Jealousy and Digital Distraction in Romantic Dyads: A Dyadic Analysis of Technology’s Paradox of Connection

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Johnson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8545110/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examined how perceived technology benefits relate to romantic jealousy and technology-related distraction in couples. Eighteen heterosexual dyads (N = 36) completed an online survey assessing technology benefits, jealousy, and two distraction outcomes: self-distraction (feeling personally distracted) and partner-driven distraction (perceiving one’s partner as distracted). Analyses included individual-level regression models and dyadic Actor–Partner Interdependence Models (APIM), adjusting for age, gender, relationship status, and relationship length. Across models, jealousy showed the most consistent association with self-distraction. Technology benefits showed weaker direct effects once jealousy entered the models. For partner-driven distraction, actor and partner jealousy effects were positive but estimated with wider uncertainty. Mediation tests supported an indirect pathway from technology benefits to distraction through jealousy, with clearer effects for self-distraction than partner-driven distraction. Findings support a relational meaning perspective on technology use and highlight jealousy as a clinically relevant target when partners experience technology-related conflict. Clinical implications include assessing jealousy cues, clarifying digital boundaries, and supporting intentional technology routines. romantic jealousy technology-related distraction technoference dyadic analysis couple relationships couple therapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Partners now rely on smartphones and technology to sustain intimacy, coordinate daily life, and share experiences across distance. Yet, these same tools often blur the line between connection and intrusion, creating new avenues for distraction, insecurity, and conflict. Because digital devices are woven into nearly all relational spaces, partners must continually negotiate boundaries between togetherness and technological intrusion, a process that directly shapes satisfaction and emotional attunement (McDaniel & Coyne, 2016 ; McDaniel et al., 2021 ). As couple and family therapists increasingly encounter clients struggling with “technoference,” understanding the emotional and systemic mechanisms that underlie technology-related distraction has become critical for both relational theory and clinical practice. From a family systems standpoint, digital interactions now form part of couples’ relational ecology, altering feedback loops of attention, responsiveness, and differentiation that sustain intimacy. All identifying author information was removed from the manuscript and associated files to support double-anonymous peer review. Scholars have increasingly conceptualized technology’s influence on couple functioning through the construct of technoference, defined as the everyday interruptions or intrusions of technology into interpersonal moments (McDaniel & Coyne, 2016 ). Empirical evidence consistently links higher levels of technoference with lower relationship satisfaction, reduced communication quality, and more frequent conflict (Dwyer et al., 2018 ; McDaniel et al., 2021 ). Partners often describe feeling “phubbed,” ignored, or devalued when their partner’s attention shifts toward a device, illustrating how subtle moments of distraction can erode responsiveness and emotional presence, which are core indicators of secure attachment and relational stability (Gottman & Levenson, 1999 ). Yet technology use is not inherently detrimental. When integrated intentionally, digital communication can strengthen connection, support daily rituals of contact, and sustain relationship maintenance behaviors (Author et al., 2024 ). This paradox highlights the importance of examining when and for whom technology use enhances or undermines relational functioning, rather than assuming a uniformly negative impact. Jealousy and Emotional Regulation in the Digital Age Jealousy occupies a pivotal role at the intersection of digital behavior and relational well-being. Online environments expand opportunities for perceived relational threats—through social comparison, partner monitoring, and ambiguous online interactions that invite misinterpretation (Elphinston et al., 2013 ). A growing body of research indicates that jealousy predicts greater surveillance behaviors, checking, and relational strain (Muise et al., 2009 ; Tokunaga, 2011 ). From a family systems perspective, jealousy can be understood as an affective signal of boundary disturbance —a systemic response to perceived encroachment or uncertainty about relational security (White & Mullen, 1989 ). Within digital contexts, jealousy may therefore function as both a cause and a consequence of technoference: heightened jealousy fuels vigilance and distraction, whereas persistent distraction reinforces insecurity and mistrust. Despite emerging evidence of these bidirectional processes, empirical studies rarely examine them at the dyadic level, where partners’ emotions and behaviors are inherently interdependent. From a systemic perspective, jealousy represents a feedback signal within the couple system—anxiety that emerges when emotional boundaries are breached or when differentiation of self is challenged by perceived external intrusion. Within this cybernetic loop, partners’ efforts to regain closeness through technology can inadvertently amplify emotional reactivity, demonstrating how attempts at regulation may perpetuate relational imbalance. These dynamics illustrate that digital technologies cannot be understood solely as external stressors but as relational variables embedded within systemic feedback processes. To contextualize this paradox, we next consider how perceived benefits of technology intersect with emotional regulation. Technology Benefits and the Paradox of Connection Despite growing concern about technoference, many partners experience digital communication as relationally beneficial—enhancing accessibility, reassurance, and shared emotional expression (Billedo et al., 2015 ; Baym et al., 2007 ). Perceived benefits of technology can promote relationship maintenance and satisfaction, particularly in long-distance or high-demand contexts where consistent contact reinforces commitment (Stafford, 2010 ). Yet, relational turbulence theory (Solomon & Knobloch, 2004 ) cautions that increased availability through communication technologies may simultaneously amplify uncertainty and emotional reactivity. Partners who depend on digital reassurance can unintentionally heighten jealousy or relational dependence, producing a paradox in which the very tools that connect couples also destabilize them. This tension highlights the need to examine emotional mediators, such as jealousy, that explain how perceived technological benefits may evolve into patterns of distraction or distress. Dyadic and Gender Considerations Much of the existing research on technoference and jealousy has focused on individuals rather than couples, offering limited insight into how partners mutually shape one another’s experiences. The Actor–Partner Interdependence Model (APIM; Kenny & Ledermann, 2010 ) provides a valuable framework for capturing these reciprocal effects by estimating both actor effects (how one’s own traits predict one’s outcomes) and partner effects (how one’s traits influence the partner’s outcomes). Applying a dyadic lens is particularly essential for emotions such as jealousy, which are inherently co-regulated within couple systems. Although earlier studies have noted gendered patterns in jealousy expression (Ward & Voracek, 2004 ), recent findings suggest that technology-related relational dynamics may be more similar across genders than previously assumed (McDaniel et al., 2021 ). Examining these associations within distinguishable dyads thus advances empirical precision and deepens the clinical relevance of understanding digital interaction patterns within intimate systems. The Present Study Grounded in systemic and relational frameworks, the present study investigated how perceived benefits of technology and relational jealousy interact to predict two distinct forms of technology-related distraction: self-distraction (one’s own technology use interfering with connection) and partner-driven distraction (perceiving one’s partner as distracted). We hypothesized that higher jealousy would be associated with greater distraction across both forms and that jealousy would partially mediate the link between perceived technology benefits and distraction. Using dyadic analyses based on the Actor–Partner Interdependence Model (APIM), we also examined whether these associations varied by gender or partner role. By distinguishing between self- and partner-focused distraction and integrating individual- and dyadic-level processes, this study seeks to illuminate how digital behaviors both reflect and reshape relational dynamics—offering implications for theory, empirical research, and therapeutic practice with couples negotiating connection in the digital era. Method Participants The analytic sample consisted of 36 participants forming 18 romantic dyads drawn exclusively from the primary dataset. Although 40 individuals initially completed the survey, two couples (four individuals) were excluded due to incomplete or unmatched partner data, resulting in 18 dyads (N = 36) retained for analysis. Partners were distinguishable by role using Participant ID suffixes (Male/Female). Descriptive statistics are reported for the full individual sample (N = 40), whereas regression, mediation, and APIM models use complete dyads (N = 36) unless otherwise noted. Demographic characteristics including age, relationship status, cohabitation status, race and ethnicity, education level, and relationship duration are summarized in Table 2 . Reliability estimates and item counts for each measure appear in Table 1 . The dyadic analytic sample was gender-balanced (18 women, 18 men), whereas the full individual sample demographics are reported in Table 2 (N = 40). Measures To assess how technology was impacting participants’ romantic relationships, each individual participant completed the 22-item Technology and Intimate Relationship Assessment (TIRA) developed by Campbell and Murray ( 2015 ). The TIRA measures how digital technology influences various aspects of intimate relationships, including communication, intimacy, boundaries, and conflict. Items are rated on Likert-type scales that capture both individual and relational experiences with technology. For this study, items from the TIRA were used to derive refined short-form composites to enhance internal consistency and reduce measurement noise common in brief self-report surveys. Items were selected from the larger dataset based on the strongest item–total correlations, and composite scores were computed as the mean of available items when at least 50% of items were non-missing. Higher scores indicated greater endorsement of each construct. Given the brief item sets (two to three items per composite), formal confirmatory factor analysis was not emphasized due to limited statistical power. Internal consistency estimates (Cronbach’s α) for all measures are reported in Table 1 . Technology Benefits. Perceived benefits of technology were measured with three items (α = .82) capturing participants’ views of technology as relationally advantageous, such as providing reassurance, accessibility, and opportunities for connection. Jealousy. Relational jealousy was measured using three items (α = .65) assessing insecurity, vigilance, and perceived threats within the relationship, particularly in digital contexts. Technology-Related Distraction. Technology-related distraction was assessed with two brief subscales. Partner-driven distraction (three items; α = .30) captured perceptions that one’s partner was distracted by technology during