Background: Pre- and intra-operative language mapping in neurosurgery patients frequently involves an object naming task. The choice of the optimal object naming paradigm remains challenging due to lack of normative data and standardization in mapping practices. The aim of this study was to identify object naming paradigms that robustly and consistently activate classical language regions and could therefore be used to improve the sensitivity of language mapping in brain tumor and epilepsy patients.Methods: Functional magnetic resonance imaging (fMRI) data from two independent groups of healthy controls  were used to generate threshold-weighted voxel-based consistency maps. This novel approach allowed us to compare inter-subject consistency of activation for naming single objects in the visual and auditory modality and naming two objects in a phrase or a sentence.Results: We found that the consistency of activation in language regions was greater for naming two objects per picture than one object per picture, even when controlling for the number of names produced in 5 s.Conclusion: More consistent activation in language areas for naming two objects compared to one object suggests that two-object naming tasks may be more suitable for delimiting language eloquent regions with pre- and intra-operative language testing. More broadly, we propose that the functional specificity of brain mapping paradigms for a whole range of different linguistic and non-linguistic functions could be enhanced by referring to databased models of inter-subject consistency and variability in typical and atypical brain responses. Awake craniotomy with intra-operative stimulation mapping is strongly advocated for patients with gliomas affecting eloquent brain regions . A growiThe purpose of this study is to demonstrate how functional consistency maps generated from large populations of neurotypical controls can be used to facilitate the selection of pre- and intra-operative language tasks that robustly and consistently activate core language areas. Below, we discuss the challenges related to current language mapping practices prior to illustrating how results from functional magnetic resonance imaging (fMRI) of neurotypical participants can be used to inform decision making.The complexity and wide distribution of language networks make it extremely challenging to predict how resection will affect language function. This is further complicated by inter-individual structural and functional variability, commonly observed in healthy individuals  and exacThe reliability of fMRI has been examined in a meta-analysis of studies comparing fMRI with direct electrical stimulation for language mapping. The authors found that the sensitivity of fMRI for detecting language areas ranged from 59–100%, with 0–97% specificity . It is aUnderstanding inter-subject variability in neurologically normal subjects is important because it can arise for multiple reasons, such as differences in the hemodynamic response, differences in task performance or differences in the neural systems used for the same task . In thisGiven the above, the results from fMRI analyses are not a substitute for intra-operative stimulation mapping; however, they are useful for helping to select tasks that are most likely to evoke a response during intra-operative stimulation mapping.The application of direct electrical stimulation (DES) for mapping of motor and sensory pathways in neuro-oncological surgery was described by Mitchel Berger in the nineties and it has subsequently become a part of the neurosurgeon’s armamentarium . DES is Functional disturbances during electrical stimulation may indicate that the stimulated region was required for the task tested, but they may also reflect false positives. For example, a reduction in speed or accuracy may not be due to disturbance at the stimulus site; it could be a consequence of (a) disruption in distant task-related regions through the spread of electrical current along connecting axons , (b) patAt present, no standardized protocol exists to reliably identify and test language regions in neurosurgery patients with many institutions assessing only one task . A surveWhile stimulation mapping with visual picture naming is considered the gold standard, the choice of stimulus modality should be carefully considered, taking into account the site of the lesion. The current study investigates how robustly and consistently different object naming paradigms engage sensory, motor and language regions in neurotypical individuals. As a proxy for neural activity, we used BOLD fMRI. By identifying object naming paradigms with the most consistent and robust BOLD responses, we generate hypotheses for optimal task selection for intra-operative and pre-operative surgical planning.Specifically, we compared how consistently four different object naming tasks activated sensory, motor and language regions in neurotypical individuals at the voxel/region level. Three of the object naming tasks involved visual (picture) naming, the third involved auditory object naming . For all four tasks, the number of stimuli presented and the fMRI acquisition time was controlled but, for two of the visual naming tasks, we presented objects in pairs every 5 s for a duration of 2.5 s, whereas in the other two tasks we presented one object at a time every 2.5 s for a duration of 1.5 s .We expected that the requirement to name two objects on a trial, rather than one, would increase demand on the regions involved in speech production  and so yield more robust activation at the individual level. If the naming of two objects results in more consistent activation at the individual level in speech production regions compared to naming a single object, then future studies could investigate whether naming two objects increases test sensitivity for intraoperative and pre-operative language mapping.Prior studies have aimed to compare the effectiveness of different language mapping fMRI paradigms e.g.,  using trWe used a large heterogeneous sample to deliberately maximize inter-subject variability . This heterogeneity makes our results more generalizable to clinical populations. As inter-subject variability in sample demographics was held constant across tasks, any task dependent differences in the degree to which a brain region is activated cannot be attributed to selection bias. Our goal was to identify tasks that result in the most consistent fMRI responses across participants, despite the heterogeneity in the subject characteristics. For tasks with low consistency  in fMRI activation across the whole sample, we investigated whether consistency differed for younger compared to older participants.The data used in this experiment were selected from the PLORAS database  rather tOur participants included 79 native English speakers, with normal or corrected-to-normal vision, and no history of neurological or psychiatric disorders. All were right handed according to the Edinburgh Handedness Inventory .n = 24) performed two different object naming tasks, counterbalanced across participants. The first object naming task  involved overtly naming a single object in a picture (see guitar”). Group 2 (n = 55) performed 4 object naming tasks including those performed by Group 1  and two tasks that presented two semantically unrelated objects per picture (see fox and boat”). In the other task, the objects interacted to depict an event and participants were instructed to overtly name the two objects within a sentence that described how the objects were interacting . To do so they used one of four pre-specified verbs that described the interaction: “eating,” “drinking,” “jumping,” or “falling.” The set of acceptable verbs was restricted to minimize inter-subject variability in verb selection. Passive constructions were ruled out by requiring the agent of the action to be named first. We expected inter-subject consistency to be highest for sentence production because this is the most challenging task and therefore most demanding on the language system.The 79 participants comprised two groups (Group 1 and Group 2). Group 1 (ture see . Successture see . In one wrundle”). The non-verbal non-semantic stimuli were colored patterns in the visual modality and meaningless humming in the auditory modality. During speech production, participants: named the objects, read or repeated the words and pseudowords, named the color of the meaningless visual stimuli or named the gender of the humming voices in the auditory modality. During 1-back matching, participants indicated whether the stimulus was identical or different to the preceding stimulus. For each subject, the stimuli presented during speech production were identical to the stimuli presented during 1-back matching.Group 1 participated in 16 different tasks including the visual and auditory single object naming tasks that we focus on in the current paper. In brief, the 16 tasks comprised a 2 × 2 × 2 × 2 factorial design . Factor The order of the 16 tasks was counterbalanced across 24 subjects. Half the subjects performed the speech production tasks first and half performed the 1-back matching tasks first. Within each of these groups, half were presented the visual stimuli first, then auditory stimuli, the other half were presented with auditory stimuli first then visual stimuli. Within each of these groups, each type of stimulus  occurred an equal number of times first, second, third, or fourth (across subjects). As exactly the same stimuli were used for speech production and 1-back matching tasks, a direct comparison of fMRI activation for speech production and 1-back matching identified brain regions involved in speech production, after controlling for stimuli. The main effects of stimulus modality , semantics versus non-semantic, and phonological versus non-phonological are reported in Group 2 participated in 13 different tasks see  includinThe second experiment in which Group 2 participated involved the 8 speech production tasks used with Group 1 . Edges and features were outlined in black to ensure that the objects were easily recognizable in the scanner see , confirmThe colors were not uniform in either the object naming or color naming tasks but pilot studies ensured that participants agreed on the predominant color of all the visual patterns. The stimuli used in the gender naming task (meaningless humming) were created by male or female voices humming with no phonological or semantic content.For Group 1, the 128 object names were assigned to four different sets of 32 stimuli . Each set also included 4 repeat stimuli that needed to be detected during 1-back matching . Sets A–C were rotated across pictures of objects, written object names and auditory object names, in different participants. Semantic and phonological content was therefore controlled across participants. Within participant, no stimulus set was repeated across the speech production tasks or across the 1-back matching tasks. Set D included the sounds of 32 objects that were always used during the object sound tasks and never used in any other task.For Group 2, the 120 stimuli were assigned to 6 different sets of 20 stimuli (A-F), with 8 stimuli in set G. Each task, except auditory object naming, presented 2 different stimulus sets. In Experiment 1, the first task presented two novel sets (A and C), and the second to fifth