Team Physiological Dynamics: A Critical Review

Teams, Teamwork, and the Input–Mediators–Output–Input (IMOI) Framework

We adopt commonly used definitions of teams (i.e., two or more individuals working interdependently toward shared and valued goals; Salas, Dickinson, Converse, & Tannenbaum, 1992), task work (i.e., performance behaviors related to individual team members completing their work; Baker & Salas, 1992), teamwork (i.e., performance behaviors associated with team member interactions; Crawford & LePine, 2013), team performance (i.e., the combination of task work and teamwork; Salas, Stagl, Burke, & Goodwin, 2007), and team effectiveness (i.e., the degree to which the team’s performance outcomes meet goals and expectations; Salas et al., 2007).

Two interrelated features of these definitions hold particular significance to this review. First, drawing on concepts from the broader science of complex systems (e.g., Anderson, 1999; Arrow, McGrath, & Berdahl, 2000), team performance is viewed as a multilevel phenomenon involving both top-down contextual influences on behavior in lower level components of the system, and bottom-up emergent processes originating from the interactions of lower level system components (Kozlowski, 2015). Conversely, physiological dynamics are measured only at the individual level. Consequently, to understand these phenomena within a team setting requires answering key conceptual and methodological questions about levels of analysis and aggregation. Second, team performance is dynamic. It unfolds over time and levels. Because physiological dynamics can be measured at a high sampling rate (e.g., 1 Hz and greater), these approaches can help address calls for more dynamic empirical studies of team processes (Kozlowski, Chao, Grand, Braun, & Kuljanin, 2013, 2016). To do so, it is first necessary to answer key questions about how TPD can be represented, associated with team processes or emergent states, and used to test theory-driven hypotheses.

In the absence of an overarching theory or framework for TPD, this review adopts the IMOI framework (Ilgen, Hollenbeck, Johnson, & Jundt, 2005) from the science of teams. The IMOI framework has served as an organizing heuristic for the science of teams for decades. Team inputs are relatively stable attributes of the team, its task, and context (e.g., team composition, interdependence structure, organizational setting; Bell, 2007; Stewart, 2006). Team mediators include process behaviors typically classified as action, transition, or interpersonal processes (LePine, Piccolo, Jackson, Mathieu, & Saul, 2008; Marks, Mathieu, & Zaccaro, 2001), and cognitive (e.g., shared situation awareness; Wildman, Salas, & Scott, 2014) and affective (e.g., team mood, stress; Lehmann-Willenbrock, Meyers, Kauffeld, Neininger, & Henschel, 2011) emergent states that fluctuate over a team’s performance episode. Outputs are the results of team interaction and typically include team effectiveness, efficiency, viability, and learning. The IMOI framework was chosen to organize this review because it is a very high-level organizational framework capable of classifying aspects of a variety of more bounded theories (e.g., around a specific physiological system like team neurodynamics, Stevens et al., 2013; or a pattern of observations like synchrony, Guastello & Peressini, 2017) as well as atheoretical empirical studies. This review aims to summarize literature across a wide-ranging literature from diverse disciplines. Although the IMOI framework does not necessarily facilitate advancement of highly bounded theories within the broad field of TPD (e.g., physiological system or pattern-specific theories), it does enable integration of these research streams into the team research community.

Physiological Measurement in Team Settings

The study of physiology in dyads and larger groups has a long history, including parent and child dyads, therapists and clients, romantic couples, and groups of friends and performers (Palumbo et al., 2017). For example, the work of John Gottman in the area of marital dynamics employed continuous measures of physiology, communication analysis, and self-report of emotional valence throughout conversations to predict long-term outcomes for relationships (Gottman, 2005). In addition, the broad multidisciplinary field of social neuroscience seeks to uncover biological and neurological processes underlying social behavior (Cacioppo & Cacioppo, 2013) and has provided new insights into social-psychological phenomenon (e.g., Social Identity Theory; Scheepers & Derks, 2016). These complex and diverse literatures indicate that physiological patterns between dyads and groups reflect meaningful changes in social processes (e.g., the presence of physiological synchrony [PS] relates to the health of an interpersonal relationship; Palumbo et al., 2017). However, much of this work has focused on individuals in social settings or nontask performing groups and is disconnected from the extensive research on work teams. Studying TPD in the context of work teams can help understand potentially unique relationships between physiological processes and tasks and variables that are relevant to work teams (e.g., metrics of team productivity).