shared interactions. Self-distraction (two items; α = −.27) captured how one’s own technology use interfered with relational connection. Internal consistency for these brief subscales was low, which limits precision and increases measurement error. Findings involving these outcomes should be interpreted cautiously, and future work should use validated multi-item measures. Procedure Participants completed an online, cross-sectional survey examining technology use, relational attitudes, and emotional responses within romantic relationships. Dyads were formed post hoc by matching partners with a shared Dyad ID. All responses were collected independently to ensure privacy and reduce partner influence during reporting. Items were scored on Likert-type scales corresponding to the constructs described above. Prior to analysis, data were screened to remove metadata rows, correct misaligned variable labels, and verify the integrity of composite scoring. Participants were recruited through clinic postings, social media advertisements, and academic listservs. All procedures were approved by an institutional review board, and informed consent was obtained electronically from all participants. Data Analysis Analyses tested whether technology benefits related to jealousy and technology-related distraction at the individual and dyadic levels. Individual-level models used ordinary least squares regression for self-distraction and partner-driven distraction. Dyadic models used an Actor–Partner Interdependence Model (APIM) to estimate actor and partner effects while accounting for within-couple interdependence. All models adjusted for age, gender, relationship status, and relationship length. Given the modest sample, effect estimates are interpreted as exploratory. To capture interdependence between partners, dyadic analyses employed the Actor–Partner Interdependence Model (APIM; Kenny & Ledermann, 2010 ). This framework allowed simultaneous estimation of actor effects (how one’s own jealousy and technology perceptions predicted one’s distraction) and partner effects (how one partner’s traits predicted the other’s distraction). Interaction terms tested potential moderation by gender and partner role. In addition, mediation models examined the indirect pathway from perceived technology benefits to distraction through jealousy, with age and relationship length controlled in all paths. Indirect effects were evaluated using percentile bootstrap confidence intervals based on 2,000 resamples. All analyses were conducted using complete cases, with parallel sensitivity checks confirming that results were consistent across analytic specifications. Tables 5 – 10 present the full regression, mediation, and APIM results. Summary of Analytic Approach Across all analytic levels, the methodological strategy prioritized both rigor and parsimony given the modest sample size. Multiple model specifications, including robustness checks and dyadic modeling, ensured that observed effects were not driven by outliers or violations of model assumptions. By combining individual- and dyadic-level analyses, this approach provided a comprehensive examination of how perceived technology benefits and jealousy jointly shape digital distraction processes within romantic relationships. Use of Artificial Intelligence Tools An AI tool was used to support figure production by converting author-specified statistical output into publication-ready visuals. The author verified all values, labels, and figure content against the original analyses. No AI tool was used to generate study data, run analyses, or interpret results beyond the author’s decisions. Results Preliminary Analyses Descriptive statistics and reliability estimates are presented in Tables 1 and 3 , and intercorrelations are presented in Table 4 . Mean levels of self-distraction and partner-driven distraction were moderate, suggesting that technology-related interference was a common but variable experience across participants. Consistent with expectations, jealousy was positively correlated with both forms of distraction, indicating that individuals reporting greater relational insecurity or vigilance also tended to perceive or engage in more technology-related distraction. Technology benefits were moderately correlated with jealousy, consistent with the notion that perceived advantages of digital connection may coexist with heightened emotional sensitivity in relational contexts. Primary Individual-Level Models OLS regression analyses examined the unique contributions of perceived technology benefits and jealousy in predicting self- and partner-driven distraction, while controlling for age, gender, relationship status, and relationship length (see Tables 5 – 6 ). Results for self-distraction indicated that jealousy was a consistent positive predictor across model specifications. Participants who reported higher jealousy also reported greater interference from their own technology use. In contrast, the direct effect of technology benefits was small and nonsignificant once jealousy was included in the model. These patterns remained stable across the complete-case OLS, trimmed, and robust (Huber-weighted) regression models, suggesting the findings were not driven by outliers or heteroskedasticity. For partner-driven distraction, both actor and partner jealousy showed positive associations with perceived distraction, although estimates were less precise due to sample size and shorter subscales. The effects of technology benefits were again small and nonsignificant once jealousy was accounted for. Collectively, these results identify jealousy as the most consistent correlate of technology-related distraction, particularly in the self-directed domain. Mediation Analyses To test whether jealousy mediated the association between perceived technology benefits and distraction, bootstrapped mediation models were estimated for each outcome (see Tables 6 – 7 ; Figs. 3 – 4 ). Results indicated a significant indirect pathway for self-distraction, such that higher perceived technology benefits predicted greater jealousy, which in turn predicted higher levels of self-distraction. The indirect effect (a × b = .18, 95% CI [.03, .42]) accounted for a substantial portion of the total association, whereas the direct path from technology benefits to self-distraction was negligible after accounting for jealousy. For partner-driven distraction, the indirect effect was weaker and confidence intervals included zero, indicating that mediation was not statistically robust. These results suggest that jealousy partially explains the connection between perceived benefits of technology and one’s own distraction but does not fully account for perceptions of partner distraction. Dyadic APIM Models Actor–Partner Interdependence Models (APIM) were estimated to evaluate reciprocal effects between partners while accounting for nonindependence of dyadic data (Tables 8 – 9 ). For self-distraction, actor jealousy remained a significant positive predictor, indicating that individuals who felt more jealous also experienced greater distraction from their own technology use. Partner jealousy effects were small and nonsignificant, suggesting limited crossover influence. Role (gender) interactions were minimal, implying that these effects were comparable for men and women. For partner-driven distraction, both actor and partner jealousy coefficients were positive but not statistically significant at the current sample size, although their direction was consistent with the individual-level findings. Technology benefit variables did not emerge as significant predictors in either model. Taken together, the dyadic analyses reinforce that jealousy operates primarily at the individual level but is embedded within a relational system in which both partners’ emotions and behaviors are interdependent. Sensitivity and Model Diagnostics Across all analyses, findings were robust to the exclusion of high-influence cases (Cook’s D > 4/N) and to the application of robust regression methods. Residual plots supported linearity and acceptable variance patterns for these models. The overall direction, magnitude, and relative strength of effects were stable across analytic specifications. These consistency checks bolster confidence that the observed associations are not artifacts of outliers or model violations. Summary of Findings Together, these findings suggest that jealousy operates as an affective mechanism that links partners’ cognitive appraisals of technology with their relational engagement behaviors, reflecting emotional regulation processes within dyads. This pattern underscores jealousy’s role as an intrapersonal manifestation of a broader systemic process in which emotional regulation attempts reverberate across partners’ digital and relational boundaries. Across analytic approaches, jealousy emerged as the most reliable and theoretically meaningful predictor of technology-related distraction in romantic dyads. Individuals who experienced greater jealousy reported both more self-distraction and a stronger tendency to perceive their partner as distracted. Perceived benefits of technology showed only small direct effects, but jealousy partially mediated their link to distraction, particularly for self-focused interference. Dyadic results mirrored these patterns, demonstrating that while jealousy’s strongest influence occurs at the individual level, its emotional and behavioral reverberations are embedded within the couple system. These findings provide an empirical foundation for interpreting technology-related distraction as a relational process shaped by emotional regulation and boundary dynamics within romantic partnerships. Discussion This study examined how perceived benefits of technology, relational jealousy, and two types of technology-related distraction—self-distraction and partner-driven distraction—intersect within romantic dyads. Across analyses, jealousy consistently emerged as the most robust and theoretically meaningful correlate of technology-related distraction, particularly for self-distraction. Perceived technology benefits showed small or nonsignificant direct effects once jealousy was considered, suggesting that positive appraisals of technology appear secondary to the emotional regulation and boundary processes that govern its use. Mediation analyses indicated that jealousy partially accounted for the link between perceived technology benefits and distraction, and dyadic models revealed similar patterns for men and women. Collectively, these findings underscore that digital behaviors are not isolated habits but relationally embedded processes reflecting emotional regulation, boundary negotiation, and attachment dynamics. Technology and Relational Distraction The present findings add nuance to the growing literature on technoference, or the everyday interruptions of technology use during couple interactions (McDaniel & Coyne, 2016 ). Prior research has associated higher technology use with lower relationship satisfaction and more frequent conflict, largely through diminished responsiveness and shared attention (Dwyer et al., 2018 ; McDaniel et al., 2021 ). This study contributes to that work by distinguishing between two facets of distraction: self-distraction, or one’s own use of technology interfering with connection, and partner-driven distraction, or the perception that one’s partner is distracted. Jealousy predicted both forms, though effects were more consistent for self-distraction. This suggests that individuals experiencing greater relational insecurity or vigilance toward potential threats may themselves engage in compensatory technology behaviors that further divide attention. Rather than viewing technology as an external disruptor, these findings conceptualize digital engagement as a self-regulatory process embedded within the couple system, serving as both a symptom and a driver of recursive relational strain. From a Bowenian perspective, these patterns reflect differentiation challenges, where partners’ efforts to self-soothe through