Available via license: CC BY 4.0
Content may be subject to copyright.
Citation: Kechagias, Evripidis P.,
Georgios A. Papadopoulos, and
Ioanna Rokai. 2024. Evaluating the
Impact of Digital Health Interventions
on Workplace Health Outcomes: A
Systematic Review. Administrative
Sciences 14: 131. https://doi.org/
10.3390/admsci14060131
Received: 2 April 2024
Revised: 13 June 2024
Accepted: 16 June 2024
Published: 20 June 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
administrative
sciences
Systematic Review
Evaluating the Impact of Digital Health Interventions on
Workplace Health Outcomes: A Systematic Review
Evripidis P. Kechagias , Georgios A. Papadopoulos * and Ioanna Rokai
Sector of Industrial Management and Operational Research, School of Mechanical Engineering,
National Technical University of Athens, 15780 Athens, Greece; eurikechagias@mail.ntua.gr (E.P.K.);
rokai20@hotmail.com (I.R.)
*Correspondence: gpapado@mail.ntua.gr
Abstract: With the increasing penetration of digital technologies into health management, digital
health interventions in workplaces have been subject to substantial interest. These interventions aim to
enhance employee well-being, minimize absenteeism and presenteeism, and augment organizational
productivity. This paper carries out a systematic review focusing on the key characteristics of
effective digital health interventions designed to enhance health-related outcomes within workplace
settings and evaluates their implications for prospective implementation in the workplace. According
to PRISMA guidelines, the current systematic review adopted the most appropriate methods to
retrieve studies from PubMed, covering interventions that included cognitive-behavioral therapy
apps, software that reduces sedentary behaviors, virtual reality for well-being, and comprehensive
health programs. The studies’ quality was assessed through standardized tools with a preference for
randomized control trials and mixed-methods research. It was found that digital health interventions
positively impact mental health, physical activity, and well-being. However, limitations were found
due to self-reported data and potential biases. This review identified long-term effectiveness, objective
outcome measures, and cost-effectiveness as areas for future research. Digital health interventions
hold promise in enhancing workplace health strategies, as they offer scalable, personalized, cost-
effective solutions. However, critically relevant research gaps have to be faced to integrate these
successfully and exploit their real potential in organizational health strategies.
Keywords: digital health interventions; workplace health; systematic review; employee well-being;
organizational productivity
1. Introduction
As an increasing part of workplace health management, digital health interven-
tions consist of using a wide range of technologies designed to support health enhance-
ment and disease management and to improve healthcare delivery (Murray et al. 2016;
Kowatsch and Fleisch 2021
). However, the final objective of such interventions is also to
improve not only individual health outcomes but the overall efficiency and productivity
of the entire process of an organization (Perski and Short 2021). The aim of this paper
is to review the literature regarding promoting the efficacy of health-related outcomes
through the use of digital health interventions in workplace settings and, mainly, the im-
plications it has for managerial decision-making. The relevance of this topic stems from
the link between employee health and organizational performance, which is a thoroughly
researched one (Kolasa and Kozinski 2020). The literature has consistently shown that
health matters such as absenteeism and presenteeism—employees physically at the work-
place but underperforming on the job due to illness—are significantly associated with both
workforce health and well-being (Howarth et al. 2018). On this note, the precise use of
digital health interventions may be interpreted as innovative ways to settle these problems
(
Guo et al. 2020
). Unlike conventional health programs, digital interventions can provide
Adm. Sci. 2024,14, 131. https://doi.org/10.3390/admsci14060131 https://www.mdpi.com/journal/admsci
Adm. Sci. 2024,14, 131 2 of 16
scalable, personal, and inexpensive services available for a bigger segment of employees
(Soobiah et al. 2020).
What is more, the development of workplace health strategies manifests a shift from
out-of-date and traditional approaches to more technology-based strategies. Before, basic
workplace health management was based on the improvement of the physical work environ-
ment and simple health services (Blandford et al. 2018). However, with the growth of digital
technology, there is momentum to include wearable health devices, intra-organization track-
ing applications, and online health promotion programs as integral parts of organizational
employee health portfolios (Jandoo 2020). Such a digital tool not only provides real-time
health monitoring but also enables personalized feedback and interventions, possibly
leading to better homeostasis outcomes (Kowatsch et al. 2019).
In this scope, the literature review of this paper is based on various models and
frameworks explaining the efficacy of any digital health intervention. These theories include
behavior change theories (Pelly et al. 2023), health promotion theories
(Stark et al. 2022
),
and technology acceptance theories (AlQudah et al. 2021), which collectively allow for
a holistic approach to elucidating how digital health tools can be helpful in shaping
employees’ health behaviors and outcomes. The Health Belief Model (Limbu et al. 2022),
for example, explains how employees would perceive the benefits of engaging with digital
health tools, whereas the Technology Acceptance Model (Nadal et al. 2020) helps explicate
the factors that influence the adoption and sustained use of the technologies.
The backbone of this paper is a systematic review of the literature, offering a critical
appraisal of the current research in the field. Digital health interventions have recently
received increased attention due to the rising interest in exploring their potential for im-
proving health outcomes in different settings, including workplaces (Brewer et al. 2020).
This review aims to determine the extent to which digital health interventions have been
successful in enhancing health outcomes in workplace-based settings, as well as the associ-
ated reasons for success or lack thereof. On this note, from this preliminary research, it was
found out that, whereas there is an emerging body of literature regarding digital health
interventions, significant gaps still exist in the literature, with particular attention required
to be given to investigating long-term effectiveness as well as the impact on managerial
decision-making processes. Based on the existing literature, digital health interventions
in the workplace have been explored in various contexts; however, there are several gaps
that need further research. For instance, there is a lack of studies on digital health interven-
tions in low- and middle-income countries (Thai et al. 2023). Additionally, while tailored
digital interventions have shown promise in improving mental health and reducing presen-
teeism, their effectiveness in addressing depression, anxiety, and absenteeism is less clear.
There is also a high heterogeneity in outcome measures, especially for work productivity
(Moe-Byrne et al. 2022
). Furthermore, the long-term and consistent effects of digital work-
place wellness interventions have not been adequately studied
(Thai et al. 2023)
. Lastly, the
literature suggests a need for more research on how digital technologies can be leveraged
to expand the reach of performance management and provide timely updates of data for
workforce planning (Long et al. 2018).
Comprehending the efficiency of such interventions would be pivotal for managers
and organizational leaders. The results emanating from this research may inform decision-
making regarding the implementation of health technologies in the workplace. Insights
gained from this review could prove invaluable in molding future workplace health strate-
gies, when employee health is fast becoming recognized as a key driver for organizational
success. Therefore, this paper sets out to contribute to the emerging field of digital health
in the workplace by offering an integrative review of the literature that seeks to examine
the effectiveness of digital health interventions and their management implications. In so
doing, it intends to offer practical insights for organizations that pursue the exploitation
of digital technologies towards the enhancement of employee health and, by extension,
overall organization performance.
Adm. Sci. 2024,14, 131 3 of 16
2. Materials and Methods
The research methodology, including the materials and methods of the present sys-
tematic review, was planned in such a manner that a complete and unbiased evaluation of
digital health interventions in places of work was ascertained. The methodology adheres to
the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) state-
ment, to keep the structure as well as transparency. This methodology builds a strong basis
upon which a systematic exploration of digital health interventions is conducted, promis-
ing a synthesis of high-quality evidence with direct future workplace health strategies
(Sarkis-Onofre et al. 2021).
