A Dyadic Perspective on Parent and Adolescent Technology Use: The Role of Open Communication, Joint Technology Use, and Validation Motives in Problematic Social Media Use

A Dyadic Perspective on Parent and Adolescent Technology Use: The Role of Open Communication, Joint Technology Use, and Validation Motives in Problematic Social Media Use | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Dyadic Perspective on Parent and Adolescent Technology Use: The Role of Open Communication, Joint Technology Use, and Validation Motives in Problematic Social Media Use Wendy Ellis, Lynda Hutchinson, Tara Dumas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7473910/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2026 Read the published version in BMC Psychology → Version 1 posted 12 You are reading this latest preprint version Abstract Background : Parents significantly influence adolescent behavior, yet research often overlooks dyadic interactions in shaping media use. Understanding how family members’ technology habits and communication relate to problematic media use (PMU) is critical for developing effective interventions. Objective : This study used actor–partner interdependence modeling (APIM) to examine associations among time on social media, PMU, and three mediators (validation motives, joint technology use, and open communication) within parent–adolescent dyads. Methods : Participants were 85 parent–child dyads (N = 170) from Ontario, Canada. Parents included 46 mothers (54.1%) and 30 fathers (35.3%). Adolescents (M age = 13.44, SD = 1.6) included 50 males (58.8%) and 32 females (37.6%). In 2023, parents and adolescents completed online surveys assessing time online, social media motives and patterns, and communication. Results : Greater social media use, stronger validation motives, and more frequent joint technology use were associated with higher adolescent PMU, while open communication predicted lower PMU. Parent social media use was linked to adolescent validation motives and joint technology use. Mediation analysis showed adolescent-reported joint use partially mediated the link between parent social media use and adolescent PMU. Conclusions : Family digital habits are interdependent. Parents’ social media use influences adolescents’ behaviors; validation seeking and joint use predict PMU, whereas open communication could be protective. Problematic Media Use (PMU) Joint Technology Use Parent–Adolescent Dyads Open Communication Online Validation Figures Figure 1 Introduction Technology use has significantly altered the social landscape for today’s adolescents. Currently, 95% of adolescents have access to a smartphone, and nearly half report using the internet “almost constantly”. 1, 2 The total amount of time adolescents spend online has received significant attention, yet another pressing issue is the rise in problematic media use (PMU). PMU is characterized by compulsive usage patterns and addiction-like symptoms, including anxiety when disconnected. Estimates suggest that one in four youth experience PMU, leading to a range of adverse outcomes, including poor mental health and academic difficulties 3,4 . To mitigate the development of PMU, previous studies have often examined time spent online and specific individual risk factors 5 , but the complex social and contextual influences remain underexplored. 6 Specifically, adolescents’ motivations for validation and parents’ pivotal role warrant closer examination. Moreover, few studies have taken a dyadic approach to consider how the behaviors of parents and adolescents interact to shape PMU in both parties. The aim of the present study is to examine the interdependent nature of parent and adolescent behaviors in influencing PMU. Problematic Media Use in Adolescence Although research highlights the potential for technology to foster positive experiences and social connections among adolescents, 7 a substantial body of evidence links technology use to poor mental health outcomes 8-10 . Historically, this research has measured self-reported time online without considering specific technology-related experiences and the near-constant connections we have with our devices 11-13 . Recent studies have shown that PMU is a much stronger predictor of poor mental health outcomes than time spent on social media or intensity alone 14-15 . PMU is a multidimensional construct that includes elements of excessive use, compulsive checking, and preoccupation with digital content, particularly social media. Behaviors are often driven by fear of missing out (FOMO) resulting in difficulty disengaging and emotional dependence. 16-18 FOMO is characterized by persistent anxiety or discomfort when excluded from socially rewarding activities, especially with social media 18,19 . Canadian data suggest that nearly 40% of youth are either classified as having problematic internet use or are at risk for its development 20 . In the present study we define PMU broadly to include FOMO, addiction behaviors and excessive use. Developmental factors further intensify vulnerability to PMU. Adolescence is marked by heightened social comparison and risk-taking along with intensified needs for autonomy and peer approval 21, 22 . These factors, combined with immature self-regulation and reward-processing brain circuits, increase sensitivity to immediate gratification and social feedback, contributing to PMU 23-25 . This is evident in behaviors such as like-seeking, where adolescents post curated or edited photos in pursuit of social validation 26 . Research indicates that up to 90% of youth engage in some form of like-seeking behavior when posting online 27 . However, increased pressure for social media attention has been linked to negative social outcomes, including reduced friendship closeness, diminished social support 28 and risk taking 26 . Consequently, adolescents motivated by validation may engage in compulsive online behaviors, driven by both developmental changes and external influences such as peers and family 29, 30 . Parent–Child Relationship As adolescents begin to assert their autonomy, parents often struggle to maintain their connection and influence 31, 32 . However, the family serves as the primary context for development and individuals remain interconnected 33 . Many researchers have examined the strategies parents enact to mitigate their adolescents’ PMU including setting limits, engaging in active discussions, joint technology use, and modeling appropriate behavior 34-36 . Recent meta-analyses provide limited evidence that any parental practices effectively mitigate PMU, especially during adolescence 37-40 . Importantly, some well-meaning parenting strategies may even have unintended negative consequences. For example, screen time restrictions have been linked to increased secrecy around phone use 40 . Similarly, while joint technology use, such as watching TikToks or playing games together, can open up opportunities for "teachable moments," it may inadvertently reinforce frequent and potentially unhealthy engagement with digital media 37 . Though less frequently studied, family warmth and cohesion may be among the most influential protective factors against PMU 6, 38, 40 . The role of strong family relationships has long been recognized as fundamental to all areas of development 41 . Positive parental relationships can help meet adolescents’ social and emotional needs, making them feel connected and supported at home and less likely to seek external validation. Autonomy-supportive parenting, which involves co-regulation and emphasizes open communication, modelling, and guidance helps adolescents develop better self-regulation skills 42-45 . Few studies have explored the influence of open communication in parent-child relationships on adolescents' digital engagement 40 . Additionally, previous research has also shown that parents’ own use of technology is related to children’s screen time and adjustment 46-48 and the overall quality of parent-child interactions 50 . Parents and adolescents may share similar motivational challenges in social media use, including attention-seeking and difficulties with excessive use and addiction. In fact, over 20% of adults aged 35–49 report at least some negative outcomes related to social media use 20 . Consistent with Social Learning Theory 51 , when parents frequently check notifications, post for approval, or involve their children in these practices, they model such behaviors as normative. This modeling may shape adolescents’ beliefs about what constitutes acceptable digital behavior. Yet, little research has examined how parents’ own PMU or their motivations for social media validation in relation to their adolescents’ media use. The Present Study The present study examines the role of adolescent and parent predictors of PMU from a dyadic perspective. That is, we explore the dynamic interplay between adolescent and parent variables to understand how their mutual influences shape PMU. The nature of dyadic relationships makes it difficult to disentangle the role of parenting in adolescents' PMU. Single-informant data on dyadic relationships provide imbalanced perspectives, often with conflicting reports 52 , yet few studies have utilized parent-child data 53, 37, 39 . Drawing on family systems theory, which emphasizes the interconnectedness of family members 32 , this study employs dyadic analysis to examine the relationships among time spent on social media, PMU, and three key mediators: motivations for validation, joint technology use, and open communication. Each construct is assessed from both the parent and adolescent perspective. All variables have parent and adolescent responses. Given the cross-sectional design of the study, we interpret associations and mediation pathways as exploratory and correlational rather than causal, recognizing that temporal order cannot be firmly established. We developed the following four hypotheses: H1: More time spent on social media will predict PMU for both adolescents and their parents; H2: Greater motivations for validation will predict PMU for both adolescents and their parents (H2a) and mediate the relationship between time spent on social media and PMU (H2b); H3: Greater joint technology use will predict higher PMU for both adolescents and their parents (H3a) and mediate the relationship between time spent on social media and PMU (H3b); H4: Greater open communication between parents and adolescents will predict lower PMU (H4a) and mediate the relationship between time spent on social media and PMU (H4b). Methods Participants The sample consisted of 170 participants (85 parent-child dyads). All participants lived in Ontario, Canada. There were 46 mothers (54.1%) and 30 fathers (35.3%). Parents reported their age, with four parents (4.7%) between age 25 – 30, 38 parents (44.7%) between age 31 – 40, 30 parents (35.3%) between age 41 – 50, 5 parents between age 51 – 60 (5.9%). Among adolescents, there were 50 males (58.8%), 32 females (37.6%), and 1 participant who identified as ‘other’ (1.2%). Adolescents’ mean age was 13.44 years ( SD = 1.6 years). Additional demographic data are provided in Table 1. Procedure The study protocol was reviewed and approved by the [blinded for review] University Research Ethics Board, ensuring compliance with established ethical guidelines, including those outlined in the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS2, 2022) and the Declaration of Helsinki. Informed consent was obtained from parents, and assent was obtained from adolescents prior to participation. Data were collected between January and July 2023. Two tactics were used to recruit individuals for study participation. First, posters were displayed at local community centers and electronic posters were advertised on the lab research pages on social media. Second, the poster advertisement was shared during an in-person movie screening and panel discussion hosted by the researchers at [blinded] University. Parents were informed they would need to complete the online survey first using a QR code. Parents were asked to provide consent for study participation for themselves and their child. Parents who provided consent and completed the survey were provided with a unique code and URL to share with their child. This code allowed their adolescent to access the survey and complete similar survey items. Parents and adolescents had an opportunity to provide an email address to receive a $10 CAD gift card as compensation for their participation. Each participant spent approximately 20 minutes completing their surveys. Measures Parent Problematic Use Questions pertaining to parental PMU measured excessive use and were assessed using 4 items developed by Matthes et al 47 . Items included: “I look at my mobile phone or check messages in between doing things”, “I pick up my mobile phone and do something with it, although I have nothing particular to do on it,” “If I get a message on my mobile phone then I just have to look at it right away,” and “I use my mobile phone so much that it has had a negative impact on my job or my family time.” Parents were asked to consider the previous month when responding to items. Items used a 5-point Likert scale ranging from 1 ( never ) to 5 ( always ). The four items were averaged to compute an overall score (α = .69). Adolescent Problematic Use Adolescent PMU was assessed with items relating to FOMO and addiction. Four items were adapted from previous FOMO items to align with the aims of the current study 17 . These items assessed anxious behaviors (e.g., worrying), related to social media use over the last month (“become restless or troubled if you have not had access to social media,” “often worry about missing important things on social media,” “get worried when you find out online that your friends are having fun without you,” and “continue to keep tabs online about what your friends are doing, even on vacation”). Three items from the Bergen Social Media Addiction Scale (BSMAS) designed by Andreassen et al. 54 were used to measure adolescents’ addictive behaviors relating to social media use (“spend a lot of time thinking about social media or planned use of social media,” “use social media so much that it has had a negative impact on your schoolwork,” and “feel it is important to update your status and share the details online when you are having a good time”). Items used a 5-point Likert scale ranging from 1 ( never ) to 5 ( always ). The FOMO scale and BSMAS were combined to create a single PMU scale for adolescents ( a = .87). Parent and Adolescent Time Spent on Social Media Parents and adolescents each reported their time spent on social media over the previous month and estimated their daily engagement 26 . They were first instructed to open the screen time summary in their phones and look under social media. The question asked: “Over the past month, how much time did you spend on social media on an average day?" Eight response options were provided ranging from 0, less than 10 minutes, to 8-10 hours, and over 10 hours. Parent and Adolescent Motivations for Validation Both parents and