presented one novel set (not presented in a previous task) and one repeated set . In Experiment 2, visual object naming presented sets D and F. The pictures in set D were novel but their names had been presented during auditory semantic matching in Experiment 1. The pictures in set F were not novel as they had previously been presented for sentence production. For auditory object naming, participants were presented with 8 new stimuli from set G and 12 stimuli that had previously been seen or heard in Sets A to E.n = 12) vs. after (n = 12) 1-back matching. For (A), the names of objects in the object naming tasks were completely novel for Group 1 but not for Group 2 (see above). For (B) the pictures of objects in the object naming tasks were completely novel for the 12 subjects who performed the speech production tasks first, but not novel for the 12 subjects who performed 1-back matching first.As described above, the stimuli presented to Group 2 during two-object naming and sentence production (Experiment 1) were less familiar than the stimuli presented to Group 2 during auditory and visual single object naming (Experiment 2). Many prior studies have demonstrated how stimulus familiarity reduces neuronal responses, see Each task (16 for Group 1 and 13 for Group 2) was presented in its own (separate) scanning run with 4 blocks of stimuli, each lasting 25 s, followed by 16 s of fixation. Within block, there were 9 stimuli of the same kind  for all Group 1 tasks; and 10 stimuli for all Group 2 tasks. The stimulus repeat in the Group 1 tasks only needed to be detected and responded to (with a finger press) in the 1-back matching tasks but was also present in the speech production tasks in order to keep the stimuli constant across tasks. The inter-stimulus interval was 2.52 s for Group 1, 2.5 s for Group 2 Experiment 2, and 5 s for Group 2 Experiment 1 (which presented pairs of object stimuli), see Prior to scanning, we trained each participant on all tasks using a separate set of training stimuli except for the environmental sounds which remained the same. When in the scanner, participants were instructed to respond as fast as possible, keeping their body and head as still as possible and their eyes open and fixated on a cross in the middle of the display screen. Scanning started with the instructions “Get Ready” written on the in-scanner screen while five dummy scans were acquired . This was followed by a written instruction , lasting 3.085 s, which indicated the forthcoming start of a new block and reminded participants of the task that needed to be performed.Auditory stimuli were presented via MRI compatible headphones , which filtered ambient in-scanner noise. Volume levels were adjusted for each participant before scanning. Spoken responses were recorded via a noise-canceling MRI microphone , and transcribed manually for off-line analysis. Correct responses were those that matched the target without delay or self-correction. For two-object naming and sentence production, the response was only correct if both objects were named correctly within the inter-trial interval. In addition, for sentence production, the correct verb also needed to be produced. For some stimuli, more than one response was considered correct. For example, a picture of a mug could be named “cup” or “mug.” All other responses were categorized as incorrect. Response times for speech production were analyzed off-line but were only available for Group 2.∗ signal. After realignment and unwarping, the realignment parameters were checked to ensure that participants moved less than one voxel (3 mm) within each scanning run. The anatomical T1w images were co-registered to the mean EPI image generated during the realignment step and then spatially normalized to the MNI space using the unified normalization-segmentation routine in SPM12.Data for Group 1 and 2 were processed independently. All preprocessing and statistical analysis were performed in SPM12 , running on MATLAB 2012a . Functional volumes were spatially realigned to the first EPI volume and unwarped to compensate for non-linear distortions caused by head movement or magnetic field inhomogeneity. The unwarping procedure was used in preference to including the realignment parameters as linear regressors in the first-level analysis because unwarping accounts for non-linear movement effects by modeling the interaction between movement and any inhomogeneity in the T23 for structural T1w and 3 × 3 × 3 mm3 for EPI images). After normalization, functional images were spatially smoothed with a 6 mm full-width-half-maximum isotropic Gaussian Kernel to compensate for residual anatomical variability and to permit application of Gaussian random-field theory for statistical inference  instructions, (ii) stimuli with correct responses, (iii) stimuli with incorrect responses and (iv) “other” responses . Stimulus functions were convolved with a canonical hemodynamic response function and high pass filtered with a cut-off period of 128 s.For each scanning session/run , we generated a single contrast that compared activation in response to the stimuli and task of interest to resting with fixation. This resulted in 16 different contrasts (one per task) for each participant for Group 1 and 13 different contrasts for Group 2. Visual inspection ensured that there were no visible artifacts  that might have been caused by within-scan head movements. These contrast images were then entered into a second-level analysis in SPM12 so that we could functionally segregate our core regions of interest.p < 0.5–0.001). Threshold-weighted consistency maps are generated by defining a complementary cumulative histogram of the number of subjects against the statistical threshold th at each voxel. Consistency is expressed as a single number by calculating the area under the curve of the complementary cumulative histogram. Prior to estimating the area under the curve, the generated histograms were multiplied by a linear weighting function Wth that monotonically increased with th:Inter-subject consistency for all object naming tasks was evaluated, at every brain voxel, using threshold-weighted voxel-based consistency maps, as described in Tmin was set to p = 0.5 (uncorrected) to exclude effects of non-interest. To account for the spatial dependency between neighboring voxels, the voxel-based consistency value summarized the effect at the voxel of interest and its 6 nearest neighbors using a spherical volume of interest with radius of 2 mm. A low consistency value (the proportion near 0) means that the voxel was consistently not activated in almost all subjects. When the proportion is 1, the voxel was activated in each subject irrespective of threshold within the range of statistical thresholds. A proportion less than 1, indicates either consistency across subjects at a low statistical threshold or that only a subset of participants activated the voxel, irrespective of threshold  and across the whole brain, for different tasks.To investigate whether inter-subject variability is greater in older than younger participants, we split our 79 participants into two approximately equal sized groups (40 under 35 years old vs. 39 over 35 years old) and compared consistency across these groups for naming one object in (i) the visual modality and (ii) the auditory modality. In addition, we split the 55 participants who named two objects per condition and produced sentences into two approximately equal sized groups (27 under 40 years old versus 28 older than 40 years old) and compared consistency across age groups for the same tasks.In addition to considering inter-subject consistency in activation at the whole brain level, we also home in on the core language areas that are involved in extracting and producing speech sounds, specifically the left posterior superior temporal cortex (Wernicke’s area) and the left inferior frontal cortex (Broca’s area). Using data from Group 1 only, these regions were segregated from the rest of the object naming network by searching for voxels that were activated during (A) object naming compared to rest and (B) 1-back matching of written words and pseudowords compared to object and color naming. Contrast (B) has already been shown to activate areas involved in speech sound processing , consistp < 0.05 after family wise error correction for multiple comparisons across the whole brain (in height). In addition, we checked and confirmed that the identified voxels were also activated by 1-back matching of words and pseudowords compared to rest.Common activation for Contrasts (A) and (B) was identified by using a global conjunction in SPM with a statistical threshold of The left temporal and frontal regions activated by the conjunction are illustrated in Average in-scanner accuracy was 89% or above for each object naming task in both groups . ResponsFor naming single objects and two objects, activation was highly consistent in sensori-motor areas, including bilateral occipital, motor, and auditory cortices see . These rIn language regions of interest  or when they name two objects in a sentence . By contrast, activation is much less consistent when naming a single object from a picture  or naming an object from its sound (single auditory object naming). These findings have implications for pre-operative and intra-operative language mapping in that such mapping may have improved sensitivity to language function if the task involves presenting two objects in the same picture rather than pictures of single objects.per se, because the hemodynamic response to neural activity is expected to stay constant within region and we are comparing inter-subject variability/consistency within region. Moreover, we found no evidence that this greater consistency was the consequence of familiarity/fatigue effects. Instead, we hypothesize that naming two objects increases demand on processes related to word retrieval and production yielding robust language-related activation at the individual subject level.We observed greater consistency in language-related activation for naming two objects rather than one even though the number of objects presented was held constant within 25 s blocks (10 for two-object naming and 10 for single object naming). The results cannot be explained by inter-subject differences in the BOLD response In the language regions of interest used in the current study , activation did not show significantly more consistency for producing two object names in a sentence compared to a noun phrase see . TherefoIn the temporal lobe language region of interest, which included the temporo-parietal junction, middle and superior temporal cortex, activation was more consistent for auditory object naming than for visual object naming see . This isIn addition, our results show how consistently sensory and motor regions are activated by all four object naming tasks . This suOur functional consistency maps offer a potential data-based solution for pre-surgical planning, accounting for inter-subject variability. Specifically, for each region of interest, functional consistency maps can be generated to calculate inter-subject consistency in response to multiple different tasks and task differences (contrasts). Neuro-surgical teams could then compare the location of regions planned for resection with the output from a database that indicates (i) which tasks engage