Next Article in Journal
Supporting Meaningful Choices: A Decision Aid for Individuals Facing Existential Distress and Considering Psilocybin-Assisted Therapy
Previous Article in Journal
Medical Complications Among Children and Adolescents with Sickle Cell Disease in Texas Medicaid
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Healthcare
Volume 13
Issue 18
10.3390/healthcare13182289
healthcare-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Wearables in Healthcare Organizations: Implications for Occupational Health, Organizational Performance, and Economic Outcomes

by
Daniele Virgillito
1,
Pierluigi Catalfo
1 and
Caterina Ledda
2,3,*
1
Department of Economics and Business, University of Catania, 95100 Catania, Italy
2
Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
3
Occupational Medicine Unit, Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
*
Author to whom correspondence should be addressed.
Healthcare 2025, 13(18), 2289; https://doi.org/10.3390/healthcare13182289
Submission received: 23 August 2025 / Revised: 5 September 2025 / Accepted: 12 September 2025 / Published: 12 September 2025
(This article belongs to the Section Artificial Intelligence in Healthcare)
Browse Figure
Versions Notes

Abstract

Background: Healthcare organizations face major challenges in protecting staff health and ensuring business continuity, particularly in high-risk settings. Wearable technologies are emerging tools to monitor occupational health indicators, improve staff safety, and strengthen organizational resilience. Objectives: This scoping review aimed to map the current evidence on wearable technologies in healthcare, focusing on their impact on occupational health, staff safety, and economic outcomes, as well as barriers and facilitators to their adoption. Methods: A systematic search was conducted in PubMed, Scopus, Web of Science, Embase, CINAHL, sources from inception to July 2025. Studies were included if they evaluated wearable technologies used by healthcare workers and assessed outcomes related to occupational health, organizational resilience, absenteeism, presenteeism, or cost-effectiveness. The review followed the Arksey and O’Malley framework and PRISMA-ScR guidelines. Results: 31 studies met the inclusion criteria. Most were pilot or feasibility studies; only two randomized controlled trials were identified. The wearable technologies evaluated included continuous physiological monitoring devices, real-time location systems, hands-free communication tools, and consumer-grade devices. Evidence suggests potential benefits in improving staff safety, reducing stress and burnout, and enhancing workflow efficiency. However, economic evidence was limited and outcomes varied considerably. Barriers included alert fatigue, privacy concerns, interoperability challenges, and limited staff engagement. Facilitators included leadership support, user-centered design, and adequate infrastructure. Conclusions: Wearable technologies show promise for supporting occupational health and organizational resilience in healthcare, but evidence remains fragmented.

1. Introduction

Healthcare organizations face significant challenges in protecting staff health and ensuring service continuity in high-risk environments. These include psychological and moral stressors like burnout and ethical dilemmas [1], difficulties with personal protective equipment compliance [2], and violence directly affecting health service delivery [3]. The COVID-19 pandemic has exacerbated these issues, necessitating enhanced infection control measures and occupational health support [4]. Environmental services personnel often lack adequate protection despite high exposure risks [5]. Military and humanitarian organizations face unique challenges in conflict zones and during disease outbreaks [6,7]. Strategies to address these challenges include improving PPE design, implementing comprehensive safety protocols, providing mental health support, and developing preparedness plans for future outbreaks [8].
Occupational health risks in healthcare settings significantly impact organizational performance, patient outcomes, and resilience. Individual and organizational resilience are positively associated with job satisfaction, stress reduction, and improved occupational health [9,10]. Organizational factors, such as work environment and culture, influence worker health, safety, and patient care quality [11,12]. Healthcare workers face unique challenges, including musculoskeletal injuries, which can lead to decreased work effectiveness and potential negative patient outcomes [13]. Employee participation in occupational health and safety practices contributes to organizational resilience, particularly during crises [14]. Resilient healthcare systems exhibit double-loop learning behaviors and adapt to disruptive events [15]. The organizational health framework provides a robust approach to managing employee well-being and organizational performance, emphasizing the link between worker health and productivity [16]. Collectively, health-related absenteeism, presenteeism, and psychosocial risks undermine organizational resilience, service delivery, and economic sustainability [15,16,17].
Healthcare worker illness and absenteeism significantly impact healthcare systems economically. Influenza and respiratory illnesses are common causes, with studies showing high rates of symptoms and missed workdays among healthcare workers [17,18]. Vaccination can reduce absenteeism and associated costs [19,20]. However, presenteeism remains a concern, with many workers continuing to work while ill [17]. The economic burden extends beyond direct wage costs, with productivity losses estimated using wage multipliers ranging from 1.54 to 1.97 [21]. Total economic impact is substantial, with one study estimating $260 billion in lost output annually due to health-related productivity losses across all U.S. workers [22]. Burnout and work stress also contribute to absenteeism in healthcare [23]. Improved management of sickness absence and occupational health services are recommended to address these challenges [24]. However, while their benefits are well documented in industrial sectors, research on wearable technologies in healthcare organizations remains scarce.
While some studies have already explored the use of wearable and visible technologies in organizational contexts, including healthcare, their findings remain limited in scope and fragmented across disciplines [25]. These contributions highlight those wearable solutions are already available, yet their integration faces substantial barriers such as cost, privacy, and organizational readiness.
Our study advances this field by systematically mapping the evidence specific to healthcare organizations, with a focus on how wearables may enhance workforce protection, resilience, and business continuity. Unlike prior contributions, this review integrates occupational health, organizational performance, and economic perspectives, offering a comprehensive view tailored to the healthcare sector.
Wearable technologies offer significant potential for improving staff safety, risk prevention, and data-driven management in various industries, particularly construction and railroads. These technologies include smart helmets with augmented reality [26], physiological sensors [26,27], and exoskeletons [26]. They enable real-time monitoring of workers’ health, environmental conditions, and potential hazards [28,29]. Wearables can enhance personal protective equipment usage [30] and provide data for predictive analytics in occupational safety [27]. Applications include fall detection, exposure evaluation, and musculoskeletal disorder recognition [28]. Integration with connected worker solutions offers contextual information and decision support [27]. These technologies contribute to continuous compliance checking, risk prevention [31], and innovation in risk management systems [32], ultimately improving workplace safety and productivity across various sectors.
Wearable devices are increasingly recognized for their potential to enhance workplace safety, health, and productivity. However, several barriers hinder their adoption, including concerns about employee privacy, compliance, and the cost–benefit ratio [33,34]. Additionally, the lack of employee involvement in device selection and insufficient data demonstrating effectiveness are significant obstacles [27,35]. Facilitators for successful implementation include strong leadership support, effective communication, and a focus on worker-centric approaches [36,37]. Ethical considerations also play a crucial role, as organizations must address potential negative impacts on performance [38]. Overall, understanding these barriers and enablers is essential for integrating wearable technologies into organizational resilience strategies [39].
Although research has examined occupational health risks, organizational resilience, and wearable technologies, the evidence remains fragmented and inconsistent. Most studies focus on single aspects, specific devices, outcomes, or sectors, without providing a comprehensive understanding of how wearables can support workforce protection and business continuity in healthcare.
In particular, little is known about how wearable technologies can be integrated into occupational health services, leveraged to strengthen resilience-oriented strategies, or adapted to sector-specific ethical and organizational challenges.
A scoping review is therefore needed to map the available evidence, clarify key concepts, and identify gaps. This approach will synthesize what is known about (i) the types of wearable technologies used in healthcare, (ii) their impact on staff safety and organizational resilience, and (iii) the barriers and enablers to their adoption. The findings will offer healthcare organizations and decision-makers an integrated perspective to guide future practice and research.
The central research question underpinning this review was how wearable technologies are currently being used to enhance occupational health, staff safety, and organizational resilience in healthcare organizations.
This review is limited to healthcare workers and organizational outcomes, excluding studies that investigated wearable technologies solely for patient-monitoring purposes. Consistent with Arksey & O’Malley and PRISMA-ScR guidance, the objective was framed as the following research question: How are wearable technologies currently being used to enhance occupational health, staff safety, and organizational resilience in healthcare organizations?

2. Materials and Methods

This scoping review was conducted following the methodological framework proposed by Arksey and O’Malley [40] and further refined by Levac et al. [41]. Reporting adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines [42].
This methodological framework was considered appropriate as it enables the synthesis of heterogeneous evidence, allows for the identification of knowledge gaps, and provides a structured approach to clarify emerging concepts in a fragmented field such as wearable technologies in healthcare.
In order to ensure methodological transparency and to precisely delineate the scope of the review, the inclusion and exclusion criteria were structured according to the PICOS framework (Population, Intervention, Comparison, Outcomes, Study design) (Table 1). This approach allowed us to systematically define the target population, the types of interventions considered, the relevant comparators, the primary and secondary outcomes of interest, and the eligible study designs, thereby strengthening the consistency and reproducibility of the review process.

2.1. Eligibility Criteria

Peer-reviewed articles and gray literature published in English were considered eligible if they:
  • addressed the use of wearable technologies in healthcare settings;
  • evaluated outcomes related to occupational health, staff safety, organizational resilience, or business continuity;
  • examined barriers, facilitators, or economic implications of implementation.
All study designs were eligible, including experimental, observational, qualitative, and mixed-methods studies. Publications focusing solely on patient-monitoring wearables without consideration of staff-related outcomes were excluded. Simulation studies and purely technical validation studies without worker-related outcomes were excluded.