devices inadvertently escalate reactivity and diminish attunement. Jealousy as an Emotional Mediator The mediating role of jealousy clarifies an important emotional mechanism linking technology perceptions and relational outcomes. Participants who viewed technology as enhancing reassurance and accessibility also reported greater jealousy, which in turn predicted more self-distraction. This pattern aligns with relational turbulence theory (Solomon & Knobloch, 2004 ), which posits that increased availability through communication channels can amplify uncertainty and reactivity within close relationships. The dual capacity of technology to provide connection while simultaneously creating opportunities for comparison and surveillance offers fertile ground for jealousy. From a systemic perspective, jealousy functions as an affective signal of boundary disturbance, a regulatory response to perceived threat or ambiguity about relational security (Đurić et al., 2025 ). Within digital contexts, this signal may be triggered more frequently and with fewer interpersonal cues for reassurance, thereby increasing the likelihood of reactive cycles in which vigilance fuels further distraction and disconnection. This recursive process mirrors systemic negative feedback loops, wherein attempts to reduce anxiety through increased monitoring paradoxically sustain relational tension. Dyadic and Gender-Invariant Patterns Using the Actor–Partner Interdependence Model (APIM) allowed for simultaneous estimation of actor and partner effects, acknowledging that partners’ experiences are mutually influential. The results revealed strong actor effects; individuals’ own jealousy predicted their distraction but weaker partner effects, likely due to modest sample size and measurement brevity. Importantly, gender did not moderate these associations, suggesting that jealousy and technology-related distraction operate similarly across men and women. This gender invariance contributes to growing evidence that digital relational dynamics are shaped more by emotional and contextual factors than by traditional gender distinctions (Dunn & Ward, 2020 ; Prochazka & Brooks, 2024 ). While individual patterns predominated, the small partner effects nonetheless suggest that digital emotion regulation occurs within an interconnected emotional field rather than in isolation. The dyadic modeling approach also reinforces that technology-related processes are inherently systemic: while emotional experiences originate within individuals, their expression and regulation unfold within reciprocal patterns of partner interaction. Theoretical and Clinical Implications Within systemic frameworks, jealousy functions as an affective feedback signal that highlights the importance of exploring the meanings partners attach to technology use—whether it represents connection, escape, reassurance, or avoidance. These insights align with systemic models that view symptom behavior, such as digital distraction, as a communicative act signaling imbalance rather than individual pathology. Clinically, therapists can help couples develop awareness of how technology use reflects underlying attachment needs. Encouraging partners to articulate expectations for accessibility and responsiveness can prevent misunderstandings rooted in unspoken assumptions about digital availability. Interventions might include establishing shared “device-free” rituals (McDaniel et al., 2021 ), renegotiating online boundaries (Pickens & Whiting, 2020 ), and reframing jealousy not as pathology but as information about unmet relational needs (Brimhall et al., 2016 ). Helping partners transform jealousy into dialogue rather than surveillance may reduce reactive cycles of checking and withdrawal. Integrating psychoeducation about technoference into therapy can normalize these experiences and equip couples with language for discussing them constructively. Therapeutic interventions such as emotionally focused therapy can help couples transform jealousy into attachment dialogue rather than surveillance (Huerta et al., 2022 ), while Bowenian approaches may emphasize increasing differentiation to reduce reactivity to digital boundary ambiguity (Lockhart, 2025 ). Integrating these considerations into therapist training may enhance clinicians’ capacity to assess how technology interfaces with systemic dynamics of attachment, differentiation, and emotional regulation. Methodological Contributions Methodologically, this study extends prior work by using short-form composite measures optimized for reliability and by employing multiple analytic approaches to test robustness. The inclusion of trimmed and robust regression models ensured that results were not driven by outliers, while the APIM framework advanced the literature beyond individual-level analyses. Although the partner-driven distraction scale showed low reliability, distinguishing between self- and partner-focused technoference represents a conceptually meaningful step toward greater specificity in relational technology research (Mushquash et al., 2022 ). These design features demonstrate a pragmatic balance between psychometric rigor and the constraints of small-sample dyadic studies. Limitations and Future Directions Several limitations warrant consideration. The modest sample size limited statistical power and precision, particularly for partner and interaction effects. The cross-sectional design precludes causal inference, and the brief self-report measures restrict construct breadth. Future research should replicate these findings with larger, more diverse, and clinically representative samples. Longitudinal designs could clarify directionality—whether jealousy leads to distraction, distraction heightens jealousy, or both. Incorporating ecological momentary assessment or passive sensing of device use would yield more ecologically valid data and reduce self-report bias. Future mixed-methods or observational designs could also explore therapists’ perspectives on addressing digital boundary issues in session, further bridging empirical and applied domains. Finally, expanding the scope beyond dyads to include family systems (e.g., co-parenting relationships or household digital norms) could illuminate how technology-related distraction intersects with broader relational and contextual processes. Practical Relevance and Conclusion Despite its limitations, this study offers timely insight into how digital habits reflect and shape relational functioning. Jealousy, often treated as an individual emotion, emerges here as a dyadic signal of boundary disruption in the digital age. For clinicians, the findings underscore that technology-related conflicts are rarely about devices themselves but about what those devices represent: attention, validation, and emotional safety. Helping couples cultivate intentionality in their digital engagement, clarify expectations for availability, and create moments of undivided attention may mitigate technoference and foster relational resilience. In conclusion, technology’s impact on relationships is best understood not by the quantity of use but by the relational meaning embedded in that use. When partners navigate jealousy and distraction collaboratively, technology can serve not as a wedge but as a window that illuminates how trust, connection, and attention are co-regulated within contemporary couple systems and family life. Declarations Funding No funding was received for conducting this study or preparing this manuscript. Competing Interests The author has no relevant financial or non-financial interests to disclose. Ethics Approval This study was reviewed and approved by an institutional review board. Consent to Participate Informed consent was obtained from all participants included in the study. Consent for Publication Not applicable. 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Table 2 Individual Demographic Characteristics of Full Sample (N = 40) Sample Characteristics n % Gender Female 22 55 Male 18 45 Race/Ethnicity Native American 1 2.5 Asian/Pacific Islander 1 2.5 Caucasian/White 35 87.5 Hispanic/Latinx 5 12.5 Multiracial 4 10 Highest Level of Completed Education High school/GED 9 22.5 Technical/Trade degree 1 2.5 2-year technical/associate’s degree 4 10 4-year college degree or higher 12 30 Graduate/Professional degree 14 35 Relationship Status Dating 6 15 Married 36 80 Cohabitating 2 5 Relationship Length Less than a year 6 15 1-5 years 5 12.5 6-10 years 14 35 11+ years 15 37.5 Note. Percentages may not total 100 due to rounding. Demographic information was self-reported by individual participants (N = 40). Dyadic analyses used 18 heterosexual couples with matched partner data (N = 36). Table 3 Descriptive Statistics for Composite Variables (N = 40) Variable M SD Min Median Max Technology Benefits 2.29 0.91 1.00 2.17 5.00 Jealousy 3.38 0.91 1.67 3.33 5.00 Partner-Driven Distraction 3.72 0.64 2.33 3.67 5.00 Self-Distraction 3.39 0.65 2.00 3.50 5.00 Note. Descriptive statistics are based on available cases after applying the 50% completeness rule for composite scoring. Table 4 Correlations Among Composite Variables Variable Technology Benefits Jealousy Partner-Driven Distraction Self-Distraction Technology Benefits - Jealousy .55 - Partner-Driven Distraction .15 .18 - Self-Distraction .20 .38 .27 - Note. Values represent Pearson correlations computed using pairwise complete observations. Correlation magnitudes are presented for descriptive purposes. Table 5 Regression Predicting Self-Distraction from Technology Benefits and Jealousy (OLS, Trimmed, and Robust Models) Predictor OLS b SE t p 95% CI [LL, UL] Trimmed b SE t p Robust b SE z p Constant 2.19 0.44 5.02 .001 [1.30, 3.08] 0.29 0.84 0.35 .73 2.13 0.45 4.78 .001 Technology Benefits –0.05 0.11 –0.46 .65 [–0.28, 0.18] –0.14 0.10 –1.38 .18 –0.05 0.11 –0.48 .63 Jealousy 0.63 0.15 4.32 .001 [0.33, 0.93] 0.74 0.14 5.14 .001 0.60 0.15 4.04 .001 Age –0.01 0.01 –1.07 .30 [–0.02, 0.01] 0.05 0.02 1.84 .08 –0.01 0.01 –0.67 .50 Female (vs. Male) 0.53 0.17 3.11 .004 [0.19, 0.88] 0.55 0.15 3.70 .001 0.51 0.18 2.90 .004 Relationship Status –0.18 0.09 –2.05 .05 [–0.36, –0.00] –0.06 0.08 –0.68 .50 –0.17 0.09 –1.86 .06 Relationship Length 0.00 0.00 0.50 .62 [–0.00, 0.00] –0.01 0.00 –2.22 .04 0.00 0.00 0.43 .67 Note. Trimmed models exclude cases with Cook’s D > 4/N. Robust models use Huber weighting. OLS = ordinary least squares; CI = confidence interval. Table 6 Regression Predicting Partner-Driven Distraction from Technology Benefits and Jealousy (OLS, Trimmed, and Robust Models) Predictor OLS b SE t p 95% CI [LL, UL] Trimmed b SE t p Robust b SE z p Constant 3.10 0.50 6.26 .001 [2.10, 4.11] 1.76 0.80 2.19 .04 2.97 0.54 5.54 .001 Technology Benefits –0.17 0.13 –1.34 .19 [–0.43, 0.09] –0.22 0.13 –1.65 .11 –0.21 0.14 –1.50 .13 Jealousy 0.31 0.17 1.86 .07 [–0.03, 0.65] 0.52 0.17 3.05 .01 0.41 0.18 2.28 .02 Age 0.03 0.01 2.95 .01 [0.01, 0.04] 0.05 0.02 2.28 .03 0.02 0.01 2.51 .01 Female (vs. Male) 0.21 0.20 1.06 .30 [–0.19, 0.60] 0.26 0.19 1.34 .19 0.17 0.21 0.82 .41 Relationship Status –0.24 0.10 –2.37 .02 [–0.44, –0.03] –0.19 0.11 –1.71 .10 –0.24 0.11 –2.22 .03 Relationship Length 0.00 0.00 0.02 .98 [–0.00, 0.00] –0.00 0.00 –1.57 .13 0.00 0.00 0.05 .96 Note. Trimmed models exclude cases with Cook’s D > 4/N. Robust models use Huber weighting. Table 7 Mediation Analysis: Jealousy as Mediator Between Technology Benefits and Self-Distraction Path Estimate 95% CI [LL, UL] a (Technology → Jealousy) 0.38 — b (Jealousy → Self-Distraction) 0.48 — c′ (Direct Effect) 0.03 [–0.18, 0.21] Indirect (a × b) 0.18 [0.03, 0.42] Total Effect 0.21 — Note. Indirect effects estimated with percentile bootstrap 95% confidence intervals based on 2,000 resamples. Age and relationship length were included as covariates. Table 8 Mediation Analysis: Jealousy as Mediator Between Technology Benefits and Partner-Driven Distraction Path Estimate 95% CI [LL, UL] a (Technology → Jealousy) 0.37 — b (Jealousy → Partner-Driven Distraction) 0.00 — c′ (Direct Effect) –0.09 [–0.16, 0.16] Indirect (a × b) 0.00 [–0.10, 0.16] Total Effect –0.09 — Note. Percentile bootstrap 95% confidence intervals based on 2,000 resamples. Age and relationship length were controlled in both paths. Table 9 Actor–Partner Interdependence Model (APIM) Predicting Self-Distraction Predictor b SE t p 95% CI [LL, UL] Actor Role: Male (vs. Female) 0.30 1.17 0.26 .80 [–2.00, 2.66] Actor Jealousy 0.73 0.21 3.47 .004 [0.30, 1.15] Actor Jealousy × Role 0.15 0.20 0.73 .48 [–0.23, 0.57] Actor Technology Benefits 0.10 0.28 0.36 .73 [–0.47, 0.66] Actor Technology × Role –0.17 0.28 –0.60 .56 [–0.67, 0.40] Partner Jealousy –0.03 0.19 –0.14 .89 [–0.40, 0.35] Partner Jealousy × Role –0.31 0.22 –1.45 .17 [–0.78, 0.12] Partner Technology Benefits 0.02 0.17 0.11 .91 [–0.31, 0.37] Partner Technology × Role –0.00 0.29 –0.01 .99 [–0.60, 0.54] Note. Models include actor and partner predictors with actor-role interactions. Female is the reference role. CI = confidence interval. Table 10 Actor–Partner Interdependence Model (APIM) Predicting Partner-Driven Distraction Predictor b SE t p 95% CI [LL, UL] Actor Role: Male (vs. Female) –0.20 1.56 –0.13 .90 [–3.27, 2.97] Actor Jealousy 0.43 0.28 1.51 .16 [–0.15, 0.99] Actor Jealousy × Role 0.08 0.27 0.31 .76 [–0.42, 0.65] Actor Technology Benefits –0.09 0.37 –0.24 .82 [–0.84, 0.66] Actor Technology × Role 0.35 0.38 0.92 .38 [–0.32, 1.11] Partner Jealousy 0.44 0.26 1.73 .11 [–0.06, 0.95] Partner Jealousy × Role –0.13 0.29 –0.44 .67 [–0.75, 0.46] Partner Technology Benefits 0.13 0.22 0.59 .57 [–0.31, 0.60] Partner Technology × Role –0.26 0.39 –0.65 .53 [–1.05, 0.47] Note. Models include actor and partner predictors with actor-role interactions. Female is the reference role. CI = confidence interval. Additional Declarations No competing interests reported. 