The decrease in nursing staff with the simultaneous increase in patients with multiple morbidities in need of care means an increase in workload of the remaining nursing staff. The digitization of health care in theory should help to counteract this change and its consequences. However, in Germany in particular, the process is proceeding very slowly; Germany is ranked 16th out of 17 countries in the Bertelsmann Digital Health Index [1]. The application of digital health technology (DHT) is an important factor in the digitalization process. DHTs in the context of this review means technologies that are directly linked to outpatient and inpatient care and are implemented by nurses or physicians. By DHT, we mean, for instance, health information systems (HISs), medical devices, and other digital applications that support patient care from the perspective of health care professionals (HCPs).

In addition to the positive effects of the use of DHT, there is also evidence which suggests that its use can cause extra workload [2] and can consequently have a negative impact on HCPs’ health [3]. However, it remains ambiguous which factors are specifically responsible for a high mental workload (MWL) during the use of DHT. Initial results show that this may be because of a lack of usability and user involvement as well as poor implementation processes [4,5].

Poor usability and other factors rooted in technology can cause a high MWL [5]. High workloads can cause errors independent of the operators status (novice or expert). Those errors often results form decision-making processes. [6]. When working with patients, however, susceptibility to errors as well as indecisiveness cannot be an option. Working in outpatient and inpatient care can be considered as working in safety-critical environments. Many tasks, varying in complexity, occur within limited time windows. Decisions could be supported by different DHTs through the structured and standardized presentation of information.

The interaction between the users and the systems is complex and interdependent, which contributes to difficulties in the prediction of effects related to the systems on the users [7].

Wickens et al [8] give a good practical example for this effect. During surgery, different complex tasks have to be performed by the surgeon in addition to observing the patient. In the event of a sudden change in the patient's vital signs, which can be potentially life-threatening, the surgeon has to promptly take an appropriate decision on how to proceed. Complex demands could result in an overload if they exceed the capacity of attentional resources [7]. Consequences of overload are an increasing vulnerability to errors and decreasing performance. In addition to serious consequences for patients, an overload also has drastic effects on employees. High workloads caused by several factors (including technology) result in consequences regarding the workers’ health; technostress, mental health issues such as depression or burnout, and decreased job satisfaction are only a few of the alarming effects [9]. There is growing evidence that DHTs are contributing to increasing mental health problems, (eg, burnout of health care workers [10,11]). The investigation of MWL in different situations is a possible approach toward identifying the main causes behind, for example, emerging incidences of burnout in physicians and nurses [12].

MWL can be defined using different approaches and is usually influenced by different and multiple factors. It is multidimensional and multifaceted and is one of the most important variables for understanding and predicting human performance.

The possible definitional approaches of workload can be derived from two different perspectives:

Eggemeier et al [15] define MWL as the “proportion of the operator’s information processing capacity or resources that is actually required to meet system demands.” Gopher and Donchin [16] state that “mental workload may be viewed as the difference between capacities of the information processing system that are required for task performance to satisfy performance expectations and the capacity available at any given time.” They define MWL as a latent variable relating to the interaction between the operator and the task. As per Proctor [6], the definition of MWL is “a task [that] represents the level of attentional resources required to meet both objective and subjective performance criteria, which may be mediated by task demands, external support and past experience.”

In summary, there is no all-encompassing, universally accepted definition of MWL. We define MWL as a construct that addresses the influence of task demands on operator resources resulting in an impact on psychological factors such as performance (Figure 1) but not in the sense of stress or acceptance.

Especially during work, inadequate workload results in poorer performance [18]. Following the above definitions, a high workload can either be caused by unsuitable task requirements or by limited resources that are available in a cognitive manner, for example certain parts of the brain. The aim of measuring MWL is to determine the tasks and work processes that cause adverse or inappropriate levels of demands to draw conclusions about user performance as well as error prevention. Furthermore, the measurement of MWL can help identify factors that cause consequences such as technostress or burnout among nurses and physicians [10].

