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. Author manuscript; available in PMC: 2025 Dec 4.
Published in final edited form as: Hum Behav Emerg Technol. 2025 Aug 1;2025:3547526. doi: 10.1155/hbe2/3547526

Objective Phone Use During Time with One’s Partner: Associations with Relationship and Individual Well-being

Brandon T McDaniel 1, Sabrina Uva 1, Victor Cornet 1, Michelle Drouin 1
PMCID: PMC12674674  NIHMSID: NIHMS2118486  PMID: 41346411

Abstract

When a person chooses to interact with their phone instead of their partner (e.g., technoference, phubbing), it may diminish interactional quality, relationship satisfaction, and well-being. However, much of the research on technology use in relationships has utilized self-reports. We extend prior work by objectively measuring smartphone use in a sample of 247 adult participants (75% women; Mean age = 30.87 years) to better understand the extent of use around one’s partner and the connection between this use and relational and personal well-being. Participants completed an online baseline survey and 8 days of phone tracking and nightly time diaries. On average, participants used their smartphone during 27% of their time around their partner; 86% used their phone every day at least some around their partner. Linear regression modeling revealed that phone use around partner (not total daily phone use) predicted lower relationship satisfaction and coparenting quality, although effects were only significant for women. We also found that phone habits in general (i.e., both phone use around partner and total phone use) predicted greater depression and lower life satisfaction, with effects trending toward being stronger for women). Overall, our results suggest that one’s own phone use is connected—especially for women—to one’s own relational and personal well-being. Our objective phone use and daily diary methods offer one potential model for studying the nuances of technoference and its effects on relational and personal well-being. Future research should continue to explore both objective and subjective measures of device use within couples and families.

Keywords: Smartphone use, technoference, phubbing, couple relationships, coparenting, depression, life satisfaction

INTRODUCTION

Smartphones are now interwoven throughout the daily life of U.S. adults (Pew Research Center, 2021). Given their prevalence, researchers have begun to explore how phones are used in and around couple time and relationship interactions (Hertlein et al., 2020; Juhasz & Bradford, 2016; McDaniel et al., 2021). Although some phone use within couples involves shared activities (e.g., Drouin & McDaniel, 2021; Salmela et al., 2019), research has shown that individuals also engage in solo phone use in the presence of their partner, and when this happens, interruptions in couple interactions—termed technoference or phubbing—can occur (McDaniel & Coyne, 2016; McDaniel et al., 2020; Roberts et al., 2022; Sbarra et al., 2019). Additionally, research shows that when a person chooses to interact with their phone instead of their partner, it may diminish the quality of interactions and relationship satisfaction (Bröning, 2022; Hipp, 2019; McDaniel et al., 2018) and, in turn, personal and relational well-being (Kostić, 2022; Lapierre & Zhao, 2022; McDaniel & Coyne, 2016). However, to date, much of the work on technology use in couple relationships has utilized self-reports. We therefore sought to extend this work by objectively measuring smartphone use to better understand the extent of use around one’s partner and the potential connection between this use and relational and personal well-being.

Prevalence and Correlates of Phone Use Around Partner

It is common and normative for smartphones to be used during couple interactions. In fact, 62% to 67% of those in American convenience samples report solo technology use in the presence of their partner at least daily (McDaniel & Coyne, 2016; McDaniel et al., 2021). There are many drivers of this phone use in the presence of partners, including strong habits, device notifications, work/social pressures, stress, boredom, loneliness, lack of self-regulation, fear of missing out, phone addiction, and more (e.g., Aranz & Schnauber-Stockmann, 2023; Billieux, 2012; Chotpitayasunondh & Douglas, 2016: Karadağ et al., 2015; McDaniel, 2019; van Deursen, 2015). Though some of these drivers are positive and can help an individual meet their needs and obligations, there are times when phone use can cause interruptions/distractions and negatively influence interactional synchrony (e.g., McDaniel & Wesselmann, 2021). For example, a 2014 report revealed that 42% of young adults in romantic relationships felt their partner was sometimes distracted by their phone during their time together (Lenhart & Duggan, 2014). More recently, approximately 51% of the those in couple relationships report being phubbed by their partner’s excessive cell phone use (Vogels & Anderson, 2020), and technoference (interruptions and intrusions of technology during face-to-face time; McDaniel & Coyne, 2016) has been reported in couple leisure time as occurring on 67% of days (McDaniel et al., 2021). A recent study by Schokkenbroek (2022) showed that this appears to affect both men and women equally, as 93% of women and 89% of men reported that their partner attended to their phones at their expense at least once over the past two weeks. Combined, this research suggests that individuals sometimes choose to attend to their phones instead of their relationship partners and that these interactions are noticed by partners and can cause disruption in the couple relationship.

