The Role and Impact of Digital-Only Health Interventions in the Workplace

Abstract

Background: Employee health is closely linked to workplace productivity, with absenteeism and presenteeism posing significant challenges. In response, organizations are increasingly adopting digital health interventions (DHIs) to promote employee wellness. However, the standalone effectiveness of fully digital interventions, independent of face-to-face or telephone-based support, remains underexplored. Objective: This systematic review aims to assess the impact of digital-only health interventions on employee health outcomes in workplace settings and to identify the factors influencing their effectiveness and implementation. Methods: A comprehensive literature search was conducted across MEDLINE, EMBASE, PubMed, and PsycINFO following PRISMA guidelines. Studies were included if they focused on digital-only workplace health interventions and measured health-related outcomes in randomized controlled trials (RCTs). Twenty-two studies met the inclusion criteria. Results: Digital-only interventions demonstrated consistent, though modest, improvements in sleep quality, physical activity, sedentary behavior, and mental well-being. While 21 out of 22 RCTs reported at least one significant health outcome, challenges included methodological heterogeneity, variable dropout rates, and inconsistent use of standardized outcome measures. Only a third of the studies showed low risk of bias. Conclusions: Digital health interventions can positively influence employee well-being, particularly in addressing behavior-driven health concerns. However, to optimize impact, future programs should focus on standardizing measurement tools, improving user engagement, and ensuring inclusive design. Bridging human–computer interaction (HCI) design practices with health sciences’ methodological rigor could enhance the development and sustainability of digital solutions in workplace health promotion.

Keywords

Digital Health, Workplace Wellness, Employee Well-Being, Systematic Review, Human–Computer Interaction, Implementation Science, Health, Health Technology, Remote Interventions.

Introduction

In today’s fast-paced work environment, employee health has become a central concern for organizations striving to maintain productivity and reduce operational costs. Two significant challenges that arise from poor employee health are absenteeism—when employees are not present at work due to illness—and presenteeism, which refers to employees who are physically present but unable to perform at full capacity due to health issues. While absenteeism is more readily observed and recorded, presenteeism often remains hidden yet can have a more substantial impact on workplace productivity and overall business outcomes. The root causes of these productivity drains are frequently linked to modifiable health behaviors and conditions such as poor sleep, stress, physical inactivity, poor diet, and unmanaged chronic illnesses like obesity, diabetes, or depression. Consequently, organizations have increasingly recognized that investing in employee health is not only a moral imperative but also a strategic advantage. As a result, workplace wellness programs have evolved from optional fringe benefits to integral components of corporate strategies. Historically, these programs have included a mix of health screenings, onsite fitness options, wellness coaching, and educational seminars. However, in recent years, digital technologies have dramatically reshaped how such programs are delivered. With the growing ubiquity of smartphones, apps, wearable trackers, and high-speed internet, digital health interventions (DHIs) now offer scalable, cost-effective alternatives that are accessible to a wide and often remote workforce. Digital health tools bring convenience and personalization, but they also introduce new challenges. While multicomponent interventions—blending digital tools with human support—are well studied, there is comparatively limited evidence on the standalone effectiveness of interventions delivered solely through digital platforms. As organizations shift toward remote work models and seek low-cost, high-impact wellness solutions, understanding the specific value and limitations of digital-only interventions is increasingly important.