MWL assessment was first developed and applied in other safety-critical environments such as aviation or aerospace or nuclear power plants. Owing to similar conditions—already described—in the sociotechnical system, workload assessment is also a useful approach in the clinical setting.

The assessment of MWL can be performed by different techniques. A distinction between analytical and empirical methods may be drawn. Analytical methods tend to be used in system development, while empirical methods are used when workload is to be measured directly in the executing system or in the simulation [13].

Analytical assessment methods are simulation models, expert opinions, or task analyses. Empirical methods are distinguished into three different categories: performance measures, subjective methods, and physiological techniques [6]. Performance measures refer to the measures of the primary and a secondary task.

Depending on the situation and the underlying question, one or more of these techniques are appropriate to apply. Several factors should be considered when selecting assessments, including sensitivity, diagnostic ability, intrusiveness, validity, reliability, simplicity of use, and user acceptance [19].

Tao et al [20] analyzed the physiological assessment of MWL across different application areas. One main result was that MWL assessments were not essentially valid in all areas, for example, for all tasks and differed in their validity.

Charles and Nixon [21] provide an overview of physiological measures that discriminate between different MWL levels. They detect varying ranges in the sensitivity of these measures but provide an evidence base for their deployment.

These reviews concentrate on physiological measures, not on all possible assessments. Although physiological measures are gaining relevance in the field of MWL assessment, methods that can be applied quickly and easily can still probably be helpful, especially in the health care sector.

The workload in health care institutions is high. A possible factor contributing to high workloads could be the use of DHT.

MWL is possibly the construct that can reflect best the workload caused by technologies. There is only light evidence for causes of MWL related to DHT. One reason might be that the health care sector has not been in the spotlight for researchers of human factors until now. To our knowledge, there currently is no review of the measurement methods for MWL caused by DHT.

As a primary objective, this systematic review intends to identify the impact of digital technologies, particularly HIS, on the workload of health care workers.

There are specific reviews investigating physiological methods assessing MWL as well as several papers studying the MWL in health care in general. We aimed to present a broader approach by looking at all methods that were used in the defined field while providing a more specific approach in focusing on DHT in particular, thus differing from already existing reviews to this topic [20,21]. We concentrated on a review of applied methods as well as their quality criteria. In addition (as secondary objectives), we aimed to assess what methods are being applied in health care to measure MWL relating to DHT. In particular, the application of eye tracking or pupillometry as a measurement method was investigated.

The research questions for this study are as follows:

Many different factors have led to a significant increase in workload in the health care sector in the past few years [22]. Work-related stress has become one of the main challenges in the health care sector [23]. Nurses in particular report high levels of work-related stress that lead to negative physical and psychological effects for them as well as for their patients [24]. Many nurses describe themselves as feeling empty and report depressive symptoms [25,26]. In Germany in particular, the number of days of sick leave taken by nurses is increasing every year. In addition to musculoskeletal diseases, which account for the majority of sick leaves, absences because of mental illness are increasing significantly [27]. The past two years (2020-2021) brought about many other challenges as well.

This systematic review is registered with PROSPERO (International Prospective Register of Systematic Reviews; CRD42021233271) and follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines [28].

We defined the inclusion criteria for this systematic review according to the population, intervention, comparison, outcome, context scheme and the corresponding research question or questions. The inclusion criteria related to the study population, measurement type (intervention or comparison), the outcome of the study, and the study setting (context). An additional inclusion criterion related to study design.

The primary outcome of this systematic review was to analyze the influence of DHT on the MWL of HCPs and medical or nursing students.

Secondary outcomes included the types of assessments that are applied to measure MWL related to DHT. Additionally, we examined the impact of eye tracking on the measurement of MWL related to DHT.