2.1. Eligibility Criteria
The criteria for eligibility for this systematic review were established meticulously
to guide the selection and ensure that only the highest quality and relevant studies were
included in the research, showing the actual benefits of these digital health interventions
in workplace settings. At the center of these criteria was the incorporation of qualitative
studies, mixed methods, pilot studies, and other forms of combined studies, as well as
randomized controlled trials (RCTs), which was important for several reasons. Different
methodologies provide diverse insights: RCTs present information about the effectiveness
of an intervention with minimal bias (Pearson et al. 2020), while qualitative research
investigates the processes and context of the use. Mixed-methods and pilot studies identify
and assess the practical and experimental possibilities and complications. Such an approach
helps to assess all the aspects of the interventions’ effectiveness in terms of health results,
which reflects the nature of Workplace Health problems as extensive and interrelated.
Furthermore, there is ample literature evidence about the utility of well-informed and
adequate managerial decisions, as well as acknowledging that novelty and flexibility are
essential to adapt and improve strategies for various settings. Overall, methodological
diversity enriches the perception and application of digital health interventions in the
workplace. The reporting of such studies should be undertaken in English, a common
requirement within any systematic review, so that ease of comprehension and comparability
can be obtained during analysis.
Moreover, the decision to consider protocols of studies together with completed
studies in this systematic review is justified because the protocols give a true picture of
current research activities and trends that may not be captured in completed studies. This
is especially important in the increasingly dynamic area of digital health interventions. It
also provides information about the methods in the upcoming research to be used so that
future studies are framed and funding and policy decisions are made. Moreover, including
protocols reduces publication bias by considering such studies that are still ongoing but are
not yet published, thus providing a more rounded and comprehensive meta-analysis of
the accessible data. This future-oriented framework improves the understanding of how
digital health interventions may impact workplace health as well as the decision-making
of managers.
In order to evaluate the quality of the selected studies, separately for each study, we
applied the Cochrane Risk of Bias Tool (RoB) to RCTs and the Mixed Methods Appraisal
Tool (MMAT) for mixed-methods and qualitative studies. These tools present a framework
of how one can assess different facets of study quality, including selection bias, performance
bias, detection bias, and reporting bias. Applying these tools enabled us to investigate all
the issues rigorously and consistently to make a detailed methodological analysis of every
study included in our review.
This research focused on digital health interventions targeting a work setting and
delivered digitally via computers, tablets, smartphones, or inboxes to improve health-
related outcomes among employees. Thus, the included studies had to evaluate the
effects of websites, apps, or downloadable software. This criterion, accompanied by the
constantly increasing prevalence of digital platforms among methods of health promotion,
allowed for the conducting of an all-inclusive analysis of modern ways of intervention
Adm. Sci. 2024,14, 131 4 of 16
(
Agarwal and Patel 2020)
. However, studies that included additional support, such as
direct meetings or feedback from health professionals, but excluded the introductory
sessions or follow-up for research purposes to isolate the impact of digital components
were excluded. This exclusion ensured the effects attributed to digital interventions were
not confounded by these additional elements.
The population of participants was restricted to employees who were above the age of
18 years, which represents the socially working age group population and thus ensures
that the results would be valid for most of the employments. Interventions could be of
any length or duration and measure any type of health-related outcome, allowing for the
capture of a broad array not only of digital health intervention formats but also of the
interventions’ varied impacts on physical and mental health as well as illness symptoms and
health-related lifestyle behaviors. This offered an inclusive approach where the objective
was to ensure review comprehensiveness and reflectivity of the applied digital health
interventions in the workplace setting.
2.2. Search Strategy
The search strategy of this systematic review was thoughtfully developed to ensure
that all the relevant studies related to digital health interventions in the workplace were
comprehensively retrieved. The search encompassed studies that have been published
from 2017 up to January 2024, thus making this review current and relevant. For this
review, PubMed served as the primary database, selected for its comprehensive coverage
of medical and health-related literature, and was searched systematically using a defined
protocol to identify relevant studies on digital health interventions and workplace health
outcomes. The selection of the search terms/keywords was performed very carefully so as
to ensure that the required studies related to digital health interventions in a workplace
were picked up. The keywords included terms such as ‘workplace’, ‘occupational’, ‘digital
health’, ‘e-health’, ‘m-health’, ‘intervention’, and other relevant Medical Subject Headings
(MeSH) terms. The combination of controlled MeSH terms as well as free-text terms
allowed the detection of pertinent studies that might have not been indexed under a
standard nomenclature.
Besides performing a database search, the reference lists of identified papers, as well
as other reviews, were screened for additional studies using the backward citation tracking
technique. This strategy increased the possibility of identifying all relevant studies with
no exception of those likely to have been missed in database searches. Therefore, the
search strategy was meticulously designed and conducted according to best practices in
the systematic reviews approach. It followed a comprehensive and systematic approach for
the minimization of study retrieval bias and to guarantee a comprehensive review of the
relevant literature for a work-based digital health intervention.
2.3. Data Collection
The process of data collection, as used in this systematic review, was purposely meant
to enable the effective and efficient collection of systemic information from the selected
studies. This important step quickly pinpointed studies that were possibly acceptable. The
researcher undertook an extensive scrutiny of the full texts of the shortlisted studies to
determine their eligibility. However, through the rigorous and systematic attention to detail
in minimizing bias, the process was performed based on conducting an exhaustive analysis
of each study with reference to its relevance against the predefined inclusion/exclusion
criteria. Each of the studies had been independently reviewed by the authors to avoid
bias. An independent, rigorous, unbiased assessment of the studies was carried out, which
minimized personal bias affecting the evaluation. Ambiguity or uncertainty that arose in
the assessment was resolved through contacting experts in the field or further reviewing
the literature to make an informed decision for the validation of the review’s findings.
Adm. Sci. 2024,14, 131 5 of 16
2.4. Selection of Studies
The systematic review for this research followed the guidelines of the PRISMA in
ensuring that the selection of studies was not only rigorous but also transparent. The flow
chart shown in Figure 1depicts a structured process that initially referred to
2236 studies,
further narrowing down to 1175 after removal of duplicates, and further targeting the final
17 studies that were eligible for inclusion following a systematic screening and eligibility
assessment. This stringent process ensured the inclusion of studies that were most relevant
and of high quality for this review. The selected papers are presented in Table A1 of
Appendix A.
Adm. Sci. 2024, 14, x FOR PEER REVIEW 5 of 16
2.4. Selection of Studies
The systematic review for this research followed the guidelines of the PRISMA in en-
suring that the selection of studies was not only rigorous but also transparent. The flow
chart shown in Figure 1 depicts a structured process that initially referred to 2236 studies,
further narrowing down to 1175 after removal of duplicates, and further targeting the final 17
studies that were eligible for inclusion following a systematic screening and eligibility assess-
ment. This stringent process ensured the inclusion of studies that were most relevant and of
high quality for this review. The selected papers are presented in Table A1 of Appendix A.
Figure 1. Flow chart of the process for selecting relevant papers.
3. Results
3.1. Study Characteristics
While the studies considered in this review covered diverse geographies and workplace
public settings, the choice of studies also reflected a global concern and interest in such inter-
ventions. These included a UK-based qualitative study on the effectiveness of the digital CBT
intervention WorkGuru by Carolan and de Visser (2018), as well as Haile et al. (2020), who
undertook mixed-methods work on an anti-sedentary app. For instance, in Pakistan, Kazi et
Figure 1. Flow chart of the process for selecting relevant papers.