2.2. Information Sources and Search Strategy

A comprehensive literature search was conducted across the following databases: PubMed, Scopus, Web of Science, Embase, and CINAHL, from inception to 24 July 2025. Gray literature was identified through Google Scholar and targeted searches of relevant institutional and organizational websites (e.g., World Health Organization, Centers for Disease Control and Prevention, European Agency for Safety and Health at Work).
The search strategy was designed in collaboration with a research librarian and combined free-text keywords and controlled vocabulary (e.g., MeSH terms, Emtree terms) related to three main domains:
  • Wearable technologies (e.g., wearable devices, sensors, smart helmets, exoskeletons, biosensors, wearable monitoring systems);
  • Healthcare workers (e.g., healthcare staff, clinical personnel, employees, nurses, physicians, allied health professionals, hospital workers);
  • Outcomes and organizational aspects (e.g., occupational health, staff safety, workplace safety, organizational resilience, business continuity, absenteeism, productivity).
The final search strings were adapted for the syntax and indexing of each database. The search strategies are reported below:
  • PubMed: ((“wearable devices” [MeSH] OR “wearable” [tiab] OR “sensors” [tiab] OR “biosensors” [MeSH] OR “exoskeleton*” [tiab] OR “smart helmet*” [tiab]) AND (“health personnel” [MeSH] OR “healthcare worker*” [tiab] OR “hospital staff” [tiab] OR “nurse*” [tiab] OR “physician*” [tiab] OR “employee*” [tiab]) AND (“occupational health” [MeSH] OR “staff safety” [tiab] OR “workplace safety” [tiab] OR “organizational resilience” [tiab] OR “business continuity” [tiab] OR “absenteeism” [MeSH] OR “productivity” [MeSH])).
  • Scopus: (TITLE-ABS-KEY (“wearable device*” OR “wearable” OR “sensor*” OR “exoskeleton*” OR “smart helmet*” OR “biosensor*”) AND TITLE-ABS-KEY (“healthcare worker*” OR “hospital staff” OR “nurse*” OR “physician*” OR “employee*”) AND TITLE-ABS-KEY (“occupational health” OR “staff safety” OR “workplace safety” OR “organizational resilience” OR “business continuity” OR “absenteeism” OR “productivity”)).
  • Web of Science: TS = (“wearable device*” OR “wearable” OR “sensor*” OR “exoskeleton*” OR “smart helmet*” OR “biosensor*”) AND TS = (“healthcare worker*” OR “hospital staff” OR “nurse*” OR “physician*” OR “employee*”) AND TS = (“occupational health” OR “staff safety” OR “workplace safety” OR “organizational resilience” OR “business continuity” OR “absenteeism” OR “productivity”).
  • Embase: (‘wearable device’/exp OR ‘wearable’:ti,ab OR ‘sensor’/exp OR ‘sensor*’:ti,ab OR ‘exoskeleton’/exp OR ‘exoskeleton*’:ti,ab OR ‘smart helmet*’:ti,ab OR ‘biosensor’/exp OR ‘biosensor*’:ti,ab) AND (‘health care personnel’/exp OR ‘healthcare worker*’:ti,ab OR ‘hospital staff’:ti,ab OR ‘nurse*’:ti,ab OR ‘physician*’:ti,ab OR ‘employee*’:ti,ab) AND (‘occupational health’/exp OR ‘staff safety’:ti,ab OR ‘workplace safety’:ti,ab OR ‘organizational resilience’:ti,ab OR ‘business continuity’:ti,ab OR ‘absenteeism’/exp OR ‘productivity’/exp).
  • CINAHL: (“wearable device*” OR “wearable” OR “sensor*” OR “exoskeleton*” OR “smart helmet*” OR “biosensor*”) AND (“healthcare worker*” OR “hospital staff” OR “nurse*” OR “physician*” OR “employee*”) AND (“occupational health” OR “staff safety” OR “workplace safety” OR “organizational resilience” OR “business continuity” OR “absenteeism” OR “productivity”).
For Google Scholar and gray literature, a simplified search was conducted using the combination: “wearable devices” AND “healthcare workers” AND (“occupational health” OR “safety” OR “resilience” OR “business continuity”).
Only studies published in English were considered eligible. All articles retrieved through the search strategy were available in full text and included for screening.
After the initial retrieval of search results, citations from peer-reviewed articles were extracted from the primary databases. To streamline the elimination of duplicates and support the management of the articles selected for inclusion, Rayyan software (https://www.rayyan.ai) was used [43]. Three independent reviewers (P.C., D.V., and C.L.) were involved in the selection process.
The screening was carried out in two phases. In the first phase, two reviewers (P.C. and D.V.) independently assessed the titles, abstracts, and keywords of the articles to evaluate their relevance to the scope of the review. Relevant review articles were also identified for potential citation searching to uncover additional studies not indexed in the selected databases [44]. In the second phase, the same reviewers evaluated the full-text articles according to the inclusion and exclusion criteria. Any disagreements were resolved through consultation with the third reviewer (C.L.), who oversaw the process and ensured consistency across both phases. Inter-rater reliability was not formally calculated. A PRISMA-ScR flow diagram was used to document the selection process. The review protocol is in the process of being registered on the Open Science Framework (OSF), and the registration details will be provided accordingly.

3. Results

3.1. Literature Search

A comprehensive search across several electronic databases initially yielded 657 records. The PRISMA flowchart (Figure 1) details the process of selecting relevant publications at each step of the review. After eliminating 401 duplicate entries, 256 records advanced to the screening phase. During this stage, 163 records were excluded based on their failure to meet the established inclusion criteria. This left 93 articles to be further assessed for eligibility. In the final phase, 31 studies were included in the qualitative/quantitative synthesis.
Among the 69 included studies, pilot or feasibility designs predominated (approximately 40%), followed by observational cohort or cross-sectional studies (about 25%). Qualitative approaches accounted for nearly 15% of the studies, while mixed-methods designs represented around 10%. Randomized controlled trials and case studies/series were less common, each contributing less than 10% of the total. In terms of technology, multiparameter wearable systems such as SensiumVitals, Philips Biosensor/Healthdot, and ViSi Mobile/HealthPatch were the most frequently investigated. Consumer-grade devices including Fitbit and WHOOP bands, as well as specialized solutions such as smart glasses and exoskeletons, were also represented, though less consistently. Overall, the distribution of study types and technologies reflects an exploratory stage of research, with significant heterogeneity across settings and outcomes.

3.2. General Characteristics of the Included Studies

A total of 32 studies met the inclusion criteria and were included in this scoping review (Table 2). These studies encompassed a diverse range of designs, healthcare settings, and wearable technologies. The majority were conducted in hospital-based environments, including academic medical centers, surgical wards, intensive care units, emergency departments, pediatric hospitals, dementia care homes, and inpatient units, while a smaller number targeted community-based or multi-center setting.
Economic implications were rarely reported in the included studies. Most publications did not provide direct analyses of cost variables and often listed this domain as “no economic analysis.” A few exceptions emerged: Rosiello et al. [56] documented direct savings of €54–90 per patient through reduced nursing time, and Weenk et al. [52] reported reductions in workload-related costs. Kooij et al. [53] highlighted shortened hospital stays and earlier home recovery associated with biosensor implementation, while Hanna [72] discussed cost as a major barrier to adoption across multiple sensor types. Beyond these cases, economic aspects were generally addressed only indirectly, for example, through references to efficiency gains, workflow changes, or resource optimization. Overall, the scarcity and inconsistency of cost-related outcomes underscore a major gap in the evidence base regarding the economic impact of wearable technologies in healthcare settings.
Regarding study design, most were pilot or feasibility studies [45,46,47,48,49,50,63,67,70,71], frequently adopting longitudinal approaches to evaluate usability, adoption, and implementation outcomes. Observational cohort or cross-sectional studies were also common [58,61,65,68,69,74,75]. Other study designs included randomized controlled trials [52,56], qualitative or mixed-methods evaluations [51,53,55,60,62,64,72,73], case studies [54,66], and case series [66].
The types of wearable technologies evaluated were heterogeneous. Frequently investigated devices included SensiumVitals [58,59,61,75], Philips Biosensor [53], ViSi Mobile and HealthPatch [52], and multiparameter wireless systems such as Wireless Vital Parameter Monitoring (WVPCM) [56] and Healthdot [57]. Consumer-grade devices were also used, including the WHOOP band [45], Fitbit Charge [48,62], and MUSE-S™ [50]. Other technologies included smart glasses [51], proximity beacons [49], real-time location systems [70], wireless sensor systems [67], hands-free communication devices [68], near-field display devices [63], and various multiparameter armbands and biosensors [47,66,72].
Primary outcomes varied but frequently focused on device usability, adoption rate, user satisfaction, and perceived impact on occupational health and safety. High user engagement and satisfaction were reported in studies where devices were user-friendly and minimally disruptive [50,51,59]. Barriers to implementation included digital literacy gaps [48], privacy and ethical concerns [51,54], alert fatigue [61], and infrastructure or interoperability issues [72,73].
Economic outcomes were rarely evaluated, although Rosiello et al. [56] demonstrated cost savings of €54–90 per patient through nursing time reduction, and Weenk et al. [52] reported reductions in workload and costs. Facilitators for successful adoption included user-centered design, leadership support, effective staff training, and adequate IT infrastructure [55,57]. Overall, the evidence suggests that wearable technologies can enhance staff safety, occupational health, and organizational resilience, although variability in study design and heterogeneity in devices and outcomes limit direct comparisons across studies.

3.2.1. Staff Safety

Several studies demonstrated improvements in staff safety through wearable monitoring systems, real-time location devices, and proximity beacons. Reported outcomes included enhanced situational awareness, reduced risk of accidents, and improved compliance with safety protocols. Devices such as location trackers [54,70] and wireless sensor systems [67] were particularly effective in high-risk environments, supporting organizational resilience by ensuring timely interventions.

