With the growing percentage of Chinese immigrants in the U.S. population, it is crucial to understand how pre-migration factors (such as reasons for migration) are related to the adjustment of families in the host country. The present study examined reasons for migration and their associations with post-migration sociocultural factors and parenting styles in a community-based sample of Chinese American immigrant families (N = 258) living in the San Francisco Bay Area. The parents’ self-reported reasons for migration included family reasons (55.1%, e.g., family reunification), betterment reasons (18.0%, e.g., better education and occupational opportunities), and both family and betterment reasons (26.9%). Those who migrated for betterment reasons reported significantly higher parental education and per capita income than the family reason group (p < 0.001) and significantly higher income than the combined reason group (p = 0.007). No significant group differences emerged in cultural orientations and parenting styles after controlling for socioeconomic factors. The findings suggested that Chinese immigrant families who migrated solely for better education and occupational opportunities had significantly higher post-migration socioeconomic status than other reason groups. These differences have relevance for programs and services for new immigrants, as families might need different types of support (e.g., socioeconomic vs. relational) depending on their motivations for migration and post-migration socioeconomic resources.

Background: COVID-19 has spread worldwide and generated tremendous stress on human beings. Unfortunately, it is often hard for distressed individuals to access mental health services under conditions of restricted movement or even lockdown. Objective: The study first aims to develop an online digital intervention package based on a commercially released coloring game. The second aim is to test the effectiveness of difference intervention packages for players to increase subjective well-being (SWB) and reduce anxiety during the pandemic. Methods: An evidence-based coloring intervention package was developed and uploaded to an online coloring game covering almost 1.5 million players worldwide in January 2021. Players worldwide participated to color either 4 rounds of images characterized by awe, pink, nature, and blue or 4 rounds of irrelevant images. Participants' SWB and anxiety and the perceived effectiveness of the game in reducing anxiety (subjective effectiveness [SE]) were assessed 1 week before the intervention (T1), after the participants completed pictures in each round (T2-T5), and after the intervention (T6). Independent 2-tailed t tests were conducted to examine the general intervention (GI) effect and the intervention effect of each round. Univariate analysis was used to examine whether these outcome variables were influenced by the number of rounds completed. Results: In total, 1390 players worldwide responded and completed at least 1 assessment. Overall, the GI group showed a statistical significantly greater increase in SWB than the general control (GC) group (N=164, t162=3.59, Cohen d=0.59, 95% CI 0.36-1.24, P<.001). Compared to the control group, the best effectiveness of the intervention group was seen in the awe round, in which the increase in SWB was significant (N=171, t169=2.51, Cohen d=0.39, 95% CI 0.10-0.82, P=.01), and players who colored all 4 pictures had nearly significant improvements in SWB (N=171, F4,170=2.34, partial ŋ2=0.053, P=.06) and a significant decrease in anxiety (N=171, F4,170=3.39, partial ŋ2=0.075, P=.01). Conclusions: These data indicate the effectiveness of online psychological interventions, such as coloring games, for mental health in the specific period. They also show the feasibility of applying existing commercial games embedded with scientific psychological interventions that can fill the gap in mental crises and services for a wider group of people during the pandemic. The results would inspire innovations to prevent the psychological problems caused by public emergencies and encourage more games, especially the most popular ones, to take more positive action for the common crises of humankind.

As a convenient device for observing neural activity in the natural environment, portable EEG technology (PEEGT) has an extensive prospect in expanding neuroscience research into natural applications. However, unlike in the laboratory environment, PEEGT is usually applied in a semiconstrained environment, including management and engineering, generating much more artifacts caused by the subjects’ activities. Due to the limitations of existing artifacts annotation, the problem limits PEEGT to take advantage of portability and low-test cost, which is a crucial obstacle for the potential application of PEEGT in the natural environment. This paper proposes an intelligent method to identify two leading antecedent causes of EEG artifacts, participant’s blinks and head movements, and annotate the time segments of artifacts in real time based on computer vision (CV). Furthermore, it changes the original postprocessing mode based on artifact signal recognition to the preprocessing mode based on artifact behavior recognition by the CV method. Through a comparative experiment with three artifacts mark operators and the CV method, we verify the effectiveness of the method, which lays a foundation for accurate artifact removal in real time in the next step. It enlightens us on how to adopt computer technology to conduct large-scale neurotesting in a natural semiconstrained environment outside the laboratory without expensive laboratory equipment or high manual costs.

The VUCA environment challenged neuropsychological research conducted in conventional laboratories. Researchers expected to perform complex multimodal testing tasks in natural, open, and non-laboratory settings. However, for most neuropsychological scientists, the independent construction of a multimodal laboratory in a VUCA environment, such as a construction site, was a significant and comprehensive technological challenge. This study presents a generalized lightweight framework for perception analysis based on multimodal cognition-aware computing, which provided practical updated strategies and technological guidelines for neuromanagement and automation. A real-life test experiment on a construction site was provided to illustrate the feasibility and superiority of the method. The study aimed to fill a technology gap in the application of multimodal physiological and neuropsychological techniques in an open VUCA environment. Meanwhile, it enabled the researchers to improve their systematic technological capabilities and reduce the threshold and trial-and-error costs of experiments to conform to the new trend of VUCA.

[This corrects the article DOI: 10.2196/37026.].