adolescents reported how important receiving attention on social media is to them 27 . One question asked: “How important is it to you to get likes/views or followers on your social media posts (e.g., TikTok, Instagram)?” Responses were recorded on a 5-point scale from 1 ( not at all important) to 5 ( extremely important) . The option of “ I don’t post on social media ” was also provided. Parent Perceptions of Joint Technology Use Parents were asked two questions, adapted from Connell et al. 55 about spending time online with their adolescent. Responses were recorded on a 5-point Likert scale from 1 ( never ) to 5 ( always ). Parents were asked: “How often do you spend time in person on the same device as your child (e.g., phone, computer/tablet, social media or gaming)?” and “How often do you ask your child to spend time online with you?” An average was computed for these items ( r = .82). Adolescent Perceptions of Joint Technology Use Adolescents were asked two questions about joint technology use with their parents. These questions were also adapted from Connell et al. 55 and modified to be relevant to adolescents. Responses were recorded on a 5-point Likert scale from 1 ( never ) to 5 ( always ). Adolescents were asked: “How often do you spend time in person on the same device as your parent (e.g., phone, computer/tablet, gaming system)” and “How often do you ask your parent to spend time online with you (e.g., watching TikToks)?” An average was computed for these items ( r = .44). Parent Perceptions of Child-Parent Open Communication Parents’ perceptions of child-parent open communication were assessed with 7 items from the positive relationship closeness subscale 56 (e.g., "My child openly shares feelings and experiences with me”). Parents rated each item on a 5-point Likert scale ranging from 1 ( never) to 5 ( always ). An average score of child-parent open communication was computed ( α =.83). Adolescents Perceptions of Parent-Child Open Communication Four items from the openness subscale of the Parent-Adolescent Communication Scale 57 were used to measure adolescents’ perspectives of open communication with a parent (e.g., “If I were in trouble, I could tell my parent”). Adolescents rated each item on a five-point Likert scale ranging from 1 ( strongly disagree ) to 5 ( strongly agree ). An average score of parent-child open communication was computed (α = .67). Results Comparing Parent and Adolescent Responses Preliminary analyses were used to examine frequency responses between the parent and adolescent samples. First, time spent on social media was compared between the two samples. Over 90% of parents reported using social media 3 hours or less per day, with the most common response being "1–2 hours per day" (35.3%), followed by "10–60 minutes per day" (29.4%). For adolescents, about 85% reported using social media 3 hours or less per day, with the most common response being "1–2 hours per day" (36.5%), followed by "2–3 hours per day" (27.1%). These responses suggest adolescents use social media more than their parents. When examining motivations for validation, 28.6% of adolescents and 25.9% of parents indicated these metrics were "moderately important." Additionally, 21.2% of parents and 14.1% of adolescents responded that they do not post on social media. Approximately 15.3% of parents and 29.6% of adolescents reported "often" or "always" spending time together online on the same device with their adolescent or parent, respectively. Next, we examined several individual survey items to compare parent and adolescent responses. When assessing adolescents’ PMU, 67.1% reported that they at least sometimes used "social media so much it has had a negative impact on their schoolwork." Among parents, 70.6% indicated that their mobile phone use at least sometimes "had a negative impact on their job or family time." Finally, when examining parent-child relationships, 71.8% of parents reported that they share an affectionate and warm relationship with their child "most of the time" or "always." Likewise, 70.6% of adolescents "agree" or "strongly agree" with the statement "If I were in trouble, I could tell my parent." Correlations Between Parent and Adolescent Responses Pearson’s correlations were conducted for all parent and adolescent variables (see Table 2). First, we examined the relationship between parent and adolescent reported variables. Parents’ and adolescents’ time spent on social media was positively correlated, r = .40, p < .001. There were also positive correlations between parent and adolescent reported open communication ( r = .54, p < .001), parent and adolescent reported joint technology use ( r = .76, p < .001), parent and adolescent motivations for validation ( r = .34, p = .006), and parent and adolescent PMU ( r = .21, p = .056). In terms of parent variables, there was a positive correlation between their PMU and time spent on social media, r = .30, p = .005, and parents’ motivations for validation, r = .26, p = .036. Adolescents’ PMU was positively correlated with joint technology use, r = .52, p <.001, motivations for validation, r = .56, p < .001, and negatively correlated with parents’ open communication, r = -.46, p < .001. Adolescents’ PMU was positively correlated with parents’ joint technology use, r = .57, p < .001, and parents’ motivations for validation, r = .30, p = .016. There was also a negative correlation between adolescents’ PMU and parent open communication, r = -.35, p < .001. Finally, adolescent age was correlated with all study variables, including parent and adolescent reported joint technology use, r = -.22, p = .042, and r = -.23, p = .035, respectively. We conducted a Multivariate Analysis of Variance (MANOVA) to examine whether there were significant gender differences on the five adolescent variables, but no significant differences emerged. A second MANOVA examined whether there were differences between mothers and fathers across the five parent variables. Mothers reported higher levels of parent-child open communication ( M = 3.88) compared to fathers ( M = 3.55), F (1, 76) = 4.41, p = .039. Conversely, fathers reported higher levels of joint technology use with their adolescents ( M = 2.27) compared to mothers ( M = 1.62), F (1, 76) = 21.48, p < .001. Analytic Plan Given the interdependence of dyadic data, it is critical to account for how parent-adolescent dyads mutually influence each other to accurately model associations between variables. To examine our main research question, we used the Actor-Partner Interdependence Model (APIM) 58 , which accounts for interdependence between participants by modeling how an individual’s characteristics influence their own outcomes (actor effects), as well as those of the other dyad member (partner effects). Our analyses treated parents and adolescents as distinguishable, as they provided data from different perspectives within the dyad. Thus, due to their distinct roles in the dyad, they could not be treated indistinguishably. We applied the APIM to examine how both parent and adolescent reports of technology use predicted their own PMU, as well as that of the other dyad member. The model simultaneously accounts for actor effects, which capture the influence of an individual’s self-reported variables on their outcomes, and partner effects, which capture how an individual’s self-reported variables impact their dyadic counterpart. By including both actor and partner effects, the model accounts for interdependence and allows for a comprehensive analysis of both direct and indirect effects. We completed multilevel modeling using SPSS (Version 29) to test the APIM and examine actor and partner effects on PMU. Using this approach, individuals were nested within the dyad, which served as the unit of analysis. Preliminary data analysis involved conducting a discriminability test on the paired data of adolescents and their parents. A significant chi-square value indicated that the data are distinguishable 59 . To examine indirect pathways, we conducted mediation analyses within the APIM framework. Specifically, we tested the relationship between time spent on social media and PMU, with motivations for validation, joint technology use, and open communication as potential mediators. Bootstrapping with 5,000 samples and a 95% confidence interval was used to estimate indirect effects and account for potential non-normality in their distribution. Confidence intervals around the unstandardized indirect effects were generated to evaluate the significance of these pathways. Unlike traditional mediation analyses, which are conducted in a series of steps, the APIM allows for a more integrated examination of both direct and indirect effects within dyads, accounting for the interdependence between parents and adolescents. Based on our previous analyses, adolescent age was included as a covariate to statistically control for potential influence on the outcomes. Model Testing We conducted a single Actor-Partner Interdependence Model (APIM), consisting of four predictors (time spent on social media, with motivations for validation, joint technology use, and open communication) to test our hypotheses. Actor effects examined how adolescents’ characteristics predicted their own outcomes, and how parents’ characteristics predicted their own outcomes. Partner effects examined how adolescents’ characteristics predicted parents’ outcomes, and how parents’ characteristics predicted adolescent outcomes. Thus, the model included eight actor effects (four per dyad member) and eight partner effects. Time spent on social media was treated as the direct effect on PMU, while motivations for validation, joint technology use, and open communication were mediators in this model. Adolescent Problematic Use Main analyses revealed significant actor effects for adolescents’ time spent on social media, motivations for validation, joint technology use, and open communication (see Table 3). Figure 1 depicts significant effects in the model. Specifically, adolescents who spent more time on social media, reported higher motivations for validation, reported higher joint technology use and experienced increased PMU. Conversely, adolescents who reported more open communication with their parents experienced lower PMU. No significant indirect actor effects emerged. Parent Problematic Use Significant actor effects emerged for parents’ time spent on social media and their PMU (see Table 3; Figure 1). Specifically, greater time spent on social media directly predicted increased PMU. Additionally, parents’ time spent on social media predicted increased motivation for validation. No significant indirect actor effects were observed. Partner Effects on Problematic Media Use There were no partner effects from adolescents to parents, indicating that adolescent variables did not predict parent outcomes. However, several significant partner effects emerged, indicating that parent variables predicted adolescent outcomes (Table 3; Figure 1). Specifically, greater time spent on social media by parents predicted higher adolescent motivations for validation and greater adolescent-reported joint technology use. There was an additional direct effect of parent-reported joint technology use on adolescent PMU, indicating more reported joint use predicted more PMU. Finally, an indirect effect emerged whereby adolescent-reported joint technology use mediated the relationship between parent time spent on social media and adolescent PMU, b = 0.04, bootstrapped SE = 0.03, 95% CI [0.0004, 0.1118] (Figure 1). Discussion The dyadic nature of parent-adolescent relationship has been largely overlooked in research on adolescent PMU. By employing a dyadic approach, this study underscores the continuing influence of parents during adolescence. The Actor-Partner Interdependence Model (APIM) allowed us to examine specifically how parents’ behaviors relate to adolescents' social media use. The findings support Bandura's Social Cognitive Theory 51 in the digital age, emphasizing the importance of modeling within the family. The results demonstrate an important link between parents’ behaviors and adolescents’ outcomes, and also highlight the importance of shared technology use and open communication in shaping media habits for both generations. Parent-Adolescent Dyad Our findings reveal that parents and adolescents engage in similar rates of social media use and PMU. Further, parents and their adolescents’ time on social media was significantly correlated. Although adolescents report slightly more time online and higher posting frequency, nearly 70% of both groups struggle to balance their technology use with everyday responsibilities. Additionally, approximately 60% of both parents and adolescents report feeling at least somewhat motivated by online attention, likes and followers, suggesting both groups value online feedback. This underscores the shared salience of digital validation across generations and may reflect a cultural shift toward seeking feedback through social media. Moreover, overlapping patterns may reflect mutual influence and shared environments, but parent behavior may remain a powerful source of influence during the adolescent period 47 . In line with the first hypothesis, time spent on social media predicted problematic media use (PMU) in both adolescents and parents. However, correlation analyses revealed only weak relationships between time online and other media behaviors. For adolescents, PMU was most strongly associated with validation motives, joint technology use, and parent–child communication. These findings align with previous research and suggest that while time on social media matters, it is only one of several important factors shaping problematic media habits 60 . Our dyadic model revealed that greater parental social media use was linked to more frequent joint technology use with adolescents. In turn, joint technology use (reported by both parents and adolescents) was associated with higher levels of adolescent PMU. As well, adolescent-reported joint technology use mediated the relationship between parental social media use and adolescent PMU. This finding suggests that joint technology use (reported from both parties) may be related to detrimental outcomes and is consistent with prior research 37 . While parents may engage in shared media use as a strategy for monitoring or fostering connection, such practices may inadvertently reinforce excessive engagement or interfere with other responsibilities. It is also possible that increased joint media use may reflect a parental response to adolescents’ negative online experiences, such as cyberbullying. Thus, the effectiveness of this parenting strategy to mitigate PMU remains unclear. The APIM also indicated that parents' time on social media was positively associated with validation-seeking motivations in both themselves and their adolescents. Indeed, both parents and adolescents experience pressure to receive likes and followers. These validation-driven motivations were associated with more time spent on social media and increased PMU, in line with other work