Abstract
Since the advent of smartphones, peer interactions over digital platforms have become a primary mode of socializing among adolescents. Despite the rapid rise in digital social activity, it remains unclear how this dramatic shift has impacted adolescent social and emotional experiences. In an intensive, longitudinal design (N = 26, n = 206 monthly observations for up to 12 months, 12–17 years), we used digital phenotyping methods to objectively measure within-person fluctuations in smartphone use (screen time, pickups, notifications) across different categories (social media, communication, entertainment, games) and examined their prospective, bidirectional associations with positive and negative mood. Bayesian hierarchical models showed that when adolescents reported better mood than usual, they subsequently spent more time on communication apps and launched social media and communication apps upon pickup less often. Meanwhile, when adolescents used entertainment apps more than usual, they subsequently reported improved mood. These preliminary findings suggest a pattern where fluctuations in mood relate to subsequent changes in smartphone use that are primarily social, whereas the fluctuations in smartphone use relating to subsequent changes in mood were primarily entertainment-related. We found little evidence that within-person fluctuations in screen time or social media use were associated with increases in negative mood, as frequently theorized. These findings highlight the importance of disentangling the distinct components of smartphone use that relate to affective processes and examining their bidirectional, prospective relationships over time, due to the possibility of differential outcomes. This work is a necessary first step in identifying targets for intervention efforts promoting resilience and wellbeing during adolescence.
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Adolescence is a unique period of social sensitivity (Somerville, 2013) when the importance of peer relationships and belonging increases (Brown, 1990; Somerville, 2013). Social interactions through smartphones have become a primary mode of connection among adolescents (Lenhart et al., 2010), becoming completely integrated with everyday life, as 95% of adolescents have a smartphone and are online frequently throughout the day (Pew Research Center, 2018; Rideout et al., 2022; Vogels et al., 2022). Yet, it remains unclear how this dramatic shift in the adolescent social landscape has impacted wellbeing. While socializing through smartphones can strengthen relationships, they may also increase exposure to negative social experiences (e.g., rejection, cyberbullying). Research examining this direct link has been inconclusive (Odgers & Jensen, 2020; Valkenburg et al., 2022) and many in the field have called for improved measurements to help clarify these relationships (Beyens et al., 2021; Kross et al., 2021; Orben & Blakemore, 2023; Valkenburg, 2022). Meanwhile, reported affect is more frequent, intense, and volatile during adolescence (Guyer et al., 2016; Steinberg, 2005) and involves reported decreases in positive affect and increases in negative affect (Abitante et al., 2022; Griffith et al., 2021; Grisanzio et al., 2023). Given the rapid socioemotional development and sharp rise in mental health problems that occur during adolescence (Blakemore & Mills, 2014; Crone & Dahl, 2012; Paus et al., 2008), it is important to understand how affective processes are related to smartphone use during adolescence (Orben et al., 2024). The current study leverages objective measures of smartphone use in an intensive, longitudinal (i.e., within-person) study that examines more granular features of smartphone use and its relation to adolescent emotional experience, a critical marker of adolescent mental health and wellbeing.
Extant research has revealed that the impact of smartphone use on adolescent outcomes is complex, varied, and context-dependent. Research in this area has recently accelerated, but consensus remains elusive (Appel et al., 2020; Hamilton et al., 2022; Jensen et al., 2019; Johannes et al., 2021; Valkenburg et al., 2022). On the one hand, youth who reported greater smartphone use showed either no change or improved associations with loneliness, strength of relationships, social support, and internalizing symptoms (Anto et al., 2023; Coyne et al., 2020; George et al., 2018; Jensen et al., 2019; Padilla‐Walker et al., 2012; Roser et al., 2016; Steinsbekk et al., 2023; Thomée et al., 2011). Work using objective, longitudinal data has yielded similarly positive or benign outcomes (Maftei et al., 2022; Marciano et al., 2022). On the other hand, reported smartphone use has been associated with worse emotional health, self-esteem, and wellbeing (Bennett et al., 2020; McNamee et al., 2021). Total time spent online and particularly high volumes of digital communication have also been correlated with worse self-harm, internalizing symptoms, and daily functioning (Coyne et al., 2019; McAllister et al., 2021). These discrepant findings could be clarified by examining intermediary factors (thoughts and emotions related to smartphone use) that shape adolescent wellbeing. Posting and interacting with peers on social media has been related to enhanced self-expression, affirmation, and positive affect (James et al., 2023; Karsay et al., 2022). However, both self-reported and objective smartphone use have been associated with social comparison and negative mood states (Dreier et al., 2024; Engeln et al., 2020; Nereim et al., 2022; Ren et al., 2023; Sequeira et al., 2020), that, in turn, modulate clinical risk (Nesi et al., 2022; Nick et al., 2022).
Ultimately, the extant literature on adolescent smartphone use exhibits considerable heterogeneity, showing associations with both positive and negative outcomes. Inconsistencies may be partially explained by the self-reported and cross-sectional nature of the majority of early research in this area, much of which combines all digital media use into a single composite rather than examining more granular types of use (Ellis, 2019; Kross et al., 2021). The current study aims to overcome these challenges by leveraging objective measures of smartphone use in an intensive, longitudinal design that examines granular features of smartphone use and its relation to adolescent mood. Adolescents use smartphones in a multitude of ways (Toh et al., 2019; Twenge & Farley, 2021), where associations with wellbeing vary by specific categories of apps (David et al., 2018) or types of smartphone use (Marciano et al., 2022; Oulasvirta et al., 2012; Rozgonjuk et al., 2018). Social (e.g., social media, communication) and non-social (e.g., entertainment, games) smartphone use can shape adolescent development (Allaby & Shannon, 2020) and are differentially associated with anxiety and depression (Elhai et al., 2017). While different phone metrics—like screen time (duration), pickups (app launched after pickup), and notifications—are related, they reflect fundamentally different behaviors and experiences (i.e., controllability) that are differentially related to positive and negative outcomes (Dreier et al., 2024; Kanjo et al., 2017; Prinstein et al., 2020; Saeb et al., 2015; Toh et al., 2023). Though screen time has been a leading metric of phone use in many early and influential studies, an emerging literature examining other metrics of phone use, such as pickups and notifications, has developed. Frequency of pickups and notifications have both been associated with positive and negative affect (Dreier et al., 2024; Kanjo et al., 2017; Saeb et al., 2015), as well as depressed mood and distraction (Rozgonjuk et al., 2018; Stothart et al., 2015; Toh et al., 2023; Upshaw et al., 2022). Distinguishing between social and non-social use may clarify these inconsistent findings (Kanjo et al., 2017). Thus, better characterization of these metrics and related psychological experiences is critical to informing best practices for smartphone management to enhance wellbeing.