2. Background and Rationale

Over the past two decades, organizations have become increasingly proactive about addressing employee well-being. Initially perceived as optional or secondary to core business objectives, workplace wellness initiatives have gained traction as evidence mounted linking poor health with reduced productivity, higher absenteeism, and increased healthcare costs. Studies consistently show that poor lifestyle choices—such as lack of physical activity, poor nutrition, insufficient sleep, and unmanaged stress—are major contributors to both chronic illness and lost productivity. Workplace wellness programs emerged as a response to these trends, with early interventions focusing on in-person screenings, group activities, and onsite counseling. These traditional approaches, while beneficial, often lacked scalability—especially for organizations with large or geographically dispersed workforces. As technology advanced, so did the delivery of health interventions. Online platforms, mobile applications, and wearable health trackers enabled more accessible and cost-efficient programs. Digital health interventions (DHIs) are now a staple of modern workplace wellness efforts. These range from basic wellness apps that track steps and sleep, to sophisticated platforms that offer guided meditation, cognitive behavioral therapy (CBT), and personalized health coaching through artificial intelligence. DHIs offer flexibility and privacy—key advantages in engaging employees who may feel reluctant to seek help in traditional formats. Despite these promising developments, a critical gap remains in the understanding of digital-only interventions. Most evidence to date focuses on hybrid or multicomponent models—blending digital tools with human interaction (such as coaching or counselling). As a result, it's unclear how much of the observed health improvements are attributable to the digital components themselves, versus the interpersonal support elements. This review was designed to address that gap by focusing specifically on interventions that are delivered entirely through digital means, with no in-person or telephone-based support. By doing so, it aims to isolate the effect of the digital component and evaluate whether such programs can independently improve employee health outcomes. Furthermore, the review explores implementation challenges and facilitators, drawing from real-world experiences and theoretical frameworks such as the Consolidated Framework for Implementation Research (CFIR) and insights from Human–Computer Interaction (HCI). The goal is not only to evaluate efficacy but also to understand what makes digital health programs succeed—or fail—when applied in organizational settings. By identifying what works, for whom, and under what conditions, this review seeks to provide actionable guidance for employers, program designers, and researchers aiming to harness the full potential of digital health interventions in the workplace.

3. METHODOLOGY

To explore the effectiveness of digital-only health interventions in the workplace, this review followed a systematic and rigorous methodology in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. This framework ensures a transparent, replicable, and comprehensive approach to evidence synthesis.

3.1 Search Strategy

A broad search was conducted across four major databases:

• Medline
• Embase
• PubMed
• PsycINFO

• The search terms combined keywords and controlled vocabulary relating to:
• Digital health (e.g., eHealth, mobile health, telehealth, online interventions)
• Workplace or employee health (e.g., occupational health, corporate wellness)
• Health outcomes (e.g., physical activity, sleep, mental health, sedentary behaviour)
• No date restrictions were applied, but the search was limited to peer-reviewed randomized controlled trials (RCTs) published in English.

3.2 Inclusion and Exclusion Criteria

Studies were included if they met the following criteria:

• Target population: Working adults (employees in any occupational setting)
• Intervention type: Digital-only health programs, delivered via mobile apps, web platforms, or wearable devices, with no face-to-face or telephonic support
• Study design: Randomized Controlled Trials
• Outcome measures: Quantifiable health-related outcomes (e.g., improvements in physical activity, sleep, well-being)
• Excluded studies included:
• Interventions involving in-person, phone, or hybrid delivery modes
• Programs targeting unemployed or retired individuals
• Non-randomized or observational studies
• Interventions focusing solely on knowledge or awareness without a behavioral component

3.3 Study Selection and Screening

• The initial search yielded 1,345 records. After removing duplicates and conducting title and abstract screening, a total of 22 studies met all inclusion criteria and were retained for full review.
• Each study was independently assessed by multiple reviewers for:
• Relevance to the research question
• Quality and risk of bias
• Clarity of outcome measures
• Disagreements during the selection process were resolved through discussion and consensus.

3.4 Data Extraction and Synthesis

Data extracted from each study included:

• Study population characteristics (e.g., sample size, demographics)
• Intervention features (e.g., duration, platform used, health behavior targeted)
• Health outcomes and metrics used
• Statistical significance of results
• Attrition and engagement rates
• Risk of bias (using established quality assessment tools)
• A narrative synthesis approach was used due to heterogeneity in study designs, intervention content, and outcome measures, which limited the feasibility of a meta-analysis.
• This methodology provided a structured and consistent way to evaluate the growing body of literature on digital-only health interventions, ensuring that findings are both reliable and applicable in real-world workplace settings.