The following databases were systematically searched between January 20, 2021, and February 28, 2021, by using defined keywords (and synonyms) such as “mental workload,” “health information system,” “assessment,” “health care professionals” and “eye tracking” that result in specified search strings (the block chain is shown in Multimedia Appendix 1): MEDLINE (PubMed), Web of Science, Academic Search Premier and CINAHL (EBSCO), and PsycINFO. In addition, we searched for relevant research in the reference sections of included studies as well as of relevant recently published reviews. The keywords were defined by reviewing thesaurus systems such as Medical Subject Headings, expert opinions, and reviews of relevant studies.

We updated our search in February 2022 by replicating this process.

Following PRISMA, we organized the search terms by database and research question in a separate document [28]. We have attached this document (Multimedia Appendix 2)

The search strategy included the following four categories, each represented by keywords and synonyms: technologies used (eg, HIS), population (eg, HCPs), methods (eg, assessment), and MWL. In addition, eye tracking was added for research questions 2.1. and 2.2. The terms are linked by the Boolean operators AND or.

We restricted our search to articles published in the period between 2000 and 2022. This search time frame was chosen because it documents the development of the current generation of prehospital communication technology, such as telemedicine and electronic patient care reports [29]. The literature search was limited to articles written in English or German, as both reviewers were sufficiently proficient in these languages.

Two evaluators independently rated the quality of the identified studies using the QualSyst Scale [31]. Disagreements were resolved via discussion (among LK and BB) or, if necessary, resolved by a third reviewer (ML and RR).

Studies were rated using a structured tool (comprising 14 items). If a study completely fulfilled a criterion, it was assigned 2 points. In case of partial fulfillment, 1 point was assigned. If the criterion was not fulfilled by the study, no point was assigned to the study.

If a criterion was not applicable to the study presented (eg, blinding of the investigator), it was removed from the assessment. The achieved points as a percentage of the possible total points were evaluated as per the following criteria: a score of <0.5 by both reviewers resulted in exclusion, studies with scores between 0.5 and 0.65 were classified as having a moderate risk of bias, and studies with scores >0.65 were classified as having a low risk of bias.

LL and BB read the full texts and extracted information concerning identified and relevant aspects of the studies. We differentiated main study characteristics, measurements, and outcomes from relevant findings and recommendations.

In addition to the descriptive presentation of study characteristics and findings, we aimed to extract factors or aspects of DHT that contributed to an increase in MWL. Furthermore, we extracted information on how the included studies assessed the workload and in which settings eye tracking was used with regard to specific outcomes. On the basis of this, we developed an overview of the methods that can be used to measure MWL caused by DHTs meaningfully and validly. Furthermore, we assessed the studies concerning the categories of types of DHT and factors that contribute to a lower MWL.

The methods, settings, and outcomes were organized into logical categories that were rated by the reviewers. The typical categories of methods referring to MWL assessments were analytical or empirical techniques. Typical categories for settings were laboratory or field. Categories referring to assessed outcomes have to be defined during the reviewing process. In each category, we extracted how often an indicator for a category was applied (eg, category % = method applied/N studies) and how often combinations of specific indicators were used (eg, for total percentage with method A with setting B and outcome C, total % = combination applied/N studies). A typical indicator for category methods would be a questionnaire or subjective method. If an indicator was identified, the reviewers filled in the row with a 1; if no indicator was identified, for example, if the method was not applied, the table was filled in with a 0.

After the initial screening of the search results, we did not conduct a meta-analysis because the results and quantifications of the measures varied widely. Instead, we performed descriptive analysis to summarize the data, in which we first compared the studies in terms of the evaluation methods used (qualitative, quantitative, or mixed methods) and then performed a comparison of their survey methods.

We used the following two nonquantitative approaches for data synthesis: tabulation and a narrative approach.

In a first step, all main characteristics of each study were extracted (study design, the setting of the target population such as a hospital, sample size, age, sex, and population type such as physicians). We only included studies with a sample size of under 20 participants provided that the risk of bias was adequate [32].

We analyzed studies in terms of objectives, outcomes, and assessments as well as types of DHT. The quality criteria of assessments and information regarding the application of eye tracking as well as outcomes assessed via eye tracking were extracted. Differing from our protocol. we did not assess data on overall MWL in studies in addition to MWL levels related to DHT because the studies did not contain this information.