3.2.2. Stress and Burnout

Wearables capable of monitoring heart rate variability (HRV), sleep quality, and other physiological indicators were frequently employed to detect stress and burnout [45,47,50]. Studies reported reductions in perceived stress and improvements in resilience and quality of life, particularly when devices were user-friendly and integrated smoothly into workflows. However, the predominance of pilot designs limits the generalizability of these findings.

3.2.3. Absenteeism and Presenteeism

Although not commonly primary outcomes, some studies indirectly reported on absenteeism and presenteeism. Improved workload management and early detection of stress symptoms [52,56] suggest potential benefits for reducing sick leave and sustaining staff presence. Nevertheless, robust evidence linking wearables directly to absenteeism reduction remains limited.

3.2.4. Economic and Cost Implications

Economic outcomes were seldom analyzed. A few studies documented measurable benefits: for instance, Rosiello et al. [56] reported direct savings of €54–90 per patient through nursing time reduction, and Weenk et al. [52] described workload-related cost savings. Kooij et al. [53] highlighted reduced hospital stays, while Hanna [72] identified high costs as a barrier to adoption. Overall, cost-effectiveness evidence remains scarce and inconsistent.

3.3. Results of Characteristics Referred to PICOS

The characteristics of the included studies were analyzed and summarized according to the PICOS framework (Table 1).
The characteristics of the included studies were analyzed according to the PICOS framework. To enhance clarity and avoid redundancy, the results are summarized in Table 3 using bullet points. This table highlights the main features of the study populations, types of interventions, comparators, outcomes, and study designs.
Population (P). All 32 studies focused on healthcare professionals working in hospital or clinical settings, including nurses, physicians, residents, allied health professionals, and support staff. The majority were conducted in high-pressure environments such as emergency departments, intensive care units, surgical wards, or academic medical centers [45,51,52,56], while others involved pediatric hospitals [55], dementia care homes [54], or general inpatient units [49,69]. The population size varied widely, from small pilot samples of fewer than 20 participants [47,67] to large observational cohorts [58,61].
Intervention (I). The interventions consisted of a wide range of wearable technologies aimed at monitoring physiological parameters, improving communication, or supporting occupational safety. Frequently studied devices included continuous physiological monitoring systems such as SensiumVitals [58,59,61,75], Philips Biosensor [53], ViSi Mobile and HealthPatch [52], and the Wireless Vital Parameter Monitoring (WVPCM) system [56]. Other interventions included consumer-grade devices like the WHOOP band [45], Fitbit [27,48], and MUSE-S™ [50], smart glasses [51], real-time location systems [70], proximity beacons [49], wireless sensor systems [67], hands-free communication devices [68], and near-field display devices [63].
Comparison (C). Most studies compared wearable technologies with standard occupational health and safety practices, while others evaluated different types of wearable devices or implementation strategies. Formal comparative analyses were limited, as many studies were exploratory or descriptive in nature [54,66,72]. Randomized controlled trials [52,56] directly compared wearable monitoring with standard care and demonstrated improvements in workload management and early detection of patient deterioration.
Outcomes (O). Primary outcomes included occupational health and safety metrics, device usability, adoption rates, user satisfaction, stress levels, and organizational resilience indicators. Positive adoption and satisfaction rates were reported when devices were user-friendly and integrated seamlessly into workflows [50,51,58]. Some studies demonstrated improvements in workload management [56,57], staff safety [54,70], and early detection of stress or burnout [45,47]. Secondary outcomes included absenteeism, presenteeism, productivity, and cost implications, though economic evaluations were infrequently performed. Rosiello et al. [56] reported direct cost savings of €54–90 per patient, while Weenk et al. [52] found reductions in workload-related costs.
Study design (S). As anticipated in a scoping review, the included studies were methodologically heterogeneous. Pilot and feasibility studies [46,48,67] predominated, followed by observational cohort or cross-sectional studies [58,59,74], qualitative studies [55,72,73], mixed-methods evaluations [51,62], and randomized controlled trials [52,56]. Sample sizes, outcomes, and study durations varied substantially, reflecting the exploratory nature of the available evidence.
Overall, the findings across all PICOS elements indicate that wearable technologies have the potential to support occupational health, staff safety, and business continuity in healthcare organizations. However, evidence remains fragmented, with significant variability in populations, interventions, and outcome measures, limiting comparability across studies.

4. Discussion

This scoping review provides an updated synthesis of the available evidence on wearable technologies used to enhance occupational health, staff safety, and organizational resilience in healthcare settings. The findings from 32 studies demonstrate that wearable devices have the potential to improve early detection of stress and fatigue, support workflow efficiency, and contribute to overall staff safety. However, the evidence base remains fragmented, with most studies focusing on feasibility and usability rather than robust clinical or organizational outcomes. This is consistent with previous reviews in occupational health and safety domains, which have similarly highlighted the exploratory nature of much of the current research on wearables [33,62].
The predominance of pilot and feasibility studies [46,48,50,67] highlights the early stage of evidence development in this area. Randomized controlled trials [52,56] remain scarce but are critical to establishing causal links between wearable device use and improved outcomes. These RCTs reported improvements in workload management and early detection of patient deterioration, aligning with findings from industrial and construction sectors where wearables have been shown to enhance hazard detection and reduce incidents [26,31].
Beyond summarizing results, it is important to critically appraise the evidence base. The predominance of pilot and feasibility studies with small sample sizes and reliance on self-reported outcomes reflects an early stage of evidence development, limiting the generalizability of findings. Few randomized controlled trials and longitudinal follow-ups were available, preventing strong causal inferences or assessment of sustained effects. Similarly, economic evaluations were scarce, with only isolated studies reporting cost savings, leaving a significant gap in understanding cost-effectiveness and long-term return on investment. Ethical and privacy concerns—such as continuous surveillance of staff performance and data governance—were acknowledged in some studies but often insufficiently integrated into their interpretation, raising important questions about acceptability and trust. These gaps mirror those described in previous reviews of occupational safety and digital health, where methodological heterogeneity, limited standardization of outcomes, and ethical challenges were identified as recurrent obstacles to large-scale implementation.
The heterogeneity of devices and outcomes makes it difficult to establish a clear evidence hierarchy. Similar challenges have been reported in previous reviews on digital health technologies, where variability in interventions often impedes meta-analytic synthesis [35,60]. Many studies in our review focused on continuous physiological monitoring devices such as SensiumVitals, Philips Biosensor, and ViSi Mobile, which have been shown to improve early warning detection in patients [58,61]. However, relatively few studies assessed staff-related health outcomes beyond usability and satisfaction, limiting our understanding of their true impact on absenteeism, presenteeism, and organizational resilience.
A key barrier identified was alert fatigue and workflow disruption [58,61], findings consistent with other sectors using real-time alerting systems [29]. Privacy concerns and data governance issues also emerged [51,54], echoing broader ethical concerns around surveillance raised by Maltseva [38] and Biswas et al. [36]. These barriers suggest that the implementation of wearable technologies must be embedded within a broader organizational strategy that emphasizes staff trust and transparent communication, as recommended in other occupational safety frameworks [37].
Wearable technologies show promise in improving occupational health outcomes for healthcare workers. Studies indicate that wearables can help manage stress [76], detect burnout [77], and enhance resilience [78]. These devices enable continuous monitoring of physiological signals, allowing for real-time assessment of work-related stress [47]. Wearables and smartphone apps can facilitate self-care strategies, including mindfulness practices, which have been shown to reduce stress and improve well-being [50,79]. Compared to standard occupational health practices, wearable-based interventions have demonstrated effectiveness in reducing stress, enhancing resilience, and improving quality of life [80]. However, more research is needed to address limitations in data standardization and long-term observations [77]. Overall, wearable technologies offer potential for improving various aspects of healthcare workers’ occupational health, including stress reduction, burnout prevention, and organizational resilience [27].
Wearable technologies in healthcare settings show promise for improving patient outcomes and organizational performance, but evidence of their cost-effectiveness remains limited. Studies indicate that wearables can increase quality-adjusted life years and potentially be cost-saving, depending on factors like device type and health condition [81]. These technologies can enhance employee health, reduce absenteeism, and increase productivity [38,82]. However, implementation challenges include ethical concerns, integration into clinical workflows, and data management [83,84]. While some studies suggest improved productivity and reduced absenteeism in depression management [85], evidence for wearables’ impact on chronic disease outcomes is mixed [86]. More high-quality research is needed to fully understand the long-term benefits and cost-effectiveness of wearable technologies in healthcare settings [81,87].
The synthesis of evidence underscores those wearable technologies alone are unlikely to produce significant improvements unless integrated with systemic organizational change. Studies reporting the most positive results emphasized user-centered design and active staff involvement in device selection and deployment [55,72]. This aligns with evidence from integrated care literature, which identifies leadership support, training, and stakeholder engagement as key enablers of successful workforce innovation [37].
From an occupational health perspective, wearable devices that monitor physiological stress and workload may provide actionable data to inform staffing decisions and targeted interventions [45,47]. Real-time location systems and duress alarms [68,70] have shown promise in enhancing staff safety during critical incidents. These findings mirror the adoption of similar technologies in high-risk industries such as mining and construction, where location-based solutions are widely used to improve situational awareness [28].
Economic evidence, while limited, suggests potential for cost savings through reduced complications and improved workload management [52,56]. However, the lack of full cost–benefit analyses remains a significant gap. Studies outside healthcare have demonstrated positive return-on-investment metrics for wearable technologies when linked to reductions in absenteeism and injury-related costs [31,33]. Similar evaluations are urgently needed in healthcare to support decision-making and large-scale investment.
Recent research highlights the potential of wearable technologies and organizational interventions to improve healthcare workers’ safety, stress levels, and occupational health outcomes. Wearable devices can monitor vital signs, physical activity, and sleep patterns, helping to prevent accidents and manage fatigue [27,77]. These technologies, combined with connected worker solutions, provide real-time insights into workplace safety and productivity [27]. Organizational interventions, such as team development and safety training programs, have been shown to reduce work-related stress [88]. Integration of wearables with health and safety management systems can support worker well-being during technological changes [89]. However, challenges remain, including privacy concerns and the need for improved data standardization [77]. While cognitive-behavioral therapy and relaxation techniques show promise in stress reduction, more research is needed to evaluate the effectiveness of various interventions [90].
Wearable technologies in healthcare settings show promising cost-effectiveness and return on investment profiles. Studies indicate that these devices can increase quality-adjusted life years and be cost-effective or cost-saving, depending on factors such as device type and health condition addressed [81]. Implementing wearable sensors for pressure injury prevention has demonstrated increased turn compliance, decreased hospital-acquired pressure injuries, and reduced organizational costs [91]. A workplace disability management program utilizing wearable technologies showed a 66.6% reduction in sickness absence days and a return on investment of 27.66 [92]. Similarly, a preventive mental health intervention for nurses yielded net savings of 244–651 euros per nurse within six months [93]. Continuous monitoring systems in medical-surgical units have also shown highly positive returns on investment, with potential cost savings ranging from $3,268,000 to $9,089,000 over five years [94].
The evidence reviewed is characterized by methodological heterogeneity and a predominance of small-scale studies. Many relied on self-reported outcomes, which may be subject to response and recall bias. Only two randomized trials were identified [52,56], and both were limited to specific clinical settings, reducing generalizability. Few studies included longitudinal follow-up to assess sustained impact on occupational health or organizational outcomes.
The scarcity of economic evaluations and the limited exploration of ethical issues represent further gaps. Privacy concerns, particularly around continuous monitoring of staff performance, could undermine user acceptance if not adequately addressed [38,54]. There is also a lack of standardized outcome measures, making it difficult to benchmark the effectiveness of different devices or implementation strategies. These gaps mirror those identified in broader digital health implementation research [27,60].
Future research should prioritize high-quality randomized controlled trials and longitudinal cohort studies that evaluate both staff-centered and organizational outcomes, including absenteeism, presenteeism, burnout, and productivity. Mixed-methods approaches will be essential to capture the complex interplay between technology, workflow, and organizational culture.
Further, robust economic evaluations are needed to determine the cost-effectiveness of wearable technologies and to justify their integration into occupational health programs. Addressing ethical considerations, particularly data governance and surveillance concerns, will also be critical to enhancing staff trust and adoption. Finally, international collaboration to develop standardized outcome metrics could facilitate cross-study comparisons and strengthen the evidence base.
Despite the growing potential of wearable technologies in healthcare, several barriers hinder their integration into organizational practice. Concerns regarding data privacy, cost–benefit ratios, and interoperability with existing systems remain key challenges. In addition, issues such as limited digital literacy, alert fatigue, and lack of staff involvement in device selection contribute to resistance. Strategies to foster adoption include strong leadership support, early and transparent communication, dedicated staff training, and worker-centered approaches that prioritize usability and ethical safeguards. Embedding these elements within organizational policies can enhance adherence and long-term sustainability.
This study has several limitations. First, only articles published in English were included, which may have excluded relevant evidence from other languages. Third, the included studies were highly heterogeneous in terms of design, sample size, and outcome measures, limiting comparability and precluding meta-analysis. Nevertheless, this scoping review contributes to the scientific field by offering an integrated overview of wearable technologies in healthcare, emphasizing their links with occupational health, staff safety, and organizational resilience. By identifying both evidence and gaps, the review provides a foundation for future research and policy development.

