From precision medicine to precision health

Precision health, derived from precision medicine, is an extension or progeny of this approach [12,13]. Precision medicine proposes customizing medical decisions and tailoring treatment to each individual’s unique genetic or biological composition [14–16]. Despite the relevance and achievements of precision medicine in the treatment of diseases, this approach has some limitations from a health promotion and prevention research perspective. Firstly, precision medicine often reduces the human being to biological data, thus ignoring a holistic biopsychosocial understanding of health and its numerous determinants [17]. Secondly, precision medicine is disease-orientated and predominantly concerned with the consideration of sick populations in the sense of curation (treatment of diseases), thereby lacking the application in health promotion and primary prevention [18].

Although the understanding of precision medicine has expanded in recent years with new concepts emerging, e.g., exposome science [19,20] or personomics research [21], there is still the limitation that it is almost exclusively used in the field of secondary or tertiary prevention [18]. Thus, disease treatment is given priority over prevention [9]. This appears paradoxical because research has shown that every dollar invested in prevention saves approximately 27 dollars in the long run [22]. For this reason, Hekler et al. [18] noted that the full potential of precision efforts will only be achieved if precision is applied across the spectrum of health, including population health and public health [23], not just medicine. Based on these limitations in precision medicine, several approaches have been developed in precision research in recent years: precision public health, precision health and precision prevention [10,12,13,24–27].

Precision prevention

Precision prevention uses biological, behavioral, socioeconomic and epidemiological data to develop and implement strategies tailored to, e.g., reducing the incidence and mortality of NCDs in a specific individual or group of individuals [24]. Although the term precision prevention turned up first in 2014, and there is a growing number of studies, the terminological use is still very variable or unclear [13,24]. In the context of using the compound term precision prevention, the most commonly used terms to describe precision are individualized, personalized, tailored, or stratified [13,24]. Biró et al. [24], for example, argue that individualized, personalized and precision prevention are not separate terms but are embedded in each other. In their understanding, precision prevention encompasses and extends both individualized and personalized prevention. Following this broad understanding of precision prevention, health professionals consider not only a person’s characteristics and lifestyle (individualized prevention) but also the personal omics profile, as well as the socioeconomic status or the opportunities offered by psychological and behavioral data of the person (personalized prevention) when making proposals to maintain or improve the individual’s quality of life [24]. In the present study, we decided to rely on the terminological description of Biró et al. [24] and therefore, use the term precision prevention and its associated definition. However, several publications use the broader concept and term of precision health [3,10,18].

Precision prevention aims to develop an expanded precision perspective and may be viable within the whole prevention paradigm–primary, secondary and tertiary prevention [24]. Precision prevention claims to not just address symptoms but to directly target genetic, biological, environmental, social and behavioral determinants of health [9,18] and to optimize non-pharmaceutical interventions based on these factors [28,29]. This ensures that the right support is provided to the right individual at the right time [18,30–33]. Accordingly, precision prevention focuses on multiple determinants of health and takes a holistic view of health according to the WHO biopsychosocial model of health [34], while also taking a lifespan perspective of health into account, therefore including interventions across the lifespan [10,18].

The current state of research in precision prevention

Although precision prevention is still in its infancy, research in this field has developed rapidly and particularly early work has produced promising findings [10]. Gambhir et al. [11] have structured the research approaches to precision prevention into a cyclical model that looks at precision prevention in the key components: (1) risk assessment, personal and environmental monitoring; (2) data analytics and (3) tailored interventions [10].

Based on this cyclical model, a scoping review by Viana et al. [10] presented the current state of international research on precision health in a very general and comprehensive way. At the same time, however, it also highlights numerous desiderata in this research area. About half of the studies focused on developing an intervention, primarily individual digital health promotion tools or public health programs, implemented in rural communities, hospitals, workplaces and many other settings. The three most commonly addressed health conditions were metabolic disorders (16%), cardiovascular disease (13%) and cancer (12%), and only 7% aimed at increasing physical activity or weight reduction [10]. Finally, medical approaches currently seem to dominate the research area, as only 15% of the studies can be assigned to the behavioral and social sciences.