suggesting that strong motivations for likes predict more compulsive social media use 29 . While seeking online feedback may support relationship maintenance and identity formation, our results suggest that parents, like adolescents, may also be vulnerable to the reinforcing effects of digital validation. Interestingly, over 20% of parents and 14% of adolescents in our sample reported not posting online, potentially relying on more traditional sources of social feedback or attention. Finally, and in line with expectations, open communication and closeness within the parent–adolescent relationship emerged as protective factors, as indicated by the negative relationship between adolescent-reported open communication and PMU. Specifically, only adolescent-reported open communication, and not the reported open communication of their parents, predicted their PMU. This suggests that adolescents’ perceptions of open communication between themselves and their parents may be particularly influential. Open communication between adolescents and parents is crucial in fostering skills that support self-regulation 43 . When adolescents feel heard and supported, they are more likely to share their experiences and challenges, providing parents with opportunities to guide the establishment of appropriate boundaries 45 . These dynamics not only promote healthier media use but also strengthen adolescents’ ability to set their own limits, an essential component of self-regulation. Implications Our findings suggest that adolescents’ technology use should be understood within the context of the entire family unit, recognizing the interdependent nature of parents' and adolescents' behaviors. Specifically, parents should be informed that their modeling is an important predictor of their child’s behavior during adolescence, as their technology use directly correlates with adolescents' time spent on social media and PMU. Furthermore, since joint technology use between parents and adolescents was associated with higher PMU in adolescents, family-based interventions might focus on the whole family while promoting mindful technology use 61 . Encouraging parents to engage in joint technology use that promotes media literacy and intentional technology use may strengthen parent-adolescent bonds without inadvertently reinforcing negative habits. Helping both adolescents and parents recognize their own motivations for validation may also be beneficial, as increased awareness of these underlying drives may reduce reward-driven engagement with social media and promote more mindful media use. Finally, our findings on the importance of open communication highlight the necessity of maintaining an open dialogue, especially from adolescents' perspectives. Encouraging parents to adopt autonomy-supporting communication strategies, validated by their adolescents, might help adolescents set their own media limits and develop self-regulation skills 62. Limitations Several limitations should be considered when interpreting the findings of the current study. First, the cross-sectional design and the limited number of dyadic participants constrain our ability to draw conclusions about directionality and the generalizability of our results. Although many parents were willing to participate in our study, recruiting both members of the parent-adolescent dyad proved challenging, which limited the sample size, and therefore, its representativeness. Second, recruitment procedures may have resulted in a sample biased toward individuals highly engaged with social media and parents actively interested in learning about this topic. Third, our study did not account for all family dynamics that could play a role in the observed relationships. For example, interactions between specific parenting roles (e.g., mother vs. father) and the gender of the child, as well as families with parents and adolescent identifying in other gender or role categories, were not explicitly examined. Nevertheless, our finding that mothers reported greater open communication with their children (compared to fathers) and fathers reported more joint technology use (compared to mothers), is in line with previous research that highlights fathers’ role as playmate and mothers as nurturers 63 . Additional household factors such as the presence of siblings and other relationship features (e.g., shared custody) were not assessed, which could have implications for the findings. Finally, some measures used in the study provided only limited information on the constructs we examined. For instance, we lacked detailed information on how adolescents and parents used technology together. Understanding whether these interactions involved passive scrolling, co-viewing, or active discussion could have provided a clearer picture of how such activities influence PMU. Furthermore, the measures of PMU between parents and adolescents were slightly different, with the adult questions focusing more on excessive use and the adolescent questions focusing more on FOMO and addiction. These differences underscore the importance of developing and applying age-appropriate but conceptually aligned measures. Conclusion In conclusion, this study took a dyadic approach to examine PMU in both adolescents and their parents. Although responses were similar on several measures, analyzing both perspectives offered valuable insights that enhance our understanding of the dynamic relationships among the variables of interest. By including both parents and adolescents, the study highlights the importance of considering the family unit when examining technology use. Key findings show that parents' time spent on social media is linked to their adolescents' behaviors, with joint technology use and motivations for validation contributing to increased PMU. Open communication was identified as a protective factor, emphasizing the importance of family dynamics in shaping healthy technology habits in adolescents. These findings point to the potential benefits of family-based interventions that promote mindful media use, encourage open conversations, and support adolescent self-regulation in an increasingly digital world. Abbreviations APIM: Actor–Partner Interdependence Model BSMAS: Bergen Social Media Addiction Scale FOMO: Fear of Missing Out PMU: Problematic Media Use TCPS2: Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (2nd edition) Declarations Ethics approval and consent to participate This study was reviewed and approved by the King’s University College Ethics Committee on December 5, 2022. All participants provided informed consent to participate. Parents provided consent for children under 18, and children provided assent. The study was conducted in accordance with established ethical guidelines, including the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to the confidentiality of participants but are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research was supported by an internal research grant at King’s University College. Authors' contributions W.E., L.H., and T.D. conceived and designed the study, supervised data collection, and provided oversight throughout the project. W.E., L.H., and T.D. coordinated recruitment, data collection, and initial data cleaning. W.E. conducted statistical analyses and prepared figures and tables. W.E. wrote the first draft of the manuscript. All authors contributed to reviewing and revising the manuscript and approved the final version for submission. Acknowledgements The authors would like to thank the families who participated in this research, as well as the student research assistants and community partners who supported recruitment and data collection. References Pew Research Center. Teens, social media and technology 2022. Washington, DC: Pew Research Center; 2022. Rideout VJ, Robb MB. Social media, social life: Teens reveal their experiences. San Francisco: Common Sense Media; 2018. Shannon H, Bush K, Villeneuve PJ, Hellemans KGC, Guimond S. Problematic social media use in adolescents and young adults: Systematic review and meta-analysis. JMIR Ment Health. 2022;9(4):e33450. doi:10.2196/33450. Sohn SY, Rees P, Wildridge B, Kalk NJ, Carter B. Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: A systematic review, meta-analysis and GRADE of the evidence. BMC Psychiatry. 2019;19(1):235. doi:10.1186/s12888-019-2350-x. van Duin C, Heinz A, Willems H. 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J Child Media. 2023;17(4):443-66. doi:10.1080/17482798.2023.2230321. Wong RS, Tung KT, Rao N, Leung C, Hui AN, Tso WW, et al. Parent technology use, parent–child interaction, child screen time, and child psychosocial problems among disadvantaged families. J Pediatr. 2020;226:258-65. doi:10.1016/j.jpeds.2020.07.006. Kildare CA, Middlemiss W. Impact of parents mobile device use on parent-child interaction: A literature review. Comput Human Behav. 2017;75:579-93. doi:10.1016/j.chb.2017.06.003. Bandura A. Self-efficacy: Toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191-215. doi:10.1037/0033-295X.84.2.191. Geurts SM, Vossen HGM, van den Eijnden RJJM, Koning IM. Adolescents’ problematic social media use: Agreement and discrepancies between self- versus mother- and father-reports. Technol Mind Behav. 2023;4(2). doi:10.1037/tmb0000110. Augenstein TM, Thomas SA, Ehrlich KB, Daruwala S, Reyes SM, Chrabaszcz JS, De Los Reyes A. Comparing multi-informant assessment measures of parental monitoring and their links with adolescent delinquent behavior. Parenting. 2016;16(3):164-86. doi:10.1080/15295192.2016.1158600. Andreassen CS, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, Pallesen S. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychol Addict Behav. 2016;30(2):252-62. doi:10.1037/adb0000160. Connell SL, Lauricella AR, Wartella E. Parental co-use of media technology with their young children in the USA. J Child Media . 2015;9(1):5-21. doi:10.1080/17482798.2015.1001360 Pianta RC. Child-parent relationship scale. Unpublished measure. Charlottesville (VA): University of Virginia; 1992. Barnes HL, Olson DH. Barnes and Olson communication scale. APA PsycTests. 1982. doi:10.1037/t56782-000. Kenny DA, Kashy DA, Cook WL. Dyadic data analysis. New York: Guilford Press; 2006. Kenny DA, Ledermann T. Detecting, measuring, and testing dyadic patterns in the actor–partner interdependence model. J Fam Psychol. 2010;24(3):359-66. doi:10.1037/a0019651. Fischer-Grote L, Kothgassner OD, Felnhofer A. Risk factors for problematic smartphone use in children and adolescents: A review of existing literature. Neuropsychiatr. 2019;33(4):179-90. doi:10.1007/s40211-019-00319-8. Werner M, Kapetanovic S, Claesdotter-Knutsson E. Family-centered treatment program for problematic gaming and excessive screen use in a clinical child and youth population (FAME): Protocol for a feasibility pilot mixed method study. JMIR Res Protoc. 2024;13:e56387. doi:10.2196/56387. Young R, Tully M. Autonomy vs. control: Associations among parental mediation, perceived parenting styles, and U.S. adolescents’ risky online experiences. Cyberpsychol. 2022;16(2):5. doi:10.5817/CP2022-2-5 Collins WA, Russell G. Mother-child and father-child relationships in middle childhood and adolescence: A developmental analysis. Dev Rev . 1991;11(2):99-136. doi:10.1016/0273-2297(91)90004-8. Tables Table 1 Demographic Data Parent Variables N (%) Adolescent Variables N (%) Parenting Role Mother Father 46 (54.1) 30 (35.3) Grade 6 7 8 9 10 9 (10.6) 22 (25.9) 25 (29.4) 16 (18.8) 12 (14.1) Ethnicity White Black Filipino Aboriginal Other 51 (60.0) 12 (14.1) 4 (4.7) 3 (3.5) 6 (7.1) Ethnicity White Black Aboriginal Other 51 (60.0) 15 (17.6) 2 (2.4) 5 (6.0) Marital Status Married Divorced Common-Law Single Widowed 54 (63.5) 12 (14.1) 4 (4.7) 3 (3.5) 3 (3.5) Gender Male Female Other 50 (58.8) 32 (27.6) 1 (1.2) Employment Status Full-time Part-time Unemployed Other 57 (67.1) 13 (15.3) 3 (3.5) 3 (3.5) Education Level Undergraduate Degree College Degree or Diploma Some College/University Masters’ Degree Secondary School or equivalent Doctoral Degree Other 22 (25.9) 13 (15.3) 11 (12.9) 11 (12.9) 9 (10.6) 5 (5.9) 5 (5.9) Note . The sample consisted of 85 parents and 85 adolescents. Some individuals did not complete all demographics measures, and thus, not all percentages add to 100. Table 2 Correlations Between Parent and Adolescent Variables Variable Mean SD 1 2 3 4 5 6 7 8 9 Adolescent Time on SM 3.4 1.15 Adolescent Communication 3.6 .70 .22* Adolescent Joint Tech Use 2.5 .91 .05 -.02 Adolescent Motivations 2.2 1.32 -.01 -.09 .28** Adolescent PMU 2.6 .81 .03 -.46** .52** .56** Parent Time on SM 3.0 1.2 .40** .20 .24* .17 .06 Parent Communication 3.7 .68 .20 .54** -.11 -.01 -.35** .23* Parent Joint Tech Use 1.88 .66 -.03 -.06 .74** .39** .57** .14 -.09 Parent Motivations 2.1 1.02 .08 .07 .35** .34** .30* .20 .01 .3* Parent PMU 2.7 .81 .15 .05 .27* -.01 .21 .30** -.02 .20 .26* Note: * p < .05, ** p < .001. Time on SM = Time spent on social media; Communication = Open communication; Joint Tech Use = Joint technology use; Motivations = Motivations for validation; PMU = Problematic media use. Table 3 Actor-Partner Interdependence Model (APIM) Predicting Parent and Adolescent PMU Variable Adolescent PMU Parent PMU Coefficient SE t (p) 95% CI Coefficient SE t (p) CI Adolescent Time on SM .11 .05 2.09 (.040) .0052-.2156 .03 .08 .34 (.736) -.1377-1940 Adolescent Communication -.42 .10 -4.44 (.000) -.6102-.2324 .04 .15 .27 (.792) -.2583-.3375 Adolescent Joint Tech Use .20 .10 2.06 (.040) .0069-.3911 .11 .15 .72 (.474) -.1935-.4123 Adolescent Motivations .22 .047 4.81 (.000) .1315-.3177 -.11 .07 -1.46 (.149) -.2543-.0394 Parent Time on SM -.03 .054 -.577 (.566) -.1380-.0761 .17 .09 1.97 (.052) -.0039-.3337 Parent Communication -.16 .10 -1.60 (.112) -.3535-.0378 -.09 .16 -.56 (.578) -.3951-.2220 Parent Joint Tech Use .28 .13 2.18 (.033) .0240-.5402 .12 .20 .60 (.553) -.2852-5289 Parent Motivations .01 .07 .15 (.885) -.1206-.1395 .11 .10 1.05 (.297) -.0970-.3131 Note . Time on SM = Time spent on social media; Communication = Open communication; Joint Tech Use = Joint technology use; Motivations = Motivations for validation; PMU = Problematic media use. Additional Declarations No competing interests reported. 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Path coefficients are unstandardized, and only significant paths are shown. Dashed lines indicate significant indirect effects.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7473910/v1/67aeea0b40a58b6445b0e903.png"},{"id":108437906,"identity":"62daa0ef-c58b-463a-a545-c085fcc12773","added_by":"auto","created_at":"2026-05-04 16:04:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":514458,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7473910/v1/b1c09cab-ed4d-4112-a0ce-b60247650229.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Dyadic Perspective on Parent and Adolescent Technology Use: The Role of Open Communication, Joint Technology Use, and Validation Motives in Problematic Social Media Use","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTechnology use has significantly altered the social landscape for today\u0026rsquo;s adolescents. Currently, 95% of adolescents have access to a smartphone, and nearly half report using the internet \u0026ldquo;almost constantly\u0026rdquo;.