## Under Pressure? A Study of Heart Rate and Heart-Rate Variability Using the SmarTerp CAI tool

## Abstract

The results of a quasi-experimental, intra-subject study are reported on the effects of the use of SmarTerp on physiological stress levels of twelve second-year students of the MA in Interpreting at the University of Bologna during a simultaneous interpreting task. The study, part of a broader project, explores the rendition of terminological units, proper names, and numbers  and  its  correlation  with  stress  levels,  to  provide  insights  into  SmarTerp's  practical  usefulness  in  the  field. Physiological  stress  levels  were  measured  through  heart  rate  and  heart-rate  variability  indicators  with  Empatica  E4 wristbands.  Participants  took  part  in  three  data-collection  sessions  over  a  month.  In  sessions  1  and  3  the  participants interpreted two speeches, one with SmarTerp and another one without it. Descriptive findings hinted at a potential stressalleviating  effect  of  interpreting  with  SmarTerp,  especially  when  interpreting  into  a  second  language.  However,  all inferential statistical results consistently revealed non-significant outcomes. Furthermore, stress levels did not decrease significantly over time when using SmarTerp. While the non-significant reduction in stress may cast doubt on the tool's efficacy, the complexity and multiple variables influencing stress in interpreting tasks should be factored in. SmarTerp may serve its primary purpose in aiding accurate rendition of terminological units, proper names, and numbers.

## Keywords

interpreting; CAI tools; SmarTerp; quasi-experiment; physiological stress; heart rate; heart-rate variability

## Introduction

The development of simultaneous interpreting technologies and the Internet has led to a technological breakthrough,  which  Fantinuoli  (2018a)  called  'technological  turn'  in  interpreting.  This  leap forward,  predicted  to  impact  all  aspects  of  the  profession,  is  based  on  advancements  in  remote interpreting, machine interpreting, and computer-assisted interpreting (CAI)-an oral translation form where interpreters use computer applications to support and enhance certain interpreting tasks and  improve  quality  and  productivity  (Fantinuoli  2018b,  p.  155).  CAI  tools  support  knowledge acquisition, terminology management, and entry retrieval, and they seem to reduce cognitive effort (Stoll 2009) and facilitate accurate rendering of terminological units (Díaz-Galaz 2015; Gacek 2015; Biagini 2016; Wang & Wang 2019) and numbers (Desmet et al. 2018; Defrancq & Fantinuoli 2021) when at task.

CAI applications can be categorized into first- and second-generation tools (Fantinuoli 2018b). First-generation tools emphasize terminology management, whereas second-generation tools further incorporate  information  retrieval  from  corpora,  terminology  extraction,  and  organization  of materials. Recent advancements in simultaneous interpreting technologies have resulted in cloudbased Remote Simultaneous Interpreting (RSI) platforms or Simultaneous Interpreting Delivery Platforms (SIDPs), where interpreters operate with virtual consoles in virtual environments (Braun 2019; Saeed et al. 2022).

SmarTerp is a hybrid remote interpreting system and AI-powered computer-assisted interpreting tool combining RSI system functionality with CAI tool capabilities. It uses an artificial intelligence engine to provide live, automatic speech recognition of pre-selected problematic units: terms, proper

* Christian Olalla Soler Facultat de Traducció i Interpretació Universitat Autònoma de Barcelona christian.olalla@uab.cat

**  Nicoletta Spinolo Dipartimento di Interpretacione e Traduzione Università di Bologna, Forlì nicoletta.spinolo@unibo.it

***  Ricardo Muñoz Martín Dipartimento di Interpretacione e Traduzione Università di Bologna, Forlì ricardo.munoz@unibo.it names, and numbers. SmarTerp basically works as follows: (1) the interpreter feeds problematic units into a glossary before the session; (2) when interpreting, live, automatic speech recognition of those units prompts their target language renditions on the screen. That is, SmarTerp identifies pre-selected units  in  the  speech  flow,  locates  them  in  the  glossary,  and  prompts  the  interpreter  with  their translations  from  the  glossary,  potentially  enhancing  accuracy  and  coherence  while  reducing cognitive effort and stress (Prandi 2018, 2023; Frittella 2022).

The problematic units are elements on which boothmate support is usually expected (Gile 2009: 202).  This  becomes  particularly  relevant  in  RSI  where,  although  not  absent,  remote  boothmate support  may  become  less  effective  (Chmiel  &  Spinolo  2022).  On  the  other  hand,  however,  the literature on visual input and RSI suggests that managing multiple input streams can be challenging and stressful for interpreters (Ziegler & Gigliobianco 2018; Saeed et al. 2023). Hence, interpreters' stress  might  increase  due  to  introducing  an  additional  stream  of  information  and  the  need  for interpreters to familiarize themselves with the tool, which imposes further processing demands.

Using tools like SmarTerp may have additional, unintended consequences for the interpreting process. Interpreters may continue to monitor the solutions being provided even when they are not or no longer needed, or when they decide to omit or generalize information. In such cases, prompts may become distractors. Other factors, such as a slow or unstable Internet connection, may lead to increased return lag, necessitating catch-up with the source speech after being prompted with the rendition. That is, interpreters may need to adjust their ear-voice span to the tool's or the connection's pace.

Using AI-powered CAI tools in the booth may have additional effects, due to the potentially more complex  visual  input  to  process  if  the  interface  is  poorly  designed  or  hinders  human-computer interaction (Saeed et al. 2022). Despite these challenges, the stress induced by using such tools might decrease over time, as users become more familiar with them and learn to manage the additional information stream effectively.

The impact of tools like SmarTerp on the interpreting process may vary among interpreters, based on  expertise  levels;  experienced  interpreters  might  adapt  more  quickly,  while  trainees  might experience increased cognitive demand (Wang & Wang 2019). This paper reports the results of a quasi-experimental, intra-subject study on the effects of using SmarTerp on physiological stress in a sample of second-year MA interpreting students at the University of Bologna. We aimed to answer two research questions:

- (I) Does interpreting with SmarTerp reduce stress for MA interpreting students?
- (II) Does exposure to SmarTerp decrease stress for MA Interpreting students over time?

While  we  acknowledge  the  inherent  potential  for  heightened  stress  attributed  to  managing supplementary input streams, these tools may, in fact, mitigate stress over time. This alleviation may be due not only to the provision of ready-made solutions to potentially problematic units, but also to users adapting to the interface and the supplementary decision-making processes inherent to using the tool. As users become familiar with it, they might develop refined strategies and more efficient decision-making processes, which can in turn contribute to reducing stress levels. This assumption is rooted in our study, which involved students as participants and was developed within a didactic setup. We piloted SmarTerp at the University of Bologna to assess its usefulness and impact within a learning environment. Interpreting students are in the process of acquiring and refining their skills, so they offer a unique perspective and set of needs that are pivotal in evaluating the tool's efficacy and applicability in educational settings.

We posit that, if tools such as SmarTerp were to increase stress, including them in interpreting courses  might  warrant  consideration.  However,  this  decision  should  not  be  made  hastily.  The potential trade-off between increased stress and the possible enhancement of interpreting accuracy and overall performance should be factored in.

Besides, we cannot ignore the rapid advancement and growing prevalence of AI-powered CAI tools in the industry. Thus, including these technologies may be beneficial to prepare new cohorts of students to effectively navigate this evolving landscape. Early exposure to CAI tools should aid them in  becoming  more  comfortable  with  such  tools,  thereby  leveraging  their  advantages  while simultaneously reducing the potential stress associated with their use. Over time, users' adaptation to these tools may be used as a valuable indicator of the tool's learnability, a key aspect of its overall usability  (Nielsen  2010;  Frittella  2023).  To  sum  up,  while  adopting  such tools might come  with certain initial challenges, their long-term benefits could well outweigh the initial increase in stress levels, especially as users become more adept at managing the additional information stream and integrating it into their interpreting process.

To explore our research questions, we collected heart rate and heart rate variability data using Empatica E4 wristbands during interpreting tasks in two conditions, one with SmarTerp and another one without it, in three data-collection sessions over a period of a month. Section 2 presents the methods, with specific outlines for ethical issues (§2.1), data-collection procedure (§2.2), the sample (§2.3),  the  data-collection  tool  (§2.4),  the  indicators  (§2.5),  the  stimuli  (§2.6),  and  statistical procedures (§2.7). Section 3 presents and discusses the results and section 4 summarizes our main conclusions.