It is now well established that smartphone use has the potential to affect interpersonal interactions (Billieux, 2012; Courtright & Caplan, 2020; Ergün et al., 2020; Ni et al., 2025). Mobile phones can serve as both a communication facilitator (e.g., via texting, phone calls, etc.) as well as a distractor, potentially disrupting face-to-face interactions and partner responsiveness (Duran et al., 2011; Lapierre et al., 2021; McDaniel et al., 2016). Overall, the research suggests that those who report increased levels of phubbing and technoference in their relationships face several adverse consequences, including lower overall relationship satisfaction, dissatisfaction regarding partner interaction responsiveness, and more conflict (Vanden Abeele et al., 2016); Ergun et al., 2020; Frackowiak et al., 2022; McDaniel & Coyne, 2016; Ni et al., 2025; Schokkenborek et al., 2022). Thus, technological disruption may have a direct effect on relationship quality. Alternatively (or additionally), the activities in which one engages online may affect relationship quality indirectly. For example, the use of social networking apps, such as Instagram and Facebook, may reduce time spent with significant others, decrease connection, promote comparison to other relationships, and increase feelings of jealousy (Bouffard et al., 2021; Gerber et al., 2018; Kashian, 2019; Krasnova et al., 2016; Kuss & Griffiths, 2011; Rast et al., 2021). In turn, this may lead their partner to engage in greater surveillance of their online activities, such as their social media messages, texts, emails, or browser history (Shafer et al., 2022). In some cases, this partner surveillance is linked to increased levels of conflict, separation, breakups, and even divorce (Wang & Zhao, 2023). Therefore, there are at least two routes—direct and indirect—in which time spent online while with a partner may affect the quality of couple relationships.

From a theoretical standpoint, previous research suggested that negative effects of technoference may manifest for various reasons. Symbolic interactionism (Denzin, 1992) suggests that interactions represent symbols that can send messages to partners about values in the relationship, potentially leading to feelings of exclusion (David & Roberts, 2017; Hales et al., 2018). Technoference, as explained by the displacement hypothesis, sees technology use sometimes displacing—even unintentionally—deep, high-quality interactions during shared time (McCombs, 1972; McDaniel & Coyne, 2016). Meanwhile, social exchange theory (Thibault & Kelley, 1959) proposes that individuals continually assess rewards and costs in relationships, and technology use may disrupt this balance, leading to fewer rewarding interactions (e.g., lower quality interactions) and increased costs (e.g., conflict, jealousy, feelings of exclusion), ultimately contributing to dissatisfaction (e.g., McDaniel et al., 2018; 2021; McDaniel & Drouin, 2019). Finally, Cameron and Webster (2011) expanded the social exchange theory to the spiral theory of incivility and suggested that communication decisions in interactions, particularly those that prioritize other people and tasks, can lead to perceptions of incivility and mistrust.

Though romantic partners may recognize the adverse effects of technoference and phubbing on their relationships, due to a fear of missing out or social media addiction, they may still feel a sense of urgency to check their phones (Ansari et al., 2024; Rast et al., 2021; Sun et al., 2021). This sense of urgency to check one’s phone may override the potential negative emotional responses to one’s own phubbing behavior (Guazzini et al., 2021) and the general perception among most individuals that phone use within groups leads to lower quality interactions (Rainie et al., 2015). In other words, even though individuals might recognize that their own phone use is causing problems in their relationship or life, they still may not curb their behavior.