We examine screen time, pickups, and notifications across five categories: overall use, social media, communication, games, and entertainment. We examine the bidirectional, prospective relationships between smartphone use and mood, a key precursor of wellbeing (Watson et al., 1988), at the within-person, monthly level for up to 12 months. This intensive, longitudinal design can capture the substantial within-person variability evident in social and affective processes (Coppersmith et al., 2019; Dewald-Kaufmann et al., 2021; Rodman et al., 2021), and characterize how these relationships unfold over time. Drawing from our previous work (Rodman et al., 2021), we expect that social media use and communication will have prospective and bidirectional associations with negative mood. Due to the limited study of other smartphone use categories and metrics, such as pickups and notifications, the examination of their relationships to mood is exploratory.
Method
All data and code are made available on Open Science Framework at: https://osf.io/t36wd/.
Participants
Participants were drawn from an ongoing intensive longitudinal parent study involving monthly assessments for 1 year. The current sample included 26 adolescents (n = 206 monthly observations) aged 12–17 years (Mage = 15.18 years, SDage = 1.12 years, 42% female-identifying; see Table 1). Our study was well-powered to examine within-person associations between smartphone use and positive and negative mood over time, with sufficient power (> 80%) to detect small within-person effects (ß = 0.12). See Supplementary Information and Figure S3 for more information on the simulated power analysis approach. Participants were recruited from schools, libraries, public transportation, and other public spaces in the general community in the greater Boston, MA area and participated in the current study between March 2021 and May 2022. Inclusion criteria required being aged 12–17 years old, possession of a smartphone with a data plan, and English fluency. Participants were excluded from the parent study based on the following criteria: presence of pervasive developmental disorders (e.g., autism), MRI scan ineligibility (e.g., metal implants, metal braces, claustrophobia, pregnancy), psychiatric medication use, active safety concerns, and inability to attend 12 study sessions at Harvard University. The community-based sample was broadly representative of diverse racial and ethnic backgrounds, with 42% of participants identifying as White, 19% as Black, 23% as Asian, and 15% as more than one race. In addition, 12% identified as Hispanic or Latino, while 88% did not. The sample represented a wide range of socioeconomic backgrounds, as measured by parental education and household income. See Table 1 for sociodemographic information. Legal guardians provided informed consent and youth provided assent. All study procedures were approved by the Institutional Review Board at Harvard University. For each monthly visit conducted, participants were compensated approximately $25/hour, amounting to about $12 for completing the assessments in the current study (20 min total).
Procedures
Continuous passive sensing of smartphone use was measured and aggregated to the monthly level. Assessments measuring positive and negative mood were administered at the end of each month. Thus, the structure of the study includes an inherently lagged design between smartphone use and affect measures (i.e., mood) that allow for prospective analyses within the same month and the following month. This monthly timescale is particularly relevant for long-form oscillations in mood and psychological symptoms that tend to occur over weeks or months (Connell & Dishion, 2006; Hammen, 2005). Long-form timescales also have significance for smartphone use and psychological symptoms (Rodman et al., 2021; Thomée et al., 2011), and may identify more cumulative effects that have relevance for clinical risk (Nesi et al., 2022). The current study resulted in a total of 229 possible monthly observations of smartphone use and mood over the study period, with participants completing 182 monthly assessments (80% completion rate).
Assessments
Positive and Negative Affect Scale
The PANAS is a 20-item self-reported measure designed to assess the two primary dimensions of mood, positive affect (PA) and negative affect (NA) (Watson et al., 1988). The PANAS lists 20 feelings, 10 referring to PA (i.e., attentive, interested, alert, excited, enthusiastic, inspired, proud, determined, strong and active) and 10 NA (i.e., distressed, upset, hostile, irritable, scared, afraid, ashamed, guilty, nervous, jittery). Respondents rated the extent to which they have felt each feeling in the past month, using a 5-point Likert scale (from 1 = ‘very slightly or not at all’ to 5 = ‘Extremely’). This scale can be used to test for affect at various timescales, including momentary, daily, weekly, or monthly reports (Watson et al., 1988). Reported affect at longer time scales over weeks or months can be described as mood (Watson et al., 1988). The alpha reliabilities are acceptably high, ranging from .86 to .90 for PA and from .84 to .87 for NA. The PANAS has demonstrated moderate to good reliability in adolescents (.76 positive, .69 negative) (Allan et al., 2015; Crawford & Henry, 2004). See Table 1 for descriptive statistics and intraclass correlation.
Smartphone Use
Each month, participants submitted data that indexed their smartphone use. For Android users (N = 6), screen time data is retrievable through MetricWire. For iOS users (N = 20), participants took screenshots of their phone usage via the Screen Time report in settings, which displays screen time (i.e., duration), pickups (i.e., first app launched after opening phone), and notifications for all apps on the weekly level going back 1 month. This data was hand-coded and quality checked by team personnel to record the number of minutes spent on each app each week for all participants. All data were calculated on a per day basis (to account for varying number of days each month) and aggregated to the monthly level. Data were then categorized into domains of app type, and we selected both social and non-social categories of use germane to adolescent socioemotional development and wellbeing (Allaby & Shannon, 2020; Marciano et al., 2022): Social Media, Communication, Games, and Entertainment. The Social Media category included apps such as TikTok, Instagram, and Snapchat. The Communication category included apps such as text messaging, FaceTime, WhatsApp, and Phone Calls. The Games category included apps such as Clash of Clans and Two Dot, smartphone-based computer games. The Entertainment category included apps such as Youtube and Netflix. App category definition was defined using guidance from The Apple Store and Google Play (see App Categorization https://osf.io/t36wd/).
Analytical Approach
We calculated descriptive statistics, correlations, and intra-class correlations (ICC) for all variables (see Table 1 and Table S2). We examined within-person fluctuations in screen time, pickups (iPhone only, Android does not have this metric), and notifications (iPhone only, Android does not have this metric) for various categories of smartphone use (e.g., social media use, communication, games, and entertainment) and examined their prospective, bidirectional associations with monthly measures of positive and negative mood in the same month and the following month (see Table 2). We isolated within-person effects, while controlling for between-person effects. Sensitivity analyses were conducted to account for pickups in notification models and vice versa (Table S1) and found that these variables act as suppressors for one another. Though correlated (maximum zero-order r = .500, p < .001), these constructs are not completely overlapping (maximum 25% shared variance). Thus, we chose to move forward with their analyses in separate models.
As in prior work (Rodman et al., 2021), all regression analyses were carried out in a Bayesian framework, due to its appropriateness for exploratory analyses and intuitive interpretation of the 95% highest posterior density (HPD) credible interval (CR), which signifies a 95% probability of the true population parameter being within the interval. We conducted Bayesian hierarchical linear models with unit of time (i.e., study month) nested within subject, with a random intercept allowed to vary across subjects. All models included study month, gender, and age as nuisance covariates. Models were estimated in R 3.5.2 (R Core Team, 2020) using the Stan language (Stan Development Team, 2018) and the brms (Bürkner, 2017) and sjstats packages (Lüdecke, 2019). Weakly informative priors specifying a Gaussian distribution (M = 0, SD = 10) were used to represent our diffuse prior knowledge of the fixed and random effects. For each parameter, we sampled from 4 stationary Markov chains that approximated the posterior distribution using the Monte Carlo No U-Turn Sampler (Hoffman & Gelman, 2014). Each Markov chain comprised 15,000 sampling iterations, including a burn-in period of 2500 iterations, which were discarded. Convergence of the 4 chains to a single stationary distribution was assessed via the Gelman-Rubin convergence statistic (Gelman & Rubin, 1992). Highest posterior density 95% CR for all parameters was then calculated from these samples and carried forward for inference, wherein CRs that did not contain zero were considered statistically significant.
