This study follows the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for reporting observational research. It aims to investigate user behavioral patterns and their potential associations with sleep behaviors by analyzing the relationship between smartphone usage patterns and nocturnal smartphone inactivity.

The analysis was conducted in 3 phases. First, the demographic characteristics and smartphone usage of all 1074 participants were analyzed. Second, participants were stratified into groups based on gender, age, highest education level, employment status, and primary type of smartphone usage. Statistical tests were used to identify differences in smartphone usage patterns across these demographic groups. Finally, correlation and regression analyses were conducted to explore the association between smartphone usage and nocturnal smartphone inactivity.

We carried out a retrospective, siteless study using data provided by the Murmuras app, in continuation of the Menthal project [31]. All data were securely stored on the Murmuras platform, with publishing rights retained by all authors, irrespective of the study outcomes.

The Murmuras app is specifically designed to capture detailed smartphone usage data, supported by a robust back-end web platform, enabling extensive and in-depth analysis. Beyond monitoring smartphone app usage, the Murmuras app tracks a wide range of device-level interactions, including screen on and off events, device unlocking, frequency of startup and shutdown, SMS activity (sent and received), and incoming and outgoing calls. In addition to behavioral data, the Murmuras app gathers user feedback on personality traits, demographic characteristics, and emotional states via structured questionnaires.

Participants were recruited through the Murmuras app between January 1, 2022, and December 31, 2022. Users who installed the app from the Google Play Store and consented to data sharing were included. The study used passively collected smartphone usage data, which were analyzed retrospectively.

This study was conducted in accordance with ethical guidelines, ensuring the protection of participant privacy and data confidentiality. The original data collection was approved by the Ethics Committee of the University of Bonn (approval #13-05-31). All participants provided informed digital consent via the mobile app prior to the initial data acquisition. The dataset includes smartphone usage behavior data only and does not contain any personally identifiable information.

As this study involved a secondary analysis of fully anonymized data, no additional institutional review board approval was required. This complies with the General Data Protection Regulation and the ethical standards outlined by the Arbeitskreis Medizinischer Ethik-Kommissionen [32]. Permission to access and use the dataset for secondary research purposes was obtained from the University of Bonn and the original data custodians. Participants did not receive any form of compensation for their involvement in the original study.

Data collection was facilitated through the Murmuras app, which confirmed eligibility, secured participant consent, and gathered phone usage data. Upon consent, participants downloaded the app and used a unique, anonymous ID code provided via the link for initial access.

The study encompassed a standard demographic survey (age, gender, education, occupation, etc) and recorded data on app usage (such as app name, usage duration, and the sequence of usage) and device interaction data (such as screen on and off events and device power cycles). All collected data were systematically formatted and organized into CSV files.

Data for this study were collected retrospectively using the Murmuras app, which is primarily marketed and widely used in Germany. As a result, the majority of participants were from Germany.

The key indicators were as follows:

The statistical analysis entailed calculating the daily duration of smartphone usage, daily duration and frequency of different smartphone app usage, and duration of nocturnal smartphone inactivity. Normality and homogeneity of variance tests were first conducted to evaluate the distribution of smartphone usage patterns within each demographic group. Based on the test results, Kruskal-Wallis tests were applied to identify differences between groups, followed by the Dunn post hoc test for pairwise comparisons. Statistical significance was determined using P values, and all indicators were reported as medians with IQRs (first and third quartiles). Correlation and regression analyses were conducted to explore relationships between smartphone usage and nocturnal smartphone inactivity. Results were presented through correlation coefficients, regression coefficients, intercepts, P values, and R² values.

Data categorized as “Other,” “Prefer not to answer,” or “Unknown” were excluded from the analysis to ensure reliability and validity. The study included 1074 participants, classified based on gender, age, employment status, education level, and purpose of smartphone use—variables answered by the vast majority of respondents. Other demographic variables, such as relationship status, living area, and income level, were excluded due to high non-response rates, with over 45% (n=470) of participants opting not to answer. This focus on core demographic factors ensured robust and reliable statistical results. All statistical analyses were encoded and analyzed using RStudio (2023.06.2 Build 561; Posit).

This research encompassed 1074 smartphone users, with most (n=724, 67.4%) of the sample being female. Participants were from 39 countries, with the majority residing in Germany (91.5%, n=983). The mean age of participants was 27.9 (SD 9.5) years.

In total, 59.3% (n=637) of participants held a high school degree or equivalent educational background, while 49.8% (n=535) were students. The data revealed that smartphones were not only primarily used for social media and leisure entertainment but also frequently for managing daily affairs. The average daily duration of smartphone usage was 3.54 hours. A total of 91.9% (n=987) of participants did not use their phones for more than 6 hours during nighttime (Tables 1 and 2).