Phone Use and Individual Well-being

The impact of mobile phone use, particularly instances of phubbing or technoference, has also been examined in relation to personal well-being. McDaniel and Coyne (2016) found that technoference predicts conflict over technology use, which affects relationship satisfaction, and then may spill out into depression and life satisfaction. Building on this work, Roberts and colleagues (2016) also found similar results—namely that the impact of phubbing on relationship satisfaction was mediated by conflict concerning cellphone use, and phubbing was identified as indirectly impacting depression through its effects on relationship and life satisfaction. A subsequent study by McDaniel & Drouin (2019) highlighted the significance of cellphone use in shaping the emotional dynamics of romantic partnerships. Daily reports from both partners in romantic relationships revealed that on days when participants rated more technoference than usual, they perceived more conflict over technology use, rated their face-to-face interactions as less positive, and experienced more negative mood. These daily associations persisted even after controlling for general feelings of relationship satisfaction, depression, and attachment anxiety, with no significant differences observed between women and men in these associations. Other studies have also found that phubbing and technoference are associated with personal well-being and feelings, such as anger, frustration, and feelings of exclusion (e.g., Beukeboom et al., 2021; McDaniel & Wesselmann, 2021; Nuñez & Radtke, 2023; Thomas et al., 2022). Thus, it is possible for phone use around one’s partner to spill out into individual well-being.

Setting aside the potential negative effects of technoference and phubbing, an individual’s technology use and phone habits are often intricately linked to their well-being, and these ties are likely bidirectional. For example, research has shown that feelings of depression or stress can propel individuals toward increased overall phone use, often as a coping mechanism or a means of distraction (e.g., Panova & Lleras, 2016). Conversely, heightened phone use, particularly passive social media use (e.g., mindless scrolling), has sometimes been associated with increased negative mood states, such as depression and stress (e.g., Hoffner & Lee, 2015; Scott et al., 2017). In sum, the research to date suggests that technology and mobile phone use may have both direct and indirect (through technoference) effects on relational and personal well-being.

Gender and Phone Use Around Partner

There may be gender differences in the experiences, perceptions, and effects of technoference and phubbing on relationships. Some work suggests that women may be more attuned to or engage in greater relationship-oriented perceptions and behaviors. For example, women often carry a heavier load of the emotional labor in the relationship (e.g., “activities that are concerned with the enhancement of others’ emotional well-being and with the provision of emotional support;” Erickson, 2005, p. 338; Grandey & Gabriel 2015; Hochschild, 1979; Umberson et al., 2015). Women are also more likely to initiate a break-up or divorce, which suggests greater ties to relationship cues and behaviors (Rosenfeld, 2018). It is also clear that men and women within relationships show different perceptions, satisfaction levels, or variability in relationship feelings day-to-day (e.g., Curran et al., 2015; Horne et al., 2019).

However, when gender differences have been examined within the context of technology use in relationships, results are often mixed, with some studies finding women perceiving greater amounts of technoference from their partner than men (e.g., Chen et al., 2022; McDaniel et al., 2018) and others finding no differences between genders (e.g., Bröning, & Wartberg, 2022). In a recent study on the topic, Ligon-Tucker (2023) found that male participants were not bothered by their partner’s phone use and only showed signs of anger when they needed their partner’s attention, whereas females demonstrated signs of anger and, in some cases, jealousy when their partner was on the phone. Meanwhile, other studies have shown that, although the overall amount of perceived pubbing or technoference may differ by gender, its associations with outcomes such as relationship satisfaction do not (Hipp & Carlson 2021; Lapierre et al., 2022; McDaniel et al., 2018; McDaniel et al., 2021). As the results across studies have been inconsistent, the role of gender in the experience of technoference merits further exploration.

