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BACKGROUND: With the rise of computer science and artificial intelligence, analyzing large data sets promises enormous potential in gaining insights for developing and improving evidence-based health interventions. One such intervention is the counseling strategy motivational interviewing (MI), which has been found effective in improving a wide range of health-related behaviors. Despite the simplicity of its principles, MI can be a challenging skill to learn and requires expertise to apply effectively. OBJECTIVE: This study aims to investigate the performance of artificial intelligence models in classifying MI behavior and explore the feasibility of using these models in online helplines for mental health as an automated support tool for counselors in clinical practice. METHODS: We used a coded data set of 253 MI counseling chat sessions from the 113 Suicide Prevention helpline. With 23,982 messages coded with the MI Sequential Code for Observing Process Exchanges codebook, we trained and evaluated 4 machine learning models and 1 deep learning model to classify client- and counselor MI behavior based on language use. RESULTS: The deep learning model BERTje outperformed all machine learning models, accurately predicting counselor behavior (accuracy=0.72, area under the curve [AUC]=0.95, Cohen κ=0.69). It differentiated MI congruent and incongruent counselor behavior (AUC=0.92, κ=0.65) and evocative and nonevocative language (AUC=0.92, κ=0.66). For client behavior, the model achieved an accuracy of 0.70 (AUC=0.89, κ=0.55). The model's interpretable predictions discerned client change talk and sustain talk, counselor affirmations, and reflection types, facilitating valuable counselor feedback. CONCLUSIONS: The results of this study demonstrate that artificial intelligence techniques can accurately classify MI behavior, indicating their potential as a valuable tool for enhancing MI proficiency in online helplines for mental health. Provided that the data set size is sufficiently large with enough training samples for each behavioral code, these methods can be trained and applied to other domains and languages, offering a scalable and cost-effective way to evaluate MI adherence, accelerate behavioral coding, and provide therapists with personalized, quick, and objective feedback.

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Tic disorders (TD), including Tourette Syndrome, are characterized by involuntary, repetitive movements and/or vocalizations that can lead to persistent disability and impairment across the lifespan. Existing research demonstrates that video-based behavioral coding (VBBC) methods can be used to reliably quantify tics, enabling a more objective approach to tic measurement above and beyond standardly used TD questionnaires. VBBC is becoming more popular given the ease and ubiquity of obtaining patient videos. However, rigor and reproducibility of this work has been limited by undescribed and unstandardized approaches to using VBBC methods in TD research. The current paper describes "best practices" for VBBC in TD research, which have been tested and refined in our research over the past 15+ years, including considerations for data acquisition, coding implementation, interrater reliability demonstration, and methods reporting. We also address ethical considerations for researchers using this method.

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PURPOSE OF REVIEW: The purpose of this review is to (1) present a conceptual definition of infant food acceptance, (2) explore methodological challenges to measuring different components of food acceptance, and (3) provide recommendations for future research on food acceptance during infancy. RECENT FINDINGS: Infant food acceptance includes separate but overlapping components: wanting, liking, and consumption. Recent evidence suggests that although these components are often correlated, there is variability in the sensitivity of these measures to detect differences in infants' responses to target foods. To date, consumption and caregiver perceptions of liking have been the primary outcome measures of interest in studies of infant food acceptance. More diverse and precise measures across the three components of infant food acceptance are needed to address a wider scope of research questions, which would in turn strengthen the interpretability and translational potential of research in this area.