To dissociate between- and within-person effects of predictors of interest in monthly level analyses, we used within-individual centering (i.e., centering each participant’s observations at the monthly level around their person-specific mean across the year-long study period) and between-subject centering at the year level (i.e., centering each participant’s mean level for the entire study period relative to the overall mean for the entire sample). Both within and between-person terms were included in all models at the same time. This approach orthogonalizes variation in a given predictor into between- and within-person variability (Enders & Tofighi, 2007), accounting for the dependent nature of the data both over time and within-subject, while controlling for trait-level characteristics of each predictor. When assessing within-person effects at the monthly level, we computed both same-month and following-month models to assess for relatively shorter and longer-range prospective relationships.
Results
Screen Time
Findings showed substantial within-person variability in screen time for social media, communication, and games (ICCs = .62–.67), and moderate within-person variability for overall use and entertainment (ICCs = .70–.75). On average, adolescents spent 321 min on their phones per day (range: 1.27–1062 m), with the most time engaged in social media for an average of 128.69 min per day (range: 0.08–403.44 m), followed by games (M = 49.54 m, 0–288.21 m), entertainment (M = 45.99 m, 0–276.70 m), and communication (M = 41.08 m, 0.07–231.11 m). See Fig. 1.
Mood and Subsequent Screen Time
Analyses examining the prospective relationships between screen time and mood at the within-person level showed that when adolescents reported greater positive mood than usual, they also engaged in more use of communication apps the following month (B = 1.343, SE = 0.480, CR = [0.395, 2.283]; Fig. 2A). We did not find other significant associations between fluctuations in positive or negative mood and subsequent changes in other categories of screen time during the following month (CRs included 0). See Table 2.
Screen Time and Subsequent Mood
Analyses examining relationships in the other direction showed that when adolescents engaged in more overall screen time than usual, they reported increased positive mood the following month (B = 0.011, SE = 0.006, CR = [0.001, 0.022]; Fig. 3A). Once again, we did not find other significant associations between fluctuations in other categories of screen time and subsequent changes in positive or negative mood during the same or following month (CRs included 0).
Pickups
Findings showed substantial within-person variability in overall pickups per day (ICC = .50), with significant within-person variability in pickups for social media, communication, and games (ICCs = .54–.66) and little within-person variability for entertainment (ICC = .91). Overall, adolescents launched apps upon picking up their phones an average of 85 times a day (range: 5.00–234.45 pickups), with pickups for social media apps occurring the most (M = 31.99 pickups, range: 0.62–102.29 pickups), followed by communication (M = 25.21 pickups, range: 1.42–94.88 pickups), games (M = 3.53 pickups, range: 0.04–30.57 pickups), and entertainment (M = 3.25 pickups, range: 0.04–24.92 pickups). Pickup analyses were restricted to iPhone users (N = 20) because Android does not provide this metric. See Supplementary Fig. 1.
Mood and Subsequent Pickups
Analyses examining the prospective relationships between pickups and mood showed that when adolescents reported greater positive mood than usual, they also engaged in fewer pickups of social media apps the following month (B = − 0.735, SE = 0.322, CR = [− 1.366, − 0.103]; Fig. 2B). Similarly, when adolescents reported less negative mood than usual, they also engaged in fewer pickups of communication apps the following month (B = 0.650, SE = 0.321, CR = [0.025, 1.280]; Fig. 2C). We did not find other significant associations between fluctuations in positive or negative mood and subsequent changes in other categories of pickups during the following month (CRs included 0). See Table 2.
Pickups and Subsequent Mood
Analyses examining relationships in the other direction showed that when adolescents engaged in more pickups of entertainment apps than usual, they reported increased positive mood (B = 0.559, SE = 0.269, CR = [0.030, 1.090]; Fig. 3B). Once again, we did not find other significant associations between fluctuations in other categories of pickups and subsequent changes in positive or negative mood during the same or following month (CRs included 0).
Notifications
Findings showed significant within-person variability in notifications overall per day (ICC = .56), with substantial within-person variability in notifications for communication, games, and entertainment apps (ICCs = .48–.66) and moderate within-person variability for social media notifications (ICCs = .82). Overall, adolescents received an average of 112 notifications a day, with the most notifications received from communication apps (M = 69.51 notifications, range: 5.95–525.00), followed by social media apps (M = 32.88 notifications, range: 0.04–187.17), games (M = 6.70 notifications, range: 0.04–25.44) and entertainment (M = 1.31 notifications, range: 0.04–7.04). Notification analyses were restricted to iPhone users (N = 20) because Android does not provide this metric. See Supplementary Fig. 2.
Mood and Subsequent Notifications
Analyses examining the prospective relationships between notifications and mood showed that when adolescents reported greater positive mood than usual, they also received fewer notifications overall (B = 2.383, SE = 1.212, CR = [− 4.765, − 0.014] and more notifications from entertainment apps the following month (B = 0.062, SE = 0.031, CR = [0.002, 0.123]; Fig. 2D). We did not find other significant associations between fluctuations in positive or negative mood and subsequent changes in other categories of notifications during the following month (CRs included 0). See Table 2.
Notifications and Subsequent Mood
Analyses examining relationships in the other direction showed that when adolescents received more notifications from entertainment apps than usual, they reported increased positive mood the same month (B = 1.382, SE = 0.657, CR = [0.093, 2.667]; Fig. 3C) and decreased negative mood the following month (B = − 1.504, SE = 0.595, CR = [− 2.666, − 0.328]; Fig. 3D). We did not find other significant associations between fluctuations in other categories of notifications and subsequent changes in positive or negative mood during the same or following month (CRs included 0).
Discussion
The current study used passive sensing data to extract smartphone use across multiple metrics (screen time, pickups, notifications) and categories (overall, social media, communication, games, and entertainment) and investigated their prospective, bidirectional relationships with monthly measures of positive and negative mood over a year. On average, adolescents used their smartphones for about 5 h a day and showed substantial within-person variation (~ 40%). When adolescents reported better mood than usual, they subsequently used communication apps more and checked social media and communication apps less. Meanwhile, when adolescents engaged in more use of entertainment apps than usual across all metrics, they subsequently reported improved mood. In all, these findings preliminarily suggest a pattern where fluctuations in mood relate to subsequent changes in smartphone behavior that were primarily in the social domain, whereas fluctuations in smartphone behavior predicting subsequent changes in mood were primarily in the entertainment domain. We found little evidence that within-person fluctuations in screen time or social media use were associated with increases in negative mood, as frequently theorized. These findings highlight the importance of disentangling the distinct components of smartphone use that relate to affective processes and examining their bidirectional, prospective relationships over time, due to the possibility of differential outcomes.