In addition to the comprehensive review by Viana et al. [10], Mauch et al. [3] included 31 studies in precision health interventions targeting modifiable health behaviors in their scoping review. Most interventions targeted physical activity (87%) and/or dietary intake (77%). Intervention content was personalized manually (via human interaction) in more than half of the studies (55%) and automatically (via a digital platform) in 35%. The information used to personalize interventions was primarily behavioral or lifestyle data (65%) and physiologic, biochemical or clinical data (48%) [3].

Although these reviews already provide a comprehensive insight into precision prevention, e.g., Viana et al. [10] conclude that their scoping review can serve as a basis for future syntheses of precision health advances. Future reviews should, therefore, first broaden the search strategy thematically in order to better capture emerging sub-disciplines and then focus specifically on certain stages of individuals’ lifespans (e.g., children, adults) and settings (e.g., school, workplace). Regarding certain lifespan stages and from the perspective of public health, especially health promotion and prevention, health-promoting programs are particularly feasible and effective when targeting people in different settings, i.e., in their most important living and working environments [35].

Worksite health promotion and precision prevention

For adults, together with the family and community setting, the workplace setting is a crucial setting for implementing and carrying out health-promoting programs [36,37], with working adults estimated to spend 30% to 40% of their waking time at work [38,39]. Among other things, the workplace provides an efficient structure to reach large groups for health-promoting interventions and uses natural social networks [40]. For example, occupational health services, which are part of the work organization, can be used with their know-how to introduce effective workplace health promotion interventions [41]. For these reasons, workplaces are an ideal setting for health promotion interventions, as they not only offer access to a large number of people but also offer the opportunity to implement interventions at multiple levels targeting individual, organizational and environmental determinants of health and health behaviors [42].

Despite the relevance of the worksite as an ideal setting for health promotion, precision prevention in the occupational context has not yet been considered comprehensively or has only targeted specific health conditions [43,44]. Viana et al. [10], for example, could only identify three studies (3/225; 1%) in their scoping review that were conducted in the workplace [43–45], while the scoping review by Mauch et al. [3] on precision health in the context of behavior change interventions identified one additional study in the workplace setting [46].

In the workplace setting, Moe-Byrne et al. [47] published a review in which they present studies on a thematically very close understanding of precision prevention (tailored interventions). The authors conducted a systematic review of randomized controlled trials (RCTs) to assess the effect of tailored digital health interventions provided in the workplace aiming to improve physical and mental health, presenteeism and absenteeism of employees. The study outcome was positive for tailored digital interventions regarding presenteeism, sleep, stress levels and physical symptoms related to somatization. However, this was less true for tailored digital interventions addressing depression, anxiety and absenteeism. Although these interventions did not reduce anxiety and depression in the general working population, they did significantly reduce depression and anxiety in employees with higher levels of psychological distress [47]. In addition, Moe-Byrne et al. [47] report that most studies showed no improvement in absenteeism but a faster return to work among long-term sick workers who had received a tailored intervention.

Research gap and objectives

In summary, despite substantial developments in recent years, research on precision prevention is still in its infancy [10]. There is a fundamental lack of reviews that address approaches and interventions in the context of health promotion and primary prevention, recruiting “healthy” or non-diseased populations [10]. Another research gap presents the fact that, to the knowledge of the authors of the present scoping review, there are currently no studies in which precision prevention is implemented holistically, i.e., according to the model components of Gambhir et al. [11] and the stages of Viana et al. [10]: 1) risk assessment/data monitoring, 2) data analytics and 3) tailored interventions. In addition, there is currently a lack of research on specific populations in their living and/or working environments. Especially in the adult age group, studies on precision prevention in the workplace have not yet been comprehensively presented. However, this setting is particularly relevant regarding health promotion and prevention [36,40,42].