## 2. Materials and methods

## 2.1. Ethical aspects

The design of this study was approved by the Bioethics Committee of the University of Bologna. The informed consent form, signed by all participants, explained our goals and included information on the kind of data that would be collected (interpreters' performances and Empatica E4 data on cardiac  activity).  It  also  explained  that  only  anonymized,  aggregate  data  would  be  published. Participants were made aware that they could withdraw from the study at any moment and their data could be deleted with no consequences for them. No financial compensation was offered for their participation.

## 2.2. Procedure

The study was conducted in October 2021 in an interpreting lab of the Department of Interpreting and Translation of the University of Bologna, Forlì Campus.

Twelve second-year students of the MA in Interpreting participated in three data-collection sessions (one per week for all participants), in which they had to simultaneously interpret two speeches, one without SmarTerp and the other one with the tool, in that order. We were unable to randomize tasks and speech order due to practical constraints linked to SmarTerp running in its beta development phase at the time of data collection.

In  both  conditions,  with  and  without  SmarTerp, the stimuli  were  derived  from  videorecorded speeches. However, the mode of stimulus presentation differed between the two conditions. In the condition  without  SmarTerp,  the  recorded  speech  was  played  using  VLC  media  player  on  the instructor's computer and mirrored to the participants' screens, allowing them to see and hear the speaker. In the SmarTerp condition, SmarTerp was run on the instructor's computer and mirrored to the participants' screens. That is, SmarTerp was playing the recorded speech and actively processing and extracting terms in real-time. This live interaction with SmarTerp was crucial to maintaining the authenticity of the tool's functionality during the study.

The participants were exposed to these two interfaces, but they did not interact with them since they were mirrored through the instructor's computer. Given this setup, all students in a specific data- collection session experienced identical stimuli with the same latency. The live prompting feature of SmarTerp  was  not  captured  on  video  for  subsequent  latency  analysis.  According  to  the  tool's developers, the estimated latency ranges from a few milliseconds to a maximum of 1 second.

Our study is aptly classified as a quasi-experiment due to several defining characteristics inherent to  its  design.  While  we  do  exert  control  over  the  independent  variable-determining  whether participants interpret with or without SmarTerp-our study lacks a distinct control group and random assignment of participants to varied conditions or groups, both of which are pivotal components of a true experimental design. The absence of a proper control group and the non-random assignment inherently limit our ability to control for all potential confounders and, consequently, to definitively attribute  observed  differences  to  the  manipulation  of  the  independent  variable.  Additionally,  the intra-subject design allows for comparisons within the same subjects under different conditions, but it  does  not  compensate  for  the  absence  of  random  assignment  and  a  proper  control  group.  By clarifying  that  ours  is  a  quasi-experiment,  we  acknowledge  the  constraints  in  making  causal inferences and generalizations from our findings.

Before the first session, participants attended a self-training online course on the use of SmarTerp, so they could become familiar with the interface and its functionalities. In the first session, and before the first interpreting task, they were orally briefed about the study and could ask questions before signing the informed consent forms. Before each task, the participants were briefed on the text, the name of the speaker, and the event where the speech took place.

The use of Empatica E4 wristbands to measure physiological indicators (see § 2.4) called for adding some additional stages in the data-collection procedure, for it required recording a baseline. Before  the  initial  interpreting  task,  participants  were  given  roughly  ten  minutes  to  relax  and  to measure their baseline. After the second task, a similar relaxation period allowed for stress recovery measurement. However, recovery data were not used in the subsequent analysis (see below).

After performing the task in the two conditions, the participants filled in a questionnaire on the perceived usefulness of the tool. However, in the second data-collection session the participants only interpreted in one condition, with SmarTerp, and they did not fill in the questionnaire (Table 1). The purpose of this second session was to observe habituation effects (research question II) by facilitating an additional session wherein participants had further exposure to SmarTerp. Besides, in the first and third  sessions,  they  were  given  a  five-minute  break  between  the  two  interpreting  tasks.  Table  1 summarizes the procedure.

Table 1. Data-collection procedure

|       | Before session 1       | Session 1 (week 1)                         | Session 2 (week 2)                      | Session 3 (week 3)                         |
|-------|------------------------|--------------------------------------------|-----------------------------------------|--------------------------------------------|
| Tasks | 1. Webinar on SmarTerp | Q&A and informed consent                   |                                         |                                            |
|       |                        | Putting wristbands on                      | Putting wristbands on                   | Putting wristbands on                      |
|       |                        | Ten-minute relaxation stage (baseline)     | Ten-minute relaxation stage (baseline)  | Ten-minute relaxation stage (baseline)     |
|       |                        | Brief + interpreting task without SmarTerp |                                         | Brief + interpreting task without SmarTerp |
|       |                        | Five-minute pause                          |                                         | Five-minute pause                          |
|       |                        | Brief + interpreting task with SmarTerp    | Brief + interpreting task with SmarTerp | Brief + interpreting task with SmarTerp    |
|       |                        | Ten-minute relaxation stage (recovery)     | Ten-minute relaxation stage (recovery)  | Ten-minute relaxation stage (recovery)     |
|       |                        | Questionnaire on SmarTerp                  |                                         | Questionnaire on SmarTerp                  |
| Time  | 1 h approx.            | 1 h 20 min. approx.                        | 40 min. approx.                         | 1 h 15 min. approx.                        |

To be able to use the beta version of SmarTerp, developers had been provided with video recordings of the speeches, along with their respective glossaries containing potential problem triggers and their translations  before  each  session  (see  §2.6).  SmarTerp's speech recognition  system  was  thus pretrained for each speech, so as to ensure accurate identification and consistent prompting. Prior to the task,  participants  were not exposed to the glossary, so they were unaware of the pre-determined problem  triggers  and  their  translations.  Product-oriented  findings  from  this  study  (Russo  et  al. forthcoming)  suggest  increased  accuracy  when  interpreting  problem  triggers  in  all  conditions involving SmarTerp. We cannot be sure whether participants arrived at their renditions by themselves or by adopting the suggestions prompted by the tool.

## 2.3. Sample

Twelve second-year students of the MA in interpreting at the University of Bologna were recruited using a non-probabilistic, convenience sampling procedure. They were all female speakers of Italian as L1 and had a mean age of 24 (minimum 22, maximum 26). No participant suffered from heartrelated issues, and none consumed alcoholic drinks at least in the 10 hours preceding the experiment, or caffeine at least one hour before data collection. Three participants were tobacco smokers (25%). None of them had any experience with using SmarTerp. The group was divided into four sub-groups of  three  students  each,  based  on  language  combination  and  direction:  (1)  Italian  Spanish;  (2) Spanish  Italian; (3) Italian  English, and  (4)  English  Italian. The  data  from  participants interpreting into their L1/A language (English and Spanish into Italian) and for participants into their L2/B languages (Italian into English and Spanish) will be analyzed and reported separately.

## 2.4. Data-collection tool: Empatica E4 wristbands

The main benefit of Empatica E4 wristbands is that they are unobtrusive at data collection, enhancing ecological  validity.  Physiological  data  on  stress  were  collected  with  the  photoplethysmography sensor of Empatica E4 wristbands. 1 This sensor measures heart rate (HR), blood volume pulse (BVP), and inter-beat interval (IBI) data. Empatica E4 wristbands have been deemed adequate for collecting heart rate (HR) and heart rate variability (HRV) data. McCarthy et al. (2016) compared the signal qualities of the Empatica E4 and the General Electric's SEER Light Extend Recorder holter portable electrocardiogram, a standard clinical device for atrial fibrillation detection. Their results showed that  the  E4  produced  similar  data  quality  to  the  holter  device  85%  of  the  time,  with  the  holter performing better only 5% of the time. Schuurmans et al. (2020) tested the accuracy and predictive value of the Empatica E4 wristband against the Vrije University Ambulatory Monitoring System (VU-AMS) and found significant correlations between them for multiple HR and HRV indicators (see § 2.5). More research is needed, but these studies support using the E4 wristband for HR and HRV data collection. Overall, the Empatica E4 wristband has potential for measuring HR and HRV under non-movement conditions.

