Recently, Direct Behavior Ratings have been shown to be a promising new tool for observing students and classrooms in an education setting for a variety of behaviors. The traditional method of observing students and classroom behavior was through tools called Systemic Direct Observations. Currently, there are only a few studies looking at the use of a Direct Behavior Rating as a device to collect peer comparison data to estimate classwide behavior problems. This study examined the estimated percentages of on-task and disruptive behavior between a Systemic Direct Observation with momentary time sampling and three random peers, a Systemic Direct Observation with momentary time sampling using the entire class, and a Direct Behavior Rating. Multiple undergraduate classrooms were taped and divided up into twenty-five 7-minute segments. The videos were then coded on all three of the observation forms with 100% reliability ratings. Results indicated that there was a strong relationship between the Direct Behavior Rating and the SDO classwide on-task estimates with 37% of the variance in the Systemic Direct Observation classwide data consistent with the Direct Behavior Rating data. There was a moderate relationship between the on-task Direct Behavior Rating and three-peer on-task with 13% of the variance in the Systemic Direct Observation data as a portion of the Direct Behavior Rating data. Results also showed that there was a significant correlation between Direct Behavior Rating both of the Systemic Direct Observation methods with 43% for the classwide Systemic Direct Observation and 39% of the three-peers Systemic Direct Observation variance consistent with the Direct Behavior Rating data. Implications and future directions were considered. The research yielded results that indicated that Direct Behavior Ratings might be a useful tool when evaluating classwide behavior, and that further research is warranted.

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Background: Few studies have focused on the contextual influences that impact negative affect (NA) and risk for mood disorders in young adults. Research using ecological momentary assessment (EMA) methods has shown that neurotypical adults with elevated social anxiety may be more sensitive to their social environment. To date, little is known about how types of social interactions impact autistic adults, who may show varying levels of social anxiety and social motivation. Aim: Our goal was to examine the heterogeneity in daily social experiences for autistic and socially anxious adults. Method: Using EMA surveys, we tracked daily self-reported face-to-face interactions and examined how these interactions influenced daily affect. We likewise examined how social anxiety (using the Liebowitz Social Anxiety Scale) and autism traits (using the Autism Spectrum Quotient) influenced day-to-day social experiences while controlling for potential covariates (age and biological sex). Participants consisted of 88 young adults who participated in a mental health longitudinal study. We used a multilevel model approach (MLM) to examine predictors of NA. Results: MLM analysis showed that a model with face-to-face interactions and social anxiety predictors best explained outcomes in NA. AQ scores, age, and sex covariates did not improve model fit. Social anxiety was a significant negative predictor of NA after controlling for face-to-face interaction. Conclusion: These findings highlight that autism traits do not predict NA after controlling for social anxiety, and that social anxiety interventions may improve overall moods by addressing types of interactions.

Suicide continues to be a critical concern for society as one of the leading causes of death in the United States, increasing from 10.4 to 13.5 per 100,000 from 2000 to 2016. This risk is further increased up to 8 times for individuals with Autism Spectrum Disorder. Suicidal thoughts and behaviors have been difficult to predict on a moment-by-moment basis, in part due to technological challenges. Suicidal ideation has been identified as an important indicator of suicidal behavior and an important measurement for predicting suicide in both neurotypical individuals and individuals with autism spectrum disorder. In particular, sleep disturbances are one risk factor for suicidal behavior. Important aims of this study include identifying personalized predictors of leading up to suicidal ideation, including how sleep activity patterns affect suicidal ideation, and how these risk factors differ between those with autism spectrum, socially anxious groups. This will give further insight into predictors of suicidal ideation, providing a better understanding for predicting changes in suicidal ideation, with aims to bring more clarity in this at-risk population and improve treatment options. To observe predictors of both long-term and short-term changes in suicidal ideation, I will analyze longitudinal data. The data includes daily phone questionnaires and actigraphy data tracking using GENEActiv wearable devices that includes sleep from individuals with autism spectrum and social anxiety with a history of suicidal ideation, and neurotypical individuals who are tracked over 24-36 weeks. Results of analysis indicate that sleep duration is not a significant predictor of suicidal ideation intensity, and that there is no difference between the autism spectrum and social anxiety groups in the predictive ability of sleep. Limitations of this study include local convenience sampling which includes a large majority of white participants, part of the study and data collected occurring during the COVID-19 pandemic, and potential response bias for such a sensitive topic. This study shows overall that there is a working model for this type of analysis, however many more features of sleep including movement during sleep and waking during sleep need to be analyzed to see if there is any predictive power in information from actigraphy, which would be a low invasive method for detecting increases in suicidal ideation risk.

Nonsuicidal self-injury (NSSI; i.e., the deliberate destruction of one’s own bodily tissue without suicidal intent and for reasons not socially sanctioned) is prevalent and associated with clinically serious consequences. There is a need for evidence-based, stand-alone treatments for this behavior as it presents across the full range of psychiatric disorders. Developing time-efficient and cost-effective interventions for NSSI has proven difficult given that the core components of treatment remain largely unknown. The aim of this study was to examine the specific effects on NSSI of mindful emotion awareness training and cognitive reappraisal, two transdiagnostic treatment strategies that directly address the functional processes that often maintain self-injury (i.e., relief or escape from aversive thoughts or feelings). Using a counterbalanced, combined series (multiple baseline and phase change) single-case experimental design, the unique and combined impact of these two four-week interventions was evaluated among diagnostically heterogeneous, self-injuring adults (N = 10; mean age = 21.3, range = 18 to 30 years). Hypotheses were that each intervention would produce clinically meaningful reductions in NSSI; adding the alternative intervention would have additive benefit for those who did not respond to the initial intervention alone; and reductions in NSSI would be maintained over a four-week follow-up phase. Results showed that 8 of 10 participants demonstrated clinically meaningful reductions in NSSI by the follow-up phase; six participants responded to one intervention alone, whereas adding the alternative intervention was associated with additive benefit for two participants. Group-based analyses indicated a statistically significant effect of study phase on NSSI (p < .001), with fewer NSSI urges and acts occurring after the interventions were introduced. The interventions were also associated with moderate to large reductions in anxiety (d = 0.89 – 1.09), depression (d = 0.79 – 1.09), and interference caused by symptoms (d = 0.61), and with improvements in skills-based mechanisms: mindful emotion awareness (d = 1.44) and reappraisal (d = 1.30). The results suggest that increasing mindful emotion awareness and cognitive reappraisal may be two key therapeutic strategies for reducing NSSI. Transdiagnostic, emotion-focused interventions delivered in time-limited formats can serve as practical yet powerful treatment approaches, especially for lower-risk self-injuring individuals.

BACKGROUND: As digital media use has increasingly become a part of daily life for adolescents, it is critical to ascertain any potential link between digital media use and poorer mental health. Many studies have explored the relationship between screen time and psychiatric symptoms, but most of these studies have been limited by cross-sectional and participant recall. Moreover, many of these studies examine youth digital media use as a whole, rather than examining qualitative use. This study uses smartphone-based ecological momentary assessment to capture daily relationships between anxiety and depression symptoms and quality and quantity of smartphone use. METHODS: Youth aged 12 to 22 years-old were recruited from Cambridge Health Alliance’s outpatient psychiatry clinics to participate in the study. Participants were asked to use the mindLAMP app daily for six weeks to record depression and anxiety symptoms (via PHQ-8 and GAD-7 screeners), as well as provide daily screen time and three most frequently used apps obtained from their smartphone’s screen time report feature. Patient psychiatric diagnosis and demographic data were obtained from the medical record upon study enrollment. Multilevel mixed effects regression models were used to assess relationships between daily depression and anxiety symptoms and digital media use; linear regression models assessed relationships between psychiatric diagnoses and average screen and social media time over the course of the study. RESULTS: The study recruited a total of 51 youths. Frequent use of a specific type of digital media was not related to an increase in daily depression or anxiety symptoms. While no one category of psychiatric diagnosis was related to higher or lower average screen time, a statistically significant relationship was observed between daily GAD-7 score and daily time spent on social media (p=0.004) and percent of daily screen time that was spent on social media (p=0.01) with increased daily GAD-7 scores. Daily PHQ-8 scores were also positively correlated to daily time spent on social media (p=0.023). CONCLUSIONS: Our findings support those of prior studies that a significant relationship exists between anxiety, depression, and time spent on screens. It is less evident whether a specific type of digital media use may be more or less associated with these symptoms. More focused research with digital phenotyping or ecological momentary assessment may help to determine whether digital media use causes the worsening of psychiatric symptoms or alternatively, is being used to help manage them. Either way, mental health clinicians should educate their adolescent patients about these existing relationships and encourage patient self-awareness regarding personal patterns of digital media use. / 2025-02-20T00:00:00Z

Introduction: Over the last decades, our understanding of the cognitive, motivational, and neural processes involved in addictive behavior has increased enormously. A plethora of laboratory-based and cross-sectional studies has linked cognitive-behavioral measures to between-subject differences in drinking behavior. However, such laboratory-based studies inevitably suffer from small sample sizes and the inability to link temporal fluctuations in task measures to fluctuations in real-life substance use. To overcome these problems, several existing behavioral tasks have been transferred to smartphones to allow studying cognition in the field. Method: In this narrative review, we first summarize studies that used existing behavioral tasks in the laboratory and self-reports of substance use with ecological momentary assessment (EMA) in the field. Next, we review studies on psychometric properties of smartphone-based behavioral tasks. Finally, we review studies that used both smartphone-based tasks and self-reports with EMA in the field. Results: Overall, studies were scarce and heterogenous both in tasks and in study outcomes. Nevertheless, existing findings are promising and point toward several methodological recommendations: concerning psychometrics, studies show that – although more systematic studies are necessary – task validity and reliability can be improved, for example, by analyzing several measurement sessions at once rather than analyzing sessions separately. Studies that use tasks in the field, moreover, show that power can be improved by choosing sampling schemes that combine time-based with event-based sampling, rather than relying on time-based sampling alone. Increasing sampling frequency can further increase power. However, as this also increases the burden to participants, more research is necessary to determine the ideal sampling frequency for each task. Conclusion: Although more research is necessary to systematically study both the psychometrics of smartphone-based tasks and the frequency at which task measures fluctuate, existing studies are promising and reveal important methodological recommendations useful for researchers interested in implementing behavioral tasks in EMA studies.

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