The main objective of this scoping review is to map the current state of precision prevention research in the workplace setting, specifically to:

1. Summarize precision prevention research study context and characteristics (e.g., year of publication, health condition, sample characteristics).
2. Describe and analyze the precision prevention approach in the stages of risk assessment/data monitoring, data analytics, and the health promotion interventions implemented by study design and data analysis.

Based on this mapping, in the discussion of the main objective, we:

1. 3. Identify knowledge gaps in precision prevention in the workplace, highlighting promising research directions that can help shape future directions for research and implementation in worksite health promotion.

Information sources

Information on precision prevention and workplace health promotion was retrieved on two dates (May 4, 2023 and May 15, 2023) due to the workload of the retrieval process from the following six electronic databases: Web of Science^TM, Scopus^®, Embase^®, Ovid MEDLINE^®, PubMed^® and APA PsycInfo^®. Google Scholar^TM was not considered since this database did not support the comprehensive search string described below. In addition to searching articles in the databases mentioned above, references of eligible articles were separately screened on August 8, 2023 to detect further topic-related articles that had not been identified by the employed search strings thus far. Within this step, a search for follow-up articles of previously published study protocols detected by the search string was carried out if the string-based literature search did not already identify them. Grey literature was not considered as this review focuses on peer-reviewed publications and the current state of research on precision prevention in workplace health promotion.

Search strategy

Databases were searched for articles on precision prevention in workplace settings published in English between January 2010 and May 2023. For this purpose, a database search string for titles and abstracts was developed, employing two sets of search term groups. One group of search terms was chosen to focus on “precision” by also using the search terms “personalized”, “individualized”, “stratified”, “tailored” as described by Viana et al. [10], as well as “targeted”. A second group of search terms was chosen to address “prevention” with the additional search terms “health promotion”, “health”, “program” and “intervention”. The Boolean operator “OR” combined search terms of each group.

To identify articles in which search terms of the two groups were employed as term combinations such as “personalized health promotion” or “targeted intervention”, this specific search aspect was introduced into the search string by suitable database-specific proximity operators if available, e.g., NEAR/x operator (Web of Science^TM), PRE/x operator (Scopus^®), adj (Embase^® and MEDLINE^®). For the PsychInfo^® and PubMed^® databases, adapted strings were individually programmed to retrieve the search term combinations.

The Boolean operator “AND” finally combined all search term pairs related to “precision”/“prevention” with the workplace-related terms “worksite”, “organizational”, “occupational”, “worker”, “employee” or “corporate”, also considering different spellings (e.g., “organizational” or “organisational”). The entire set of electronic search strings adapted to the specific requirements of the six electronic databases are listed in S1 Table 1 in S1 File.

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Table 1. Absolute and relative frequencies of occupational groups (n = 162, multiple mentioning possible).

https://doi.org/10.1371/journal.pone.0304951.t001

Selection of sources of evidence–eligibility criteria

The following types of journal articles were included: primary empirical research studies (intervention studies, e.g., RCTs and observational studies, e.g., cross-sectional studies), study protocols and conference proceedings. We included different study types to capture prospective and retrospective designs and even planned studies to include and describe the broad research field of precision prevention in occupational health [50]. Reviews and meta-analyses were not included because they typically considered secondary studies and therefore these studies could be included twice, but the reference list of eligible reviews was screened to identify additional research [51]. Furthermore, editorial articles, book chapters, dissertations, abstracts and posters were excluded due to the lack of a structural quality assessment such as peer reviews. If the full text of articles could not be retrieved, these articles were excluded. Articles found by the search strategy were only included, if they reported on adult participants in workplace settings, human tissue samples (e.g., genetic material) or historical datasets (e.g., health records) of workers. Articles unrelated to workplace settings, such as community settings, were excluded. All eligible articles had to contain descriptions related to “precision prevention” or similar in title and/or abstract, introduction, methods or results. A detailed summary of the eligibility criteria, focusing on population, context (i.e., setting), concept and source of evidence, is listed in S1 Table 2 in S1 File.