4. Overview of Included Studies

The 22 randomized controlled trials (RCTs) included in this review span a wide range of industries, geographic regions, and digital intervention types. Collectively, these studies provide a comprehensive snapshot of how digital-only health programs are being implemented and evaluated in workplace settings.

4.1 Study Characteristics

Population Size:

Study sample sizes ranged from fewer than 100 participants to more than 1,000, representing a mix of corporate, public sector, and healthcare employees.

Duration:

Intervention durations varied widely, from 4 weeks to 12 months, with most studies averaging around 8 to 12 weeks.

Delivery Platforms:

• Programs were delivered through:
• Mobile health apps
• Web-based platforms
• Wearable tracking devices These tools provided features such as activity monitoring, educational content, automated reminders, goal setting, and feedback.

Intervention Focus Areas:

Digital-only interventions targeted a range of employee health outcomes:

• Physical activity promotion
• Sedentary behaviour reduction
• Sleep improvement
• Mental health and stress reduction
• Dietary behaviour and weight management

4.2 Types of Interventions

While the specific tools and platforms varied, most interventions shared common features:

• Self-monitoring capabilities, such as step counters or sleep trackers
• Behavioural nudges, like motivational messages or push notifications
• Goal setting and progress tracking
• Educational content, often delivered in short, digestible formats (videos, infographics, interactive quizzes)
• None of the included interventions involved direct contact with coaches, clinicians, or support staff—maintaining the integrity of a fully digital-only format.

4.3 Common Findings

Despite differences in delivery and structure, nearly all studies (21 out of 22) reported at least one statistically significant improvement in the targeted health behaviour. Positive outcomes were most consistently observed in:

• Increased physical activity
• Improved sleep quality
• Reduced sedentary time
• Enhanced mental well-being

4.4 Study Quality and Risk of Bias

• Only about one-third of the studies were rated as having a low risk of bias, based on their methodology and reporting standards. The remainder were either unclear or moderate, typically due to:
• Incomplete reporting of participant flow or randomization methods
• Inconsistent use of validated measurement tools
• High attrition rates, which can threaten internal validity
• Despite these limitations, the consistency of positive outcomes across studies supports the view that digital-only interventions have meaningful potential to enhance employee health.
• This overview sets the stage for a deeper exploration of the results, including specific health outcomes and the challenges associated with engagement, adherence, and measurement reliability.

5. Results Overview

The 22 randomized controlled trials reviewed demonstrated a generally positive impact of digital-only interventions on a range of employee health outcomes. Although effect sizes varied, the consistency of improvement across multiple domains suggests that digital health tools can play a valuable role in workplace wellness—especially when designed for simplicity and behavioural engagement.

5.1 Primary Health Outcomes Improved

The most commonly observed improvements were in the following areas:

Physical Activity:

Nearly all studies targeting physical activity reported increases in daily step counts, frequency of exercise, or duration of moderate-to-vigorous physical activity. Many interventions utilized wearables or smartphone-based tracking to encourage goal-setting and self-monitoring.

Sedentary Behaviour:

Several studies successfully reduced the number of prolonged sitting periods during the workday. Interventions that included reminders or gamification features—such as hourly standing prompts—were particularly effective.

Sleep Quality:

Digital interventions focused on sleep hygiene education, behavioural cues, and relaxation techniques led to better self-reported sleep quality and reduced symptoms of insomnia in participants.

Mental Well-being:

Studies addressing stress, anxiety, and emotional regulation showed modest but statistically significant improvements. Tools that included mindfulness training, cognitive behavioural therapy (CBT) modules, or journaling features were most effective.

Diet and Weight Management (less frequent):
A smaller subset of studies focused on dietary behaviours or weight loss. These showed mixed results, often depending on the intensity and interactivity of the digital content.