The Current Study

In the current study, we sought to extend previous work by objectively measuring smartphone use to better understand the extent of use around one’s partner and the potential connection between this use and relational and personal well-being. We focused on objective measurement, as much of the previous research has consisted only of individuals’ perceptions, and research has shown that self-reports of technology use are often inaccurate compared with actual use (Yuan et al., 2019). Moreover, measures of technoference are often framed from the perspective of a partner’s phone use (instead of one’s own use). In this study, we sought to extend this previous work by measuring objectively two aspects of personal smartphone use (use around one’s partner and total daily use), along with individuals’ self-reports of relational well-being (indicated by measures of relationship satisfaction and coparenting quality) and personal well-being (indicated by measures of depression and life satisfaction).

RQ1: We first examined the overall prevalence of objective phone use during time around one’s partner.

Then, based on previous work and theory (e.g., social exchange theory, displacement, spiral theory of incivility), we predicted that:

H1a: Greater objective phone use during one’s time with their partner would be associated with worse relational well-being.

H1b: Greater objective phone use during one’s time with their partner would be associated with worse personal well-being.

RQ2: We also explored whether differences emerged by gender.

RQ3: Finally, to confirm that effects are tied specifically to phone use around one’s partner and not simply to the typical heaviness/frequency of one’s phone use in general, we also examined total general phone use as a predictor of relational and personal well-being.

METHODS

Participants and Procedures

Data are from 247 U.S. adults who were currently in a relationship. These individuals are a subset of a larger NIH-funded study on 299 adults who were parents of infants (Healthy Digital Habits in Parents of Infants, R21NR019402). More details concerning the full sample can be found in McDaniel et al. (2023). In short, participants completed a baseline survey; then about three weeks later they completed 8 days of nightly surveys (M = 7.63 days completed) while their phone use was tracked continuously across those same 8 days (via an app installed on their smartphone; RescueTime for iOS devices and Chronicle for Android devices; M = 7.72 days of phone use data measured); and about 3 weeks after the daily phase of the study participants completed a follow-up survey. All surveys were online. In all, 288 participants were in a relationship at baseline and/or during the daily surveys and, within these participants, 259 participated in the daily phase of the study. Among these, two were missing daily data, eight reported never being around their partner during the 8 days, and a further two were missing phone use tracking data, leaving an analytic sample of 247 participants.

In our analytic sample (n = 247), 75% were female, 80% were Non-Hispanic Caucasian, median family income was $75,000 (Interquartile Range = $55,000; M = $82,377; SD = $47,606), and 61% had a Bachelor’s degree or higher. Participants were on average 30.87 years old (SD = 4.86), their child was 6.69 months old (SD = 3.51), and 67% had more than one child living in their home. On average, participants had been in their relationship for 7.68 years (SD = 4.31). About 35% used an Android smartphone, while 65% used an iPhone. We also provide the sample demographics for women and men in Table 1.

Table 1.

Demographic characteristics for women and men

Women (n = 185) Men (n = 62) Difference
Mean SD Mean SD t-value
Age (years) 29.84 (4.62) 33.95 (4.22) −6.19***
Infant age (months) 6.88 (3.49) 6.10 (3.51) 1.51
Number of children 2.30 (1.19) 1.92 (1.01) 2.25*
Family income $77,295 ($42,474) $97,623 ($58,235) −2.93**
Education 4.54 (1.69) 5.06 (1.49) −2.34*
Length of relationship (years) 7.65 (4.28) 7.75 (4.44) −0.15
n % n % Chi-square
Ethnic minority 37 20.00% 11 17.74% 0.15
Android user 54 29.19% 32 51.61% 10.29**
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***

p < .001,

**

p < .01,

*

p < .05.

As Levene’s test for equality of variances was significant, the adjusted t-value was used for the mean difference between women and men on income and education.

Measures

Relationship satisfaction.

Participants responded to the 4-item Couple Satisfaction Index (CSI-4; Funk & Rogge, 2007) at baseline (e.g., “I have a warm and comfortable relationship with my partner”) on a 6-point scale 0 (Not at all) to 5 (Completely). We averaged across the 4 items, with higher scores representing greater relationship satisfaction (Cronbach’s alpha = .95). Four participants indicated they were not currently in a relationship at baseline but indicated they were in a relationship during the daily surveys. Thus, to include them in the analyses, we calculated their scores from their follow-up survey.

Coparenting quality.