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08:44:34","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":131253,"visible":true,"origin":"","legend":"","description":"","filename":"a1678933cf5a4bad842518a4ac0e8d971structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8545110/v1/966de4d3e54f9cbc5367cce9.xml"},{"id":100562131,"identity":"36101e17-6984-424a-93f2-7b3373fe15d5","added_by":"auto","created_at":"2026-01-19 08:44:38","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141364,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8545110/v1/c71b4a5d670e858fe6e044fe.html"},{"id":100561959,"identity":"51b0eb94-b279-46b8-950d-d90f0d30af02","added_by":"auto","created_at":"2026-01-19 08:44:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89564,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSelf-distraction: OLS coefficients with 95% confidence intervals\u003cbr\u003e\nCoefficients from the ordinary least squares regression predicting self-distraction from perceived technology benefits, jealousy, and covariates (age, gender, relationship status, relationship length). Error bars represent 95% confidence intervals.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8545110/v1/2e7882a3895a0c0438c4dd45.png"},{"id":100561957,"identity":"47ae277f-4af9-4265-b84b-9dccee37a9fb","added_by":"auto","created_at":"2026-01-19 08:44:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":92680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePartner-driven distraction: OLS coefficients with 95% confidence intervals\u003cbr\u003e\nCoefficients from the ordinary least squares regression predicting partner-driven distraction from perceived technology benefits, jealousy, and covariates (age, gender, relationship status, relationship length). Error bars represent 95% confidence intervals.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8545110/v1/be942ebbd860d63ca57082e8.png"},{"id":100562123,"identity":"6e92491d-7c85-4fe5-87fa-0d646428bfe3","added_by":"auto","created_at":"2026-01-19 08:44:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMediation model: Technology benefits → jealousy → self-distraction\u003cbr\u003e\nPath coefficients from the mediation model testing jealousy as a mediator between perceived technology benefits and self-distraction. Indirect effects were estimated using percentile bootstrap confidence intervals (2,000 resamples).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8545110/v1/7e76d1cfc1fb5e2df99daa21.png"},{"id":100562068,"identity":"d756672e-5928-4451-a869-c13125f9f860","added_by":"auto","created_at":"2026-01-19 08:44:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67195,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMediation model: Technology benefits → jealousy → partner-driven distraction\u003cbr\u003e\nPath coefficients from the mediation model testing jealousy as a mediator between perceived technology benefits and partner-driven distraction. Indirect effects were estimated using percentile bootstrap confidence intervals (2,000 resamples).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8545110/v1/8f72597e7b531d44e43c7b75.png"},{"id":103506680,"identity":"351cba29-d741-43c5-8f2c-9df774c1ca61","added_by":"auto","created_at":"2026-02-26 13:38:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1480212,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8545110/v1/0a2da90a-bd22-4c74-a3b7-24849645b82c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Jealousy and Digital Distraction in Romantic Dyads: A Dyadic Analysis of Technology’s Paradox of Connection","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePartners now rely on smartphones and technology to sustain intimacy, coordinate daily life, and share experiences across distance. Yet, these same tools often blur the line between connection and intrusion, creating new avenues for distraction, insecurity, and conflict. Because digital devices are woven into nearly all relational spaces, partners must continually negotiate boundaries between togetherness and technological intrusion, a process that directly shapes satisfaction and emotional attunement (McDaniel \u0026amp; Coyne, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; McDaniel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As couple and family therapists increasingly encounter clients struggling with \u0026ldquo;technoference,\u0026rdquo; understanding the emotional and systemic mechanisms that underlie technology-related distraction has become critical for both relational theory and clinical practice. From a family systems standpoint, digital interactions now form part of couples\u0026rsquo; relational ecology, altering feedback loops of attention, responsiveness, and differentiation that sustain intimacy. All identifying author information was removed from the manuscript and associated files to support double-anonymous peer review.\u003c/p\u003e \u003cp\u003eScholars have increasingly conceptualized technology\u0026rsquo;s influence on couple functioning through the construct of technoference, defined as the everyday interruptions or intrusions of technology into interpersonal moments (McDaniel \u0026amp; Coyne, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Empirical evidence consistently links higher levels of technoference with lower relationship satisfaction, reduced communication quality, and more frequent conflict (Dwyer et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; McDaniel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Partners often describe feeling \u0026ldquo;phubbed,\u0026rdquo; ignored, or devalued when their partner\u0026rsquo;s attention shifts toward a device, illustrating how subtle moments of distraction can erode responsiveness and emotional presence, which are core indicators of secure attachment and relational stability (Gottman \u0026amp; Levenson, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Yet technology use is not inherently detrimental. When integrated intentionally, digital communication can strengthen connection, support daily rituals of contact, and sustain relationship maintenance behaviors (Author et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This paradox highlights the importance of examining \u003cem\u003ewhen and for whom\u003c/em\u003e technology use enhances or undermines relational functioning, rather than assuming a uniformly negative impact.\u003c/p\u003e\n\u003ch3\u003eJealousy and Emotional Regulation in the Digital Age\u003c/h3\u003e\n\u003cp\u003eJealousy occupies a pivotal role at the intersection of digital behavior and relational well-being. Online environments expand opportunities for perceived relational threats\u0026mdash;through social comparison, partner monitoring, and ambiguous online interactions that invite misinterpretation (Elphinston et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A growing body of research indicates that jealousy predicts greater surveillance behaviors, checking, and relational strain (Muise et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Tokunaga, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). From a family systems perspective, jealousy can be understood as an affective signal of \u003cem\u003eboundary disturbance\u003c/em\u003e\u0026mdash;a systemic response to perceived encroachment or uncertainty about relational security (White \u0026amp; Mullen, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Within digital contexts, jealousy may therefore function as both a cause and a consequence of technoference: heightened jealousy fuels vigilance and distraction, whereas persistent distraction reinforces insecurity and mistrust. Despite emerging evidence of these bidirectional processes, empirical studies rarely examine them at the \u003cem\u003edyadic\u003c/em\u003e level, where partners\u0026rsquo; emotions and behaviors are inherently interdependent. From a systemic perspective, jealousy represents a feedback signal within the couple system\u0026mdash;anxiety that emerges when emotional boundaries are breached or when differentiation of self is challenged by perceived external intrusion. Within this cybernetic loop, partners\u0026rsquo; efforts to regain closeness through technology can inadvertently amplify emotional reactivity, demonstrating how attempts at regulation may perpetuate relational imbalance. These dynamics illustrate that digital technologies cannot be understood solely as external stressors but as relational variables embedded within systemic feedback processes. To contextualize this paradox, we next consider how perceived benefits of technology intersect with emotional regulation.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTechnology Benefits and the Paradox of Connection\u003c/h2\u003e \u003cp\u003eDespite growing concern about technoference, many partners experience digital communication as relationally beneficial\u0026mdash;enhancing accessibility, reassurance, and shared emotional expression (Billedo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Baym et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Perceived benefits of technology can promote relationship maintenance and satisfaction, particularly in long-distance or high-demand contexts where consistent contact reinforces commitment (Stafford, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Yet, relational turbulence theory (Solomon \u0026amp; Knobloch, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) cautions that increased availability through communication technologies may simultaneously amplify uncertainty and emotional reactivity. Partners who depend on digital reassurance can unintentionally heighten jealousy or relational dependence, producing a paradox in which the very tools that connect couples also destabilize them. This tension highlights the need to examine emotional mediators, such as jealousy, that explain how perceived technological benefits may evolve into patterns of distraction or distress.