Predicting problematic smartphone use over time in adolescence: A latent class regression analysis of online and offline activities

2020 ◽  
pp. 146144482094880
Author(s):  
Anne-Linda Camerini ◽  
Tiziano Gerosa ◽  
Laura Marciano
Keyword(s):  
Social Desirability ◽  
Latent Class ◽  
Class Membership ◽  
Problematic Smartphone Use ◽  
Future Studies ◽  
Latent Class Regression ◽  
The Social ◽  
Smartphone Use ◽  
Over Time ◽  
Time Future

Despite today’s ubiquitous nature of smartphones among adolescents, little is known about behavioural online and offline longitudinal predictors of problematic smartphone use (PSU). Guided by Uses and Gratifications Theory, we applied latent class analysis on survey data collected in 2017 from a cohort of 1096 adolescents ( Mage = 12.4, SDage = 0.56) and regressed PSU measured 1 year later on class membership, controlling for socio-demographic characteristics, social desirability and autoregressive effects. We extracted four distinct classes: social-recreational onliners ( n = 228), weekend onliners ( n = 331), balanced ( n = 404) and noninvolved ( n = 153). Characterised by significantly more time spent online for recreational and social networking activities, both during weekdays and weekend days, as well as less time for sleep, the social-recreational onliners class showed significantly higher levels of PSU over time. Future studies should assess not only duration but also the frequency of daily online activities to provide further insights into behavioural predictors of PSU.

scholarly journals Does Smartphone Addiction Fall on a Continuum of Addictive Behaviors?

2020 ◽  
Vol 17 (2) ◽  
pp. 422 ◽  
Author(s):  
Sheila Yu ◽  
Steve Sussman
Keyword(s):  
Behavioral Addiction ◽  
Smartphone Addiction ◽  
Gaming Addiction ◽  
Addictive Behaviors ◽  
Problematic Smartphone Use ◽  
Causal Pathways ◽  
Current Review ◽  
The Social ◽  
Smartphone Use ◽  
Meta Analyses

Due to the high accessibility and mobility of smartphones, widespread and pervasive smartphone use has become the social norm, exposing users to various health and other risk factors. There is, however, a debate on whether addiction to smartphone use is a valid behavioral addiction that is distinct from similar conditions, such as Internet and gaming addiction. The goal of this review is to gather and integrate up-to-date research on measures of smartphone addiction (SA) and problematic smartphone use (PSU) to better understand (a) if they are distinct from other addictions that merely use the smartphone as a medium, and (b) how the disorder(s) may fall on a continuum of addictive behaviors that at some point could be considered an addiction. A systematic literature search adapted from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was conducted to find all relevant articles on SA and PSU published between 2017 and 2019. A total of 108 articles were included in the current review. Most studies neither distinguished SA from other technological addictions nor clarified whether SA was an addiction to the actual smartphone device or to the features that the device offers. Most studies also did not directly base their research on a theory to explain the etiologic origins or causal pathways of SA and its associations. Suggestions are made regarding how to address SA as an emerging behavioral addiction.

Smartphones and Social Support: Longitudinal Associations Between Smartphone Use and Types of Support

2021 ◽  
pp. 089443932098876
Author(s):  
Matthew A. Lapierre ◽  
Pengfei Zhao
Keyword(s):  
Social Support ◽  
Social Networking ◽  
Social Bonds ◽  
Problematic Smartphone Use ◽  
Personal Lives ◽  
The World ◽  
Different Types ◽  
Smartphone Use ◽  
Tangible Support ◽  
Over Time

Smartphones provide users with a vast array of tools to reach out to the world. Smartphones can be used to reach out interpersonally with family, friends, and acquaintances, they can be used to scroll through social networking platforms where one can post comments on a friend’s status update or read about the personal lives of their favorite celebrity, and they can be used to surf the web or read the news. Yet, research has also shown that problematic smartphone use (PSU) can be harmful. Of interest in the current study is whether smartphones can help or harm social bonds longitudinally via social support. Working with a sample of 221 college students who were surveyed twice over a 3-month span, this study explored whether various types of smartphone use (e.g., person-to-person, social networking, and mass-mediated) along with PSU predicted different types of social support over time. The results showed that person-to-person smartphone use was associated with greater belonging support (i.e., feeling accepted by people around you) and tangible support (i.e., feeling that you can find people to help with practical needs) over time. In addition, increased PSU was associated with less tangible support longitudinally. Lastly, there were no effects for social networking or mass-mediated smartphone use on any type of social support. These results offer important insights into how smartphones potentially affect our ability to connect with others along with greater detail about specific kinds of use are implicated.

