Robert Richer

Import and reconstruct saliva-sampling studies recorded by the 'CARWatch' application. Registration metadata and raw barcode events are converted into auditable study days and scheduled sample positions using a two-pass issue-review workflow. Functions assess sampling-time compliance, merge laboratory saliva measurements, calculate response features, and create quality-control visualizations. The application is described by Richer et al. (2023) .

Background: Chronic primary low back pain (CPLBP) is highly prevalent and associated with physical impairment and psychological distress. Fear avoidance (FA) is one contributing factor in the development and persistence of CPLBP. Previous studies proved existing connections between pain, physical activity, stress, and FA, which is also present in pain-free individuals. However, the roles of FA and psychophysiological stress responses have not been systematically disentangled and related to movement patterns combined in one standardized experiment involving pain induction. Methods: To address this gap, 76 pain-free participants (20: phase 1; 56: phase 2) will perform standardized movement tasks (Back Performance Scale, Sit-To-Stand-Test) with and without wearing a back pain simulator in a within-subject design. Movement will be recorded with wearable sensor-based and novel radar-based motion capture technology. Additionally, psychophysiological stress parameters (e.g., salivary cortisol, heart rate variability), and self-reports on stress, pain, and FA will be assessed. This will be followed by a qualitative feasibility and acceptability interview in phase 1. In this phase, we will evaluate the feasibility and acceptability of a novel back pain simulator enabling a standardized simulation of low back pain, and the motion capture technology. Phase 2 will investigate the effects of pain induction on self-reported pain, back pain-related movement parameters, and stress responses. Associations of these factors with FA will be analyzed. We anticipate high feasibility and acceptability. We expect higher pain ratings, lower mobility, and higher stress responses in the pain condition compared to the control condition. We expect FA to moderate mobility and stress responses in the pain condition. Discussion: This study protocol establishes an experimental paradigm to investigate the association of pain, FA, movement, and stress in healthy samples under controlled pain conditions. This paradigm aims to contribute to a deeper understanding of the interplay of psychological and physiological mechanisms in CPLBP. Trial registration: OSF, osf-registrations-rt2ca-v1. Registered April 2, 2025, https://doi.org/10.17605/OSF.IO/RT2CA ; osf-registrations-vsmce-v1. Registered September 18, 2025, https://doi.org/10.17605/OSF.IO/VSMCE .

In psychoneuroendocrinology (PNE), data is valuable due to its high clinical and health-related relevance and resource-intensive data collection yet sharing it openly alongside publications remains uncommon. If shared, data remain highly fragmented and non-standardized, which hinders data reuse and integration across studies. To address this, we formed a consortium and, as a first step, conducted this study using a modified Delphi approach across two survey rounds to inform about important considerations regarding data structure, metadata, subject-level variables, and implementation requirements before a data standard for PNE may be developed. The panel in the final round consisted of 50 responders from the field of PNE. In two online survey rounds, respondents rated their agreement with 84 statements anonymously on a 5-point Likert scale. Consensus (> 70% [dis]agreement) was achieved for 80 items. The results highlight a broad agreement within the PNE community on the need for, and key components of, a standardized data structure, including a balance between flexibility and standardization to allow for adaptability across many use cases as well as comparability. Developing and establishing a standard for PNE data will be a central next step to foster transparency, sustainable use of resources, and the translational potential of our work.

BACKGROUND Depression is one of the most prevalent mental illnesses worldwide, negatively impacting numerous aspects of individuals’ lives. It is typically diagnosed by trained professionals through structured interviews and questionnaires, yet the condition often remains undetected due to stigma, limited access to clinical services, and the reliance on self-reported symptoms. Recent advances in digital health have positioned digital biomarkers as promising complementary tools that enable passive, continuous monitoring of behavioral markers to support clinical assessment. However, existing approaches frequently rely on specialized sensing hardware or controlled recording environments, limiting their applicability in real-world settings and often remaining restricted to single-modality data, preventing them from unfolding their full potential by not considering relevant complementary data streams. OBJECTIVE This work evaluates the feasibility of detecting depressive symptoms through data captured by earables, including voice-related features, gait parameters, and head motion to enable unobtrusive, real-world mental health monitoring. METHODS A study was conducted with 25 participants, of whom 21 were included in the analysis (12 healthy, 9 with clinically elevated depressive symptoms, defined as a PHQ-8 (Patient Health Questionnaire-8) cutoff score of 10). Voice and motion data were captured via hearing aid–embedded sensors during semi-structured interviews and a free walking task designed to elicit behavioral and emotional responses in naturalistic settings. Audio features included a minimalistic set of paralinguistic and emotion-related features. Motion analysis incorporated spatio-temporal gait features, generic time-series descriptors, and expert-designed features. Group differences were assessed using Mann-Whitney U tests and age- and BMI-corrected ANCOVAs. Classification was performed using logistic regression (LR), k-nearest neighbors (kNN), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). RESULTS Multi-modal fusion of voice-, gait-, and motion-derived features yielded the best classification performance, with a balanced accuracy of 0.85 (SD 0.13) using a Random Forest classifier. An ablation study revealed that performance declined as feature sets were reduced, though configurations restricted to the interview condition or IMU-derived features alone retained competitive accuracies of up to 0.80. Unimodal configurations – voice or gait features only – achieved moderate balanced accuracies of 0.62, indicating that each modality independently carries relevant discriminative information, while their combination provides the most robust representation of depressive symptomatology. CONCLUSIONS These findings demonstrate the feasibility of passive, unobtrusive depression detection using a single ear-worn device. Rather than relying on specialized hardware or single-modality pipelines, the proposed approach simultaneously captures rich multimodal behavioral information across voice, gait, and motion. The results support earables as viable platforms for multimodal mental health assessment, establishing a foundation for future large-scale validation and real-world deployment in continuous mental health monitoring.

