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. Author manuscript; available in PMC: 2026 Oct 7.
Published before final editing as: J Prev (2022). 2026 Jul 28:10.1007/s10935-026-00938-2. doi: 10.1007/s10935-026-00938-2

Social Media Use Soon After Waking and Depression among U.S. Adults

Hyosin Kim a, Memuna Aslam a, Geethika Koneru a, Brian Primack a
PMCID: PMC13640952  NIHMSID: NIHMS2209380  PMID: 42518083

Abstract

As social media use has rapidly grown and widely been used, its association with depression has been increasingly documented. However, little is known about the relative association between social media use soon after waking versus total use and depression among adults. This study examines the associations between each of these independent variables and depression in a national sample of U.S. adults. We collected data from U.S. adults aged 30 to 70 years through surveys administered in July and August of 2023. Measures included social media soon after waking, total time spent on social media, seven sociodemographic characteristics, and depression using the Patient Health Questionnaire-9. The response rate was 61% (1512/2463). One-third of participants (33%) had depression. In separate survey-weighted multivariable logistic models, both total social media use and social media use soon after waking were significantly associated with depression. When both variables were included in the same model, only social media use soon after waking remained significantly associated with depression. Compared to almost no use soon after waking, frequent use after waking was associated with more than twice the odds of depression (Adjusted Odds Ratio=2.05; 95% CI=1.40, 3.00) after adjusting for total social media use and all covariates. The observed association may be bidirectional. Future studies should clarify the direction of these observed associations and determine whether modifying social media use soon after waking improves mental health outcomes or whether depressive symptoms increase engagement with social media upon waking.

Keywords: social media, waking, depression, adult, PHQ

INTRODUCTION

An estimated 21 million U.S. adults experience at least one major depressive episode each year (Substance Abuse and Mental Health Services Administration, 2023). Depression is associated with reduced quality of life, higher healthcare costs, and increased risks of morbidity and mortality (Greenberg et al., 2021; Pratt et al., 2016). While severe depression is treated by clinical and pharmacological interventions, evidence-based behavioral changes incorporated into daily life have emerged as a safe and cost-effective approach to preventing and managing depression (Battista et al., 2025; Sarris et al., 2014).

Over the past few decades, social media use (SMU) has increased rapidly and has become an integral part of many people’s personal and social lives. More than three-quarters of U.S. adults report using at least one social media platform, with four in ten U.S. adults indicating they are online “almost constantly” (Pew Research Center, 2024). Increasing engagement with social media has simultaneously raised concerns about its potential negative impact on emotional well-being (Jafar et al., 2023). Findings regarding the association between SMU and depression have been mixed depending on the study design and definition of key study concepts and measures (Seabrook et al., 2016; Valkenburg, 2022), and there is ongoing discussion to confirm the causal relationship between SMU and mental health outcomes (Ferguson et al., 2025; Thrul et al., 2025). One potential explanation for the mixed findings is that the motivations for SMU may differentially influence the engagement patterns and mental health outcomes (Ewing et al., 2023). In particular, when social media is used as a coping mechanism or means of escaping stressors, it may be associated with maladaptive patterns of use and adverse mental health outcomes (Chao et al., 2023; Stone & Veksler, 2022; Ulvi et al., 2022).

To help hone health-related recommendations around social media, prior work has examined various facets of SMU, including overall time spent, frequency of use, passive vs. active use, positive vs. negative experiences, addictive tendencies, and the use of specific social media platforms (Escobar-Viera et al., 2018; Perlis et al., 2021; Primack et al., 2019; Shensa et al., 2017; Wright et al., 2021). However, insufficient research has explored the timing of SMU, and relative contribution of the timing of SMU to depression, compared with overall time spent on SMU, remains understudied. SMU soon after waking may influence mental health differently than overall SMU quantity, and timing may represent a distinct dimension of exposure or behavioral patterns. The period soon after waking is a particularly important time of day both biologically and behaviorally for mental health (Chen et al., 2024; Jones et al., 2019). For example, light therapy routines soon after waking reduce daily mood fluctuations and depression severity, and decreased early activity is associated with depressive symptoms (Bonatto et al., 2024; Mei et al., 2020). Furthermore, among children and adolescents, device use immediately upon waking has been found to be associated with poor concentration, lower academic performance, and mood-related symptoms (Spina et al., 2021). These findings provide empirical grounding for examining the association between SMU immediately after waking and depression.

