Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination.

Traditional interventions for academic procrastination often fail to capture the nuanced, individual-specific factors that underlie them. Large language models (LLMs) hold immense potential for addressing this gap by permitting open-ended inputs, including the ability to customize interventions to individuals' unique needs. However, user expectations and potential limitations of LLMs in this context remain underexplored. To address this, we conducted interviews and focus group discussions with 15 university students and 6 experts, during which a technology probe for generating personalized advice for managing procrastination was presented. Our results highlight the necessity for LLMs to provide structured, deadline-oriented steps and enhanced user support mechanisms. Additionally, our results surface the need for an adaptive approach to questioning based on factors like busyness. These findings offer crucial design implications for the development of LLM-based tools for managing procrastination while cautioning the use of LLMs for therapeutic guidance.

Development of a COVID-19-Related Anti-Asian Tweet Data Set: Quantitative Study.

BACKGROUND: Since the advent of the COVID-19 pandemic, individuals of Asian descent (colloquial usage prevalent in North America, where "Asian" is used to refer to people from East Asia, particularly China) have been the subject of stigma and hate speech in both offline and online communities. One of the major venues for encountering such unfair attacks is social networks, such as Twitter. As the research community seeks to understand, analyze, and implement detection techniques, high-quality data sets are becoming immensely important. OBJECTIVE: In this study, we introduce a manually labeled data set of tweets containing anti-Asian stigmatizing content. METHODS: We sampled over 668 million tweets posted on Twitter from January to July 2020 and used an iterative data construction approach that included 3 different stages of algorithm-driven data selection. Finally, we found volunteers who manually annotated the tweets by hand to arrive at a high-quality data set of tweets and a second, more sampled data set with higher-quality labels from multiple annotators. We presented this final high-quality Twitter data set on stigma toward Chinese people during the COVID-19 pandemic. The data set and instructions for labeling can be viewed in the Github repository. Furthermore, we implemented some state-of-the-art models to detect stigmatizing tweets to set initial benchmarks for our data set. RESULTS: Our primary contributions are labeled data sets. Data Set v3.0 contained 11,263 tweets with primary labels (unknown/irrelevant, not-stigmatizing, stigmatizing-low, stigmatizing-medium, stigmatizing-high) and tweet subtopics (eg, wet market and eating habits, COVID-19 cases, bioweapon). Data Set v3.1 contained 4998 (44.4%) tweets randomly sampled from Data Set v3.0, where a second annotator labeled them only on the primary labels and then a third annotator resolved conflicts between the first and second annotators. To demonstrate the usefulness of our data set, preliminary experiments on the data set showed that the Bidirectional Encoder Representations from Transformers (BERT) model achieved the highest accuracy of 79% when detecting stigma on unseen data with traditional models, such as a support vector machine (SVM) performing at 73% accuracy. CONCLUSIONS: Our data set can be used as a benchmark for further qualitative and quantitative research and analysis around the issue. It first reaffirms the existence and significance of widespread discrimination and stigma toward the Asian population worldwide. Moreover, our data set and subsequent arguments should assist other researchers from various domains, including psychologists, public policy authorities, and sociologists, to analyze the complex economic, political, historical, and cultural underlying roots of anti-Asian stigmatization and hateful behaviors. A manually annotated data set is of paramount importance for developing algorithms that can be used to detect stigma or problematic text, particularly on social media. We believe this contribution will help predict and subsequently design interventions that will significantly help reduce stigma, hate, and discrimination against marginalized populations during future crises like COVID-19.

Personality-targeted persuasive gamified systems: exploring the impact of application domain on the effectiveness of behaviour change strategies.

Persuasive gamified systems for health are interventions that promote behaviour change using various persuasive strategies. While research has shown that these strategies are effective at motivating behaviour change, there is little knowledge on whether and how the effectiveness of these strategies vary across multiple domains for people of distinct personality traits. To bridge this gap, we conducted a quantitative study with 568 participants to investigate (a) whether the effectiveness of the persuasive strategies implemented vary within each domain (b) whether the effectiveness of various strategies vary across two distinct domains, (c) how people belonging to different personality traits respond to these strategies, and (d) if people high in a personality trait would be influenced by a persuasive strategy within one domain and not in the other. Our results show that there are significant differences in the effectiveness of various strategies across domains and that people's personality plays a significant role in the perceived persuasiveness of different strategies both within and across distinct domains. The Reward strategy (which involves incentivizing users for achieving specific milestones towards the desired behaviour) and the Competition strategy (which involves allowing users to compete with each other to perform the desired behaviour) were effective for promoting healthy eating but not for smoking cessation for people high in Conscientiousness. We provide design suggestions for developing persuasive gamified interventions for health targeting distinct domains and tailored to individuals depending on their personalities.

A Review of the Effectiveness, Feasibility, and Acceptability of Art Therapy for Children and Adolescents during the COVID-19 Pandemic.

Art therapy has been widely offered to reduce symptoms of psychological disturbance. Pooled evidence about its effectiveness in epidemic contexts, particularly during the COVID-19 pandemic, has not been yet established. This study reviewed the effectiveness, feasibility, and acceptability of art therapy on children and adolescents during the COVID-19 pandemic and past epidemics. We searched PubMed/Medline, PsycINFO, CENTRAL (Cochrane Library), and CINAHL for articles on art therapy during COVID-19. Included studies reported improvements in measures of mental health, sleep quality, and psychological well-being in children with or without disabilities in the epidemic context. Results also showed that art therapy was highly feasible and accepted by children and adolescents as well as their families during epidemics in reviewed studies. Art therapy can be effective at improving various aspects of mental health, sleep quality, and psychological well-being. More empirical evidence is needed with larger sample sizes and longer duration of interventions.

Understanding the Social Determinants of Mental Health of Undergraduate Students in Bangladesh: Interview Study.

BACKGROUND: The undergraduate student population has been actively studied in digital mental health research. However, the existing literature primarily focuses on students from high-income nations, and undergraduates from limited-income nations remain understudied. OBJECTIVE: This study aims to identify the broader social determinants of mental health among undergraduate students in Bangladesh, a limited-income nation in South Asia; study the manifestation of these determinants in their day-to-day lives; and explore the feasibility of self-monitoring tools in helping them identify the specific factors or relationships that affect their mental health. METHODS: We conducted a 21-day study with 38 undergraduate students from 7 universities in Bangladesh. We conducted 2 semistructured interviews: one prestudy and one poststudy. During the 21-day study, participants used an Android app to self-report and self-monitor their mood after each phone conversation. The app prompted participants to report their mood after each phone conversation and provided graphs and charts so that the participants could independently review their mood and conversation patterns. RESULTS: Our results show that academics, family, job and economic condition, romantic relationship, and religion are the major social determinants of mental health among undergraduate students in Bangladesh. Our app helped the participants pinpoint the specific issues related to these factors, as the participants could review the pattern of their moods and emotions from past conversation history. Although our app does not provide any explicit recommendation, the participants took certain steps on their own to improve their mental health (eg, reduced the frequency of communication with certain persons). CONCLUSIONS: Although some of the factors (eg, academics) were reported in previous studies conducted in the Global North, this paper sheds light on some new issues (eg, extended family problems and religion) that are specific to the context of the Global South. Overall, the findings from this study would provide better insights for researchers to design better solutions to help the younger population from this part of the world.

Grant Report on mCARE: Mobile-Based Care for Children with Autism Spectrum Disorder (ASD) for Low- and Middle-Income Countries (LMICs).

In low- and middle-income countries, especially in Bangladesh, Autism Spectrum Disorder (ASD) may be considered an anathema, and social-cultural-financial constraints mean that there are few facilities available for treatment for ASD children. The revolution in the use of the mobile phone (~80%) by the majority of people in Bangladesh in recent years has created an opportunity to improve the overall scenario in the treatment or remote monitoring process for children with ASD. In this grant project, we planned and developed a mobile phone-based system to remotely monitor children with ASD and help their treatment process both at the caregiver and care practitioner ends. In developing mCARE, we utilized a Remote Experience Sampling Method to design, build, deploy, and study the impact of mobile based monitoring and treatment of children with ASD in Bangladesh. We developed a mobile application using the Experience Sampling Method (ESM). A caregiver routinely reported the behavioral and milestone parameters of their children with ASD. The care practitioners monitored the longitudinal data that helped them in decision-making in a particular patient's treatment process. The Value Sensitive Design (VSD) was used to make this mobile application more user friendly with consideration of the local economic, social, and cultural values in Bangladesh.

Informing Developmental Milestone Achievement for Children With Autism: Machine Learning Approach.