scholarly journals Latent class regression improves the predictive acuity and clinical utility of survival prognostication amongst chronic heart failure patients

2020 ◽  
Author(s):  
John L Mbotwa ◽  
Marc de Kamps ◽  
Paul D Baxter ◽  
George TH Ellison ◽  
Mark S Gilthorpe
Keyword(s):  
Heart Failure ◽  
Clinical Utility ◽  
Latent Class ◽  
Machine Learning Algorithms ◽  
Continuous Variables ◽  
Class Membership ◽  
Heart Failure Patients ◽  
Latent Class Regression ◽  
Candidate Predictor ◽  
The Uk

AbstractThe present study aimed to compare the predictive acuity of latent class regression (LCR) modelling with: standard generalised linear modelling (GLM); and GLMs that include the membership of subgroups/classes (identified through prior latent class analysis; LCA) as alternative or additional candidate predictors. Using real world demographic and clinical data from 1,802 heart failure patients enrolled in the UK-HEART2 cohort, the study found that univariable GLMs using LCA-generated subgroup/class membership as the sole candidate predictor of survival were inferior to standard multivariable GLMs using the same four covariates as those used in the LCA. The inclusion of the LCA subgroup/class membership together with these four covariates as candidate predictors in a multivariable GLM showed no improvement in predictive acuity. In contrast, LCR modelling resulted in a 10-14% improvement in predictive acuity and provided a range of alternative models from which it would be possible to balance predictive acuity against entropy to select models that were optimally suited to improve the efficient allocation of clinical resources to address the differential risk of the outcome (in this instance, survival). These findings provide proof-of-principle that LCR modelling can improve the predictive acuity of GLMs and enhance the clinical utility of their predictions. These improvements warrant further attention and exploration, including the use of alternative techniques (including machine learning algorithms) that are also capable of generating latent class structure while determining outcome predictions, particularly for use with large and routinely collected clinical datasets, and with binary, count and continuous variables.

Temporary Communitas at an Iconic Cricket Event: An Ethnographic Study

2022 ◽  
Author(s):  
Leonie Lockstone-Binney ◽  
Judith Mair ◽  
Tom Baum ◽  
Faith Ong
Keyword(s):  
Qualitative Analysis ◽  
Factor Structure ◽  
Social Relations ◽  
Reliability And Validity ◽  
Ethnographic Study ◽  
Future Studies ◽  
Sporting Event ◽  
The Social ◽  
Insight Into ◽  
Over Time

The nature of events demand uniqueness and memorability, but the specific elements of experience that produce these have not been deeply examined, particularly over the course of the event experience. Much of this relies heavily on event places and the social relations they facilitate. This research used the concept of temporary communitas and built on the Event Experience Scale (EES) through an ethnographic study of an iconic multi-day, spectator driven sporting event. Solicited participant diaries of eight friends and family who travelled to attend the 2017 Boxing Day Ashes Test in Melbourne, Australia, were collected pre, during and post-event to capture the event experience as it emerged over time. Qualitative analysis of the ethnographic accounts revealed four event experience themes (competition, emotions and atmosphere, special experience and interactions), which collectively were connected to a strong sense of temporary communitas. These themes were evident across the event cycle, providing insight into the nuances of the event experience, and highlighting the importance of understanding the social relations generated in the event place pre- and post-event. Consequently, it is suggested that revision to the existing EES instrument is required to more comprehensively assess for temporary communitas as part of the event experience. Future studies could usefully test the factor structure of the EES with and without the suggested additional temporary communitas items and compare both models on the basis of their reliability and validity.

scholarly journals Differences in Reward Sensitivity between High and Low Problematic Smartphone Use Adolescents: An ERP Study

2021 ◽  
Vol 18 (18) ◽  
pp. 9603
Author(s):  
Xinmei Deng ◽  
Qiufeng Gao ◽  
Lijun Hu ◽  
Lin Zhang ◽  
Yanzhen Li ◽  
...  
Keyword(s):  
Reward Condition ◽  
Reward Sensitivity ◽  
Social Reward ◽  
Social Feedback ◽  
Problematic Smartphone Use ◽  
Punishment Condition ◽  
Reward Positivity ◽  
The Social ◽  
Smartphone Use ◽  
Reward And Punishment