Passive sensing data allowed for the characterization of how teens use their phones across various metrics and categories over time. The data revealed that smartphone use fluctuates substantially from month to month, whereby 30–38% of the variance is driven by within-person variability in screen time, 34–50% in pickups (except entertainment at 8%), and 18–44% in notifications, which is in line with prior work using objective measures (Bradley & Howard, 2023; Harari et al., 2019; Rodman et al., 2021). Average smartphone use in the present study is also comparable to other studies using objective smartphone data finding average daily screen time of 3–8 h (Andrews et al., 2015; Bradley & Howard, 2023; Ellis et al., 2019; Sewall et al., 2020), and around 100 pickups and notifications a day (Bradley & Howard, 2023; Ellis et al., 2019), with most occurring for social apps (Bradley & Howard, 2023). Importantly, the descriptive statistics and ICCs from subjective measures of smartphone use differ markedly from the type of objective measures used here. ICCs of self-reported smartphone use are substantially lower than objective measures (ICCs ~ .15–.30 lower), (Coyne et al., 2020; Marciano et al., 2022) which may, in part, reflect bias in subjective reports (Boyle et al., 2022; Sewall et al., 2020) and underscores the need for objective measurement of smartphone use. Indeed, self-reported screen time tends to be underestimated and social media app use tends to be overestimated relative to objective metrics (Andrews et al., 2015; Boyle et al., 2022; Parry et al., 2021; Sewall et al., 2020). Furthermore, adolescent smartphone use fluctuates substantially from month to month, which highlights the need to consider within-person (vs. between-person) associations between smartphone use and affective processes.
We examined the prospective, bidirectional relationships between smartphone use (across varied categories and metrics) and mood. Findings revealed a weakly positive relationship between greater overall screen time than usual and subsequent increased positive mood. While subjective reports of phone use are primarily related to worse wellbeing or mood (Anderl et al., 2023; Sewall et al., 2020), actual phone use often shows small or non-existent associations with mood (Marciano et al., 2022; Sewall et al., 2020; Shaw et al., 2020). When examining the reverse direction, adolescents who reported greater positive mood than usual showed greater subsequent screen time on communication apps. Prior work has also shown that positive mood relates to subsequent increases in smartphone use (Marciano et al., 2022), and it stands to reason that the enhanced sociability that follows improved mood in real life (Whelan & Zelenski, 2012) would extend to social smartphone use. When examining pickup behavior (app launched after pickup), launching entertainment apps more than usual was associated with subsequent increases in positive mood, in line with prior work showing online leisure activities relate to positive affect (Stoeber et al., 2011). By contrast, better mood than usual was associated with subsequent decreases in pickups for social media and communication apps. Pickups of social apps are often conceptualized as checking behavior that reflects an updating process within the context of social communication or milieu. Indeed, the majority of pickups are related to social media checking (Bradley & Howard, 2023) and have been associated with negative mood (Saeb et al., 2015). Finally, when adolescents received more notifications from entertainment apps than usual, they reported better subsequent mood. While more data is needed to parse meaningful contextual factors, notifications from entertainment apps may reflect other participant behaviors (e.g., leisure time; Fennell et al., 2019). Indeed, increased independence during adolescence is accompanied by increased leisure time (Larson, 2001). Smartphone use is highly shaped by availability, where adolescents check social apps when free for short durations, but when available for longer durations, they watch shows and videos (Toh et al., 2019). Leisure time has been associated with improved mood and reduced stress (Zawadzki et al., 2015), and leisure-time smartphone use was found to be a protective factor against depression (Kremer et al., 2014). Thus, notifications from entertainment apps may reflect relaxed scheduling restraints that, in turn, relate to improved mood.
Taken together, the current findings illustrate a pattern where the directionality of influence between smartphone use and mood may depend on the category of phone use, such that fluctuations in mood appear to influence socially related smartphone behaviors, and smartphone use that is related to subsequent changes in mood is primarily entertainment-related (perhaps, as a marker of leisure time). While this differential pattern must be replicated, it preliminarily demonstrates the importance of examining bidirectional relationships in a longitudinal design. Much extant research examining smartphone use and adolescent wellbeing is based on cross-sectional studies testing between-person differences at a single point in time (Boer et al., 2020; Liu et al., 2022; Shannon et al., 2022; Twenge et al., 2018) and these designs cannot capture the substantial within-person variability evident in smartphone use (Beyens et al., 2021; Valkenburg et al., 2022). Moreover, repeated sampling designs permit the examination of bidirectional relationships, where wellbeing can shape subsequent smartphone behaviors (Katevas et al., 2018; Rodman et al., 2021) or clarify relationships that exist in one direction only (Anderl et al., 2023; Marciano et al., 2022). Therefore, examination of bidirectional, prospective relationships, which is possible in a high-frequency longitudinal design as in the current study, is crucial to observe how these relationships unfold over time (Orben, 2020).
Critically, very weak or no associations were found with overall phone use, and patterns primarily emerged when examining more granular features of smartphone use. Prior work supports this finding, wherein overall trends in smartphone use and wellbeing were further qualified by specific types of platform and metrics of phones use (David et al., 2018; Marciano et al., 2022; Oulasvirta et al., 2012; Rozgonjuk et al., 2018). Presently, most research examining smartphone use and adolescent functioning has largely taken a monolithic approach focused on total screen time as a basis for extrapolating outcomes (Mougharbel & Goldfield, 2020; Nesi & Prinstein, 2015). This may obfuscate meaningful relationships, as adolescents use smartphones in a multifarious manner, all with varying effects (Toh et al., 2019; Twenge & Farley, 2021). Thus, understanding how smartphone use impacts adolescent wellbeing necessitates measures that make meaningful distinctions between platforms, features, or uses (Prinstein et al., 2020).
Limitations and Future Directions
Given that adolescence is a period of social and affective development that is accompanied by risk for mental health problems, it is critical to improve measurements to examine how smartphone use relates to wellbeing (Orben et al., 2024). In this study, we use objective smartphone measures across multiple categories of phone use and utilize a longitudinal design to examine within-person, bidirectional relationships. However, the current findings have several limitations. While the study is powered to examine these questions at the within-person level (see Supplementary Information), the current sample is small and may not generalize; however, the range in gender, racial/ethnic, and socioeconomic representation helps to mitigate some of these concerns (Table 1). Furthermore, the sample size did not permit examination of between-person or age-related effects, which are an important consideration (Beyens et al., 2021; George et al., 2018; Orben et al., 2022). While the current study focuses on monthly fluctuations that capture long-form oscillations in variables of interest, future work should also include examination of short-form dynamic oscillations on the within-day or daily level. Additionally, participants were freely able to use other devices, put time limits on their screen time or customize the silencing of their notifications. While we aimed to capture naturalistic behavior, this may have inadvertently introduced some noise to our data, driving down metrics of smartphone use in ways that may systematically vary by category. Future work should account for such participant-set stipulations and behaviors in analyses. Finally, a future work should combine quantitative and qualitative approaches to further understand the motivations and functions of smartphone use.