Following Palumbo and colleagues’ (2017) definition of the more general concept of interpersonal autonomic physiology as “the general methodology for studying temporal interactions in physiological processes between multiple people” (p. 100), the current review and definition of TPD focuses on the measurement of physiological dynamics over time of team members captured during team performance episodes (i.e., while team members are interacting interdependently toward shared goals). Consequently, TPD occurs concurrently with team interaction processes and emergent states. In this review, we consider how TPD relates to team constructs broadly represented by the IMOI framework introduced earlier.

As illustrated in Figure 1, overlaying the IMOI and team member physiological processes yields four broad categories of potential research questions or conceptual relationships, and related practical applications. First, research can test relationships between team inputs and physiological dynamics of team members. This could include the use of team composition variables (e.g., different aggregations of individual difference variables; Mathieu, Tannenbaum, Donsbach, & Alliger, 2014) or manipulations of task, technology, or team design (e.g., task-induced workload; Toppi et al., 2016) to predict aspects of physiological dynamics in teams. Once established, these team input-to-TPD relationships could be used in team selection or composition systems (e.g., adding predictive validity to traditional trait measures important for team members), diagnostic competency assessment systems within intelligent tutoring systems (e.g., diagnosing different levels of skill proficiency within the team), or to evaluate the impact of different technologies or other structural interventions on team functioning.

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Figure 1.

Integrating the inputs–mediators–outcomes model with team physiological dynamics.

Second, research can test relationships be-tween TPD and team processes or emergent states. Here, the goal is to understand how physiological signals can be used to understand how certain team processes (e.g., communication patterns; Gorman et al., 2016) or emergent states unfold within a team over time. If validity evidence exists for these TPD measures, they can help to remove resource barriers (e.g., time to manually code team process behaviors) currently hindering more research on dynamic team processes and resulting applications. Understanding how patterns in physiological dynamics relate to team processes and emergent states can also contribute to real-time team management tools (e.g., team interaction analytics and displays to aid team reflexivity and self-correction or leader-driven interventions) or the design of interactive and synthetic agent systems (e.g., driving more responsive and effective models for artificial agents as team members).

Third, research can test relationships between TPD and team outcomes. This includes task (e.g., error rate, completion time; Henning, Boucsein, & Claudia Gil, 2001; Henning & Korbelak, 2005), affective (e.g., group potency, mutual trust), and team learning outcomes. With TPD patterns associated with output variables, predictive aids can be developed to use TPD signals to prompt the team to potential failures. Given the cyclical nature of the IMOI framework, this can include relationships between patterns of physiology within teams and future performance episodes of that team.

Fourth, in addition to TPD relationships with aspects of the IMOI framework, researchers can focus exclusively on the physiological measurements without further connection to the IMOI model. These studies could explore the psychometric properties of TPD, just as studies focus on the reliability or internal structure of other measurement methods within teams, or relationships between measures of different types of physiological dynamics (Strube & Neuman, 2007). Psychometric evaluation studies could include, for example, evaluating the impact of different instrumentation, pre-processing, or data aggregation and analysis techniques on physiological signal quality.

Search Strategy

We searched the PsycINFO and EBSCOHost databases with search terms related to teams, physiological measurement systems, and teamwork constructs (see Appendix A for details under each search term). The search was first conducted in February 2017 and updated in December 2017 and December 2018. Our inclusion criteria included (1) peer-reviewed journal articles, (2) teams comprising members who were unrelated to each other (i.e., excludes mother–child or romantic relationships), (3) team tasks and processes that could be generalizable to work settings (e.g., constructing objects, process control, flight simulation, etc.), and (4) TPD measurements obtained when team members were engaged in team tasks.