An additional gap in the literature is that most research examining SMU and mental health outcomes has focused on youth due to their developmental vulnerability and the unregulated nature of social media content (Nagata et al., 2024; Saleem et al., 2024; Zimmermann & Tomczyk, 2025). However, social media use is also common among adults. For instance, recent national survey data show that 92% of U.S. adults aged 30–49 years, 85% of those aged 50–64 years, and 64% of those aged 65 and older use YouTube, while 80%, 74%, and 57% use Facebook, respectively (Pew Research Center, 2025). At the same time, adulthood is a critical period for morbidity, mortality, and healthcare costs associated with depression. Few existing studies on adults examined associations between specific social media platforms and depression or other psychosocial outcomes (Ho et al., 2023; Lei et al., 2024; Perlis et al., 2021). Existing evidence found that use of some platforms, such as Facebook, Snapchat, and TikTok, was associated with higher levels of depression, whereas platforms that facilitate instant messaging and social connection were associated with positive psychosocial outcomes, including a greater sense of connectedness (Ho et al., 2023; Lei et al., 2024; Perlis et al., 2021). However, less attention has been paid to how the timing of social media use may relate to depression, particularly social media use soon after waking. Among adults aged 30 and older, SMU is likely to occur outside of typical working hours, such as early in the morning, for stress relief, information gathering, and social networking (Stockinger et al., 2024).

Therefore, this study addresses important gaps in the literature by examining the associations between SMU soon after waking, total time spent on SMU, and depression among a sample of U.S. adults. First, we hypothesized that social media use soon after waking would be associated with depression (H1). Second, we hypothesized that greater total SMU time would be associated with depression (H2). Third, we hypothesized that both SMU soon after waking (H3a) and greater total SMU time (H3b) would be independently associated with depression when included in the same model. Identifying modifiable aspects of SMU is critical for informing prevention strategies. While existing interventions have primarily focused on reducing total daily usage, SMU occurring at the start of the day, if shown to be associated with depression, may represent more actionable and feasible targets for behavioral intervention. Thus, assessing the potential impact of SMU soon after waking may provide insights into targeted intervention points to reduce depression risk.

METHODS

Participants

The study sample was recruited by Qualtrics Sampling Service. Eligibility criteria included being aged between 30 to 70 years, able to read English and complete an online survey, and residing in the U.S. Participants were recruited considering age, sex, race, education, household income, and geographic region to ensure diverse demographic, socioeconomic, geographical representation of the U.S. adult population. In addition, survey weights were available to further improve the generalizability of findings. Participants were compensated with redeemable Qualtrics credits, and informed consent was completed as part of the questionnaire. Data collection took place July through August 2023. This study protocol was approved by the Institutional Review Board of the authors’ institution (Protocol HE-2023–374).

Of the 2461 individuals initially invited to participate, 418 did not complete the survey. Qualtrics implemented its standard data-quality screening procedures, including digital fingerprinting, IP tracking, timestamp analysis, and live data-scrubbing processes to identify potentially fraudulent, duplicate, or low-quality responses. Responses identified as low-quality (e.g., inconsistent or duplicate responses, usually rapid completion times, or straight-lining) were removed (n = 531). The median survey completion time was 18 minutes (mean=30 minutes). This resulted in a final sample of 1512 participants, representing 61.4% of those eligible. Excluded respondents were more likely to be younger (46.8 years vs. 49.6 years), men (42.4% vs 31.5%), and non-White (44.5% vs. 23.3%) compared to those included in the study (p < 0.001 for all). Post-stratification survey weights were applied to address this nonresponse bias and so that results would be representative of the U.S. general population according to age, gender, sexuality, race/ethnicity, and educational attainment. In the weighted sample, 50% were women and 92% identified as heterosexual. Regarding racial/ethnic composition, 75% were non-Hispanic White, followed by 15% non-Hispanic Black, and 11% from other racial/ethnic categories (Table 1).