[1   https://www.empatica.com/research/e4/, accessed 05/10/2022](https://www.empatica.com/research/e4/)

To minimize confounding effects due to stress induced by starting the study and by fatigue at the end of the task, only recordings of the same length were compared, following the guidelines of the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996). As recommended by Rojo & Korpal (2020), we extracted four 5-minutelong spans from the central minutes of each condition, following the rule of the three R's-Resting, Reactivity, and Recovery (Laborde et al. 2018)-i.e., baseline (resting, no task), interpreting without SmarTerp (task 1), interpreting with SmarTerp (task 2), and recovery (relaxation, after task 2). The baseline was measured with the participants sitting alone in their booths with both feet on the floor. Laborde et al. (2017) lists further recommendations for an optimal baseline-ankles at an angle of 90 degrees, hands on thighs, and eyes closed-but we thought that forcing the posture of participants in such detail would negatively affect the ecological validity. Again, following Laborde et al. (2017), Recovery was measured with the participants sitting in the same position as in the baseline one (Resting). Recovery data was not employed in this study (see § 2.5). Kubios HRV Premium software was used to automatically detect and correct artifacts, based on Lipponen & Tarvainen (2019). On average, 9.3% (Mdn = 9.4; SD = 2.0) of beats in each participant's recording were corrected.

## 2.5. Indicators

Before describing the indicators that we used to measure physiological stress, we need to frame and define  this  construct.  The  term  arousal  describes  a  state  of  physiological  activation  or  cortical responsiveness,  associated  with  sensory  stimulation  and  activation  of  fibers  from  the  reticular activating  system.  It  may  also  refer  to  a  state  of  excitement  or  energy  expenditure  linked  to  an emotion. Usually, arousal is closely related to a person's appraisal of the significance of an event or to  the  physical  intensity  of  a  stimulus.  Arousal  can  either  facilitate  or  debilitate  performance. Responses can be positive (eustress), promoting focused attention and cognitive flow (Moneta 2020), or negative (distress), leading to anxiety or frustration (Kemeny 2003; but see Bienertova-Vasku et al. 2020 for a critique on the difference between these two notions). Stress is here understood as a stimulus, response, or physiological consequence, triggered by physical/environmental, task-related, and interpersonal stressors in interpreting (Cooper et al. 1982; Kemeny 2003). Stressful situations elicit  physiological  responses,  including  sympathetic  nervous  system  activation,  hypothalamicpituitary-adrenal  axis  activation,  and  immunological  cell  level  fluctuations  (Kemeny  2003). However,  repeated  exposures  to  aversive  stimuli  or  situations  may  lead  people  to  adapt  their physiological responses to stress. Adaptation here describes changes that reduce the physiological strain produced by stressful components of the total environment. Many organisms, not only humans, adapt  to  repeated  stimuli  by  reducing  their  response,  a  feature  described  as  habituation.  With habituation we may refer here both to the process of growing accustomed to a situation or stimulus, or the diminished effectiveness of a stimulus in eliciting a response, following repeated exposure to the stimulus. 2

Our study focused on sympathetic nervous system activation, measured through heart rate (HR) and heart rate variability (HRV). HR indicates the average number of heartbeats over a period, while HRV measures the variations in time between successive heartbeats. The indicators of stress based on HR and HRV employed in our study (see below) were computed from the BVP data measured with the wristbands. HR alone is mainly an indicator of physical exertion (Rojo & Korpal 2020), and it is often studied in the literature in conjunction with other HRV indicators or as a part of them (Kim et al. 2018). HRV is a complex measure, with different indicators providing information about the contribution of the autonomic nervous system to cardiac activity and regulation (Rojo & Korpal 2020: 196). That is, HRV accounts for the activity of the vagus nerve, thus 'the focus is on vagal tone and its correlation with better executive cognitive performance, as well as better emotional and health regulation' (Rojo & Korpal 2020: 196). However, Rojo & Korpal also report that HRV is more sensitive to artifacts (for instance, induced by participants moving during data collection) and needs greater accuracy in measurement.

An increased HR mainly points at higher physical exertion, while an increased HRV-i.e., more variation between heartbeats over a certain period-would suggest a 'greater ability to tolerate stress or [...] recovery from prior accumulated stress' (Rojo & Korpal 2020: 196), while a reduced variation between heartbeats suggests 'stress from exercise, psychological events, or other internal or external stressors' (ibid.). Researchers have derived many indicators based on HR and HRV data, but not all of them are adequate to measure stress. Based on Rojo et al. (2021), on metanalyses (Castaldo et al. 2015; Schaffer & Ginsberg 2017; Kim et al. 2018) and on Spinolo et al. (2022), the HR and HRV indicators examined were the ones listed and defined in Table 2.

Table 2. Heart rate and heart-rate variability indicators

| indicator   | description (based on Schaffer et al. 2014 and Schaffer& Ginsberg 2017)                                                                                                                                                                                                                                                                          |
|-------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Mean HR     | The mean heart rate associated to higher physical exertion is also expected to increase in a situation of stress, pointing at sympathetic activity, rather than to parasympathetic activity.                                                                                                                                                     |
| Mean RR     | R is the peak point of successive R-peaks, or QRS complex, of the electrocardiogram wave. The mean of RR intervals is expected to decrease in a situation of stress, pointing again at sympathetic activity.                                                                                                                                     |
| RMSSD       | The square Root of the Mean Squared Differences between Successive RR intervals. It reflects the influence of parasympathetic activity on HRV. Under stress, RMSSD values tend to decrease, indicating a reduction in parasympathetic (vagal) activity and an increase in sympathetic activity, as part of the physiological response to stress. |
| LF-HF ratio | The ratio between Low Frequency and High Frequency band powers. A ratio above 1 indicates sympathetic activity (i.e., stress), while a value below 1 shows parasympathetic activity (i.e., relaxation).                                                                                                                                          |

Both Mean HR and Mean RR pertain to heart rate. So, they are related, but they differ in the aspects of heart rate they represent. Mean HR is the pulse rate, calculated by dividing the number of recorded heartbeats by the duration of that recording, in minutes. This yields and is expressed as an average, number of heartbeats per minute. Mean HR provides an overall estimate of the heart's activity level, but it does not convey information about heart rate variability. In contrast, Mean RR focuses on the time intervals between successive heartbeats. It refers to the average time interval between successive R-peaks  (QRS  complex)  in  an  electrocardiogram  recording,  that  is,  the  period  between  two heartbeats.  This  measure,  typically  expressed  in  milliseconds,  captures  the  time  span  between heartbeats and informs on the variability of heart rate over time. By including both Mean RR and Mean  HR,  our  study  aimed  to  provide  a  comprehensive  analysis  to  better  understand  the physiological responses under investigation.

2 Definitions from the APA Dictionary of Psychology . For the physiology and neurobiology of stress and adaptation, see McEwen (2007). For workplace pressure at teleworking, see Day et al (2021) and Seeman et al (2023).

In HRV research, baseline measurements work as reference points for the individual's autonomic function under neutral conditions, reflecting their inherent level of HRV (Veltman & Gaillard 1993; Moses et al. 2007; Loudon & Deininger 2016). The main method for evaluating the physiological responses  elicited  by  a  task  is  comparing  baseline  measurements  with  on-task  indicators.  The rationale lies in the ability to capture changes in autonomic nervous system (ANS) function and regulation that occur in response to the task, while accounting for individual differences in baseline HRV values. HRV indicators collected while at task are compared with the baseline measurements to  assess  the  relative  impact  of  the  task  on  ANS  activity-specifically,  the  balance  between sympathetic (fight-or-flight) and parasympathetic (rest-and-digest) components. Changes in HRV indicators between the baseline and on-task conditions can reveal the extent to which the task induces stress or cognitive effort. To compare the baseline and on-task measurements of the four selected indicators (Table 2), on-task measurements were subtracted from the baseline values measured in that session. The results of this procedure indicate the direction of change , and are interpreted as follows:

- Mean HR increases with stress. If the difference between baseline and on-task measurement is positive, stress levels were higher at baseline measurement.
- Mean RR decreases with stress. If the difference between baseline and on-task measurement is negative, stress levels were higher at baseline measurement.
- RMSSD decreases with stress. If the difference between baseline and on-task measurement is negative, stress levels were higher at baseline measurement.
- A positive difference in LF/HF ratio points to a tendency towards parasympathetic activity (relaxation), while a negative difference shows a tendency towards sympathetic activity (stress).

To assess the magnitude of difference between baseline and on-task measurements, we calculated the  median  percent  difference,  which  represents  the  central  tendency  of  individual  percent differences. Compared to the mean, the median is a more robust measure of central tendency. It is less susceptible to the influence of extreme values, thereby providing a more accurate representation of the data's center in limited sample sizes. The larger the median percent difference, the greater the difference  between  baseline  and  on-task  measurement.  The  interpretation  of  the  direction  of  the change follows the guidelines provided for each indicator.

## 2.6. Source texts as stimuli

Given the varied language-combinations involved in this study, the three data-collection sessions, and the two conditions (with and without SmarTerp), we needed to ensure that the main stimulithe source speeches-were comparable in terms of topic, word count, text complexity, and delivery speed.