All included studies were evaluated with regard to their risk of bias.

A textual narrative synthesis of all included studies was made and comparable findings were synthesized. In addition, a descriptive analysis of eye tracking measures was extracted.

In the ongoing process, we had to perform a few amendments.

Contrary to what was defined in the protocol to this review, research questions 1.1 and 1.2 were not substituted to this final paper [32]. Deviating from the protocol’s attempt, we decided to use a different assessment tool to evaluate the risk of bias (QualSyst, [31]). In contrast to our protocol, we also included studies with a sample size under 20 participants under the condition that their risk of bias was adequate. Deviating from our protocol. we did not assess data on overall MWL in studies as well as MWL levels related to DHT because the studies did not contain this information.

The database search resulted in 7952 hits. Additional searches in the bibliographies of the identified publications and through discussions with experts yielded 152 more search results (N=8104). After removal of duplicates, 6122 (75.54%) publications remained in the review process. On the basis of the title and abstract screening, 6003 (74.07%) publications were excluded. Of the remaining 117 (1.4%) that were included in the full-text analysis, 72 (62%) were excluded for the following reasons: another concept of stress than defined in our paper (eg, technostress) was used, DHT was not part of the study, the study outcome was not workload, the paper was not an original work, the scope of the paper was alert-related workload, the population consisted of patients, it was a scoping or narrative review, there was no health care setting, or the full text was not available. In total, 46 (0.6%) studies were included in the qualitative synthesis and assessed for risk of bias. Of these, 17 (37%) studies were excluded because of their high risk of bias. The systematic search and the search strategy that followed resulted in 25 included studies (Figure 3).

In total, 17 (37%) studies had a high risk of bias and were therefore excluded from the review because of scores <0.5.

A total 15 (33%) studies had scores between 0.5 and 0.65 and were therefore considered to have a moderate risk of bias. Furthermore, 10 (22%) studies had a low risk of bias (as shown in Multimedia Appendix 3; interrater agreement on scoring was r=0.91; P=.01).

Discrepancies in scoring generally resulted in different scores for item 1 (objectives) or 7 (blinding).

The main characteristics of the included studies are displayed in Table 1. Most studies were published between the years 2010 and 2022 [33-54]. Only 2 studies were published between the years 2002 and 2009 [55,56]. Most studies were conducted and published in the United States [33,35,37-40,42-50,52,53,55,57].

Most studies were carried out in laboratory or simulation settings [33,35,36,38-43,46-50,52,55,56], a few were done in field settings [37,45,54,57], and some were conducted only on the web [34,44,51].

A total of 10 studies were quasi-experimental [33,36,40,41,46,47,49,50,55,57], 8 were cross-sectional [34,37,39,44,48,51,54,56], 2 were observational [38,53], 1 was a longitudinal design study [45], and 4 were RCTs [35,42,43,52].

The included participants consisted of physicians (14 studies [33-35,37-39,42-44,46-52,54,56,57]), nurses (4 studies [40,45,53,55]), and medical or nursing students (1 study [41]) as well as mixed populations out of these 3 groups (6 studies [36,37,42,47,50,57]). The sample size in most included studies ranged from 10 to 50 participants [33,35,36,38-40,42,43,46-50,52-57], 1 study ranged from 50 to 100 participants [34], and 5 studies included >100 participants [37,41,44,45,51]. Furthermore, 16 studies reported times of experience with DHT [33,34,36,40,42,43,45,48,49,51-53,55-57].

The included studies did not apply a homogenous definition approach for MWL: 13 studies did not provide a definition of their underlying concept at all [35,36,38,40-43,45,50,52-54,57], 2 studies applied a classic definition of MWL [37,51], 3 studies defined MWL as mental effort [34,46,47], 2 as information overload [33,39], and 5 studies applied a definition of cognitive load [41,44,48,49,56]. All the applied definition had a common base that could be summed up under the concept of MWL that we defined for inclusion.