5.2 Engagement and Retention

While digital interventions showed promise, one of the most consistent challenges across studies was participant engagement:

• Dropout rates ranged from 10% to over 40%, with a median attrition of approximately 25%.
• Engagement declined over time, particularly in longer studies (≥6 months).
• Features that improved retention included:
• Interactive interfaces
• Goal reminders and progress tracking
• Personalized feedback
• Social or gamified elements (e.g., virtual challenges, leaderboards)
• Low engagement was especially pronounced among participants with lower digital literacy or minimal initial motivation, highlighting the need for adaptive and inclusive design strategies.

5.3 Measurement Challenges

One critical limitation was the lack of standardized outcome measures across studies. Many relied on self-reported data, which introduces bias, and only a few used validated tools or objective metrics (e.g., actigraphy for sleep, or wearable device data for physical activity). This inconsistency makes cross-study comparisons difficult and undermines the ability to perform quantitative synthesis (e.g., meta-analysis).

5.4 Risk of Bias Summary

• Only about one-third of the studies demonstrated low risk of bias, while the rest had concerns related to:
• Randomization procedures
• Participant blinding (often not feasible in behavioral trials)
• Incomplete data reporting
• Attrition without appropriate intent-to-treat analysis
• Despite these methodological challenges, the overall picture is encouraging: Digital-only interventions are associated with modest but significant improvements in key health behaviors, especially in areas like sleep and physical activity, where behavior tracking and habit reinforcement are critical.

6. Challenges Identified

While digital-only workplace health interventions offer flexibility and cost efficiency, several recurring challenges emerged across the reviewed studies. These challenges must be addressed to enhance the reliability, scalability, and impact of future programs.

6.1 Lack of Standardized Outcome Measures

• One of the most significant obstacles was the wide variability in outcome measurement:
• Many studies relied on self-reported data, which is subject to recall bias and social desirability effects.
• There was inconsistent use of validated tools, making it difficult to compare findings across studies.
• Objective metrics (e.g., device-logged step counts, heart rate monitoring) were underutilized despite being more accurate.
• This heterogeneity hampers efforts to conduct meta-analyses and draw generalizable conclusions about the efficacy of digital interventions.

6.2 Methodological Inconsistencies

The studies varied in their design quality and rigor:

• Not all trials used randomized allocation procedures robustly, and some lacked adequate control groups.
• Blinding of participants was often not possible due to the nature of the interventions, but many failed to implement alternative strategies to reduce bias.
• Follow-up durations were inconsistent, with some interventions only lasting a few weeks, limiting insights into long-term efficacy.
• These methodological differences reduce confidence in the internal and external validity of the findings.

6.3 Participant Engagement and Attrition

• High dropout rates were one of the most pervasive problems, affecting both the credibility and scalability of interventions:
• Participants frequently lost interest over time, especially when interventions lacked interactive elements.
• Programs that didn’t provide feedback or personalized content struggled with sustained user engagement.
• Technological fatigue, lack of perceived relevance, or competing work demands were commonly cited barriers to retention.
• Solutions such as gamification, social incentives, and adaptive content delivery may help improve user adherence in future programs.

6.4 Diversity and Equity Limitations

Many studies lacked sufficient attention to diversity in participant demographics, including:

Age: Older employees may face digital literacy barriers.

Socioeconomic status: Access to smartphones or reliable internet is not universal.

Cultural relevance: Few interventions were adapted for non-Western populations or minority groups. 0As a result, the findings may not fully represent the experiences of all workforce segments, limiting equity and inclusiveness.

6.5 Limited Transparency in Intervention Components

• In some cases, the intervention design was not fully described:
• Researchers failed to specify which features (e.g., reminders, feedback, educational content) were included.
• This makes it difficult to determine which components drove positive outcomes, a crucial insight for replication and scaling.
• A move toward transparent and modular reporting (e.g., TIDieR checklists) could greatly improve clarity in future studies. These challenges underline the need for more robust, standardized, and inclusive research designs. Addressing them will be critical for improving the real-world effectiveness and sustainability of digital-only workplace health interventions.

7. Implementation Science Perspective

Understanding the effectiveness of digital-only health interventions is only part of the equation. To ensure these tools achieve sustained real-world impact, it's essential to also consider how they are implemented within workplace settings. This section draws on implementation science—particularly the Consolidated Framework for Implementation Research (CFIR)—to identify the conditions that help or hinder successful adoption.

7.1 The CFIR Framework

• CFIR organizes implementation factors into five major domains:
• Intervention Characteristics (e.g., complexity, adaptability)
• Inner Setting (e.g., organizational culture, workflows)
• Outer Setting (e.g., policies, market forces)
• Characteristics of Individuals (e.g., attitudes, knowledge)
• Process of Implementation (e.g., planning, engagement)
• Each of these domains plays a key role in determining whether a digital health program is embraced or ignored in a workplace context.

7.2 Key Insights from the Literature

A systematic review of 44 implementation studies yielded several recurring lessons that are particularly relevant to digital workplace health interventions:

Fit with Existing Workflows:

Interventions must integrate smoothly into current routines and systems. If a new digital tool disrupts daily processes without clear benefit, it’s unlikely to be adopted.

Stakeholder Involvement:

Programs succeed more often when end users, supervisors, and organizational leaders are engaged from the beginning. Their support can help overcome resistance and drive participation.

Planning and Readiness:

Successful implementations are rarely spontaneous. Organizations must be prepared for change, with adequate time, infrastructure, and communication plans in place before rollout.

Training and Technical Support:

Digital interventions are only as effective as users’ ability to navigate them. Structured training, onboarding sessions, and ongoing support are critical to avoid early disengagement.

Continuous Adaptation Post-Launch:
Implementation should not end once the platform goes live. Programs that include feedback loops—regularly monitoring usage data and adjusting content or delivery—perform better over time.

7.3 Implications for Workplace Settings

• In the context of workplace wellness, implementation science suggests that:
• Leadership buy-in is essential. When executives actively promote and participate in digital wellness programs, employees are more likely to engage.
• Organizational culture must support health. Programs introduced in high-stress or unsupportive environments may fail regardless of their digital sophistication.
• Timing matters. Launching a health initiative during peak work seasons or organizational transitions may undermine participation.
• Ultimately, implementation success hinges on contextual alignment—ensuring that the intervention, organization, and individuals are ready and able to engage meaningfully.

8. Key Facilitators for Implementation

While digital health interventions can be deployed quickly, their long-term success depends on thoughtful planning and integration into the workplace environment. Drawing from the CFIR framework and real-world case studies, this section highlights several key facilitators that support effective implementation in organizational settings.

8.1 Alignment with Existing Systems and Workflows

• One of the most crucial success factors is how well a digital health intervention aligns with current workplace processes:
• Tools that augment existing practices (e.g., replacing paper-based health tracking with an app) are more likely to be adopted.
• Interventions that require minimal behavioral disruption are easier to integrate, especially in busy or high-demand work environments.
• Seamless integration with HR systems, wellness portals, or employee communication channels helps embed the program into the daily workflow.

8.2 Early and Ongoing Stakeholder Engagement

Involving key stakeholders early in the design and rollout process builds trust and encourages participation:

• Health and safety teams, HR leaders, IT staff, and frontline managers should be consulted from the beginning.
• Employee ambassadors or champions can serve as early adopters and advocates, helping promote the program through peer influence.
• Gathering feedback throughout the process enables timely adaptations and reinforces a sense of ownership among participants.

8.3 Organizational Readiness and Planning

Workplaces that are prepared for change are more likely to implement digital health tools successfully:

Assessing readiness involves evaluating digital infrastructure, employee needs, leadership support, and competing priorities. Clear planning includes setting goals, identifying success metrics, allocating resources, and preparing contingency plans for technical or engagement issues.

8.4 Structured Training and Onboarding

• Even the most intuitive digital platforms can fail without proper user orientation:
• Initial training sessions—either in person or online—should introduce key features and demonstrate how to use the platform effectively.
• Providing step-by-step guides, FAQs, and troubleshooting support helps build user confidence.
• Training should be inclusive, accounting for diverse levels of digital literacy across the workforce.

8.5 Post-Launch Monitoring and Flexibility

Successful programs don’t end after rollout—they evolve:

Usage data and feedback should be reviewed regularly to identify drop-off points, usability issues, or emerging needs.

Iterative updates—such as adding new content, refining notifications, or simplifying navigation—can keep the platform relevant and engaging. Adaptability is especially important in dynamic work environments where job roles, schedules, and stressors may shift over time.

9. Challenges in Measuring Effectiveness of Digital Health Interventions

Even though digital health interventions (DHIs) have shown promising results in workplace settings, accurately measuring their effectiveness remains a serious challenge. This section explains the key difficulties that researchers face while evaluating DHIs, step by step.

Step 1: Inconsistency in Study Designs

• The biggest challenge comes from the lack of uniformity in how studies are conducted.
• Different studies use different time durations (some run for a few weeks, others for several months).
• The focus areas also vary — some target physical activity, others mental health, sleep, or nutrition.
• Some are app-based, while others use websites, emails, or a combination of platforms.
• This inconsistency makes it very difficult to compare results or identify which interventions are truly effective across various settings.

Step 2: Non-Standardized Outcome Measures

• Another issue is the variety of tools used to measure results.
• Some studies use self-reported questionnaires.
• Others rely on wearable fitness trackers or app usage data.
• The definition of success (e.g., better sleep, improved mental health) also varies.
• Because different tools are used and success is measured differently, researchers cannot reliably combine or compare data across studies.

Step 3: Low Participant Engagement and High Dropout Rates

• Many digital health studies face problems with participant engagement and retention.
• Some users lose interest after a few days or weeks.
• Dropout rates are often high — in some cases, more than 50% of participants do not complete the study.
• Lack of motivation, boring content, or poor user experience can be contributing factors.
• This affects the reliability of the study findings, because it becomes unclear whether the intervention itself was effective or participants simply lost interest.

Step 4: Risk of Bias and Weak Research Designs

• Only about one-third of the studies reviewed were rated as having a low risk of bias.
• Many studies failed to include a proper control group.
• Some had very small sample sizes.
• Others didn’t fully explain their methodology, which raises questions about the accuracy of their results.
• Studies that are not designed or reported properly can lead to misleading conclusions about the success of digital health programs.

Step 5: Difficulty in Performing Meta-Analysis

Because of all the above issues — varying designs, outcome measures, and dropout rates — performing a meta-analysis (which combines results from multiple studies to draw strong conclusions) becomes nearly impossible. This limits our ability to make clear, evidence-based recommendations about which digital tools actually work and in which situations.

10. Lessons from Human–Computer Interaction (HCI) vs Health Science

Digital health interventions (DHIs) are influenced by two distinct fields — Human–Computer Interaction (HCI) and Health Science. These two domains bring different priorities, methods, and perspectives to designing, developing, and evaluating digital tools. Understanding their differences helps us design better health solutions.

Below are step-by-step lessons comparing HCI and Health Science approaches across different stages of digital health intervention development.

Step 1: Literature Review Approaches

HCI Approach: Informal and flexible. Researchers often review literature based on current trends or design needs. They prioritize relevance and innovation over completeness.

Health Science Approach: Formal and systematic. Reviews follow structured methods such as PRISMA to ensure all relevant evidence is included. They value completeness, transparency, and reproducibility.

Lesson: HCI can benefit from more rigorous review processes, while Health Science could learn from HCI’s flexibility in adapting to new topics quickly.

Step 2: Development Lifecycle

HCI Approach: Highly iterative. Design, test, gather feedback, and redesign multiple times before finalization. Focus is on usability and end-user experience.

Health Science Approach: Structured and linear. Follows a fixed path: development → pilot testing → full trial → implementation. Focus is on proving impact through evidence.