Each participant took part in five data-collection tasks, so they interpreted five speeches. The source languages were three: English, Italian, and Spanish. To reduce variation in source speeches, we created a source text for each session that was similar for the three source languages. To do so, one text was borrowed from Frittella (2023) and four texts with similar characteristics were crafted in English. The five texts were then translated into Spanish and Italian and proofread by L1 speakers of the respective languages. To further enhance comparability, all texts were of the same genre or type (opening speeches at international events), and they all had the same structure: an introduction, greetings, an agenda of the event, the body of the speech, and a conclusion.

We also calculated a readability measure for the five texts in each source language (English, Italian, and Spanish), using the Flesch-Kincaid index for English (Kincaid et al. 1975), GULPEASE index  for  Italian  (Lucisano  &  Piemontese  1988),  and  Fernández's  (1959)  readability  scale  for Spanish. 3 The three indices have the same range (100 = very easy to read; 0 = very difficult to read). The English texts  yielded a  mean  readability  score  of  49.9  (mdn  =  47.9;  SD  =  5.9),  suggesting difficulty, while Italian texts had a mean of 53 (mdn = 54; SD = 2.5) and Spanish texts a mean of 59.5 (mdn = 58.9; SD = 4.3), both suggesting moderate difficulty. The low data dispersion within each language reveals comparable readability levels, with texts ranging from moderately difficult to difficult across the three indices.

To  ensure  that  the  complexity  of  the  five  texts  was  similar,  they  were  scanned  for  possible problem triggers, which were in turn categorized based on Frittella (2023). The aim was to obtain well-balanced  texts  that  contained  parallel  potential  problem  triggers.  For  each  language  pair, SmarTerp was fed with a glossary with the corresponding specialized terms, proper names (entities and persons), and numbers in the appropriate language pair, so that the output offered by the tool would be the same for all participants. Of course, the difficulty of the texts may have been perceived differently for each participant, due to their personal characteristics.

The five speeches were videorecorded by L1 speakers. The average delivery rate for the five speeches in English was 107.4 words per minute (mdn = 108.3; SD = 9.7), 101.7 words per minute for the Italian speeches (mdn = 101.7; SD = 3.0), and 104.8 for the Spanish speeches (mdn = 106.1; SD = 5.9). The mean duration of the recordings of the English speeches was 09:48 minutes (mdn = 09:55; SD = 00:31), 10:45 minutes for the Italian speeches (mdn = 10:47; SD = 00:43), and 10:54 for the Spanish speeches (mdn = 10:55; SD = 01:02).

## 2.7. Statistical analysis

Given the intra-subject nature of our design, a single datapoint per (1) participant, (2) measurement (baseline, interpreting without SmarTerp, interpreting with SmarTerp), and (3) directionality-into the A or B language-makes a sample size of N = 6 too small to apply statistical procedures such as regression analyses. Language pair may have had an impact on the participants' stress levels, yet we merged all participants interpreting into their A language in one group and those interpreting into their B language in another group, since the facilitation effect of the tool and subsequent reduction in stress should be equally present in all cases.

Log transforming HR and HRV indicators to normalize the data (as recommended by Laborde et al.  2017)  would  have  had  little  impact  on  the  analysis  with  such  limited  sample  size.  We  thus employed the non-parametric procedures described in Table 3.

The significance level was preestablished at α = 0.05. To establish a minimum effect size of interest, we performed a sensitive analysis of the Wilcoxon signed-rank test for matched pairs for a unilateral  test  with  α  =  0.05,  power  =  0.8,  and  a  sample  size  of  6  using  G*Power  3.1.9.6.  The minimum effect size of interest is d = 1.226. Converted into r (Ruscio 2008), it equals 0.522. Hence, effect sizes under 0.522 were considered irrelevant even if statistically significant. Statistical analysis was carried out in RStudio Desktop and jamovi 2.3 (jamovi project 2023).

3 Readability formulas are atheoretical and unreliable, but there is no consensus in the field as to how to objectively profile texts and quantify relevant text features. However, the indicators combined into these formulas (e.g., word frequency, sentence length) do have supporting evidence of their merit in isolation. CTIS research has often used readability formulas for text profiling, so these results are offered merely as a reference, whereas the comparability between the texts was rather achieved by the other steps. The differences between English and the romance language translations might be the consequence of having been processed and revised by professional communicators.

Table 3. Non-parametric statistical procedures

| Aim                                                                                                                            | Test                                                                                                     | Contrast type                             | Effect size   |
|--------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|-------------------------------------------|---------------|
| Comparing within-group baseline vs. on- task measurement for a given indicator in a given session                              | Wilcoxon signed-rank test with Bonferroni correction                                                     | Unilateral                                | r             |
| Comparing within-group median percent difference for a given indicator between two conditions                                  | Wilcoxon signed-rank test with Bonferroni correction                                                     | Unilateral                                | r             |
| Comparing between-group median percent difference for a given indicator in a given session and condition                       | Mann-Whitney U test                                                                                      | Unilateral                                | r             |
| Comparing within-group median percent difference for a given indicator among the three sessions in the with-SmarTerp condition | Friedman test as omnibus test and Wilcoxon signed- rank test with Bonferroni correction as post-hoc test | Omnibus: bilateral; post- hoc: unilateral | W             |
| Comparing within-group median percent difference for a given indicator between the two sessions in the no-SmarTerp condition   | Wilcoxon signed-rank test with Bonferroni correction                                                     | Unilateral                                | r             |

## 3. Results and discussion

Table  4  presents  the  descriptive  results  of  the  difference  between  the  baseline  and  the  on-task measurement  of  each  direction  (into  A  and  into  B  language),  indicator  (Mean  HR,  Mean  RR, RMSSD, and LF/HF ratio), and condition (with and without SmarTerp). It also provides the median percent difference between baseline and on-task measurement.

| Difference (Baseline - on-task measurement)   | Difference (Baseline - on-task measurement)   | Difference (Baseline - on-task measurement)   |   Session 1 Mean HR |   Mean RR |   RMSSD |   LF/HF ratio |   Session 2 Mean HR Mean | RR    | RMSSD   |   LF/HF ratio |   Session 3 Mean HR |   Mean RR |   RMSSD | LF/HF ratio   |
|-----------------------------------------------|-----------------------------------------------|-----------------------------------------------|---------------------|-----------|---------|---------------|--------------------------|-------|---------|---------------|---------------------|-----------|---------|---------------|
| A lang.                                       | With                                          | Median                                        |                -4.0 |      57.0 |    28.0 |          -0.1 |                      0.5 | -5.5  | 18.5    |           0.2 |                -3.0 |      35.0 |    94.0 | -0.1          |
|                                               |                                               | SD                                            |                21.4 |     217.2 |   160.6 |           1.3 |                     17.1 | 201.1 | 136.7   |           1.1 |                 9.4 |     124.4 |   176.5 | 1.7           |
|                                               |                                               | Median %diff.                                 |                -6.4 |       6.0 |     6.8 |         -10.0 |                      0.8 | -4.3  | -0.6    |           9.1 |                -4.0 |       4.3 |    48.8 | - 3.6         |
|                                               | Without                                       | Median                                        |                -5.5 |      79.5 |    68.5 |           0.0 |                          |       |         |               |                -6.5 |      62.5 |    73.2 | -0.6          |
|                                               |                                               | SD                                            |                23.7 |     245.3 |   230.0 |           0.9 |                          |       | -       |               |                 7.8 |     109.4 |   161.7 | 1.3           |
|                                               |                                               | Median %diff.                                 |                -8.8 |       8.3 |    16.4 |           4.8 |                          |       |         |               |                -8.1 |       8.2 |    41.7 | -24.1         |
| B lang.                                       | With                                          | Median                                        |                 5.0 |     -68.0 |   -45.7 |           0.0 |                     -4.0 | 54.5  | 27.9    |           0.1 |                -9.0 |      91.0 |   124.0 | -0.4          |
|                                               |                                               | SD                                            |                11.7 |     123.3 |   113.1 |           0.4 |                      8.0 | 102.1 | 83.8    |           0.7 |                10.1 |     104.8 |   123.2 | 1.8           |
|                                               |                                               | Median %diff.                                 |                 7.5 |      -7.6 |   -11.3 |          -1.0 |                     -6.2 | 6.4   | 6.1     |           7.4 |               -12.0 |      12.0 |    44.7 | - 47.0        |
|                                               | Without                                       | Median                                        |               -19.0 |     187.5 |   177.5 |           0.2 |                          |       |         |               |                -8.0 |      83.5 |    55.8 | -1.9          |
|                                               |                                               | SD                                            |                17.2 |     167.5 |   136.8 |           1.3 |                          | -     |         |               |                10.6 |     110.1 |   154.8 | 2.2           |
|                                               |                                               | Median %diff.                                 |               -25.5 |      25.6 |    58.2 |          14.5 |                          |       |         |               |               -10.3 |      10.5 |    39.3 | -57.1         |

Table 4. Descriptive statistics of the difference between baseline and on-task measurement for each indicator, direction, and condition

## Interpretation of results is as follows:

- Mean HR increases with stress. If baseline - on -task measurement is positive, stress levels were higher during baseline.
- Mean RR decreases with stress. If baseline - on -task measurement is negative, stress levels were higher during baseline.
- RMSSD decreases with stress. If baseline - on -task measurement is negative, stress levels were higher during baseline.
- LF/HF ratio. Positive difference  tendency towards parasympathetic activity (relaxation). Negative difference  tendency towards sympathetic activity (stress).
- Median percent difference indicates the size of the difference between the two measurements (the larger the value, the larger the difference). The interpretation of the direction of the change follows the guidelines provided for each indicator.