The analyzed types of DHT were grouped into one of six categories as appropriate electronic health records or electronic medical records (EMRs), computerized decision support systems, information display or vital sign display, e-prescribing systems, anesthesia system, and computerized clinical reminders. More than half of the studies (13/25, 52%) analyzed electronic health records or EMRs.

Studies with various outcomes reflecting the association of DHT and MWL were included.

Overall, 20 (83%) of the included studies investigated the MWL related to DHT in general [33,38,40,53,54,56], 8.33% (2/25) compared MWL before and after redesign of DHT [51,52], and 12.5% (3/25) of the studies analyzed MWL before and after implementation of a new DHT [37,50,55]. A further 12.5% (3/25) of the studies compared MWL among different DHT or systems [34,35,40].

Furthermore, 33^,33% (5/25) of the included studies investigated the relationship between the usability of the DHT and MWL [39,43,44,48,57], 16.67% (4/25) assessed MWL related to task demands and performance during the use of DHT [39,42,46,47], 8.33% (2/25) of the studies examined the influence of decision support on MWL [41,49], and 4% (1/25) examined other influences [36].

The included studies identified various factors of the systems that contributed to the MWL of HCPs. Some factors were rooted in the systems themselves; other factors were caused by influences and circumstances on an organizational level. We grouped the results by organizational and system-related factors (Figure 2).

A total of 8 studies identified the task to be performed by the use of the DHT as the relevant factor that contributes to an increasing MWL [34,36,37,41,42,47,50,54,56]. In all cases, the tasks did not fit the processes already implemented in the system.

Of these, 2 studies stated the overall workload in the working environment as the contributing factor [53,54]: the higher the general workload, the higher the MWL related to DHT.

Other relevant organizational factors that were identified by a study was the amount of time since implementation [45]: the longer a system was implemented, the lower the MWL, which initially increased significantly immediately after implementation.

In addition to direct influences, a study examined mediating factors and specifically identified gender and total hours worked. Women, as well as those who worked fewer hours, had a smaller increase in MWL from the DHT [44,57].

In addition to organizational factors, most studies (23/25, 92%) identified factors based predominantly in the underlying system of the DHT.

A total of 4 studies cited weaknesses in the interface design as main factors for an increasing MWL of HCPs [39,48,50,51].

In addition to the interface design, 6 studies identified deficiencies in the usability as an influencing factor for increasing workload [39,43-45,50,51]. Studies refer to longer task completion times, higher error rates, a higher number of clicks, and differences in usability ratings between men and women (women contributed to higher rankings) [39]. There were also reports of less MWL because of automatically sorted and displayed test results in an electronic health record [43] and a significant correlation between MWL and usability [44].

A further 5 studies identified nonfunctioning decision support as a critical factor in increasing MWL [35,41,47,49,56], and 4 studies detected the organization of data and information as influencing factors [33,36,38,56].

A study showed that integrated displays cause less MWL than nonintegrated traditional displays [40]. In addition to precisely identifiable factors, 2 studies indicated that high MWL is particularly because of system functionality of the system in itself [34,39].

The combination of setting and applied measure that was detected in most cases was a laboratory setting combined with a subjective measurement method. Further, it can be identified that the outcome relationship between MWL and usability related to DHT measured by subjective methods or performance measures in the laboratory was established in most cases. Other frequently applied combinations were subjective method, MWL related to DHT, decision support, usability, system comparison, or other as well as physiological measures combined with task demands or other, in the laboratory. The results of the combinations of settings, assessments, and outcomes are displayed in Multimedia Appendix 4.

This systematic review investigated 25 studies that applied various measurement methods to assess the MWL related to DHT. The aim of the review was to show which factors of DHT contribute to a high MWL for HCPs in health care settings. In addition, the review was intended to identify methods that are currently used to measure MWL in health care. In this context, the role of eye tracking as a measurement method in particular was considered.