3. Results
3.1. Study Characteristics
While the studies considered in this review covered diverse geographies and work-
place public settings, the choice of studies also reflected a global concern and interest
in such interventions. These included a UK-based qualitative study on the effectiveness
of the digital CBT intervention WorkGuru by Carolan and de Visser (2018), as well as
Haile et al. (2020
), who undertook mixed-methods work on an anti-sedentary app. For
Adm. Sci. 2024,14, 131 6 of 16
instance, in Pakistan, Kazi et al. (2020) depicted such geographical diversity whilst
characterizing the strengths and weaknesses of projects relating to digital health, and
Nwaogu et al. (2021)
in Nigeria respectively assessed barriers as well as motivators regard-
ing the prospects of interventions for digital mental health. The study characteristics were
broad, with many being mixed-methods designs suggesting a rich collection of qualitative
data. Participant demographics represented the working adult population, with interven-
tions spanning mobile health apps to virtual reality sessions and comprehensive health
portals integrated into workplace intranets.
This observed the contents as well as the modes of delivery for these interventions
at both extremes, exemplifying a paradigm shift that was seen from traditional programs
towards technologically advanced approaches in regard to workplace health. For example,
the period length for those interventions varied, including those that were spread over
a span of 8 weeks, such as the WorkGuru program, as well as studies with a one-year
duration, like one under study by Crane et al. (2019). The outcomes of the interventions on
digital health were different, and they were measured by both psychological and physical
outcome measures. The reports consisted of improved mental well-being, apart from
a decrease in sedentary behavior, as well as self-efficacy and stress management. The
research reported mainly positive findings, although mixed and negative findings were
also discussed so as to provide a balanced opinion of the current evidence referred to in
relation to the effects of the interventions.
All of the included studies were assessed for their risk of bias, and concerns were
identified with regard to recall bias as well as self-selection and academic research bias.
However, a careful quality assessment process was undertaken by using independent
quality assessments and consensus discussions, which gave assurance to the authors that
the findings from the studies were believable and the evidence as a whole strong.
Therefore, this systematic review presents a comprehensive academic discourse on
the effectiveness of digital health interventions within workplace contexts. It illuminates
the transformative power that such interventions may have over health outcomes amongst
a workforce and has significant implications for managerial decisions as well as organi-
zational health policies. The chosen studies demonstrate the benefits and pitfalls that
digital health interventions entail and provide a platform for future research in this rapidly
expanding area.
3.2. Participant Characteristics
The reviewed studies depict a cross-sectional representation of the participating work-
force in digital health interventions. The cohort comprised a wide array of professions,
geographical locations, and organizational settings, thereby giving an inclusive view of
the reach and applicability of digital health at work. From Carolan and de Visser’s (2018)
research based on 18 participants recruited across a range of UK organizations, who took
part in an 8-week digital CBT intervention, to Haile et al.’s (2020) research with a sample of
80 employees drawn from four companies, aimed at sedentary behavior, the participant
range widely varied.
Kazi et al. (2020) from Pakistan canvassed 51 stakeholders of digital health projects,
giving a snapshot of opinions from behind the innovation of healthcare. Meanwhile,
another local study, implemented by Nwaogu et al. (2021), explored the use of digital
mental health intervention among 62 personnel in construction from Nigeria. Moreover,
Blewitt et al. (2022)
partnered with MacKillop Family Services staff in Australia to estab-
lish a health portal reflective of not necessarily the particular approach in the digital
health sector.
In research in Switzerland performed by Kerr et al. (2023), 170 employees investigated
the adoption of a digital stress-management informing intervention, while Adhyaru and
Kemp (2022) performed similar research in the UK and narrowed their lens to 39 NHS
clinicians using VR as a wellness tool. Njoku et al. (2023) further widened the scope in nine
SMEs by interviewing the business perspective on health technology.
Leigh et al. (2020)
and
Adm. Sci. 2024,14, 131 7 of 16
Nadav et al. (2021) expanded their pool of participants to include healthcare professionals
and social care professionals, respectively, putting the total over 250 in order to study the
barriers and facilitators while trying to find out the areas of mHealth adoption and digital
service integration, respectively.
Simons et al.’s (2015) study from the Netherlands may offer a more controlled set-
ting with its RCT involving Delft University employees, while Kowalski et al.’s (2024)
Swedish study shines through its large sample of 1267 healthcare workers, who partook
in a mHealth stress-management study. The Dutch protocol of study by
Smit et al. (2022)
cut across sectors, where employees working in organizations that were completely un-
related to each other engaged with a workplace health promotion program. Apprais-
ing the EMPOWER intervention in their protocols of studies, Olaya et al. (2022) tar-
geted employees drawn from SMEs and public agencies distributed in a number of Euro-
pean countries,
Engels et al. (2022)
MSEs in Germany and their specific needs, and, finally,
Thomson et al. (2023)
UK line managers and direct reports within a digital
training program.
Therefore, the varying layers of employment, as well as sectors and even national
contexts indicated by these sets of characteristics, demonstrate the potential capacity for
digital health interventions but also underline both their likely appeal to a broad audience
on the basis of the problems they help solve and the particular adaptability such solutions
are expected to possess in order to conform to a wide range of occupational health needs.
3.3. Attrition Rates
In digital health intervention studies, attrition rates are a measure of the level of
participant engagement and, in turn, reflect the feasibility of interventions. Overall, the
range of attrition rates between the studies presented was quite wide, but this reflected the
kind of interventions deployed and the populations targeted by the deployment.
In the qualitative study design adopted in Carolan and de Visser’s (2018) research,
the approach did not pay too much attention to attrition rates, as they had a thematic
analysis throughout the interviews. The attrition should have been significantly curbed in
the qualitative approach for the reason that individual engagement was far more compre-
hensive. Haile et al. (2020) reported in a study to reduce sedentary behavior that there was
increased standing time and transitions per hour, with a high dropout rate as well. This
might indicate that, although the intervention effectiveness remained for those participants
retained within it, very low long-term participant engagement and retention within a
digital intervention is problematic.
The mixed-methods study by Kazi et al. (2020) did not explicitly mention attri-
tion, but, considering the broad stakeholder involvement in the digital health projects,
the perceived relevance and immediate benefits could have determined the retention.
Nwaogu et al. (2021)
faced challenges attributed to biases in the size and method of the
sample, as, in general, a web-based survey would typically have higher attrition rates, since
participants can drop out with ease in context.
Blewitt et al. (2022) reported success in embedding the HiPPP Portal within a staff
intranet of MacKillop Family Services. Full interoperability with daily work tools likely
supported sustained use with lower attrition, but details were lacking in relation to attrition
rates. This value-sensitive design for digital stress-management interventions has been
explored by Kerr et al. (2023) despite consistently moderate to high reports of intention of
use but unreported actual attrition rates.
Another difference between the two groups was that clinicians using VR in the NHS
reported significant increases in happiness and relaxation but did not comment on attri-
tion rates for participants, which are often quite high when piloting novel technologies
(
Adhyaru and Kemp 2022)
. While Njoku et al. (2023) and Leigh et al. (2020) gave the SMEs’
and HCPs’ perspectives on the adoption of digital health technology, they did not give
specific attrition data.
Simons et al. (2015) created a clear RCT structure with the effect that this reduces likely
attrition, though self-selection issues combined with reliance on self-reporting may have
Adm. Sci. 2024,14, 131 8 of 16
skewed dropout rates. Smit et al. (2022) and Olaya et al. (2022) conducted research on the
workplace health promotion program evaluation and assessment of eHealth intervention
effectiveness without reporting details of attrition, which are often overlooked in the
reports published.