Figure 2 illustrates the screening process. The initial search in February 2017 produced a total of 1,902 hits. After title and abstract review and limiting to empirical peer-reviewed articles, we retained 19 articles. Forward- and backward-citation searches from these articles produced an additional 13 articles. After further culling articles that were not generalizable to work settings or that had not obtained physiological measurements while participants were engaged in team tasks, our final article count was 32.

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Figure 2.

Article search strategy.

Coding

Coding methodology was iteratively developed. First, we extracted characteristics of teams that were judged as potentially influencing the development and maintenance of TPD, including team size, mutual familiarity of team members, and the context of measurement, including the team task, task setting, sample expertise, collaboration duration, and times at which measurements were taken.

Next, we coded study findings using the four categories rooted in the IMOI framework as illustrated in Figure 1: inputs, mediators (i.e., processes or emergent states), outputs, and psychometric factors. The intent of this coding was to extract common features relevant to team research from studies originating from diverse disciplines. Examples of psychometric variables included the timing of physiological measurements (e.g., Ahonen, Cowley, Hellas, & Puolamäki, 2018) and attempts to decompose the source of the variance underlying TPD (e.g., shared task demands, co-location; Fusaroli, Bjørndahl, Roepstorff, & Tylén, 2016; Strang, Funke, Russell, Dukes, & Middendorf, 2014). Owing to the wide variation in methodology and reporting of results, including establishment of individual and team physiological baselines, task structures, and the exact nature of experimental manipulations, it was difficult to extract covariates to enable conducting a meta-analysis.

How Do Researchers Use Physiological Measurement in Team Settings?

We characterize how researchers use physiological measures to detect team phenomena, including the types of physiological systems measured, characteristics of teams studied, and methodological details.

What physiological systems are measured?

Figure 3 lists the variables measured within each physiological subsystem. Researchers measure both the central nervous system (CNS; for example, Stevens, Galloway, Wang, & Berka, 2012; Toppi et al., 2016) and the peripheral nervous system (PNS; for example, Chanel, Kivikangas, & Ravaja, 2012; Fusaroli et al., 2016; Henning & Korbelak, 2005).

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Figure 3.

Classification of physiological systems and their respective measures.

Measures of PNS activity primarily focus on the autonomic nervous system. Cardiac measures reflect activity of the sympathetic and parasympathetic nervous systems and were the most commonly used indicator of physiological activity, accounting for more than half (n = 18; 56%) of the articles. The electrodermal system (n = 11; 34%), in contrast, is a pure reflection of sympathetic activation because it measures the activity of eccrine sweat glands innervated by the sympathetic nervous system alone. Finally, facial electromyography (EMG; n = 5; 16%), a measure of somatic nervous system activity, was used to detect affect through zygomaticus major (ZM; that is, smiling), corrugator supercilli (CS; frowning), and orbicularis oculi (OO; frowning, stress) muscle activity during team events. Ten (31%) studies measured CNS activity using the electroencephalogram (EEG).

A majority of the articles (n = 23; 72%) measured only one system of physiological activation. Of these, cardiovascular measures were the most common (n = 10), followed by EEG (n = 8), electrodermal activity (EDA; n = 4), and facial EMG (n = 1). Simultaneous measurement of multiple systems can help inform the robustness of TPD and the generalizability of findings. Six (19%) articles measured at least two physiological systems or subsystems; of these, four articles measured cardiac activity and EDA. Only two (6%) studies measured both CNS (EEG) and PNS (cardiac, facial EMG) activity. Finally, only three articles (9%) measured multiple subsystems of the PNS: cardiac activity, EDA, and facial EMG activity.

How is individual-level physiological data treated at the team level?

In understanding TPD, it is important to consider how signals from individual team members are aggregated to the team level. Three main types of aggregation strategies were employed: (1) overall activation or arousal levels, (2) synchrony, and (3) organization or stability of patterns of activation. First, researchers used some form of overall arousal levels for individuals (e.g., averaging activation levels over some time) in multivariate analyses or created a team-level measure (e.g., averaging individual scores for the team). Four studies used an overall physiological arousal or activation measure, referred to as physiological activation (PA) in the following sections, including averages (Kivikangas, Kätsyri, Järvelä, & Ravaja, 2014) and magnitude of change in physiological responses compared with a baseline (Sariñana-González, Romero-Martínez, & Moya-Albiol, 2017, 2019).