Table 1.

Sociodemographic Characteristics of a National Sample of 30–70-Year-Old US Adults by Depression Status

No Depression 1 Depression 1 p-value for Χ2 Total
Weighted % Weighted %
Actual sample size, n (%) 1005 (66) 507 (34) 1512 (100)
Weighted % 67 33 100
Social Media Use within 30 Minutes of Waking <0.001
 Almost never 35 19 29
 Sometimes/Half of the time 32 35 33
 Most of the time/Almost always 33 46 37
Social Media Use Hours per Week (in quartiles) <0.001
 0–7 27 16 23
 8–20 24 22 23
 21–41 23 29 25
 42 or more 26 33 28
Gender Identity 0.43
 Man 51 49 50
 Woman 49 51 50
Sexuality <0.001
 Heterosexual 94 87 92
 Non-Heterosexual 6 13 8
Age <0.001
 30–39 26 39 30
 40–49 23 28 25
 50–59 22 21 21
 60–70 29 12 24
Race/Ethnicity 0.66
 Non-Hispanic White 75 74 75
 Non-Hispanic Black 14 13 14
 Other (Hispanic, APAA, multiple race) 11 12 11
Education 0.05
 No Degree 47 54 49
 Associate's or Bachelor's Degree 35 29 33
 Master's or Doctorate Degree 18 16 17
Employment 0.07
 Employed 65 64 65
 Partially Employed 2 29 27 28
 Unemployed 6 9 7
Marital Status <0.001
 Single 23 31 25
 Married 58 47 55
 Other 3 19 21 20
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Abbreviations. APAA: Asian-American, Pacific Islander, American Indian, or Alaskan Native.

1

As defined by the Patient Health Questionnaire (PHQ-9), using the standard cutoff of 10.

2

Includes part-time or sporadic work.

3

Includes divorced, separated, or widowed.

Measures

Outcome.

Depression was assessed using the Patient Health Questionnaire (PHQ-9). The PHQ-9 is a self-administered instrument for measuring both the presence and the severity of depression. It consists of nine items assessing depressive symptoms experienced over the past 2 weeks, such as loss of interest, fatigue, difficulty concentrating, and feelings of worthlessness. Each item is scored on a scale of 0 (“Not at all”) to 3 (“Nearly every day”), yielding a total score ranging from 0 to 27. Respondents with a PHQ-9 score of 10 or higher were classified as having depression, guided by current recommendations (Kroenke et al., 2001). We selected the binary measure to focus on clinically meaningful depressive symptoms. We also conducted sensitivity analyses using cutoffs of 9 and 11 and using the measure as continuous to examine robustness of our findings.

Exposures.

There were two exposures of interest, and they are intended to capture distinct dimensions of SMU: a time-specific behavioral pattern (frequency of use soon after waking) and overall quantity of use (total time). Two exposure variables were measured using different measurement formats accordingly. Specifically, the first was the frequency of social media use soon after waking. For this study, we selected a 30-minute window of waking to operationalize “soon” after waking based on a prior study that used a similar time frame (Levenson et al., 2017). Participants responded to the question, “How often during the past year have you checked social media in the first 30 minutes after waking up in the morning?” with the following options: almost never, sometimes, about half the time, most of the time, and almost always. Based on the distribution of data and to facilitate identifying potentially meaningful thresholds, for primary analyses we collapsed these five responses into three categories: almost never (coded as 0), sometimes or about half the time (1), and most of the time or almost always (2). The second exposure of interest was overall time using social media. For this, participants reported amounts of time they use social media on an average week. Separate responses were elicited for weekdays and weekends because SMU is known to be different during these times (Li et al., 2017). In order to help participants with their estimates, we listed the top ten social media platforms at the time of the questionnaire (e.g. TikTok, Instagram, Twitter/X, Facebook, Reddit), which at that time constituted about 95% of all social media use (Pew Research Center, 2024). For analysis, we collapsed time into quartiles because the distributions of SMU measures were highly skewed and not linear; hence, quartiles were chosen to avoid assuming a linear relationship and to improve interpretability. While self-reported estimates on social media may not precisely capture absolute use, prior studies suggest that such measures are reliable and useful for ranking individuals and examining associations across relative groups (e.g., quartile 4 vs. quartile 3) (Scharkow, 2016).

Covariates.

We adjusted our multivariable models for key sociodemographic factors potentially related to SMU and/or mental health outcomes. These included age, gender identity (man vs. woman), sexuality (heterosexual vs. not heterosexual), race/ethnicity (non-Hispanic White, Black, other category including Hispanic, Asian American/Pacific Islander/American Indian/Alaskan Native, or multiracial), education level (no degree, Bachelor’s degree, Master’s or Doctorate degree), employment status (employed, partially employed, or unemployed), and marital status (single, married, divorced/separated/widowed) (Kaiser et al., 2021; Kyei-Blankson et al., 2016; Zhai et al., 2024).

Statistical Analysis

We described the characteristics of the weighted sample using percentages and examined bivariate associations between sociodemographic factors and depression using chi-squared tests. We conducted three survey-weighted, separate logistic regression models using Stata’s -svyset- and -svy:- commands. The first model assessed the multivariable association between SMU within 30 minutes of waking and depression while adjusting for all sociodemographic factors. The second model was similar but used only SMU time as the independent variable. In the final model, we included both independent variables in the same model to examine their independent associations with depression. Regression coefficients were exponentiated as odds ratios (ORs) to facilitate interpretation. We further assessed the linear associations between the independent variables and the outcome by including the independent variables in their continuous forms as opposed to using the variables in quartiles.

To assess the robustness of our findings, we conducted several sensitivity analyses. These included (1) performing unweighted analyses, (2) using different cutoffs for PHQ-9 scores (i.e., 9 and 11), (3) operationalizing social media use after waking as a five-level variable instead of a three-level variable, and (4) modeling PHQ-9 scores and SMU time as continuous variables rather than using the binary or categorical measures. All analyses were conducted using Stata version 18 (Statacorp, College Station, TX), and significance was defined using a two-tailed α of 0.05.

RESULTS

Sample Characteristics

Table 1 presents the characteristics of the study sample (n = 1512). In the weighted sample, 34% were depressed. Within 30 minutes of waking, 29% of respondents almost never used social media, 33% used it “sometimes” or “about half of the time,” and 37% used it “most of the time” or “almost always.” Respondents in the lowest quartile reported seven or fewer hours (23%), whereas those in the highest quartile reported 42 or more hours (28%) on social media per week. Bivariate analyses indicated that depression was associated with more frequent SMU within 30 minutes of waking (46% vs. 33% in the “most of the time/almost always” group, p < 0.001), higher SMU (p < 0.001), identifying as non-heterosexual, younger age, and being unmarried (p < 0.001) (Table 1).

Association Between Independent Variables and Depression

Regression results are found in Table 2. In Model 1, there was a significant association between more frequent social media use within 30 minutes of waking and depression. For example, compared to those who almost never use social media within 30 minutes of waking, those who almost always use social media within 30 minutes of waking had an AOR of 2.36 (95% CI = 1.68, 3.31). We also found a significant linear association between SMU within 30 minutes of waking and depression (p < 0.001). In Model 2, which focused on the independent variable of SMU time, compared with those who spent 0–7 hours per week, those who spent 42 or more hours per week had about twice the odds of depression (AOR = 1.94; 95% CI = 1.32, 2.86). A significant linear association between SMU time and depression was confirmed (p < 0.001).