the region; (ii) the consistency with which the region is engaged for these tasks across neurotypical individuals; and (iii) which tasks might be optimal for pre-operative fMRI or intra-operative DES.The current study explored the consistency of object naming activation in healthy controls. These findings may not necessarily translate to patient populations because the object naming networks may have already re-organized in patients with brain tumors or epilepsy . To furtOur results suggest that successful implementation of the two-object naming paradigm in the intra-operative setting may allow for more sensitive language mapping. This motivates an explicit evaluation of whether the two-object naming paradigm provides a more reliable probe of language function than single object naming during intraoperative mapping. Further fMRI studies of neurotypical populations could also test how inter-subject variability/consistency changes with different inter-stimulus intervals. Based on the current results, our hypotheses are that (A) activation will be higher when participants are under time pressure but can still produce correct responses and (B) presenting two or more objects simultaneously provides a practical way to increase time pressure compared to presenting each object one at a time at a fast rate.The approach illustrated in the current study can be extended to map networks of regions activated by different language tasks. For instance, Object naming is a widely utilized task in patients undergoing neurosurgery and allows the mapping of a widely distributed network of speech production regions. In this study, we examined inter-subject consistency in activation during four different object naming tasks in neurotypical participants. Naming two depicted objects either in a phrase or in a sentence resulted in more consistent activation in core language areas  in comparison to single object naming . We therefore propose that requiring two objects to be named on a trial may optimize sensitivity to DES effects during awake language mapping. In addition, single object naming in the auditory modality (naming object from sounds) resulted in higher consistency of activation in temporal language regions in comparison to single object naming in the visual modality (naming objects from pictures). Our findings highlight the importance of selecting a stimulus modality based on lesion site.The data that support the findings of this study are available upon request from the senior author CP.The studies involving human participants were reviewed and approved by London Queen Square Research Ethics Committee. The patients/participants provided their written informed consent to participate in this study.CP, JE, and MS contributed to conception and design of the study. JE performed the statistical analysis. JE and CP wrote the first draft of the manuscript. DG and MK wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Mindfulness has become increasingly popular, and positive outcomes have been reported for mindfulness-based interventions (MBIs) in reducing stress. These findings make room for innovative perspectives on how MBIs could be applied, for instance through mobile health .The aim of this study is to investigate whether a nonguided mindfulness mobile app can decrease perceived stress in a nonclinical Dutch population over the course of 8 weeks, with follow-up at 6 months.A randomized controlled trial was performed to compare an experimental group that made use of a structured 8-week mHealth mindfulness program and a control group after 8 weeks, with follow-up after 6 months. Participants were recruited via a national television program. The primary outcome measure was perceived stress as measured by the Perceived Stress Scale, secondary outcomes were symptoms of burnout  and psychological symptoms . Outcomes were analyzed using a multilevel regression model.emotional exhaustion and physical exhaustion showed significantly lower scores for the experimental group after 8 weeks , but not at follow-up. There were no differences between groups for psychological symptoms measured using the 4DSQ.At baseline, 587 respondents were included. Results showed no postintervention differences between groups for the level of perceived stress. With regard to the secondary outcome measures, the VAS for These findings do not support our hypothesis that using the mindfulness app would reduce stress levels. However, our findings related to diminished exhaustion at 8 weeks are encouraging and require further investigation.ClinicalTrials.gov NCT05246800; https://clinicaltrials.gov/show/NCT05246800 Mindfulness practice has become increasingly popular, both in the general public ,2 and inMBIs are traditionally delivered face to face. New types of applications include web-based programs and mobile health  . AdvantaKassa, in the context of 4 broadcasts on the topic “stress” in March 2018. The study was announced during this TV show, and in that particular moment, viewers were invited to visit the program’s website and click the weblink with more information about the study and the possibility to apply for it. There were no eligibility criteria, other than being an adult. After having read and accepted the terms and conditions (informed consent), participants were referred to the web-based questionnaires. Randomization took place after they had filled out the baseline (T0) questionnaires and was performed with a built-in randomization algorithm. The experimental group was provided access to the mindfulness mobile app, which contained an 8-week nonguided mindfulness program developed to reduce stress symptoms. The control group was suggested to read information about stress and burnout on the Kassa website. Participants were not blinded to their condition, as they knew whether or not they received access to the mindfulness app. There were three time points of measurement: T0 , T1 , and T2 (6 months after randomization).A randomized controlled trial with follow-up measures at 6 months was performed. Participants were recruited through the television (TV) program The mindfulness application was developed by Minddistrict, an eHealth company in the Netherlands. The content of the app was developed by professionals in the field of mental health care and in accordance with the principles of mindfulness-based stress reduction and mindfulness-based cognitive therapy ,26. ThisParticipants age, sex, level of education, and occupation were assessed.The PSS measures perceived stress levels . The 14-control over emotions, memory and concentration, sleep, work interest, work performance, interest in others, emotional exhaustion, and physical exhaustion. Each symptom was rated on a 0-100 scale, with higher scores indicating higher difficulty.Burnout symptoms were assessed using 8 visual analogue scales (VASs). The symptoms measured were as follows: The Four-Dimensional Symptom Questionnaire (4DSQ) consists of 50 items rated on a 4-point Likert scale . The 50 t test (2-tailed). Two-sided P values of <.05 were considered significant. Data were analyzed using descriptive statistics in SPSS .The experimental group and the control group were compared using a multilevel regression analysis, with participants as the upper level and their repeated measures as the lower level. Time , treatment group , and the time–group interaction were postulated as fixed effects. The difference in change in the PSS and VAS between the groups at follow-up is considered the primary contrast. At T2, differences between groups for the 4DSQ were analyzed with an independent samples The study was approved by the Erasmus University Medical Ethical Committee and evaluated as not subject to the Dutch act on medical scientific research involving human subjects (METC 2017-1117).The final sample at T0 included 587 participants. This sample consisted mostly of highly educated (64.5%), employed (74.7%), and female (74.6%) individuals with a mean age of 46.05 (SD 13.64) years. More detailed information about the initial and final sample is provided in F1,501.26=0.70, P=.40) and T2 , for the time–group interaction, with the experimental group presenting lower exhaustion scores. Upon 6-month follow-up (T2), this effect was not maintained with the outcomes . Changes between the two groups over time for the other 6 VAS scales did not differ at T1 and T2. Outcomes for the 4DSQ subscales at T2 showed no significant differences between both groups.There were 8 VASs measuring burnout symptoms at T0, T1, and T2. Changes between groups over time were measured with a multilevel regression model. The VAS scales of Our primary research question served to investigate whether an 8-week nonguided mindfulness mobile app can decrease perceived stress levels in a nonclinical Dutch population. Our findings do not support our hypothesis that using a nonguided mindfulness app reduces perceived stress levels. Not observing an effect on stress in this study might be explained by the complete lack of personal contact in this study; that is, there was no mindfulness trainer reachable through the app, nor was there the possibility to engage in social contact with other participants. This potential hypothesis is supported by the recent review of Borghouts , which pAnother finding of our study was that the experimental group reported lower levels of both emotional and physical exhaustion after 8 weeks of using the app. This finding is particularly interesting, as different definitions of burnout all share exhaustion as a central component -38. For Future research on the effects of mobile mindfulness apps on burnout is warranted. Given the long waiting lists for mental health care, an ideal setting for further testing this or other mobile mindfulness apps would be general practitioner clinics and mental health care institutions. That is, driven by these long waiting lists, it has become more common to offer patients bridge interventions with low personnel costs, including eHealth modules. This provides a great opportunity to implement a study design with conditions that vary in the amount and type of personal contact. Such a design could even be advanced by using randomization schemes that allow for including patient preferences with regard to these aspects. Next, given our findings that both mental and physical exhaustion decreased in the app user group, it would be of great interest to conduct frequent measures of individual stress and burnout symptoms with an experience sampling method . This caThe fact that participants were unequally distributed between the experimental and the control group was a limitation of this study. This was owing to a previously unnoticed error in the automated randomization algorithm, which could unfortunately not be repaired afterward. Another limitation is the lack of an adherence measure, which makes it impossible to look at a possible dose-response relationship . FurtherOur study did not find an effect of using a mindfulness app on perceived stress levels in a nonclinical Dutch population. However, this may be owing to the type of respondent recruitment and a lack of control on adherence levels. Our results show diminished emotional and physical exhaustion in the app user group after 8 weeks. These findings are encouraging as they suggest that a mindfulness app has potential to be used as a preventive intervention for burnout.