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Background and Objectives: Nursing home (NH) staff mealtime care approaches are associated with behaviors of residents with dementia, but their impact on food intake remains unexplored. This study examined the role of staff person-centered and task-centered approaches and resident positive, neutral, and challenging behaviors on food intake. Research Design and Methods: Videotaped mealtime observations (N = 160) involving 36 staff and 27 residents (53 unique staff-resident dyads) in 9 NHs were coded using the refined Cue Utilization and Engagement in Dementia mealtime video-coding scheme. The dependent variable was resident food intake. The independent variables were staff person-centered approaches that support resident abilities, staff-resident (dyadic) interactions, and dining environments, staff task-centered approaches, and resident positive, neutral, and challenging behaviors. Resident challenging behaviors included mealtime functional impairments and resistive behaviors. Linear mixed modeling was used. Moderating effects of staff approaches, food type, and length of dyadic mealtime interactions (ie, video duration) were examined. Results: The relationship between food intake and resident mealtime functional impairments was moderated by food type (p < .001). The relationship between food intake and resident resistive behaviors was moderated by food type (p = .002) and staff person-centered verbal approaches (p = .001). The relationships between food intake and staff person-centered nonverbal approaches (p = .003) and resident positive/neutral nonverbal behaviors (p = .004) were moderated by the length of dyadic mealtime interactions. Discussion and Implications: Food intake was associated with staff person-centered approaches and resident positive/neutral and challenging behaviors. Findings emphasize the importance of facilitating positive dyadic interactions using individualized, context-based, multifaceted, person-centered care. Future research on temporal and causal relationships is warranted in larger diverse samples.

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This study examined differences in observed mealtime behaviors between children preparing to transition to oral feeding and children with various other chronic illnesses using a standardized measure of mealtime beaviors. The parent-child mealtime relationship can become strained due to problematic mealtime behaviors that limit food intake, as well as inadvertent reinforcement of disruptive behavior by caregivers. Frequency/rate of behaviors were compared between children with tube feeding (CwTF) and from previous studies of children with chronic illnesses using the Dyadic Interactive Nomenclature for Eating (DINE). Parents of CwTF used more coaxing, physical prompts, and reinforcement during meals, while parents of children with chronic illnesses used more direct commands and engaged in more parent talk. Findings support differences in parent-child mealtime interactions and eating behaviors across pediatric illness subgroups.

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Observational coding of children's eating behaviors and meal microstructure (e.g., bites, chews) provides an opportunity to assess complex eating styles that may relate to individual differences in energy intake and weight status. Across studies, however, similar terms are often defined differently, which complicates the interpretation and replication of coding protocols. Therefore, this study aimed to compile methods of coding meal microstructure in children. To limit bias and ensure a comprehensive review, a systematic search was conducted in January 2021 across three databases (PubMed, PsychInfo, Web of Science) resulting in 46 studies that coded at least one meal-related behavior in healthy children (i.e., no medical/psychological disorders) who were able to self-feed (i.e., no spoon-, breast-, or bottle-feeding). While the majority of studies had good interrater reliability, the details reported about study foods and the clarity of the definitions used for behavioral coding varied considerably. In addition to reported microstructure behaviors, a non-exhaustive review of individual differences was included. While few studies reported individual differences related to age and sex, there was a larger literature related to weight status that provided evidence for an 'obesogenic' style of eating characterized by larger Bites, faster Eating and Bite Rates, and shorter Meal Durations. However, some studies may not have been optimally designed or powered to detect individual differences because they did not set out a priori to examine them. Based on this systematic review, best practices for the field are recommended and include reporting more details about foods served and coded eating behaviors to improve reproducibility. These suggestions will improve the ability to examine patterns of individual differences across studies, which may help identify novel targets for intervention.

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Thin slices are used across a wide array of research domains to observe, measure, and predict human behavior. This article reviews the thin-slice method as a measurement technique and summarizes current comparative thin-slice research regarding the reliability and validity of thin slices to represent behavior or social constructs. We outline decision factors in using thin-slice behavioral coding and detail three avenues of thin-slice comparative research: (1) assessing whether thin slices can adequately approximate the total of the recorded behavior or be interchangeable with each other (representativeness); (2) assessing how well thin slices can predict variables that are different from the behavior measured in the slice (predictive validity), and (3) assessing how interpersonal judgment accuracy can depend on the length of the slice (accuracy-length validity). The aim of the review is to provide information researchers may use when designing and evaluating thin-slice behavioral measurement.