Initially, we analyzed the periods of nocturnal smartphone inactivity among participants. Participants were categorized into 2 groups based on results: group 1 comprised participants with no nightly smartphone inactivity period of 6 hours or longer, and group 2 comprised participants who recorded at least one nocturnal smartphone inactivity period of 6 hours or longer.

This study demonstrated that smartphone usage patterns were significantly associated with factors such as gender, educational attainment, employment status, and usage purpose. Generally, female participants showed a higher frequency of phone usage compared to male participants, predominantly for online shopping and social interaction apps. Male participants were more likely to engage with their smartphones during nighttime hours. Younger participants exhibited more extensive use of smartphones, primarily for playing games, watching videos, and chatting with friends. Older individuals, meanwhile, showed lower levels of smartphone engagement. Participants with higher levels of education generally spent less time on smartphones and were more likely to use information acquisition apps, such as Google and news platforms. Individuals not employed full-time, including part-time employees or homemakers, often used their smartphones more intensively, especially at night, mainly for activities such as gaming, video streaming, and social interactions. Additionally, homemakers displayed a preference for financial apps, such as those used for stock trading, financial management, and online shopping. Conversely, individuals who primarily used smartphones for work-related purposes had lower usage compared to those who used them for personal purposes. Their smartphone activities were predominantly focused on work-related tasks, including communicating with colleagues, sending emails, and using functionalities like alarm clocks and notepads. Notably, they also exhibited a significant reduction in smartphone usage after work hours.

The correlation and regression analysis revealed a negative correlation of varying strengths between smartphone and app usage and nocturnal smartphone inactivity. Specifically, participants who frequently used social media apps exhibited higher smartphone usage at night. In contrast, this pattern was reversed among participants with a doctoral degree. They predominantly used their smartphones for gathering information and reducing their nighttime usage. As a result, the more time dedicated to acquiring information throughout the day, the longer the duration they tended to refrain from using their smartphones at night.

In this study, nocturnal smartphone inactivity was used as an indirect indicator of sleep behavior. Nocturnal smartphone inactivity exceeding 6 hours was used as a threshold to assess whether participants achieved sufficient sleep duration. The results showed that over 92% (n=988) of participants exhibited smartphone inactivity for more than 6 hours at nighttime, demonstrating a high degree of consistency in this behavior. Additionally, for some participants, smartphone inactivity consistently occurred between 22:00 and 06:00, aligning closely with the National Sleep Foundation’s recommendation of 7-9 hours of sleep per night for adults [34].

Numerous studies indicate that women tend to have a higher reliance on cell phones compared to men, a trend driven by societal pressures and safety concerns [35,36]. Women predominantly used their smartphones for socializing, often dedicating more time to social media and shopping apps, activities that offered them pleasure and relaxation [37]. In contrast, men showed a preference for gaming and watching videos [38]. Research analysis has indicated a clear trend: the total time spent on smartphone usage and the dependence on it tend to decrease with age. Individuals younger than 20 years, especially teenagers, emerge as the most frequent users [39,40]. These young people predominantly engage with their smartphones for communication, socializing, gaming, watching videos, and listening to music [41,42]. In contrast, older adults, facing relatively lower social costs and pressures, show less frequent and shorter durations of smartphone usage, primarily focusing on reading news, using the phone as a clock for reminders, and communicating with peers in a traditional phone manner [41]. Notably, a significant issue among many young people is the continuous nighttime use of smartphones, which leads to sleep disturbances. Furthermore, it has been observed that the earlier the age at which individuals start using smartphones, the greater their dependence on these devices tends to be [43]. Some research indicates that students from families with higher cultural and economic statuses tend to have an increased dependency on smartphones [44]. This trend also correlates with the educational levels of the parents; children of more educated parents generally exhibit a healthier relationship with smartphone usage [45]. Individuals with academic accomplishments often rely on smartphones as a primary tool for accessing valuable information. Specifically in India, research shows that an impressive 90% of medical students consider smartphones to be extremely beneficial for accessing educational content, listening to lectures, and taking notes [46].

In our study, we discovered a negative correlation between smartphone usage and nocturnal smartphone inactivity. Particularly, participants who frequently used social media apps were found to engage with their phones more extensively at night. Social media facilitates self-exploration and emotional expression while serving as a pivotal platform for medical communication, education, and disaster response [47,48]. Given these capabilities, it is clear that social media has emerged as the most favored smartphone app. Excessive engagement with social media has been associated with significant psychological risks, including heightened anxiety, increased vulnerability to depression, and diminished overall well-being [49]. Moreover, engaging with social media during nighttime can significantly impair sleep quality and duration [50,51]. Numerous research findings indicate that late-night smartphone use can lead to sleep disturbances, resulting in diminished sleep quality and daytime fatigue [52]. This adverse effect is possibly due to presleep smartphone usage, which can reduce the latency of rapid eye movement sleep, elevate electroencephalogram spectrum power, delay the onset of melatonin secretion, and potentially cause mood fluctuations in users [19,53].