Background: Problematic smartphone use is highly prevalent in adolescent populations compared to other age groups (e.g., adults and young children). Previous studies suggested that higher levels of reward sensitivity were associated with problematic smartphone use. Therefore, the current study investigated the neural processing of monetary and social reward and punishment feedbacks between high and low problematic smartphone use adolescents. Methods: 46 adolescents participated in the current study and they were categorized into two groups based on their level of problematic smartphone use: those who obtained low scores on the measure of problematic smartphone use were categorized as Low Problematic Smartphone Use (LPSU), and those who obtained high scores on the measure of problematic smartphone use were categorized as High Problematic Smartphone Use (HPSU). Electrocortical activities were recorded during the processing of monetary and social reward and punishment feedback. Results: (1) LPSUs evoked larger P3 in the social punishment condition than in the monetary punishment condition. HPSUs evoked larger P3 in the social reward condition than in the monetary condition. (2) The feedback-related negativity (FRN) amplitudes in the reward condition were significantly larger than those in the punishment condition. (3) HPSUs induced larger reward positivity in social feedback conditions than in monetary feedback conditions, while there were no significant differences between the two types of conditions in the LPSUs. Discussion: The results provide neural underpinning evidence that high sensitivity to social rewards may be related to problematic smartphone use in adolescence.

scholarly journals A Latent Class Approach to Modeling Trajectories of Health Care Cost in Pediatric Cystic Fibrosis

2019 ◽  
Vol 39 (5) ◽  
pp. 593-604 ◽  
Author(s):  
Joseph F. Levy ◽  
Marjorie A. Rosenberg
Keyword(s):  
Risk Factors ◽  
Cystic Fibrosis ◽  
Bayesian Estimation ◽  
Latent Variables ◽  
Latent Class ◽  
Low Cost ◽  
Risk Profile ◽  
Cost Of Care ◽  
Class Membership ◽  
Over Time

Introduction. Estimating costs of medical care attributable to treatments over time is difficult due to costs that cannot be explained solely by observed risk factors. Unobserved risk factors cannot be accounted for using standard econometric techniques, potentially leading to imprecise prediction. The goal of this work is to describe methodology to account for latent variables in the prediction of longitudinal costs. Methods. Latent class growth mixture models (LCGMMs) predict class membership using observed risk factors and class-specific distributions of costs over time. Our motivating example models cost of care for children with cystic fibrosis from birth to age 17. We compare a generalized linear mixed model (GLMM) with LCGMMs. Both models use the same covariates and distribution to predict average costs by combinations of observed risk factors. We adopt a Bayesian estimation approach to both models and compare results using the deviance information criterion (DIC). Results. The 3-class LCGMM model has a lower DIC than the GLMM. The LCGMM latent classes include a low-cost group where costs increase slowly over time, a medium-cost group with initial higher costs than the low-cost group and with more rapidly increasing costs at older ages, and a high-cost group with a U-shaped trajectory. The risk profile-specific mixtures of classes are used to predict costs over time. The LCGMM model shows more delineation of costs by age by risk profile and with less uncertainty than the GLMM model. Conclusions. The LCGMM approach creates flexible prediction models when using longitudinal cost data. The Bayesian estimation approach to LCGMM presented fits well into cost-effectiveness modeling where the estimated trajectories and class membership can be used for prediction.

scholarly journals Problematic Smartphone Use during the COVID-19 Pandemic: Its Association with Pandemic-Related and Generalized Beliefs

2021 ◽  
Vol 18 (11) ◽  
pp. 5724
Author(s):  
Meng Xuan Zhang ◽  
Juliet Honglei Chen ◽  
Kwok Kit Tong ◽  
Eilo Wing-yat Yu ◽  
Anise M. S. Wu
Keyword(s):  
Social Cognitive Theory ◽  
Social Cognitive ◽  
Cognitive Theory ◽  
Formal Analysis ◽  
Substantial Effect ◽  
Problematic Smartphone Use ◽  
Chinese Adult ◽  
The Social ◽  
Social Cynicism ◽  
Smartphone Use

Smartphone technologies have played a crucial role in the fight against the COVID-19 pandemic; however, the increased use of smartphones during the pandemic period may expose the general public to a higher risk of problematic smartphone use (PSU). This study aimed to estimate the prevalence of PSU among Chinese community adults and adopted a social-cognitive theory and social axiom framework to evaluate the effects of beliefs on PSU. A Chinese adult sample (N = 616) was obtained through probability sampling via a telephone survey from Macao, China and included 591 smartphone users’ data (39.4% men) for formal analysis. The prevalence of PSU was 43.3% in the overall sample, with 41.9% in women, and 45.5% in men. Two types of beliefs derived from the social-cognitive theory, pandemic-related self-efficacy and government efficacy, both showed significant and negative correlations with PSU (r = −0.13 and −0.10, p < 0.05). As for the two beliefs from the social axiom framework, reward for application was negatively correlated with PSU (r = −0.10, p < 0.05), whereas social cynicism was positively associated with PSU (r = 0.25, p < 0.001). Among those four beliefs, social cynicism exerted the most substantial effect on PSU when controlling for demographics. Our findings enriched the understanding of PSU during the pandemic and provided empirical direction regarding cognition-based intervention strategies for reducing PSU.