Lesson: Combining rapid HCI prototyping with the structured rigor of Health trials could make interventions both user-friendly and scientifically valid.

Step 3: Who Leads the Design Process

HCI Approach: End-users play a central role. Tools are shaped around user needs, preferences, and feedback. Designers often co-create with users.

Health Science Approach: Experts and professionals drive the design. They use theories and prior evidence to structure the intervention.

Lesson: A balance is essential — tools should be both evidence-based and user-centered for maximum effectiveness and engagement.

Step 4: Timing of Implementation

HCI Approach: Implementation happens early. Tools are rolled out in real-world settings quickly to gather feedback during development.

Health Science Approach: Implementation comes after proven effectiveness through controlled trials. There is caution about premature rollout.

Lesson: Health Science can adopt earlier, smaller-scale testing to get real-world input sooner, while HCI can benefit from delayed implementation until solid evidence is available.

Step 5: How Success is Measured

HCI Approach: Success is based on usability, satisfaction, and engagement. Focus is on how the tool performs in real-life usage.

Health Science Approach: Success is measured through impact on health outcomes using methods like randomized controlled trials (RCTs). Focus is on effectiveness.

Lesson: Combining HCI’s process-focused evaluation with Health’s outcome-based metrics would create a more complete picture of success.

Step 6: Ethical Frameworks

HCI Approach: Ethics are centered on user rights, consent, and autonomy. Ethics reviews may be less formal but emphasize respect for users.

Health Science Approach: Strict ethical standards with institutional reviews, informed consent, and protection from harm. Stronger regulations apply.

Lesson: HCI should adopt more structured risk assessments, while Health Science could benefit from more user-centered ethical thinking.

Step 7: Publication Practices

HCI Approach: Focuses on in-depth case studies and design innovation. Papers are often long and difficult to publish without significant technical contribution.

Health Science Approach: Publishes a variety of formats including short reports, opinion pieces, and trials. Word limits are strict and format is formal.

11.CONCLUSION

Digital-only health interventions in the workplace are emerging as a scalable and cost-effective strategy to promote employee well-being and productivity. This systematic review found that, despite the variability in methods and outcomes, the vast majority of included randomized controlled trials reported at least one significant improvement in health outcomes such as sleep quality, mental well-being, physical activity, and reduction in sedentary behavior. These findings highlight the modest but promising potential of digital interventions, especially when applied to relatively simple, behavior-based health issues. However, several limitations temper the strength of these findings. First, there was substantial heterogeneity in intervention design, measurement tools, and target behaviors, which makes cross-study comparisons and meta-analysis challenging. Many studies lacked standardized outcome measures, and only a minority demonstrated a low risk of bias. Furthermore, participant engagement and retention emerged as critical issues, with dropout rates varying widely—an indication that digital platforms must do more to hold users’ attention over time. Beyond efficacy, the implementation of digital interventions in real-world workplace settings faces several practical barriers and facilitators. Drawing on frameworks like the Consolidated Framework for Implementation Research (CFIR), this review emphasized that successful implementation hinges on contextual alignment—how well the intervention fits within existing organizational routines—and the presence of supportive infrastructure, leadership buy-in, and staff training. Involving stakeholders early in the process and designing systems that are flexible, intuitive, and minimally disruptive to workflows were also shown to enhance adoption and sustainability. From a methodological perspective, the review highlights a critical gap between the fields of Human–Computer Interaction (HCI) and Health Sciences. HCI excels at user-centered design and rapid iteration, while health research brings methodological rigor and standardized evaluation. Bridging these disciplines could yield more robust, user-friendly, and effective interventions. The case study on diabetes education in the NHS and the U.S.-based Fit & Quit trial further emphasized the importance of equity, cultural sensitivity, and tailored outreach. Recruitment and engagement strategies that considered demographic differences were essential in reaching and retaining diverse populations. However, disparities in engagement and retention persisted, underscoring the need for more inclusive and accessible digital health solutions.

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