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The Video Activity Coder is a free, open-source computer program that can assist in coding behaviors in videorecorded datasets. Several programs already exist with this purpose; however, some of these are quite expensive, whereas some open-source, free alternatives have limitations. The Video Activity Coder allows a user to view recorded observations with the ability to play, pause, rewind, and fast-forward video clips easily. Users can mark the beginnings and endings of behavioral codes by using an onscreen keypad. For example, one could note every instance of a child manipulating toys by pressing a key or note the beginning and end of when a parent assists a child. In addition, the program offers descriptive statistics and output that captures second-by-second streams of codes that can be analyzed with other programs.

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Mobile phone use (MPU) while driving is an important road safety challenge worldwide. Naturalistic driving studies (NDS) emerged as one of the most sophisticated methodologies to investigate driver behavior; however, NDS have not been implemented in low- or middle-income countries. The aim of this research is to investigate MPU while driving and compare the results to those reported in international studies. An analysis of 61.32 h and 1350 km driven in Curitiba (Brazil) showed that MPU lasted for an average of 28.51 s (n = 627) and occurred in 58.71% of trips (n = 201) with an average frequency of 8.37 interactions per hour (n = 201). The proportion of the trip time using a mobile phone was 7.03% (n = 201), and the average instantaneous speed was 12.77 km/h (n = 627) while using the phone. Generally, drivers spent less time on more complex interactions and selected a lower speed when using the phone. MPU was observed more during short duration than longer trips. Drivers in this study engaged in a larger number of MPU compared to drivers from Netherlands and the United States; and the percentage of trip time with MPU was between North American and European values.

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This study explored how mothers' observed and self-reported child feeding practices (child control over food choices, encouragement of balance and variety, and teaching about nutrition) were associated with mother-child snack food selections and child snack food consumption in a laboratory setting. Mothers (N = 107) and their 4.5-year-old children (52% female) selected up to 5 snack foods (out of 9 snack foods: 6 higher-energy-density [ED] and 3 lower-ED) for optional child consumption throughout a one-hour laboratory visit. Mothers' in-the-moment child feeding practices during the snack food selection task were coded using observational coding schemes, and mothers' global child feeding practices (i.e., across meals and snacking occasions) were self-reported using the Comprehensive Feeding Practices Questionnaire (Musher-Eizenman & Holub, 2007). Results of multiple linear regression analyses with covariates showed that higher-ED snack food selections were positively associated with observed child control over food choices (B = 0.35, SE = 0.12, p = .006) and self-reported teaching about nutrition (B = 0.49, SE = 0.19, p = .010), and negatively associated with self-reported encouragement of balance and variety (B = -0.66, SE = 0.24, p = .007). Lower-ED snack food selections were positively associated with self-reported encouragement of balance and variety (B = 0.53, SE = 0.20, p = .008). Child consumption of higher-ED or lower-ED snack foods were not significantly associated with mothers' child feeding practices (observed or self-reported). We discuss the implications of these findings for future research on children's snack food selection and consumption.

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BACKGROUND: Nursing home residents with dementia commonly experience low food intake, leading to negative functional and nutritional consequences. While the importance of staff-resident (dyadic) interactions during mealtime is acknowledged, little research has examined the role of dyadic verbal interactions on food intake. OBJECTIVES: This study aimed to examine the relationship between food intake and dyadic verbal interactions. METHODS: This study was a secondary analysis of 110 videotaped observations of mealtime care interactions among 25 residents with dementia and 29 staff (42 unique dyads) in 9 nursing homes. Staff positive utterances and resident positive and negative utterances (independent variables) and food intake (dependent variable) were coded from the videotaped observations using the Cue Utilization and Engagement in Dementia video coding scheme. A linear mixed model was fit to the data. The two-way interaction effects of food type and video duration with each independent variable as well as two-way interaction effects among the independent variables were tested. Covariates included in the model were the number of years staff worked as a caregiver, and resident age, gender, and eating function. RESULTS: The model included three significant interaction effects involving verbal variables: the interaction effect of staff positive utterances with resident positive utterances (p=.030), the interaction effect of staff positive utterances with food type (p=.027), and the interaction effect of resident negative utterances with video duration (p=0.002). Increased number of intakes of liquid food per minute was associated with increased number of staff positive utterances per minute when residents did not make positive utterances. Decreased number of intakes of solid food per minute was associated with increased number of staff positive utterances per minute, especially when residents made between 0 and 3 positive utterances per minute. As the duration of the videos increased, the number of intakes per minute increased for residents who made one or more negative utterances and decreased for residents who made no negative utterances in the videos. The number of intakes per minute was associated with resident gender in that male residents had increased number of intakes per minute compared with female residents (p=.017), and was not associated with other participant characteristics. CONCLUSION: Intake was associated with dyadic verbal interactions, and such relationship was complex in that it was moderated by food type and video duration. Findings support the significant role of dyadic verbal interactions on intake, and inform the development of effective, tailored mealtime care interventions to promote intake.