Thomson et al. (2023) reported recruitment participation from 24 organizations and
data received from 224 line managers for analysis, indicating reasonable participation rates,
but the attrition rates were not specified. Similar to them, Engels et al. (2022), in their
protocol study, described barriers to stress prevention intervention with no specification
for dropouts.
Overall, attrition rates were variable and often not explicitly reported, implying that
more rigorous documentation and analysis of participant retention are needed for future
digital health intervention studies. Attrition is a complex issue which is influenced by the
engagement of the user, the perceived relevancy of the intervention, the ease of using the
technology, and the personalization of the content of the intervention to meet user needs.
3.4. Features of the Intervention
The reviewed studies paint a broad qualitative range of approaches from the workplace
to improve health outcomes, thereby showing the landscape of digital health interventions.
Carolan and de Visser (2018) further presented ‘WorkGuru’, an 8-week program that was
built on cognitive-behavioral therapy (CBT), treating workplace mental health with a suite
of interactive elements, along with e-coaching. When they are engaged through the Welbot
app, it interjects sedentary behavior among employees with regular nudges that encourage
movement. Haile et al. (2020) stated: Kazi et al. (2020) take a broader perspective in which
they investigate smartphone apps in combination with other artificial intelligence (AI) and
machine learning (ML) tools that were put in place across different digital health initiatives
in Pakistan, operating through a SWOT analysis paradigm.
A study by Nwaogu et al. (2021) examined the subsequent intervention levels for the
mental health of Nigerian construction personnel with usage barriers and motivators using
mobile and web-based tools. ‘HiPPP Portal’ digital health interventions targeting precon-
ception, pregnant, and postpartum women were developed by Blewitt et al. (2022) using
intervention mapping on the one hand, whilst Kerr et al. (2023) described the application
of value-sensitive design during the development of just-in-time adaptive interventions (JI-
TAIs) that are digital stress-management interventions (dSMIs).
Adhyaru and Kemp (2022)
delved into the use of virtual reality (VR) in improving general mental health well-being
for NHS clinicians.
For example, Njoku et al. (2023) focused on the relevance of accelerator programs
in enhancing the adoption and adaptation of digital health technologies amongst small
to medium-sized enterprises, while Leigh et al. (2020) unveiled barriers and facilita-
tors to mobile health (mHealth) adoption by health care professionals (HCPs) in the UK.
Nadav et al. (2021)
researched digital service execution in Finnish health and social care set-
tings to expose practices of successful integration of digital services into
day-to-day work.
3.5. Duration of Interventions
Interventions of this duration have wide variations, which are indicative of the tailored
approach that has to be applied with a view to addressing particular health outcomes and
organizational contexts. ‘WorkGuru’ comprised an 8-week program, allowing a defined
period within which participants were enabled to engage with mental health content. The
‘Welbot’ intervention was carried out within a period of 6 months, which permits the
drawing of an accurate judgment regarding the longitudinal character of alterations in
sedentary behavior. Concerning Kazi et al. (2020), the length of the intervention was not
revealed, as the article is dedicated to a retrospective assessment of already existing digital
health initiatives.
Nwaogu et al. (2021) did not give the lengths of individual participation, since it was a
more qualitative and survey kind of study. Note that the ‘HiPPP Portal’ was embedded as
Adm. Sci. 2024,14, 131 9 of 16
a continual resource within an organizational intranet, purporting it to be of fixed duration
rather than a continual access model. And so, as with Kerr et al.’s (2023) study, since the
intervention did not explicitly mark off the duration, one of the attributed factors was
perhaps that this kind of study tends to be more devoted to design principles and not
predominantly to a time-based aspect.
Adhyaru and Kemp (2022) delivered a 30-min VR session that exemplifies an intense
but short-lived vignette regarding wellbeing. The engagement with the accelerator program,
as described by Njoku et al. (2023), was variable, depending on the involvement of
individual SMEs with the program. While Leigh et al.’s (2020) research was a discrete choice
experiment that lasted for some months, data for Nadav et al.’s (2021) study were acquired
through the usage of focus groups, and an intervention period was not clearly stipulated.
Other studies, such as that of Kowalski et al. (2024), advanced a month-long mHealth
stress-management intervention, giving a window from which to assess the digital in-
tervention’s effectiveness. Simons et al. (2015) decided to implement a hybrid eHealth
solution with an immediate and a 6-week post-measurement, mixing the short-term as well
as medium-term assessment. Smit et al.’s (2022) intervention lasted one year in terms of the
integrated Dutch Workplace Health Promotion Program, while that of Olaya et al. (2022)
lasted seven weeks as an EMPOWER intervention.
For instance, the ‘MMW’ virtual learning by Thomson et al. (2023) and the ‘System
P’ platform by Engels et al. (2022) provided flexible timelines without specificity as to the
exact durations. The ‘Get Healthy at Work’ program (Crane et al. 2019) is embedded with a
12-month evaluation period that allows for ample time to capture changes to the health
culture and practices in the workplace.
Overall, these studies underscore the need to consider intervention duration in the
planning and assessment of digital health strategies. The variation in durations between
what could be regarded as short and focused as compared to months-long interventions is
indicative of the goals, context, and abilities of the digital tools and platforms in use.
3.6. Controls and Comparisons
Generally, control, as well as comparison mechanisms, significantly contribute to
efficacy in any digital health intervention. Carolan and de Visser (2018) did not state a
control group as this was a qualitative study whose focus was thematic analyses of the
participant’s experiences with the digital CBT intervention ‘WorkGuru’. On the other hand,
Haile et al. (2020) operationalized a mixed-methods design with an intervention labelled
‘Welbot’, and, presumably, compared it to common workplace routines as an implicit
control. Kazi et al. (2020) sidestepped a traditional control group in favor of interpreting
the landscape of digital health projects within Pakistan via a SWOT analysis.
Nwaogu et al. (2021) provided insights into the barriers and motivators for the use
of digital mental health intervention with a muddled comparator, given the qualitative
nature of the study. Similarly, Blewitt et al. (2022) were emphatic about the process of
developing and using the ‘HiPPP Portal’ without the involvement of a control group, since
the study sought intervention mapping as opposed to comparative efficacy. Similarly,
Kerr et al. (2023)
utilized a value-sensitive design in the study, where users’ responses
on dSMIs with and without JITAI components were compared as part of a digital stress-
management intervention.
Adhyaru and Kemp (2022) carried out a pilot study to evaluate the effect of VR
intervention without having a control group, thereby restricting the interpretation of the
effectiveness of a VR session. Njoku et al. (2023) and Leigh et al. (2020), respectively, used
qualitative interviews and discrete choice experiments without control groups to elicit
perspectives and preferences towards digital health technology adoption and mHealth
app prescription.
Adm. Sci. 2024,14, 131 10 of 16
3.7. Outcome Measures
The outcome measures capture combined physical, psychological, and other health-
related measures based on the objectives of these interventions. Haile et al. (2020) used the
Office of Sedentary Patterns Assessment Questionnaire (OSPAQ) and Nordic Musculoskele-
tal Questionnaire (NMQ) to measure physical changes brought in behavior due to reduced
sedentariness. Adhyaru and Kemp (2022) resort to physical measures of physiological
arousal and subjective mood, pre- and post-exposure to the VR experience, in order to take
cognizance of signs of physical changes towards stress or relaxation.
