Data collection procedure
There has been sparse research into apps for OHS&W, though apps for mental health and well-being have been studied [15,16,17]. In order to gain an overview, the first step was to develop a search strategy. We mapped OHS&W apps by searching the two major app outlets, Apple’s App Store and Google’s Google Play. We used the following keywords in both databases: work environment, occupational health, occupation, work environment authority, productivity, leader, safety representative, union representative, sick leave, safety, lift, pain, well-being, stress, and mental health. The search terms were selected based on in-depth knowledge of the Danish field of occupational health.
We used two smartphones (one iPhone running IOS and one OnePlus running Android operating system) for the search. The two smartphones used had access to apps available through the Danish versions of the App Store and Google Play. The access we had through our Danish phones was limited to the apps available via the Danish version of the App Store and Google Play. Most likely, some apps were excluded due to this. Additionally, we searched in Google Play Store using a browser, a possibility that Apple’s App Store does not support. To search in Apple’s App Store using a browser, we used the homepage https://fnd.io/, a service that allowed us to adjust which national market edition of the App Store we wished to search. The search terms were in Danish; however, apps in English were also included if they appeared based on the search terms.
In the App Store and Google Play Store, it is not possible to set up search criteria and run the search as one would do in bibliographic databases; therefore, each search term was searched individually on App Store and Google Play Store. The apps suggested based on the search (“you might also like” section) were included in the study, if relevant. Each keyword served as a starting point for what resembled a snowball data collection method [24]. This resulted in several cross-references for each keyword; therefore, it is not telling to make a table showing how many apps we found on each keyword. The approach also made it necessary to make the first selection process part of the data collection phase. Hence, the first of two rounds of the data selection process took place while extracting the data. We assessed the apps based on the following criteria:
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• Were the apps aimed at working life, workplaces, and occupational health?
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• Did they concern an operationalization of occupational health topics?
The criteria meant that, for instance, coloring apps (coloring books for adults) found by the keyword search: stress were not included, as they were not aimed particularly at reducing stress at work. The second criteria meant that, for instance, apps on the correct usage of ladders were included as researchers deemed it an operationalization of an OSH&W topic, specifically safety.
This study is based only on the apps available through the App Store and Google Play Store. The present study does not include apps not available via these two channels, such as those specifically developed for or by a specific company for internal use. We conducted searches on the internet using the names of the apps. The identified data material about each app (webpages, articles, reviews, detailed descriptions of the application, etc.) was saved using the Ncapture web tool. We kept all the data material in Nvivo11, enabling us to code the data material.
In addition to the search on the App Store and Google Play Store, we searched InfoMedia, a database containing all Danish newspaper articles. This was done to identify apps described in the press and which might subsequently be searched in either the App Store or Google Play Store via the name of the app in question. InfoMedia was searched in the period 2011–2021 using the search criteria: “app”/”apps” combined with “occupational health”. Twenty-nine articles were found in InfoMedia, referring to ten unique apps. Three new apps were identified through this method. The articles providing additional knowledge about apps identified in the App Store and Google Play Stores became part of the data material.
This process yielded 63 apps for OHS&W. These apps were entered in Nvivo11. Hereafter, we did a second round of exclusion processes following the same criteria as in the first round (mentioned above) but accessing the apps and the description of the apps more thoroughly (full-text screening). In the second exclusion process, six apps were excluded. See Fig. 1 for a diagram of the app identification and selection process.
Data analyzing processes
We developed a coding system in Nvivo11 to analyze the apps. Codes were made in a dialectical process where two of the authors applied predefined principles (target group, sender of the app, area of occupational health, type of app, reference to research) to the apps, met to discuss them with the third author, made code alterations to analyze the data material better, and applied the new set of codes. This was done in three iterations until a comprehensive categorization was found.
In the following, we present the final taxonomy we have developed in the study to categorize the apps. We have divided the apps according to which OHS&W area they cover, the target group for the app, the app provider, and the type of app in question (intervention or information/communication).
Different fields of occupational health
To create an overview of the field, we categorized the mapped apps according to the type of occupational health area they cover. The apps are coded according to the following categories: Musculoskeletal disorders, psychosocial work environment, work accidents/safety, chemistry, noise, management, rights/legislation, OHS&W coordination (including, e.g., apps for handling workplace assessment or apps that could be used for the safety representatives’ work).
Sender and recipients
In addition, we have categorized who the developer/owner of the app is (private company, public institution, public/private partnership, cooperative, foundation/non-profit, social partners, industry associations, UN, research institutions) and who the target recipient/audience is (companies, HR-personnel, safety representatives, managers, employees, and occupational health professionals).
The type of app
In the initial search, we found that OHS&W apps cover a wide range of diverse apps. To operationalize the type of apps, we divided them into two qualitatively different categories: 1) apps that primarily present information (information apps) and 2) apps that aim to create a change in the workplace (intervention apps).
The first category includes apps presenting information and tools for communication, for example, datasheets, information on materials/chemistry, etc., in a digital form. The second category includes apps that introduce a form of intervention in OHS&W, such as prompting workers to answer questionnaires or undergo training. However, the categories are difficult to keep completely separate, as comprehensive and well-accessible information might be a basis for a change in, e.g., work performance and thereby have a derived OHS&W significance. An example is the Danish Emergency Management Agency’s App “Dangerous substances”, an inventory of relevant information on harmful chemical substances. The app contains instructions for the safest possible action in an accident with dangerous substances and the possibility of looking up facts and legislation regarding chemical substances. We thus categorize the app as an information app [1], corresponding to the Danish Emergency Management Agency’s characterization of the app as a reference work; however, the app provides an obvious potential for adapting the work and creating better working environment conditions based on the data provided.
Nevertheless, we have kept the distinction between ‘information apps’ and ‘intervention apps’ as it allowed us to take a closer look at the apps used for OHS&W interventions and examine the degree of documentation for the promised effect. An examination of the effect is not equally relevant for apps that have the format of fact sheets/reference works or apps that make knowledge accessible quickly (contact information, legislation regarding OHS&W, recommended strain in physical work, etc.).
Assessment of research basis for the effect of the app
We assessed how the 37 intervention apps documented the app effects by assessing if they referenced research. We did this using two methods: First, we screened the publicly available data material collected in our app store and InfoMedia searches. This comprised of online information on the app (often the homepage for the app), newspaper articles found in InfoMedia, and the descriptions provided in the App Store and Google Play Store. Second, to ensure that all scientific publications on the specific apps were found, we searched PubMed, Web of Science, and PsycInfo for articles between 2002 and 2021 (see Fig. 2). We searched on the app name plus “app or application” (i.e., “Wysa” + “app”) to identify relevant studies on the identified apps (see Additional file 2). We did three rounds of screening based on the title, abstract and full text of the identified studies counting the number of apps evaluated and the number of publications.
On this basis, we divide the intervention apps into two categories: “not research-based” (apps where we did not find any reference to research in either method) and “research-based” (apps where we find reference(s) to research for all or parts of the mechanisms within the app).