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Animal personality is defined as consistent individual differences across time and situations, but little is known about how or when those differences are established during development. Likewise, several studies described the personality structure of adult capuchin monkeys, without assessing the ontogeny of these personality traits. We analyzed the behavioral repertoire of 12 wild infants (9 males, 3 females) yellow-breasted capuchin monkeys (Sapajus xanthosternos), in Una Biological Reserve (Bahia, Brazil). Each infant was observed and filmed weekly from birth until 36 months, through daily focal sampling. We analyzed the behavior of each individual in 10 developmental points. By means of component reduction (principal component analysis), we obtained four behavioral traits: Sociability, Anxiety, Openness, and Activity. We investigated whether there were developmental effects on those traits by fitting regression models for the effect of time on behavioral traits, controlling for monkey identity, sex, and cohort. Sociability (decreasing) and Anxiety (increasing) changed significantly along development. By means of repeatability analysis, we did not find intra-individual consistency across time in those traits, so we cannot discriminate stable personality traits in early ontogeny. Our results show that the personality structure of capuchin monkeys is not established during early development, in agreement with the literature on human personality.

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Visual avoidance of unpleasant stimuli (i.e., strategic positioning of eyes, head and torso away from an environmental stimulus) is a common attentional control behavior that may down-regulate emotion by reducing visual input. Despite its ubiquity, relatively little is known about how visual avoidance is affected by neurological diseases that impact neural circuits involved in emotional functioning. We examined visual avoidance in 56 behavioral variant frontotemporal dementia (bvFTD) patients, 43 Alzheimer's disease (AD) patients, and 34 healthy controls. Participants came to our laboratory and viewed an extremely disgusting film clip while visual avoidance was measured using behavioral coding of head, body, and eye position. Controlling for differences in cognitive functioning, bvFTD patients were less likely to engage in visual avoidance behaviors than both AD patients and healthy controls. Additional analyses revealed that diminished visual avoidance in this task was associated with lower levels of real-world emotion regulation but not with emotion reactivity as reported by the primary caregiver.

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Children with autism spectrum disorder (ASD) show reduced gaze to social partners. Eye contact during live interactions is often measured using stationary cameras that capture various views of the child, but determining a child's precise gaze target within another's face is nearly impossible. This study compared eye gaze coding derived from stationary cameras to coding derived from a "point-of-view" (PoV) camera on the social partner. Interobserver agreement for gaze targets was higher using PoV cameras relative to stationary cameras. PoV camera codes, but not stationary cameras codes, revealed a difference between gaze targets of children with ASD and typically developing children. PoV cameras may provide a more sensitive method for measuring eye contact in children with ASD during live interactions.