Table 2:

Social Media Use After Waking and Depression among a National Sample of 30–70-Year-Old US Adults

Variable Model 1 Model 2 Model 3
AOR a (95% CI) AOR a (95% CI) AOR a (95% CI)
Social Media Use within 30 Minutes of Waking
 Almost never 1.00 1.00 b
 Sometimes/Half of the time 1.79*** (1.29, 2.48) 1.63** (1.15, 2.31)
 Most of the time/Almost always 2.36*** (1.68, 3.31) 2.05*** (1.40, 3.00)
Social Media Use Hours per Week
 0–7 1.00 1.00 c
 8–20 1.36 (0.94, 1.96) 1.13 (0.76, 1.66)
 21–41 1.83** (1.27, 2.64) 1.35 (0.91, 2.01)
 42 or more 1.94*** (1.32, 2.86) 1.34 (0.86, 2.08)
Gender Identity
 Man 1.00 1.00 1.00
 Woman 1.16 (0.90, 1.51) 1.18 (0.92, 1.53) 1.17 (0.90, 1.51)
Sexuality
 Heterosexual 1.00 1.00 1.00
 Not Heterosexual 1.81** (1.16, 2.83) 1.83** (1.17, 2.85) 1.80* (1.15, 2.82)
Age
 30–39 1.00 1.00 1.00
 40–49 0.68* (0.49, 0.94) 0.70* (0.50, 0.97) 0.70* (0.50, 0.97)
 50–59 0.49*** (0.34, 0.72) 0.48*** (0.33, 0.71) 0.51*** (0.35, 0.75)
 60–70 0.19*** (0.12, 0.30) 0.18*** (0.12, 0.29) 0.20*** (0.13, 0.31)
Race
 Non-Hispanic White 1.00 1.00 1.00
 Non-Hispanic Black 0.71 (0.48, 1.05) 0.67* (0.46, 0.99) 0.68 (0.46, 1.01)
 Other (Hispanic, APAA, multiple race) 1.06 (0.71, 1.58) 1.00 (0.67, 1.49) 1.05 (0.70, 1.57)
Education
 No Degree 1.00 1.00 1.00
 Associate's or Bachelor's Degree 0.78 (0.59, 1.03) 0.82 (0.62, 1.07) 0.80 (0.60, 1.05)
 Master's or Doctorate Degree 0.69 (0.46, 1.05) 0.71 (0.47, 1.07) 0.68 (0.45, 1.04)
Employment
 Employed 1.00 1.00 1.00
 Partially Employed d 1.95*** (1.40, 2.70) 1.85*** (1.34, 2.57) 1.96*** (1.41, 2.73)
 Unemployed 1.62* (1.02, 2.58) 1.60* (1.02, 2.50) 1.65* (1.04, 2.61)
Marital Status
 Single 1.00 1.00 1.00
 Married 0.66** (0.48, 0.89) 0.65** (0.48, 0.89) 0.65** (0.47, 0.88)
 Other e 1.19 (0.82, 1.75) 1.19 (0.81, 1.73) 1.19 (0.81, 1.74)
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Abbreviations. AOR = Adjusted Odds Ratio. APAA = Asian American, Pacific Islander, American Indian, or Alaskan Native. CI = Confidence Interval. SMU = Social Media Use. Levels of Statistical Significance.

***

= p < 0.001,

**

= p < 0.01,

*

= p < 0.05.

a

Odds ratio, adjusted for all variables in the table, for depression as defined by the Patient Health Questionnaire (PHQ-9), using the standard cutoff of 10.

b

p < 0.001 for the association (SM use after waking vs. depression)

c

p = 0.09 for the association (duration vs. depression)

d

Includes part-time or sporadic work.

e

Includes divorced, separated, or widowed.