Gasterosteus aculeatus) and its sister species, the Japan Sea stickleback (G. nipponicus), have been well characterized. Little is known, however, about the sex chromosomes of their congener, the blackspotted stickleback (G. wheatlandi). We used pedigrees to obtain experimentally phased whole genome sequences from blackspotted stickleback X and Y chromosomes. Using multispecies gene trees and analysis of shared duplications, we demonstrate that Chromosome 19 is the ancestral sex chromosome and that its oldest stratum evolved in the common ancestor of the genus. After the blackspotted lineage diverged, its sex chromosomes experienced independent and more extensive recombination suppression, greater X–Y differentiation, and a much higher rate of Y degeneration than the other two species. These patterns may result from a smaller effective population size in the blackspotted stickleback. A recent fusion between the ancestral blackspotted stickleback Y chromosome and Chromosome 12, which produced a neo-X and neo-Y, may have been favored by the very small size of the recombining region on the ancestral sex chromosome. We identify six strata on the ancestral and neo-sex chromosomes where recombination between the X and Y ceased at different times. These results confirm that sex chromosomes can evolve large differences within and between species over short evolutionary timescales.How consistent are the evolutionary trajectories of sex chromosomes shortly after they form? Insights into the evolution of recombination, differentiation, and degeneration can be provided by comparing closely related species with homologous sex chromosomes. The sex chromosomes of the threespine stickleback ( Takifugu rubripes), which are 1 My old, differ by only a single nucleotide  have degenerated extensively  present an opportunity to address these questions, as they show variation in sex determination among closely related species. They have experienced sex chromosome turnovers , transitGasterosteus wheatlandi. Gasterosteus sticklebacks (G. aculeatus) have been well characterized (G. nipponicus), which is sister to the threespine stickleback, shares those three strata on Chr 19, indicating that recombination ceased in their common ancestor , which split from the ancestor of threespine and Japan Sea stickleback about 14 Ma , so that chromosome is another candidate for the ancestral sex chromosome of the genus Gasterosteus  Homology controls for the effects of evolutionary time, as sex chromosome turnovers “reset the clock” of Y chromosome evolution. Observing that the same chromosome pair determines sex is not sufficient to establish homology, however, as the same autosome can be independently recruited as a sex chromosome in closely related species . In otheGasterosteus sticklebacks, and that it has degenerated much more rapidly in the blackspotted stickleback. Although the younger strata evolved independently in the blackspotted stickleback and the ancestor of threespine and Japan Sea sticklebacks, suppressed recombination evolved much more rapidly in blackspotted stickleback. The fused neo-Y of Chr 12 in blackspotted stickleback has an SDR with little degeneration, but it is much larger than the neo-Y on Chr 9 in Japan Sea stickleback, which is of similar age. These results demonstrate that homologous sex chromosomes can evolve striking differences over short evolutionary timescales.Here, we present the first genomic investigation of the blackspotted stickleback sex chromosomes. Using whole genome sequencing of parents and offspring in 15 families, we obtained experimentally phased sequences from 15 X and 15 Y chromosomes. We find that the blackspotted stickleback SDR extends along nearly the entire length of Chr 19, and that the Y is extremely degenerated over this whole region. This situation contrasts with that in threespine and Japan Sea sticklebacks, where extreme degeneration is limited to the oldest stratum on Chr 19 S1 in . We demoWe obtained phased sequences of X and Y chromosomes using 15 small pedigrees. These comprised interspecific crosses, each consisting of a blackspotted stickleback father, a threespine stickleback mother, one daughter, and one son  online. FST) because lack of recombination allows the two sex chromosomes to accumulate different substitutions , the SDRs on all Ys in the population descend from a single ancestral Y. Consequently, all Y sequences are monophyletic with respect to the X sequences in gene trees from this region. If enough time has passed since recombination ceased, the X chromosomes will also be monophyletic. The argument works conversely when recombination is suppressed via an X-linked modifier. By contrast, recombination in the PAR causes the Xs and Ys to be intermingled on the gene tree for that region of the sex chromosome. The monophyly criterion is a sensitive test for recombination because departure from monophyly is the cumulative result of many generations of recombination.The third pattern seen in SDRs is Y monophyly . When anFST between the X and Y is also highly elevated over nearly the entire chromosome (In a later section (Multispecies Gene Trees), we will establish that a stratum on Chr 19 is the ancestral SDR for all three species, and for convenience we will refer to it here as such. Both the read depth ratio and the monophyly criterion show that the SDR spans nearly all of the ancestral sex chromosome (Chr 19) in blackspotted stickleback . Most SNromosome B, suggesFST between the X and Y  at the end of Chr 19 distal to the fusion is a PAR  and 3. T X and Y B.Among the 100 kb windows in the SDR, 74% (150/203) exhibit X monophyly, as expected if recombination between the X and Y ceased long ago C. PatterAlong the length of the SDR, we see that read depth ratios vary, suggesting the presence of strata A. We ideStratum S1 is the oldest, and we show later in this paper that it is homologous to the oldest stratum on the threespine Y . Thus thWe refer to the second oldest stratum on the Y as stratum R1 in order to distinguish it from strata on the threespine and Japan Sea Y chromosomes. Here, the extent of degeneration on the Y is similar to that seen in stratum S1: the average read depth ratio is 0.60 A, and 83dS are not elevated in this region  to the youngest (R3) . The lack of a positive correlation between stratum age and sex chromosome differentiation has also been seen in other species with degenerate sex chromosomes, and is thought to result from bioinformatic artifacts .The pattern of est (R3) B. The dirtifacts . When a dS) between the blackspotted X and Y at sites that are not hemizygous varies between the four strata on Chr 19, as expected if they stopped recombining at different times . Divergence between the blackspotted and threespine stickleback lineages is dated at 14 Ma . This fusion doubled the size of the Y, as Chrs 12 and 19 are both approximately 20 Mb . Y monopnd neo-Y E. Thus, The read depth ratio in the SDR of the neo-sex chromosomes is nearly equal to the autosomal mean, indicating that the neo-Y is young and has not degenerated much  online. FST . It has significantly greater dS and dN than stratum R5 , but is not significantly different in dN/dS (P = 0.09) (dS by divergence with the threespine Chr 12 homolog (see above), we estimate that stratum R4 stopped recombining 1.7 Ma, and stratum R5 stopped recombining less than 1.0 Ma. These are likely overestimates because they represent the most recent common ancestor of all neo-X and neo-Y sequences, but the neo-X chromosomes are not monophyletic in many windows of the SDR   online.  