5. Conclusions

This scoping review highlights the emerging but still fragmented evidence on wearable technologies as tools to support occupational health, staff safety, and business continuity in healthcare organizations. While many devices demonstrated potential benefits in reducing stress, preventing burnout, and improving workflow efficiency, the heterogeneity of interventions and outcomes limits definitive conclusions.
From a practical perspective, the findings indicate that wearable technologies can complement traditional occupational health and safety programs by enabling continuous monitoring, supporting early risk detection, and enhancing organizational resilience. However, successful adoption requires addressing challenges such as privacy concerns, staff acceptance, digital literacy, and system interoperability. Ethical considerations, particularly regarding surveillance and worker autonomy, remain critical to ensure that implementation enhances, rather than undermines, trust in healthcare workplaces. The lack of robust economic evaluations further limits understanding of cost-effectiveness, representing a key gap for both researchers and decision-makers.
Future research should prioritize transdisciplinary approaches that combine occupational health, organizational science, and economic evaluation to generate integrated evidence. This will allow for a better understanding of how wearable technologies can deliver sustained improvements in healthcare worker well-being and productivity, while ensuring cost-effectiveness and scalability. Robust randomized controlled trials, longitudinal cohort studies, and standardized outcome measures are essential to advance the field and guide policy and investment decisions.

Author Contributions

Conceptualization, D.V. and C.L.; methodology, P.C. and C.L.; software, C.L.; validation, D.V. and C.L.; formal analysis, P.C. and C.L.; resources, C.L.; data curation, D.V.; writing—original draft preparation, C.L.; writing—review and editing, D.V.; supervision, P.C. and C.L.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.