BACKGROUND: Care for children with autism spectrum disorder (ASD) can be challenging for families and medical care systems. This is especially true in low- and- middle-income countries such as Bangladesh. To improve family-practitioner communication and developmental monitoring of children with ASD, mCARE (Mobile-Based Care for Children with Autism Spectrum Disorder Using Remote Experience Sampling Method) was developed. Within this study, mCARE was used to track child milestone achievement and family sociodemographic assets to inform mCARE feasibility/scalability and family asset-informed practitioner recommendations. OBJECTIVE: The objectives of this paper are threefold. First, it documents how mCARE can be used to monitor child milestone achievement. Second, it demonstrates how advanced machine learning models can inform our understanding of milestone achievement in children with ASD. Third, it describes family/child sociodemographic factors that are associated with earlier milestone achievement in children with ASD (across 5 machine learning models). METHODS: Using mCARE-collected data, this study assessed milestone achievement in 300 children with ASD from Bangladesh. In this study, we used 4 supervised machine learning algorithms (decision tree, logistic regression, K-nearest neighbor [KNN], and artificial neural network [ANN]) and 1 unsupervised machine learning algorithm (K-means clustering) to build models of milestone achievement based on family/child sociodemographic details. For analyses, the sample was randomly divided in half to train the machine learning models and then their accuracy was estimated based on the other half of the sample. Each model was specified for the following milestones: Brushes teeth, Asks to use the toilet, Urinates in the toilet or potty, and Buttons large buttons. RESULTS: This study aimed to find a suitable machine learning algorithm for milestone prediction/achievement for children with ASD using family/child sociodemographic characteristics. For Brushes teeth, the 3 supervised machine learning models met or exceeded an accuracy of 95% with logistic regression, KNN, and ANN as the most robust sociodemographic predictors. For Asks to use toilet, 84.00% accuracy was achieved with the KNN and ANN models. For these models, the family sociodemographic predictors of "family expenditure" and "parents' age" accounted for most of the model variability. The last 2 parameters, Urinates in toilet or potty and Buttons large buttons, had an accuracy of 91.00% and 76.00%, respectively, in ANN. Overall, the ANN had a higher accuracy (above ~80% on average) among the other algorithms for all the parameters. Across the models and milestones, "family expenditure," "family size/type," "living places," and "parent's age and occupation" were the most influential family/child sociodemographic factors. CONCLUSIONS: mCARE was successfully deployed in a low- and middle-income country (ie, Bangladesh), providing parents and care practitioners a mechanism to share detailed information on child milestones achievement. Using advanced modeling techniques this study demonstrates how family/child sociodemographic elements can inform child milestone achievement. Specifically, families with fewer sociodemographic resources reported later milestone attainment. Developmental science theories highlight how family/systems can directly influence child development and this study provides a clear link between family resources and child developmental progress. Clinical implications for this work could include supporting the larger family system to improve child milestone achievement.

An mCARE study on patterns of risk and resilience for children with ASD in Bangladesh.

Community-wide lockdowns in response to COVID-19 influenced many families, but the developmental cascade for children with autism spectrum disorder (ASD) may be especially detrimental. Our objective was to evaluate behavioral patterns of risk and resilience for children with ASD across parent-report assessments before (from November 2019 to February 2020), during (March 2020 to May 2020), and after (June 2020 to November 2020) an extended COVID-19 lockdown. In 2020, our study Mobile-based care for children with ASD using remote experience sampling method (mCARE) was inactive data collection before COVID-19 emerged as a health crisis in Bangladesh. Here we deployed "Cohort Studies", where we had in total 300 children with ASD (150 test group and 150 control group) to collect behavioral data. Our data collection continued through an extended COVID-19 lockdown and captured parent reports of 30 different behavioral parameters (e.g., self-injurious behaviors, aggression, sleep problems, daily living skills, and communication) across 150 children with ASD (test group). Based on the children's condition, 4-6 behavioral parameters were assessed through the study. A total of 56,290 behavioral data points was collected (an average of 152.19 per week) from parent cell phones using the mCARE platform. Children and their families were exposed to an extended COVID-19 lockdown. The main outcomes used for this study were generated from parent reports child behaviors within the mCARE platform. Behaviors included of child social skills, communication use, problematic behaviors, sensory sensitivities, daily living, and play. COVID-19 lockdowns for children with autism and their families are not universally negative but supports in the areas of "Problematic Behavior" could serve to mitigate future risk.

Measuring exposure of e-waste dismantlers in Dhaka Bangladesh to organophosphate esters and halogenated flame retardants using silicone wristbands and T-shirts.

Silicone (polydimethylsiloxane or PDMS) wristbands and cotton T-shirts were used to assess the exposure of e-waste recyclers in Dhaka, Bangladesh to polybrominated diphenyl ethers (PBDEs), novel brominated flame retardants (NBFRs), dechlorane plus (DPs), and organophosphate esters (OPEs). The median surface-normalized uptake rates of PBDEs, NBFRs, DPs, and OPEs were 170, 8.5, 4.8, and 270 ng/dm2/h for wristbands and 5.4, 2.0, 0.94, and 23 ng/dm2/h for T-shirts, respectively. Concentrations of Tris(2-chloroethyl) phosphate (TCEP), Tris(1,3-dichloro-2-propyl) phosphate (TDCIPP), Tri-m-cresyl phosphate (TmCP), Bis(2-ethlyhexyl) tetrabromophthalate (BEH-TEBP), and Dechlorane plus (DPs) in wristbands were significantly correlated with those in T-shirts. Wristbands accumulated ~7 times more mass than T-shirts, especially of compounds expected to be mainly in the gas phase. We introduce the silicone "sandwich" method to approximate the easily releasable fraction (ERF) from T-shirts, hypothesized to be related to dermal exposure. ERFs varied from 6 to 75% of total chemical accumulated by T-shirts and were significantly negatively correlated with compounds' octanol-air partition coefficient (log Koa). The median daily exposure doses via dermal transfer from the front of the T-shirt to the front body trunk were 0.32, 0.13, 0.11, and 9.1 ng/kg-BW/day for PBDEs, NBFRs, DPs, and OPEs, respectively. The evidence of e-waste recycler exposure to flame retardants in this low income country, lacking protective personal equipment, calls for measures to minimize their exposure and for chemical management regulations to consider exposures to chemicals in waste products.