the SDR F.FST, dS, and dN) we find that there are two young strata (R4 and R5) on the neo-sex chromosome (Chr 12).In sum, based on several statistics  between males and females, which is an artifact that results when sequencing reads from the duplicates on the Y chromosome mismap to their autosomal paralogs when using the female reference genome.Homology between Y chromosomes can be inferred from shared Y-specific duplications. Gasterosteus species. We first calculated FST between the sons and daughters in our pedigrees for the autosomes that they inherited from their blackspotted father. Values were averaged over 10 kb windows. We repeated this analysis using comparable pedigrees from the Japan Sea stickleback studied by FST values fall in the top 2% of the distribution. Of those, 183 windows are shared between blackspotted and Japan Sea sticklebacks. This number is far greater than expected by chance . In those windows, 98 SNPs are shared by both species. Three of these windows contain multiple SNPs where both species have high FST (>0.25) between the X and Y and the same male-specific allele (Amhy) in threespine stickleback, which arose by a duplication from Chr 8 to Chr 19 , but not in the congeneric Japan Sea or threespine sticklebacks. Gene trees across the SDR of Chr 12 in the blackspotted stickleback confirm that its neo-X and neo-Y are closely related to one another, and that its neo-Y is young since its sequences are embedded with the neo-X sequences (The neo-sex chromosome (Chr 12) is sex linked in blackspotted sticklebacks as well as the distantly related ninespine stickleback  and insights  not otherwise possible, and allow us to contrast the rates of evolutionary divergence among sex chromosomes.Clades sharing sex chromosomes descended from a relatively recent common ancestor offer unique insights into the repeatability of sex chromosome evolution, as they control for sex chromosome age and identity. We observed striking differences between the ancestral sex chromosomes (Chr 19) of the blackspotted stickleback and the threespine stickleback. Most notably, the SDR in the blackspotted stickleback is larger and more degenerate. Overall, 85% of genes have been deleted from the blackspotted Y, whereas only 56% have been lost from the threespine Y . In straThe difference in degeneration of the blackspotted and threespine stickleback Ys reflects both the differing ages of their strata and differing rates of evolution. Strata R1 and R2 in blackspotted stickleback are much older (13 and 12 My) than strata S2 and S3 in threespine stickleback (6 and 4.7 My) , and it −8, a naïve estimate of the effective population size based on θ  has degenerated much more rapidly in the blackspotted than in the threespine stickleback is not known. Degeneration of the Y chromosome can be accelerated by higher mutation rates, shorter generation times, and increased reproductive skew among males , but theon θ see , Chap. 3Gasterosteus sticklebacks contrasts with the high rates of sex chromosome turnover in other sticklebacks. Species in the genus Pungitius, for example, which have diverged over the last 4 My, vary both in which chromosome pair determines sex and in which sex is heterogametic  degeneration. The best examples come from birds. Although all birds share an homologous W chromosome, the number of strata, the degree of their degeneration, and size of the PAR vary widely between species that are distantly related . There dRumex hastatulus and R. rothschildianus are not homologous, they are of similar age, and yet they have degenerated to dramatically different degrees  are the only known cases of sex chromosome turnover events in which a new Y originated from an ancestral X chromosome  and blackspotted stickleback (G. wheatlandi) from Canal Lake, Nova Scotia, Canada , blackspotted stickleback from York Harbour, Newfoundland, Canada , and threespine stickleback from Humber Arm, Newfoundland, Canada . Collections were carried out with permits for the Maritime Region  and Newfoundland .All procedures involving live fish were approved by the Veterinary Service of the Department of Agriculture and Nature of the Canton of Bern (VTHa# BE4/16 and BE17/17) and the St. Mary’s University Animal Care Committee (17-18A2). All procedures involving live fish were approved by the Veterinary Service of the Department of Agriculture and Nature of the Canton of Bern (VTHa# BE4/16 and BE17/17) and the St. Mary’s University Animal Care Committee (17-18A2). During June and July 2017, we collected threespine stickleback  and 19  that were previously shown to be sex-linked in blackspotted stickleback   . We useds v.1.15  to filtehttps://stickleback.genetics.uga.edu/, whereas this manuscript was in review (dN and dS), the fraction of genes that are hemizygous, and the fraction of genes with premature stop codons. To do so, we generated a hard masked reference genome using the repeat annotations file with the maskfasta function in BEDtools v.2.26  to minimize genotyping errors at hemizygous sites. Finally, we used VCFtools v.1.15 to calculate weighted FST between the phased X and Y chromosomes. The maximum FST for all of our comparisons is 1 as we calculated differentiation between X and Y chromosomes rather than between males and females.Before further analyses, we filtered our genotypes using -f a) over 1,000 bootstraps, to generate gene trees in 100 kb windows across Chromosomes 19 and 12. We calculated the fraction of Ys (or Xs) falling within the largest monophyletic clade of Ys (or Xs) using a custom R script that employs the R packages ape (phytools (Gene trees within an SDR are expected to be “Y (or X) monophyletic” . This coages ape  and phytphytools . Gene trVCFtools –geno-depth v.1.15  and Tajima’s Our first approach for testing whether the sex chromosomes are homologous was to test whether chromosome duplications involving the Y are shared between species. We remapped the blackspotted stickleback sequences from this study to the unmasked assembly of the VCFtools v.1.15 to calculate weighted FST between the phased X and Y sequences separately for each species in 10 kb nonoverlapping windows across all autosomes. We then used custom R scripts to identify windows in which mean FST falls within the top two percent of windows in both species. Within each of these shared outlier windows, we calculated FST between the X and Y sequences at each SNP, and identified all SNPs with FST >0.25 in both species. We further filtered the set of shared high-FST SNPs to include only those sites in which an allele is restricted to males in both species . Windows that contain multiple SNPs satisfying these criteria are interpreted as conclusive evidence that the Y chromosomes in blackspotted and Japan Sea sticklebacks are homologous. The alternative hypothesis of independent duplications would require that the same autosomal region was independently duplicated onto the Y multiple times, and also that it then independently accrued the same point mutations at multiple loci in less than 14 My.We tested for homology between the blackspotted and Japan Sea stickleback Y chromosomes by identifying autosomal regions that show extreme differences in allele frequencies between males and females in both species. We first used blastn v.2.8.1 with default settings to search for similar sequences in the threespine stickleback Y reference  or monophyletic clades for a species (as expected of PARs or new SDRs).All windows within a nonrecombining stratum should share the same topology since they all descend from the same individual chromosome on which recombination was first suppressed . As described above, false topological inferences can arise from genotyping or phasing errors in regions of the chromosomes with large deletions on the Y. Therefore, we assume that the most common topology probably represents the true evolutionary history. Additionally, in our pedigrees, deletions on the Y are most likely to result in topologies in which one or both species’ Ys cluster with the threespine X sequences, since SNP-calling programs will wrongly impute the maternal threespine X allele as the blackspotted or Japan Sea Y genotype. Thus, topologies consistent with independent Y origins B or Y tuMolecular Biology and Evolution online.msab179_Supplementary_DataClick here for additional data file.