Funding

The research activity of Daniele Virgillito was funded by the European Union—NextGenerationEU, in the framework of the GRINS—Growing Resilient, INclusive and Sustainable project (GRINS PE00000018—CUP E63C22002120006). The views and opinions expressed are solely those of the authors and do not necessarily reflect those of the European Union, nor can the European Union be held responsible for them. The APC was funded by resources derived from the SOGIS Master’s Program of the University of Catania.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank Chiara Cantarella, Venera Fasone, Salvatore Garozzo, and Rita Pennisi (listed in alphabetical order) for their valuable support to the SOGIS Master’s Program.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Alabduljabbar, Z.M.; Al Beshri, Z.S.; Alhejji, E.A.; Qismayn, A.I.; Al Shuhaib, J.Y.; Abushomi, H.Q.; A Musad, A.A.; Alshammari, B.S.S.; Alyahya, A.A.; Alghanem, A.M.A. A Review of Psychological and Moral Challenges Faced by Medical Staff in High-Stress Environments. J. Ecohumanism 2024, 3, 983–990. [Google Scholar] [CrossRef]
  2. Honda, H.; Iwata, K. Personal Protective Equipment and Improving Compliance among Healthcare Workers in high-risk settings. Curr. Opin. Infect. Dis. 2016, 29, 400–406. [Google Scholar] [CrossRef]
  3. Foghammar, L.; Jang, S.; Kyzy, G.A.; Weiss, N.; Sullivan, K.A.; Gibson-Fall, F.; Irwin, R. Challenges in Researching Violence Affecting Health Service Delivery in Complex Security Environments. Soc. Sci. Med. 2016, 162, 219–226. [Google Scholar] [CrossRef]
  4. Ahmad, I.A.; Osei, E. Occupational Health and Safety Measures in Healthcare Settings during COVID-19: Strategies for Protecting Staff, Patients and Visitors. Disaster Med. Public Health Prep. 2021, 17, e48. [Google Scholar] [CrossRef]
  5. Lowe, A.; Gibbs, S. How Should Health Care Organizations Protect Personnel in Environmental Services and Related Fields? AMA J. Ethics 2022, 24, E846–E852. [Google Scholar] [CrossRef]
  6. Lamb, L.E.; Cox, A.; Fletcher, T.; McCourt, A. Formulating and Improving Care While Mitigating Risk in a Military Ebo La Virus Disease Treatment Unit. J. R. Army Med. Corps 2016, 163, 2–6. [Google Scholar] [CrossRef]
  7. Fast, L.; Wille, C. To Stay or Go? The Complexities of Providing Healthcare in Insecure Environments. World Health Popul. 2016, 16, 38–42. [Google Scholar] [CrossRef]
  8. Edgar, M.; Selvaraj, S.A.; Lee, K.E.; Caraballo-Arias, Y.; Harrell, M.; Rodríguez-Morales, A. Healthcare Workers, Epidemic Biological Risks—Recommendations Based on the Experience with COVID-19 and Ebolavirus. Infez. Med. 2022, 30, 168. [Google Scholar] [CrossRef] [PubMed]
  9. Gonçalves, L.; Sala, R.; Navarro, J.-B. Resilience and Occupational Health of Health Care Workers: A Moderator Analysis of Organizational Resilience and Sociodemographic Attributes. Int. Arch. Occup. Env. Health 2021, 95, 223–232. [Google Scholar] [CrossRef] [PubMed]
  10. Tamanna, M.; Sharma, S. Do Resilient Organizations Enhance Organizational Performance? Int. J. Sci. Technol. Res. 2020, 9, 3610–3612. [Google Scholar]
  11. Lundstrom, T.; Pugliese, G.; Bartley, J.; Cox, J.; Guither, C. Organizational and Environmental Factors That Affect Worker Health and Safety and Patient Outcomes. Am. J. Infect. Control 2002, 30, 93–106. [Google Scholar] [CrossRef] [PubMed]
  12. Gaspar, T.; Gomez-Baya, D.; Guedes, F.B.; Correia, M.F. Health Management: Evaluating the Relationship between Organizational Factors, Psychosocial Risks at Work, Performance Management, and Hospital Outcomes. Healthcare 2023, 11, 2744. [Google Scholar] [CrossRef]
  13. Miller, K. Risk Factors and Impacts of Occupational Injury in Healthcare Workers: A Critical Review. OA Musculoskelet. Med. 2013, 1, 4. [Google Scholar] [CrossRef]
  14. Boczkowska, K.; Niziołek, K. Health and Safety Strategy in Medical Institutions: Employee Participation. In Organizational Change and Relational Resources; Routledge: Abingdon, UK, 2021; pp. 197–210. ISBN 978-1-00-317260-4. [Google Scholar]
  15. Darrow, L. Exploring the Factors That Drive Organizational Resilience: Lessons from Healthcare. Master’s Thesis, Oregon State University, Corvallis, OR, USA, 2017. [Google Scholar]
  16. Cotton, P.; Hart, P.M. Occupational Wellbeing and Performance: A Review of Organisational Health Research. Aust. Psychol. 2003, 38, 118–127. [Google Scholar] [CrossRef]
  17. Kuster, S.P.; Böni, J.; Kouyos, R.D.; Huber, M.; Schmutz, S.; Shah, C.; Bischoff-Ferrari, H.A.; Distler, O.; Battegay, E.; Giovanoli, P.; et al. Absenteeism and Presenteeism in Healthcare Workers Due to Respiratory Illness. Infect. Control Hosp. Epidemiol. 2020, 42, 268–273. [Google Scholar] [CrossRef]
  18. Gianino, M.M.; Politano, G.; Scarmozzino, A.; Stillo, M.; Amprino, V.; Di Carlo, S.; Benso, A.; Zotti, C.M. Cost of Sickness Absenteeism during Seasonal Influenza Outbreaks of Medium Intensity among Health Care Workers. Int. J. Environ. Res. Public Health 2019, 16, 747. [Google Scholar] [CrossRef]
  19. Van Buynder, P.G.; Konrad, S.; Kersteins, F.; Preston, E.; Brown, P.D.; Keen, D.; Murray, N.J. Healthcare Worker Influenza Immunization Vaccinate or Mask Policy: Strategies for Cost Effective Implementation and Subsequent Reductions in Staff Absenteeism Due to Illness. Vaccine 2015, 33, 1625–1628. [Google Scholar] [CrossRef] [PubMed]
  20. Imai, C.; Toizumi, M.; Hall, L.; Lambert, S.; Halton, K.; Merollini, K. A Systematic Review and Meta-Analysis of the Direct Epidemiological and Economic Effects of Seasonal Influenza Vaccination on Healthcare Workers. PLoS ONE 2018, 13, e0198685. [Google Scholar] [CrossRef] [PubMed]
  21. Strömberg, C.; Aboagye, E.; Hagberg, J.; Bergström, G.; Lohela-Karlsson, M. Estimating the Effect and Economic Impact of Absenteeism, Presenteeism, and Work Environment–Related Problems on Reductions in Productivity from a Managerial Perspective. Value Health 2017, 20, 1058–1064. [Google Scholar] [CrossRef]
  22. Davis, K.; Collins, S.; Doty, M.; Ho, A.; Holmgren, A.L. Health and Productivity Among U.S. Workers. Issue Brief 2005, 856, 1–10. [Google Scholar]
  23. Nechita, P.; Oroian, B.A.; Schipor, P.D.; Enache, A.L.; Popa, M.V. The Link Between Work Stress, Absenteeism, and Sickness absence: The Impact of Burnout on Healthcare Workers. Bull. Integr. Psychiatry 2025, 2, 29–39. [Google Scholar] [CrossRef]
  24. Naidoo, R.; Naidoo, S.; Hariparsad, S. Disabling health: The Challenge of Incapacity Leave and Sickness Abse Nce Management in the Public Health Sector in KwaZulu-Natal Province. S. Afr. Health Rev. 2016, 2016, 61–72. [Google Scholar]
  25. Tarakci, H.; Kulkarni, S.; Ozdemir, Z.D. The Impact of Wearable Devices and Performance Payments on Health Outcomes. Int. J. Prod. Econ. 2018, 200, 291–301. [Google Scholar] [CrossRef]
  26. Rane, N.; Choudhary, S.; Rane, J. Leading-Edge Wearable Technologies in Enhancing Personalized Safety on Construction Sites: A Review. SSRN J. 2023. [Google Scholar] [CrossRef]
  27. Patel, V.; Chesmore, A.; Legner, C.M.; Pandey, S. Trends in Workplace Wearable Technologies and Connected-Worker Solutions for Next-Generation Occupational Safety, Health, and Productivity. Adv. Intell. Syst. 2021, 4, 2100099. [Google Scholar] [CrossRef]
  28. Pavón, I.; Sigcha, L.; Arezes, P.; Costa, N.; Arcas, G.; López, J.M. Wearable Technology for Occupational Risk Assessment: Potential Avenue s for Applications. In Occupational Safety and Hygiene VI; CRC Press: Boca Raton, FL, USA, 2018; pp. 447–452. ISBN 978-1-351-00888-4. [Google Scholar]
  29. Banerjee, S.; Hempel, M.; Sharif, H. A Review of Workspace Challenges and Wearable Solutions in Railroads a Nd Construction. In Proceedings of the 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, Spain, 26–30 June 2017; IEEE: New York, NY, USA, 2017; pp. 91–96. [Google Scholar]
  30. Kritzler, M.; Bäckman, M.; Tenfält, A.; Michahelles, F. Wearable Technology as a Solution for Workplace Safety. In Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia, Linz, Austria, 30 November–2 December 2015; ACM: New York, NY, USA, 2015; pp. 213–217. [Google Scholar]
  31. Barata, J.; da Cunha, P.R. Safety Is the New Black: The Increasing Role of Wearables in Occupational Health and Safety in Construction. In Lecture Notes in Business Information Processing; Springer International Publishing: Berlin/Heidelberg, Germany, 2019; pp. 526–537. ISBN 978-3-030-20484-6. [Google Scholar]
  32. De Vasconcelos, F.M.; Almeida, C.F.M.; Gualtieri, S.R.; Danesin, L.A.; Kagan, N.; Cruz, L.H.; Mollica, D.; de Demuner, S.V.R.; Dominice, A.; Filho, J.S. Wearable Sensing for Work Safety and Occupational Health Applied to an Electric Power Distributor. IET Conf. Proc. 2021, 2021, 1717–1721. [Google Scholar] [CrossRef]
  33. Schall, M.C., Jr.; Sesek, R.F.; Cavuoto, L.A. Barriers to the Adoption of Wearable Sensors in the Workplace: A Surve y of Occupational Safety and Health Professionals. Hum. Factors 2018, 60, 351–362. [Google Scholar] [CrossRef] [PubMed]
  34. Reid, C.R.; Schall, M.C., Jr.; Amick, R.Z.; Schiffman, J.M.; Lu, M.-L.; Smets, M.; Moses, H.R.; Porto, R. Wearable Technologies: How Will We Overcome Barriers to Enhance Worker Performance, Health, And Safety? Proc. Hum. Factors Ergon. Soc. Annu. Meet. 2017, 61, 1026–1030. [Google Scholar] [CrossRef]