## 3.1. Stress levels with vs. without SmarTerp

## 3.1.1. Interpreting into the A language (L1)

In the results from participants interpreting into their A language using SmarTerp in session 1, the median values for Mean HR (-4.0) and Mean RR (57.0) suggest increased stress levels in the on-task measurement compared to the baseline. Concurrently, RMSSD and LF/HF ratio exhibit the same directional changes (28.0 for RMSSD; -0.1 for LF/HF ratio). However, inferential tests reveal no significant differences among the four indicators between measurements. They support that, in session 1, interpreting with SmarTerp into the A language did not yield lower stress levels than the baseline (Table 5).

Table  5.  Results  of  the  Wilcoxon  signed-rank  test  for  the  with-SmarTerp  condition  in  session  1  for participants interpreting into their A language. Note: all tests are unilateral

| Indicator   |    W |   p (Bonferroni) |      r |
|-------------|------|------------------|--------|
| Mean HR     | 11.0 |            1.000 |  0.048 |
| Mean RR     | 10.0 |            1.000 | -0.048 |
| RMSSD       | 10.0 |            1.000 | -0.048 |
| LF/HF ratio |  7.0 |            1.000 | -0.333 |

Also in session 1, when interpreting into the participants' A language without SmarTerp, the median  values  of  Mean  HR  (-5.5),  Mean  RR  (79.5),  and  RMSSD  (68.5)  show  a  consistent direction of change between baseline and on-task measurements. The three indicators suggest increased stress levels during task performance, compared to the baseline. However, the LF/HF ratio exhibits no variation between measurements (0.0). Despite these observations, inferential analysis reveals no significant differences in stress levels between the baseline and interpreting without SmarTerp (Table 6).

Table  6.  Results  of  the  Wilcoxon  signed-rank  test  for  the  no-SmarTerp  condition  in  session  1  for participants interpreting into their A language. Note: all tests are unilateral

| Indicator   |    W |   p (Bonferroni) |      r |
|-------------|------|------------------|--------|
| Mean HR     |  5.0 |            1.000 | -0.333 |
| Mean RR     | 14.0 |            1.000 |  0.333 |
| RMSSD       | 15.0 |            1.000 |  0.429 |
| LF/HF ratio | 10.0 |            1.000 | -0.048 |

Elevated stress levels when at task compared to the baseline were anticipated, given the inherently stressful nature of interpreting, irrespective of SmarTerp usage. The relative reduction in stress levels with SmarTerp, compared to the task without the tool, is assessed by comparing median percent differences. Comparing this indicator between the with- and no-SmarTerp conditions for participants  interpreting  into  their  A  language  in  session  1,  a  larger  magnitude  of  difference between baseline and on-task measurements is observed in the without condition for Mean HR (-8.8 vs. -6.4), Mean RR (8.3 vs. 6.0), and RMSSD (16.4 vs. 6.8). Conversely, LF/HF ratio yields an  opposite  trend  (without  4.8  vs.  with  -10).  However,  these  differences  are  not  statistically significant (Table 7), suggesting that interpreting with or without SmarTerp in session 1 does not impact the magnitude of difference between baseline and on-task measurements for participants' A language.

| Indicator   |    W |   p (Bonferroni) |     r |
|-------------|------|------------------|-------|
| Mean HR     | 16.0 |            1.000 | 0.524 |
| Mean RR     |  6.0 |            0.876 | 0.429 |
| RMSSD       |  7.0 |            1.000 | 0.333 |
| LF/HF ratio |  6.0 |            1.000 | 0.200 |

Table 7. Wilcoxon signed-rank test results comparing median percent difference in session 1 under both SmarTerp conditions for participants interpreting into their A language. Note: all tests are unilateral

Session 2 results for participants using SmarTerp indicate an opposite trend compared to session 1. The median values for Mean HR (0.5), Mean RR (-5.5), and LF/HF ratio (0.2) suggest elevated stress  levels  in  the  baseline  compared  to  on-task  measurements,  while  the  median  value  for RMSSD (18.5) suggests the opposing trend. No significant differences were identified between baseline and on-task measurements (Table 8).

Table  8.  Results  of  the  Wilcoxon  signed-rank  test  for  the  no-SmarTerp  condition  in  session  2  for participants interpreting into their A language. Note: all tests are unilateral

| Indicator   |    W |   p (Bonferroni) |      r |
|-------------|------|------------------|--------|
| Mean HR     |  8.0 |            1.000 |  0.067 |
| Mean RR     |  9.0 |            1.000 | -0.143 |
| RMSSD       | 12.0 |            1.000 |  0.143 |
| LF/HF ratio | 10.0 |            1.000 | -0.048 |

In session 3, participants exhibit increased stress levels in the on-task measurement, relative to the baseline for three of the four indicators in the withSmarTerp condition: Mean HR (-3.0), Mean RR (35.0), and RMSSD (94.0). The direction of change for LF/HF ratio suggests increased sympathetic activity when interpreting, albeit with a minimal magnitude of difference (-0.1). Nonetheless, inferential tests do not identify significant differences between baseline and on-task measurements (Table 9).

Table  9.  Results  of  the  Wilcoxon  signed-rank  test  for  the  with-SmarTerp  condition  in  session  3  for participants interpreting into their A language. Note: all tests are unilateral

| Indicator   |    W |   p (Bonferroni) |      r |
|-------------|------|------------------|--------|
| Mean HR     |  5.5 |            1.000 | -0.476 |
| Mean RR     | 15.0 |            1.000 |  0.429 |
| RMSSD       | 17.0 |            1.000 |  0.619 |
| LF/HF ratio |  8.0 |            1.000 | -0.238 |

In session 3, participants interpreting into their A language without SmarTerp display increased stress levels in on-task measurements, compared to the baseline for three of the four indicators: Mean HR (-6.5), Mean RR (62.5), and RMSSD (73.2). The LF/HF r atio presents a direction of change indicating sympathetic activity (-0.6). As in prior cases, inferential analysis reveals no significant differences between baseline and on-task measurements (Table 10).

Table  10.  Results  of  the  Wilcoxon  signed-rank  test  for  the  no-SmarTerp  condition  in  session  3  for participants interpreting into their A language. Note: all tests are unilateral

| Indicator   |    W |   p (Bonferroni) |      r |
|-------------|------|------------------|--------|
| Mean HR     |  4.0 |            1.000 | -0.619 |
| Mean RR     | 17.0 |            1.000 |  0.619 |
| RMSSD       | 16.0 |            1.000 |  0.524 |
| LF/HF ratio |  9.0 |            1.000 | -0.143 |

Comparing the median percent difference between with- and no-SmarTerp conditions in session 3, the no-SmarTerp condition shows a larger magnitude of difference between baseline and ontask measurements for all indicators except RMSSD: Mean HR (with: -4.0; without: -8.1), Mean RR  (with:  4.3;  without:  8.2),  RMSSD  (with:  48.8;  without:  41.7),  LF/HF  ratio  (with:  -3.6; without:  -24.1).  This  suggests  that,  although  participants  experienced  increased  stress  levels during tasks in both conditions, these levels were higher without SmarTerp relative to the baseline compared to the with-SmarTerp condition. Nevertheless, inferential statistics reveal no significant differences (Table 11).