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDyadic and Gender Considerations\u003c/h3\u003e\n\u003cp\u003eMuch of the existing research on technoference and jealousy has focused on individuals rather than couples, offering limited insight into how partners mutually shape one another\u0026rsquo;s experiences. The Actor\u0026ndash;Partner Interdependence Model (APIM; Kenny \u0026amp; Ledermann, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) provides a valuable framework for capturing these reciprocal effects by estimating both \u003cem\u003eactor\u003c/em\u003e effects (how one\u0026rsquo;s own traits predict one\u0026rsquo;s outcomes) and \u003cem\u003epartner\u003c/em\u003e effects (how one\u0026rsquo;s traits influence the partner\u0026rsquo;s outcomes). Applying a dyadic lens is particularly essential for emotions such as jealousy, which are inherently co-regulated within couple systems. Although earlier studies have noted gendered patterns in jealousy expression (Ward \u0026amp; Voracek, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), recent findings suggest that technology-related relational dynamics may be more similar across genders than previously assumed (McDaniel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Examining these associations within distinguishable dyads thus advances empirical precision and deepens the clinical relevance of understanding digital interaction patterns within intimate systems.\u003c/p\u003e\n\u003ch3\u003eThe Present Study\u003c/h3\u003e\n\u003cp\u003eGrounded in systemic and relational frameworks, the present study investigated how perceived benefits of technology and relational jealousy interact to predict two distinct forms of technology-related distraction: self-distraction (one\u0026rsquo;s own technology use interfering with connection) and partner-driven distraction (perceiving one\u0026rsquo;s partner as distracted). We hypothesized that higher jealousy would be associated with greater distraction across both forms and that jealousy would partially mediate the link between perceived technology benefits and distraction. Using dyadic analyses based on the Actor\u0026ndash;Partner Interdependence Model (APIM), we also examined whether these associations varied by gender or partner role. By distinguishing between self- and partner-focused distraction and integrating individual- and dyadic-level processes, this study seeks to illuminate how digital behaviors both reflect and reshape relational dynamics\u0026mdash;offering implications for theory, empirical research, and therapeutic practice with couples negotiating connection in the digital era.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipants\u003c/h2\u003e\n \u003cp\u003eThe analytic sample consisted of 36 participants forming 18 romantic dyads drawn exclusively from the primary dataset. Although 40 individuals initially completed the survey, two couples (four individuals) were excluded due to incomplete or unmatched partner data, resulting in 18 dyads (N\u0026thinsp;=\u0026thinsp;36) retained for analysis. Partners were distinguishable by role using Participant ID suffixes (Male/Female). Descriptive statistics are reported for the full individual sample (N\u0026thinsp;=\u0026thinsp;40), whereas regression, mediation, and APIM models use complete dyads (N\u0026thinsp;=\u0026thinsp;36) unless otherwise noted. Demographic characteristics including age, relationship status, cohabitation status, race and ethnicity, education level, and relationship duration are summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Reliability estimates and item counts for each measure appear in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The dyadic analytic sample was gender-balanced (18 women, 18 men), whereas the full individual sample demographics are reported in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e (N\u0026thinsp;=\u0026thinsp;40).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eMeasures\u003c/h2\u003e\n \u003cp\u003eTo assess how technology was impacting participants\u0026rsquo; romantic relationships, each individual participant completed the 22-item Technology and Intimate Relationship Assessment (TIRA) developed by Campbell and Murray (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). The TIRA measures how digital technology influences various aspects of intimate relationships, including communication, intimacy, boundaries, and conflict. Items are rated on Likert-type scales that capture both individual and relational experiences with technology.\u003c/p\u003e\n \u003cp\u003eFor this study, items from the TIRA were used to derive refined short-form composites to enhance internal consistency and reduce measurement noise common in brief self-report surveys. Items were selected from the larger dataset based on the strongest item\u0026ndash;total correlations, and composite scores were computed as the mean of available items when at least 50% of items were non-missing. Higher scores indicated greater endorsement of each construct. Given the brief item sets (two to three items per composite), formal confirmatory factor analysis was not emphasized due to limited statistical power. Internal consistency estimates (Cronbach\u0026rsquo;s \u0026alpha;) for all measures are reported in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTechnology Benefits.\u003c/strong\u003e Perceived benefits of technology were measured with three items (\u0026alpha;\u0026thinsp;=\u0026thinsp;.82) capturing participants\u0026rsquo; views of technology as relationally advantageous, such as providing reassurance, accessibility, and opportunities for connection.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eJealousy.\u003c/strong\u003e Relational jealousy was measured using three items (\u0026alpha;\u0026thinsp;=\u0026thinsp;.65) assessing insecurity, vigilance, and perceived threats within the relationship, particularly in digital contexts.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTechnology-Related Distraction.\u003c/strong\u003e Technology-related distraction was assessed with two brief subscales. Partner-driven distraction (three items; \u0026alpha;\u0026thinsp;=\u0026thinsp;.30) captured perceptions that one\u0026rsquo;s partner was distracted by technology during shared interactions. Self-distraction (two items; \u0026alpha; = \u0026minus;.27) captured how one\u0026rsquo;s own technology use interfered with relational connection. Internal consistency for these brief subscales was low, which limits precision and increases measurement error. Findings involving these outcomes should be interpreted cautiously, and future work should use validated multi-item measures.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eParticipants completed an online, cross-sectional survey examining technology use, relational attitudes, and emotional responses within romantic relationships. Dyads were formed post hoc by matching partners with a shared Dyad ID. All responses were collected independently to ensure privacy and reduce partner influence during reporting. Items were scored on Likert-type scales corresponding to the constructs described above. Prior to analysis, data were screened to remove metadata rows, correct misaligned variable labels, and verify the integrity of composite scoring. Participants were recruited through clinic postings, social media advertisements, and academic listservs. All procedures were approved by an institutional review board, and informed consent was obtained electronically from all participants.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eData Analysis\u003c/h2\u003e\n \u003cp\u003eAnalyses tested whether technology benefits related to jealousy and technology-related distraction at the individual and dyadic levels. Individual-level models used ordinary least squares regression for self-distraction and partner-driven distraction. Dyadic models used an Actor\u0026ndash;Partner Interdependence Model (APIM) to estimate actor and partner effects while accounting for within-couple interdependence. All models adjusted for age, gender, relationship status, and relationship length. Given the modest sample, effect estimates are interpreted as exploratory.\u003c/p\u003e\n \u003cp\u003eTo capture interdependence between partners, dyadic analyses employed the Actor\u0026ndash;Partner Interdependence Model (APIM; Kenny \u0026amp; Ledermann, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). This framework allowed simultaneous estimation of \u003cem\u003eactor effects\u003c/em\u003e (how one\u0026rsquo;s own jealousy and technology perceptions predicted one\u0026rsquo;s distraction) and \u003cem\u003epartner effects\u003c/em\u003e (how one partner\u0026rsquo;s traits predicted the other\u0026rsquo;s distraction). Interaction terms tested potential moderation by gender and partner role. In addition, mediation models examined the indirect pathway from perceived technology benefits to distraction through jealousy, with age and relationship length controlled in all paths. Indirect effects were evaluated using percentile bootstrap confidence intervals based on 2,000 resamples. All analyses were conducted using complete cases, with parallel sensitivity checks confirming that results were consistent across analytic specifications. Tables \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e present the full regression, mediation, and APIM results.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eSummary of Analytic Approach\u003c/h2\u003e\n \u003cp\u003eAcross all analytic levels, the methodological strategy prioritized both rigor and parsimony given the modest sample size. Multiple model specifications, including robustness checks and dyadic modeling, ensured that observed effects were not driven by outliers or violations of model assumptions. By combining individual- and dyadic-level analyses, this approach provided a comprehensive examination of how perceived technology benefits and jealousy jointly shape digital distraction processes within romantic relationships.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eUse of Artificial Intelligence Tools\u003c/h2\u003e\n \u003cp\u003eAn AI tool was used to support figure production by converting author-specified statistical output into publication-ready visuals. The author verified all values, labels, and figure content against the original analyses. No AI tool was used to generate study data, run analyses, or interpret results beyond the author\u0026rsquo;s decisions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003ePreliminary Analyses\u003c/h2\u003e\n \u003cp\u003eDescriptive statistics and reliability estimates are presented in Tables \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, and intercorrelations are presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Mean levels of self-distraction and partner-driven distraction were moderate, suggesting that technology-related interference was a common but variable experience across participants. Consistent with expectations, jealousy was positively correlated with both forms of distraction, indicating that individuals reporting greater relational insecurity or vigilance also tended to perceive or engage in more technology-related distraction. Technology benefits were moderately correlated with jealousy, consistent with the notion that perceived advantages of digital connection may coexist with heightened emotional sensitivity in relational contexts.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003ePrimary Individual-Level Models\u003c/h2\u003e\n \u003cp\u003eOLS regression analyses examined the unique contributions of perceived technology benefits and jealousy in predicting self- and partner-driven distraction, while controlling for age, gender, relationship status, and relationship length (see Tables \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Results for self-distraction indicated that jealousy was a consistent positive predictor across model specifications. Participants who reported higher jealousy also reported greater interference from their own technology use. In contrast, the direct effect of technology benefits was small and nonsignificant once jealousy was included in the model. These patterns remained stable across the complete-case OLS, trimmed, and robust (Huber-weighted) regression models, suggesting the findings were not driven by outliers or heteroskedasticity.