  35. Dos Schwambach, S.G.C.; Sott, M.K.; Schwambach, R.E. Wearable Devices and Workplace Productivity: A Bibliometric Analysis of Their Integration into Professional Environments. Dataset Rep. 2024, 3, 101–106. [Google Scholar] [CrossRef]
  36. Biswas, A.; Begum, M.; Van Eerd, D.; Johnston, H.; Smith, P.M.; Gignac, M.A.M. Integrating Safety and Health Promotion in Workplaces: A Scoping Revie w of Facilitators, Barriers, and Recommendations. Health Promot. Pract. 2021, 23, 984–998. [Google Scholar] [CrossRef]
  37. Busetto, L.; Luijkx, K.; Calciolari, S.; Ortiz, L.G.G.; Vrijhoef, H.J.M. Barriers and Facilitators to Workforce Changes in Integrated Care. Int. J. Integr. Care 2018, 18, 17. [Google Scholar] [CrossRef] [PubMed]
  38. Maltseva, K. Wearables in the Workplace: The Brave New World of Employee Engagement. Bus. Horiz. 2020, 63, 493–505. [Google Scholar] [CrossRef]
  39. Bhullar, G. “Wear” Is the Manufacturing Future: The Latest Fashion Hitting the Workplace. In Lecture Notes in Computer Science; Springer International Publishing: Berlin/Heidelberg, Germany, 2015; pp. 49–56. ISBN 978-3-319-26137-9. [Google Scholar]
  40. Arksey, H.; O’Malley, L. Scoping Studies: Towards a Methodological Framework. Int. J. Soc. Res. Methodol. 2005, 8, 19–32. [Google Scholar] [CrossRef]
  41. Levac, D.; Colquhoun, H.; O’Brien, K.K. Scoping Studies: Advancing the Methodology. Implement. Sci. 2010, 5, 69. [Google Scholar] [CrossRef] [PubMed]
  42. Tricco, A.C.; Lillie, E.; Zarin, W.; O’Brien, K.K.; Colquhoun, H.; Levac, D.; Moher, D.; Peters, M.D.J.; Horsley, T.; Weeks, L.; et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann. Intern. Med. 2018, 169, 467–473. [Google Scholar] [CrossRef]
  43. Rayyan. Available online: https://www.rayyan.ai/ (accessed on 12 August 2025).
  44. Briscoe, S.; Bethel, A.; Rogers, M. Conduct and Reporting of Citation Searching in Cochrane Systematic Reviews: A Cross-Sectional Study. Res. Synth. Methods 2020, 11, 169–180. [Google Scholar] [CrossRef]
  45. Agarwal, A.K.; Gonzales, R.; Scott, K.; Merchant, R. Investigating the Feasibility of Using a Wearable Device to Measure Physiologic Health Data in Emergency Nurses and Residents: Observational Cohort Study. JMIR Form. Res. 2024, 8, e51569. [Google Scholar] [CrossRef] [PubMed]
  46. Paradiso, R.; Krijnen, E.; Vannetti, F. Wearable System for the evaluation of Well-Being in the Workplace. In Cognitive Computing and Internet of Things; AHFE International: New York, NY, USA, 2024. [Google Scholar] [CrossRef]
  47. Shishavan, H.H.; Garza, J.; Henning, R.; Cherniack, M.; Hirabayashi, L.; Scott, E.; Kim, I. Continuous Physiological Signal Measurement over 24-Hour Periods to Assess the Impact of Work-Related Stress and Workplace Violence. Appl. Ergon. 2023, 108, 103937. [Google Scholar] [CrossRef]
  48. Clingan, C.A.; Dittakavi, M.; Rozwadowski, M.; Gilley, K.N.; Cislo, C.R.; Barabas, J.; Sandford, E.; Olesnavich, M.; Flora, C.; Tyler, J.; et al. Monitoring Health Care Workers at Risk for COVID-19 Using Wearable Sensors and Smartphone Technology: Protocol for an Observational mHealth Study. JMIR Res. Protoc. 2021, 10, e29562. [Google Scholar] [CrossRef]
  49. Keller, S.C.; Salinas, A.B.; Oladapo-Shittu, O.; Cosgrove, S.E.; Lewis-Cherry, R.; Osei, P.; Gurses, A.P.; Jacak, R.; Zudock, K.K.; Blount, K.M.; et al. The Case for Wearable Proximity Devices to Inform Physical Distancing among Healthcare Workers. JAMIA Open 2021, 4, ooab095. [Google Scholar] [CrossRef]
  50. Ghosh, K.; Nanda, S.; Hurt, R.T.; Schroeder, D.R.; West, C.P.; Fischer, K.M.; Bauer, B.A.; Fokken, S.C.; Ganesh, R.; Hanson, J.L.; et al. Mindfulness Using a Wearable Brain Sensing Device for Health Care Professionals During a Pandemic: A Pilot Program. J. Prim. Care Community Health 2023, 14, 21501319231162308. [Google Scholar] [CrossRef] [PubMed]
  51. Park, S.; Lee, H. The Effect of Communication in Emergency Department Isolation Rooms Using Smart Glasses: A Mixed-Methods Study. J. Clin. Nurs. 2025, 34, 4340–4350. [Google Scholar] [CrossRef]
  52. Weenk, M.; Bredie, S.J.; Koeneman, M.; Hesselink, G.; van Goor, H.; van de Belt, T.H. Continuous Monitoring of Vital Signs in the General Ward Using Wearable Devices: Randomized Controlled Trial. J. Med. Internet Res. 2020, 22, e15471. [Google Scholar] [CrossRef]
  53. Kooij, L.; Peters, G.M.; Doggen, C.J.M.; van Harten, W.H. Remote Continuous Monitoring with Wireless Wearable Sensors in Clinical Practice, Nurses Perspectives on Factors Affecting Implementation: A Qualitative Study. BMC Nurs. 2022, 21, 53. [Google Scholar] [CrossRef]
  54. Scott, A. Confronting Challenge: Enabling Care Home Staff to Understand and Work Effectively with Challenging Behaviours in Dementia. Eur. Psychiatry 2008, 23, S395. [Google Scholar] [CrossRef]
  55. O’Dwyer, B.; Jaana, M.; Hui, C.; Chreim, S.; Ellis, J. Digital Contact Tracing Implementation Among Leaders and Health Care Workers in a Pediatric Hospital During the COVID-19 Pandemic: Qualitative Interview Study. JMIR Public Health Surveill. 2024, 10, e64270. [Google Scholar] [CrossRef] [PubMed]
  56. Rosiello, F.; Pietrantonio, F.; Di Lorenzo, J.; Bertani, G.; Anzidei, A.; Laurelli, G.; Cipriano, E.; Di Iorio, C.; Montagnese, F.; Pascucci, M. Could the Miniaturize Techonologies Improve Patients Adherence and Assure Better Quality of Life? Eur. J. Public Health 2021, 31, iii398. [Google Scholar] [CrossRef]
  57. Schonck, F.; Luyer, M.; van der Meer, N.; Bouwman, A.; Nienhuijs, S. Implementation of a Surgical Ward Innovation: Telemonitoring Controlled by Healthdot [SWITCH-Trial PROTOCOL]. PLoS ONE 2025, 20, e0322472. [Google Scholar] [CrossRef]
  58. Joshi, M.; Archer, S.; Morbi, A.; Ashrafian, H.; Arora, S.; Khan, S.; Cooke, G.; Darzi, A. Perceptions on the Use of Wearable Sensors and Continuous Monitoring in Surgical Patients: Interview Study Among Surgical Staff. JMIR Form. Res. 2022, 6, e27866. [Google Scholar] [CrossRef]
  59. Iqbal, F.M.; Joshi, M.; Khan, S.; Ashrafian, H.; Darzi, A. Implementation of Wearable Sensors and Digital Alerting Systems in Secondary Care: Protocol for a Real-World Prospective Study Evaluating Clinical Outcomes. JMIR Res. Protoc. 2021, 10, e26240. [Google Scholar] [CrossRef]
  60. Brewster, L.; Mountain, G.; Wessels, B.; Kelly, C.; Hawley, M. Factors Affecting Front Line Staff Acceptance of Telehealth Technologies: A Mixed-method Systematic Review. J. Adv. Nurs. 2013, 70, 21–33. [Google Scholar] [CrossRef]
  61. Iqbal, F.M.; Joshi, M.; Fox, R.; Koutsoukou, T.; Sharma, A.; Wright, M.; Khan, S.; Ashrafian, H.; Darzi, A. Outcomes of Vital Sign Monitoring of an Acute Surgical Cohort with Wearable Sensors and Digital Alerting Systems: A Pragmatically Designed Cohort Study and Propensity-Matched Analysis. Front. Bioeng. Biotechnol. 2022, 10, 895973. [Google Scholar] [CrossRef] [PubMed]
  62. Patel, K.; Moukhtarian, T.R.; Russell, S.; Daly, G.; Walasek, L.; Tang, N.K.Y.; Toro, C.; Meyer, C. Digital Cognitive Behavioural Therapy Intervention in the Workplace: Study Protocol for a Feasibility Randomised Waitlist-Controlled Trial to Improve Employee Mental Well-Being, Engagement and Productivity. BMJ Open 2022, 12, e060545. [Google Scholar] [CrossRef] [PubMed]
  63. Santomauro, C.; McCurdie, T.; Pollard, C.; Shuker, M. Exploring the Feasibility of Wearable Technologies to Provide Interactive Telepresence Sub-Specialist Support to Remote Clinicians Treating Patients with Traumatic Injuries. Prehospital Disaster Med. 2019, 34, s85. [Google Scholar] [CrossRef]
  64. Shah, A.; Guessi, M.; Wali, S.; Ware, P.; McDonald, M.; O’Sullivan, M.; Posada, J.D.; Ross, H.; Seto, E. The Resilience of Cardiac Care Through Virtualized Services During the COVID-19 Pandemic: Case Study of a Heart Function Clinic. JMIR Cardio 2021, 5, e25277. [Google Scholar] [CrossRef]
  65. Buss, A.; Areia, C.; Biggs, C.; Edmundson, H.; Young, L.; Roman, C.; Santos, M.; Tarassenko, L.; Watkinson, P.; Vollam, S. Using a Novel Ambulatory Monitoring System to Support Patient Safety on an Acute Infectious Disease Ward During an Unfolding Pandemic. J. Adv. Nurs. 2023, 80, 2452–2461. [Google Scholar] [CrossRef] [PubMed]
  66. Meltzer, A.J.; Hallbeck, M.S.; Morrow, M.M.; Lowndes, B.R.; Davila, V.J.; Stone, W.M.; Money, S.R. Measuring Ergonomic Risk in Operating Surgeons by Using Wearable Technology. JAMA Surg. 2020, 155, 444. [Google Scholar] [CrossRef]
  67. Claudio, D.; Velázquez, M.A.; Bravo-Llerena, W.; Okudan, G.E.; Freivalds, A. Perceived Usefulness and Ease of Use of Wearable Sensor-Based Systems in Emergency Departments. IIE Trans. Occup. Ergon. Hum. Factors 2015, 3, 177–187. [Google Scholar] [CrossRef]
  68. Carreon, H.F.; Dutra, D. Inpatient Outcomes of a Hands-Free, Wireless Communication Device Implementation. CIN Comput. Inform. Nurs. 2020, 38, 323–328. [Google Scholar] [CrossRef]
  69. Yesmin, T.; Carter, M.W.; Gladman, A.S. Internet of Things in Healthcare for Patient Safety: An Empirical Study. BMC Health Serv. Res. 2022, 22, 278. [Google Scholar] [CrossRef]