Participants rated the quality of their coparenting relationship with their partner via the Coparenting Relationship Scale – Brief Form (CRS-Brief; Feinberg, 2012), which includes 14 items (e.g., “My partner undermines my parenting”). Scale points ranged from 0 (Not true of us) to 6 (Very true of us). Negatively worded items were first reverse scored, and then all items were averaged with higher scores indicating greater coparenting quality (alpha = .85). Again, we utilized the coparenting scores from the follow-up survey for four participants.

Depression.

We measured depressive symptoms via the CES-D-SF (Levine, 2013). This scale contains 7 items (e.g., “I felt depressed) rated on a 4-point scale from 0 (Rarely or none of the time—less than 1 day) to 3 (Most or all of the time—5 to 7 days). Items were averaged with higher scores representing greater depressive symptoms (alpha = .81).

Life satisfaction.

Participants responded to 5 items (e.g., “In most ways my life is close to ideal”) from the Satisfaction with Life Scale (Diener et al., 1985) on a 7-point scale, from 1 (Strongly disagree) to 7 (Strongly agree). Items were averaged where higher scores indicate grater life satisfaction (alpha = .89).

Parenting stress.

Participants responded to the Parental Stress Scale (PSS; Berry & Jones, 1995), which includes 18 items (e.g., “The major source of stress in my life is my child”) on a 5-point scale from 1 (Strongly disagree) to 5 (Strongly agree). Items were averaged with higher scores representing greater parenting stress (alpha = .86).

Phone use around partner.

On the 8 days of nightly surveys, participants tracked the following items in 15-minute intervals throughout their day from 6am to the nighttime when they completed their survey (e.g., around 9pm to 12am): (1) when physically near their partner, (2) when sleeping, and (3) when someone other than the participant was utilizing the participant’s phone. To combine the continuously tracked smartphone use data with the time diary data, we developed a MATLAB script that first binned phone use data into 15-minute intervals across the 8 days, then matched and merged it with the time diary data. We also developed a Python script which calculated (a) the amount of time the participant used their phone during the times they were around their partner (but only for those times when the participant was not asleep and someone else was not using the participant’s phone) and (b) the amount of time they were around their partner (not including when the participant was asleep). We then calculated a proportion variable for each day per participant by dividing the total phone use during partner time by total partner time; if a participant was never around their partner on a given day, then this proportion variable was coded as missing. Finally, we averaged this variable across all days of data within each participant.

Total phone use.

A Python script calculated the total amount of time, in hours, each participant used their phone each day. We then averaged across the 8 days for each participant.

Analysis

We first ran descriptive statistics and bivariate correlations utilizing SPSS 26. We also ran t-tests to compare mean values across women and men on the main study variables. Then, utilizing a series of four hierarchical linear regression models, we examined associations between phone use around partner and relationship satisfaction, coparenting quality, depression, and life satisfaction—while controlling for demographics and parenting stress. The codings of the demographic variables are included in the regression model table. We then performed the same series of four regression models, but this time examined associations between total phone use and the outcomes. In these models, we entered the demographics and parenting stress in step 1. In step 2, we entered phone use as a predictor. Finally, in step 3, we entered the interaction between phone use and parent gender (phone use X gender). We report the standardized betas for the final step 3 models, along with the changes in the R2 for each step.

RESULTS

RQ1: Prevalence of phone use around partner

On average (across the 8 days and across all participants), participants used their smartphone during 27.75% of their time around their partner; however, this ranged from no phone use to as high as phone use during 91.81% of their partner time. Almost 100% of participants used their phone at least sometime around their partner across the 8 days (only one participant never used their phone around their partner), and about 81% of all days of data collected included at least 20 minutes of phone use around one’s partner. We also found that 86% of participants used their phone at least some around their partner every single day. On average, women showed significantly greater total phone use and phone use around their partner (see Table 2).

Table 2.