\u003csup\u003e1, 2\u0026nbsp;\u003c/sup\u003eThe total amount of time adolescents spend online has received significant attention, yet another pressing issue is the rise in problematic media use (PMU). PMU is characterized by compulsive usage patterns and addiction-like symptoms, including anxiety when disconnected. Estimates suggest that one in four youth experience PMU, leading to a range of adverse outcomes, including poor mental health and academic difficulties \u003csup\u003e3,4\u003c/sup\u003e. To mitigate the development of PMU, previous studies have often examined time spent online and specific individual risk factors\u003csup\u003e5\u003c/sup\u003e, but the complex social and contextual influences remain underexplored.\u003csup\u003e6\u003c/sup\u003e Specifically, adolescents\u0026rsquo; motivations for validation and parents\u0026rsquo; pivotal role warrant closer examination. Moreover, few studies have taken a dyadic approach to consider how the behaviors of parents and adolescents interact to shape PMU in both parties. The aim of the present study is to examine the interdependent nature of parent and adolescent behaviors in influencing PMU.\u003cbr\u003e\u003cstrong\u003eProblematic Media Use in Adolescence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough research highlights the potential for technology to foster positive experiences and social connections among adolescents,\u003csup\u003e7\u003c/sup\u003e a substantial body of evidence links technology use to poor mental health outcomes\u003csup\u003e8-10\u003c/sup\u003e. Historically, this research has measured self-reported time online without considering specific technology-related experiences and the near-constant connections we have with our devices\u003csup\u003e11-13\u003c/sup\u003e. Recent studies have shown that PMU is a much stronger predictor of poor mental health outcomes than time spent on social media or intensity alone\u0026nbsp;\u003csup\u003e14-15\u003c/sup\u003e.\u003cbr\u003e\u0026nbsp;\u003cbr\u003ePMU is a multidimensional construct that includes elements of excessive use, compulsive checking, and preoccupation with digital content, particularly social media. Behaviors are often driven by fear of missing out (FOMO) resulting in difficulty disengaging and emotional dependence.\u003csup\u003e16-18\u003c/sup\u003e FOMO is characterized by persistent anxiety or discomfort when excluded from socially rewarding activities, especially with social media \u003csup\u003e18,19\u003c/sup\u003e. Canadian data suggest that nearly 40% of youth are either classified as having problematic internet use or are at risk for its development\u003csup\u003e20\u003c/sup\u003e. In the present study we define PMU broadly to include FOMO, addiction behaviors and excessive use.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDevelopmental factors further intensify vulnerability to PMU. Adolescence is marked by heightened social comparison and risk-taking along with intensified needs for autonomy and peer approval \u003csup\u003e21, 22\u003c/sup\u003e. These factors, combined with immature self-regulation and reward-processing brain circuits, increase sensitivity to immediate gratification and social feedback, contributing to PMU \u003csup\u003e23-25\u003c/sup\u003e. This is evident in behaviors such as like-seeking, where adolescents post curated or edited photos in pursuit of social validation\u003csup\u003e26\u003c/sup\u003e. Research indicates that up to 90% of youth engage in some form of like-seeking behavior when posting online\u003csup\u003e27\u003c/sup\u003e. However, increased pressure for social media attention has been linked to negative social outcomes, including reduced friendship closeness, diminished social support\u003csup\u003e28\u003c/sup\u003e and risk taking\u003csup\u003e26\u003c/sup\u003e. Consequently, adolescents motivated by validation may engage in compulsive online behaviors, driven by both developmental changes and external influences such as peers and family\u0026nbsp;\u003csup\u003e29, 30\u003c/sup\u003e.\u003cbr\u003e\u003cstrong\u003eParent\u0026ndash;Child Relationship\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs adolescents begin to assert their autonomy, parents often struggle to maintain their connection and influence\u003csup\u003e31, 32\u003c/sup\u003e. However, the family serves as the primary context for development and individuals remain interconnected \u003csup\u003e33\u003c/sup\u003e. Many researchers have examined the strategies parents enact to mitigate their adolescents\u0026rsquo; PMU including setting limits, engaging in active discussions, joint technology use, and modeling appropriate behavior\u003csup\u003e34-36\u003c/sup\u003e. Recent meta-analyses provide limited evidence that any parental practices effectively mitigate PMU, especially during adolescence \u003csup\u003e37-40\u003c/sup\u003e. Importantly, some well-meaning parenting strategies may even have unintended negative consequences. For example, screen time restrictions have been linked to increased secrecy around phone use \u003csup\u003e40\u003c/sup\u003e. Similarly, while joint technology use, such as watching TikToks or playing games together, can open up opportunities for \u0026quot;teachable moments,\u0026quot; it may inadvertently reinforce frequent and potentially unhealthy engagement with digital media\u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThough less frequently studied, family warmth and cohesion may be among the most influential protective factors against PMU \u003csup\u003e6, 38, 40\u003c/sup\u003e. The role of strong family relationships has long been recognized as fundamental to all areas of development \u003csup\u003e41\u003c/sup\u003e. Positive parental relationships can help meet adolescents\u0026rsquo; social and emotional needs, making them feel connected and supported at home and less likely to seek external validation. Autonomy-supportive parenting, which involves co-regulation and emphasizes open communication, modelling, and guidance helps adolescents develop better self-regulation skills \u003csup\u003e42-45\u003c/sup\u003e. Few studies have explored the influence of open communication in parent-child relationships on adolescents\u0026apos; digital engagement \u003csup\u003e40\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, previous research has also shown that parents\u0026rsquo; own use of technology is related to children\u0026rsquo;s screen time and adjustment \u003csup\u003e46-48\u003c/sup\u003e and the overall quality of parent-child interactions\u003csup\u003e50\u003c/sup\u003e. Parents and adolescents may share similar motivational challenges in social media use, including attention-seeking and difficulties with excessive use and addiction. In fact, over 20% of adults aged 35\u0026ndash;49 report at least some negative outcomes related to social media use\u003csup\u003e20\u003c/sup\u003e. Consistent with Social Learning Theory\u003csup\u003e51\u003c/sup\u003e, when parents frequently check notifications, post for approval, or involve their children in these practices, they model such behaviors as normative. This modeling may shape adolescents\u0026rsquo; beliefs about what constitutes acceptable digital behavior. Yet, little research has examined how parents\u0026rsquo; own PMU or their motivations for social media validation in relation to their adolescents\u0026rsquo; media use.\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;The Present Study\u003c/p\u003e\n\u003cp\u003eThe present study examines the role of adolescent and parent predictors of PMU from a dyadic perspective. That is, we explore the dynamic interplay between adolescent and parent variables to understand how their mutual influences shape PMU. The nature of dyadic relationships makes it difficult to disentangle the role of parenting in adolescents\u0026apos; PMU. Single-informant data on dyadic relationships provide imbalanced perspectives, often with conflicting reports \u003csup\u003e52\u003c/sup\u003e, yet few studies have utilized parent-child data \u003csup\u003e53, 37, 39\u003c/sup\u003e. Drawing on family systems theory, which emphasizes the interconnectedness of family members\u003csup\u003e32\u003c/sup\u003e, this study employs dyadic analysis to examine the relationships among time spent on social media, PMU, and three key mediators: motivations for validation, joint technology use, and open communication. Each construct is assessed from both the parent and adolescent perspective. All variables have parent and adolescent responses. Given the cross-sectional design of the study, we interpret associations and mediation pathways as exploratory and correlational rather than causal, recognizing that temporal order cannot be firmly established. We developed the following four hypotheses:\u003cbr\u003e\u0026nbsp;H1: More time spent on social media will predict PMU for both adolescents and their parents;\u003cbr\u003e\u0026nbsp;H2: Greater motivations for validation will predict PMU for both adolescents and their parents (H2a) and mediate the relationship between time spent on social media and PMU (H2b);\u003cbr\u003e\u0026nbsp;H3: Greater joint technology use will predict higher PMU for both adolescents and their parents (H3a) and mediate the relationship between time spent on social media and PMU (H3b);\u003cbr\u003e\u0026nbsp;H4: Greater open communication between parents and adolescents will predict lower PMU (H4a) and mediate the relationship between time spent on social media and PMU (H4b).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample consisted of 170 participants (85 parent-child dyads). All participants lived in Ontario, Canada. There were 46 mothers (54.1%) and 30 fathers (35.3%). Parents reported their age, with four parents (4.7%) between age 25 \u0026ndash; 30, 38 parents (44.7%) between age 31 \u0026ndash; 40, 30 parents (35.3%) between age 41 \u0026ndash; 50, 5 parents between age 51 \u0026ndash; 60 (5.9%). Among adolescents, there were 50 males (58.8%), 32 females (37.6%), and 1 participant who identified as \u0026lsquo;other\u0026rsquo; (1.2%). Adolescents\u0026rsquo; mean age was 13.44 years (\u003cem\u003eSD\u003c/em\u003e = 1.6 years). Additional demographic data are provided in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedure\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the [blinded for review] University Research Ethics Board, ensuring compliance with established ethical guidelines, including those outlined in the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS2, 2022) and the Declaration of Helsinki. Informed consent was obtained from parents, and assent was obtained from adolescents prior to participation. Data were collected between January and July 2023. Two tactics were used to recruit individuals for study participation. First, posters were displayed at local community centers and electronic posters were advertised on the lab research pages on social media. Second, the poster advertisement was shared during an in-person movie screening and panel discussion hosted by the researchers at [blinded] University. Parents were informed they would need to complete the online survey first using a QR code. Parents were asked to provide consent for study participation for themselves and their child. Parents who provided consent and completed the survey were provided with a unique code and URL to share with their child. This code allowed their adolescent to access the survey and complete similar survey items. Parents and adolescents had an opportunity to provide an email address to receive a $10 CAD gift card as compensation for their participation. Each participant spent approximately 20 minutes completing their surveys.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParent Problematic Use\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuestions pertaining to parental PMU measured excessive use and were assessed using 4 items developed by Matthes et al \u003csup\u003e47\u003c/sup\u003e. Items included: \u0026ldquo;I look at my mobile phone or check messages in between doing things\u0026rdquo;, \u0026ldquo;I pick up my mobile phone and do something with it, although I have nothing particular to do on it,\u0026rdquo; \u0026ldquo;If I get a message on my mobile phone then I just have to look at it right away,\u0026rdquo; and \u0026ldquo;I use my mobile phone so much that it has had a negative impact on my job or my family time.\u0026rdquo; Parents were asked to consider the previous month when responding to items. \u0026nbsp; Items used a 5-point Likert scale ranging from 1 (\u003cem\u003enever\u003c/em\u003e) to 5 (\u003cem\u003ealways\u003c/em\u003e). The four items were averaged to compute an overall score (\u0026alpha; = .69).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAdolescent Problematic Use\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdolescent PMU was assessed with items relating to FOMO and addiction. Four items were adapted from previous FOMO items to align with the aims of the current study \u003csup\u003e17\u003c/sup\u003e. These items assessed anxious behaviors (e.g., worrying), related to social media use over the last month (\u0026ldquo;become restless or troubled if you have not had access to social media,\u0026rdquo; \u0026ldquo;often worry about missing important things on social media,\u0026rdquo; \u0026ldquo;get worried when you find out online that your friends are having fun without you,\u0026rdquo; and \u0026ldquo;continue to keep tabs online about what your friends are doing, even on vacation\u0026rdquo;). Three items from the Bergen Social Media Addiction Scale (BSMAS) designed by Andreassen et al.\u003csup\u003e54\u003c/sup\u003e were used to measure adolescents\u0026rsquo; addictive behaviors relating to social media use (\u0026ldquo;spend a lot of time thinking about social media or planned use of social media,\u0026rdquo; \u0026nbsp;\u0026ldquo;use social media so much that it has had a negative impact on your schoolwork,\u0026rdquo; and \u0026nbsp; \u0026ldquo;feel it is important to update your status and share the details online when you are having a good time\u0026rdquo;). Items used a 5-point Likert scale ranging from 1 (\u003cem\u003enever\u003c/em\u003e) to 5 (\u003cem\u003ealways\u003c/em\u003e). The FOMO scale and BSMAS were combined to create a single PMU scale for adolescents (\u003cem\u003ea\u003c/em\u003e = .87).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParent and Adolescent Time Spent on Social Media\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParents and adolescents each reported their time spent on social media over the previous month and estimated their daily engagement \u003csup\u003e26\u003c/sup\u003e. They were first instructed to open the screen time summary in their phones and look under social media. The question asked: \u0026ldquo;Over the past month, how much time did you spend on social media on an average day?