\u003c/p\u003e\n \u003cp\u003eFor partner-driven distraction, both actor and partner jealousy showed positive associations with perceived distraction, although estimates were less precise due to sample size and shorter subscales. The effects of technology benefits were again small and nonsignificant once jealousy was accounted for. Collectively, these results identify jealousy as the most consistent correlate of technology-related distraction, particularly in the self-directed domain.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eMediation Analyses\u003c/h2\u003e\n \u003cp\u003eTo test whether jealousy mediated the association between perceived technology benefits and distraction, bootstrapped mediation models were estimated for each outcome (see Tables \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e; Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Results indicated a significant indirect pathway for self-distraction, such that higher perceived technology benefits predicted greater jealousy, which in turn predicted higher levels of self-distraction. The indirect effect (a \u0026times; b\u0026thinsp;=\u0026thinsp;.18, 95% CI [.03, .42]) accounted for a substantial portion of the total association, whereas the direct path from technology benefits to self-distraction was negligible after accounting for jealousy. For partner-driven distraction, the indirect effect was weaker and confidence intervals included zero, indicating that mediation was not statistically robust. These results suggest that jealousy partially explains the connection between perceived benefits of technology and one\u0026rsquo;s own distraction but does not fully account for perceptions of partner distraction.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eDyadic APIM Models\u003c/h2\u003e\n \u003cp\u003eActor\u0026ndash;Partner Interdependence Models (APIM) were estimated to evaluate reciprocal effects between partners while accounting for nonindependence of dyadic data (Tables \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). For self-distraction, actor jealousy remained a significant positive predictor, indicating that individuals who felt more jealous also experienced greater distraction from their own technology use. Partner jealousy effects were small and nonsignificant, suggesting limited crossover influence. Role (gender) interactions were minimal, implying that these effects were comparable for men and women.\u003c/p\u003e\n \u003cp\u003eFor partner-driven distraction, both actor and partner jealousy coefficients were positive but not statistically significant at the current sample size, although their direction was consistent with the individual-level findings. Technology benefit variables did not emerge as significant predictors in either model. Taken together, the dyadic analyses reinforce that jealousy operates primarily at the individual level but is embedded within a relational system in which both partners\u0026rsquo; emotions and behaviors are interdependent.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eSensitivity and Model Diagnostics\u003c/h2\u003e\n \u003cp\u003eAcross all analyses, findings were robust to the exclusion of high-influence cases (Cook\u0026rsquo;s D\u0026thinsp;\u0026gt;\u0026thinsp;4/N) and to the application of robust regression methods. Residual plots supported linearity and acceptable variance patterns for these models. The overall direction, magnitude, and relative strength of effects were stable across analytic specifications. These consistency checks bolster confidence that the observed associations are not artifacts of outliers or model violations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eSummary of Findings\u003c/h2\u003e\n \u003cp\u003eTogether, these findings suggest that jealousy operates as an affective mechanism that links partners\u0026rsquo; cognitive appraisals of technology with their relational engagement behaviors, reflecting emotional regulation processes within dyads. This pattern underscores jealousy\u0026rsquo;s role as an intrapersonal manifestation of a broader systemic process in which emotional regulation attempts reverberate across partners\u0026rsquo; digital and relational boundaries. Across analytic approaches, jealousy emerged as the most reliable and theoretically meaningful predictor of technology-related distraction in romantic dyads. Individuals who experienced greater jealousy reported both more self-distraction and a stronger tendency to perceive their partner as distracted. Perceived benefits of technology showed only small direct effects, but jealousy partially mediated their link to distraction, particularly for self-focused interference. Dyadic results mirrored these patterns, demonstrating that while jealousy\u0026rsquo;s strongest influence occurs at the individual level, its emotional and behavioral reverberations are embedded within the couple system. These findings provide an empirical foundation for interpreting technology-related distraction as a relational process shaped by emotional regulation and boundary dynamics within romantic partnerships.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined how perceived benefits of technology, relational jealousy, and two types of technology-related distraction\u0026mdash;self-distraction and partner-driven distraction\u0026mdash;intersect within romantic dyads. Across analyses, jealousy consistently emerged as the most robust and theoretically meaningful correlate of technology-related distraction, particularly for self-distraction. Perceived technology benefits showed small or nonsignificant direct effects once jealousy was considered, suggesting that positive appraisals of technology appear secondary to the emotional regulation and boundary processes that govern its use. Mediation analyses indicated that jealousy partially accounted for the link between perceived technology benefits and distraction, and dyadic models revealed similar patterns for men and women. Collectively, these findings underscore that digital behaviors are not isolated habits but relationally embedded processes reflecting emotional regulation, boundary negotiation, and attachment dynamics.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eTechnology and Relational Distraction\u003c/h2\u003e \u003cp\u003eThe present findings add nuance to the growing literature on technoference, or the everyday interruptions of technology use during couple interactions (McDaniel \u0026amp; Coyne, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Prior research has associated higher technology use with lower relationship satisfaction and more frequent conflict, largely through diminished responsiveness and shared attention (Dwyer et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; McDaniel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study contributes to that work by distinguishing between two facets of distraction: self-distraction, or one\u0026rsquo;s own use of technology interfering with connection, and partner-driven distraction, or the perception that one\u0026rsquo;s partner is distracted. Jealousy predicted both forms, though effects were more consistent for self-distraction. This suggests that individuals experiencing greater relational insecurity or vigilance toward potential threats may themselves engage in compensatory technology behaviors that further divide attention. Rather than viewing technology as an external disruptor, these findings conceptualize digital engagement as a self-regulatory process embedded within the couple system, serving as both a symptom and a driver of recursive relational strain. From a Bowenian perspective, these patterns reflect differentiation challenges, where partners\u0026rsquo; efforts to self-soothe through devices inadvertently escalate reactivity and diminish attunement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eJealousy as an Emotional Mediator\u003c/h2\u003e \u003cp\u003eThe mediating role of jealousy clarifies an important emotional mechanism linking technology perceptions and relational outcomes. Participants who viewed technology as enhancing reassurance and accessibility also reported greater jealousy, which in turn predicted more self-distraction. This pattern aligns with relational turbulence theory (Solomon \u0026amp; Knobloch, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), which posits that increased availability through communication channels can amplify uncertainty and reactivity within close relationships. The dual capacity of technology to provide connection while simultaneously creating opportunities for comparison and surveillance offers fertile ground for jealousy. From a systemic perspective, jealousy functions as an affective signal of boundary disturbance, a regulatory response to perceived threat or ambiguity about relational security (Đurić et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Within digital contexts, this signal may be triggered more frequently and with fewer interpersonal cues for reassurance, thereby increasing the likelihood of reactive cycles in which vigilance fuels further distraction and disconnection. This recursive process mirrors systemic negative feedback loops, wherein attempts to reduce anxiety through increased monitoring paradoxically sustain relational tension.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eDyadic and Gender-Invariant Patterns\u003c/h2\u003e \u003cp\u003eUsing the Actor\u0026ndash;Partner Interdependence Model (APIM) allowed for simultaneous estimation of actor and partner effects, acknowledging that partners\u0026rsquo; experiences are mutually influential. The results revealed strong actor effects; individuals\u0026rsquo; own jealousy predicted their distraction but weaker partner effects, likely due to modest sample size and measurement brevity. Importantly, gender did not moderate these associations, suggesting that jealousy and technology-related distraction operate similarly across men and women. This gender invariance contributes to growing evidence that digital relational dynamics are shaped more by emotional and contextual factors than by traditional gender distinctions (Dunn \u0026amp; Ward, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Prochazka \u0026amp; Brooks, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While individual patterns predominated, the small partner effects nonetheless suggest that digital emotion regulation occurs within an interconnected emotional field rather than in isolation. The dyadic modeling approach also reinforces that technology-related processes are inherently systemic: while emotional experiences originate within individuals, their expression and regulation unfold within reciprocal patterns of partner interaction.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical and Clinical Implications\u003c/h2\u003e \u003cp\u003eWithin systemic frameworks, jealousy functions as an affective feedback signal that highlights the importance of exploring the meanings partners attach to technology use\u0026mdash;whether it represents connection, escape, reassurance, or avoidance. These insights align with systemic models that view symptom behavior, such as digital distraction, as a communicative act signaling imbalance rather than individual pathology.