Exploratory machine learning modeling of adaptive and maladaptive personality traits from passively sensed behavior

Yan, Runze; Ringwald, Whitney R.; Hernandez, Julio Vega; Kehl, Madeline; Bae, Sangwon; Dey, Anind K.; Low, Carissa A.; Wright, Aidan G.C.; Doryab, Afsaneh

doi:10.1016/j.future.2022.02.010

Cited by 15 publications

(8 citation statements)

References 68 publications

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“…Insight is regarded as the clarity of an individual's feelings, thinkings, and behaviours (Grant et al, 2002;Yan et al, 2022). Insight can be considered as the novel understanding of oneself gained by reviewing one's past and present experiences, making connections among the experiences, and realising the interrelations among one's thoughts, feelings, and behaviours, which promotes the adaptation of new and more helpful beliefs as well as the associated behaviours (Falon et al, 2021;Hill et al, 2007;Loyd et al, 2023).…”

Section: Insightmentioning

confidence: 99%

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2023

GJPR

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The study aimed to test whether insight mediates decentering and cognitive coping processes in adaptive and maladaptive types. One hundred and fifty-five Chinese adult participants were recruited to complete an online survey, to report their level of decentering, insight, and cognitive coping strategies. The results of the simple regression analyses showed decentering positively predicted adaptive cognitive coping, and negatively predicted maladaptive cognitive coping process to a moderate degree. Mediation analyses revealed the mediating effect of insight only occurred in the relationship between decentering and maladaptive cognitive coping strategies, suggesting less decentering predicted greater maladaptive cognitive coping processes via a lower level of insight. The study contributed to the new understanding of the decentering mechanism of the cognitive coping process. Insight had a stronger effect on maladaptive cognitive coping than on adaptive cognitive coping strategies. The study provided recommendations to improve outcome effectiveness in mental health intervention.

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Section: Insightmentioning

confidence: 99%

Untitled

2023

GJPR

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“…Their results revealed that programmer's personality traits and knowledge collection behavior play a key role in shaping their intention to be creative. Yan et al [44] presented a novel analytic approach to extract and understand these behavioral patterns and their impact on predicting adaptive and maladaptive personality traits. Their machine learning analysis shows that both traits are associated with passively sensed behavior.…”

Section: Related Workmentioning

confidence: 99%

“…The personality traits and knowledge collection behavior play a key role in shaping their intention to be creative. 22 Yan et al [44] Reg 128 Adaptive and maladaptive personality traits are associated with passively sensed behavior 23 Qamar & Malik [12] Exp Stu (135) Teams with higher values of Team Homogeneity Index (THI) were more productive and produced better quality software during the implementation and testing phases 24 Qamar & Malik [62] Exp Stu (215) & Pro (…”

Section: Related Workmentioning

confidence: 99%

A Quantitative Assessment of the Impact of Homogeneity in Personality Traits on Software Quality and Team Productivity

Qamar

Malik

2022

IEEE Access

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Software industry is increasingly focusing on the improvement of software quality and team performance to survive in a competitive and rapidly growing environment. Previous studies reveal the importance of social and human factors in software engineering. Despite the importance of these personality factors, only a handful of empirical studies have quantitatively evaluated the impact of personality traits on software quality and effectiveness of software engineering teams. This study quantifies the abstract notion of trait-wise team homogeneity based on the Five-Factor Model (FFM). Additionally, this study also aims to measure its impact on team productivity and software quality. Trait-wise team homogeneity measures the Team Homogeneity Index (THI) for each of the five personality traits of FFM. Therefore, five new metrics i.e. Openness THI (O-THI), Conscientiousness THI (C-THI), Extraversion THI (E-THI), Agreeableness THI (A-THI), and Neuroticism THI (N-THI) are proposed. The utility of these five metrics is evaluated by conducting experiments in academic and industrial environments for three different phases of the Software Development Life Cycle (SDLC) i.e. analysis and design, implementation, and testing. During the analysis and design phase, it was observed that teams (composed of either students or professionals) with greater values of C-THI and A-THI produced better quality design models and teams with greater values of C-THI were more productive. In the implementation phase, teams with higher values of O-THI, C-THI, and E-THI were noticeably more productive and produced better quality code. For the testing phase, teams with greater values of C-THI and A-THI produced better quality test cases and were more productive. Results obtained so far indicate that the newly proposed five metrics -O-THI, C-THI, E-THI, A-THI, and N-THIappear to have the potential to aid managers and academics alike in building productive teams which can produce better quality software.

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“…The rules for excluding participants’ data are often arbitrarily chosen, which can affect the reproducibility of research findings. For example, Yan and colleagues (2022) excluded a participant’s data for a day if all sensor data from that day were missing and assumed that their data were missing at random, whereas Nickels and colleagues (2021) included a day only if at least half a day of data from one frequently sampled sensor (e.g., GPS) was present and if the app measuring behavior passively was active for at least 18 hr of the day. Thus, studies differed in the measures they chose to exclude data and the time window of missing data they considered acceptable.…”

Section: Introductionmentioning

confidence: 99%

It’s All About Timing: Exploring Different Temporal Resolutions for Analyzing Digital-Phenotyping Data

Langener,

Stulp,

Jacobson

et al. 2024

Advances in Methods and Practices in Psychological Science

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The use of smartphones and wearable sensors to passively collect data on behavior has great potential for better understanding psychological well-being and mental disorders with minimal burden. However, there are important methodological challenges that may hinder the widespread adoption of these passive measures. A crucial one is the issue of timescale: The chosen temporal resolution for summarizing and analyzing the data may affect how results are interpreted. Despite its importance, the choice of temporal resolution is rarely justified. In this study, we aim to improve current standards for analyzing digital-phenotyping data by addressing the time-related decisions faced by researchers. For illustrative purposes, we use data from 10 students whose behavior (e.g., GPS, app usage) was recorded for 28 days through the Behapp application on their mobile phones. In parallel, the participants actively answered questionnaires on their phones about their mood several times a day. We provide a walk-through on how to study different timescales by doing individualized correlation analyses and random-forest prediction models. By doing so, we demonstrate how choosing different resolutions can lead to different conclusions. Therefore, we propose conducting a multiverse analysis to investigate the consequences of choosing different temporal resolutions. This will improve current standards for analyzing digital-phenotyping data and may help combat the replications crisis caused in part by researchers making implicit decisions.

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Exploratory machine learning modeling of adaptive and maladaptive personality traits from passively sensed behavior

Cited by 15 publications

References 68 publications

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A Quantitative Assessment of the Impact of Homogeneity in Personality Traits on Software Quality and Team Productivity

It’s All About Timing: Exploring Different Temporal Resolutions for Analyzing Digital-Phenotyping Data

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