Carolan and de Visser (2018) used a thematic analysis of interviews for the psycho-
logical impacts of ‘WorkGuru’. Kerr et al. (2023) evaluated user acceptance and concerns
related to value to gauge the reaction around the psyche towards digital stress-management
interventions. Central in the studies by Nwaogu et al. (2021) and Kowalski et al. (2024),
where the former identified psychological barriers and motivators, while the latter focused
on mental health measures like mood self-monitoring and stress-management education,
was also the copious use of psychological measures.
Some studies reported other health-related outcomes. Simons et al. (2015) introduced
productivity measures as health-related outcome measures of a hybrid eHealth intervention.
Olaya et al. (2022) measured presenteeism, mental health measures, and absenteeism of
the ‘EMPOWER’ intervention. Crane et al. (2019) assessed workplace culture changes and
work productivity outcomes in the ‘Get Healthy at Work’ program.
This cluster of studies demonstrates how the outcome measures that have been se-
lected are entirely bound up with the aims of digital health interventions. The physical
mainly focuses on phenomena that are primarily quantifiable changes in health status or
behavior, though the psychological is often based on the mental and emotional impact
of the intervention. Other measures are usually broader in terms of the impact on work
and lifestyle, such as productivity, absenteeism, and general well-being in workplaces—all
interventions will affect these in particular ways and measure them as such. The varied
metrics shown depict the complex nature of health and the need for a holistic vantage point
when developing studies that assess digital health interventions.
3.8. Design and Aims of Studies
The studies were wide-ranging in design, and this was an indication that these spanned
from qualitative, quantitative to mixed-methods design, all of which were relevant to the
purpose. For example, Carolan and de Visser (2018) deployed a qualitative study intending
to explore the experiences of the people who were participants in the ‘WorkGuru’ digital
CBT intervention, with the intention of highlighting the facilitators and barriers within
digital interventions designed for mental health. Haile et al. (2020) conducted mixed-
methods research in order to answer the question of whether the ‘Welbot’ app was capable
of sedentary behavior reduction, as this last also brings up users’ feelings by means of
qualitative data, apart from the physical outcomes measured.
Kazi et al. (2020) undertook a mixed-methods evaluation of digital health projects with
the intention of applying strengths, weaknesses, opportunities, and threats approaches
to draw lessons that would inform future digital health initiatives. Utilizing a qualitative
methodology approach, data collected by Nwaogu et al. (2021) identified barriers and moti-
vators for using digital interventions for mental health problems among building workers
with the aim of increasing the level of user engagement and program design. The ‘HiPPP
Portal’ for healthy lifestyles of preconception, pregnant, and postpartum women was
developed and integrated using an intervention mapping modality by
Blewitt et al. (2022).
Kerr et al. (2023) used a value-sensitive design to design a mixed-methods approach
to a digital stress-management intervention that would consider ethical issues and user
perceptions. The research subjects were employees, and the aim of the research was to
investigate how employees construed digital stress-management tools and their choice for
such interventions.
Adm. Sci. 2024,14, 131 11 of 16
3.9. Effects of Intervention
In both reports, the effects to anticipate from interventions that were in place for
publicizing featured a variation in range. That is, the WorkGuru program readily identified
facilitators and barriers that could hamper or make uptake more viable in applying digital
CBT interventions. The outcome of the ‘Welbot’ app showed an increase in standing time
and number of transitions per hour, thereby suggesting that a positive effect was noted
with regard to physical activity at work.
Strengths and challenges to the digital health fold in Pakistan have been exhibited by
digital health projects reviewed in the study by Kazi et al. (2020), hence providing a basis
for strategic improvements. Notably, this study identifies important obstacles as well as
motivators in the use of digital mental health tools, thereby providing insights for better
deployment of such interventions.
Blewitt et al.’s (2022) ‘HiPPP Portal’ portrays high integration into the UTAUT and
also accessibility and usability, which underlines the potential of digital health platforms in
addressing women’s health in workplace settings. Kerr et al. (2023) identified moderate
to high intention to use the developed dSMI, and, consequently, this suggests that the
intervention is likely to receive good attention upon its use in a workplace setting.
This, therefore, implies that digital health interventions could apply favorably in
influencing health-related outcomes. However, the success of these interventions often lies
in understanding the complex interplay between user needs, the design of the intervention,
and contextual factors. The findings of the studies indicate that, in some interventions,
there is hope for better health behaviors and outcomes, but challenges for implementation
in real activities, technology acceptance, and users’ engagement are pretty common.
3.10. Mixed and Negative Findings
The discoveries identifiable from the digital health interventions divulged not only
success but also a spectrum of mixed or negative findings. Haile et al. (2020), for example,
reported an increasing efficacy lent by the ‘Welbot’ application to physical activities, even
though a high dropout and reliance on self-reported data put a questionable tone on the
conclusiveness of the study. On the other hand, Carolan and de Visser (2018) identified
substantive difficulties, such as personal motivation and technical issues, which could
preclude consistent use, as exemplified by the ‘WorkGuru’ programme.
The authors say that, though digital health projects face critical obstacles, such as a
lack of infrastructure and technical know-how, they have potential. Motivators, such as
efficiency and effectiveness, were identified by Nwaogu et al. (2021) but so were barriers,
such as cost and usability issues, that suggest that, while the desire for digital mental health
tools exists, practical difficulties could hamper actual usage.
Furthermore, as pointed out by Kerr et al. (2023), the fact that privacy and autonomy
can lead to ethical issues means that there could be barriers regarding the adoption of
digital stress-management interventions. In this respect, while there will be room for
positive outcomes, negative sentiments and experiences can hinder the effect and diffusion
of the said interventions.
3.11. Risk of Bias
The studies under review also present various risks of bias, which could influence
their findings. Self-selection bias proves to be a recurrent problem, as demonstrated in the
study of Carolan and de Visser (2018) and Kerr et al. (2023), where participants with an
inherently increased interest in digital health may be more likely to engage and, at least,
might not possibly represent the general population. Studies such as that of Adhyaru and
Kemp (2022) raise the question of the validity of the observed effects in the absence of
control groups.
Response bias is an internal threat to the validity of qualitative works such as
Nwaogu et al.’s (2021)
and Nadav et al.’s (2021) studies, in which participants’ responses
could be influenced by what they believe to be socially acceptable or by them upholding
Adm. Sci. 2024,14, 131 12 of 16
the researchers’ ideal expectations. In addition, studies using self-report data, like that
performed by Haile et al. (2020), may have results that incline towards inaccuracies or
misjudgments by people when reporting on their behaviors or feelings.
The Risk of Bias was assessed using the Cochrane RoB Tool for RCTs and for mixed-
methods and qualitative trials, the MMAT. Thus, the degree of bias in the studies under
consideration also turned out to be rather high but not uniform. For instance, several RCTs
assessed low selection bias because adequate randomization procedures were undertaken,
although a few trials showed high performance bias, since blinding was not conducted.
Clear challenges existed for cross-sectional and mixed-methods studies in relation to
response bias when collecting self-reported data. The most significant threat to validity
within qualitative research was response bias and researcher bias. This way, it is possible to
demonstrate how each study addresses the quality criteria and to make a more accurate
assessment of the reliability and validity of the obtained results, thus providing a sound
basis for synthesizing the evidence.
4. Discussion
This study focused on the implementation of digital health interventions in the work-
place, particularly in supporting the health outcomes of employees and facilitating their
productivity. Consistent with the findings of Brewer et al. (2020), our analysis shows that
digital health programs significantly improve the mental health and physical activity of
employees. This finding reinforces the conclusion made by Carolan and de Visser (2018),
who observed a positive correlation between the use of digital health tools and greater
employee well-being.
