Questionnaire and interview assessment can provide reliable data on attitudes and self-perceptions on emotion, and experimental laboratory assessment can examine functional relations between stimuli and reactions under controlled conditions. On the other hand, ambulatory assessment is less constrained and provides naturalistic data on emotion in daily life, with the potential to (1) assure external validity of laboratory findings, (2) provide normative data on prevalence, quality and intensity of real-life emotion and associated processes, (3) characterize previously unidentified emotional phenomena, and (4) model real-life stimuli for representative laboratory research design. Technological innovations now allow for detailed ambulatory study of emotion across domains of subjective experience, overt behavior and physiology. However, methodological challenges abound that may compromise attempts to characterize biobehavioral aspects of emotion in the real world. For example, emotional effects can be masked by social engagement, mental and physical workloads, as well as by food intake and circadian and quasi-random variation in metabolic activity. The complexity of data streams and multitude of factors that influence them require a high degree of context specification for meaningful data interpretation. We consider possible solutions to typical and often overlooked issues related to ambulatory emotion research, including aspects of study design decisions, recording devices and channels, electronic diary implementation, and data analysis.

Measuring electrodermal activity (EDA) on the wrist with the use of dry electrodes is a promising method to help identify person-specific stressors during prolonged recordings in daily life. While the feasibility of this method has been demonstrated, detailed testing of validity of such ambulatory EDA is scarce. In a controlled laboratory study, we examine SCL and ns.SCR derived from wrist-based dry electrodes (Philips DTI) and palm-based wet electrodes (VU-AMS) in 112 healthy adults (57% females, mean age = 22.3, SD = 3.4) across 26 different conditions involving mental stressors or physical activities. Changes in these EDA measures were compared to changes in the Pre-ejection period (PEP) and stressor-induced changes in affect. Absolute SCL and ns.SCR frequency were lower at the wrist compared to the palm. Wrist-based ns.SCR and palm-based ns.SCR and SCL responded directionally consistent with our experimental manipulation of sympathetic nervous system (SNS) activity. Average within-subject correlations between palm-based and wrist-based EDA were significant but modest (r SCL = 0.31; r ns.SCR = 0.42). Changes in ns.SCR frequency at the palm (r = −0.44) and the wrist (r = −0.36) were correlated with changes in PEP. Both palm-based and wrist based EDA predicted changes in affect (6.5%–14.5%). Our data suggest that wrist-based ns.SCR frequency is a useful addition to the psychophysiologist’s toolkit, at least for epidemiology-sized ambulatory studies of changes in sympathetic activity during daily life.

Speech production interferes with the measurement of changes in cardiac vagal activity during acute stress by attenuating the expected drop in heart rate variability. Speech also induces cardiac sympathetic changes similar to those induced by psychological stress. In the laboratory, confounding of physiological stress reactivity by speech may be controlled experimentally. In ambulatory assessments, however, detection of speech episodes would be necessary to separate the physiological effects of psychosocial stress from those of speech. Using machine learning (https://osf.io/bk9nf), we trained and tested speech classification models on data from 56 participants (ages 18–39) under controlled laboratory conditions. They were equipped with privacy-secure wearables measuring thoracoabdominal respiratory inductance plethysmography (RIP from a single and a dual-band set-up), thoracic impedance pneumography, and an upper sternum positioned unit with triaxial accelerometers and gyroscopes. Following an 80/20 train-test split, nested cross-validations were run with the machine learning algorithms XGBoost, gradient boosting, random forest, and logistic regression on the training set to get generalized performance estimates. Speech classification by the best model per method was then validated in the test set. Speech versus no-speech classification performance (AUC) for both nested cross-validation and test set predictions was excellent for thorax–abdomen RIP (nested cross-validation: 96.6%, test set prediction: 98.5%), thorax-only RIP (97.5%, 99.1%), impedance (97.0%, 97.8%), and accelerometry (99.3%, 99.6%). The sternal accelerometer method outperformed others. These open-access models leveraging biosignals have the potential to also work in daily life settings. This could enhance the trustworthiness of ambulatory psychophysiology, by enabling detection of speech and controlling for its confounding effects on physiology.

Using heart rate (HR) measurements to detect mental stress in naturalistic settings is hampered by the physiological impact of hemodynamic and metabolic demands. Correcting HR for these demands can help isolate fluctuations in HR associated with psychosocial stress responses, a concept termed additional heart rate (aHR). This study examined whether adding predictors for posture, activity type, and lagged movement intensity for the prolonged impact of physical activity (PA) improved aHR estimation across various manipulations of mental stress, posture, and PA in a controlled laboratory environment (n = 197). Accelerometer signals were used to obtain the movement intensity and to classify posture and activity type. Posture, activity type, and lagged movement intensity each led to a significant improvement in HR estimation, as measured by adjusted R2 and root mean squared error. However, HR was overestimated during quiet sitting. The aHR, computed as the difference between observed and predicted HR, generally underestimated observed task-baseline reactivity but was sensitive to individual differences in reactivity to mental stressors. Between-subject correlations of aHR with task-baseline reactivity ranged from 0.62 to 0.93 across conditions. On a within-subject level, the ability of aHR to differentiate between exposure to physical stress and mental stress was limited (recall = 0.32, precision = 0.31), but better than that of observed HR (recall = 0.02, precision = 0.02). Future research should explore the potential of this novel aHR estimation method in differentiating physical and mental demands on HR in daily life, and its predictive value for health outcomes.

Respiratory sinus arrhythmia (RSA) is the heart period variability observed in synchrony with respiration. RSA amplitude is widely used in psychophysiological research to non-invasively index cardiac vagal activity. However, RSA measures are significantly affected by respiratory behavior, even in the absence of changes in cardiac vagal activity. Fifty-to-sixty percent of the variation in RSA can be attributed to respiration rate and tidal volume. This poses a notable challenge for ambulatory RSA measurement where respiratory behavior cannot be experimentally controlled and can show substantial variation. This pre-registered two-day ambulatory study (https://osf.io/57es4) compared four approaches to control for respiratory influences on RSA, to make an empirical recommendation on how to best capture cardiac vagal activity in daily life. We evaluated how well the RSA metric of each approach predicted (1) Minute-to-minute heart period, assumed to be predominantly governed by cardiac vagal activity, and (2) Perceived stress, positive affect, negative affect, and safety—states expected to elicit fluctuations in cardiac vagal activity— at smartphone prompts. The tidal volume-normalized RSA approach was optimal, explaining 1.47 times as much within-individual variance in heart period as that explained by uncontrolled-RSA. The need to use respiratory-controlled RSA was further highlighted by results on safety. Perceived safety was associated with uncontrolled-RSA (p = .033) but not with any of the controlled-RSA metrics. This relationship was driven by higher respiration rate co-occurring with lower safety. We recommend using tidal volume-normalized RSA in ambulatory research to avoid reporting spurious within-individual correlations between psychological states and cardiac vagal activity.