In Model 3, which included both independent variables, only SMU within 30 minutes of waking remained associated with depression (Table 2). For example, compared with those who almost never use social media within 30 minutes of waking, those who did so mostly or almost always had twice the odds of depression (AOR = 2.05; 95% CI = 1.40, 3.00). While SMU within 30 minutes of waking showed a linear association with depression (p < 0.001), no significant association was found between SMU time and depression (p = 0.09).

As an additional sensitivity analysis, we examined depression as a continuous outcome (PHQ-9 score, ranging from 0 to 27) and modeled SMU time as continuous variables. Results of our sensitivity analyses were consistent with the primary analyses. Greater weekly SMU was associated with higher PHQ-9 scores (β = 0.02, p < 0.001), and more frequent SMU within 30 minutes of waking was also associated with higher PHQ-9 scores (β = 1.23, p < 0.001). When both measures were included simultaneously, both remained independently associated with depression, although the association was stronger for SMU within 30 minutes of waking (β = 1.05, p < 0.001) than for overall weekly SMU time (β = 0.01, p = 0.04). These findings support the robustness of the primary results. Because all sensitivity analyses described in the Methods (Statistical Analysis section) yielded substantively similar findings in terms of significance and magnitude of effects, only the results from primary analyses are reported in Table 2.

DISCUSSION

In a nationally representative sample of 1512 U.S. adults aged 30–70 years, we found that both SMU soon after waking and overall SMU time were individually associated with depression. These findings were consistent with our first and second hypotheses (H1, H2). However, when both SMU variables were included in the same model, only SMU after waking remained statistically significant. These findings were consistent with H3a but not H3b. These findings extend prior research focused on total exposure and suggest that the timing of SMU, particularly immediately after waking up, may have a stronger association with depression than overall cumulative usage time.

While these findings do not confirm causality and may reflect bidirectional relationships, several mechanisms may help explain the observed association between SMU soon after waking and depression. One possible pathway is that engaging with social media soon after waking may disrupt healthy morning routines. Another possible pathway relates to cognitive functioning. Early exposure of SMU is likely to shape emotional and cognitive processes at the start of the day, influencing subsequent psychological states during the rest of the day. For instance, engaging in mindless scrolling or consuming social media news after waking may draw individuals instantly into reactive and analytical thinking patterns, counteracting the benefits of remaining in a mindful state upon waking and rather causing mental exhaustion (MacKenzie & Kocovski, 2016). Another recent study showed that social media use changes individual’s brainwaves, reducing alpha waves associated with calm and restful states while increasing other waves, such as beta and gamma, which are linked to cognitive excitation and emotional engagement, and mental fatigue (Satani et al., 2025). The overwhelming influx of information, exposure to negative content and hostile online interactions, or idealized portrayals of others may evoke stronger negative emotions and increase susceptibility to depression, particularly at the start of the day compared to daytime periods where individuals are engaged in more varied interactions and distractions (Chao et al., 2023; Stockinger et al., 2024; Wright, Rodriguez Espinosa, et al., 2024). These neurocognitive mechanisms may help explain the observed association between SMU within 30 minutes of waking and depressive symptoms.

An alternative explanation for the findings is the reverse direction of the association. In other words, individuals experiencing depressive symptoms may be more likely to engage with social media soon after waking as an avoidant coping mechanism (Sun et al., 2023; Wegmann et al., 2023). Depression is often characterized by loss of interest and motivation, difficulty concentrating, persistent low mood, and social withdrawal (Chand & Arif, 2026). Individuals experiencing these symptoms may turn to social media soon after waking to distract themselves from anticipated daily stressors or to seek immediate emotional stimulation to avoid negative emotions. This interpretation highlights the need for studies that clarify the temporal ordering and causal relationship between SMU after waking and depression.