In previous studies on digital interventions, diverse results were found, depending on
the type of intervention considered and the indicators that were drawn upon for judgment.
For example, while Engels et al. (2022) indicated significant gains in terms of reductions
in absenteeism and increases in productivity, the study by Nadav et al. (2021) is closer
to our findings, showing no significant difference in absenteeism rates between pre- and
post-intervention. The difference thus highlights the complexity involved in measuring the
outcomes of digital interventions and suggests that intervention design, workplace culture,
and implementation act as critical mediating factors.
In contrast, our analysis did not identify a significant difference in short-term pro-
ductivity measures, a finding also in line with the work of Kerr et al. (2023). Indeed, they
argued that the actual value of digital health interventions could lie in the long-term health
outcomes and changes in organizational culture rather than in immediate productivity
gains. A second key result from our study relates to the core outcome measure of employee
engagement. In line with the findings by Thomson et al. (2023), greater engagement with
digital health tools was directly connected to better health outcomes. This supports the
need for engaging and user-friendly digital health solutions, as already highlighted by
Njoku et al. (2023).
Finally, our findings add to the debate on the cost-effectiveness of digital health
interventions. Similar to the economic analyses undertaken by Kowalski et al. (2024),
this study indicates that, although notable up-front costs exist, the long-term benefits are
realized through reduced healthcare costs and improved employee health. A comparison
of the findings of other systematic reviews shows that the effectiveness of digital health
interventions varies. According to Blewitt et al. (2022), the ineffectiveness is because of a
lack of standardization in implementing these interventions. Our study extends this and
suggests that interventions specifically adjusted to the needs of a workforce are likely to be
more effective.
In line with the advice of Nwaogu et al. (2021), our study highlighted a recommen-
dation that digital health interventions should be continuously evaluated and adjusted.
Implementation of these instruments alone will not do; an entity should be ready to con-
stantly assess the impact brought in by these initiatives and make amends as required.
More important is the role of management in ensuring the successful implementation of
Adm. Sci. 2024,14, 131 13 of 16
digital health interventions. This is in line with the belief of Thomson et al. (2023) that
managerial support is at the heart of promoting a culture that harnesses and uses digital
health resources in an effective way.
In summary, our study contributes to the mounting evidence supporting the effective-
ness of digital health interventions for enhancing various health outcomes in the workplace.
Although challenges remain for the quantification of immediate productivity impacts, the
long-term implications for the health of employees and the well-being of organizations
are apparently positive. The major requirement for future research will be towards the
development of standardized frameworks of implementation and exploration of manage-
ment roles in facilitative tool establishment, leading to the successful adoption of digital
health interventions.
In conclusion, digital health interventions have the potential to inform and contribute
significantly towards strategies for workplace health, given the evidence in their favor for
improving various health outcomes. The results suggest that user engagement, ethical
issues, and practical implementation challenges need to be taken into account in the
design of such interventions. The focus in the design of interventions in workplace health
management is to achieve easy success by clearly designing the interventions with user
needs at the center and making those interventions accessible to them with some clear
benefits. While the existing body of evidence shows good promise, it is clear that more
rigorous, diverse, and long-haul studies are needed in order to reach a clear understanding
of the efficacy and utility of digital health interventions in enhancing employee well-being
and organizational productivity.
Author Contributions: Conceptualization, E.P.K. and I.R.; methodology, E.P.K.; software, I.R.; valida-
tion, E.P.K., G.A.P. and I.R.; formal analysis E.P.K. and I.R.; investigation, G.A.P. and I.R.; resources,
G.A.P.; data curation, I.R.; writing—original draft preparation, E.P.K. and I.R.; writing—review and
editing, E.P.K.; visualization, G.A.P.; supervision, G.A.P.; project administration, G.A.P.; funding
acquisition, G.A.P. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is
not applicable to this article.
Conflicts of Interest: The authors declare no conflicts of interest.
Appendix A. Selected Papers for Review
Table A1. Reviewed Papers.
Paper No. Authors Title Year Journal Type of Study
1 Adhyaru, J. S. and C. Kemp Virtual reality as a tool to promote
wellbeing in the workplace 2022 Digital Health Pilot
2
Blewitt, C., M. Savaglio, S. K. Madden,
D. Meechan, A. O’Connor, H.
Skouteris, and B. Hill
Using Intervention Mapping to Develop
a Workplace Digital Health Intervention
for Preconception, Pregnant, and
Postpartum Women: The Health
in Planning
2022
International
Journal of
Environmental
Research and
Public Health
Pilot
3 Carolan, S. and R. O. de Visser
Employees’ Perspectives on the
Facilitators and Barriers to Engaging
With Digital Mental Health
Interventions in the Workplace:
Qualitative Study
2018 JMIR mental
health Qualitative
Adm. Sci. 2024,14, 131 14 of 16
Table A1. Cont.
Paper No. Authors Title Year Journal Type of Study
4
Crane, M., E. Bohn-Goldbaum, B.
Lloyd, C. Rissel, A. Bauman, D. Indig,
S. Khanal, and A. Grunseit
Evaluation of Get Healthy at Work, a
state-wide workplace health promotion
program in Australia
2019 BMC Public
Health
Mixed
Methods
5
Engels, M., L. Boß, J. Engels, R.
Kuhlmann, J. Kuske, S. Lepper, L.
Lesener, V. Pavlista, M. Diebig, T.
Lunau, S. A. Ruhle, F. B. Zapkau, P.
Angerer, J. Hoewner, D. Lehr, C.
Schwens, S. Süß, I. C. Wulf, and
N. Dragano
Facilitating stress prevention in micro
and small-sized enterprises: protocol for
a mixed method study to evaluate the
effectiveness and implementation
process of targeted web-based
interventions
2022 BMC Public
Health
Mixed
Methods
6
Haile, C., A. Kirk, N. Cogan, X.
Janssen, A. M. Gibson, and B.
Macdonald
Pilot Testing of a Nudge-Based Digital
Intervention (Welbot) to Improve
Sedentary Behaviour and Wellbeing in
the Workplace
2020
International
Journal of
Environmental
Research and
Public Health
Pilot
7
Kazi, A. M., S. A. Qazi, N. Ahsan, S.
Khawaja, F. Sameen, M. Saqib, M. A.
K. Mughal, Z. Wajidali, S. Ali, R. M.
Ahmed, H. Kalimuddin, Y. Rauf, F.
Mahmood, S. Zafar, T. A. Abbasi, K.
Khalil-Ur-Rahmen, M. A. Abbasi, and
L. K. Stergioulas
Current challenges of digital health
interventions in Pakistan: Mixed
methods analysis
2020
Journal of
Medical
Internet
Research
Mixed
Methods
8
Kerr, J. I., M. Naegelin, M. Benk, F. v
Wangenheim, E. Meins, E. Viganò,
and A. Ferrario
Investigating Employees’ Concerns and
Wishes Regarding Digital Stress
Management Interventions With Value
Sensitive Design: Mixed Methods Study
2023 J Med Internet
Res
Mixed
Methods
9Kowalski, L., A. Finnes, S. Koch, and
A. Bujacz
User engagement with organizational
mHealth stress management
intervention—A mixed methods study
2024 Internet
Interventions
Mixed
Methods
10 Leigh, S., L. Ashall-Payne, and
T. Andrews
Barriers and Facilitators to the Adoption
of Mobile Health among Health Care
Professionals from the United Kingdom:
Discrete Choice Experiment
2020 JMIR mHealth
and uHealth Pilot
11
Nadav, J., A. M. Kaihlanen, S. Kujala,
E. Laukka, P. Hilama, J. Koivisto, I.
Keskimäki, and T. Heponiemi
How to Implement Digital Services in a
Way That They Integrate into Routine
Work: Qualitative Interview Study
among Health and Social Care
Professionals
2021
Journal of
Medical
Internet
Research
Qualitative
12
Njoku, C., S. Green Hofer, G.