Bivariate correlations and descriptive statistics for main study variables for women and men

Phone use around partner Total phone use Relationship Satisfaction Coparenting quality Depression Life Satisfaction Parenting stress
Phone use around partner -- .76*** −.23** −.16* .34*** −.23** .01
Total phone use .71*** -- −.11 −.04 .27*** −.26*** .00
Relationship satisfaction .25* .12 -- .69* −.38*** .41*** −.23**
Coparenting quality .19 .00 .66*** -- −.32*** .38*** −.26***
Depression −.05 .04 −.48*** −.32* -- −.44*** .35***
Life satisfaction .15 .16 .53*** .29* −.45*** -- −.50***
Parenting stress −.22 −.15 −.36** −.33** .30* −.45*** --
Women (n = 185)
 Mean 0.29 5.23 3.92 4.88 0.85 5.38 1.99
 Std. Dev. (0.15) (2.03) (1.06) (0.81) (0.61) (1.13) (0.48)
Men (n = 62)
 Mean 0.23 4.41 3.76 4.92 0.71 5.00 2.12
 Std. Dev. (0.10) (1.81) (0.99) (0.82) (0.51) (1.30) (0.55)
Mean difference
 t-value 3.58*** 2.84** 1.06 −0.32 1.64 2.18* −1.76
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***

p < .001,

**

p < .01,

*

p < .05.

Mother correlations are presented above the diagonal, and father correlations are presented below the diagonal. Levene’s test for equality of variances was significant, so the adjusted t-value was used for the mean difference between mothers and fathers on phone use around partner.

Bivariate correlations

Bivariate correlations and descriptives for women and men are reported in Table 2. Phone use around partner and total phone use were highly correlated for both women and men (r > .70). Phone use around partner was negatively correlated with both relationship satisfaction and coparenting quality for women; however, phone use around partner was positively correlated with relationship satisfaction and unrelated to coparenting quality for men. Women with greater phone use (around partner and total) also showed greater depression and lower life satisfaction (although not higher parenting stress), while men’s phone use was unrelated to any of the well-being variables.

Phone use and relational well-being

Standardized beta estimates from the final full regression models (Step 3) are presented in Table 3. We found support for our hypothesis (H1a): phone use around one’s partner was significantly predictive of lower relationship satisfaction (Step 2, β = −.17, p = .01), although not worse coparenting quality (Step 2, β = −.10, p = .11). However, (RQ2) a significant interaction between phone use around one’s partner and gender emerged for predicting relationship satisfaction (Step 3, β = .19, p = .01), indicating that the effect was non-significant for men (β = .25, p = .15) but significant for women (β = −.23, p < .001). A significant interaction was also found in predicting coparenting quality (Step 3, β = .14, p = .048), indicating that the effect was non-significant for men (β = .22, p = .21) but significant for women (β = −.15, p = .03).

Table 3.

Standardized betas for phone use predicting relational and individual well-being

Phone around partner Total phone use Phone around partner Total phone use Phone around partner Total phone use Phone around partner Total phone use
Relationship Satisfaction Relationship Satisfaction Coparenting Quality Coparenting Quality Depression Depression Life Satisfaction Life Satisfaction
Variable β β β β β β β β
Step 1: Control variables
 Gender .04 .02 .09 .06 −.13ǂ −.13ǂ −.06 −.06
 Age −.16* −.15ǂ −.14* −.13 −.02 −.03 −.02 −.02
 Phone type −.03 −.03 .03 .03 .06 .04 −.01 .01
 Education .07 .07 .11 .11 −.16* −.15* .05 .04
 Income .07 .07 .09 .09 −.10 −.10 .18** .17*
 Work status −.03 −.02 −.03 −.02 .02 .02 −.10 −.11ǂ
 Ethnic minority status −.04 −.04 −.13* −.13* .02 .02 .02 .02
 Child age −.07 −.07 −.10 −.10 −.11ǂ −.12* −.05 −.04
 Number of children .03 .02 .01 .001 −.09 −.07 .07 .06
 Parenting stress −.26*** −.27*** −.28*** −.29*** .35*** .36*** −.50*** −.50***
Step 2: Phone use
 Phone use −.23*** −.10 −.15* −.02 .29*** .22** −.21*** −.24***
Step 3: Interaction with Gender
 Gender X Phone use .19* .09 .14* −.01 −.12ǂ −.07 .11ǂ .15*
Full model F-value 3.60*** 2.56** 3.70*** 3.06*** 7.55*** 6.41*** 9.75*** 10.06***
Full model R 2 .158 .117 .161 .137 .282 .250 .336 .343
Step 1 R 2 .107** .107** .137*** .137*** .215*** .215*** .300*** .30***
Step 2 Change in R 2 .025** .004 .009 .000 .056*** .031** .028** .028**
Step 3 Change in R 2 .025** .006 .014* .000 .011ǂ .004 .009ǂ .015*
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Note:

***

p < .001,

**

p < .01,

*

p < .05,

ǂ

p < .10.

Parent age, education, income, child age, number of children, parenting stress, and phone use were grand mean centered. Income was in $1,000 units. Other demographic variables were coded as: Parent gender (1 = male, 0 = female), phone type (1 = Android, 0 = iPhone), work status (1 = working, 0 = not working), ethnic minority status (1 = minority, 0 = Non-Hispanic White).

Moreover, (RQ3) total phone use was not predictive of relationship satisfaction (Step 2, β = −.07, p = .31) or coparenting quality (Step 2, β = −.02, p = .74), and the effect did not differ by gender for predicting relationship satisfaction (Step 3, β = .09, p = .22) or coparenting quality (Step 3, β = −.01, p = .94). Results thus suggest that effects were more strongly tied to phone use around one’s partner than total phone use.

Phone use and personal well-being

We found support for our hypothesis (H1b): phone use around one’s partner was significantly predictive of greater depression (Step 2, β = .25, p < .001) and lower life satisfaction (Step 2, β = −.18, p < .01). (RQ2) Gender moderated the association with depression at the trend level (Step 3, β = −.12, p = .06), indicating that the effect was non-significant for men (β = −.02, p = .88) but significant for women (β = .29, p < .001). A trend level interaction was also found for predicting life satisfaction (Step 3, β = .11, p = .08), indicating that the effect was non-significant for men (β = .07, p = .64) but significant for women (β = −.21, p < .01).

Finally, (RQ3) total phone use showed associations with depression (Step 2, β = .19, p < .01) and life satisfaction (Step 2, β = −.18, p < .01). The effect on depression did not differ by gender (Step 3, β = −.07, p = .29), although a significant interaction with gender was found in predicting life satisfaction (Step 3, β = .19, p = .02), with the effect being non-significant for men (β = .07, p = .56) and significant for women (β = −.24, p < .001). As the total phone use associations were similar to those found for phone use around one’s partner, results suggest that effects on personal well-being may stem from both phone use around one’s partner and total phone use or simply be associated with phone habits in general.

DISCUSSION

Excessive phone use has been portrayed as damaging to couples and relationships; however, much of this previous work has relied upon general perceptions of relational and personal well-being and self-reports of phone use rather than objective measures. In this study, which included a sample of U.S. adults, phone use around relationship partners was common. Moreover, objective phone use measures and daily surveys showed that phone use around one’s partner was related to worse relationship quality (relationship satisfaction and coparenting quality), depression, and lower life satisfaction.

Overall, the participants in our sample spent about one fourth of their time with their partners on their mobile phones. Interestingly, the range of phone use on average around partners was wide, from no time at all to 92% of their partner time; however, most participants (86%) spent at least some time on their phone when they were with their partners every day. This aligns with previous research showing that most individuals report engaging in some solo phone use while with their partners at least daily (McDaniel & Coyne, 2016; McDaniel et al., 2021), and it also extends this research by utilizing an objective phone use measure rather than simply self-reports. Interestingly, we also found phone use (both around partner and total) to be higher among women as compared to men.