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Table 2. Absolute and relative frequencies of health conditions (n = 160 conditions, multiple mentioning possible).

https://doi.org/10.1371/journal.pone.0304951.t002

Data extraction

All authors extracted relevant data from eligible articles, with each author reviewing 25% of the relevant studies. Data extraction focused on general characteristics of the study (e.g., health condition or working context), information on the study’s aims, design, main analysis, results and implications for future research, as well as qualitative data related to precision prevention, including measured health outcomes, tailored variables and definitions of precision health.

Data analysis and synthesis

Data analysis and synthesis were initiated in the Rayyan App based on highlighted and commented text sections relevant to our review’s aims. Building on this process, comprehensive data analysis and synthesis were performed in a spreadsheet using Excel^® software [Microsoft Cooperation, Redmond, Washington, USA], where the relevant articles’ extracted data were transferred. The authors also added comments to this spreadsheet and discussed the content to condense the findings in group meetings and reach an agreement on data synthesis. Lastly, data synthesis was conducted by summarizing the findings of our review in text and tables and visualizing the data in charts using Word^® and Excel^® software [Microsoft Cooperation, Redmond, Washington, USA].

Articles screened

Database searches retrieved 3,249 articles after removing duplicates. After title, abstract and full-text screening, and detection of additional 28 articles in a snowballing approach using the reference lists and citation tracking, 129 included articles (see S2 File for a reference list) remained for data analysis (Fig 1). The data and the categorizations for analyses can be found in S3 File.

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Fig 1. PRISMA flow chart for the procedure of this scoping review.

https://doi.org/10.1371/journal.pone.0304951.g001

Study context and characteristics

To address the main objective of this study, we summarized the precision prevention research study context and characteristics. The number of articles with precision prevention focus in the work context ascended with a mean of 9 articles per year. Approximately half of the studies (n = 71 of 129; 55%) were published in the years 2018 to 2023, whereas only 20% of the studies (n = 26 of 129) were published in the first five years (2010 to 2014). Most articles (n = 19) were published in 2022 (Fig 2).

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Fig 2. Published articles per year.

https://doi.org/10.1371/journal.pone.0304951.g002

With 30 articles, the majority of articles was published by authors from the United States of America (USA with 23%), followed by the Netherlands (11%) and Canada (9%). Fig 3 shows the global distribution of the articles.

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Fig 3. Amount and (percentage) of publications per country.

The country of origin of the first author was used. A zoom was placed on Europe, as smaller countries in terms of area were difficult to represent; n = 129 studies. Made with Natural Earth.

https://doi.org/10.1371/journal.pone.0304951.g003

Sample sizes at the baseline of the studies in our included articles varied between two and 12,696 participants, with a median of 169 participants. A priori estimated sample sizes from study protocols were not included. The mean age of participants was 42.5 years (Median = 43.6; Min = 24.0; Max = 57.1 years). For articles that mentioned participant gender (n = 94 of 129; 73%), the median percentage of female gender participants was 61%. Six articles only focused on the female gender, while 12 articles focused on the male gender. In 33% of the articles, information about the social background of the participants was given. Most of the articles concentrated on educational level (19%) or occupational level (5%) and only six articles included factors such as income or salary, followed by three articles including ethnicity. Regarding occupational groups (see Table 1), most articles (30%) addressed healthcare workers, e.g., hospital and nursing staff. The interventions rarely addressed people with physically demanding jobs, such as working in the agriculture or transport sector (e.g., professional drivers).