Conclusion
The current study aimed to leverage intensive, longitudinal sampling of objective smartphone data across multiple platforms (i.e., app types) and metrics to characterize the prospective, bidirectional associations between fluctuations in smartphone use and adolescent mood over time. We found little evidence that within-person fluctuations in screen time or social media use were associated with increases in negative mood, as frequently theorized. Instead, when differentiating between various smartphone features, preliminary patterns emerged: fluctuations in mood related to subsequent changes in smartphone use that are primarily social, whereas fluctuations in smartphone use related to subsequent changes in mood were primarily entertainment-related. This work highlights the need to make meaningful distinctions between platforms, features, or uses as a necessary first step in identifying targets for intervention efforts to promote resilience and wellbeing during adolescence.
References
Abitante, G., Haraden, D. A., Pine, A., Cole, D., & Garber, J. (2022). Trajectories of positive and negative affect across adolescence: Maternal history of depression and adolescent sex as predictors. Journal of Affective Disorders, 315, 96–104. https://doi.org/10.1016/j.jad.2022.07.038
Allaby, M., & Shannon, C. S. (2020). “I just want to keep in touch”: Adolescents’ experiences with leisure-related smartphone use. Journal of Leisure Research, 51(3), 245–263. https://doi.org/10.1080/00222216.2019.1672506
Allan, N. P., Lonigan, C. J., & Phillips, B. M. (2015). Examining the factor structure and structural invariance of the PANAS across children, adolescents, and young adults. Journal of Personality Assessment, 97(6), 616–625. https://doi.org/10.1080/00223891.2015.1038388
Anderl, C., Hofer, M. K., & Chen, F. S. (2023). Directly-measured smartphone screen time predicts well-being and feelings of social connectedness. Journal of Social and Personal Relationships, 02654075231158300. https://doi.org/10.1177/02654075231158300
Andrews, S., Ellis, D. A., Shaw, H., & Piwek, L. (2015). Beyond self-report: Tools to compare estimated and real-world smartphone use. PLoS ONE, 10(10), e0139004. https://doi.org/10.1371/journal.pone.0139004
Anto, A., Asif, R. O., Basu, A., Kanapathipillai, D., Salam, H., Selim, R., Zaman, J., & Eisingerich, A. B. (2023). Exploring the impact of social media on anxiety among university students in the United Kingdom: Qualitative study. JMIR Formative Research, 7, e43037. https://doi.org/10.2196/43037
Appel, M., Marker, C., & Gnambs, T. (2020). Are social media ruining our lives? A review of meta-analytic evidence. Review of General Psychology, 24(1), 60–74. https://doi.org/10.1177/1089268019880891
Bennett, B. L., Whisenhunt, B. L., Hudson, D. L., Wagner, A. F., Latner, J. D., Stefano, E. C., & Beauchamp, M. T. (2020). Examining the impact of social media on mood and body dissatisfaction using ecological momentary assessment. Journal of American College Health, 68(5), 502–508. https://doi.org/10.1080/07448481.2019.1583236
Beyens, I., Pouwels, J. L., van Driel, I. I., Keijsers, L., & Valkenburg, P. M. (2021). Social media use and adolescents’ well-being: Developing a typology of person-specific effect patterns. Communication Research, 00936502211038196. https://doi.org/10.1177/00936502211038196
Blakemore, S.-J., & Mills, K. L. (2014). Is adolescence a sensitive period for sociocultural processing? Annual Review of Psychology, 65(1), 187–207. https://doi.org/10.1146/annurev-psych-010213-115202
Boer, M., Stevens, G. W. J. M., Finkenauer, C., de Looze, M. E., & van den Eijnden, R. J. J. M. (2020). Social media use intensity, social media use problems, and mental health among adolescents: Investigating directionality and mediating processes. Computers in Human Behavior, 106645. https://doi.org/10.1016/j.chb.2020.106645
Boyle, S. C., Baez, S., Trager, B. M., & LaBrie, J. W. (2022). Systematic bias in self-reported social media use in the age of platform swinging: Implications for studying social media use in relation to adolescent health behavior. International Journal of Environmental Research and Public Health, 19(16), 9847. https://doi.org/10.3390/ijerph19169847
Bradley, A. H. M., & Howard, A. L. (2023). Stress and mood associations with smartphone use in university students: A 12-week longitudinal study. Clinical Psychological Science, 11(5), 921–941. https://doi.org/10.1177/21677026221116889
Brown, B. B. (1990). Peer groups and peer cultures. In S. S. Feldman & G. R. Elliott (Eds.), At the threshold: The developing adolescent (pp. 171–196). Harvard University Press.
Bürkner, P.-C. (2017). brms: An R package for Bayesian multilevel models using stan. Journal of Statistical Software, 80(1). https://doi.org/10.18637/jss.v080.i01
Connell, A. M., & Dishion, T. J. (2006). The contribution of peers to monthly variation in adolescent depressed mood: A short-term longitudinal study with time-varying predictors. Development and Psychopathology, 18(1), 139–154. https://doi.org/10.1017/S0954579406060081
Coppersmith, D. D. L., Kleiman, E. M., Glenn, C. R., Millner, A. J., & Nock, M. K. (2019). The dynamics of social support among suicide attempters: A smartphone-based daily diary study. Behaviour Research and Therapy, 120, 103348. https://doi.org/10.1016/j.brat.2018.11.016
Coyne, S. M., Stockdale, L., & Summers, K. (2019). Problematic cell phone use, depression, anxiety, and self-regulation: Evidence from a three year longitudinal study from adolescence to emerging adulthood. Computers in Human Behavior, 96, 78–84. https://doi.org/10.1016/j.chb.2019.02.014
Coyne, S. M., Rogers, A. A., Zurcher, J. D., Stockdale, L., & Booth, M. (2020). Does time spent using social media impact mental health?: An eight year longitudinal study. Computers in Human Behavior, 104, 106160. https://doi.org/10.1016/j.chb.2019.106160
Crawford, J. R., & Henry, J. D. (2004). The positive and negative affect schedule (PANAS): Construct validity, measurement properties and normative data in a large non-clinical sample. British Journal of Clinical Psychology, 43(3), 245–265. https://doi.org/10.1348/0144665031752934
Crone, E. A., & Dahl, R. E. (2012). Understanding adolescence as a period of social–affective engagement and goal flexibility. Nature Reviews Neuroscience, 13(9), 636–650. https://doi.org/10.1038/nrn3313
David, M. E., Roberts, J. A., & Christenson, B. (2018). Too much of a good thing: Investigating the association between actual smartphone use and individual well-being. International Journal of Human-Computer Interaction, 34(3), 265–275. https://doi.org/10.1080/10447318.2017.1349250
Dewald-Kaufmann, J. F., Wüstenberg, T., Barton, B. B., Goerigk, S., Reinhard, M. A., Musil, R., Werle, J., Falkai, P., Jobst, A., & Padberg, F. (2021). Dynamics of the immediate behavioral response to partial social exclusion. Scientific Reports, 11(1), 1. https://doi.org/10.1038/s41598-020-80039-0