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OBJECTIVES: Alcohol Behavioral Couple Therapy (ABCT) is an efficacious treatment for alcohol use disorders. Coding treatment integrity can shed light on the active ingredients of ABCT, but there are no published studies of treatment integrity instruments for ABCT. The present study describes the development and initial reliability of the Treatment Integrity Rating System - Couples Version (C-TIRS) for ABCT. METHODS: The C-TIRS was used to rate 284 first- and mid-treatment ABCT sessions of 188 couples in four randomized clinical trials. RESULTS: Average inter-rater reliability for distinguishing ratings between C-TIRS items was fair-to-good for quantity items (intraclass correlation [ICC] = 0.64) and poor-to-fair for quality items (ICC = 0.41). Five C-TIRS subscales were defined a priori to measure treatment components involving cognitive-behavioral therapy, spouse involvement, couple therapy, common therapeutic factors, and overall adherence to the treatment protocol and had adequate internal reliability (α = 0.74-0.89). Inter-rater reliability was fair to good on seven of ten scales but poor on three scales (ICC range = 0.17-0.72). CONCLUSIONS: The C-TIRS was designed to provide information about quantity and quality of the delivery of ABCT components; however, further refinement of the C-TIRS is warranted before it should be used in frontline practice. Clinical implications and recommendations for future research are discussed.

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Motivational interviewing (MI) is an efficacious treatment for substance use disorders and other problem behaviors. Studies on MI fidelity and mechanisms of change typically use human raters to code therapy sessions, which requires considerable time, training, and financial costs. Natural language processing techniques have recently been utilized for coding MI sessions using machine learning techniques, rather than human coders, and preliminary results have suggested these methods hold promise. The current study extends this previous work by introducing two natural language processing models for automatically coding MI sessions via computer. The two models differ in the way they semantically represent session content, utilizing either 1) simple discrete sentence features (DSF model) and 2) more complex recursive neural networks (RNN model). Utterance- and session-level predictions from these models were compared to ratings provided by human coders using a large sample of MI sessions (N=341 sessions; 78,977 clinician and client talk turns) from 6 MI studies. Results show that the DSF model generally had slightly better performance compared to the RNN model. The DSF model had "good" or higher utterance-level agreement with human coders (Cohen's kappa>0.60) for open and closed questions, affirm, giving information, and follow/neutral (all therapist codes); considerably higher agreement was obtained for session-level indices, and many estimates were competitive with human-to-human agreement. However, there was poor agreement for client change talk, client sustain talk, and therapist MI-inconsistent behaviors. Natural language processing methods provide accurate representations of human derived behavioral codes and could offer substantial improvements to the efficiency and scale in which MI mechanisms of change research and fidelity monitoring are conducted.

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OBJECTIVE: The aim of this study was to assess the accuracy of thin slices to characterize the verbal communication behavior of counselors and patients engaged in Motivational Interviewing sessions relative to fully coded sessions. METHODS: Four thin slice samples that varied in number (four versus six slices) and duration (one- versus two-minutes) were extracted from a previously coded dataset. In the parent study, an observational code scheme was used to characterize specific counselor and patient verbal communication behaviors. For the current study, we compared the frequency of communication codes and the correlations among the full dataset and each thin slice sample. RESULTS: Both the proportion of communication codes and strength of the correlation demonstrated the highest degree of accuracy when a greater number (i.e., six versus four) and duration (i.e., two- versus one-minute) of slices were extracted. CONCLUSION: These results suggest that thin slice sampling may be a useful and accurate strategy to reduce coding burden when coding specific verbal communication behaviors within clinical encounters. PRACTICE IMPLICATIONS: We suggest researchers interested in using thin slice sampling in their own work conduct preliminary research to determine the number and duration of thin slices required to accurately characterize the behaviors of interest.

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OBJECTIVES: To provide a concise and practical guide to the development, modification, and use of behavioral coding schemes for observational data in pediatric psychology. METHODS: This article provides a review of relevant literature and experience in developing and refining behavioral coding schemes. RESULTS: A step-by-step guide to developing and/or modifying behavioral coding schemes is provided. Major steps include refining a research question, developing or refining the coding manual, piloting and refining the coding manual, and implementing the coding scheme. Major tasks within each step are discussed, and pediatric psychology examples are provided throughout. CONCLUSIONS: Behavioral coding can be a complex and time-intensive process, but the approach is invaluable in allowing researchers to address clinically relevant research questions in ways that would not otherwise be possible.

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