Analysis of the targeted health conditions

Multiple health conditions were mentioned in the analyzed articles (see Table 2). Therefore, 160 health conditions occurred, including health outcomes with a psychological health focus (e.g., mental health, stress, strain or depression) and a physiological health focus (e.g., musculoskeletal complaints or obesity). Furthermore, the health conditions could be classified as behavioral outcomes, for example, physical activity/sitting behavior, eating habits, smoking/alcohol consumption or work-related outcomes, for example, work performance, job satisfaction and sick leave days of absence as well as communicable diseases such as the Coronavirus disease 2019 (COVID-19).

Analyzing the stages of the precision prevention approach

Furthermore, we used the model of Gambhir et al. [11] to determine the precision prevention stages and combined the stages 1) risk assessment and data monitoring, followed by 2) data analytics and 3) tailored interventions. In our scoping review with 129 articles, 18 articles (14%) primarily focused on risk assessment/data monitoring, 16 articles (12%) mainly included data analytics studies, and 95 articles (74%) implemented and/or evaluated an intervention. When an article was categorized as risk assessment/data monitoring, we analyzed whether resources, risks or a combination of the two were addressed. Almost half of the articles on risk assessment/data monitoring focused on risks, while one-third of the articles used a combination of risks and resources. In the stage of data analytics, we analyzed the methods of the studies and most of the articles examined a profile approach with latent profile analysis or cluster analysis, followed by machine learning methods.

In the stage of interventions, first, we analyzed how the feedback for tailoring was given, in a way whether the feedback was personal, digital or in a blended manner. Second, we counted how often the feedback was given in interventions, for example, just once or repeatedly (see Table 3). Most interventions were delivered digitally, often in the form of health portals, platforms or tools on websites or in apps. However, the digital interventions also included, for example, a virtual reality-based occupational stress management component [53], automated stress tracking methods [54] or digital behavior change interventions with calendars [55]. If a personal component involving contact with people was used, this usually consisted of consultations and individual counseling. A blended intervention combined these aspects of human contact with digital feedback, either in the form of online consultations on outcomes or an additional opportunity for in-person contact. This is often associated with several feedback loops. For example, the outcome is presented digitally and then discussed in person, whereby the further intervention can be digital again. One of the success factors in reaching employees was the direct use of the intervention in the workplace setting, for example, the delivery of a smoking cessation intervention at a lunch truck [56].

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Table 3. Categorization of different article types and feedback or tailoring for interventions.

https://doi.org/10.1371/journal.pone.0304951.t003

Regarding study designs, more than a third of all studies used randomized controlled trials (39%), followed by cross-sectional studies (18%) and study protocols or pilot studies (12%). Fig 4 shows the distribution of study designs for the different precision prevention stages.

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Fig 4. Overview of studies with precision prevention stages and study designs.

The inner layer displays the stages of precision prevention addressed by the articles and the outer layer represents study designs within each stage of precision prevention, adding up to 100% of the respective stage; (N/A = not available).

https://doi.org/10.1371/journal.pone.0304951.g004

The main data analyses of all studies were regression analyses (44% with analyses of variance or linear mixed models), followed by univariate analyses (Chi-squared tests, Student’s t-tests, Fisher’s test, nonparametric tests; 12%), and thematic analyses (12%). Machine learning methods (Algorithms, Markov Models) were used in 8% of the articles. A profile approach was conducted by latent profile analyses (7%) or cluster analyses (4%). Only 3% of the articles used other analyses like Generalized Estimating Equations or N-of-1 trials, whereas 9% of the articles stated no precise main data analysis (N/A). Fig 5 shows the distribution of main data analyses for the different precision prevention stages.

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Fig 5. Overview of studies with precision prevention stages and main data analysis.

The inner layer displays the stages of precision prevention addressed by the articles and the outer layer represents main data analyses within each stage of precision prevention, adding up to 100% of the respective stage; (N/A = not available).

https://doi.org/10.1371/journal.pone.0304951.g005

General aspects

More generally, our scoping review shows that contrary to the findings of Viana et al. [10], many studies exist in the broad context of precision prevention research in occupational settings. While Viana et al. [10] found only three studies on precision prevention in the workplace in their scoping review, we identified 129 publications by use of a more specific search terminology. Although there are numerous studies on precision prevention in an occupational setting, there still does not appear to be a unified understanding in all these publications, due to the numerous, sometimes very different definitional descriptions of precision prevention. Nevertheless, this scoping review provides a good overview of the studies published to date on precision prevention, especially in the stages of risk assessment/monitoring, data analytics and interventions [11].