Dreier, M. J., Boyd, S. I., Jorgensen, S. L., Merai, R., Fedor, J., Durica, K. C., Low, C. A., & Hamilton, J. L. (2024). Adolescents’ daily social media use and mood during the COVID-19 lockdown period. Current Research in Ecological and Social Psychology, 100196. https://doi.org/10.1016/j.cresp.2024.100196
Elhai, J. D., Levine, J. C., Dvorak, R. D., & Hall, B. J. (2017). Non-social features of smartphone use are most related to depression, anxiety and problematic smartphone use. Computers in Human Behavior, 69, 75–82. https://doi.org/10.1016/j.chb.2016.12.023
Ellis, D. A. (2019). Are smartphones really that bad? Improving the psychological measurement of technology-related behaviors. Computers in Human Behavior, 97, 60–66. https://doi.org/10.1016/j.chb.2019.03.006
Ellis, D. A., Davidson, B. I., Shaw, H., & Geyer, K. (2019). Do smartphone usage scales predict behavior? International Journal of Human-Computer Studies, 130, 86–92. https://doi.org/10.1016/j.ijhcs.2019.05.004
Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12(2), 121–138. https://doi.org/10.1037/1082-989X.12.2.121
Engeln, R., Loach, R., Imundo, M. N., & Zola, A. (2020). Compared to Facebook, Instagram use causes more appearance comparison and lower body satisfaction in college women. Body Image, 34, 38–45. https://doi.org/10.1016/j.bodyim.2020.04.007
Fennell, C., Barkley, J. E., & Lepp, A. (2019). The relationship between cell phone use, physical activity, and sedentary behavior in adults aged 18–80. Computers in Human Behavior, 90, 53–59. https://doi.org/10.1016/j.chb.2018.08.044
Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7(4), 457–472. https://doi.org/10.1214/ss/1177011136
George, M. J., Russell, M. A., Piontak, J. R., & Odgers, C. L. (2018). Concurrent and subsequent associations between daily digital technology use and high-risk adolescents’ mental health symptoms. Child Development, 89(1), 78–88. https://doi.org/10.1111/cdev.12819
Griffith, J. M., Clark, H. M., Haraden, D. A., Young, J. F., & Hankin, B. L. (2021). Affective development from middle childhood to late adolescence: Trajectories of mean-level change in negative and positive affect. Journal of Youth and Adolescence, 50(8), 1550–1563. https://doi.org/10.1007/s10964-021-01425-z
Grisanzio, K. A., Flournoy, J. C., Mair, P., & Somerville, L. H. (2023). Shifting qualities of negative affective experience through adolescence: Age-related change and associations with functional outcomes. Emotion, 23(1), 278–288. https://doi.org/10.1037/emo0001079
Guyer, A. E., Silk, J. S., & Nelson, E. E. (2016). The neurobiology of the emotional adolescent: From the inside out. Neuroscience and Biobehavioral Reviews, 70, 74–85. https://doi.org/10.1016/j.neubiorev.2016.07.037
Hamilton, J. L., Nesi, J., & Choukas-Bradley, S. (2022). Reexamining social media and socioemotional well-being among adolescents through the lens of the COVID-19 pandemic: A theoretical review and directions for future research. Perspectives on Psychological Science, 17(3), 662–679. https://doi.org/10.1177/17456916211014189
Hammen, C. (2005). Stress and depression. Annual Review of Clinical Psychology, 1(1), 293–319. https://doi.org/10.1146/annurev.clinpsy.1.102803.143938
Harari, G. M., Müller, S. R., Stachl, C., Wang, R., Wang, W., Bühner, M., Rentfrow, P. J., Campbell, A. T., & Gosling, S. D. (2019). Sensing sociability: Individual differences in young adults’ conversation, calling, texting, and app use behaviors in daily life. Journal of Personality and Social Psychology. https://doi.org/10.1037/pspp0000245
Hoffman, M. D., & Gelman, A. (2014). The no-U-turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(47), 1593–1623.
James, K. M., Silk, J. S., Scott, L. N., Hutchinson, E. A., Wang, S., Sequeira, S. L., Lu, C., Oppenheimer, C., & Ladouceur, C. D. (2023). Peer connectedness and social technology use during COVID-19 lockdown. Research on Child and Adolescent Psychopathology, 1–12. https://doi.org/10.1007/s10802-023-01040-5
Jensen, M., George, M. J., Russell, M. R., & Odgers, C. L. (2019). Young adolescents’ digital technology use and mental health symptoms: Little evidence of longitudinal or daily linkages. Clinical Psychological Science, 7(6), 1416–1433. https://doi.org/10.1177/2167702619859336
Johannes, N., Nguyen, T., Weinstein, N., & Przybylski, A. K. (2021). Objective, subjective, and accurate reporting of social media use: No evidence that daily social media use Correlates with personality traits, motivational states, or well-being. Technology, Mind, and Behavior, 2(2). https://doi.org/10.1037/tmb0000035
Kanjo, E., Kuss, D. J., & Ang, C. S. (2017). NotiMind: Utilizing responses to smart phone notifications as affective sensors. IEEE Access, 5, 22023–22035. https://doi.org/10.1109/ACCESS.2017.2755661
Karsay, K., Matthes, J., Schmuck, D., & Ecklebe, S. (2022). Messaging, posting, and browsing: A mobile experience sampling study investigating youth’s social media use, affective well-being, and loneliness. Social Science Computer Review, 41, 089443932110583. https://doi.org/10.1177/08944393211058308
Katevas, K., Arapakis, I., & Pielot, M. (2018). Typical phone use habits: Intense use does not predict negative well-being. Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services, 1–13. https://doi.org/10.1145/3229434.3229441
Kremer, P., Elshaug, C., Leslie, E., Toumbourou, J. W., Patton, G. C., & Williams, J. (2014). Physical activity, leisure-time screen use and depression among children and young adolescents. Journal of Science and Medicine in Sport, 17(2), 183–187. https://doi.org/10.1016/j.jsams.2013.03.012
Kross, E., Verduyn, P., Sheppes, G., Costello, C. K., Jonides, J., & Ybarra, O. (2021). Social media and well-being: Pitfalls, progress, and next steps. Trends in Cognitive Sciences, 25(1), 55–66. https://doi.org/10.1016/j.tics.2020.10.005
Larson, R. (2001). How U.S. children and adolescents spend time: What it does (and doesn’t) tell us about their development. Current Directions in Psychological Science, 10(5), 160–164. https://doi.org/10.1111/1467-8721.00139
Lenhart, A., Ling, R., Campbell, S., & Purcell, K. (2010). Teens and mobile phones: Text messaging explodes as teens embrace it as the centerpiece of their communication strategies with friends. Pew Internet & American Life Project. https://eric.ed.gov/?id=ED525059. Accessed 2/4/24.
Liu, M., Kamper-DeMarco, K. E., Zhang, J., Xiao, J., Dong, D., & Xue, P. (2022). Time spent on social media and risk of depression in adolescents: A dose–response meta-analysis. International Journal of Environmental Research and Public Health, 19(9), 9. https://doi.org/10.3390/ijerph19095164
Lüdecke, D. (2019). sjstats: Statistical functions for regression models (0.17.6) [Computer software]. https://CRAN.R-project.org/package=sjstats. Accessed 2/4/24.