scholarly journals Using the Biopsychosocial Model for Identifying Subgroups of Detained Juveniles at Different Risk of Re-offending in Practice: a Latent Class Regression Analysis Approach

2021 ◽  
Author(s):  
Esther Laura de Ruigh ◽  
Samantha Bouwmeester ◽  
Arne Popma ◽  
Robert Vermeiren ◽  
Lieke van Domburgh ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Regression Analysis ◽  
Juvenile Offenders ◽  
Juvenile Justice ◽  
Latent Class ◽  
Assessment Tool ◽  
Individual Risk ◽  
Class Membership ◽  
Latent Class Regression ◽  
Individual Risk Assessment

Abstract Background: Juvenile delinquents constitute a heterogeneous group, which complicates decision-making based on risk assessment. Various psychosocial factors have been used to define clinically relevant subgroups of juvenile offenders, while neurobiological variables have not yet been integrated in this context. Moreover, translation of neurobiological group differences to individual risk assessment has proven difficult. We aimed to identify clinically relevant subgroups associated with differential youth offending outcomes, based on psychosocial and neurobiological characteristics, and to test whether the resulting model can be used for risk assessment of individual cases. Methods: A group of 263 detained juveniles from juvenile justice institutions was studied. Latent class regression analysis was used to detect subgroups associated with differential offending outcome (recidivism at 12 month follow-up). As a proof of principle, it was tested in a separate group of 76 participants whether individual cases could be assigned to the identified subgroups, using a prototype ‘tool’ for calculating class membership. Results: Three subgroups were identified: a ‘high risk – externalizing’ subgroup, a ‘medium risk – adverse environment’ subgroup, and a ‘low risk – psychopathic traits’ subgroup. Within these subgroups, both autonomic nervous system and neuroendocrinological measures added differentially to the prediction of subtypes of reoffending (no, non-violent, violent). The ‘tool’ for calculating class membership correctly assigned 92.1% of participants to a class and reoffending risk. Conclusions: The LCRA approach appears to be a useful approach to integrate neurobiological and psychosocial risk factors to identify subgroups with different re-offending risk within juvenile justice institutions. This approach may be useful in the development of a biopsychosocial assessment tool and may eventually help clinicians to assign individuals to those subgroups and subsequently tailor treatment based on their re-offending risk.

scholarly journals The Social Life of Pigs: Changes in Affiliative and Agonistic Behaviors following Mixing

Animals ◽  
2022 ◽  
Vol 12 (2) ◽  
pp. 206
Author(s):  
Carly I. O’Malley ◽  
Juan P. Steibel ◽  
Ronald O. Bates ◽  
Catherine W. Ernst ◽  
Janice M. Siegford
Keyword(s):  
Mixed Models ◽  
Social Life ◽  
Generalized Linear Mixed Models ◽  
Least Square ◽  
Agonistic Interactions ◽  
Future Studies ◽  
Aggressive Interactions ◽  
The Social ◽  
Affiliative Behaviors ◽  
Over Time

This study investigated potentially affiliative behaviors in grow-finish pigs, how these behaviors changed over time and their relationship to agonistic behaviors. A total of 257 Yorkshire barrows were observed for agonistic (reciprocal fights, attacks) and affiliative (nosing, play, non-agonistic contact) behaviors after mixing (at 10 weeks of age), and weeks 3, 6, and 9 after mix. The least square means of affiliative behaviors were compared across time points. Relationships among affiliative and agonistic behaviors were assessed using generalized linear mixed models. Non-agonistic contact with conspecifics increased until week 6 then remained stable between weeks 6 and 9. Nosing was highest at mix, then decreased in the following weeks. Play was lowest at mix and highest at week 3. Affiliative behaviors were negatively related with aggression at mix (p < 0.001). Pigs who engaged in play and nosing behaviors were more likely to be involved in agonistic interactions in the weeks after mixing (p < 0.05), while pigs engaging in non-agonistic contact were less likely to be involved in agonistic interactions (p < 0.001). There appear to be relationships between affiliative and agonistic behaviors in pigs, with contact being the most predictive of less aggression. Future studies could focus on promoting positive non-agonistic contact in unfamiliar pigs as a way to mitigate aggressive interactions.

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