\u0026quot; Eight response options were provided ranging from 0, less than 10 minutes, to 8-10 hours, and over 10 hours.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParent and Adolescent Motivations for Validation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eparents and adolescents reported how important receiving attention on social media is to them \u003csup\u003e27 . \u0026nbsp;\u003c/sup\u003e One question asked: \u0026ldquo;How important is it to you to get likes/views or followers on your social media posts (e.g., TikTok, Instagram)?\u0026rdquo; Responses were recorded on a 5-point scale from 1 (\u003cem\u003enot at all important)\u003c/em\u003e to 5 (\u003cem\u003eextremely important)\u003c/em\u003e. The option of \u0026ldquo;\u003cem\u003eI don\u0026rsquo;t post on social media\u003c/em\u003e\u0026rdquo; was also provided.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParent Perceptions of Joint Technology Use\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParents were asked two questions, adapted from Connell et al. \u003csup\u003e55\u003c/sup\u003e about spending time online with their adolescent. Responses were recorded on a 5-point Likert scale from 1 (\u003cem\u003enever\u003c/em\u003e) to 5 (\u003cem\u003ealways\u003c/em\u003e). Parents were asked: \u0026ldquo;How often do you spend time in person on the same device as your child (e.g., phone, computer/tablet, social media or gaming)?\u0026rdquo; and \u0026ldquo;How often do you ask your child to spend time online with you?\u0026rdquo; An average was computed for these items (\u003cem\u003er\u003c/em\u003e = .82).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAdolescent Perceptions of Joint Technology Use\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdolescents were asked two questions about joint technology use with their parents. These questions were also adapted from Connell et al.\u003csup\u003e55\u0026nbsp;\u003c/sup\u003eand modified to be relevant to adolescents. Responses were recorded on a 5-point Likert scale from 1 (\u003cem\u003enever\u003c/em\u003e) to 5 (\u003cem\u003ealways\u003c/em\u003e). \u0026nbsp;Adolescents were asked: \u0026ldquo;How often do you spend time in person on the same device as your parent (e.g., phone, computer/tablet, gaming system)\u0026rdquo; and \u0026ldquo;How often do you ask your parent to spend time online with you (e.g., watching TikToks)?\u0026rdquo; An average was computed for these items (\u003cem\u003er\u0026nbsp;\u003c/em\u003e= .44).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParent Perceptions of Child-Parent Open Communication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParents\u0026rsquo; perceptions of child-parent open communication were assessed with 7 items from the positive relationship closeness subscale \u003csup\u003e56\u003c/sup\u003e (e.g., \u0026quot;My child openly shares feelings and experiences with me\u0026rdquo;). \u0026nbsp;Parents rated each item on a 5-point Likert scale ranging from 1 (\u003cem\u003enever)\u003c/em\u003e to 5 (\u003cem\u003ealways\u003c/em\u003e). An average score of child-parent open communication was computed (\u003cem\u003e\u0026alpha;\u0026nbsp;\u003c/em\u003e=.83).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAdolescents Perceptions of Parent-Child Open Communication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour items from the openness subscale of the Parent-Adolescent Communication Scale \u003csup\u003e57\u003c/sup\u003e were used to measure adolescents\u0026rsquo; perspectives of open communication with a parent (e.g., \u0026ldquo;If I were in trouble, I could tell my parent\u0026rdquo;). Adolescents rated each item on a five-point Likert scale ranging from 1 (\u003cem\u003estrongly disagree\u003c/em\u003e) to 5 (\u003cem\u003estrongly agree\u003c/em\u003e). An average score of parent-child open communication was computed \u003cem\u003e(\u0026alpha;\u003c/em\u003e = .67).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eComparing Parent and Adolescent Responses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreliminary analyses were used to examine frequency responses between the parent and adolescent samples. First, time spent on social media was compared between the two samples. Over 90% of parents reported using social media 3 hours or less per day, with the most common response being \u0026quot;1\u0026ndash;2 hours per day\u0026quot; (35.3%), followed by \u0026quot;10\u0026ndash;60 minutes per day\u0026quot; (29.4%). For adolescents, about 85% reported using social media 3 hours or less per day, with the most common response being \u0026quot;1\u0026ndash;2 hours per day\u0026quot; (36.5%), followed by \u0026quot;2\u0026ndash;3 hours per day\u0026quot; (27.1%). These responses suggest adolescents use social media more than their parents.\u003c/p\u003e\n\u003cp\u003eWhen examining motivations for validation, 28.6% of adolescents and 25.9% of parents indicated these metrics were \u0026quot;moderately important.\u0026quot; Additionally, 21.2% of parents and 14.1% of adolescents responded that they do not post on social media. Approximately 15.3% of parents and 29.6% of adolescents reported \u0026quot;often\u0026quot; or \u0026quot;always\u0026quot; spending time together online on the same device with their adolescent or parent, respectively.\u003c/p\u003e\n\u003cp\u003eNext, we examined several individual survey items to compare parent and adolescent responses. When assessing adolescents\u0026rsquo; PMU, 67.1% reported that they at least sometimes used \u0026quot;social media so much it has had a negative impact on their schoolwork.\u0026quot; Among parents, 70.6% indicated that their mobile phone use at least sometimes \u0026quot;had a negative impact on their job or family time.\u0026quot; Finally, when examining parent-child relationships, 71.8% of parents reported that they share an affectionate and warm relationship with their child \u0026quot;most of the time\u0026quot; or \u0026quot;always.\u0026quot; Likewise, 70.6% of adolescents \u0026quot;agree\u0026quot; or \u0026quot;strongly agree\u0026quot; with the statement \u0026quot;If I were in trouble, I could tell my parent.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelations Between Parent and Adolescent Responses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson\u0026rsquo;s correlations were conducted for all parent and adolescent variables (see Table 2). First, we examined the relationship between parent and adolescent reported variables. Parents\u0026rsquo; and adolescents\u0026rsquo; time spent on social media was positively correlated, \u003cem\u003er\u003c/em\u003e = .40, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001. There were also positive correlations between parent and adolescent reported open communication (\u003cem\u003er\u003c/em\u003e = .54, \u003cem\u003ep\u003c/em\u003e \u0026lt; \u0026nbsp;.001), parent and adolescent reported joint technology use (\u003cem\u003er\u003c/em\u003e = .76, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), parent and adolescent motivations for validation (\u003cem\u003er\u003c/em\u003e = .34, \u003cem\u003ep\u003c/em\u003e = .006), and parent and adolescent PMU (\u003cem\u003er\u003c/em\u003e = .21, \u003cem\u003ep\u003c/em\u003e = .056).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn terms of parent variables, there was a positive correlation between their PMU and time spent on social media, \u003cem\u003er\u003c/em\u003e = .30, \u003cem\u003ep\u003c/em\u003e = .005, and parents\u0026rsquo; motivations for validation, \u003cem\u003er\u003c/em\u003e = .26, \u003cem\u003ep\u003c/em\u003e = .036. Adolescents\u0026rsquo; PMU was positively correlated with joint technology use, \u003cem\u003er\u003c/em\u003e = .52, \u003cem\u003ep\u003c/em\u003e \u0026lt;.001, motivations for validation, \u003cem\u003er\u003c/em\u003e = .56, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, and negatively correlated with parents\u0026rsquo; open communication, \u003cem\u003er\u003c/em\u003e = -.46, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001. \u0026nbsp;Adolescents\u0026rsquo; PMU was positively correlated with parents\u0026rsquo; joint technology use, \u003cem\u003er\u0026nbsp;\u003c/em\u003e= .57, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, and parents\u0026rsquo; motivations for validation, \u003cem\u003er\u003c/em\u003e = .30, \u003cem\u003ep\u003c/em\u003e = .016. There was also a negative correlation between adolescents\u0026rsquo; PMU and parent open communication, \u003cem\u003er\u003c/em\u003e = -.35, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001. Finally, adolescent age was correlated with all study variables, including parent and adolescent reported joint technology use, \u003cem\u003er\u0026nbsp;\u003c/em\u003e= -.22, p = .042, and \u003cem\u003er\u003c/em\u003e = -.23, \u003cem\u003ep\u003c/em\u003e = .035, respectively.\u003c/p\u003e\n\u003cp\u003eWe conducted a Multivariate Analysis of Variance (MANOVA) to examine whether there were significant gender differences on the five adolescent variables, but no significant differences emerged. \u0026nbsp;A second MANOVA examined whether there were differences between mothers and fathers across the five parent variables. Mothers reported higher levels of parent-child open communication (\u003cem\u003eM\u003c/em\u003e = 3.88) compared to fathers (\u003cem\u003eM\u003c/em\u003e = 3.55), \u003cem\u003eF\u003c/em\u003e(1, 76) = 4.41, \u003cem\u003ep\u003c/em\u003e = .039. Conversely, fathers reported higher levels of joint technology use with their adolescents (\u003cem\u003eM\u003c/em\u003e = 2.27) compared to mothers (\u003cem\u003eM\u003c/em\u003e = 1.62), \u003cem\u003eF\u003c/em\u003e(1, 76) = 21.48, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytic Plan\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the interdependence of dyadic data, it is critical to account for how parent-adolescent dyads mutually influence each other to accurately model associations between variables. To examine our main research question, we used the Actor-Partner Interdependence Model (APIM) \u003csup\u003e58\u003c/sup\u003e, which accounts for interdependence between participants by modeling how an individual\u0026rsquo;s characteristics influence their own outcomes (actor effects), as well as those of the other dyad member (partner effects). \u0026nbsp; Our analyses treated parents and adolescents as distinguishable, as they provided data from different perspectives within the dyad. Thus, due to their distinct roles in the dyad, they could not be treated indistinguishably.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe applied the APIM to examine how both parent and adolescent reports of technology use predicted their own PMU, as well as that of the other dyad member. The model simultaneously accounts for actor effects, which capture the influence of an individual\u0026rsquo;s self-reported variables on their outcomes, and partner effects, which capture how an individual\u0026rsquo;s self-reported variables impact their dyadic counterpart. By including both actor and partner effects, the model accounts for interdependence and allows for a comprehensive analysis of both direct and indirect effects. We completed multilevel modeling using SPSS (Version 29) to test the APIM and examine actor and partner effects on PMU. Using this approach, individuals were nested within the dyad, which served as the unit of analysis. Preliminary data analysis involved conducting a discriminability test on the paired data of adolescents and their parents. A significant chi-square value indicated that the data are distinguishable \u003csup\u003e59\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo examine indirect pathways, we conducted mediation analyses within the APIM framework. Specifically, we tested the relationship between time spent on social media and PMU, with motivations for validation, joint technology use, and open communication as potential mediators. Bootstrapping with 5,000 samples and a 95% confidence interval was used to estimate indirect effects and account for potential non-normality in their distribution. Confidence intervals around the unstandardized indirect effects were generated to evaluate the significance of these pathways. Unlike traditional mediation analyses, which are conducted in a series of steps, the APIM allows for a more integrated examination of both direct and indirect effects within dyads, accounting for the interdependence between parents and adolescents. Based on our previous analyses, adolescent age was included as a covariate to statistically control for potential influence on the outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a single Actor-Partner Interdependence Model (APIM), consisting of four predictors (time spent on social media, with motivations for validation, joint technology use, and open communication) to test our hypotheses. Actor effects examined how adolescents\u0026rsquo; characteristics predicted their own outcomes, and how parents\u0026rsquo; characteristics predicted their own outcomes. Partner effects examined how adolescents\u0026rsquo; characteristics predicted parents\u0026rsquo; outcomes, and how parents\u0026rsquo; characteristics predicted adolescent outcomes. Thus, the model included eight actor effects (four per dyad member) and eight partner effects. Time spent on social media was treated as the direct effect on \u0026nbsp; \u0026nbsp;PMU, while motivations for validation, joint technology use, and open communication were mediators in this model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAdolescent Problematic Use\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMain analyses revealed significant actor effects for adolescents\u0026rsquo; time spent on social media, motivations for validation, joint technology use, and open communication (see Table 3). Figure 1 depicts significant effects in the model. Specifically, adolescents who spent more time on social media, reported higher motivations for validation, reported higher joint technology use and experienced increased PMU. Conversely, adolescents who reported more open communication with their parents experienced lower PMU. No significant indirect actor effects emerged.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParent Problematic Use\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Significant actor effects emerged for parents\u0026rsquo; time spent on social media and their PMU (see Table 3; Figure 1). Specifically, greater time spent on social media directly predicted increased PMU. Additionally, parents\u0026rsquo; time spent on social media predicted increased motivation for validation. No significant indirect actor effects were observed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePartner Effects on Problematic Media Use\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were no partner effects from adolescents to parents, indicating that adolescent variables did not predict parent outcomes. However, several significant partner effects emerged, indicating that parent variables predicted adolescent outcomes (Table 3; Figure 1). Specifically, greater time spent on social media by parents predicted higher adolescent motivations for validation and greater adolescent-reported joint technology use. There was an additional direct effect of parent-reported joint technology use on adolescent PMU, indicating more reported joint use predicted more PMU. Finally, an indirect effect emerged whereby adolescent-reported joint technology use mediated the relationship between parent time spent on social media and adolescent PMU, \u003cem\u003eb\u003c/em\u003e = 0.04, bootstrapped \u003cem\u003eSE\u003c/em\u003e = 0.03, 95% CI [0.0004, 0.1118] (Figure 1).