Sathyamoorthy, N. Patel, and
H. W. W. Potts
The role of accelerator programmes in
supporting the adoption of digital
health technologies: A qualitative study
of the perspectives of small- and
medium-sized enterprises
2023 Digital Health Mixed
Methods
13
Nwaogu, J. M., A. P. C. Chan, J. A.
Naslund, C. K. H. Hon, C. Belonwu,
and J. Yang
Exploring the Barriers to and Motivators
for Using Digital Mental Health
Interventions Among Construction
Personnel in Nigeria: Qualitative Study
2021
JMIR formative
research
Mixed
Methods
14
Olaya, B., C. M. Van der
Feltz-Cornelis, L. Hakkaart-van
Roijen, D. Merecz-Kot, M. Sinokki, P.
Naumanen, J. Shepherd, F. van
Krugten, M. de Mul, K. Staszewska, E.
Vorstenbosch, C. de Miquel, R. A.
Lima, J. L. Ayuso-Mateos, L.
Salvador-Carulla, O. Borrega, C.
Sabariego, R. M. Bernard, C.
Vanroelen, J. Gevaert, K. Van Aerden,
A. Raggi, F. Seghezzi, and J. M. Haro
Study protocol of EMPOWER: A cluster
randomized trial of a multimodal
eHealth intervention for promoting
mental health in the workplace
following a stepped wedge trial design
2022 Digital Health RCT
15 Simons, L., D. van Bodegom, A.
Dumaij, and C. Jonker
Design Lessons from an RCT to Test
Efficacy of a Hybrid eHealth Solution
for Work Site Health
2015 BLED 2015
Proceedings RCT
Adm. Sci. 2024,14, 131 15 of 16
Table A1. Cont.
Paper No. Authors Title Year Journal Type of Study
16
Smit, D. J. M., S. H. van Oostrom,
J. A. Engels, A. J. van der Beek, and
K. I. Proper
A study protocol of the adaptation and
evaluation by means of a cluster-RCT of
an integrated workplace health
promotion program based on a
European good practice
2022 BMC Public
Health RCT
17
Thomson, L., J. Hassard, A. Frost,
C. Bartle, J. Yarker, F. Munir, R.
Kneller, S. Marwaha, G. Daly, S.
Russell, C. Meyer, B. Vaughan,
K. Newman, and H. Blake
Digital Training Program for Line
Managers (Managing Minds at Work):
Protocol for a Feasibility Pilot Cluster
Randomized Controlled Trial
2023 JMIR research
protocols RCT
References
Adhyaru, Jai Shree, and Charlotte Kemp. 2022. Virtual reality as a tool to promote wellbeing in the workplace. Digital Health 8:
20552076221084473. [CrossRef]
Agarwal, Anish, and Mitesh Patel. 2020. Prescribing Behavior Change: Opportunities and Challenges for Clinicians to Embrace Digital
and Mobile Health. JMIR mHealth and uHealth 8: e17281. [CrossRef] [PubMed]
AlQudah, Adi A., Mostafa Al-Emran, and Khaled Shaalan. 2021. Technology Acceptance in Healthcare: A Systematic Review. Applied
Sciences 11: 10537. [CrossRef]
Blandford, Ann, Jo Gibbs, Nikki Newhouse, Olga Perski, Aneesha Singh, and Elizabeth Murray. 2018. Seven lessons for interdisciplinary
research on interactive digital health interventions. Digital Health 4: 205520761877032. [CrossRef] [PubMed]
Blewitt, Claire, Melissa Savaglio, Seonad K. Madden, Donna Meechan, Amanda O’Connor, Helen Skouteris, and Briony Hill. 2022.
Using Intervention Mapping to Develop a Workplace Digital Health Intervention for Preconception, Pregnant, and Postpartum
Women: The Health in Planning, Pregnancy and Postpartum (HiPPP) Portal. International Journal of Environmental Research and
Public Health 19: 15078. [CrossRef]
Brewer, LaPrincess C., Karen L. Fortuna, Clarence Jones, Robert Walker, Sharonne N. Hayes, Christi A. Patten, and Lisa A. Cooper.
2020. Back to the Future: Achieving Health Equity Through Health Informatics and Digital Health. JMIR mHealth and uHealth
8: e14512. [CrossRef]
Carolan, Stephany, and Richard O. de Visser. 2018. Employees’ Perspectives on the Facilitators and Barriers to Engaging With Digital
Mental Health Interventions in the Workplace: Qualitative Study. JMIR Mental Health 5: e8. [CrossRef] [PubMed]
Crane, Melanie, Erika Bohn-Goldbaum, Beverley Lloyd, Chris Rissel, Adrian Bauman, Devon Indig, Santosh Khanal, and Anne
Grunseit. 2019. Evaluation of Get Healthy at Work, a state-wide workplace health promotion program in Australia. BMC Public
Health 19: 183. [CrossRef]
Engels, Miriam, Leif Boß, Judith Engels, Rebekka Kuhlmann, Johanna Kuske, Sarah Lepper, Lutz Lesener, Valeria Pavlista, Mathias
Diebig, Thorsten Lunau, and et al. 2022. Facilitating stress prevention in micro and small-sized enterprises: Protocol for a mixed
method study to evaluate the effectiveness and implementation process of targeted web-based interventions. BMC Public Health
22: 591. [CrossRef]
Guo, Chaohui, Hutan Ashrafian, Saira Ghafur, Gianluca Fontana, Clarissa Gardner, and Matthew Prime. 2020. Challenges for
the evaluation of digital health solutions—A call for innovative evidence generation approaches. NPJ Digital Medicine 3: 110.
[CrossRef]
Haile, Caitlin, Alison Kirk, Nicola Cogan, Xanne Janssen, Ann-Marie Gibson, and Bradley MacDonald. 2020. Pilot Testing of a
Nudge-Based Digital Intervention (Welbot) to Improve Sedentary Behaviour and Wellbeing in the Workplace. International Journal
of Environmental Research and Public Health 17: 5763. [CrossRef] [PubMed]
Howarth, Ana, Jose Quesada, Jessica Silva, Stephanie Judycki, and Peter R. Mills. 2018. The impact of digital health interventions on
health-related outcomes in the workplace: A systematic review. Digital Health 4: 205520761877086. [CrossRef] [PubMed]
Jandoo, Tarveen. 2020. WHO guidance for digital health: What it means for researchers. Digital Health 6: 205520761989898. [CrossRef]
[PubMed]
Kazi, Abdul Momin, Saad Ahmed Qazi, Nazia Ahsan, Sadori Khawaja, Fareeha Sameen, Muhammad Saqib, Muhammad Ayub Khan
Mughal, Zabin Wajidali, Sikander Ali, Rao Moueed Ahmed, and et al. 2020. Current challenges of digital health interventions in
Pakistan: Mixed methods analysis. Journal of Medical Internet Research 22: e21691. [CrossRef] [PubMed]
Kerr, Jasmine I., Mara Naegelin, Michaela Benk, Florian v Wangenheim, Erika Meins, Eleonora Viganò, and Andrea Ferrario. 2023.