Second, researchers used various forms of linear and nonlinear synchrony across team members’ physiological dynamics (for a review, see Guastello, 2017), referred to as physiological synchrony (PS) hereafter. These measures capture the degree of similarity over time between TPD but not overall activation levels. Nineteen studies used some form of PS measure, for example, cross-correlation and signal matching, making PS the most common TPD aggregation method.

Third, researchers used methods based in dynamical systems approaches (e.g., recurrence analysis; Knight, Kennedy, & McComb, 2016) to characterize the relative stability, organization, or disorganization (e.g., entropy) of patterns of activation within the team over time. Thirteen studies used some form of stability or organization metric, referred to as physiological organization (PO) hereafter, including cross-recurrence quantification analysis (CRQA) and multidimensional recurrence quantification analysis and cross-fuzzy entropy. Table 1 defines the most common statistical methods that are used for aggregating physiological data within each of these three categories, and the number of papers that use each method.

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Table 1

Definitions of Aggregation Methods Used in TPD

Aggregation Strategy	Method Name	Description	EDA	Cardiac	EMG	EEG	Sum	Category Total
Physiological activation	No aggregation	Overall arousal level of individual participants	2	3	2	3	10	10
Physiological synchrony	Cross-correlation	Correlation of bivariate time-series data at lag = 0	8	10	2	0	20	41
	Signal matching	The difference in area under the curve of two signals	1	2	0	0	3
	Instantaneous derivative matching	The average difference in the instantaneous derivative of the same point between the curves of two signals	1	2	0	0	3
	Directional agreement	The average change in direction of a point on a curve from the previous point	1	2	0	0	3
	Classic coherence	Linear relationship between two signals at a given frequency as a value between 0 (no correlation) and 1 (perfect correlation)	0	0	0	1	1
	Partial directed coherence	Granger causality-based statistic to calculate directed influence between a pair of signals in a multivariate data set	0	0	0	1	1
	Weighted coherence	Correlation of signals in specific frequency bands	3	4	0	0	7
	Autoregressive models	Observations from previous time points are used to predict the value of observations from subsequent time points	3	0	0	0	3
Physiological organization	Recurrence analysis	A time-series analysis that examines whether nonlinear dynamic systems return to similar states at different time points in their cycle	1	5	1	0	7	16
	Cross-fuzzy entropy	The use of fuzzy sets to examine the similarity of vectors based on exponential functions and their shapes	0	1	0	0	1
	Minimum-width envelope	A generalization of univariate confidence intervals to multivariate time-series data	1	0	0	0	1
	Neurodynamic entropy	The symbolic representations of the momentary power levels of a neurodynamic marker for each team member. By analyzing how the neurodynamic symbol changes over time, that stability or change patterns of activation across team members can be assessed	NA	NA	NA	7	7
		Sum	21	29	5	12

Note. TPD = team physiological dynamics; EDA = electrodermal activity; EMG = electromyography; EEG = electroencephalogram.

It is important to distinguish between measures of TPD that preserve temporal information or characterize sequence or order effects in physiological dynamics, and those that do not. Measures of PS and PO represent temporal dynamics in different ways (e.g., synchrony vs. entropy, respectively) whereas measures of PA aggregate physiological data in a way that much of the temporal information is lost (e.g., a mean level of activation over a performance period includes no information about trends over time or interrelationships between team members). Therefore, PS and PO measures represent either within-person or team changes over time, and PA measures collapse observations across time.

What Relationships Exist Between Physiological Measures and Team Constructs?

Research has explored relationships between TPD and all three categories of team variables described by the IMOI model as well as a variety of psychometric factors. Appendix B presents summarized findings about specific variables investigated under each general teamwork variable. Findings are organized under three categories to correspond to whether findings about the specific variables are unequivocal, equivocal, or fail to establish a relationship with TPD. Team input and outcome variables dominate the literature. Psychometric variables concern aspects of study design and measurement, and influence the interpretation of the nature of the relationship between TPD and many team input and output variables. Consequently, we report results of the main psychometric factors investigated before summarizing findings for team inputs, mediators, and outputs.

Psychometric Variables

The majority of psychometric evaluations have focused on measures of PS and PO and sought to understand the impact of different statistical methods for detecting synchrony or organization in TPD (e.g., linear vs. nonlinear methods, differences in lag lengths for cross-correlations). Psychometric variables also consider contextual or task factors influencing the magnitude of PS or PO. Three main categories of psychometric variables were identified in the literature: (1) measurement time, (2) aggregation method, and (3) differentiating shared task demands, co-presence, and team-specific or team-generic processes.

Team Inputs

Existing research primarily explores three categories of team input constructs: individual differences in team composition, team role and task structure, and team task demands.

Team Mediators

Two categories of team mediator variables dominate the relatively small extant research connecting TPD to team mediators: affective emergent states and team process behaviors.

Team Outputs

TPD studies cover four main types of team outputs: objective task performance, perceived workload, team affective outcomes, and team learning outcomes. Team learning outcomes (i.e., likelihood of adopting new production routines) have been discussed in relation to induced emotions, so this section will focus on the first three team output variables.

Develop a Theory-Driven Approach to TPD

The TPD field may benefit from teamwork frameworks and theories to formulate research questions and testable hypotheses, and teamwork researchers may benefit from exploring more bounded theories of physiology in groups and dyads not yet applied to work team research. For example, examining the current research in terms of the IMOI framework makes it apparent that TPD has focused primarily on team inputs and outputs. The study of mediators and processes is underdeveloped. Existing studies linking process variables such as communication with TPD are promising (Gorman et al., 2016), and may help in developing predictive aids to diagnosing maladaptive team states and cue appropriate interventions or countermeasures. Theoretical conceptualizations of TPD can be improved by specifying the evolution of TPD over time (Luciano et al., 2018). Team process behaviors are conceptualized as emerging through two types of processes: compositional or compilational (Kozlowski & Klein, 2000). Compositional emergence is characterized by convergent dynamics (i.e., people within a team become more homogeneous over time on some dimension; for example, trust increases, a shared mental model develops). A compositional form of emergence in TPD could include increasing similarity in measures of cardiac functioning as a team progresses through a task. Compilational emergence involves divergent dynamics (i.e., people within a team become more heterogeneous over time; for example, specialized skills or knowledge for different team roles are developed as the team interacts). These dynamics may show fluctuations over time, within-team variability, and different growth trajectories (Kozlowski, 2015). In the case of TPD, it is important to understand what types of emergence have been observed in the literature and what these dynamics indicate about the team.

In the current literature, TPD is most commonly treated as a compositional emergent phenomenon (PA, PS) and sometimes as a compilational phenomenon (PO), but rarely as both within the same study unless they are being compared with one another. There is evidence that composition and compilation conceptualizations can be differentially appropriate based on the situation. For example, using linear methods for generating metrics of PS may be appropriate for tightly coupled teams where all members share similar task demands (e.g., Järvelä et al., 2014). Conversely, nonlinear methods may be more appropriate for teams with more diverse collaboration patterns (e.g., Fusaroli et al., 2016). Characterizing the entire performance period through a single process ignores the potential of TPD to vary and evolve over time within the same team (Kozlowski, 2015). We need a better understanding of situations under which either or both conceptualizations of TPD are appropriate.

An increasingly large set of theories and streams of research outside of the traditional work team literature can help inform the development of theory for work team TPD. For example, work in joint action (Sebanz, Bekkering, & Knoblich, 2006), interpersonal coordination (Marsh & Richardson, 2009), interactive alignment (Garrod & Pickering, 2009), and mirroring and coupled dynamics (Hassan & Firth, 2016) from basic cognitive science research address related phenomenon. These research streams do not necessarily focus on work teams as a context but do explore physiology and interpersonal interactions in ways that can inform TPD research in work teams. Expanding studies in the process/mediators domain can also help inform conceptualizations of teams as complex systems (Arrow et al., 2000), and dynamic theories of team interaction such as Interactive Team Cognition (ITC; Cooke, Gorman, Myers, & Duran, 2013). The ITC theory proposes that team cognition is an activity that emerges from interactions between team members. Team cognition can be understood by considering patterns of communication and coordination within the context of work. Using dynamic theories of team cognition as a theoretical lens can help TPD researchers answer a variety of questions, including the reciprocal effect of team-level interactions on individual- and team-level physiological dynamics, evolving trajectories of multiple dynamical phenomena (i.e., individual physiological dynamics and TPD, communication), and patterns of overlap in these trajectories.

Operationalize Physiological Signals in Terms of Team Constructs

Physiological signals within team settings should be operationalized in terms of the team construct of interest. Operationalization should also include clarification of the measurement context, including occurrence of convergent and/or divergent work processes during the measurement period. This is especially important for physiological dynamics because the same phenomenon (e.g., elevated heart rate variability) can have competing interpretations. Using a “measure fit” process (Luciano et al., 2017), including defining the team construct (e.g., team affect, cognition, behavior, workload) and features of the measurement method and context, can help in aligning the theoretical construct with its appropriate measure.

Cacioppo, Tassinary, and Berntson’s (2007) three dimensions of psychophysiological relationships can also guide operationalization. First, researchers should consider generality, or whether psychophysiological phenomena are dependent on the measurement context. It is possible for the same phenomenon (e.g., elevated heart rate) to occur during different measurement contexts (e.g., manipulation of psychological stress vs. manipulation of task engagement). Specificity is concerned with whether a physiological phenomenon can be mapped to a single team variable or whether many-to-one mappings exist. Sensitivity concerns the degree of change required to occur in a psychological variable to be reflected as a change in a physiological process. Appropriate operationalization in terms of the measurement context can help successfully describe how physiological data represent manifestations of specific team constructs.

Develop Methodological and Reporting Standards in TPD

There is wide variability in study methodologies and reporting of key variables, which makes it difficult to not only interpret the findings of individual studies but also establish the reliability and validity of the field in general. Therefore, it is critically important for the TPD field to establish standards to guide the study of physiology in teams. This entails extracting best practices from the fields of physiological dynamics and team research for study design, data collection, and technical reporting on variables affecting teamwork and the development of TPD. For example, there is inconsistency in reporting teamwork and task work variables affecting TPD. Based on current findings, we recommend reporting prior familiarity between team members, whether team members have a history of shared work experiences, and familiarity with the task and the setting.

There is also a need to improve baseline measurement of individual physiological dynamics and TPD. When establishing baselines, it may be helpful to consider the sources of variance attributable to shared team physiology. At the most basic level, we can consider the baseline physiology of individual team members. To control for the effects of social interaction, it may be advisable to measure baselines of individual team members at rest and in isolation. Another source of variance, the effect of others’ presence on individual physiology, can be obtained through simultaneous measurements of individual team members who are collocated but not interacting. In the next step, the effect of nontask interaction can be obtained by having collocated individuals engaged in conversation while measuring their individual physiologies. Finally, the effect of task work and teamwork on baseline physiology can be measured during practice trials in which team members collaborate to accomplish the task.

Researchers should also follow best practice guidelines for statistical processing and analysis. This begins with using standard methods to detect and remove artifacts from physiological data. If linear statistical analyses (e.g., correlation coefficient) are planned then the appropriate transformations (e.g., converting nonstationary data to stationary) should be performed on the data. Developments in statistical analysis software (e.g., LedaLab, Physioscripts; Christie & Gianaros, 2013) on different types of programs (i.e., MATLAB, R, Python) have automated many steps. During statistical analyses, it is important to be mindful of Type I errors by adjusting the alpha value by the total number of desired tests. This statistical correction is rarely computed or reported in the current literature (see Järvelä et al., 2014; Montague et al., 2014).

Expand TPD Beyond Measures of Single Systems

Cardiovascular measures, either by themselves or in conjunction with other measures, are the most commonly used indicator of TPD. It is relatively rare for researchers to simultaneously measure more than one subsystem under the PNS, and rarer still for measurements to span across the CNS and PNS. However, due to the complex nature of interpersonal physiology, studies that only use a single measure of physiology may not capture a meaningful representation of general patterns in TPD.

The simultaneous analysis of multiple signals offers many benefits. First, it can help establish the relative sensitivity of different physiological systems to team research manipulations (e.g., lag differences in inter-physiological measurement in detecting team phenomena) and can improve predictive power. Second, it is a conceptually robust approach as human responses naturally involve multiple interacting physiological and psychological subsystems. However, it is a difficult undertaking, as it involves parsing intra-individual variability across time within the same measure and different measures, while also capturing this variability across the team.

There are two general approaches to combine information across sensors: across subjects and within subjects. Couplings between physiological parameters within subjects is rare but could generate new insights. Inter-parameter coupling might change, for example, under stressors of various kinds. Individual parameters might stay within their normative bounds while their interactions might deviate from these bounds; this could provide additional discriminatory information.

Extending measurements across multiple physiological subsystems can also add to contextual information that helps clarify the nature of the team construct. For example, knowing that there has been excitation in EDA without further context makes it unclear if the excitation is representing a positive or negative emotional response. To address this, Håkonsson and colleagues (2016) used two measures of facial EMG, indicating smiling and frowning, to show that heighted EDA was associated with positive emotions. In addition, use of multiple measures can help advance inter-measure reliability in capturing psychological and sociological phenomena.

One challenge to multiphysiology measurement is contending with the highly obtrusive nature of measures. Such measures of physiological activity typically impose physical constraints by limiting mobility. The use of newer technology, such as higher quality physiological wrist sensors, and emerging methods of contactless measurement of physiological states (Wei et al., 2018) present great promise to the field because they allow for the unobtrusive collection of multiple physiological measurements.

Develop Methods of Statistical Modeling and Aggregation of TPD

Currently, there are three main strategies of data aggregation (PA, PS, and PO). PS is the most frequently used aggregation strategy and characterizes compositional TPD. In addition to the extant approaches in PS, statistical advances in modeling physiological patterns in dyads can also help understand compositional patterns in TPD (Gates & Liu, 2016; Liu, Zhou, Palumbo, & Wang, 2016). The stability and influence model (Thorson, West, & Mendes, 2018) uses multilevel modeling approaches to model dependencies between repeated measures of data within the same individual (i.e., stability), and influences from partners. Helm, Miller, Kahle, Troxel, and Hastings (2018) describe three patterns of dyad dynamics. Trend synchrony describes a pattern in which physiological dynamics of both members of a dyad fluctuates around similar statistical trends (i.e., linear, quadratic, cubic) across time. Concurrent synchrony is concerned with a micro level of temporal analysis and investigates moment-to-moment de-trended changes in dyad dynamics. Lagged synchrony is also de-trended and examines the direction of dynamic influence between dyad members.

In general, there is the need for analysis and modeling methods that naturally incorporate and visualize multidimensional data. Some of the PO-based nonlinear dynamics approaches (e.g., Guastello et al., 2016; Stevens et al., 2016) that use attractors in high-dimensional state spaces could be helpful. Such methods have been used to examine nonlinear properties such as fractal representations (Likens et al., 2014), and system entropy or complexity (e.g., Gorman et al., 2016). Recurrence analysis (Knight et al., 2016) is another PO-based approach that has been used to uncover and model the temporal structure of data from two or more time-series over time, understand when systems return to similar states (e.g., Fusaroli et al., 2016), and reveal the predictability of temporal patterns (e.g., Mønster et al., 2016). Nonlinear forecasting is another type of nonlinear method in which future points are predicted based on nearest neighbors in the state space. These are model-free methods.

Model-based methods driven by specific hypotheses derived from known individual physiology and team dynamics would be more powerful. Structural Equation Modeling (SEM) embodies a structure or a hypothesis-driven model, and naturally incorporates latent or unobserved variables. This could be especially useful in the realm of TPD as the data produced by physiological sensors are not necessarily the variables that are of most interest (i.e., psychosocial and team constructs). SEM can also incorporate cofounding variables such as environmental factors (time of day, lighting level, etc.), contextual factors (e.g., interaction dynamics), or physical state of team members (fatigue, nutritional status, etc.) that are known to affect individual and team physiology.