Study context and characteristics of included studies

The first part of this scoping review’s main objective was to provide a systematic summary of precision prevention research in terms of study contexts and characteristics.

Interpreting the stages of the precision prevention approach

The second part of this scoping review’s main objective was to describe and analyze how the precision prevention approach was used in the stages of risk assessment/data monitoring, data analytics and interventions. For this purpose, we used an adapted model from Gambhir et al. [11] to determine the stages in the precision prevention ecosystem on which our identified articles focus. Similar to the review by Viana et al. [10] (47%), most publications in our review applied precision approaches to develop and evaluate interventions (74%). In the stage of data analytics, precision approaches are currently used very little (12%) in the publications identified by our review, but this result is also comparable to Viana et al. [10] (19%). However, in contrast to the results of Viana et al. [10], who found that 35% of the studies target precision prevention approaches in the stage of risk assessment/data monitoring, our review examined 14% of the studies addressing this stage in the worksite context. The high percentage of intervention studies can be explained, among other things, by the fact that some of the studies found are protocol papers and pilot studies, which we assigned to the interventions. The relatively low use of precision approaches in data analytics is surprising, as it forms the “core” between risk assessment/data monitoring and personalized interventions in the model of Gambhir et al. [11]. Without this stage, holistic precision prevention cannot be fully implemented.

Strength and limitations

The strength of this review lies in the comprehensive search that used a variety of synonyms for precision prevention [24] to capture relevant studies in the occupational setting. Another strength is the consistent, systematic approach and rigor of this scoping review, in which each step was carried out independently and at least in duplicate. The consideration of a two-sided snowballing to identify further relevant studies can be seen as a further methodological strength.

Nevertheless, we cannot exclude the possibility that further relevant studies could not be identified. Thus, despite our comprehensive search term, it is conceivable that the inclusion of other scientific disciplines, such as occupational safety, and therefore, additional terms would identify further studies. Another limitation of this scoping review is that we did not consider a few types of publications (books, dissertations, grey literature, etc.) and that we have focused on English-language publications only. Finally, another limitation is that we performed the screening in pairs, but not the data extraction, which may lead to errors or inaccuracies in the extracted data. Therefore, this scoping review can serve as a basis for further synthesis of precision prevention in the occupational setting, expanding the current review’s scope and filling in its limitations. Future reviews should include non-English publications and expand the search strategy to better capture emerging subdisciplines such as precision public health, precision mental health [10] or economic aspects of precision prevention.

Implications and future research directions

Based on these findings, a further aim of this scoping review was to identify current trends and knowledge gaps in precision prevention research in the workplace and to suggest future research directions. Future research should initially focus on developing a unified understanding of precision prevention in the occupational setting and on clearly describing and defining this still very broad construct. This could lead to precision prevention becoming more established and applied in worksite health promotion in the future, both in research and in practice. Regarding the objectives of this scoping review, the following four steps would be recommendable in future research:

1. In the future, precision prevention research should be increasingly implemented in regions and sectors that have been neglected so far to better exploit health potentials in the workplace setting. More widespread use could also lead to better targeting of vulnerable groups, especially workers from low socioeconomic groups, for health-promoting interventions, thereby better promoting their health and ultimately helping reduce health inequalities [70]. In this context, it would also be desirable, if a holistic, integrated understanding of health is considered in precision prevention research, considering different data sources to identify health needs and target groups. For example, determinants of health behaviors, behaviors and health or work-related outcomes of workers should be assessed and analyzed together in the future, as well as their environmental and societal factors [70]. According to the latest findings in personomics and exposome research [19–21], all these factors have important effects on individuals, which is why precision prevention must include information about a person’s environment and living conditions in particular, in addition to personal information. Ultimately, linking the well-founded findings of medical -omics research with the behavioral and environmental findings of social and behavioral science research would make a decisive contribution to considering human health in the future not only holistically but also in terms of reciprocal human-environment interaction.
2. Based on such a comprehensive understanding of health and a more holistic database of employees [24], new methods of data analysis could then also be increasingly used. In particular, approaches in machine learning, algorithms, AI and other person-centered methods could be used more extensively. An example of such an innovative approach is using machine learning to identify psychosocial-behavioral phenotypes by Burgermaster and Rodriguez [94]. To achieve this goal, it is also necessary to involve different expertise in science and practice, e.g., health science, sports science, psychology, medicine, etc. Such inter- and transdisciplinary collaboration would reduce the current dominance of medicine and the often associated one-sided view of precision prevention and expand a holistic approach in precision prevention research [10], thereby promoting further potential.
3. Although most of the 129 identified studies tested or evaluated an intervention, more innovative study designs should be used for interventions in the future to meet the demands of a precision prevention approach–that means targeting the right intervention to the right population at the right time [58,95]. For example, adaptive or continuous-tuning interventions [18], such as JITAIs (just-in-time adaptive interventions), MRTs (micro-randomization trials), N-of-1 designs/idiographic designs or even system identification experiments have rarely been tested or evaluated in worksite health promotion interventions. As there are already some best practice studies for the implementation of JITAIs/MRTs (HeartSteps [96,97]; SitCoach–office workers; [98]), or of system identification approaches (MyBehaviour, Just Walk [99]), with promising effects on health behaviors or even workers’ health, such study designs with the use of digital technology (apps, health portals, health platforms, etc.) should be used even more in the future. The use of digital technologies offers numerous potentials for implementing the precision prevention approach, as, for example, employees can be reached regardless of their place of work and digital interventions can also be individually and continuously adapted to the needs of employees.
4. Furthermore, interventions should not only target individuals (e.g., health behaviors) but also address changes in the environment and society, as such system approaches are particularly promising but are currently rarely used in workplace health promotion [70]. Future studies should therefore be guided by the latest findings in personomics and exposome research [19–21] and, for example, include environmental exposures and living conditions of employees in behavioral interventions, and specifically try to change them in interventions.

In addition to these recommendations for research, it would also be important in the future for stakeholders from occupational practice and also from politics (health, economy, labor, etc.) to address the possibilities and opportunities of precision prevention in occupational health and to shape this field in the future.

In the future, stakeholders from occupational practice could, among other things, focus on the numerous possibilities and opportunities of innovative, digital technologies in health-promoting measures and utilize them even more. In recent years, the numerous changes in work (digitalization, work from home, etc.) have meant, among other things, that many employees (especially white-collar workers) work much less frequently at a fixed company location and are therefore hardly accessible for health-promoting programs on site. In this respect, the greater use of digital technologies in health-promoting interventions (apps, wearables, health platforms, messengers, etc.) could, on the one hand, help to ensure that employees can regularly participate in these programs regardless of where they work. On the other hand, these interventions can also be tailored to the individual needs and preferences of employees in terms of precision prevention. In order to be able to utilize the potential of precision prevention in occupational practice even better in the future, it is also important to improve the quality of counseling in the company, especially in occupational medicine [100]. This ensures that employees receive health-related recommendations that they can understand with their individual health literacy and implement in their everyday professional and private lives.

In order to support awareness and greater use of precision prevention approaches in occupational health, it would be desirable, for example, for health policy to promote financial or tax incentives for the use of digital technologies in health-promoting programs. In addition, it would also be important for politicians to initiate large-scale campaigns, such as former US President Obama with the precision medicine initiative, to further strengthen the importance of precision prevention in society.