Maftei, A., Merlici, I.-A., & Dănilă, O. (2022). Social media use as a coping mechanism during the COVID-19 pandemic: A multidimensional perspective on adolescents’ well-being. Frontiers in Public Health, 10, 1062688. https://doi.org/10.3389/fpubh.2022.1062688
Marciano, L., Driver, C. C., Schulz, P. J., & Camerini, A.-L. (2022). Dynamics of adolescents’ smartphone use and well-being are positive but ephemeral. Scientific Reports, 12(1), 1. https://doi.org/10.1038/s41598-022-05291-y
McAllister, C., Hisler, G. C., Blake, A. B., Twenge, J. M., Farley, E., & Hamilton, J. L. (2021). Associations between adolescent depression and self-harm behaviors and screen media use in a nationally representative time-diary study. Research on Child and Adolescent Psychopathology, 49(12), 1623–1634. https://doi.org/10.1007/s10802-021-00832-x
McNamee, P., Mendolia, S., & Yerokhin, O. (2021). Social media use and emotional and behavioural outcomes in adolescence: Evidence from British longitudinal data. Economics and Human Biology, 41, 100992. https://doi.org/10.1016/j.ehb.2021.100992
Mougharbel, F., & Goldfield, G. S. (2020). Psychological correlates of sedentary screen time behaviour among children and adolescents: A narrative review. Current Obesity Reports, 9(4), 493–511. https://doi.org/10.1007/s13679-020-00401-1
Nereim, C., Bickham, D., & Rich, M. (2022). Exploring use patterns and racial and ethnic differences in real time affective states during social media use among a clinical sample of adolescents with depression: Prospective cohort study. JMIR Formative Research, 6(5), e30900. https://doi.org/10.2196/30900
Nesi, J., & Prinstein, M. J. (2015). Using social media for social comparison and feedback-seeking: Gender and popularity moderate associations with depressive symptoms. Journal of Abnormal Child Psychology, 43(8), 1427–1438. https://doi.org/10.1007/s10802-015-0020-0
Nesi, J., Rothenberg, W. A., Bettis, A. H., Massing-Schaffer, M., Fox, K. A., Telzer, E. H., Lindquist, K. A., & Prinstein, M. J. (2022). Emotional responses to social media experiences among adolescents: Longitudinal associations with depressive symptoms. Journal of Clinical Child & Adolescent Psychology, 51(6), 907–922. https://doi.org/10.1080/15374416.2021.1955370
Nick, E. A., Kilic, Z., Nesi, J., Telzer, E. H., Lindquist, K. A., & Prinstein, M. J. (2022). Adolescent digital stress: Frequencies, correlates, and longitudinal association with depressive symptoms. Journal of Adolescent Health, 70(2), 336–339. https://doi.org/10.1016/j.jadohealth.2021.08.025
Odgers, C. L., & Jensen, M. R. (2020). Annual research review: Adolescent mental health in the digital age: Facts, fears, and future directions. Journal of Child Psychology and Psychiatry, 61(3), 336–348. https://doi.org/10.1111/jcpp.13190
Orben, A. (2020). Teenagers, screens and social media: A narrative review of reviews and key studies. Social Psychiatry and Psychiatric Epidemiology, 55(4), 407–414. https://doi.org/10.1007/s00127-019-01825-4
Orben, A., & Blakemore, S.-J. (2023). How social media affects teen mental health: A missing link. Nature, 614(7948), 410–412. https://doi.org/10.1038/d41586-023-00402-9
Orben, A., Przybylski, A. K., Blakemore, S.-J., & Kievit, R. A. (2022). Windows of developmental sensitivity to social media. Nature Communications, 13(1), 1. https://doi.org/10.1038/s41467-022-29296-3
Orben, A., Meier, A., Dalgleish, T., & Blakemore, S.-J. (2024). Mechanisms linking social media use to adolescent mental health vulnerability. Nature Reviews Psychology, 1–17. https://doi.org/10.1038/s44159-024-00307-y
Oulasvirta, A., Rattenbury, T., Ma, L., & Raita, E. (2012). Habits make smartphone use more pervasive. Personal and Ubiquitous Computing, 16(1), 105–114. https://doi.org/10.1007/s00779-011-0412-2
Padilla-Walker, L. M., Coyne, S. M., & Fraser, A. M. (2012). Getting a high-speed family connection: Associations between family media use and family connection. Family Relations, 61(3), 426–440. https://doi.org/10.1111/j.1741-3729.2012.00710.x
Parry, D. A., Davidson, B. I., Sewall, C. J. R., Fisher, J. T., Mieczkowski, H., & Quintana, D. S. (2021). A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use. Nature Human Behaviour, 5(11), 11. https://doi.org/10.1038/s41562-021-01117-5
Paus, T., Keshavan, M., & Giedd, J. N. (2008). Why do many psychiatric disorders emerge during adolescence? Nature Reviews Neuroscience, 9(12), 947–957. https://doi.org/10.1038/nrn2513
Pew Research Center. (2018). Teens’ social media habits and experiences. 4. https://www.pewresearch.org/internet/2018/11/28/teens-social-media-habits-andexperiences/
Prinstein, M. J., Nesi, J., & Telzer, E. H. (2020). Commentary: An updated agenda for the study of digital media use and adolescent development – Future directions following Odgers & Jensen (2020). Journal of Child Psychology and Psychiatry, 61(3), 349–352. https://doi.org/10.1111/jcpp.13219
R Core Team. (2020). R: A language and environment for statistical computing. (3.5.2) [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/. Accessed 2/4/24.
Ren, B., Balkind, E. G., Pastro, B., Israel, E. S., Pizzagalli, D. A., Rahimi-Eichi, H., Baker, J. T., & Webb, C. A. (2023). Predicting states of elevated negative affect in adolescents from smartphone sensors: A novel personalized machine learning approach. Psychological Medicine, 53(11), 5146–5154. https://doi.org/10.1017/S0033291722002161
Rideout, V., Peebles, A., Mann, S., & Robb, M. B. (2022). The common sense census: Media use by tweens and teens, 2021. Common Sense. https://www.commonsensemedia.org/sites/default/files/research/report/8-18-census-integrated-report-final-web_0.pdf. Accessed 2/4/24.