Aim: In Germany, the willingness to be vaccinated against COVID-19 is rather low among medical staff. We collected data on symptoms, antibody titers and vaccination readiness from clinic employees at a municipal clinic who had already been through a COVID-19 infection (asymptomatic to moderate). We also examined the antibody titers for their possible importance as an individual decision-making aid with regard to vaccination.Method: 200 employees of our municipal clinics were included in the study. COVID-19 antibody determination was performed using an ELISA . The participants were given an anonymous questionnaire containing anthropometrical issues, symptoms of the infection and questions concerning the vaccination decision. Finally, the antibody levels were reported to the participants and the attitude towards a vaccination was reevaluated.Results: In all 200 participants who had already gone through a COVID-19 infection, 75 employees were in favor of a vaccination (37.5%), 96 were opposed to vaccination (48%), and 29 were undecided (14.5%). In the different occupational groups, the positive trend in terms of willingness to be vaccinated was highest among physicians and is least among nurses. The antibody results showed considerable variation in titer levels and therefore did not correlate with disease severity in asymptomatic to moderately ill persons. We also observed a pro-vaccination trend with increasing age of the participants. The specifically-asked symptom of cutaneous hyperesthesia during COVID-19 infection occurred in 5% of the participants.Conclusion: In medical personnel who had already suffered from a COVID-19 infection, the willingness to receive a vaccination tends to be highest among physicians, and lowest in nurses, and increases with age. For the vast majority of those affected, knowledge of the antibody titers only reinforces the vaccination decision made beforehand and thus does not contribute to a change in vaccination decision. The specifically-requested symptom of cutaneous hyperesthesia during COVID-19 infection was unexpectedly frequent. The various problems raised by vaccination against COVID-19 include three points, some of which are addressed in the following investigation:There is currently a shortage of vaccines.The willingness to be vaccinated among the medical and nursing staff prioritized in Germany leaves much room for improvement , 2], , . CDC cannot comment on whether people who had COVID-19 should get a COVID-19 vaccine“ ) against vaccination; n=39 [39.80%] for vaccination; indecisive in the sense of waiting/waiting for more recent scientific findings: n=14 [14.29%]. (The percentages are given for each group).Group 2 (n=45 [51.14%]) against vaccination; n=33 [37.50%] for vaccination; indecisive in the sense of waiting/waiting for more recent scientific findings: n=10 [11.36%]Group 3 (n=6 [42.86%]) against vaccination; n=3 [21.43%] for vaccination; indecisive in the sense of waiting/waiting for more recent scientific findings: n=5 [35.71%]de novo decided against vaccination.Initially, 44% of the employees (Group 2) were undecided about a vaccination. Decisions were made in this group at the second timepoint of the survey with knowledge of the antibody titers, but were only rarely influenced by the knowledge of the antibody titers . In all three groups, the decision to be vaccinated due to a change in the information base, e.g., antibody titers, but also due to several other factors, was made by six individuals, but mostly due to concerns about possible restrictions in social life or when traveling (n=9). Two people Of all 200 participants, 75 employees were finally in favor of a vaccination (37.5%), 96 were against vaccination (48%) and 29 could not make a definitive decision (14.5%).Information to facilitate vaccination decision-making on the corresponding homepages of the RKI or the STIKO, which literally contains the question: “Should people who have been through a SARS-CoV-2 infection or have recovered from COVID-19 be vaccinated?” (8) was used/known by 21 participants (8%).In an attempt to analyze the position concerning vaccination, we separated the 200 persons into their occupational groups Table 3 .Even if there is a positive trend in terms of willingness to vaccinate, especially among physicians, and although the willingness to vaccinate seemed to be lowest among nurses, statistical testing was not possible due to the vastly different numbers of people in the various occupational groups.In an attempt to evaluate their position on vaccination, we also separated the 200 persons into three cohorts according to their age  Table 4 .There seemed to be a positive trend in terms of age: the older the participants were, the more willing they were to be vaccinated However, statistical testing is not possible due to the vastly different numbers of people in the various occupational groups.Due to the high prevalence of COVID-19 infections in Thuringia during the “second wave”, it was not surprising that the number of infections was also high among the staff of our municipal clinic. Although the majority of participants reported “work” as the assumed most likely source of infection, this is an estimate which does not necessarily reflect reality, since household contacts are still regarded as the main source of transmission in the international literature . As alreOur data also suggest a more positive attitude towards vaccination with increasing age. COVID-19 vaccination-rate monitoring in Germany (COVIMO Study)  by the Rth, 2021). However, the term “medical staff” was generally used in that study, which was not further differentiated according to occupational groups. Although this was the case in our work, there does not necessarily have to be a contradiction. One problem with our study lies in the fact that the information regarding a vaccination decision was given by the participants. It cannot be clarified whether the vaccination actually took place. The study evaluation was also completed before vaccination with the Astra Zeneca vaccine were temporarily stopped (March 15Overall, the willingness to be vaccinated did not seem high in our study population. However, it should be remembered that these are people who have already had a COVID-19 infection. It would be interesting to compare it with a population of healthcare workers who have not yet been had this infection. If the willingness to be vaccinated were significantly higher in the latter group, a trust in the natural, acquired immunity in our population would most likely be the reason for the difference. It was already mentioned in the introduction that the duration and reliability of naturally acquired immunity has not yet been definitely clarified , 7]. Th. Th7]. TIn terms of the antibody titers, we observed a relatively heterogeneous distribution of titer levels. Thirty-nine persons had IgA or IgG below the detectable range. This heterogeneous distribution has also been confirmed by other studies . The sevIn view of the previous individual case descriptions of cutaneous hyperesthesia in COVID-19 infection, we used the opportunity to specifically ask about these symptoms in a larger cohort. These symptoms were reported by 10 people, which allows the cautious conclusion of an incidence of 5% for a clinically asymptomatic to mild COVID-19 infection. The fact that only women were affected is more likely due to the high proportion of women in our study; cases in men are also described in the literature .In the case of medical personnel who have already suffered from a COVID-19 infection, the willingness to receive a vaccination tends to be highest among physicians and lowest in nurses, and increases with age. The antibody tests showed different titer levels and therefore did not correlate with the disease intensity in an asymptomatic to moderately-ill group. For the majority of those affected, knowledge of the antibody titer only reinforced the vaccination decision made beforehand, and thus did not contribute to a change in vaccination behavior. The specifically requested symptom of cutaneous hyperesthesia during COVID-19 infection is unexpectedly frequent at 5%.The authors declare that they have no competing interests.There was no financial support.We are grateful to the persons who provided the blood samples to support our scientific research.


Wearable cardiac sensors pave the way for advanced cardiac monitoring applications based on heart rate variability (HRV). In real-life settings, heart rate (HR) measurements are subject to motion artifacts that may lead to frequent data loss , especially for commercial devices based on photoplethysmography (PPG). The current study had two main goals: (i) to provide a white-box quality index that estimates the amount of missing samples in any piece of HR signal; and (ii) to quantify the impact of data loss on feature extraction in a PPG-based HR signal. This was done by comparing real-life recordings from commercial sensors featuring both PPG (Empatica E4) and ECG (Zephyr BioHarness 3). After an outlier rejection process, our quality index was used to isolate portions of ECG-based HR signals that could be used as benchmark, to validate the output of Empatica E4 at the signal level and at the feature level. Our results showed high accuracy in estimating the mean HR (median error: 3.2%), poor accuracy for short-term HRV features , and mild accuracy for longer-term HRV features . These levels of errors could be reduced by using our quality index to identify time windows with few or no data loss . This quality index should be useful in future work to extract reliable cardiac features in real-life measurements, or to conduct a field validation study on wearable cardiac sensors. Quantified Self nebula  bpm (kept wide to account for sport training events). The variation criterion considered that the value of an IBI sample cannot deviate from the mean of its neighbors by more than 30%. The whole outlier rejection procedure followed the guidelines of ,34. SincOutputs of this two-step outlier removal process are illustrated in In Empatica E4, IBI samples are provided only when the PPG signal is considered good enough by the sensor’s own algorithm. This results in a scarcer (yet more trustful) signal than the standard tacogram provided by BioHarness. After the outlier rejection step, however, the IBI samples from BioHarness were also scarcer when ECG signal quality was low. If the heartbeat detection algorithm truly detects heartbeats (this hypothesis will be discussed later), it can be assumed that sample scarcity (after outlier rejection) reflects the quality