Table 11. Wilcoxon signed-rank test results comparing median percent difference in session 3 under both SmarTerp conditions for participants interpreting into their A language. Note: all tests are unilateral

| Indicator   |    W |   p (Bonferroni) |     r |
|-------------|------|------------------|-------|
| Mean HR     |  8.0 |            1.000 | 0.067 |
| Mean RR     | 11.0 |            1.000 | 0.048 |
| RMSSD       | 10.0 |            1.000 | 0.048 |
| LF/HF ratio | 11.0 |            1.000 | 0.048 |

To sum up, from a descriptive standpoint, sessions 1 and 3 exhibited elevated stress levels in Alanguage interpreting, both with and without SmarTerp, relative to the baseline. The magnitude of this difference was more pronounced in the no-SmarTerp condition in both sessions, potentially signaling the stress-mitigating assistance of  SmarTerp.  Conversely,  session  2  displayed contrasting trends, with the majority of indicators suggesting higher stress levels at the baseline than during the on-task measurements. This anomaly might be effects of extraneous variables.

## 3.1.2. Interpreting into the B language (L2)

In session 1, participants interpreting with SmarTerp into their B language exhibited lower stress levels during tasks compared to the baseline for Mean HR (5.0), Mean RR (-68.0), and RMSSD (-45.7). The LF/HF ratio demonstrated no change, with a median of 0.0. However, inferential statistics reveal no significant differences between on-task measurements and the baseline (Table 12).

Table  12.  Results  of  the  Wilcoxon  signed-rank  test  for  the  with-SmarTerp  condition  in  session  1  for participants interpreting into their B language. Note: all tests are unilateral

| Indicator   |    W |   p (Bonferroni) |      r |
|-------------|------|------------------|--------|
| Mean HR     | 16.0 |            0.624 |  0.524 |
| Mean RR     |  5.0 |            0.624 | -0.524 |
| RMSSD       |  8.0 |            1.000 | -0.238 |
| LF/HF ratio |  8.0 |            1.000 | -0.238 |

The no-SmarTerp condition exhibited an opposite trend. Participants seemed to have increased stress levels during tasks compared to the baseline: Mean HR (-19.0), Mean RR (187.5), and RMSSD (177.5). The median (0.2) of the  LF/HF  ratio  displays  a  minimally  divergent  trend. Inferential statistics reveal no significant differences (Table 13).

Table  13.  Results  of  the  Wilcoxon  signed-rank  test  for  the  no-SmarTerp  condition  in  session  1  for participants interpreting into their B language. Note: all tests are unilateral

| Indicator   |    W |   p (Bonferroni) |      r |
|-------------|------|------------------|--------|
| Mean HR     |  3.0 |            1.000 | -0.714 |
| Mean RR     | 18.0 |            1.000 |  0.714 |
| RMSSD       | 18.0 |            1.000 |  0.714 |
| LF/HF ratio | 10.0 |            1.000 | -0.048 |

The median percent difference in session 1 reveals a larger magnitude of difference in the noSmarTerp condition for all indicators: Mean HR (with: 7.5; without: -25.5), Mean RR (with: -7.6;  without:  25.6),  RMSSD (with: -11.3; without: 58.2), LF/HF ratio (with: -1.0; without: 14.5).  Inferential  results  are  nonsignificant  (Table  14),  but  descriptive  findings  for  session  1 indicate decreased stress levels, compared to the baseline, when participants interpret into their B language  using  SmarTerp;  increased  levels,  without  it;  and  a  substantially  larger  difference between the baseline and on-task measurements in the no-SmarTerp condition. This contrasts with results for participants interpreting into their A language in session 1, where stress levels were not reduced with SmarTerp and the magnitude of difference between baseline and on-task measurement was less pronounced.

Table 14. Wilcoxon signed-rank test results comparing median percent difference in session 1 under both SmarTerp conditions for participants interpreting into their B language. Note: all tests are unilateral

| Indicator   |    W |   p (Bonferroni) |     r |
|-------------|------|------------------|-------|
| Mean HR     | 21.0 |            1.000 | 1.000 |
| Mean RR     |  0.0 |            0.064 | 1.000 |
| RMSSD       |  0.0 |            0.064 | 1.000 |
| LF/HF ratio | 12.0 |            1.000 | 0.143 |

In contrast to session 1, session 2 results exhibit increased stress levels during task performance compared to the baseline for Mean HR (-4.0), Mean RR (54.5), and RMSSD (27.9). The LF/HF ratio (0.1) indicates almost no change. Nevertheless, differences between baseline and on-task measurements were not significant (Table 15).

Table  15.  Results  of  the  Wilcoxon  signed-rank  test  for  the  no-SmarTerp  condition  in  session  2  for participants interpreting into their B language. Note: all tests are unilateral

| Indicator   |    W |   p (Bonferroni) |      r |
|-------------|------|------------------|--------|
| Mean HR     |  5.0 |            1.000 | -0.524 |
| Mean RR     | 16.0 |            1.000 |  0.524 |
| RMSSD       | 20.0 |            1.000 |  0.905 |
| LF/HF ratio | 12.0 |            1.000 |  0.143 |

Session 3 with SmarTerp exhibits a trend contrary to that in session 1 and follows the one detected in  session  2.  Participants  display  increased  stress  during  task  performance,  compared  to  the baseline: Mean HR (-9.0), Mean RR (91.0), and RMSSD (124.0). Th e LF/HF ratio also indicates sympathetic activity (-0.4). Inferential results reveal no significant differences between baseline and on-task measurements (Table 16).

Table  16.  Results  of  the  Wilcoxon  signed-rank  test  for  the  with-SmarTerp  condition  in  session  3  for participants interpreting into their B language. Note: all tests are unilateral

| Indicator   |    W |   p (Bonferroni) |      r |
|-------------|------|------------------|--------|
| Mean HR     |  5.5 |            1.000 | -0.476 |
| Mean RR     | 14.0 |            1.000 |  0.333 |
| RMSSD       | 17.0 |            1.000 |  0.619 |
| LF/HF ratio |  8.0 |            1.000 | -0.238 |

In session 3, participants interpreting without SmarTerp exhibit the same trend as with the tool, with increased stress levels while at task, compared to the baseline: Mean HR (-9.0), Mean RR (91.0), RMSSD (124.0), and LF/HF ratio (-1.9). Inferential analy sis again reveals no significant differences between the baseline and on-task measurements (Table 17).

Table  17.  Results  of  the  Wilcoxon  signed-rank  test  for  the  no-SmarTerp  condition  in  session  3  for participants interpreting into their B language. Note: all tests are unilateral

| Indicator   |    W |   p (Bonferroni) |      r |
|-------------|------|------------------|--------|
| Mean HR     |  4.5 |            1.000 | -0.571 |
| Mean RR     | 17.0 |            1.000 |  0.619 |
| RMSSD       | 17.0 |            1.000 |  0.619 |
| LF/HF ratio |  3.0 |            1.000 | -0.714 |

The median percent difference in session 3 reveals that, except for the LF/HF ratio (with: -47.0; without:  -57.1),  the  remaining  indicators  exhibit  a  larger  magnitude  of  difference  between baseline  and  on-task  measurements  in  the  with-SmarTerp  condition:  M ean  HR  (with:  -12.0; without: -10.3), Mean RR (with: 12.0; without: 10.5), and RMSSD (with: 44.7; without: 39.3). Nonetheless, these differences are not statistically significant (Table 18).