The following aspects can be considered the most relevant while summarizing the main results:

In particular, the negative effect of DHT on MWL in health care was consistent across studies. At the same time, DHT could support HCPs, but it must fulfill different criteria to achieve this. In addition to the system-related factors, organizational issues contribute to the influence of DHT on high MWL.

Consistent with previous reviews, we identified the application of subjective measurement methods to be the most frequently used approach for the assessment of the MWL. [66]. Although we were able to identify a most frequently applied method, one of the main findings of this review was the heterogeneity of applied assessments, which is also in line with previous analyses [20,66]. Some studies used a combination of methods; for example, eye tracking and NASA-TLX. Reviews that investigate methods to measure MWL usually focus on 1 type of method, such as physiological measures [17,18], or a specific field of application (eg, driving distraction) [62]. The health care domain—although it can be seen as a safety-critical environment—was not the focus of these reviews. Charles and Nixon [21] included 58 studies in their review, none of which addressed MWL in health care. First, while other studies focused on nonhealth care domains, our review revealed methodological shortcomings in the health care area.

Second, our idea was to provide a holistic review of methods being used for the application of DHT in health care.

Previous reviews also checked for combined measure assessments; in line with our findings, Charles and Nixon [21] and Tao et al [20] found several studies that combined physiological measures and the NASA-TLX.

In contrast with our findings, Charles and Nixon [21] found many studies reporting quality criteria such as sensitivity and validity, also for physiological measures. However, they found differences for validity and sensitivity of measures comparing field and laboratory settings. This finding corresponds to the findings of Tao et al [20] and also partially to our findings.

Kabilmiharbi et al [22] reviewed studies concerning multiple driving distractions. In contrast to health care settings, MWL assessment during driving is mainly conducted via physiological or performance measures [63]. In line with our results, NASA-TLX was the most commonly used subjective assessment.

We identified 4 different eye tracking measures applied in the studies included in our review (fixation frequency, blink rate, pupil dilation, and visit frequency). Tao et al [20] identified blink rate, pupil diameter, and fixation duration as correlates of MWL, but—in contrast with our results—identified additional eye tracking measures that were relevant.

Besides a strong heterogeneity, a rather homogeneous approach with regard to the setting was revealed. This is equivalent to findings of Tao et al [20]. Most studies were performed in the laboratory. Outcomes differed marginally but were still differentiated for more discriminative analysis.

Factors contributing to MWL in health care can be identified as occupational or individual. Occupational factors can be level of education, type of working unit (eg, intensive care unit), work shifts, and number of patients under care [64]. Studies from other domains show, for example, an enhancement in situation complexity, task-related and individual factors as well as organizational factors such as time pressure as possible predictors of MWL [65]. However, none of the studies mentioned in this section explicitly addresses the relationship between MWL and HIS/DHT.

Many studies also consider MWL as a starting point for further consequences on the performance of the HCPs, for example, a hazard to patient safety or job satisfaction (66), rather than the factors contributing to a high MWL.

This review has some limitations with respect to the included studies.

First, because of the heterogeneity of the assessment methods, analyses, and study designs of the included studies as well as their methodological quality, a meta-analysis could not be conducted.

Second, many studies performed retrospective measurements of MWL that did not allow for causal conclusions in the results. The restriction of causality is further limited by nonreported quality criteria.

Third, the results as well as the review itself are further limited by the search process. Part of the results are aspects of factors that contribute to MWL related to DHT. These aspects were not explicitly searched for in the literature examination. It can therefore be assumed that not all relevant studies concerning these factors have been included. The search process can also be considered to be limited in the sense that it became apparent during the review process that many authors integrate the constructs of mental or cognitive workload into other constructs or refer to concepts similar to these. Other constructs that may follow a similar definition, such as mental effort, were not considered in this search. It can therefore be assumed that these studies were not included in the review.