Rodman, A. M., Vidal Bustamante, C. M., Dennison, M. J., Flournoy, J. C., Coppersmith, D. D. L., Nook, E. C., Worthington, S., Mair, P., & McLaughlin, K. A. (2021). A year in the social life of a teenager: Within-persons fluctuations in stress, phone communication, and anxiety and depression. Clinical Psychological Science, 9(5), 791–809. https://doi.org/10.1177/2167702621991804
Roser, K., Schoeni, A., Foerster, M., & Röösli, M. (2016). Problematic mobile phone use of Swiss adolescents: Is it linked with mental health or behaviour? International Journal of Public Health, 61(3), 307–315. https://doi.org/10.1007/s00038-015-0751-2
Rozgonjuk, D., Levine, J. C., Hall, B. J., & Elhai, J. D. (2018). The association between problematic smartphone use, depression and anxiety symptom severity, and objectively measured smartphone use over one week. Computers in Human Behavior, 87, 10–17. https://doi.org/10.1016/j.chb.2018.05.019
Saeb, S., Zhang, M., Karr, C. J., Schueller, S. M., Corden, M. E., Kording, K. P., & Mohr, D. C. (2015). Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: An exploratory study. Journal of Medical Internet Research, 17(7), e175. https://doi.org/10.2196/jmir.4273
Sequeira, L., Perrotta, S., LaGrassa, J., Merikangas, K., Kreindler, D., Kundur, D., Courtney, D., Szatmari, P., Battaglia, M., & Strauss, J. (2020). Mobile and wearable technology for monitoring depressive symptoms in children and adolescents: A scoping review. Journal of Affective Disorders, 265, 314–324. https://doi.org/10.1016/j.jad.2019.11.156
Sewall, C. J. R., Bear, T. M., Merranko, J., & Rosen, D. (2020). How psychosocial well-being and usage amount predict inaccuracies in retrospective estimates of digital technology use. Mobile Media & Communication, 8(3), 379–399. https://doi.org/10.1177/2050157920902830
Shannon, H., Bush, K., Villeneuve, P. J., Hellemans, K. G., & Guimond, S. (2022). Problematic social media use in adolescents and young adults: Systematic review and meta-analysis. JMIR Mental Health, 9(4), e33450. https://doi.org/10.2196/33450
Shaw, H., Ellis, D. A., Geyer, K., Davidson, B. I., Ziegler, F. V., & Smith, A. (2020). Quantifying smartphone “use”: Choice of measurement impacts relationships between “usage” and health. Technology, Mind, and Behavior, 1(2). https://doi.org/10.1037/tmb0000022
Somerville, L. H. (2013). The teenage brain: Sensitivity to social evaluation. Current Directions in Psychological Science, 22(2), 121–127. https://doi.org/10.1177/0963721413476512
Stan Development Team. (2018). The Stan Core Library (2.18.0) [Computer software]. http://mc-stan.org. Accessed 2/4/24.
Steinberg, L. (2005). Cognitive and affective development in adolescence. Trends in Cognitive Sciences, 9(2), 69–74. https://doi.org/10.1016/j.tics.2004.12.005
Steinsbekk, S., Nesi, J., & Wichstrøm, L. (2023). Social media behaviors and symptoms of anxiety and depression. A four-wave cohort study from age 10–16 years. Computers in Human Behavior, 147, 107859. https://doi.org/10.1016/j.chb.2023.107859
Stoeber, J., Harvey, M., Ward, J. A., & Childs, J. H. (2011). Passion, craving, and affect in online gaming: Predicting how gamers feel when playing and when prevented from playing. Personality and Individual Differences, 51(8), 991–995. https://doi.org/10.1016/j.paid.2011.08.006
Stothart, C., Mitchum, A., & Yehnert, C. (2015). The attentional cost of receiving a cell phone notification. Journal of Experimental Psychology: Human Perception and Performance, 41(4), 893–897. https://doi.org/10.1037/xhp0000100
Thomée, S., Härenstam, A., & Hagberg, M. (2011). Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults—A prospective cohort study. BMC Public Health, 11(1), 66. https://doi.org/10.1186/1471-2458-11-66
Toh, S. H., Howie, E. K., Coenen, P., & Straker, L. M. (2019). “From the moment I wake up I will use it…every day, very hour”: A qualitative study on the patterns of adolescents’ mobile touch screen device use from adolescent and parent perspectives. BMC Pediatrics, 19(1), 30. https://doi.org/10.1186/s12887-019-1399-5
Toh, W. X., Ng, W. Q., Yang, H., & Yang, S. (2023). Disentangling the effects of smartphone screen time, checking frequency, and problematic use on executive function: A structural equation modelling analysis. Current Psychology, 42(5), 4225–4242. https://doi.org/10.1007/s12144-021-01759-8
Twenge, J. M., & Farley, E. (2021). Not all screen time is created equal: Associations with mental health vary by activity and gender. Social Psychiatry and Psychiatric Epidemiology, 56(2), 207–217. https://doi.org/10.1007/s00127-020-01906-9
Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6(1), 3–17. https://doi.org/10.1177/2167702617723376
Upshaw, J. D., Stevens, C. E., Jr., Ganis, G., & Zabelina, D. L. (2022). The hidden cost of a smartphone: The effects of smartphone notifications on cognitive control from a behavioral and electrophysiological perspective. PLOS ONE, 17(11), e0277220. https://doi.org/10.1371/journal.pone.0277220
Valkenburg, P. M. (2022). Social media use and well-being: What we know and what we need to know. Current Opinion in Psychology, 45, 101294. https://doi.org/10.1016/j.copsyc.2021.12.006
Valkenburg, P. M., Meier, A., & Beyens, I. (2022). Social media use and its impact on adolescent mental health: An umbrella review of the evidence. Current Opinion in Psychology, 44, 58–68. https://doi.org/10.1016/j.copsyc.2021.08.017
Vogels, E. A., Gelles-Watnick, R., & Massarat, N. (2022). Teens, social media and technology 2022. https://policycommons.net/artifacts/2644169/teens-social-media-and-technology-2022/3667002/. Accessed 2/4/24.
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063–1070. https://doi.org/10.1037/0022-3514.54.6.1063
Whelan, D. C., & Zelenski, J. M. (2012). Experimental evidence that positive moods cause sociability. Social Psychological and Personality Science, 3(4), 430–437. https://doi.org/10.1177/1948550611425194
Zawadzki, M. J., Smyth, J. M., & Costigan, H. J. (2015). Real-time associations between engaging in leisure and daily health and well-being. Annals of Behavioral Medicine, 49(4), 605–615. https://doi.org/10.1007/s12160-015-9694-3
Acknowledgements
This work was supported by the National Institute of Health (R56-MH119194 and R37-MH119194 to KAM; and R00-MH126163 to AMR). The authors would also like to acknowledge Morgan Blind, Jesus De Luna, Nayeli Galvan, David Gonzalez, Katie Hennessey, Riley Hoffman, Delaney Ignace, and Sandra Mwangi for their help with data collection, coding, and processing.
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The conceptualization of this study and design of the research was done by K.A.M. and A.M.R.; A.M.R., Y.B.O., and R.R. performed the research; A.M.R. and J.A.B. analyzed the data; A.M.R., J.A.B, and G.K.C. drafted the manuscript; K.A.M and A.M.R. provided critical comments and revisions. All authors approved the final version of the manuscript for submission.
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Rodman, A.M., Burns, J.A., Cotter, G.K. et al. Within-Person Fluctuations in Objective Smartphone Use and Emotional Processes During Adolescence: An Intensive Longitudinal Study. Affec Sci (2024). https://doi.org/10.1007/s42761-024-00247-z
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DOI: https://doi.org/10.1007/s42761-024-00247-z