Table 18. Wilcoxon signed-rank test results comparing median percent difference in session 3 under both SmarTerp conditions for participants interpreting into their B language. Note: all tests are unilateral.

| Indicator   |    W |   p (Bonferroni) |     r |
|-------------|------|------------------|-------|
| Mean HR     | 14.0 |            1.000 | 0.333 |
| Mean RR     |  7.0 |            1.000 | 0.333 |
| RMSSD       |  7.0 |            1.000 | 0.333 |
| LF/HF ratio | 17.0 |            1.000 | 0.619 |

Table  19.  Mann-Whitney U test  results  comparing  median  percent  difference  between  participants interpreting  into  their  A  and  B  languages  for  each  session,  condition,  and  indicator.  Note:  all  tests  are unilateral. All p -values were non-significant at p = 1.000.

|   Session | Condition   | Median percent difference for...   |   A language |   B language |    U |     r |
|-----------|-------------|------------------------------------|--------------|--------------|------|-------|
|         1 | With        | Mean HR                            |         -6.4 |          7.5 | 16.0 | 0.111 |
|         1 |             | Mean RR                            |          6.0 |         -7.6 | 16.0 | 0.111 |
|         1 |             | RMSSD                              |          6.8 |        -11.3 | 16.0 | 0.111 |
|         1 |             | LF/HF ratio                        |        -10.0 |         -1.0 | 17.0 | 0.056 |
|         1 | Without     | Mean HR                            |         -8.8 |        -25.5 | 13.0 | 0.278 |
|         1 |             | Mean RR                            |          8.3 |         25.6 | 13.0 | 0.278 |
|         1 |             | RMSSD                              |         16.4 |         58.2 | 14.0 | 0.222 |
|         1 |             | LF/HF ratio                        |          4.8 |         14.5 | 17.0 | 0.056 |
|         2 | With        | Mean HR                            |          0.8 |         -6.2 | 15.0 | 0.167 |
|         2 |             | Mean RR                            |         -4.3 |          6.4 | 15.0 | 0.167 |
|         2 |             | RMSSD                              |         -0.6 |          6.1 | 14.0 | 0.222 |
|         2 |             | LF/HF ratio                        |          9.1 |          7.4 | 18.0 | 0.000 |
|         3 | With        | Mean HR                            |         -4.0 |        -12.0 | 16.0 | 0.111 |
|         3 |             | Mean RR                            |          4.3 |         12.0 | 16.0 | 0.111 |
|         3 |             | RMSSD                              |         48.8 |         44.7 | 15.0 | 0.167 |
|         3 |             | LF/HF ratio                        |         -3.6 |        -47.0 | 14.0 | 0.222 |
|         3 | Without     | Mean HR                            |         -8.1 |        -10.3 | 17.0 | 0.056 |
|         3 |             | Mean RR                            |          8.2 |         10.5 | 17.0 | 0.056 |
|         3 |             | RMSSD                              |         41.7 |         39.3 | 17.0 | 0.056 |
|         3 |             | LF/HF ratio                        |        -24.1 |        -57.1 | 12.0 | 0.333 |

In  short,  the  descriptive  analysis  indicates  reduced  stress  levels  when  interpreting  into  the  B language with SmarTerp only in the first session. The stress difference between baseline and task performance was notably larger without SmarTerp. Although interpreting with SmarTerp remains stressful,  it  is  less  so  than  without  it.  Nonetheless,  uncontrolled  confounding  variables  may influence these results.

When  comparing  the  results  of  participants  interpreting  into  their  A  and  B  languages  as independent groups, the median percent differences are generally larger when interpreting into B language in both conditions and all sessions, albeit nonsignificant (Table 19). This result may suggest that interpreting with SmarTerp has a stronger stress-reducing effect when done into the L2.

## 3.2. Stress levels with and without SmarTerp over time 3.2.1. Interpreting into the A language (L1)

In  interpreting  into  the  A  language  using  SmarTerp,  descriptive  results  reveal  no  consistent reduction in stress levels over time. In Session 1, most indicators suggested increased stress levels during  the  task,  compared  to  the  baseline  (Mean  HR:  -4.0;  Mea n  RR:  57.0;  RMSSD:  28.0; LF/HF: -0.1). Session 2 exhibited a contrasting pattern, with Mean HR (0.5), Mean RR (-5.5), and LF/HF ratio (0.2) displaying increased stress levels during the baseline, while only RMSSD demonstrated  increased  stress  levels  when  at  task  (18.5).  In  Session  3,  stress  levels  were noticeably higher during the task than at the baseline for all indicators (Mean HR: -3.0; Mean RR: 35.0; RMSSD: 94.0; LF/HF ratio: -0.1). However, due to the lack of task randomization, it is unclear if the increase in stress in session 3 resulted from tool usage, increased text difficulty perception, poor performance, or a combination thereof. The omnibus Friedman test for median percent difference across the three sessions produced no significant findings (Table 20).

Table 20. Friedman test results comparing median percent difference for the three sessions for participants interpreting into their A language with SmarTerp. Note: degrees of freedom for all tests = 2

| Median percent difference for…   |   χ 2 |     p |     W |
|----------------------------------|-------|-------|-------|
| Mean HR                          |   0.0 | 1.000 | 0.000 |
| Mean RR                          |   0.0 | 1.000 | 0.000 |
| RMSSD                            |   0.3 | 0.846 | 0.028 |
| LF/HF ratio                      |   0.3 | 0.846 | 0.028 |

As  for  interpreting  without  SmarTerp  into  the  A  language,  there  was  a  consistent  trend  of increased stress levels during the task, compared to the baseline, was observed in both session 1 (Mean HR: -5.5; Mean RR: 79.5; RMSSD: 68.5; LF/HF ratio: 0.0) and se ssion 3 (Mean HR: -6.5;  Mean  RR:  62.5;  RMSSD:  73.2;  LF/HF  ratio:  -0.6).  The  median  percent  difference  in magnitude was comparable for two indicators (Mean HR in session 1: -8.8; session 3: -8.1; Mean RR in session 1: 8.3; session 3: 8.2), while for RMSSD (session 1: 16.4; session 3: 41.7) and LF/HF ratio (session  1:  4.8;  session  3:  -24.1),  the  stress  level  differences  were  substantially greater  in  the  third  session.  Inferential  statistics  revealed  no  significant  differences  between Session 1 and 3 in terms of median percent difference (Table 21).

Table 21. Results of the Wilcoxon signed-rank test for the no-SmarTerp condition in session 1 and 3 for participants interpreting into their A language. Note: all tests are unilateral

| Indicator   |    W |   p (Bonferroni) |      r |
|-------------|------|------------------|--------|
| Mean HR     |  9.0 |            1.000 | -0.143 |
| Mean RR     | 11.0 |            1.000 |  0.048 |
| RMSSD       |  7.0 |            1.000 |  0.333 |
| LF/HF ratio | 10.0 |            1.000 | -0.048 |

## 3.2.2. Interpreting into the B language

When interpreting into the B language, stress levels when using SmarTerp were lower, compared to the baseline, but only in session 1 (Mean HR: 5.0; Mean RR: -68.0; RMSSD: -45.7). Session 2 (Mean HR: -4.0; Mean RR: 54.5; RMSSD: 27.9) and session 3 (Mean HR: -9.0; Mean RR:

91.0; RMSSD m: 124.0) revealed increased stress levels when at task, compared to the baseline. The LF/HF ratio indicated negligible change in sessions 1 (0.0) and 2 (0.1), while in session 3, it suggested sympathetic activity (-0.4). As for the magnitude o f median percent difference, session 3  displayed  a  higher  stress  level  increase  (Mean  HR:  -12.0;  Mean  RR:  12.0;  RMSSD:  44.7; LF/HF ratio: -47.0) than session 2 (Mean HR: -1.0; Mean RR: -6.2; RMSSD: 6.4; LF/HF ratio: 7.4). Nonetheless, inferential statistics did not reveal significant differences between the three sessions (Table 22).

| Median percent difference for…   |   χ 2 |     p |     w |
|----------------------------------|-------|-------|-------|
| Mean HR                          |   2.3 | 0.311 | 0.194 |
| Mean RR                          |   2.3 | 0.311 | 0.194 |
| RMSSD                            |   3.0 | 0.223 | 0.250 |
| LF/HF ratio                      |   1.0 | 0.607 | 0.083 |

Table 22. Friedman test results comparing median percent difference for the three sessions for participants interpreting into their B language with SmarTerp. Note: degrees of freedom for all tests = 2

Interpreting into the B language without SmarTerp revealed increased stress levels during tasks compared to the baseline in  sessions  1 (Mean  HR: -19.0;  Mean  RR:  187.5;  RMSSD: 177.5; LF/HF ratio: 0.2) and 3 (Mean HR: -8.0; Mean RR: 83.5; RMSSD: 55.8; LF/HF ratio: -1.9). However, the magnitude of median percent difference was higher in session 1 (Mean HR: -25.5; Mean RR: 25.6; RMSSD: 58.2; LF/HF ratio: 14.5) compared to session 3 (Mean HR: -10.3; Mean RR: 10.5; RMSSD: 39.3; LF/HF ratio: -57.1). This suggests a decrease in stress levels in the final session, but there was no statistical difference between the two sessions (Table 23).

Table 23. Results of the Wilcoxon signed-rank test for the no-SmarTerp condition in session 1 and 3 for participants interpreting into their B language. Note: all tests are unilateral

| Indicator   |    W |   p (Bonferroni) |      r |
|-------------|------|------------------|--------|
| Mean HR     |  6.0 |            0.876 | -0.429 |
| Mean RR     | 15.0 |            1.000 |  0.429 |
| RMSSD       | 12.0 |            1.000 |  0.143 |
| LF/HF ratio | 18.0 |            1.000 |  0.714 |

In  brief,  stress  when  using  SmarTerp  does  not  seem  to  decrease  with  exposure  to  the  tool, regardless of whether participants interpret into their A or B languages. Perhaps the exposure period was too short for the participants to really be able to adapt their behavior to the features of the  software,  which  might,  as  explained,  require  increases  in  their  ear-voice  span.  Novice interpreters might need more time to adapt their ways than experienced interpreters, and they might have a harder time when faced with multiple sources of input (audio, video, written) when facing additional instances of decision-making to their interpreting (e.g., 'shall I use the suggested term?'), and additional attention-drawing areas on the screen. Moreover, the speeches used in the study  were  different  from  each  other.  Their  order  was  not  randomized,  which  might  have influenced  how  stressful  a  session  was,  whether  using  SmarTerp  or  not.  Follow-up  research comparing novices and experts, randomization of texts and tasks, and a design that includes a longer and more intense use of the tool should shed light on this issue.

Similarly, stress does not change between sessions 1 and 3 when the tool is not used. This, again, might be due to text and order effects, as interpreting without the tool was always the first task in a session. However, interpreting without SmarTerp was more stressful in all cases, which may indicate that the use of SmarTerp slightly reduces stress levels.

Finally, even if SmarTerp did not appear to reduce stress while interpreting, user experience and  cost-benefit  considerations  should  be  taken  on  board  as  well.  It  is  unclear  whether  this increased stress is perceived as relevant by participants. Self-reported measures, such as those obtained through validated psychometric tests, focus groups, and interviews would be useful to understand the effects of speech and speech order effects.

The  focus  of  attention  may  also  modulate  the  emotional  response  and  influence  the physiological response to potentially stressful situations. Wadlinger & Isaacowitz (2011) argue that attention focus on a stimulus can also regulate emotions by suppressing the processing of irrelevant stimuli. Translating is taken to be a more demanding task than reading for information. For instance, Rojo & Naranjo (2021) show that negative affect and stress levels of translation students were higher after simply reading an emotionally charged text than after translating it. Other studies, such as Rojo, Cifuentes & Espín (2021), Rojo, Foulquié, Espín & Martínez (2021), and Rojo, Cifuentes & López (2021) have shown that attention focus while translating can also modulate the participants' physiological response to emotional stress (e.g., cortisol or heart rate).

## 4. Concluding remarks

This paper has presented the results of a quasi-experimental, intra-subject study on the effects of the use of SmarTerp on physiological stress levels in simultaneous interpreting tasks carried out by MA students in interpreting with the aim to answer (I) whether it is less stressful to interpret with SmarTerp for such students, and (II) whether exposure to SmarTerp decreases stress over time.

The descriptive results suggest elevated stress levels when using SmarTerp in two out of the three  sessions,  compared  to  their  baselines.  This  was  anticipated,  considering  the  inherently stressful  nature  of  interpreting  tasks,  especially  for  students.  The  stress  levels  during  task execution  were  predicted  to  surpass  those  in  a  relaxed  state,  irrespective  of  SmarTerp  use. However, these descriptive observations did not translate into significant inferential outcomes: all inferential statistical results were non-significant.

Another descriptive trend was higher stress levels in the no-SmarTerp condition compared to using it,  hinting  at  a  potential  stress-alleviating  effect  of  the  tool.  This  descriptive  trend  was discernible in both B→A (into L1) and A→B (into L2) interpreting, but  the  effect  was more pronounced  when  interpreting  into  the  B  language.  This  could  potentially  be  attributed  to SmarTerp aiding in facing terminological units, proper names, and numbers, allowing students to focus more on linguistic quality during their performance. However, these descriptive trends were not substantiated by inferential statistics, which consistently revealed no significant differences.

One may ponder, then, how useful such a tool can be, in view that such stress reduction is nonsignificant and that, as discussed, the tool may lead to higher cognitive effort due to more complex decision-making and information processing. The answer to this question is not straightforward due to several impacting factors.

First, there can be many reasons for the interpreter to feel stress. For instance, the environment where data-collection sessions were conducted might have induced stress in the participants, or the elements SmarTerp provides assistance with might not have been stress triggers, whereas the topic, the delivery speed, or the speaker's diction were. Second, the use of SmarTerp could be adding extra layers of decision-making in the interpreting process (i.e., considering whether the solution provided is necessary, adequate, accurate, etc.), thereby increasing stress.

Third, even if stress levels are not lowered with SmarTerp to a significant extent, the tool may serve its purpose of aiding the interpreter in accurately rendering terminological units, proper names,  and  numbers.  The  project  to  which  this  study  belongs  also  aims  at  investigating  the accuracy  of  the  participants'  renditions  of  such  units  when  interpreting  with  and  without SmarTerp, and if and how the number of accurate renditions correlates with stress. These results will be reported elsewhere.

Both  descriptive  and  inferential  statistics  showed  no  decrease  in  stress  levels  when  using SmarTerp over time, with various potential explanations. Apart from discussed factors, the singlemonth period encompassing three interpreting sessions with SmarTerp may have been insufficient to create habituation effects, thus preventing the observation of stress reduction related to tool usage.

This study has several limitations, so the results should be interpreted with caution. A first, obvious limitation is sample size (N = 12), which inevitably affected the power of statistical tests. A second limitation is that texts and task order were not randomized. As for stress analysis, no self-reported measures of stress were collected through questionnaires, interviews or focus groups to cross-reference results with those obtained with Empatica E4 wristbands through the analysis of HRV as a physiological indicator of stress. Finally, the speeches had to be necessarily different through the tasks and sessions. While they were controlled for several features that may affect performance and stress levels, other potentially relevant features could not be controlled, such as each participant's subjective perception of task difficulty.

In addition to correlating stress levels with the participants' renditions, an analysis which is underway, the results of this study suggest further scopes for follow-up studies. For instance, our participants were at the same stage of their training, so they may have had comparable levels of interpreting expertise. The use of SmarTerp at different stages of training could be studied to determine whether it has positive effects on reducing stress levels. A longitudinal study would also allow us to observe whether student participants adapt their interpreting strategies to the platform. A further line of work might compare the stress levels of interpreting students with those of professional interpreters when using SmarTerp while at task.

## Funding and conflict of interest

This  research  was  funded  by  EIT  Digital  within  the  SmarTerp  project  (ref.  number  21184). Although the software will be commercialized, the authors had no conflict of interest, for they only participated in the piloting stage of the product and will not profit from its commercialization.

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## Appendix. Full data in aggregated form

|             | Session 1              | Session 2              | Session 2              | Session 2              | Session 2              | Session 3              | Session 3              | Session 3              |
|-------------|------------------------|------------------------|------------------------|------------------------|------------------------|------------------------|------------------------|------------------------|
|             | Baseline               | Without                | With                   | Baseline               | With                   | Baseline               | Without                | With                   |
| Mean HR     | Mdn = 68.5 SD = 15.7   | Mdn = 71.5 SD = 18.2   | Mdn = 60.0 SD = 9.2    | Mdn = 66.0 SD = 7.20   | Mdn = 67.5 SD = 14.4   | Mdn = 68.0 SD = 10.4   | Mdn = 74.0 SD = 13.5   | Mdn = 74.0 SD = 10.0   |
| Mean RR     | Mdn = 880.5 SD = 167.5 | Mdn = 840.0 SD = 152.9 | Mdn = 963.0 SD =114.5  | Mdn = 910.5 SD = 91.4  | Mdn = 890.5 SD = 175.9 | Mdn = 844.5 SD = 114.6 | Mdn = 801.5 SD = 119.3 | Mdn = 812.0 SD = 111.2 |
| RMSSD       | Mdn = 367.3 SD = 101.6 | Mdn = 308.3 SD = 168.0 | Mdn = 415.5 SD = 126.4 | Mdn = 377.2 SD = 105.2 | Mdn = 405.1 SD = 149.6 | Mdn = 354.6 SD = 104.8 | Mdn = 243.9 SD = 163.8 | Mdn = 218.5 SD = 149.6 |
| LF/HF ratio | Mdn = 1.179 SD = 0.515 | Mdn = 1.400 SD = 0.844 | Mdn = 1.273 SD = 0.910 | Mdn = 1.102 SD = 0.632 | Mdn = 1.154 SD = 0.805 | Mdn = 1.188 SD = 1.022 | Mdn = 1.711 SD = 2.306 | Mdn = 1.483 SD = 1.658 |