The definition of the MWL construct was not consistent across the studies examined. In addition to MWL, stress, cognitive load, fatigue, and mental effort, and other similar concepts have been grouped under the term information overload and limited workload capacity resulting from perceptual load. However, other studies have developed their own concepts (eg, stress related to information systems) that mean slightly different things but include parts of the definition of MWL. Our results are limited in terms of not including these studies as they also included aspects of stress (eg, acceptance) that do not refer to the MWL classification that was relevant for our paper.

However, in order to develop a gold standard for measuring MWL in health care settings, it seems highly relevant to precisely define the construct. Identifying studies referring to a selective definition of MWL was therefore particularly challenging for this review. Because of the strong heterogeneity of the research field, we cannot eliminate the possibility that some studies were not included, which were not identified by our search terms because of variations in construct naming.

The combination of the different approaches toward the assessment of MWL also showed strong heterogeneity. Some of the methods—especially the physiological ones—require extensive preparation and equipment and are very time-consuming, particularly in their evaluation. Thus, not every method can be considered suitable for every setting (eg, in a clinical setting).

The approach of analysis in the laboratory seems understandable on the one hand, because content validity and reliability are easy to achieve. On the other hand, the small number of field studies ensures that results cannot be transferred to other settings easily (external validity) and that various bias effects at least partly due to presumably weak quality of the study implementation also led to erroneous results. This also applies to the generalizability across populations; therefore, studies referring to MWL of patients were not included.

The applied quality criteria assessment revealed shortcomings in methodological quality across many studies. There was only a small amount of studies with a quality rating of >65% (10 studies [37,39,41-44,49,50,54-56]). However, a possible explanation for such a low rate might be that many of the remaining studies could be regarded as first or exploratory approaches.

Most studies did not report quality criteria such as content validity or reliability. Reliability indicates the degree to which an assessment can differ between high and low workloads [67]. Content validity refers to the degree to which an assessment reflects all aspects of MWL [67]. Studies that reported reliability measures reported acceptable to high levels of internal consistency of the assessments. Studies that reported content validity reported moderate levels of internal consistency of the assessments. To develop a gold standard in the assessment of MWL in health care, the reporting of quality criteria as indications for the quality of a measurement method is essential.

Studies that were not published in full text or in English were excluded; consequently, additional information on measurement properties and descriptions of methods for assessing masticatory performance that may have potentially affected the level of evidence might have been missed.

All included papers were published in the period between 2002 and 2022; the literature search was limited to papers with publication years between 2000 and 2022.

We detected an increase in the 2010s that could give a hint regarding the increasing interest in the topic during this time. On the other hand, the term MWL, as already described, was not defined in as much detail as it should have been. Therefore, the detected increase could have also been produced by more specific definitions in the last years.

In addition, it is possible that we did not find all relevant articles, despite having thoroughly defined which terms to include and having conducted a systematic search using Medical Subject Heading terms

Our results show a very heterogenic approach toward the assessment of MWL related to DHT in health care settings. Although the assessments are heterogeneous, it can be assumed that there are 2 groups of contributing factors to MWL related to DHT, factors rooted in the system itself and organizational factors such as the task for which the system is being used.

When it comes to implementing or applying already implemented DHT in health care, these factors should be considered holistically.

The following steps should be taken for implementing and developing a gold standard and conducting future research in this field of study:

Future research is required to further investigate the relations between factors that might be contributing to MWL while using a DHT and MWL in general. Our results show a first step forward for grouping these factors. However, further primary research and review work is necessary for the development of a theoretical framework.

Our review of 25 papers shows a diverse assessment approach toward the MWL of HCPs related to DHT as well as 2 groups of relevant contributing factors to MWL. The most frequently applied method has been the NASA-TLX (subjective measurement approach) in laboratory settings. The contributing factors can be divided into system-related factors and organizational factors.

Our results show a few new approaches being used for assessing MWL in relation to systems in a valid, reliable and practical way; eye tracking could be one of these measurement techniques.

Although methodological biases were identified, we recommend further research concentrating on adequate assessments of MWL of HCPs for relevant settings. We would also like to recommend the evaluation of quality criteria.