to characterize controlled
 vocabulary and author terminology applied to a subset of CHI literature on
 wearable technologies. A descriptive analysis of articles
 (N=2,522) from 2019 identified 308 (12.2%) CHI-related articles for which
 the citations with PubMed identifiers for the included and excluded
 studies are provided. The 308 articles were published in 181
 journals which we classified by type of journal—health, informatics,
 technology and other—as shown in the third file. We provide an aggregated
 file of the author-assigned keywords as they appeared in the PubMed
 records of the included studies along with our decision about whether they
 represented consumer engagement. We also included an aggregated file of
 the Medical Subject Headings assigned to the included studies. 
 The top 100 terms and their frequency scores for the title and abstracts
 are also included. We did not include any of the terminology from CINAHL,
 and Engineering Databases (Compendex and Inspec together) due to copyright
 concerns.
description: This data set includes 14 files,
 7 Microsoft Excel (.xlsx) and those same 7 files as comma-delimited CSV
 files. We
 searched PubMed on December 19, 2020 using the strategy published in the
 associated article, limiting to the publication year 2019, and retrieved
 2,522 citations for the feasibility study and uploaded them to Rayyan.ai for independent double
 screening by four reviewers with CHI expertise (CAS, KA, CM, SS). All
 2,522 abstracts were divided equally across the team of four reviewers.
 Each reviewer independently screened 1261 abstracts (resulting in each
 abstract being reviewed by two reviewers); then, discussion and consensus
 were used as needed, with a third reviewer making the final decision. The
 inclusion and exclusion criteria that resulted in this data set appear
 below and also as a table in the published
 article.  Final Inclusion and Exclusion Criteria Applied in
 Screening and Selecting Articles for the Terminology
 Analysis  
 Inclusion 
 Exclusion 
 At
 least one device in the article meets the following criteria, as
 identified by the screener but not necessarily stated explicitly by the
 author(s):  All
 devices in the article meet at least one of the following criteria, as
 identified by the screener, but not necessarily stated explicitly by the
 author(s): The
 article is not solely a product advertisement or announcement and has an
 abstract or other substantive content in English. The article itself can
 be in a language other than English. 
 Article is solely a product advertisement or
 announcement. The
 device is consumer/patient-focused and can be worn and
 removed.  A
 device that is implanted or designed as part of a larger clinical medical
 device or system that is not typically available to
 consumers. The
 wearable device measures a health or physiological characteristic relevant
 to health or well-being. A
 device that does not measure anything relevant to
 health. The
 consumer/patient can observe the device’s data.
 Monitoring or data that is generated is not
 available to the consumer/patient. 
 The files 1 and 2 contain a subset of the citation
 data exported from the Rayyan system.  
 File 3 was created by
 two authors (KA and RW) categorizing source journals
 into 4 groups: health, informatics (based on Wang et al.’s core journal
 list), technology, and other. The “health” category includes journals
 covering health topics exclusive to informatics or technology. Journals
 that did not focus on health, informatics or technology were categorized
 as “Other;” examples included Systematic Reviews, and Evaluation and
 Program Planning.  Files 4 and 7, counting terms as single words or pre-existing
 phrases in author keywords and MeSH, were created by removing spaces in
 phrases to create a single entity and counted those with a unique word
 calculator, PlanetCalc. Unique words count | Online calculators.
 https://planetcalc.com/3205/
 Term frequencies for Files 5 and 6 were created by
 using the Monkey Learn automated word cloud generator that uses artificial
 intelligence to identify multi-word concepts and remove common stop
 words.  MonkeyLearn. Word Cloud Generator.  https://monkeylearn.com/word-cloud
description: This data set consists of
 Microsoft Excel (.xlsx) and comma-delimited CSV files for the seven
 supplementary data files published as PDFs with our
 article.   Supplemental Data File 1. Citations for 308
 included papers from 2019  Supplemental Data File 2: Citations
 for 2214 excluded papers from 2019 Supplemental Data File 3: Journal category
 analysis Supplemental Data File 4: Counts for the 855 unique
 author keywords Supplemental Data File 5: Frequency data for the
 word clouds top 100 generated from titles. 
 Supplemental Data File 6:
 Frequency data for the word clouds top 100 generated from
 abstracts.  Supplemental Data File 7: Counts of unique MeSH
 terms.  Any program
 that can open comma-delimited files, such as Microsoft Excel or other
 spreadsheet programs can be used. The secondary copies in .xlsx format
 require Microsoft Excel. The first several rows of each
 data table contain the title and description and definitions for that
 table, so please open the file and examine those and delete them or take
 them into consideration when sorting or importing the data into other
 programs.  There is no missing data in the data
 tables.  In Supplementary Data 3, those titles that were not noted as
 Informatics in the cited article have no in that column.  In Supplementary
 Data 4, those terms that were not classified as consumer engagement now
 have a dot (.) in the consumer engagement field.