of the original IBI signal.t of IBI samples that should be found in a standard tacogram with no outlier rejection. Let a time window of duration W, ranging within , in which N heart beats are detected at times {t1, t2, …, tN}. The corresponding interbeat intervals are computed as: IBIn = tn − tn−1. In a raw IBI signal , all samples add up to the total time duration between the first and the last heart beat used for IBI computation. This can be expressed as:0 is the latest beat detected before the beginning of current time window (t = 0).A simple index of “sample scarcity” can be designed by comparing the practical number N of IBI samples actually found in a given time window, with a theoretical number Nt that is close to N:The mean IBI (µ) in the current time window is computed by dividing this sum of IBIs by the number of samples (N) found in this time window. Consequently, this number of samples N can be recovered by dividing the sum of IBIs by their mean µ. Since this sum of IBIs is close to W, according to Equation (1), one can also compute a theoretical number of samples Nt) derived from the window size (W) and the mean IBI (µ). When samples are removed from the IBI signal, however, the actual number N should decrease while the theoretical number Nt should remain stable. From the relative difference between these two numbers, we designed an estimator of sample scarcity, hereafter named the Lack Index (L). As shown in Equation (3) below, the Lack Index L can also be seen as the relative error between the sum of IBI samples and the time window length:In other words, when there is no missing sample in the IBI signal, the actual number of samples (N) that can be counted in any time window can also be estimated with a theoretical number  were respectively computed out of the three IBI estimators , on non-overlapping successive time windows where W was equal to 60 s. Regarding signal IBIe4, the Lack Index was used to quantify the lack of samples in real-life settings; and its impact on signal quality at the feature level. Regarding all IBI signals, the Lack Index was used to isolate time windows which can be considered as flawless, i.e., without any missing beat after outlier removal. This could be done by setting a rigorous threshold on its value, as shown in the next paragraph.The Lack Index estimates the proportion of missing beats in a given time window. Combined with an outlier rejection process, it evaluates the quality of the original IBI signal. In this study, three Lack Indexes  and tN+1 (located after t = W). Therefore, we can set the following inequality:By definition, a given time window of length W encompasses all heartbeat times {tWhen the heartbeat detection is flawless , Equation (1) applies for the sum of IBI samples and our Lack Index L is framed by two critical values:N+1/W, then the current time window contains flawed beat detections for the current IBI estimator. Since there is no confidence that IBIN+1 has been well estimated, the minimum IBI value found in the time window is a more secure alternative to prevent adjacent time windows from influencing each other. Therefore, flawless time windows  can be identified for each of the three IBI estimators  when the following criterion is satisfied:Consequently, if the Lack Index L is superior to IBIhr, extracted from online properties of the ECG, whose accuracy is not under scope in the current study. This confidence level can also be used to select reliable time windows of cardiac signal from the BioHarness sensor, since it guarantees that heartbeat detection is safe each time its value reaches 100%. Hence, one may identify a flawless window of cardiac signal when the minimum value of Chr over the time window is equal to 100%. This additional criterion can be used to validate the previous one in our attempt to find flawless windows on signals IBIbh and IBIpt:Alternatively, BioHarness provides its own SQI: the confidence level Cbh that were identified as flawless according to Equation (6) were used as benchmarks to assess the quality of signal IBIe4. This was done at the signal level and at the feature level through two distinct procedures: a beat-to-beat comparison and feature extraction from both signals.In the second part of this study, time segments of signal IBIe4 can be identified to one beat in IBIbh. This was done by splitting the timeframe of signal IBIbh at the middle between each couple of successive heartbeats, defining a set of time intervals surrounding each heartbeat  one overdetected beat. In each time interval where no heartbeat was missing or overdetected, a pair of matching IBI samples was identified between the two signals. The absolute difference was computed between all matching pairs of IBI samples.When both signals are properly synchronized, each beat in IBIbeat see  below. Sbh had no missing sample, according to the criterion provided in Equation (6). The following parameters were computed on each single time window: (i) the probability for a sample in signal IBIbh to be missing in signal IBIe4 (pmiss), (ii) the probability for a sample in IBIe4 to be an overdetection (pover), and (iii) the mean absolute difference in IBI value between pairs of matched samples in signals IBIbh and IBIe4 (μdiff). These parameters allow to quantify three types of errors that can occur during a beat detection: a false negative, a false positive, and a true positive with a wrong value. Let us call Nbh the number of samples for IBIbh in this time window, Ne4 the number of samples for IBIe4, nmiss the number of missing samples in IBIe4, nover the number of overdetections, and This beat-to-beat analysis was run for every time window where signal IBIIn advanced cardiac monitoring applications, descriptive features are usually computed over a full time window. For example, the mean interbeat interval (µ) and their standard deviation  are frequently used in the assessment of mental states. Some features are especially designed to describe HRV: for example, the root mean square of successive differences in the IBI (rmssd), and the low- and high-frequency components in its power spectrum ). The two frequency features were computed by integrating a Lomb-Scargle periodogram  ∗ 2 ∗ 0.15[Hz] = 18).The minimum number of samples needed to estimate the frequency features depends on the range and resolution of the Lomb-Scargle periodogram, which is different for each window of unevenly sampled IBI signal . To avoii is one of the three bandpass-filtered acceleration components, and  delimits a given time window. Activity A was thus computed for both sensors to provide one activity estimate for each body location: Abh for the chest and Ae4 for the wrist. Over the set of all available 60 s time windows in the database, our activity estimate Abh got a Spearman correlation coefficient of 0.91 with the mean activity estimate from BioHarness.Since previous error rates were computed under ambulatory conditions, the amount of body movement stands as a major feature to monitor in order to explain potentially low performances for each IBI estimator. Using data from its 3-axis accelerometer, the BioHarness sensor computes and returns an estimate of activity which, according to device documentation, is derived from the Euclidean norm on the 3 bandpass-filtered acceleration components. Although this parameter might accurately reflect the amount of chest movement, there is no equivalent indicator for the wrist-worn sensor E4. A common Activity level estimator was thus computed from the raw acceleration data of both sensors, to quantify body movements on both locations on comparable magnitude scales. Each component was first bandpass-filtered in the frequency range  Hz, with a digital second-order Butterworth filter, to account for non-human artefacts and the low-frequency contributions of gravity. According to previous studies in human movement quantification ,38, the e4 against estimator IBIbh at the signal level and (iii) at the feature level.This section presents validation results on sensor Empatica E4 through the three stages of our validation method: (i) selection of time intervals with no missing sample; then (ii) validation of estimator IBIbh, IBIpt, IBIe4), the criterion shown in Equation (6) was applied to isolate 60 s time windows where no sample was missing. The criterion based on BioHarness native SQI (Equation (7)) was also applied as a reference. Following each criterion, the size of the selected subset of time windows is displayed in bh) or the Lack index  had been used to select flawless time windows.For each of the three IBI estimators (IBIbh) and wrist movement (Ae4). Within each subset, the range of these indicators reflects the amount of body movement that can be monitored when the corresponding criterion is satisfied. The two last lines of bh and maximum Ae4 over the time windows selected following each criterion. A lower figure means that fewer degrees of physical activity could be monitored through the corresponding signal without losing IBI samples.Each subset of time windows also contained various amounts of chest movement : about half the original database was selected according to each SQI . Three conclusions can be drawn from these three figures. First, no cardiac sensor is immune to real-life artefacts, since half the database showed at least one missing beat after outlier rejection. Second, BioHarness algorithm for heartbeat detection (column 3) performed slightly better than the academic reference  on the same ECG recordings (with resp. 48% and 43% selected time windows). Third, the two criteria used for columns 2 and 3 validated each other in assessing BioHarness cardiac signals. Indeed, the proportions of the selected time windows are close to each other (resp. 51.4% and 48.2%); and 91% of the time windows that satisfied Equation (6) (based on the Lack Index Lbh) also satisfied Equation (7) (based on the Confidence Level Chr). The native BioHarness SQI thus validated the ability of the Lack Index to select IBI segments with good signal quality.In e4 . This significant shrinkage of the subset size (line 2) comes with a significant drop in the amounts of body movement that can be monitored in this subset (lines 3 and 4). Indeed, the maximum levels of chest activity (Abh) and wrist activity (Ae4) are identical across columns 2, 3, and 4 (for sensor BioHarness), up to 36% (for chest) and 50% (for wrist) of the maximum activity level in the original database. In column 5 (sensor E4), though, the maximum levels of chest and wrist activity are 20 times lower, down to 1.8% (for chest) and 2.3% (for wrist) of the maximum activity levels in the database.Compared to the three cardiac signals from BioHarness, however, our criterion selected a very small subset of time windows showing no data loss for signal IBIe4 can be considered flawless are very rare in real-life recordings. As expected, the quality of data from sensor Empatica E4 is strongly related to the amount of body movement. The next paragraphs address this issue more accurately, by validating the IBI estimate of Empatica E4 at the signal and the feature level.In a nutshell, bh showed missing samples (according to Equation (6)) were removed from the original dataset. This left 3370 time windows (56 h of recordings) where signal IBIbh could be used as benchmark to validate signal IBIe4. Following the heartbeat-to-heartbeat analysis proposed in e4 under real-life conditions , half the time windows have a pmiss over 98%. Actually, 42% of the time windows did not show any sample of signal IBIe4. In 90% of the time windows, the heartbeat detection algorithm missed at least one beat over two .Results on the missed sample rate pover = 0%). Overdetection is thus a marginal phenomenon in signal IBIe4. In plot (c), one may also notice that deviations between matched samples of signals IBIbh and IBIe4 are typically low: 90% of the time windows show a mean deviation (µdiff) below 130ms, which represents roughly 15% of a typical IBI value.In plot (b), however, one may notice that 81% of the time windows show no ovedetected heartbeat  is very low and the estimation accuracy is typically high. That said, plot (c) also demonstrates that such an estimation is not perfect: the mean value of µOne may still argue that histograms of e4 . In each of these subsets, the median and interquartile range were computed for the two error rates pmiss and µdiff . The results are displayed in To distinguish several amounts of wrist movement, the dataset was divided in 10 subsets of time windows corresponding to each decile of wrist activity Amiss) together show a steep rise across the lower deciles of wrist activity (Ae4 < 17 mG), and remain stable afterwards (pmiss ≈ 100%). According to the Student’s two-sample t-test, there was a highly significant difference in pmiss between the two first deciles of wrist activity . These results confirm that wrist movements strongly increased the probability of missing a heartbeat, even at the lowest rates (median pmiss ≈ 80% in the second decile of Ae4).In chart (a), the median (thick line) and the interquartile range (colored area) of the proportion of missing beats (pdiff) shows a comparable trend: the median error increases across the first deciles of wrist activity (from 21 ms at 4 mG to 36 ms at 17 mG); and tends to remain stable afterwards. A one-way ANOVA across all deciles of Ae4 showed a significant impact of this factor on parameter µdiff . However, the median curve remains encased in parameter variability (colored area); and there is no significant difference between the two first deciles of wrist activity . Compared to pmiss, the mean deviation µdiff was thus only mildly affected by the amount of wrist movement.In chart (b), the mean absolute deviation  and parameter pmiss are two distinct measures of the proportion of missing beats in a given time window. Over all time windows where signal IBIbh could be used as benchmark, the linear correlation coefficient found between Le4 and pmiss was 0.999. This suggests that, despites the underlying hypotheses, our Lack index precisely estimates the actual proportion of missing samples in a time window. The mean absolute deviation between the two indicators is 0.6%. Since an extremely low IBI  covers 0.8% of a 60 s time window, the minimum IBI divided by window length (60 s) was a relevant upper limit on Le4 to claim that a time window had no missing sample (pmiss = 0%). This validates the criterion proposed in Equation (6).In the current study, the Lack Index .Finally, the reliability of signal IBIbh could be used as benchmark ). The results are displayed in f. This function corresponds to the probability for error Ef to be less than or equal to a given value. The median error is obtained when the curve reaches 50% in ordinate. The full curve can be interpreted like a ROC curve: when it rises steeply from 0% to 100%, lower error rates come with higher probabilities, which means that feature extraction from signal IBIe4 is more reliable.These error rates were first computed over the 3370 time windows where signal IBIWhen features are blindly computed over all time windows (plain blue line) the median errors are, in ascendant order: µ: 3%, σ: 25%, lf: 25%, rmssd: 62%, hf: 63%. The mean IBI (µ) by far is the more accurately estimated: almost 90% of the time windows have an error rate below 10%. The curve of each other temporal feature resembles the curve of a frequency feature: σ and lf on the one hand, rmssd and hf on the other hand. Such similarities, which justified the positioning of the features on e4 should be more reliable. As stated in e4) defines a subset of time windows where signal IBIe4 shows lower error rates at the signal level. The cumulative probability of error for this “Low Wrist Activity” subset is materialized by the red dashed lines in all panels of To reduce such error rates and ensure a reliable feature extraction, one may set a criterion to select time windows where signal IBIe4), the first decile of the Lack Index (Le4) made another subset of time windows with “Low Lack index”. The cumulative probabilities of error in this subset are provided by the green, dash-dotted line in each chart of Since we have designed a SQI (the Lack Index) that estimates the proportion of missing samples in a time window, we also questioned the influence of this parameter on feature estimation quality. To mirror the previous segmentation over wrist activity . This more stringent criterion was designed to suppress the effect of data loss during feature extraction. In each chart of Finally, a third subset of data was made from the time windows where signal IBIAs expected, the “Low Wrist Activity” data (red dashed lines) yielded lower error rates than the full dataset. The “Low Lack Index” subset, however, yields better results for every feature, except hf. Regarding signal quality for feature extraction, therefore, the Lack Index seems to have greater discriminative power than physical activity estimates.Indeed, the “No Missing Sample” criterion (based on the Lack Index) performed by far the best segment selection. In chart (d), the green dotted curve is merged with the top of the graph: this means that all time windows showed error rates close to 0%. In the other charts, however, the feature estimation is still imperfect; the median error rates being: σ: 10%, lf: 6%, rmssd: 34%, hf: 27%. If the long-term HRV features  now display low error rates, this error remains high for the short-term HRV features , even when no sample is missing in the time window.e4 also reduced the number of time windows available for feature computation. Over all time windows where IBIbh could be used as reference, the mean IBI (µ) could be extracted 58% of the time (since at least 1 sample was required); features σ and rmssd, 51% of the time (since 2 samples were required); and features lf and hf, 23% of the time (since 18 samples were required).Beyond its impact on feature extraction, the low number of available samples in IBIAs a reminder, the contributions of this study are threefold: (i) a validation experiment of Empatica E4 on the field; (ii) a white-box SQI (the Lack Index) and a methodological framework to qualify heart rate signals in real-life settings; (iii) the use of the Lack Index to reduce the impact of data loss during feature extraction. The following paragraphs will discuss these three contributions.e4 was not biased. These results confirm the potential of PPG measurements for monitoring instantaneous pulse rates, which is already intended by a number of wrist-worn cardiac sensors in commercial settings.Technically speaking, Empatica’s built-in algorithm  is working quite well. In our dataset, overdetection of heartbeats remained a marginal phenomenon. The IBI estimation error was typically low and this misestimation degree increased only moderately with physical activity b. In othmiss in At the signal level, such an accuracy is achieved at the price of a very scarce IBI estimation, as shown by parameter pTo produce these results, we used a two-steps method to conduct a validation study in real-life settings: (i) select time intervals where a reference measurement  shows no missing sample after outlier rejection; and then (ii) compare the target heart rate signal with the benchmark IBI segments. Our method relies on a new white-box SQI: the Lack Index, which precisely estimates the proportion of missing samples in the IBI signal see . This SQThat said, the Lack Index assumes that all IBI samples in a time window are valid heart rate data. To be used as an SQI, it should be combined with an efficient outlier rejection procedure to ensure that all IBI samples have the properties of a valid cardiac signal. The procedure implemented in Regarding Empatica E4, the results of In In this study, we did not interpolate the IBI signal since we were interested in the impact of data loss on feature extraction. Based on the Lack Index, future work may consider the use of interpolation approaches at different missing sample rates on real-world measurements (similar work has been done with simulated data loss ). AnotheAs illustrated in this article, there is still work to be done on cardiac monitoring systems (using either PPG or ECG) to make the estimation of heart rate more robust to real-life conditions. Cardiac measurement will likely remain prone to artefacts, in spite of the technical advances made in the recent years. This illustrates the need for system-specialized SQIs (like the one proposed by BioHarness) to ensure that higher-level algorithms will not run on false information. In advanced cardiac monitoring applications, however, there is also need for white-box indicators to implement strong quality management strategies in the feature extraction process. In that perspective, the current study proposed a method to timely control the risk of error due to data loss in real-life settings. At a time when cardiac monitoring techniques are being developed beyond ECG and PPG, this method should also help the researcher to characterize several sensors outputs comparatively.The work reported in this manuscript has led to patent applications, currently registered under N° FR3098390-EP3763283-US2021007674.