This study investigates the intricacies of smartphone usage patterns, emphasizing the profound correlations between variables such as gender, age, and education level on smartphone usage. The results underscore a trend: the escalation in smartphone use perpetuates habitual behaviors, particularly nocturnal usage, potentially disrupts users’ sleep patterns. Nonetheless, amid the digital era, prudent smartphone usage holds promise in significantly enhancing life satisfaction across diverse demographics, including older adult populations [54]. Thus, a thorough exploration of mobile phone usage patterns and the identification of their determinants are crucial for fostering a balanced and health-conscious engagement with these technological tools.

This study has several limitations that may affect the interpretation and generalizability of its findings.

First, the study lacks direct physiological data related to sleep patterns and instead relies on nocturnal smartphone inactivity as a proxy for sleep duration. While this proxy provides a reasonable approximation of an individual's sleep window, it may be influenced by several confounding factors, including nonsleep behaviors (eg, reading and chatting with friends), interrupted sleep (eg, waking up and briefly using the smartphone), and sleep behaviors (eg, falling asleep while their phone continues to play music or videos). These potential discrepancies between smartphone inactivity and actual sleep behavior could introduce bias into the analysis.

Second, the study encountered limitations in participant demographic data collection. A substantial proportion of respondents failed to provide complete demographic information during the questionnaire phase, resulting in a reduced response rate and limiting the ability to analyze associations between specific demographic characteristics and smartphone usage behaviors. Furthermore, although participants were categorized by gender, age, education level, and employment status, the uneven sample distribution across categories may have led to statistical imbalances, thereby affecting the robustness and stability of results in certain subgroups. Additionally, as the data sample was primarily drawn from participants in Germany, the generalizability of the findings to other cultural or regional contexts remains limited.

Third, despite the study’s year-long design (365 days), the actual data recording period varied significantly among participants, with some contributing data for only a few days. This limited recording duration reduces the ability to capture long-term smartphone usage patterns and behaviors, which in turn may compromise the representativeness of the findings and hinder the identification of sustained behavioral trends over time.

To enhance the reliability and generalizability of future research on the relationship between smartphone use and sleep, several improvements are recommended. First, incorporating multidimensional data validation is crucial. Objective data from wearable devices, such as smartwatches or fitness trackers, can provide precise measurements of key sleep metrics, including total sleep duration, sleep latency, wake-up times, and sleep quality (eg, proportion of deep sleep). In addition, standardized subjective sleep reports, such as the Pittsburgh Sleep Quality Index and Epworth Sleepiness Scale, can capture participants’ self-reported sleep quality and patterns, adding valuable context to the analysis. These combined data sources will improve the accuracy of the proxy indicator of nocturnal smartphone inactivity while enhancing the scientific rigor of future studies. Second, ensuring sample diversity and representativeness is essential to generalize findings to broader populations. This includes recruiting participants with diverse demographic characteristics such as age, gender, education level, and cultural background. Comprehensive questionnaires should also address potential correlations in smartphone usage, such as socioeconomic status, work environment, and psychological stress, to uncover complex behavioral patterns and mechanisms. Stratified sampling or targeted recruitment strategies can help balance subgroup distributions, minimizing statistical bias caused by uneven sample sizes. Lastly, optimizing data collection processes will reduce omissions and improve consistency. This could involve developing reminder functions within smartphone apps or wearable devices to prompt participants to record sleep data or complete questionnaires regularly. Furthermore, promoting sleep health awareness during the study could enhance participants’ engagement and the quality of the data collected. Together, these methodological enhancements will provide a more comprehensive and accurate understanding of the complex relationship between smartphone use and sleep behavior while ensuring the findings are robust, representative, and actionable.

Based on the results, we can recommend measures to curb excessive smartphone and social media usage. Such interventions include advocating for periodic digital detoxes; implementing smartphone usage reminders and constraints; using apps to oversee and control screen time; minimizing unnecessary notifications from news feeds, game updates, and shopping alerts; and developing educational programs to deepen users’ understanding of smartphones and raise awareness about the potential risks associated with their use [55,56]. Additionally, it is suggested that app developers consider tailoring smartphone apps to meet the unique emotional and physiological needs of older adults, thereby enhancing their digital satisfaction and online well-being [57,58].

This study offers valuable insights into smartphone usage behaviors, enhancing our understanding of the nuanced relationship between smartphone usage and sleep. The findings establish a robust basis for developing targeted health interventions tailored to diverse demographic groups. Moreover, this research enhances our comprehension of how various population segments engage with smartphones and the potential ramifications of these behaviors on their daily lives.