Background: Chronic primary low back pain (CPLBP) is highly prevalent and associated with physical impairment and psychological distress. Fear avoidance (FA) is one contributing factor in the development and persistence of CPLBP. Previous studies proved existing connections between pain, physical activity, stress, and FA, which is also present in pain-free individuals. However, the roles of FA and psychophysiological stress responses have not been systematically disentangled and related to movement patterns combined in one standardized experiment involving pain induction. Methods: To address this gap, 76 pain-free participants (20: phase 1; 56: phase 2) will perform standardized movement tasks (Back Performance Scale, Sit-To-Stand-Test) with and without wearing a back pain simulator in a within-subject design. Movement will be recorded with wearable sensor-based and novel radar-based motion capture technology. Additionally, psychophysiological stress parameters (e.g. salivary cortisol, heart rate variability), and self-reports on stress, pain, and FA will be assessed. This will be followed by a qualitative feasibility and acceptability interview in phase 1. In this phase, we will evaluate the feasibility and acceptability of a novel back pain simulator enabling a standardized simulation of low back pain, and the motion capture technology. Phase 2 will investigate the effects of pain induction on self-reported pain, back pain-related movement parameters, and stress responses. Associations of these factors with FA will be analyzed. We anticipate high feasibility and acceptability. We expect higher pain ratings, lower mobility, and higher stress responses in the pain condition compared to the control condition. We expect FA to moderate mobility and stress responses in the pain condition.Discussion: This study protocol establishes an experimental paradigm to investigate the association of pain, FA, movement, and stress in healthy samples under controlled pain conditions. This paradigm aims to contribute to a deeper understanding of the interplay of psychological and physiological mechanisms in CPLBP.

Accurate sleep monitoring is essential to assess sleep quality and diagnose sleep disorders. Although sleep laboratories provide precise assessments, they are expensive, labor-intensive, and unsuitable for long-term or large-scale monitoring. Radar-based sensing offers a fully contactless alternative, enabling unobtrusive real-world sleep monitoring. However, the lack of large, labeled datasets has limited the development of robust sleep stage classification models. We address this with transfer learning to improve classification accuracy and generalization to unseen participants within the radar cohort. An LSTM model was pretrained on movement, HRV, and respiratory features from the MESA Sleep dataset ($>$1,100 participants) and fine-tuned using radar data from 44 synchronized polysomnography recordings. Transfer learning increased the Matthews Correlation Coefficient from 0.25 to 0.47 (five-class staging), particularly for Wake, N3, and REM sleep. Future work should explore domain-adaptation across modalities and cohorts. Our results highlight the potential of radar-based sleep analysis for scalable, contactless long-term sleep monitoring.

Background: Experiencing stress is a normative part of human life. Due to its major impact on health and longevity, effort has been put into understanding underlying determinants and consequences. For stress measurement, a vast number of different methods, including physiological, subjective, and behavioral assessments, can be used. Previous measurement methods have already provided valuable insights into the role of the stress systems. However, their application has some drawbacks, highlighting the need for more convenient, less expensive, less intrusive, and continuous methods. A promising alternative could be the assessment of macroscopic body movements, which we define as body movements that are actively, consciously, or unconsciously, initiated by the muscular system. Methods: The study protocol was registered with PROSPERO, and PRISMA-P guidelines were followed. We will screen MEDLINE, PsycInfo, Web of Science, Embase, and Google Scholar for suitable studies in German or English with no restrictions towards the publication date. To do so, an a priori defined search strategy will be employed. Furthermore, we will carry out backward and forward citation searches. Inclusion criteria comprise studies examining macroscopic body movements in response to stress or related psychological constructs induced by standardized protocols. Exclusion criteria include studies on non-human populations as well as studies that do not report on a control group/condition nor pre-/post-comparisons nor traditional stress markers. The search, selection, and data extraction processes will be performed by two reviewers independently, while a third reviewer will be consulted in case of discrepancy. We will assess Risk of Bias using the revised Cochrane Risk of Bias tool or ROBINS-I, as appropriate. We will conduct random-effects meta-analyses where feasible. The quality of evidence will be evaluated using the GRADE approach. Furthermore, results will undergo critical examination towards meta-biases. Discussion: Due to the growing number of studies assessing macroscopic movements in the context of stress, the field is becoming complex, highlighting the need for a summary of current findings. Thus, here we outline a study protocol for a systematic review and meta-analysis on macroscopic movements as a potential novel marker for stress. Systematic review registration: PROSPERO CRD42024539659

Accurate detection of the aortic valve opening, represented by the B-point in the impedance cardiogram (ICG), is a critical step in non-invasive hemodynamic monitoring. The B-point is essential for deriving key parameters like the pre-ejection period (PEP) and left ventricular ejection time (LVET). However, accurately identifying this fiducial point is challenging due to a high intra-individual difference in the waveform, interfering factors like body movement, and a historical lack of publicly available datasets for evaluation. Capitalizing on recently published datasets, we propose a novel machine learning-based approach for beat-to-beat B-point detection that leverages domain knowledge from 12 different established ICG-based fiducial point detection algorithms. Using these in addition to the RR-interval as the feature set, we trained different machine learning pipelines on over 11,000 cardiac cycles. The best-performing pipeline, based on a Random Forest Regressor, achieved a considerable improvement in B-point detection accuracy and robustness. Compared to the best-performing traditional algorithm, our approach reduced the mean absolute error (MAE) by 54.7 % and its standard deviation by 47.4 %, resulting in a final MAE of 8.13 ± 12.12 ms. Our novel B-point detection method will be made available for the research community by integrating it in the open-source framework PEPbench .