  70. Jones, D.D.; Heaton, H.A.; Wallerich, T.J.; Riekmann, L.A.; Handmacher, B.H.; Hatton, R.L. Wearable Real Time Location Services Technology for Staff Duress in Healthcare. In Proceedings of the 2023 IEEE 13th International Conference on RFID Technology and Applications (RFID-TA), Aveiro, Portugal, 4–6 September 2023; IEEE: New York, NY, USA, 2023; pp. 127–129. [Google Scholar]
  71. Jovanov, E.; Frith, K.; Anderson, F.; Milosevic, M.; Shrove, M.T. Real-Time Monitoring of Occupational Stress of Nurses. In Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 30 August–3 September 2011; IEEE: New York, NY, USA, 2011; pp. 3640–3643. [Google Scholar]
  72. Hanna, S. Sensors and Wearables in Oncology: A Study of the Barriers and Facilitators to Adoption. Ph.D. Thesis, George Washington University, Washington, DC, USA, 2020. [Google Scholar] [CrossRef]
  73. McLean, K.A.; Sgrò, A.; Brown, L.R.; Buijs, L.F.; Mozolowski, K.; Daines, L.; Cresswell, K.; Potter, M.A.; Bouamrane, M.-M.; Harrison, E.M. Implementation of Digital Remote Postoperative Monitoring in Routine Practice: A Qualitative Study of Barriers and Facilitators. BMC Med. Inform. Decis. Mak. 2024, 24, 307. [Google Scholar] [CrossRef]
  74. Lokmic-Tomkins, Z.; Lin, Y.-F.; Robinson, E.; Bichel-Findlay, J.; Faisal, M.; Dowding, D. Insights from the Rapid Implementation of Digital Technologies in Nursing Practice during COVID-19—A Survey. Contemp. Nurse 2025. [Google Scholar] [CrossRef]
  75. Joshi, M.; Archer, S.; Morbi, A.; Arora, S.; Kwasnicki, R.; Ashrafian, H.; Khan, S.; Cooke, G.; Darzi, A. Short-Term Wearable Sensors for In-Hospital Medical and Surgical Patients: Mixed Methods Analysis of Patient Perspectives. JMIR Perioper Med. 2021, 4, e18836. [Google Scholar] [CrossRef] [PubMed]
  76. González Ramírez, M.L.; García Vázquez, J.P.; Rodríguez, M.D.; Padilla-López, L.A.; Galindo-Aldana, G.M.; Cuevas-González, D. Wearables for Stress Management: A Scoping Review. Healthcare 2023, 11, 2369. [Google Scholar] [CrossRef]
  77. Barac, M.; Scaletty, S.; Hassett, L.C.; Stillwell, A.; Croarkin, P.E.; Chauhan, M.; Chesak, S.; Bobo, W.V.; Athreya, A.P.; Dyrbye, L.N. Wearable Technologies for Detecting Burnout and Well-Being in Health Care Professionals: Scoping Review. J. Med. Internet Res. 2024, 26, e50253. [Google Scholar] [CrossRef]
  78. Vries, H.D.; Kamphuis, W.; Oldenhuis, H.K.E.; Schans, C.; Sanderman, R. Modelling Employee Resilience Using Wearables and Apps: A Conceptual Framework and Research Design. Int. J. Adv. Life Sci. 2019, 11, 110–118. [Google Scholar]
  79. Roussel, L.A. On-the-Go Strategies to Enhance Resilience and Self-Care. Nurs. Clin. N. Am. 2022, 57, 501–512. [Google Scholar] [CrossRef]
  80. Mistretta, E.G.; Davis, M.C.; Temkit, M.; Lorenz, C.; Darby, B.; Stonnington, C.M. Resilience Training for Work-Related Stress Among Health Care Workers. J. Occup. Environ. Med. 2018, 60, 559–568. [Google Scholar] [CrossRef] [PubMed]
  81. De Sario Velasquez, G.D.; Borna, S.; Maniaci, M.J.; Coffey, J.D.; Haider, C.R.; Demaerschalk, B.M.; Forte, A.J. Economic Perspective of the Use of Wearables in Health Care: A Systematic Review. Mayo Clin. Proc. Digit. Health 2024, 2, 299–317. [Google Scholar] [CrossRef] [PubMed]
  82. Souza, M.; Miyagawa, T.; Melo, P.; Maciel, F. Wellness Programs: Wearable Technologies Supporting Healthy Habits and Corporate Costs Reduction. In Communications in Computer and Information Science; Springer International Publishing: Berlin/Heidelberg, Germany, 2017; pp. 293–300. ISBN 978-3-319-58752-3. [Google Scholar]
  83. Lewy, H. Wearable Technologies—Future Challenges for Implementation in Health Care Services. Healthc. Technol. Lett. 2015, 2, 2–5. [Google Scholar] [CrossRef] [PubMed]
  84. Leenen, J.P.L.; Leerentveld, C.; van Dijk, J.D.; van Westreenen, H.L.; Schoonhoven, L.; Patijn, G.A. Current Evidence for Continuous Vital Signs Monitoring by Wearable Wireless Devices in Hospitalized Adults: Systematic Review. J. Med. Internet Res. 2020, 22, e18636. [Google Scholar] [CrossRef]
  85. Rost, K.; Smith, J.L.; Dickinson, M. The Effect of Improving Primary Care Depression Management on Employee Absenteeism and Productivity. Med. Care 2004, 42, 1202–1210. [Google Scholar] [CrossRef] [PubMed]
  86. Mattison, G.; Canfell, O.; Forrester, D.; Dobbins, C.; Smith, D.; Töyräs, J.; Sullivan, C. The Influence of Wearables on Health Care Outcomes in Chronic Disease: Systematic Review. J. Med. Internet Res. 2022, 24, e36690. [Google Scholar] [CrossRef]
  87. Kuratomi, D.; Shin, C.; Duffy, V.G. Systematic Literature Review on the Advances of Wearable Technologies. In Lecture Notes in Computer Science; Springer Nature: Cham, Switzerland, 2023; pp. 78–95. ISBN 978-3-031-48046-1. [Google Scholar]
  88. D’ettorre, G.; Greco, M. Healthcare Work and Organizational Interventions to Prevent Work-Related Stress in Brindisi, Italy. Saf. Health Work 2015, 6, 35–38. [Google Scholar] [CrossRef] [PubMed]
  89. Haas, E.J.; Cauda, E. Using Core Elements of Health and Safety Management Systems to Support Worker Well-Being During Technology Integration. Int. J. Environ. Res. Public Health 2022, 19, 13849. [Google Scholar] [CrossRef]
  90. Ruotsalainen, J.; Verbeek, J.; Mariné, A.; Serra, C. Preventing Occupational Stress in Healthcare Workers. Cochrane Database Syst. Rev. 2015. [Google Scholar] [CrossRef]
  91. Crotty, A.; Killian, J.M.; Miller, A.; Chilson, S.; Wright, R. Using Wearable Technology to Prevent Pressure Injuries: An Integrative Review. Worldviews Evid.-Based Nurs. 2023, 20, 351–360. [Google Scholar] [CrossRef]
  92. Santoro, A.; Di Prinzio, R.R.; Camisa, V.; Vinci, M.R.; Dalmasso, G.; Dosi, A.; De Santis, M.; Macina, N.M.; Raponi, M.; Mesolella, V.; et al. P-596 the Evaluation of Workplace Disability Management Program for Healthcare Workers in the Total Worker Health® Approach: The Return on Investment. Occup. Med. 2024, 74. [Google Scholar] [CrossRef]
  93. Noben, C.; Evers, S.; Nieuwenhuijsen, K.; Ketelaar, S.; Gärtner, F.; Sluiter, J.; Smit, F. Protecting and Promoting Mental Health of Nurses in the Hospital Setting: Is It Cost-Effective from an Employer’s Perspective? Int. J. Occup. Med. Environ. Health 2015, 28, 891–900. [Google Scholar] [CrossRef]
  94. Slight, S.P.; Franz, C.; Olugbile, M.; Brown, H.V.; Bates, D.W.; Zimlichman, E. The Return on Investment of Implementing a Continuous Monitoring Syste m in General Medical-Surgical Units. Crit. Care Med. 2014, 42, 1862–1868. [Google Scholar] [CrossRef] [PubMed]
Healthcare 13 02289 g001
Figure 1. PRISMA flow diagram illustrating the study selection process for the scoping review.
Figure 1. PRISMA flow diagram illustrating the study selection process for the scoping review.
Healthcare 13 02289 g001
Table 1. PICOS framework summarizing the population, interventions, comparators, outcomes, and study designs included in the scoping review.
Table 1. PICOS framework summarizing the population, interventions, comparators, outcomes, and study designs included in the scoping review.
Population (P)Healthcare workers, including nurses, physicians, hospital staff, and allied health professionals, employed in clinical and hospital settings. These individuals operate in high-risk occupational environments characterized by psychosocial stressors, absenteeism, and the need for continuous service delivery.
Intervention (I)Implementation of wearable technologies (e.g., physiological sensors, biometric patches, smart glasses, real-time location systems, exoskeletons, and continuous monitoring devices). The intervention focuses on enhancing occupational health, staff safety, and organizational resilience through continuous monitoring, predictive analytics, and integration with occupational health protocols.
Comparison (C)Standard occupational health and safety practices without the use of wearable devices, or alternative types of wearable technologies/implementation strategies.
Outcomes (O)
  • Primary outcomes: Improvements in staff safety and occupational health, reductions in stress and burnout, enhanced organizational resilience, and business continuity.
  • Secondary outcomes: Reduction in absenteeism and presenteeism, improvements in productivity, acceptance and adoption rates of wearable technologies, user satisfaction, and economic/financial implications of implementation.
Study design (S)All study designs were eligible, including experimental (e.g., randomized controlled trials, pilot studies), observational (e.g., cohort, cross-sectional, longitudinal), qualitative, and mixed-methods studies.
Table 2. General characteristics of the 32 studies included in the scoping review, including setting, study design, population, type of wearable technology, and primary outcomes.
Table 2. General characteristics of the 32 studies included in the scoping review, including setting, study design, population, type of wearable technology, and primary outcomes.
StudyStudy DesignHealthcare SettingTechnology TypePrimary OutcomesAdoption RateUser SatisfactionImplementation ChallengesMeasured ImpactCost ImplicationsImplementation Requirements
Agarwal et al., 2024 [45]Pilot, LongitudinalAcademic, urban, level 1 trauma center (US)WHOOP bandDevice use, acceptance, burnout, HRV, sleep50% consistent useResidents more engagedDevice features, engagementDevice use, mild anxiety, low fulfillmentNo economic analysisDevice optimization