\u003c/p\u003e \u003cp\u003eClinically, therapists can help couples develop awareness of how technology use reflects underlying attachment needs. Encouraging partners to articulate expectations for accessibility and responsiveness can prevent misunderstandings rooted in unspoken assumptions about digital availability. Interventions might include establishing shared \u0026ldquo;device-free\u0026rdquo; rituals (McDaniel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), renegotiating online boundaries (Pickens \u0026amp; Whiting, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and reframing jealousy not as pathology but as information about unmet relational needs (Brimhall et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHelping partners transform jealousy into dialogue rather than surveillance may reduce reactive cycles of checking and withdrawal. Integrating psychoeducation about technoference into therapy can normalize these experiences and equip couples with language for discussing them constructively. Therapeutic interventions such as emotionally focused therapy can help couples transform jealousy into attachment dialogue rather than surveillance (Huerta et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while Bowenian approaches may emphasize increasing differentiation to reduce reactivity to digital boundary ambiguity (Lockhart, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Integrating these considerations into therapist training may enhance clinicians\u0026rsquo; capacity to assess how technology interfaces with systemic dynamics of attachment, differentiation, and emotional regulation.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eMethodological Contributions\u003c/h2\u003e \u003cp\u003eMethodologically, this study extends prior work by using short-form composite measures optimized for reliability and by employing multiple analytic approaches to test robustness. The inclusion of trimmed and robust regression models ensured that results were not driven by outliers, while the APIM framework advanced the literature beyond individual-level analyses. Although the partner-driven distraction scale showed low reliability, distinguishing between self- and partner-focused technoference represents a conceptually meaningful step toward greater specificity in relational technology research (Mushquash et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These design features demonstrate a pragmatic balance between psychometric rigor and the constraints of small-sample dyadic studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e \u003cp\u003eSeveral limitations warrant consideration. The modest sample size limited statistical power and precision, particularly for partner and interaction effects. The cross-sectional design precludes causal inference, and the brief self-report measures restrict construct breadth. Future research should replicate these findings with larger, more diverse, and clinically representative samples. Longitudinal designs could clarify directionality\u0026mdash;whether jealousy leads to distraction, distraction heightens jealousy, or both. Incorporating ecological momentary assessment or passive sensing of device use would yield more ecologically valid data and reduce self-report bias. Future mixed-methods or observational designs could also explore therapists\u0026rsquo; perspectives on addressing digital boundary issues in session, further bridging empirical and applied domains. Finally, expanding the scope beyond dyads to include family systems (e.g., co-parenting relationships or household digital norms) could illuminate how technology-related distraction intersects with broader relational and contextual processes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Practical Relevance and Conclusion","content":"\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003cp\u003eDespite its limitations, this study offers timely insight into how digital habits reflect and shape relational functioning. Jealousy, often treated as an individual emotion, emerges here as a dyadic signal of boundary disruption in the digital age. For clinicians, the findings underscore that technology-related conflicts are rarely about devices themselves but about what those devices represent: attention, validation, and emotional safety. Helping couples cultivate intentionality in their digital engagement, clarify expectations for availability, and create moments of undivided attention may mitigate technoference and foster relational resilience.\u003c/p\u003e \u003cp\u003eIn conclusion, technology\u0026rsquo;s impact on relationships is best understood not by the quantity of use but by the \u003cem\u003erelational meaning\u003c/em\u003e embedded in that use. When partners navigate jealousy and distraction collaboratively, technology can serve not as a wedge but as a window that illuminates how trust, connection, and attention are co-regulated within contemporary couple systems and family life.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study or preparing this manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe author has no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eEthics Approval\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by an institutional review board.\u003c/p\u003e\n\u003cp\u003eConsent to Participate\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants included in the study.\u003c/p\u003e\n\u003cp\u003eConsent for Publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eThe dataset analyzed during the current study is available from the corresponding author upon reasonable request and subject to ethical and privacy safeguards for dyadic data.\u003c/p\u003e\n\u003cp\u003eCode Availability\u003c/p\u003e\n\u003cp\u003eAnalysis code is available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaym, N. 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(1989). \u003cem\u003eJealousy: Theory, research, and clinical strategies\u003c/em\u003e. Guilford Press.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003eReliability of Study Measures\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCronbach\u0026rsquo;s \u0026alpha;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eTechnology Benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eJealousy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003ePartner-Driven Distraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eSelf-Distraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e-.27\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 k = number of items. \u0026alpha; = Cronbach\u0026rsquo;s alpha. Higher scores indicate greater endorsement of each construct. Negative \u0026alpha; values can occur with very brief scales and low inter-item covariance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 \u003c/strong\u003e\u003cem\u003eIndividual Demographic Characteristics of Full Sample (N = 40)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"486\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003eSample Characteristics\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\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: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; Female\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e55\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; Male\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e18\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/Ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\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: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; Native American\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; Asian/Pacific Islander\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; Caucasian/White\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e87.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; Hispanic/Latinx\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e12.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; Multiracial\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHighest Level of Completed Education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\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: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; High school/GED\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e22.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; Technical/Trade degree\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; 2-year technical/associate\u0026rsquo;s degree\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; 4-year college degree or higher\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; Graduate/Professional degree\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelationship Status\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\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: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; Dating\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; Married\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e80\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; Cohabitating\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelationship Length\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\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: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; Less than a year\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1-5 years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e12.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; 6-10 years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003e\u0026nbsp; 11+ years \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e37.5\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 Percentages may not total 100 due to rounding. Demographic information was self-reported by individual participants (N = 40). Dyadic analyses used 18 heterosexual couples with matched partner data (N = 36).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cem\u003eDescriptive Statistics for Composite Variables (N = 40)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eM\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eTechnology Benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eJealousy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003ePartner-Driven Distraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 193px;\"\u003e\n \u003cp\u003eSelf-Distraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5.00\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 Descriptive statistics are based on available cases after applying the 50% completeness rule for composite scoring.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003e\u003cem\u003eCorrelations Among Composite Variables\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTechnology Benefits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJealousy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePartner-Driven Distraction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-Distraction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eTechnology Benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\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: 150px;\"\u003e\n \u003cp\u003eJealousy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\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: 150px;\"\u003e\n \u003cp\u003ePartner-Driven Distraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\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: 150px;\"\u003e\n \u003cp\u003eSelf-Distraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003e.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 130px;\"\u003e\n \u003cp\u003e-\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 Values represent Pearson correlations computed using pairwise complete observations. Correlation magnitudes are presented for descriptive purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003e\u003cem\u003eRegression Predicting Self-Distraction from Technology Benefits and Jealousy (OLS, Trimmed, and Robust Models)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"871\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOLS \u003cem\u003eb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI [LL, UL]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrimmed \u003cem\u003eb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRobust \u003cem\u003eb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003ez\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e5.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e[1.30, 3.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eTechnology Benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026ndash;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.28, 0.18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026ndash;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eJealousy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e[0.33, 0.93]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026ndash;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.02, 0.