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe dyadic nature of parent-adolescent relationship has been largely overlooked in research on adolescent PMU.\u0026nbsp;\u0026nbsp;By employing a dyadic approach, this study underscores the continuing influence of parents during adolescence. The Actor-Partner Interdependence Model (APIM) allowed us to examine specifically how parents\u0026rsquo; behaviors relate to adolescents\u0026apos; social media use. The findings support Bandura\u0026apos;s Social Cognitive Theory \u003csup\u003e51\u003c/sup\u003e in the digital age, emphasizing the importance of modeling within the family. The results demonstrate an important link between parents\u0026rsquo; behaviors and adolescents\u0026rsquo; outcomes, and also highlight the importance of shared technology use and open communication in shaping media habits for both generations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParent-Adolescent Dyad\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings reveal that parents and adolescents engage in similar rates of social media use and PMU. Further, parents and their adolescents\u0026rsquo; time on social media was significantly correlated. Although adolescents report slightly more time online and higher posting frequency, nearly 70% of both groups struggle to balance their technology use with everyday responsibilities. \u0026nbsp;Additionally, approximately 60% of both parents and adolescents report feeling at least somewhat motivated by online attention, likes and followers, suggesting both groups value online feedback. This underscores the shared salience of digital validation across generations and may reflect a cultural shift toward seeking feedback through social media. Moreover, overlapping patterns may reflect mutual influence and shared environments, but parent behavior may remain a powerful source of influence during the adolescent period \u003csup\u003e47\u003c/sup\u003e. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn line with the first hypothesis, time spent on social media predicted problematic media use (PMU) in both adolescents and parents. However, correlation analyses revealed only weak relationships between time online and other media behaviors. For adolescents, PMU was most strongly associated with validation motives, joint technology use, and parent\u0026ndash;child communication. These findings align with previous research and suggest that while time on social media matters, it is only one of several important factors shaping problematic media habits \u003csup\u003e60\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur dyadic model revealed that greater parental social media use was linked to more frequent joint technology use with adolescents. In turn, joint technology use (reported by both parents and adolescents) was associated with higher levels of adolescent PMU. As well, adolescent-reported joint technology use mediated the relationship between parental social media use and adolescent PMU. This finding suggests that joint technology use (reported from both parties) may be related to detrimental outcomes and is consistent with prior research \u003csup\u003e37\u003c/sup\u003e. While parents may engage in shared media use as a strategy for monitoring or fostering connection, such practices may inadvertently reinforce excessive engagement or interfere with other responsibilities. It is also possible that increased joint media use may reflect a parental response to adolescents\u0026rsquo; negative online experiences, such as cyberbullying. Thus, the effectiveness of this parenting strategy to mitigate PMU remains unclear.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe APIM also indicated that parents\u0026apos; time on social media was positively associated with validation-seeking motivations in both themselves and their adolescents. Indeed, both parents and adolescents experience pressure to receive likes and followers. These validation-driven motivations were associated with more time spent on social media and increased PMU, in line with other work suggesting that strong motivations for likes predict more compulsive social media use \u003csup\u003e29\u003c/sup\u003e. While seeking online feedback may support relationship maintenance and identity formation, our results suggest that parents, like adolescents, may also be vulnerable to the reinforcing effects of digital validation. Interestingly, over 20% of parents and 14% of adolescents in our sample reported not posting online, potentially relying on more traditional sources of social feedback or attention.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, and in line with expectations, open communication and closeness within the parent\u0026ndash;adolescent relationship emerged as protective factors, as indicated by the negative relationship between adolescent-reported open communication and PMU. Specifically, only adolescent-reported open communication, and not the reported open communication of their parents, predicted their PMU. This suggests that adolescents\u0026rsquo; perceptions of open communication between themselves and their parents may be particularly influential. Open communication between adolescents and parents is crucial in fostering skills that support self-regulation \u003csup\u003e43\u003c/sup\u003e. When adolescents feel heard and supported, they are more likely to share their experiences and challenges, providing parents with opportunities to guide the establishment of appropriate boundaries \u003csup\u003e45\u003c/sup\u003e. These dynamics not only promote healthier media use but also strengthen adolescents\u0026rsquo; ability to set their own limits, an essential component of self-regulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings suggest that adolescents\u0026rsquo; technology use should be understood within the context of the entire family unit, recognizing the interdependent nature of parents\u0026apos; and adolescents\u0026apos; behaviors. Specifically, parents should be informed that their modeling is an important predictor of their child\u0026rsquo;s behavior during adolescence, as their technology use directly correlates with adolescents\u0026apos; time spent on social media and PMU. Furthermore, since joint technology use between parents and adolescents was associated with higher PMU in adolescents, family-based interventions might focus on the whole family while promoting mindful technology use \u003csup\u003e61\u003c/sup\u003e. Encouraging parents to engage in joint technology use that promotes media literacy and intentional technology use may strengthen parent-adolescent bonds without inadvertently reinforcing negative habits. Helping both adolescents and parents recognize their own motivations for validation may also be beneficial, as increased awareness of these underlying drives may reduce reward-driven engagement with social media and promote more mindful media use. Finally, our findings on the importance of open communication highlight the necessity of maintaining an open dialogue, especially from adolescents\u0026apos; perspectives. \u0026nbsp;Encouraging parents to adopt autonomy-supporting communication strategies, validated by their adolescents, might help adolescents set their own media limits and develop self-regulation skills \u003csup\u003e62.\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be considered when interpreting the findings of the current study.\u003c/p\u003e\n\u003cp\u003eFirst, the cross-sectional design and the limited number of dyadic participants constrain our ability to draw conclusions about directionality and the generalizability of our results. Although many parents were willing to participate in our study, recruiting both members of the parent-adolescent dyad proved challenging, which limited the sample size, and therefore, its representativeness. Second, recruitment procedures may have resulted in a sample biased toward individuals highly engaged with social media and parents actively interested in learning about this topic. \u0026nbsp;Third, our study did not account for all family dynamics that could play a role in the observed relationships. For example, interactions between specific parenting roles (e.g., mother vs. father) and the gender of the child, as well as families with parents and adolescent identifying in other gender or role categories, were not explicitly examined. Nevertheless, our finding that mothers reported greater open communication with their children (compared to fathers) and fathers reported more joint technology use (compared to mothers), is in line with previous research that highlights fathers\u0026rsquo; role as playmate and mothers as nurturers \u003csup\u003e63\u003c/sup\u003e. Additional household factors such as the presence of siblings and other relationship features (e.g., shared custody) were not assessed, which could have implications for the findings. Finally, some measures used in the study provided only limited information on the constructs we examined. For instance, we lacked detailed information on how adolescents and parents used technology together. Understanding whether these interactions involved passive scrolling, co-viewing, or active discussion could have provided a clearer picture of how such activities influence PMU. Furthermore, the measures of PMU between parents and adolescents were slightly different, with the adult questions focusing more on excessive use and the adolescent questions focusing more on FOMO and addiction. These differences underscore the importance of developing and applying age-appropriate but conceptually aligned measures.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study took a dyadic approach to examine PMU in both adolescents and their parents. Although responses were similar on several measures, analyzing both perspectives offered valuable insights that enhance our understanding of the dynamic relationships among the variables of interest. By including both parents and adolescents, the study highlights the importance of considering the family unit when examining technology use. Key findings show that parents' time spent on social media is linked to their adolescents' behaviors, with joint technology use and motivations for validation contributing to increased PMU. Open communication was identified as a protective factor, emphasizing the importance of family dynamics in shaping healthy technology habits in adolescents. These findings point to the potential benefits of family-based interventions that promote mindful media use, encourage open conversations, and support adolescent self-regulation in an increasingly digital world.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAPIM: Actor\u0026ndash;Partner Interdependence Model\u003c/p\u003e\n\u003cp\u003eBSMAS: Bergen Social Media Addiction Scale\u003c/p\u003e\n\u003cp\u003eFOMO: Fear of Missing Out\u003c/p\u003e\n\u003cp\u003ePMU: Problematic Media Use\u003c/p\u003e\n\u003cp\u003eTCPS2: Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (2nd edition)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study was reviewed and approved by the King\u0026rsquo;s University College Ethics Committee on December 5, 2022. All participants provided informed consent to participate. Parents provided consent for children under 18, and children provided assent. The study was conducted in accordance with established ethical guidelines, including the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The datasets generated and/or analyzed during the current study are not publicly available due to the confidentiality of participants but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This research was supported by an internal research grant at King\u0026rsquo;s University College.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;W.E., L.H., and T.D. conceived and designed the study, supervised data collection, and provided oversight throughout the project. W.E., L.H., and T.D. coordinated recruitment, data collection, and initial data cleaning. W.E. conducted statistical analyses and prepared figures and tables. W.E. wrote the first draft of the manuscript. All authors contributed to reviewing and revising the manuscript and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors would like to thank the families who participated in this research, as well as the student research assistants and community partners who supported recruitment and data collection.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePew Research Center. Teens, social media and technology 2022. Washington, DC: Pew Research Center; 2022.\u003c/li\u003e\n\u003cli\u003eRideout VJ, Robb MB. Social media, social life: Teens reveal their experiences. 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Developing scales to measure parental mediation of young children\u0026apos;s internet use. Learn Media Technol. 2014;39(2):250-66. doi:10.1080/17439884.2013.782038.\u003c/li\u003e\n\u003cli\u003eTotland TH, Bjelland M, Lien N, Bergh IH, Gebremariam MK, Grydeland M, et al. Adolescents\u0026rsquo; prospective screen time by gender and parental education, the mediation of parental influences. Int J Behav Nutr Phys Act. 2013;10:89. doi:10.1186/1479-5868-10-89.\u003c/li\u003e\n\u003cli\u003eBoberska M, Horodyska K, Kruk M, Knoll N, Hohl DH, Keller J, et al. Parental strategies restricting screen use among children, screen home environment, and child screen use as predictors of child body fat: A prospective parent\u0026ndash;child study. Br J Health Psychol. 2019;24(2):298-314. doi:10.1111/bjhp.12354.\u003c/li\u003e\n\u003cli\u003eFam JY, M\u0026auml;nnikk\u0026ouml; N, Juhari R, K\u0026auml;\u0026auml;ri\u0026auml;inen M. Is parental mediation negatively associated with problematic media use among children and adolescents? A systematic review and meta-analysis. Can J Behav Sci. 2023;55(2):89-99. doi:10.1037/cbs0000320.\u003c/li\u003e\n\u003cli\u003eLukavsk\u0026aacute; K, Hrabec O, Lukavsk\u0026yacute; J, Demetrovics Z, Kir\u0026aacute;ly O. The associations of adolescent problematic internet use with parenting: A meta-analysis. Addict Behav. 2022;135:107423. doi:10.1016/j.addbeh.2022.107423.\u003c/li\u003e\n\u003cli\u003eNielsen P, Favez N, Liddle H, Rigter H. Linking parental mediation practices to adolescents\u0026apos; problematic online screen use: A systematic literature review. J Behav Addict. 2019;8(4):649-63. doi:10.1556/2006.8.2019.61.