Paradiso et al., 2024 [46]Pilot studyNo mention foundWearable Wellness System (WWS) by SmartexHeart rate (HR), HRV, stress, postureNo mention foundDesigned for comfortNo mention foundStress, postureNo economic analysisComfortable design
Shishavan et al., 2022 [47]Pilot, LongitudinalIntensive care unit, no mention foundMultiparameter armbandHRV, pulse transit time (PTT), stressHighWell acceptedNo mention foundStress detectionNo economic analysisHigh signal quality
Clingan et al., 2021 [48]Longitudinal, PilotAcademic institution (Michigan, US)Fitbit Charge 3, TempTraqFeasibility, HR, temperature, COVID-19 detectionNo mention foundNo mention foundInternet access, digital literacyFeasible, mood/symptom trackingNo economic analysisDigital literacy
Keller et al., 2021 [49]Pilot studyInpatient unit, no mention foundProximity beaconsNo mention foundNo mention foundNo mention foundFeasibility, physical distancingNo mention foundNo economic analysisFeasibility
Ghosh et al., 2023 [50]Pilot studyNo mention foundMUSE-S™Stress, resilience, quality of life (QoL), cognitionHigh91.9% felt relaxed, 73% would continueNo mention foundImproved stress, resilienceNo economic analysisEase of use
Park and Lee, 2025 [51]Mixed-methods, pretest-posttestEmergency departmentSmart glassesCommunication clarity, workloadNo mention foundImproved communicationUser interface, privacy, workloadImproved clarity, workloadNo economic analysisTraining, privacy
Weenk et al., 2019 [52]Randomized controlled trial (RCT)University hospital (Netherlands)ViSi Mobile, HealthPatchPsychological distress, usabilityNo mention foundGenerally positiveDevice size, trainingPositive, reduced workloadReduced workload, costsTraining, monitoring
Kooij et al., 2022 [53]QualitativeTeaching hospitals (Netherlands)Philips Biosensor, SensiumPatchNo mention foundNo mention foundMixedSensor quality, Wi-Fi, workloadMixed, reduced staysReduced stays, home recoveryInteroperability
Scott, 2008 [54]Case study, qualitativeDementia care homes (UK)Location-tracking devicesNo mention foundNo mention foundNo mention foundPrivacy, alarms, trainingEnhanced safetyNo economic analysisStakeholder involvement
O’Dwyer et al., 2024 [55]QualitativePediatric hospital (Canada)No mention foundNo mention foundNo mention foundHigh satisfactionRisk assessment, staffingPositive, feasibleNo economic analysisHuman-centered design
Rosiello et al., 2021 [56]Pilot, RCTMultiple hospitals, no mention foundWireless Vital Parameter Monitoring (WVPCM) systemClinical-economic impactNo mention foundNo mention foundNursing time reductionReduced complications, readmissions€54-90 saved per patientNursing time reduction
Schonck et al., 2025 [57]Observational, LongitudinalSurgical ward, no mention foundHealthdot (Philips)Workload (Individual Workload Scale), implementation outcomesNo mention foundAssessed via System Usability Scale,
Evidence-Based Practice Attitude Scale		Workload, complexityNo mention foundNo economic analysisEfficiency, workload
Joshi et al., 2021 [58]Cross-sectionalAcademic hospital (UK)SensiumVitalsNo mention foundNo mention foundHigh engagementAlerts, workloadNo mention foundNo economic analysisAlerts, comfort
Iqbal et al., 2021 [59]Pre-post, ObservationalNational Health Service (NHS) hospital (UK)SensiumVitalsClinical outcomesNo mention foundNo mention foundNo mention FoundNo mention foundNo economic analysisIntegration, training
Brewster et al., 2014 [60]Mixed-method reviewNo mention foundTelehealthNo mention foundNo mention foundNo mention foundService change, technicalNo mention foundNo economic analysisService change
Iqbal et al., 2022 [61]Observational cohortSurgical unit (UK)SensiumVitalsClinical outcomesNo mention foundFavorableAlert fatigue, workflowNo mention foundNo economic analysisWorkflow, policies
Patel et al., 2022 [62]Mixed methodsGeneral Medicine (Canada)Fitbit Charge 2/3No mention foundNo mention foundPatients positive, nurses mixedDevice design workflowNo mention foundNo economic analysisDevice design
Santomauro et al., 2019 [63]Pilot studyRural/regional hospitalsNear-field display devicesNo mention foundNo mention foundPositiveFunctionality preferencesNo mention foundNo economic analysisUsability testing
Shah et al., 2021 [64]Case study, qualitativeAcademic hospital (Canada)Medly (mobile)No mention foundNo mention foundNo mention foundSiloed systems workflowNo mention foundNo economic analysisLeadership, connectivity
Buss et al., 2023 [65]Qualitative evaluationAcademic hospital (UK)VitalPatch, WristOx2No mention foundNo mention foundSupportive over timeDevice numbers contactSupportive, workloadNo economic analysisDevice numbers
Meltzer et al., 2020 [66]Case seriesAcademic hospital (US)IMUsErgonomic riskNo mention foundEncouragedSample sizeRisk factors identifiedNo economic analysisMindfulness
Claudio et al., [67]Pilot studyEmergency departmentsWireless sensor systemNo mention foundNo mention foundHighNo mention foundPositiveNo economic analysisUser feedback
Carreon and Dutra, 2020 [68]Observational no mention found)No mention foundHands-free communication deviceNo mention foundNo mention foundNo mention foundCommunication failuresImproved workflowNo economic analysisInfrastructure
Yesmin et al., 2022 [69]LongitudinalHospital ward (Canada)No mention foundPatient safety outcomesNo mention foundPositiveTraining, device issuesImproved care timeNo economic analysisTraining, device
Joshi et al., 2022 [58]Interview studyAcademic hospital (UK)SensiumVitalsNo mention foundNo mention foundHigh engagementEquipment, staffNo mention foundNo economic analysisComfort, future
Jones et al., 2023 [70]Pilot study (no mention found)Academic medical center (US)Real-time location system (RTLS)No mention foundNo mention foundNo mention foundNo mention foundImproved safetyNo economic analysisReal-time location system rollout
Jovanov et al., 2011 [71]Pilot studyAcademic nursing (US)UAHealth (iPhone, Ant+)Stress index, HRVNo mention foundFacilitated by user interfaceNo mention foundStress index, heart rate variabilityNo economic analysisUser interface, support
Hanna, 2020 [72]Qualitative studyMultiple (US)Multiple sensor typesNo mention foundNo mention foundNo mention foundCost, communicationNo mention foundHigh cost barrierCollaboration
Mclean et al., 2024 [73]Qualitative implementationTertiary hospitals (UK)No mention foundNo mention foundNo mention foundMixedInformation technology, workloadNo mention foundNo economic analysisInformation technology, strategy
Lokmic-Tomkins et al., 2025 [74]Cross-sectionalClinical settings (Australia)No mention foundNo mention foundNo mention foundNo mention foundInfrastructure, supportNo mention foundNo economic analysisInfrastructure, support
Table 3. Summary of included studies according to the PICOS framework.
Table 3. Summary of included studies according to the PICOS framework.
ElementSummary (Bullet Points)
Population (P)• Healthcare professionals (nurses, physicians, residents, allied health staff, support staff). • Predominantly hospital and clinical settings (e.g., ICUs, surgical wards, EDs, academic hospitals). • Smaller number in community care, dementia homes, pediatric hospitals.
Intervention (I)• Continuous physiological monitoring systems (e.g., SensiumVitals, Philips Biosensor/Healthdot, ViSi Mobile/HealthPatch, WVPCM). • Consumer-grade wearables (Fitbit, WHOOP, MUSE-S™) • Specialized technologies (smart glasses, proximity beacons, real-time location systems, exoskeletons, wireless sensors).
Comparison (C)• Standard occupational health and safety practices. • Alternative wearable devices or implementation strategies. • Limited number of formal comparative analyses.
Outcomes (O)• Primary: Staff safety, occupational health, stress/burnout reduction, organizational resilience, workflow efficiency. • Secondary: Absenteeism/presenteeism, productivity, adoption and satisfaction, usability, cost implications.
Study Design (S)• Pilot/feasibility studies (~40%). • Observational cohort or cross-sectional (~25%). • Qualitative (~15%). • Mixed-methods (~10%). • RCTs and case studies/series (<10%).
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.

Share and Cite

MDPI and ACS Style

Virgillito, D.; Catalfo, P.; Ledda, C. Wearables in Healthcare Organizations: Implications for Occupational Health, Organizational Performance, and Economic Outcomes. Healthcare 2025, 13, 2289. https://doi.org/10.3390/healthcare13182289

AMA Style

Virgillito D, Catalfo P, Ledda C. Wearables in Healthcare Organizations: Implications for Occupational Health, Organizational Performance, and Economic Outcomes. Healthcare. 2025; 13(18):2289. https://doi.org/10.3390/healthcare13182289

Chicago/Turabian Style

Virgillito, Daniele, Pierluigi Catalfo, and Caterina Ledda. 2025. "Wearables in Healthcare Organizations: Implications for Occupational Health, Organizational Performance, and Economic Outcomes" Healthcare 13, no. 18: 2289. https://doi.org/10.3390/healthcare13182289

APA Style

Virgillito, D., Catalfo, P., & Ledda, C. (2025). Wearables in Healthcare Organizations: Implications for Occupational Health, Organizational Performance, and Economic Outcomes. Healthcare, 13(18), 2289. https://doi.org/10.3390/healthcare13182289

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop