A Video Ethnography Approach for Linking Naturalistic Behaviors to Research Constructs of Neurocognition in Schizophrenia

Abstract

Few methods are available to explore the impact of neurocognition in schizophrenia on behaviors performed in usual contexts. The authors developed a video ethnography approach to examine the relationship between naturalistic behaviors and research constructs of neurocognition. Video ethnographers accompanied subjects through usual routines gathering continuous video data. Researchers developed codes to measure four behavioral domains observed on video. This paper describes the psychometric characteristics to be considered in the development of observational approaches. It also highlights differences between behaviors performed in usual environments and neuropsychological constructs. The authors demonstrate that everyday behaviors that have been shown to correspond to neurocognitive skills in a pilot feasibility study¹ can be identified and rated. They further suggest that observational methods could provide novel strategies for linking research findings and clinical concerns.

I. Introduction

Hundreds of recent studies have examined the neuropsychological features of schizophrenia. The neurocognitive deficits associated with schizophrenia have been identified as key pathological factors in the disease and as strong contributors to functional outcomes.² However, whether and in what ways measures of neurocognition predict the everyday behaviors of those with schizophrenia remain largely unknown. Almost no studies have compared standard assessments of cognition with assessments based directly on what patients do day-today in naturalistic settings.^3,4 As a result, we do not know whether and in what manner the syndrome of cognitive deficits impacts day-to-day behaviors, and the ways in which cognitive deficits correspond to visible symptom domains is still unclear.^5–9 Overall, our research constructs of neurocognition remain largely decoupled from the lived experience of the illness.

There is growing interest in linking dimensions of observable behaviors to markers of disease pathology.^10,11 Data on the everyday behaviors that correspond to neurocognition could help characterize endophenotypes of disorders, decompose clinical diagnostic categories, and facilitate new insights into etiology. Such data may also clarify the impact of cognitive deficits on the everyday behaviors that treatment interventions aim to improve. After all, mental health treatments are not intended to change how an individual behaves in a laboratory or interview setting.^12,13 Yet few observational methods are available to document the everyday behaviors in context that are the ultimate target of treatments and that could facilitate basic research in schizophrenia.

In this paper, we describe a novel method to measure everyday behaviors and to test their relationship to constructs of neurocognition in schizophrenia. The method uses video ethnography, a naturalistic, non-directive technique for recording everyday behaviors as they unfold in community settings. We developed a theoretically-driven measurement strategy with which to rate a range of everyday behaviors that seem relevant to the skills assessed with standardized measures of neurocognition. Findings from 93 hours of naturalistic data from 9 subjects with schizophrenia are presented elsewhere.¹ In this paper, we describe the rationale for a non-directive observational approach to the study of everyday behaviors, we detail the process used to measure domains of behavior seen in the video, and we present the psychometric characteristics of the behavioral observation measures. We demonstrate that everyday behaviors that we have shown to correspond to neurocognitive measures in a pilot feasibility study¹ can be identified and rated. We also highlight significant distinctions between the skills measured in the laboratory and the skills used in day-to-day environments, and we argue that this method can be used to bridge clinical and research questions in translational science.

Naturalistic Inquiry

Video ethnography involves discrete videorecording of a subject as he pursues regular daily activities in usual contexts. An ethnographer carries a video camera while following along with the subject during his everyday routine. As described below, video ethnography is naturalistic, in that it samples subjects’ spontaneous everyday behaviors in their moment-to-moment flow. It is also non-directive, such that no instructions are given to guide the subject as he makes choices. Ethnographers provide space for the subject to structure his behaviors within the options normally available to him, with only the incentives that normally predominate in his everyday life.¹⁴ Video ethnography can be grouped with other methods of ecological momentary assessment, such as experience sampling and diary methods.¹⁵ However, video ethnography differs from these approaches because it is entails direct observation. Observational approaches like video ethnography are understood to be the gold standard approach to the assessment of ecological validity.¹⁶

Ecological validity^11,17,18 concerns the predictive relationship between performance on standardized tests and behavior in real-world settings.¹⁹ Two approaches to ecological validity are available: veridicality and verisimilitude. Most approaches to ecological validity use a verisimilitude approach,^20,21 in which tests are constructed to mimic the tasks an individual might perform in life. For instance, some functional measures assess individuals’ capacity to perform cognitive skills in simulated settings, such as using a bus map to plan a trip to a standard location (e.g., the zoo).²² In contrast, veridicality approaches assess the degree to which existing tests are empirically associated with everyday experiences.²³ Veridicality is very rarely assessed because it requires systematic evaluation of everyday behaviors.²⁴ The observational method described here is an example of a veridicality approach to ecological validity. A recent review concludes that, in establishing ecological validity, naturalistic observation is “technically the ideal” (p. 813) approach⁴ because it shows how behaviors manifest in the real world.²⁵

Because they capture behavior as it unfolds in natural contexts, observational methods have been used as a benchmark in the study of complex social and psychological behaviors since the 1930s.^26–29 In addition to their widespread use in ethology,^30,31 naturalistic observation methods have been used extensively to characterize the dynamics of children’s behavior,^32–37 pathological and normal family interactions,^38–46 the quality of medical care,^47–49 and a range of aspects of education⁵⁰ such as teacher quality⁵¹ and curriculum pacing.^52,53 Advances in video technology have facilitated this work because video offers an exceptionally high degree of behavioral detail for observational analyses. The video record can be reviewed and re-reviewed by multiple researchers. Videotaped behaviors can be objectively measured and/or rated through video coding. Video observational approaches have been used rarely in the study of psychosis to analyze social problem solving⁵⁴ and social interaction.^55–57 Taken as a whole, this literature suggests observational approaches may hold untapped promise in the study of everyday functioning in schizophrenia.

Video ethnography captures cognition and functioning not as static capacities, but as dynamic, flexibly-deployed resources that unfold minute-by-minute. They are tracked as naturalistic behaviors: spontaneous, fluent, and performed as a matter of course,⁵⁸ or what qualitative researchers call naturally organized ordinary activities.⁵⁹ The observational record includes rather than eliminates the environmental and interactional factors that form part of everyday behavior.^60–62 Aspects of the subject’s world are not external to cognition, but are integral to it, to the extent that the environment is replete with tools, foils, and motivators for action.^63–65 Naturalistic data can be contrasted with data that emerge through the suppression of normal routines (such as in the lab) or the structure imposed by research tools.⁶⁶ Some argue that naturalistic inquiry provides a particularly apt strategy for investigating causality because it characterizes “the qualitative nature of social objects and relations on which causal mechanisms depend (p.4).”⁶⁷ Indeed, many causative agents of interest in schizophrenia (e.g., social support, motivation) probably function contingently rather than deterministically, exerting salutary or detrimental – and substantial or minimal -- effects, depending upon adjacent factors. Observations are also closely tied to the types of specific behaviors that could be targeted for intervention or that serve as indicators of treatment outcomes.²⁹

Several psychometric properties are critical to consider in the development of observational approaches. In general, face and content validity are the most important to establish in behavioral observation^29,36,46,68 because they clarify how well the targeted phenomena have been operationalized in the data collection. Face validity is a judgment based on how reasonable the connection is between a construct and its measurement. Content validity addresses the degree to which the measure captures important aspects of the construct as it has been defined, and is often based on expert opinion or theoretical consensus. Questions such as, “To what degree is the behavior coded in the data reflective of the daily life situations that are most relevant to patients’ progress?” and “To what degree do the codes capture meaningful aspects of daily life that we would expect the pathological condition to impact?” reflect issues of face and content validity. Because of method variance, convergent validity with structured assessments of related constructs may not be a critical attribute of the validity of observational approaches; but demonstrating that observational coding strategies can distinguish between subjects who score high or low on structured assessments of related constructs can support their concurrent validity, as we describe below. Adequate behavioral sampling (also called time sampling) is another critical aspect of observational method validation, as it indicates that researchers have observed for a sufficient duration to capture the true variation in the sample.^69,70 Inter-rater reliability, which ascertains that multiple coders rate behavior similarly, must also be established to verify the consistency of the protocol. Test-retest reliability (i.e., day-to-day stability of coded behaviors across observation sessions) is important to establish because it can suggest that the data collection has captured consistent attributes, rather than situation dependent or state related individual differences.^71,72 Finally, the use of strategies to minimize participant reactivity⁴³ to the data collection can improve the ecological validity of the data, although multiple studies that have assessed it empirically have not found evidence of participant reactivity to natural observation.^40,73–78

The detail and validity of video ethnography distinguish it from other approaches to tracking everyday behaviors. We attempted naturalistic observation using audiorecording and field notes but behaviors could not be captured with audio alone, and the observer struggled to take adequate field notes. Methods that do not use direct observation (e.g., ecological momentary assessment) can assess the affective and behavioral dynamics of everyday contexts through self-report,^79–81 and they are feasible in individuals with schizophrenia.⁸² But the data collected through these methods lack the detail of the video record and only include reports of actions after the fact. In contrast, video ethnography directly records everyday activities, thus minimizing recall and response bias. Video ethnography also avoids other limitations of laboratory or interview-based measures of functioning, including retrospective recall and state-dependent biases and the artificiality of simulations used in performance-based assessments.⁸³ Moreover, many behaviors indicating cognitive skill are performed spontaneously without explicit thought; most of the coded actions described below reflect interactional patterns that were not accessible to subjects themselves and thus could not have been reported.^40,46 Finally, video allows for what Schwabe et al. call “unmotivated looking (p.715).”⁸⁴ Given the paucity of data on the everyday manifestations of cognitive deficits, a protocol that assesses deficits through prompts would require countless a priori assumptions, and potentially crucial behaviors would inevitably be missed.

Using methods that have special validity in the assessment of everyday behaviors could have significant clinical and research impact. We see three potential implications of establishing empirical links between research constructs and everyday behaviors. First, these links can demonstrate the relevance of research findings to clinicians, facilitating translation and dissemination. Clinicians focus on global, visible, or articulable aspects of an illness, such as hallucinations or poor insight.^85,86 Both self⁸⁷ and clinician⁸⁸ reports of cognition are poorly correlated with laboratory-based measures. Second, empirical links between neurocognitive constructs and everyday behaviors could facilitate biological research into intermediate phenotypes of schizophrenia and psychosis. For instance, neurocognition has emerged as a promising intermediate endophenotype of schizophrenia,^89–91 and the everyday behaviors that correlate with cognitive deficits may be visible or downstream markers of genetic liability. Finally, clarifying the link between research constructs and everyday behavior can establish the ecological validity of measures that are widely-used in clinical research.^4,92

II. Methods

Here we present our approach to the video data collection and data analysis in order to describe the choices required in the development of these methods. Because we recognize that many other choices could have been made, we include substantial methodological detail so that readers may evaluate the validity and potential value of our approach and of naturalistic observation in general. Study procedures were approved by the institutional review board of the University of Southern California. The full pilot protocol is described elsewhere.¹ We recruited community-dwelling subjects who had completed a diagnostic interview and neurocognitive battery as part of an ongoing study. Subjects with SCID-diagnosed schizophrenia or schizoaffective disorder were approached for enrollment if their MATRICS Consensus Cognitive Battery (MCCB) composite score fell within the top or bottom 1/3 of all subjects (n=155) in the ongoing study. We approached 12 eligible clients to enroll 9 subjects: five scoring in the bottom 1/3 and 4 scoring in the top 1/3 of the cohort. After consenting, we videotaped subjects in their natural living contexts across multiple sessions. We taped individuals for from 1 to 6 sessions (average 4.4) during different times of week and weekend days. Sessions lasted 2–5 hours. During piloting, we reviewed as a team the degree of behavioral variability observed with each subsequent observation sessions. We varied the length of observation sessions to verify the portion of a day required to capture longer events, and we varied the days (weekend, weekday) and times (morning, late afternoon) of observation to capture the widest range of behavioral patterns. Total taping time per subject ranged from 3 to 18.5 hours (average 10.3 hours). We gathered a total of 93 hours of video.

We modeled our ethnographic technique on the go-along ethnography.⁹³ In go-alongs, an ethnographer accompanies the subject on his daily routines, participating and listening while documenting. As described in Table 1, ethnographers are non-directive and behave naturalistically. They carry a semi-professional, high-definition camera measuring 14″×6″×6″ with all accessories. The act of recording requires no monitoring. The ethnographer passively allows the camera to run, carrying it with a shoulder strap or setting it down. The camera has a retractable monitor. Ethnographers check the frame by glancing down and never look through the eyepiece. A tiny, highly-sensitive omnidirectional microphone is pinned to the ethnographer and a positional microphone is mounted on top of the camera. The subject is not wired. This technique is extremely discrete. Onlookers often do not know filming is taking place, only that someone is carrying a camera, which is a common site in Los Angeles where data collection took place. The action is imperfectly framed at times (e.g., while ethnographer and subject walk to the store, the subject is seen obliquely from waist to nose only), but this did not impede data coding, as the audio track combines with visual cues is sufficient to clarify subject behaviors.

TABLE 1.

KEY FEATURES OF THE ETHNOGRAPHIC METHOD

Feature	Strategy
Non-directive	remind subject: “do whatever you’d do if I were not here,”73 as often as necessary; give no other instructions; do not express preferences for subject’s plans; do not help the subject make decisions; be explicit that you can follow subject anywhere
Naturalistic	be within action alongside subject; passively allow camera to run; let camera hang at hip or sit on table; never hold camera to face or look through eyepiece; when appropriate (e.g., at a bank counter), step back from action; dress casually; be friendly and relaxed; ask and answer questions as a peer would do

As stated above, participant reactivity to the method is an important consideration. We debated several aspects of data collection with regard to their potential to create reactivity. We filmed subjects from afar, but subjects found this to be unnerving, and onlookers took more notice. Mounting a camera and leaving it to run has produced unusual subject reactivity in other studies,^14,64 and our subjects rarely stayed in one room throughout data collection. Leaving a camera to run also yielded less rich data, because people do not narrate their actions while alone, but when accompanied, their motivations and intentions are often verbalized. Pilot subjects verbalized planning (e.g., “I need to get off in two stops,” or “We’d better leave now to get there before 4.”) and described their behaviors (e.g., to whom a phone call was made). Without this narration, coders would need to make countless inferences, such as whether the subject planned to link errands together. While accompanying the subject introduced a stimulus, many pilot subjects found the situation mundane because they were often accompanied by case managers or peers. Finally, we aimed to minimize reactivity by not informing subjects of the coding categories and by giving them no instructions for performance so they would not know how to demonstrate skills or hide deficits. Pilot data collected on the issue of reactivity is provided below.

Code Development

Following data collection, we developed data analytic approaches to convert the spontaneous, unstructured behavior captured on video into reliable units of data.^94,95 We drew on neuropsychological theory and practice to guide code development. The research team began by reviewing existing paradigms for measuring domains of neurocognition. We also studied strategies for decomposing and scoring complex behaviors, the literature on goal formulation, problem-solving therapy, and diary methods for functional assessment. Each literature provided hypotheses about the categories of behaviors that might represent the everyday manifestations of cognitive and functional skill. An author (EB) conducted surveys or consultation with six senior neuropsychologists and two rehabilitation researchers to explore ideas about the everyday behaviors that correspond to cognitive domains. The responses were diverse but did provide some viable suggestions for assessing cognitive skill from daily activities and were relevant to the content validity of our measures.

Some behaviors identified by these experts as cognitive could not be rated from video. Respondents mentioned behaviors that most subjects would not perform spontaneously (e.g., memorizing a grocery list, listening to a 60-minute lecture). For most behaviors, the subject’s skill would also be difficult to rate because a normative standard for performance has not been established. Finding one’s way around a new building -- identified as an everyday example of visual learning and memory -- is not a skill that could be reliably rated because no standard sequence or time limit could signify when it is done well. Remembering a piece of information may reflect memory skills, but having forgotten information cannot always be judged as a significant instance of forgetting. Moreover, many skills are not verbalized. Reading others’ emotional states is typically performed tacitly. The most obviously cognitive tasks (e.g., making change, reading a map) were too rare, varied, or incompletely verbalized to be scored.

Next, we began a collaborative process of viewing the video to consider which visible behaviors could be coded and which could be reflective of the skills and domains of cognition and functioning. We avoided potential bias due to expectancy effects on the part of raters⁷¹ by keeping all members of the research team blind to subjects’ performance on the MCCB throughout code development and coding. We devised four criteria for the codes. Coded behaviors: 1) are observed, not inferred or reported; 2) exclude the ethnographer; 3) include reliable descriptors; and 4) reflect skills rather than deficits.

(1) Observed, not reported, action

The validity of self-reported information can be difficult to gauge.^96,97 Subjects frequently described social interactions, plans for solving problems, or intentions for the future. Subjects talked about hopes to return to school or about tasks like refilling medications that they planned to finish that day. Such statements seemed functionally relevant, but they could not be reliably differentiated from unverifiable reports. One subject described a plan to move into a shelter and get a job registering voters in order to qualify for a housing voucher. Another talked frequently about the businesses in his neighborhood he frequented, but only once left his apartment after 5 days of visits. Unlike reports, subjects’ actions were observable. Subjects set plans for the day and then saw them through. They worked through challenges, such as looking for information on the computer. The team focused on accurately measuring these examples of action in the moment.

(2) Exclude the ethnographer

As described above, the presence of a friendly ethnographer accompanying the subject improved the quality of the data we gathered. However, to minimize the impact of observer effect on the data, interactions with the ethnographer are not coded as activities or as social interactions. Nonetheless, subjects did sometimes engage with ethnographers in problem-solving behavior that appeared ethnographer-independent. For instance, when a subject spontaneously suggests to the ethnographer that, “you can get change for the bus at the liquor store,” he has inadvertently engaged the ethnographer in problem solving. As described below, naturalistic problem solving often involves the people around the subject, and our measure of subjects’ problem solving skill includes behaviors in which the ethnographer is engaged, such as in the above example. However, in all cases, we witnessed subjects pursue identical problem solving with others in their environment. To lessen their potential impact on subjects’ problem solving, ethnographers were explicitly trained not to help subjects solve problems or make decisions (e.g., to wait for the subject to push the elevator button, even if the ethnographer knows where the subject intends to go).

(3) Reliable indicators

We take advantage of the continuous time record of the video to code the duration of many activities. Other reliable indicators include concrete behaviors used to measure relatedness (e.g., using the interlocutor’s name, making eye contact, asking follow up questions, gestures) and the activities that indicate the complexity of subjects’ time use (e.g., making a phone call, going to the library, watching TV).

(4) Code skills, not deficits

In general, skills are more reliably observed than deficits. The team often could not concur in identifying the absence of an action, a lack of memory for facts, or inappropriate inactivity. For instance, we aimed to identify instances in which a subject ignored problems in his or her environment yet rarely agreed. Reliable codes and rating scales were developed by focusing on observed skills (e.g., seeking out a companion, generating multiple solutions to a problem).

III. Results

Community Performance Indicators (CPIs)

We call the final codes community performance indicators (CPIs). The codes measure four domains of everyday behavior as seen in the video: problem solving, social interaction, behavioral activity level, and goal pursuit. Here we describe the development of these codes, drawing on findings from 93 hours of videotape. The four CPI codes are briefly summarized in Table 2. In what follows we describe in detail the justification, logic, and coding of the CPIs.

TABLE 2.

Summary of Four Community Performance Indicators (CPIs)

CPI Domain	Description of Behavior Tracked	Selected Variables Generated
Problem-solving (PRO)	Management of a novel circumstance, satisfaction of a need, or fixing of present complications; planning ahead; some helping behaviors; type of problem-solving skill used	Frequency of problem-solving behavior; average problem-solving skill level
Social interaction (INT)	Quality and quantity of observed social interactions	Average duration, frequency, relatedness of interactions; frequency of initiation of interactions; percent of total time spent in interaction
Activity Level (ARS)	Complexity and initiative of activities pursued in each 15 minutes of video; subject’s use of sequencing and multitasking in daily behaviors	Average highest activity level; average predominant activity level; variability of activity level from day-to-day
Goal Pursuit (GOL)	Time allocation across six domains of activity pursuit; whether subject pursues or receives these activities	Percent time spent on no goal pursuit; percent time spent on pursued goals; percent time spent on received goals

1). Naturalistic Reasoning and Problem Solving (PRO code)

Deficits in executive functioning, or reasoning and problem solving, are some of the most consistently identified cognitive problems in schizophrenia.^98,99 In addition, individuals with schizophrenia consistently demonstrate deficits in social problem solving, including problem recognition, problem definition, and generating alternatives.^100–102 Naturalistic instances of everyday problem solving are common, often verbalized, and observable on videotape.

For guidance to define a category of problem solving as performed in situ, the team looked to neuropsychological test paradigms. In these paradigms, reasoning and problem solving is a process by which problems are identified and solved by abstract reasoning.¹⁰³ Test paradigms for this domain assess a variety of skills, including spatial skills,^104,105 set-shifting, response initiation and suppression, and non-verbal components rule selection.¹⁰⁶ We focused on the Wisconsin Card Sort Test (WCST), the Rey-Osterrieth Complex Figure Test (RCFT),¹⁰⁷ the Tower of London,¹⁰⁸ the Independent Living Scales-Problem-Solving factor subscale (ILS-PB),¹⁰⁹ and the Neuropsychological Assessment Battery-Mazes (NAB-Mazes). The Tower of London and the RCFT involve manipulating unusual stimuli to achieve a highly-structured goal, making these tests difficult to compare with to real-world problems.¹¹⁰ We could find in the video corollaries to the set-shifting skills tested by the WCST, time-limited problem-solving required for the NAB MAZES, and the skills required to identify a solution to a hypothetical problem as posed in the ILS-PB. These tests require the subject to recognize that a novel circumstance presents itself, to respond effectively to that circumstance, to generate solutions in a timely manner, and/or to weigh a range of potential solutions.

Social problem solving assessments and problem solving therapies emphasize similar skills. Problem solving therapies stress the identification of problems, positive problem-solving orientation, solution generation, and effective response. Social problem solving assessments test skills in identifying problems (called receiving), generating solutions (processing), and choosing effective solutions (sending). To map these skills to video data, we looked for reliable indicators that the subject faced a challenge (e.g., a subject did not know how to turn on the oven). However, mere identification of a problem (“I need some cigarettes”; “I need a bigger apartment”) could not be coded, because, in some of these instances, problem solving could reasonably be postponed or might never be pursued. We code problem-solving behavior only when the identification of a problem is followed by action.

Our first few subjects demonstrated an aptitude for problem identification and solution generation in the course of efforts to help others. One subject repeatedly clarified what the ethnographer would need to do to take the bus back to his car. He explained how much change was needed, pointed out the corner at which to wait, estimated the wait time, advised the ethnographer how to know when the bus approached the appropriate street, and so on. On other occasions, he provided information to orient the ethnographer (“I’m going to change my shirt and then we can go.”). The subject was anticipating hypothetical problems and aiming to prevent them.

Then, we recognized many anticipatory behaviors that did more than fix a present challenge. Subjects converted a situation that was not problematic per se into an opportunity to improve the present. These behaviors involved generating options. One subject took a seat on a bus bench as she and the ethnographer walked past. “I’m not taking the bus; I’m just going to rest here,” she said; she was tired, and she took advantage of the bench to address that. Another subject looked at his watch as he took the bus to class; “we will have time to have another cigarette before class,” he said. Another subject, recalling the need to go downstairs, pointed to the elevator and asked, “We could take the elevator, or we could take the stairs. Which should we take?” Both anticipating potential problems and identifying opportunities require the same planning and reasoning skills as problem-solving.

After review of these examples and successive trials, we defined the problem-solving code. We mark problem-solving when we see problem identification, positive problem orientation, and solution generation together. We include helping behaviors and explanations for others when they are both a response to an explicit need and when they have implications for action in the moment. In other words, problem-solving (PRO) behaviors are those in which subjects are seen to manage a novel circumstance, satisfy a need, or fix complications in the present. PRO behaviors may also be explicit, in-the-moment attempts to improve a situation. PRO behaviors include planning whether there is time to stop at the store and telling another how to get to a landmark when such a need is expressed. In pilot data, raters agreed 78.3% (SE .09) of the time that problem solving was present or absent. Across all nine subjects, PRO behaviors occurred approximately once per hour. However, problems could be identified and solved in successive stages over a considerable period of time (such as when a subject said, “I need to get a ride from my sister,” and made three phone calls to three different people over an hour). The time required to solve a problem was not rated. Instead, we rated the kinds of skills used to solve each problem. Problem solving skill, a 3-point scale, rewards anticipatory and creative -- as opposed to rote and rudimentary --problem-solving. Since in our pilot work problem-solving instances occurred approximately once per hour, we could achieve adequate sampling in an observational period of 10–15 hours. The inter-rater reliability for PRO skill rating was excellent (kappa .87, SE .05), and the internal consistency reliability, or day-to-day stability of PRO skill use (i.e., from one session to the next), was good (Cronbach’s alpha .68).

2). Social Interactions (INT code)

Social cognitive deficits and impairments in social functioning are high-priority areas in schizophrenia research.^111,112 Social cognitive deficits may mediate the relationship between neurocognition and functioning,² and social functioning itself is an important outcome.¹¹³ A NIMH workshop defined social cognition in schizophrenia as comprised of 5 skill areas: theory of mind (or mental state attribution), social perception, social knowledge, attributional bias, and emotional processing.¹¹⁴ In general, these processes are too discrete or implicit to be identified in videos. Statements of attributional bias can be coded in transcripts,¹¹⁵ but transcription of all hours of video would have been prohibitive, and we did not have structured narratives for comparison across subjects.

Pilot subjects spent a great deal of time reporting social behaviors (e.g., “I’m going to class tomorrow with my brother.”). One subject described friends down the hall, a boyfriend, and a granddaughter. Yet subjects’ reports of interactions did not always correspond to social activity. One subject mentioned frequently talking to God, and another talked intimately about Oprah Winfrey; a third spoke aloud to someone else when the ethnographer left the room; and many of the individuals mentioned by subjects were never seen to visit or call. We found these reports of social interactions difficult to evaluate. If we could not confirm that the subject really spent the afternoon with a brother, as reported, we could not justify coding this information as an indicator of social activity. And, while we found it significant that some subjects reported receiving substantial psychosocial support from relationships that were best understood as intra- rather than inter- personal (e.g., Oprah Winfrey), we felt we could not code these as social ties. Ultimately, we instead chose to design a coding strategy to take advantage of video ethnography’s unique ability to document manifest social interactions. Video can document the interactions with others that actually take place in the course of subjects’ everyday routines, an aspect of subjects’ social lives that is very rarely studied.

In our coding, social interactions are timed from beginning to end. They begin when the subject engages in an interaction and end when the interlocutors part; the joining in of a third individual is counted as a new interaction. Interactions that take place over the phone, by text message, or email are rated identically to in-person interactions. Tracking duration allows for calculation of the average length of interactions, the percent of interactions that are short (e.g., less than 30 seconds) or long (e.g., longer than 3 minutes), and the percent of a subject’s total time spent in interaction. Interaction duration measurements reached very high inter-rater agreement (kappa .99), but stability of mean interaction duration from session-to-session was not high; alpha reliability improved to .51 when sessions one and two were compared with sessions three and four, suggesting that longer periods of observation may be required to assess average interaction length.

In addition, we rated the quality of the interaction with a scale of relatedness. We adapted the Maryland Assessment of Social Competence¹⁰⁰ to develop a 3-point scale that rates whether the subject displays efforts to extend the length of an interaction or facilitate the level of engagement. Use of the interlocutor’s name, follow-up questions, eye contact, reactive affect, gestures, and intonation all indicate relatedness. The presence of several of these features yields a relatedness score of ‘2’ for the interaction, and their absence yields a score of ‘0’. Inter-rater reliability was good for the relatedness scale (kappa .75, SE .10) and stability across sessions was high (alpha .84).

3). Behavioral Activity Level (ARS code)

Time budget approaches have demonstrated a clear relationship between activity level and clinical improvement in schizophrenia.^116,117 Engagement in social activities, employment, and ADL competence are key components of assessments of functioning.^118,119 In video examples, behavioral activity level was an obvious difference between subjects. The initiative, effort, goal-direction, and complexity of daily activities varied across subjects and across sessions. Behavioral activity level appeared salient to neurocognitive capacity and could be reliably tracked in the video.

We adapted an existing scale of activity level to rate behavioral activity in brief increments of video. The original scale^120,121 rates the activities in a diary the subject completes with an interviewer, scoring four time periods in the day. It is scaled toward active, organized, and motivated behaviors, with subjects who participate in passive, solitary activities receiving lowest scores. Our adaptation, the 5-point Activity Rating Scale (ARS), rates the level of complexity and initiative required for subjects’ behaviors seen in the video. We added activities commonly seen on video (e.g., phone calls) to the scale. The five points of the ARS scale are anchored by both specific behaviors (e.g., eating a meal, using a microwave) and behavioral descriptors (e.g., doing simple tasks one after the other, an isolated activity requiring some effort). The scale remains weighted toward complex and effortful behaviors and toward multitasking (ARS = 4) and sequencing of behaviors (ARS = 3). Time in which a subject cooks from scratch while fielding a phone call receives a ‘4’; time in which a subject watches TV receives a ‘0’. We give an ARS rating for each 15-minutes of video. Fifteen-minute increments showed better range and inter-rater reliability than 10- or 20-minute increments.

The ARS scale captures the quantity of activity (e.g., how busy subjects were) and its organizational quality (e.g., whether tasks were sequenced together toward an explicit goal). For example, two subjects spent most of their first observed sessions in their neighborhoods. The first subject walked from class to the library to return a video and pick up another. Then he went across the street to pick up food for dinner. Then he walked to the bus stop and caught a bus home. In 45 minutes, he pursued tasks requiring some effort and initiative, and he also sequenced these tasks together to pursue them efficiently. He received ARS scores between 2 and 3 for the time, with a total score of 8. The second subject also spent 45 minutes walking in his neighborhood. He stopped at stores, often to say hello to a clerk, but he did not buy anything. He greeted those he recognized on the street. He decided which direction to walk in the moment, without a planned route or a final destination. Once, he yelled across the street to someone he knew. While his walk through the neighborhood was as busy as the first subject, and while superficially he was engaged in the same tasks (e.g., going to the store), he lacked initiative and organization. He passively received an activity as it came into view (e.g., wandering into a store). He moved from one activity to the next without direction. As a result, this subject received ARS scores between 1 and 2 for the 45 minutes with a total score of 4 (the score of ‘2’ was the result of seeking to engage the company of the acquaintance across the street).

In fact, each 15 minutes is assigned 2 ARS scores, a highest and a predominant score, because some complex activities were extremely brief. If a subject is watching TV, but then makes a phone call and leaves a message, which takes 30 seconds, the subject receives a highest score of ‘2’ and a predominant score (defined as the complexity of activity pursued for at least 7.5 minutes) of ‘0’ for the 15 minutes. Use of the highest score increases the range of the scale used by coders and it gives credit to subjects for short but complex behaviors. Yet because it credits very brief behaviors within a background of inactivity, the predominant score is also used to reflect the overall level of activity. Both ARS highest (kappa .77, SE .09) and ARS predominant (kappa .93, SE .05) showed excellent inter-rater reliability and adequate alpha reliability across sessions (.79 and .70 respectively).

4). Naturalistic Goal Pursuit (GOL code)

Finally, we developed the goal pursuit scale (GOL) to describe time use over longer blocks of time than the ARS scale and to indicate whether subjects actively pursue certain experiences. The GOL scale provides a global assessment of subjects’ time use. While the skills tracked with the GOL scale reflect categories of functioning, cognitive skills, such as executive functioning,¹²² metacognition,¹²³ social cognition,¹²⁴ and verbal memory¹²⁵ could play substantial roles in the pursuit of these activities. The GOL scale is modeled on time budget approaches.^126–128 We categorize the activity seen on video into one of six domains denoting the type of activity (socializing, leisure, spirituality, activities of daily living, health, and vocation/education). The domains were adapted from the Activity Pattern Indicators scale^129,130 and from a taxonomy developed by Bogardus and colleagues.¹³¹ In addition to these six goal domains, subjects occasionally pursued no goal, such as when napping or conversing only with the ethnographer. A seventh category -- no goal pursuit – was created for those time blocks. Multiple raters showed good reliability (84.6% agreement, SE .06) in assigning goal domains.

In addition to the goal domain, blocks of time are rated as pursued or received, denoting whether subjects passively take part or actively seek out the activity. In this sense, like the ARS scale, the GOL scale credits motivated activities. For example, time during which a subject travels from home to attend church receives a GOL code of ‘spirituality; pursued’; time during which a subject sits on the porch at a group home talking with peers receives a GOL code of ‘socializing; received’. This emphasis is consistent with self-determination theory, which states that facilitating internal motivation fosters competence and potential;¹³² it is also consistent with mental health and medical interventions¹³³ that aim to foster autonomy and competence. For these reasons, we use the GOL code to distinguish between activities that are a response to stimuli in the immediate environment (received) and those that reflect an intentional effort to expand beyond those stimuli (pursued). Inter-rater reliability was excellent in distinguishing pursued versus received goal (92.3% agreement, SE .04), and session-to-session stability was adequate (alpha .59).

CPIs: Additional Psychometric Properties and Correlations with the MCCB

We pursued a further check on face validity by asking subjects to rate the degree to which they believed the CPIs represent important domains of their community functioning. On Likert scale items, subjects agreed that setting goals and pursuing them, being able to solve problems, and setting plans are important to them. All agreed that talking with others and being active are activities they pursue more often when doing well. All but one subject agreed that it is important to them to leave their apartment in the day. Regarding reactivity, we asked subjects to complete Likert scale items after the second and final observation session. Almost all subjects reported forgetting about the videotaping after 30 minutes or after the first session. In open-ended responses, 8 of 9 subjects described their behavior as the same as their usual behavior during filming; the ninth reported “being more polite.” We also asked three members of the recruitment team who knew the subjects from the clinic context to comment on their behavior on video. All three strongly agreed that the subjects behaved naturally on videotape. These results mirror reactivity findings in other studies,^134,135 the majority of which find no reactivity to observational approaches.^74–78

Results of correlational analyses between the MCCB and CPIs are described more fully elsewhere.¹ We comment here on a few findings that support the validity of the approach. First, our sampling strata based on MCCB scores was achieved, with statistically significant differences (Kruskall-Wallis chi-square 6.0, df 1, p<.01) between the two groups of subjects high and low on MCCB composite and domain scores. Second, the high and low neurocognition subjects showed statistically significantly differences on 4 CPI variables, one from each CPI domain, in the predicted direction (better neurocognition corresponding to better CPI scores) [Chi-square, (df 1) 3.0–6.0, p<.05 (one-tailed), in all cases using the Kruskal-Wallis test statistic]. Third, the Pearson correlations between MCCB composite scores and these 4 CPI variables ranged from .54 to .77 (p<.01 to .07), mirroring the between group findings. Fourth, the Pearson correlation between the MCCB composite and a z-score CPI composite derived from these 4 variables was .82 (p<.01). We partially attribute this elevated correlation to our strategy of sampling extreme groups. Fifth, the CPI domains showed a pattern of specific correlations with neurocognitive domains. For example, while Working Memory and Visual Learning had strong associations across the CPI domains, Verbal Learning, Reasoning/Problem Solving, and Social Cognition had strong associations with CPI domains of social interaction and problem solving (r > .42 or p < .05 in all cases). These findings were very similar when using a nonparametric correlation coefficient.

IV. Discussion

This paper provides a thorough description of the video ethnography method and the coding categories we developed to explore the link between everyday behaviors and neurocognitive constructs. As described elsewhere,¹ we achieved high inter-rater reliability in using these CPIs to code 93 hours of video. In our sample of 9 community-dwelling subjects who scored high or low on the MCCB, variables from all 4 CPI domains demonstrated correlations with both MCCB composite and domain scores. Based on our pilot data, we believe the CPIs constitute a valid approach to exploring the cognitive content of everyday behaviors and to testing the ecological validity of neurocognitive and functional measures. The four CPI domains do not track all aspects of everyday behavior, but they capture a wide range of behaviors and correspond theoretically to domains assessed with neurocognitive and functional measures. In addition, because they describe fundamental features of everyday life (e.g., time use, activity complexity, level of social interaction), these domains of behavior seem likely to represent mechanisms by which functioning occurs. They also provide a framework for future observational approaches that develop alternative or elaborated measurement strategies.

Despite remarkable advances in our understanding of neurocognition in schizophrenia, we know very little about how neurocognition impacts everyday behaviors in usual contexts. Video ethnography provides a rich record of behavior in context, allows for viewing and reviewing of data to reliably measure a range of behaviors, does not require prompts or self-report, and is feasible with individuals with schizophrenia. Measurements like the CPIs that assess naturalistic behaviors can rate the quality and quantity of the everyday experiences that treatment interventions ultimately aim to improve. Practically, such data could facilitate translation of neurocognitive research findings to clinicians and could stimulate neurobiological research by decomposing complex phenotypes (e.g., cognitive impairment) and disease manifestations (e.g., functional impairment). Fundamentally, data about naturally occurring behaviors may help to clarify the relevance of research constructs to the lived experience of those with schizophrenia.

Differences between naturalistic behaviors and neurocognitive constructs

We encountered several complexities to the behavioral observation of cognition. These complexities require attention in future research. As described above, numerous skills assessed in neurocognitive tests could not be directly viewed or reliably rated. The team could find no reliable markers in the video for skills in working memory, verbal learning and memory, visual learning and memory, attention and vigilance, and speed of processing. We tried but failed to track several other cognitive behaviors, including perspective-taking and emotional attribution. We also attempted to track joking and sarcasm as they emerged naturalistically. A scale was developed to assess aspects of metacognition, like noting shared circumstances (e.g., the weather) or making self-evaluative statements. In all cases, we faced immediate reliability challenges. Many of these behaviors would have required the coder to make inferences about the subject’s intentions or to score activities for which no normative standard is available. Given the relevance of these skills to the psychopathology of schizophrenia, we would encourage further exploratory work to develop rating scales for the naturalistic manifestations of these cognitive skills.

Moreover, neuropsychological problem-solving test paradigms differ in important ways from the problem-solving behaviors we witnessed in everyday life. First, everyday problem-solving can rarely be divided into social and non-social types. Both identifying and solving problems involve interactions with others, and the skills a subject uses in solving his own problems are also used in anticipating others’ problems. Second, the salience of identification, solution generation, and effectiveness varies for everyday problems. In real life, as Koren et al. say, “the person is free to choose which aspects of the question to relate to and which to ignore, what degrees of confidence to impart, what perspective to adopt, how much detail to volunteer, and so forth (p.310).”¹⁸ In everyday life, problems do not announce themselves; they emerge slowly or are brought to the fore by the subject, and they usually evolve in ways that depend upon the subject’s reaction. The nature of the appropriate response is rarely highly determined. Problem-solving can continue over a long period of time not due to lack of skill but because a subject continues to generate new options. Subjects can convert a problem into an opportunity, pursue adaptive avoidance, or focus selectively on a sub-problem that can be successfully managed.

Finally, the team could not rate the degree of difficulty of naturalistic problems and could not always agree when problems were completely solved. Some naturalistic problems were readily solved (e.g., a subject forgot a key and walked back to get it), yet even here we could not judge the complexity of the challenge. Were problems like figuring out the VCR difficult? Should we use our perception or the subject’s? For other problems, it was not easy to judge whether the problem had been adequately solved or whether a better possible solution remained. For instance, in response to an inquiry, a subject said that she believed that parking was allowed on a particular street at that time, but she neither seemed certain nor went down to the street to confirm this. Self-corrections -- like the subject who realized she got on the bus going in the wrong direction -- could be equally difficult to judge as effective or timely. Unlike mazes or set-shifting exercises, real-world problems present a variety of potential resolutions, and they may lack closure through no fault of the problem-solver.

Limitations & Future Directions

We believe that the videorecording protocol provides a representative snapshot of subjects’ everyday functional performance. However, the pilot study had several limitations and some questions could only be explored in a larger, heterogeneous sample. Clearly, data from such a small, select sample does not establish the ecological validity of the MCCB. Only data from a larger sample could test correlations between CPIs and a range of cognitive and functional measures. Data from a large sample size study would also be needed to further decompose and characterize the everyday behaviors that show strong correlations with neurocognition. Moreover, while our pilot sample included subjects with paranoid symptoms, selectivity bias of the approach should be assessed to clarify whether disorganized and paranoid patients are able to consent and participate, as this would impact the generalizability of the results. Consistent with findings from other video studies,^134,135 our subjects reported little reactivity to the video approach. We cannot exclude the possibility that subjects avoided functional behaviors or underperformed when with ethnographers, though we did not see evidence for this in pilot subjects. In a larger study, selective sampling of videotaped hours (i.e., excluding the first hour from all sessions; excluding the first session for each subject) could be used to explore subject reactivity during different periods of videorecording. Other psychometric features of the CPIs can be further explored, including the variance of these measures in different settings and the day-to-day stability (i.e., test-retest) of the variables. In addition, a large study sample could clarify the total observation time required to conclusively document CPI domains.

The video ethnography approach we used is labor intensive and not feasible for routine research or clinical use at this point. We are continuing this work to explore the psychometric attributes mentioned above and the potential for adaptations. For instance, a variety of briefer observational techniques (e.g., structured observation instruments, in vivo coding techniques, scan-sampling methods) have been developed in other fields and used in large studies or as outcome measures in controlled trials.^136–139 For example, brief observational instruments^140,141 have been used to explore whether the frequency of child and parent negative behavior in the home or clinic decreases following an intervention.¹⁴² The video ethnography method may also have uses in other research areas, such as autism and neurodegenerative disorders, where there is a need to link neurobiological findings to everyday behaviors. Findings on executive control in autism, for instance, can show conflicting results because of a lack of consensus about the construct itself and its real-world behavioral correlates.¹⁴³ CPIs or related measures could be used to analyze naturalistic video data from autistic individuals in order to understand executive control in everyday contexts. The video ethnography approach could also be used with individuals with limited or no expressive communication, such as those with aphasia or non-verbal autism, or with limited insight into their condition, such as those with frontal-temporal dementia.¹⁴⁴ Overall, this approach holds promise for many areas of research that desire or require attention to the ecological validity of its measures and constructs.

FIGURE 1.

DEVELOPMENT OF THE FOUR COMMUNITY PERFORMANCE INDICATORS (CPIs)