\u003c/li\u003e\n\u003cli\u003eHefner D, Knop K, Schmitt S, Vorderer P. Rules? Role model? Relationship? The impact of parents on their children\u0026rsquo;s problematic mobile phone involvement. Media Psychol. 2019;22(1):82-108. doi:10.1080/15213269.2018.1433544.\u003c/li\u003e\n\u003cli\u003eLaursen B, Collins WA. Parent-child relationships during adolescence. In: Lerner RM, Steinberg L, editors. Handbook of adolescent psychology: Vol 2. Contextual influences on adolescent development. 3rd ed. Hoboken: Wiley; 2009. p. 3-42.\u003c/li\u003e\n\u003cli\u003eBradt L, Grosemans E, De Cock R, Dupont B, Vansteenkiste M, Soenens B. Does parents\u0026apos; perceived style of setting limits to gaming matter? The interplay between profiles of parental mediation and BIS/BAS sensitivity in problematic gaming and online gambling. J Adolesc. 2024;96:580-97. doi:10.1002/jad.12271.\u003c/li\u003e\n\u003cli\u003eMoilanen KL, Rasmussen KE, Padilla-Walker LM. Bidirectional associations between self-regulation and parenting styles in early adolescence. J Res Adolesc. 2015;25(2):246-62. doi:10.1111/jora.12125.\u003c/li\u003e\n\u003cli\u003ePadilla-Walker LM, Christensen KJ. Empathy and self-regulation as mediators between parenting and adolescents\u0026apos; prosocial behavior toward strangers, friends, and family. J Res Adolesc. 2011;21(3):545-51. doi:10.1111/j.1532-7795.2010.00695.x.\u003c/li\u003e\n\u003cli\u003eSteinfeld N. Parental mediation of adolescent Internet use: Combining strategies to promote awareness, autonomy and self-regulation in preparing youth for life on the web. Educ Inf Technol. 2021;26(2):1897-920. doi:10.1007/s10639-020-10342-w.\u003c/li\u003e\n\u003cli\u003eFardouly J, Magson NR, Rapee RM, Oar EL, Johnco C, Richardson C, Freeman JYA. Investigating longitudinal and bidirectional relationships between parental factors and time spent on social media during early adolescence. New Media Soc. 2022;24(11):2492-513. doi:10.1177/14614448221076166.\u003c/li\u003e\n\u003cli\u003eMatthes J, Thomas MF, Stevic A, Schmuck D. Fighting over smartphones? Parents\u0026apos; excessive smartphone use, lack of control over children\u0026apos;s use, and conflict. Comput Human Behav. 2021;116:106618. doi:10.1016/j.chb.2020.106618.\u003c/li\u003e\n\u003cli\u003eSwit CS, Coyne SM, Shawcroft J, Gath M, Barr R, Holmgren HG, Stockdale L. Problematic media use in early childhood: The role of parent-child relationships and parental wellbeing in families in New Zealand and the United States. J Child Media. 2023;17(4):443-66. doi:10.1080/17482798.2023.2230321.\u003c/li\u003e\n\u003cli\u003eWong RS, Tung KT, Rao N, Leung C, Hui AN, Tso WW, et al. Parent technology use, parent\u0026ndash;child interaction, child screen time, and child psychosocial problems among disadvantaged families. J Pediatr. 2020;226:258-65. doi:10.1016/j.jpeds.2020.07.006.\u003c/li\u003e\n\u003cli\u003eKildare CA, Middlemiss W. Impact of parents mobile device use on parent-child interaction: A literature review. Comput Human Behav. 2017;75:579-93. doi:10.1016/j.chb.2017.06.003.\u003c/li\u003e\n\u003cli\u003eBandura A. Self-efficacy: Toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191-215. doi:10.1037/0033-295X.84.2.191.\u003c/li\u003e\n\u003cli\u003eGeurts SM, Vossen HGM, van den Eijnden RJJM, Koning IM. Adolescents\u0026rsquo; problematic social media use: Agreement and discrepancies between self- versus mother- and father-reports. Technol Mind Behav. 2023;4(2). doi:10.1037/tmb0000110.\u003c/li\u003e\n\u003cli\u003eAugenstein TM, Thomas SA, Ehrlich KB, Daruwala S, Reyes SM, Chrabaszcz JS, De Los Reyes A. Comparing multi-informant assessment measures of parental monitoring and their links with adolescent delinquent behavior. Parenting. 2016;16(3):164-86. doi:10.1080/15295192.2016.1158600.\u003c/li\u003e\n\u003cli\u003eAndreassen CS, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, Pallesen S. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychol Addict Behav. 2016;30(2):252-62. doi:10.1037/adb0000160.\u003c/li\u003e\n\u003cli\u003eConnell SL, Lauricella AR, Wartella E. Parental co-use of media technology with their young children in the USA. \u003cem\u003eJ Child Media\u003c/em\u003e. 2015;9(1):5-21. doi:10.1080/17482798.2015.1001360\u003c/li\u003e\n\u003cli\u003ePianta RC. Child-parent relationship scale. Unpublished measure. Charlottesville (VA): University of Virginia; 1992.\u003c/li\u003e\n\u003cli\u003eBarnes HL, Olson DH. Barnes and Olson communication scale. APA PsycTests. 1982. doi:10.1037/t56782-000.\u003c/li\u003e\n\u003cli\u003eKenny DA, Kashy DA, Cook WL. Dyadic data analysis. New York: Guilford Press; 2006.\u003c/li\u003e\n\u003cli\u003eKenny DA, Ledermann T. Detecting, measuring, and testing dyadic patterns in the actor\u0026ndash;partner interdependence model. J Fam Psychol. 2010;24(3):359-66. doi:10.1037/a0019651.\u003c/li\u003e\n\u003cli\u003eFischer-Grote L, Kothgassner OD, Felnhofer A. Risk factors for problematic smartphone use in children and adolescents: A review of existing literature. Neuropsychiatr. 2019;33(4):179-90. doi:10.1007/s40211-019-00319-8.\u003c/li\u003e\n\u003cli\u003eWerner M, Kapetanovic S, Claesdotter-Knutsson E. Family-centered treatment program for problematic gaming and excessive screen use in a clinical child and youth population (FAME): Protocol for a feasibility pilot mixed method study. JMIR Res Protoc. 2024;13:e56387. doi:10.2196/56387.\u003c/li\u003e\n\u003cli\u003eYoung R, Tully M. Autonomy vs. control: Associations among parental mediation, perceived parenting styles, and U.S. adolescents\u0026rsquo; risky online experiences. Cyberpsychol. 2022;16(2):5. doi:10.5817/CP2022-2-5\u003c/li\u003e\n\u003cli\u003eCollins WA, Russell G. Mother-child and father-child relationships in middle childhood and adolescence: A developmental analysis. \u003cem\u003eDev Rev\u003c/em\u003e. 1991;11(2):99-136. doi:10.1016/0273-2297(91)90004-8.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 \u003cem\u003eDemographic Data\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9359%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParent Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7885%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.7756%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdolescent Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9359%;\"\u003e\n \u003cp\u003e\u003cu\u003eParenting Role\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003eMother\u003c/p\u003e\n \u003cp\u003eFather\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7885%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46 (54.1)\u003c/p\u003e\n \u003cp\u003e30 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.7756%;\"\u003e\n \u003cp\u003e\u003cu\u003eGrade\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9 (10.6)\u003c/p\u003e\n \u003cp\u003e22 (25.9)\u003c/p\u003e\n \u003cp\u003e25 (29.4)\u003c/p\u003e\n \u003cp\u003e16 (18.8)\u003c/p\u003e\n \u003cp\u003e12 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9359%;\"\u003e\n \u003cp\u003e\u003cu\u003eEthnicity\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003cp\u003eFilipino\u003c/p\u003e\n \u003cp\u003eAboriginal\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7885%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e51 (60.0)\u003c/p\u003e\n \u003cp\u003e12 (14.1)\u003c/p\u003e\n \u003cp\u003e4 (4.7)\u003c/p\u003e\n \u003cp\u003e3 (3.5)\u003c/p\u003e\n \u003cp\u003e6 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.7756%;\"\u003e\n \u003cp\u003e\u003cu\u003eEthnicity\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003cp\u003eAboriginal\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e51 (60.0)\u003c/p\u003e\n \u003cp\u003e15 (17.6)\u003c/p\u003e\n \u003cp\u003e2 (2.4)\u003c/p\u003e\n \u003cp\u003e5 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9359%;\"\u003e\n \u003cp\u003e\u003cu\u003eMarital Status\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003cp\u003eCommon-Law\u003c/p\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7885%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e54 (63.5)\u003c/p\u003e\n \u003cp\u003e12 (14.1)\u003c/p\u003e\n \u003cp\u003e4 (4.7)\u003c/p\u003e\n \u003cp\u003e3 (3.5)\u003c/p\u003e\n \u003cp\u003e3 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.7756%;\"\u003e\n \u003cp\u003e\u003cu\u003eGender\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e50 (58.8)\u003c/p\u003e\n \u003cp\u003e32 (27.6)\u003c/p\u003e\n \u003cp\u003e1 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9359%;\"\u003e\n \u003cp\u003e\u003cu\u003eEmployment\u003c/u\u003e \u003cu\u003eStatus\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003eFull-time\u003c/p\u003e\n \u003cp\u003ePart-time\u003c/p\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7885%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e57 (67.1)\u003c/p\u003e\n \u003cp\u003e13 (15.3)\u003c/p\u003e\n \u003cp\u003e3 (3.5)\u003c/p\u003e\n \u003cp\u003e3 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.7756%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.9359%;\"\u003e\n \u003cp\u003e\u003cu\u003eEducation Level\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003eUndergraduate Degree\u003c/p\u003e\n \u003cp\u003eCollege Degree or Diploma\u003c/p\u003e\n \u003cp\u003eSome College/University\u003c/p\u003e\n \u003cp\u003eMasters\u0026rsquo; Degree\u003c/p\u003e\n \u003cp\u003eSecondary School or equivalent\u003c/p\u003e\n \u003cp\u003eDoctoral Degree\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7885%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22 (25.9)\u003c/p\u003e\n \u003cp\u003e13 (15.3)\u003c/p\u003e\n \u003cp\u003e11 (12.9)\u003c/p\u003e\n \u003cp\u003e11 (12.9)\u003c/p\u003e\n \u003cp\u003e9 (10.6)\u003c/p\u003e\n \u003cp\u003e5 (5.9)\u003c/p\u003e\n \u003cp\u003e5 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.7756%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. The sample consisted of 85 parents and 85 adolescents. Some individuals did not complete all demographics measures, and thus, not all percentages add to 100.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 \u003cem\u003eCorrelations Between Parent and Adolescent Variables\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"756\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1546%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.39762%;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.19022%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.79392%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.92602%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.32232%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.66182%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.73712%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.47292%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.60502%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1546%;\"\u003e\n \u003col\u003e\n \u003cli\u003eAdolescent Time on SM\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.39762%;\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.19022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.79392%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.92602%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.32232%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.66182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.73712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.47292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.60502%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1546%;\"\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003eAdolescent Communication\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e3.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.39762%;\"\u003e\n \u003cp\u003e.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.19022%;\"\u003e\n \u003cp\u003e.22*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.79392%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.92602%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.32232%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.66182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.73712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.47292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.60502%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1546%;\"\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003eAdolescent Joint Tech Use\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.39762%;\"\u003e\n \u003cp\u003e.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.19022%;\"\u003e\n \u003cp\u003e.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.79392%;\"\u003e\n \u003cp\u003e-.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.92602%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.32232%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.66182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.73712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.47292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.60502%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1546%;\"\u003e\n \u003col start=\"4\"\u003e\n \u003cli\u003eAdolescent Motivations\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.39762%;\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.19022%;\"\u003e\n \u003cp\u003e-.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.79392%;\"\u003e\n \u003cp\u003e-.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.92602%;\"\u003e\n \u003cp\u003e.28**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.32232%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.66182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.73712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.47292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.60502%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1546%;\"\u003e\n \u003col start=\"5\"\u003e\n \u003cli\u003eAdolescent PMU\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.39762%;\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.19022%;\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.79392%;\"\u003e\n \u003cp\u003e-.46**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.92602%;\"\u003e\n \u003cp\u003e.52**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.32232%;\"\u003e\n \u003cp\u003e.56**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.66182%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.73712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.47292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.60502%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1546%;\"\u003e\n \u003col start=\"6\"\u003e\n \u003cli\u003eParent Time on SM\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.39762%;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.19022%;\"\u003e\n \u003cp\u003e.40**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.79392%;\"\u003e\n \u003cp\u003e.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.92602%;\"\u003e\n \u003cp\u003e.24*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.32232%;\"\u003e\n \u003cp\u003e.