Though the prevalence of phone use around one’s partner was interesting, our main objectives were to determine whether this phone use was related to relational and personal well-being. In support of our first hypothesis, those who reported using their phones more often around their partner reported lower relationship quality (both relationship satisfaction and coparenting quality). This was even the case after controlling for parenting stress. However, when moderation by parent gender was taken into account, the effects were significant only for women. This is an important extension of previous work. More specifically, previous research has shown that technoference has negative effects on the partner who perceives themselves as being ignored or excluded by their partner (McDaniel et al., 2016; Roberts et al., 2016; McDaniel et al., 2017; Halpern et al., 2019, McDaniel et al., 2019); however, our study demonstrates that technoference also has an effect on perceptions of relationship and coparenting quality for the individual who is using their device, at least in the case of women. Moreover, some past research has shown that women may be more likely to perceive technoference as compared with men (McDaniel et al., 2018), and our study shows that they might also be greater attuned to their own phone use and the effects it has on their relationship.

Additionally, our results showed that both phone use around one’s partner and overall phone use were related to higher levels of depression and lower life satisfaction for the phone user. Again, it is notable that (a) these are not perceptions of one’s phone use, but one’s own objective use, and that (b) this objective use was linked with individual well-being. Gender also showed trends toward moderating these associations, with effects being stronger for women. A wide body of research over the last decade has shown that negative mood states are associated with prolonged time online, especially time on social media (Hoffner & Lee, 2015; Panova & Lleras, 2016; Scott et al., 2017). Our findings align with prior work and extend it to adults in relationships who are also parents of infants, who in our sample show greater feelings of depression and lower life satisfaction the more time they spend on their phone.

Finally, our analyses allowed us to parse phone use around partner from overall phone use (using an objective phone tracking measure), and it was phone use around partner and not overall phone use that was negatively related to relationship quality. When we consider these results in terms of displacement, social exchange, and the spiral theory of incivility, it is possible that time spent on phones around partners may be a sign of lesser devotion or care to one’s relationship partner, or that those who are already feeling relational distance escape to their phones for comfort. Either way, phone use around one’s partner rather than overall phone use may be a sign of a struggling relationship from both the phone user’s perspective and (from previous work) the person being phubbed. Our results also suggest that when it comes to one’s own personal well-being (e.g., depression, life satisfaction), phone use around one’s partner does not carry more weight than simply one’s own general phone use.

There are several limitations of this study that must be noted. First, our sample consisted of adults who were also parents of infants, and it is possible that the stress of caring for an infant creates phone dynamics that are different than during other parts of the life course. Although we controlled for parenting stress in our analyses, future studies should explore the extent to which these patterns replicate in adults without children as well as parents with older children. Second, although we used an objective measure for overall phone use, time spent around one’s partner was still self-reported, albeit daily and in 15-minute time intervals. Daily diaries offer more nuanced impressions of experiences than general measures at a single time point; however, they are still perceptions and not objective measures. As such, it is possible that those with lower/higher relationship quality may perceive times around their partner differently. Ideally, future studies would also passively observe couples in their home environments to obtain actual measures of phone use around partners, possibly through a combination of naturalistic observation and smartphone sensors (Cornet & Holden, 2018) or other monitoring technologies like smart homes (Wang et al., 2021). Finally, as this study was correlational, we cannot determine the direction of influence between variables. It is possible that phone use is not the culprit for relationship deterioration but a symptom of it (or quite likely both).

CONCLUSIONS

Our objective phone use and daily diary methods offer a potential model for studying the nuances of technoference and its effects on relational and personal well-being. Future research should continue to explore objective and subjective measures of device use within couples and families. Overall, our results suggest that one’s own phone use (objectively measured) is connected—especially for women—to their relational and personal well-being.

ACKNOWLEDGMENTS:

We would like to thank the participants, research study team, and research assistants who made this research possible. Preliminary results from this work were also previously presented at the National Council on Family Relations conference in 2023.

FUNDING STATEMENT:

The research reported in this publication was supported by the National Institute of Nursing Research of the National Institutes of Health under Award Number R21NR019402. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Nursing Research or the National Institutes of Health.

Footnotes

CONFLICT OF INTEREST: The authors declare that there are no conflicts of interest regarding the publication of this paper.

DATA AVAILABILITY:

The de-identified data may be shared on reasonable request to the corresponding author, and a data sharing agreement may be required.

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Data Availability Statement

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