Investigating Employees’ Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design:
Mixed Methods Study. Journal of Medical Internet Research 25: e44131. [CrossRef]
Kolasa, Katarzyna, and Grzegorz Kozinski. 2020. How to Value Digital Health Interventions? A Systematic Literature Review.
International Journal of Environmental Research and Public Health 17: 2119. [CrossRef]
Kowalski, Leo, Anna Finnes, Sabine Koch, and Aleksandra Bujacz. 2024. User engagement with organizational mHealth stress
management intervention—A mixed methods study. Internet Interventions 35: 100704. [CrossRef]
Adm. Sci. 2024,14, 131 16 of 16
Kowatsch, Tobias, and Elgar Fleisch. 2021. Digital Health Interventions. In Connected Business. Cham: Springer International Publishing,
pp. 71–95.
Kowatsch, Tobias, Lena Otto, Samira Harperink, Amanda Cotti, and Hannes Schlieter. 2019. A design and evaluation framework for
digital health interventions. IT Information Technology 61: 253–63. [CrossRef]
Leigh, Simon, Liz Ashall-Payne, and Tim Andrews. 2020. Barriers and Facilitators to the Adoption of Mobile Health among Health
Care Professionals from the United Kingdom: Discrete Choice Experiment. JMIR mHealth and uHealth 8: e17704. [CrossRef]
Limbu, Yam B., Rajesh K. Gautam, and Long Pham. 2022. The Health Belief Model Applied to COVID-19 Vaccine Hesitancy: A
Systematic Review. Vaccines 10: 973. [CrossRef]
Long, Lesley-Anne, George Pariyo, and Karin Kallander. 2018. Digital Technologies for Health Workforce Development in Low- and
Middle-Income Countries: A Scoping Review. Global Health: Science and Practice 6: S41–S48. [CrossRef] [PubMed]
Moe-Byrne, Thirimon, Jessie Shepherd, Dorota Merecz-Kot, Marjo Sinokki, Päivi Naumanen, Leona Hakkaart-van Roijen, and Christina
Van Der Feltz-Cornelis. 2022. Effectiveness of tailored digital health interventions for mental health at the workplace: A systematic
review of randomised controlled trials. PLoS Digital Health 1: e0000123. [CrossRef] [PubMed]
Murray, Elizabeth, Eric B. Hekler, Gerhard Andersson, Linda M. Collins, Aiden Doherty, Chris Hollis, Daniel E. Rivera, Robert West,
and Jeremy C. Wyatt. 2016. Evaluating Digital Health Interventions: Key Questions and Approaches. American Journal of
Preventive Medicine 51: 843–51. [CrossRef] [PubMed]
Nadal, Camille, Corina Sas, and Gavin Doherty. 2020. Technology Acceptance in Mobile Health: Scoping Review of Definitions,
Models, and Measurement. Journal of Medical Internet Research 22: e17256. [CrossRef] [PubMed]
Nadav, Janna, Anu-Marja Kaihlanen, Sari Kujala, Elina Laukka, Pirjo Hilama, Juha Koivisto, Ilmo Keskimäki, and Tarja Heponiemi.
2021. How to Implement Digital Services in a Way That They Integrate into Routine Work: Qualitative Interview Study among
Health and Social Care Professionals. Journal of Medical Internet Research 23: e31668. [CrossRef] [PubMed]
Njoku, Chidi, Stuart Green Hofer, Ganesh Sathyamoorthy, Neelam Patel, and Henry W. W. Potts. 2023. The role of accelerator
programmes in supporting the adoption of digital health technologies: A qualitative study of the perspectives of small- and
medium-sized enterprises. Digital Health 9: 20552076231173303. [CrossRef]
Nwaogu, Janet Mayowa, Albert P. C. Chan, John A. Naslund, Carol K. H. Hon, Christopher Belonwu, and Jackie Yang. 2021.
Exploring the Barriers to and Motivators for Using Digital Mental Health Interventions Among Construction Personnel in Nigeria:
Qualitative Study. JMIR Formative Research 5: e18969. [CrossRef] [PubMed]
Olaya, Beatriz, Christina M. Van der Feltz-Cornelis, Leona Hakkaart-van Roijen, Dorota Merecz-Kot, Marjo Sinokki, Päivi Naumanen,
Jessie Shepherd, Frédérique van Krugten, Marleen de Mul, Kaja Staszewska, and et al. 2022. Study protocol of EMPOWER: A
cluster randomized trial of a multimodal eHealth intervention for promoting mental health in the workplace following a stepped
wedge trial design. Digital Health 8: 20552076221131145. [CrossRef]
Pearson, Nicole, Patti-Jean Naylor, Maureen C. Ashe, Maria Fernandez, Sze Lin Yoong, and Luke Wolfenden. 2020. Guidance for
conducting feasibility and pilot studies for implementation trials. Pilot and Feasibility Studies 6: 167. [CrossRef]
Pelly, Melissa, Farhad Fatehi, Danny Liew, and Antonio Verdejo-Garcia. 2023. Novel behaviour change frameworks for digital health
interventions: A critical review. Journal of Health Psychology 28: 970–83. [CrossRef]
Perski, Olga, and Camille E. Short. 2021. Acceptability of digital health interventions: Embracing the complexity. Translational Behavioral
Medicine 11: 1473–80. [CrossRef]
Sarkis-Onofre, Rafael, Ferrán Catalá-López, Edoardo Aromataris, and Craig Lockwood. 2021. How to properly use the PRISMA
Statement. Systematic Reviews 10: 117. [CrossRef] [PubMed]
Simons, Luuk, David van Bodegom, Adrie Dumaij, and Catholijn Jonker. 2015. Design Lessons from an RCT to Test Efficacy of a
Hybrid eHealth Solution for Work Site Health. Paper presented at 28th Bled Econference, Bled, Slovenia, June 7–10; pp. 191–204.
Smit, Denise J. M., Sandra H. van Oostrom, Josephine A. Engels, Allard J. van der Beek, and Karin I. Proper. 2022. A study protocol
of the adaptation and evaluation by means of a cluster-RCT of an integrated workplace health promotion program based on a
European good practice. BMC Public Health 22: 1028. [CrossRef] [PubMed]
Soobiah, Charlene, Madeline Cooper, Vanessa Kishimoto, R. Sacha Bhatia, Ted Scott, Shelagh Maloney, Darren Larsen, Harindra C
Wijeysundera, Jennifer Zelmer, Carolyn Steele Gray, and et al. 2020. Identifying optimal frameworks to implement or evaluate
digital health interventions: A scoping review protocol. BMJ Open 10: e037643. [CrossRef] [PubMed]
Stark, Anna Lea, Cornelia Geukes, and Christoph Dockweiler. 2022. Digital Health Promotion and Prevention in Settings: Scoping
Review. Journal of Medical Internet Research 24: e21063. [CrossRef]
Thai, Yi Chiann, Deanna Sim, Tracy A. McCaffrey, Amutha Ramadas, Hema Malini, and Jessica L. Watterson. 2023. A scoping review
of digital workplace wellness interventions in low- and middle-income countries. PLoS ONE 18: e0282118. [CrossRef]
Thomson, Louise, Juliet Hassard, Alexandra Frost, Craig Bartle, Joanna Yarker, Fehmidah Munir, Richard Kneller, Steven Marwaha,
Guy Daly, Sean Russell, and et al. 2023. Digital Training Program for Line Managers (Managing Minds at Work): Protocol for a
Feasibility Pilot Cluster Randomized Controlled Trial. JMIR Research Protocols 12: e48758. [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