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.66182%;\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.73712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.47292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.60502%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1546%;\"\u003e\n \u003col start=\"7\"\u003e\n \u003cli\u003eParent Communication\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.39762%;\"\u003e\n \u003cp\u003e.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.19022%;\"\u003e\n \u003cp\u003e.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.79392%;\"\u003e\n \u003cp\u003e.54**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.92602%;\"\u003e\n \u003cp\u003e-.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.32232%;\"\u003e\n \u003cp\u003e-.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.66182%;\"\u003e\n \u003cp\u003e-.35**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e.23*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.73712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.47292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.60502%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1546%;\"\u003e\n \u003col start=\"8\"\u003e\n \u003cli\u003eParent Joint Tech Use\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.39762%;\"\u003e\n \u003cp\u003e.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.19022%;\"\u003e\n \u003cp\u003e-.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.79392%;\"\u003e\n \u003cp\u003e-.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.92602%;\"\u003e\n \u003cp\u003e.74**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.32232%;\"\u003e\n \u003cp\u003e.39**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.66182%;\"\u003e\n \u003cp\u003e.57**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.73712%;\"\u003e\n \u003cp\u003e-.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.47292%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.60502%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1546%;\"\u003e\n \u003col start=\"9\"\u003e\n \u003cli\u003eParent Motivations\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.39762%;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.19022%;\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.79392%;\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.92602%;\"\u003e\n \u003cp\u003e.35**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.32232%;\"\u003e\n \u003cp\u003e.34**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.66182%;\"\u003e\n \u003cp\u003e.30*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.73712%;\"\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.47292%;\"\u003e\n \u003cp\u003e.3*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.60502%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1546%;\"\u003e\n \u003col start=\"10\"\u003e\n \u003cli\u003eParent PMU\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.39762%;\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.19022%;\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.79392%;\"\u003e\n \u003cp\u003e.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.92602%;\"\u003e\n \u003cp\u003e.27*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.32232%;\"\u003e\n \u003cp\u003e-.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.66182%;\"\u003e\n \u003cp\u003e.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.86922%;\"\u003e\n \u003cp\u003e.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.73712%;\"\u003e\n \u003cp\u003e-.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.47292%;\"\u003e\n \u003cp\u003e.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.60502%;\"\u003e\n \u003cp\u003e.26*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: * \u003cem\u003ep\u003c/em\u003e \u0026lt; .05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; .001. Time on SM = Time spent on social media; Communication = Open communication; Joint Tech Use = Joint technology use; Motivations = Motivations for validation; PMU = Problematic media use.\u003c/p\u003e\n\u003cp\u003eTable 3 Actor-Partner Interdependence Model (APIM) Predicting Parent and Adolescent PMU\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"727\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7989%;\"\u003e\n \u003cp\u003eVariable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3306%;\"\u003e\n \u003cp\u003eAdolescent\u003c/p\u003e\n \u003cp\u003ePMU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81543%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6061%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.57576%;\"\u003e\n \u003cp\u003eParent PMU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30028%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8815%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7989%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003eCoefficient \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3306%;\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81543%;\"\u003e\n \u003cp\u003et (p)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6061%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.57576%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30028%;\"\u003e\n \u003cp\u003et (p)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8815%;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7989%;\"\u003e\n \u003cp\u003eAdolescent Time on SM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3306%;\"\u003e\n \u003cp\u003e.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81543%;\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003cp\u003e(.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6061%;\"\u003e\n \u003cp\u003e.0052-.2156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.57576%;\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30028%;\"\u003e\n \u003cp\u003e.34\u003c/p\u003e\n \u003cp\u003e(.736)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8815%;\"\u003e\n \u003cp\u003e-.1377-1940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7989%;\"\u003e\n \u003cp\u003eAdolescent\u003c/p\u003e\n \u003cp\u003eCommunication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e-.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3306%;\"\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81543%;\"\u003e\n \u003cp\u003e-4.44\u003c/p\u003e\n \u003cp\u003e(.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6061%;\"\u003e\n \u003cp\u003e-.6102-.2324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.57576%;\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30028%;\"\u003e\n \u003cp\u003e.27 (.792)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8815%;\"\u003e\n \u003cp\u003e-.2583-.3375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7989%;\"\u003e\n \u003cp\u003eAdolescent Joint Tech Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3306%;\"\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81543%;\"\u003e\n \u003cp\u003e2.06 (.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6061%;\"\u003e\n \u003cp\u003e.0069-.3911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.57576%;\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30028%;\"\u003e\n \u003cp\u003e.72 (.474)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8815%;\"\u003e\n \u003cp\u003e-.1935-.4123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7989%;\"\u003e\n \u003cp\u003eAdolescent Motivations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3306%;\"\u003e\n \u003cp\u003e.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81543%;\"\u003e\n \u003cp\u003e4.81 (.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6061%;\"\u003e\n \u003cp\u003e.1315-.3177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e-.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.57576%;\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30028%;\"\u003e\n \u003cp\u003e-1.46 (.149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8815%;\"\u003e\n \u003cp\u003e-.2543-.0394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7989%;\"\u003e\n \u003cp\u003eParent Time on SM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e-.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3306%;\"\u003e\n \u003cp\u003e.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81543%;\"\u003e\n \u003cp\u003e-.577\u003c/p\u003e\n \u003cp\u003e(.566)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6061%;\"\u003e\n \u003cp\u003e-.1380-.0761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.57576%;\"\u003e\n \u003cp\u003e.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30028%;\"\u003e\n \u003cp\u003e1.97 (.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8815%;\"\u003e\n \u003cp\u003e-.0039-.3337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7989%;\"\u003e\n \u003cp\u003eParent Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e-.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3306%;\"\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81543%;\"\u003e\n \u003cp\u003e-1.60\u003c/p\u003e\n \u003cp\u003e(.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6061%;\"\u003e\n \u003cp\u003e-.3535-.0378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e-.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.57576%;\"\u003e\n \u003cp\u003e.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30028%;\"\u003e\n \u003cp\u003e-.56 (.578)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8815%;\"\u003e\n \u003cp\u003e-.3951-.2220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7989%;\"\u003e\n \u003cp\u003eParent Joint Tech Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3306%;\"\u003e\n \u003cp\u003e.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81543%;\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003cp\u003e(.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6061%;\"\u003e\n \u003cp\u003e.0240-.5402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.57576%;\"\u003e\n \u003cp\u003e.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30028%;\"\u003e\n \u003cp\u003e.60 (.553)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8815%;\"\u003e\n \u003cp\u003e-.2852-5289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20.7989%;\"\u003e\n \u003cp\u003eParent Motivations\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.3306%;\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.81543%;\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003cp\u003e(.885)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6061%;\"\u003e\n \u003cp\u003e-.1206-.1395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8457%;\"\u003e\n \u003cp\u003e.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.57576%;\"\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.30028%;\"\u003e\n \u003cp\u003e1.05 (.297)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8815%;\"\u003e\n \u003cp\u003e-.0970-.3131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Time on SM = Time spent on social media; Communication = Open communication; Joint Tech Use = Joint technology use; Motivations = Motivations for validation; PMU = Problematic media use.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Problematic Media Use (PMU), Joint Technology Use, Parent–Adolescent Dyads, Open Communication, Online Validation","lastPublishedDoi":"10.21203/rs.3.rs-7473910/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7473910/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Parents significantly influence adolescent behavior, yet research often overlooks dyadic interactions in shaping media use. Understanding how family members’ technology habits and communication relate to problematic media use (PMU) is critical for developing effective interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study used actor–partner interdependence modeling (APIM) to examine associations among time on social media, PMU, and three mediators (validation motives, joint technology use, and open communication) within parent–adolescent dyads.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Participants were 85 parent–child dyads (N = 170) from Ontario, Canada. Parents included 46 mothers (54.1%) and 30 fathers (35.3%). Adolescents (M age = 13.44, SD = 1.6) included 50 males (58.8%) and 32 females (37.6%). In 2023, parents and adolescents completed online surveys assessing time online, social media motives and patterns, and communication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Greater social media use, stronger validation motives, and more frequent joint technology use were associated with higher adolescent PMU, while open communication predicted lower PMU. Parent social media use was linked to adolescent validation motives and joint technology use. Mediation analysis showed adolescent-reported joint use partially mediated the link between parent social media use and adolescent PMU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Family digital habits are interdependent. Parents’ social media use influences adolescents’ behaviors; validation seeking and joint use predict PMU, whereas open communication could be protective.\u003c/p\u003e","manuscriptTitle":"A Dyadic Perspective on Parent and Adolescent Technology Use: The Role of Open Communication, Joint Technology Use, and Validation Motives in Problematic Social Media Use","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 23:07:32","doi":"10.21203/rs.3.rs-7473910/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-05T05:43:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-30T20:49:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223208838246992999740499638094944269795","date":"2025-10-21T16:00:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87368286159169059275804377988594015858","date":"2025-10-13T11:01:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278604384461074893856577858503648612272","date":"2025-10-10T19:17:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-08T13:55:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117971565362601811559213373033958043641","date":"2025-10-08T11:49:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-08T06:30:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-23T10:12:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-05T08:46:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-03T21:26:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-09-03T21:23:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff700083-319d-477f-81f1-1e478d3077a0","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:03:39+00:00","versionOfRecord":{"articleIdentity":"rs-7473910","link":"https://doi.org/10.1186/s40359-026-04613-3","journal":{"identity":"bmc-psychology","isVorOnly":false,"title":"BMC Psychology"},"publishedOn":"2026-04-29 15:58:24","publishedOnDateReadable":"April 29th, 2026"},"versionCreatedAt":"2025-10-21 23:07:32","video":"","vorDoi":"10.1186/s40359-026-04613-3","vorDoiUrl":"https://doi.org/10.1186/s40359-026-04613-3","workflowStages":[]},"version":"v1","identity":"rs-7473910","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7473910","identity":"rs-7473910","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}