Some aggregation approaches draw from both PS and PO (e.g., Guastello, 2016; Guastello et al., 2016) making the boundaries between these types of analysis and conceptualizations of TPD less discrete. This is an opportunity to expand research on clarifying the nature and construct of TPD, including understanding its primary dimensions.

Advance Study and Methods of Psychometric Evaluation in TPD

The TPD field has already made an encouraging start in understanding the sources of variance underlying TPD. For example, Strang and colleagues (2014) demonstrate the contribution of variables such as common task demands and collocation on TPD. By shedding light on nonteam factors (e.g., similar physical and cognitive demands induced by engaging in the same task) that may contribute to PA, such research can inform consideration of control variables in future studies on team physiology. Generalizability theory (Shavelson, Webb, & Rowley, 1989) involves identification and quantification of sources of systematic and error variance underlying behavioral measurements, and can be applied to TPD research to inform understanding of facets (e.g., team role) that drive variance in team-level data.

Multitrait multimethod measurement using time-series data can also help advance psychometrics in TPD. Robust methods of data collection should be used to capture a broader range of variables across the IMOI spectrum. Levenson and Gottman (1983) established the validity of cardiovascular, vascular, muscle, and skin conductance activity in measuring marital satisfaction by comparing them against communication content and self-reported valence. Establishing the construct and criterion validity of physiological measures in team research is critical and can be similarly established by comparing them against more traditional measures of teamwork constructs including observational forms, self-report surveys, or other organizational outcomes. Once physiological measures have been established as reliable and valid indicators of team states and processes, they can then be used in place of other measures of team constructs that require intensive coding, for example, communication.

Move TPD Research From the Lab to the Field

Historically, physiological measurement required extensive instrumentation and limited participant mobility. Consequently, a majority of the research on TPD is conducted in the laboratory. Although laboratory studies afford experimental control, they also have drawbacks. First, tasks used in laboratory research typically either have low realism and fidelity, or fail to embody critical features about work settings that may influence teamwork. Second, the sample pool of a typical participant in a laboratory experiment (i.e., college students) may not have access to strategies and work processes of experts, thus limiting the generalizability of research to work settings. A third cost is the relative expense of establishing experiments or simulations with expert samples compared with instrumenting them in field settings.

Advances have been made in studying TPD of experts such as submarine operators, pilots, and health care workers in simulated work settings using EEG (e.g., Stevens et al., 2016; Stevens et al., 2012; Toppi et al., 2016). There is a need to expand TPD research to field settings by also studying other autonomic nervous system processes. Unobtrusive and wearable sensors provide the opportunity to study teamwork in situ. The ability to freely ambulate in the environment can also enable greater diversity in the types of tasks and teams that could then be brought under the scrutiny of systematic physiological research. In addition, it can also allow for better understanding of the influence of the physical environment on emergent processes such as task restructuring, formation of subgroups, team collaboration, and strategy use. In addition, study setting is an important moderator of relationships between other team constructs and performance with effect sizes being larger in field studies (e.g., Bell, 2007).

Replicate Studies in TPD

At the present time, it is challenging to make conclusive statements about the relationship between physiological variables and team constructs. One reason is that there are insufficient studies investigating the link between specific team variables and TPD. In addition, diversity in task structure, measurement processes, and analysis and reporting standards make it difficult to compare results across studies, even for the same construct. Increasing research focused on disambiguating these findings is important.

Some research groups appear to use the same sample across multiple studies (e.g., Gorman et al., 2016; Kivikangas et al., 2014; Stevens et al., 2012; Spapé et al., 2013). It is essential to replicate these studies with different samples to investigate the generalizability of findings. Replication studies can also enrich the field by establishing reliability of findings across different types of samples and across different physiological systems, and test for boundary conditions of TPD, for example, by varying interaction structures.