Existing frameworks on the determinants of depression have emphasized biological and genetic factors (e.g., neurotransmitter imbalances), psychological processes (e.g., rumination), and social determinants (e.g., discrimination, isolation, adverse living conditions) (Kirkbride et al., 2024; Remes et al., 2021). However, lifestyle behaviors as risk factors for depression have received less attention, despite their potential to interact with biological and psychological mechanisms and serve readily modifiable targets for prevention. Our study builds on the growing literature examining behavioral aspects of SMU and underscores the need to consider SMU soon after waking as a potential behavioral risk factor for depression.

This study has several limitations. First, the cross-sectional design precludes causal inference. It is possible that individuals with depressive symptoms are more likely to engage in SMU immediately upon waking, reflecting greater reliance on social media or more passive forms of engagement. Longitudinal research is needed to address reverse causality and pre-existing mental health differences. In addition, because the data were collected in July and August of 2023, following the COVID-19, the findings should be interpreted within the context of post-pandemic SMU patterns, which may differ from those observed before or during the COVID-19 pandemic (Wright, Larson, et al., 2024). Second, we sought to obtain a nationwide sample that approximated the demographic composition of the U.S. adult population in terms of age, gender, sexual orientation, race/ethnicity, and education through national recruitment facilitated by Qualtrics and the application of survey weights. However, we recognize that weighting cannot fully eliminate potential selection bias or guarantee full representativeness. In addition, the number of respondents from certain marginalized populations (e.g., transgender individuals and specific racial/ethnic groups) was limited at initial sampling. Future studies should consider oversampling these groups to improve representation. Third, both SMU measures were based on self-report and therefore subject to recall error and reporting bias. Participants may have inaccurately estimated their frequency or duration of SMU, which could have introduced measurement error. However, prior research suggests that self-reported measures can reliably capture relative differences between individuals and allow for meaningful estimation of associations with health outcomes (Scharkow, 2016). Additionally, the two exposure variables were measured using different approaches, which may raise comparability concerns. However, these measures were intentionally chosen to capture distinct constructs and any measurement error in SMU would likely attenuate associations rather than overestimate them. Finally, we did not measure the details of activities involved in early SMU. Future research should incorporate objective measures, examine qualitative differences in SMU behaviors across times of day, and test interventions that modify SMU soon after waking to determine their impact on depression independent of total use and to identify critical time thresholds associated with risk.

Despite noted limitations, to our knowledge, this is one of the first survey-based empirical studies with a large adult sample examining the association between SMU soon after waking and depression. While there is general agreement to caution against checking social media soon after waking, empirical evidence that reflects actual social media users’ behaviors and self-reported mental health assessments has been scarce. By distinguishing between total use and time-specific use, our findings show that when social media is used particularly at the start of the day may be more strongly associated with depression than how much it is used overall. Future studies should examine other temporal patterns of SMU, such as use before bedtime, to determine whether associations with depression differ by the timing of use during the day.

CONCLUSIONS

This study examined the relative associations of overall SMU time and SMU after waking up with the likelihood of reporting depression. We found that SMU soon after waking was more strongly associated with depression than total time spent on social media among U.S. adults. Although both factors were individually related to depression, only use within the first 30 minutes of waking remained significant when examined together. These findings suggest that the timing of SMU may be an important consideration in mental health promotion programs and public health guidance, in addition to overall SMU time. Future longitudinal and experimental studies are needed to confirm causal relationships and to determine whether modifying social media habits in the early part of the day could serve as a feasible lifestyle intervention to improve mental well-being.

Acknowledgements

This work was supported by the National Institutes of Health (R01-CA225773 to BP). We thank Nhi Phan for her valuable administrative and research support on this project.

Footnotes

Conflicts of interest: The authors have no relevant financial or non-financial interests to disclose.

Ethics approval: This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Oregon State University (Protocol HE-2023–374, 4 May 2023).

Consent to participate: Informed consent was obtained from all individual participants included in the study.

Data and code availability:

The data and code that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data and code that support the findings of this study are available from the corresponding author upon reasonable request.

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