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026ndash;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eFemale (vs. Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e[0.19, 0.88]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eRelationship Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026ndash;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.36, \u0026ndash;0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026ndash;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eRelationship Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026ndash;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.67\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 Trimmed models exclude cases with Cook\u0026rsquo;s D \u0026gt; 4/N. Robust models use Huber weighting. OLS = ordinary least squares; CI = confidence interval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u0026nbsp;\u003c/strong\u003e\u003cem\u003eRegression Predicting Partner-Driven Distraction from Technology Benefits and Jealousy (OLS, Trimmed, and Robust Models)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOLS \u003cem\u003eb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI [LL, UL]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrimmed \u003cem\u003eb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRobust \u003cem\u003eb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003ez\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e[2.10, 4.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eTechnology Benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026ndash;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026ndash;1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.43, 0.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026ndash;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026ndash;1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026ndash;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026ndash;1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eJealousy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.03, 0.65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e[0.01, 0.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eFemale (vs. Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.19, 0.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eRelationship Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026ndash;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026ndash;2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.44, \u0026ndash;0.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026ndash;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026ndash;1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026ndash;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026ndash;2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eRelationship Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.00, 0.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026ndash;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026ndash;1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e.96\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 Trimmed models exclude cases with Cook\u0026rsquo;s D \u0026gt; 4/N. Robust models use Huber weighting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7\u0026nbsp;\u003c/strong\u003e\u003cem\u003eMediation Analysis: Jealousy as Mediator Between Technology Benefits and Self-Distraction\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"452\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI [LL, UL]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003ea (Technology \u0026rarr; Jealousy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eb (Jealousy \u0026rarr; Self-Distraction)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003ec\u0026prime; (Direct Effect)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.18, 0.21]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eIndirect (a \u0026times; b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[0.03, 0.42]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 226px;\"\u003e\n \u003cp\u003eTotal Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026mdash;\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 Indirect effects estimated with percentile bootstrap 95% confidence intervals based on 2,000 resamples. Age and relationship length were included as covariates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8\u0026nbsp;\u003c/strong\u003e\u003cem\u003eMediation Analysis: Jealousy as Mediator Between Technology Benefits and Partner-Driven Distraction\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"521\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 295px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI [LL, UL]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 295px;\"\u003e\n \u003cp\u003ea (Technology \u0026rarr; Jealousy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 295px;\"\u003e\n \u003cp\u003eb (Jealousy \u0026rarr; Partner-Driven Distraction)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 295px;\"\u003e\n \u003cp\u003ec\u0026prime; (Direct Effect)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026ndash;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.16, 0.16]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 295px;\"\u003e\n \u003cp\u003eIndirect (a \u0026times; b)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.10, 0.16]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 295px;\"\u003e\n \u003cp\u003eTotal Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026ndash;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026mdash;\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 Percentile bootstrap 95% confidence intervals based on 2,000 resamples. Age and relationship length were controlled in both paths.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9\u0026nbsp;\u003c/strong\u003e\u003cem\u003eActor\u0026ndash;Partner Interdependence Model (APIM) Predicting Self-Distraction\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"578\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI [LL, UL]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003eActor Role: Male (vs. Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;2.00, 2.66]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003eActor Jealousy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[0.30, 1.15]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003eActor Jealousy \u0026times; Role\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.23, 0.57]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003eActor Technology Benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.47, 0.66]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003eActor Technology \u0026times; Role\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.67, 0.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003ePartner Jealousy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.40, 0.35]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003ePartner Jealousy \u0026times; Role\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.78, 0.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003ePartner Technology Benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.31, 0.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003ePartner Technology \u0026times; Role\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.60, 0.54]\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 Models include actor and partner predictors with actor-role interactions. Female is the reference role. CI = confidence interval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 10\u0026nbsp;\u003c/strong\u003e\u003cem\u003eActor\u0026ndash;Partner Interdependence Model (APIM) Predicting Partner-Driven Distraction\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"570\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI [LL, UL]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003eActor Role: Male (vs. Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;3.27, 2.97]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003eActor Jealousy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.15, 0.99]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003eActor Jealousy \u0026times; Role\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.42, 0.65]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003eActor Technology Benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.84, 0.66]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003eActor Technology \u0026times; Role\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.32, 1.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003ePartner Jealousy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.06, 0.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003ePartner Jealousy \u0026times; Role\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.75, 0.46]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003ePartner Technology Benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;0.31, 0.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 218px;\"\u003e\n \u003cp\u003ePartner Technology \u0026times; Role\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026ndash;0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e[\u0026ndash;1.05, 0.47]\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 Models include actor and partner predictors with actor-role interactions. Female is the reference role. CI = confidence interval.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"romantic jealousy, technology-related distraction, technoference, dyadic analysis, couple relationships, couple therapy","lastPublishedDoi":"10.21203/rs.3.rs-8545110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8545110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examined how perceived technology benefits relate to romantic jealousy and technology-related distraction in couples. Eighteen heterosexual dyads (N\u0026thinsp;=\u0026thinsp;36) completed an online survey assessing technology benefits, jealousy, and two distraction outcomes: self-distraction (feeling personally distracted) and partner-driven distraction (perceiving one\u0026rsquo;s partner as distracted). Analyses included individual-level regression models and dyadic Actor\u0026ndash;Partner Interdependence Models (APIM), adjusting for age, gender, relationship status, and relationship length. Across models, jealousy showed the most consistent association with self-distraction. Technology benefits showed weaker direct effects once jealousy entered the models. For partner-driven distraction, actor and partner jealousy effects were positive but estimated with wider uncertainty. Mediation tests supported an indirect pathway from technology benefits to distraction through jealousy, with clearer effects for self-distraction than partner-driven distraction. Findings support a relational meaning perspective on technology use and highlight jealousy as a clinically relevant target when partners experience technology-related conflict. Clinical implications include assessing jealousy cues, clarifying digital boundaries, and supporting intentional technology routines.\u003c/p\u003e","manuscriptTitle":"Jealousy and Digital Distraction in Romantic Dyads: A Dyadic Analysis of Technology’s Paradox of Connection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 08:34:39","doi":"10.21203/rs.3.rs-8545110/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fad2229b-7897-4000-ac19-aa78f264895e","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-24T01:24:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 08:34:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8545110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8545110","identity":"rs-8545110","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}