Turkish validation study examines sleep procrastination tied to smartphone use in bed
Smartphone use before sleep has moved from a behavioral nuisance to a measurable sleep-health issue, and the Turkish adaptation of the While-In-Bed-Smartphone-Use-Induced Sleep Procrastination Scale now adds a structured way to quantify it. In a sample of 658 undergraduate students, investigators tested whether the instrument could reliably capture the habit of delaying sleep because of phone use after getting into bed. The focus was psychometric rather than therapeutic, but the clinical relevance is obvious: when bedtime smartphone use becomes routine, sleep onset can shift later, sleep duration can shrink, and next-day functioning can suffer.
The scale sits at the intersection of digital behavior and sleep medicine. It measures a form of procrastination that is not about lack of knowledge or poor intent alone; it reflects an immediate conflict between bedtime physiology and a device engineered to hold attention. Bright light exposure, cognitive arousal, social reward loops, and endless content streams all compete with the body’s sleep drive. In that setting, a validated Turkish instrument gives researchers and clinicians a way to move beyond vague self-report and into standardized measurement.
A 658-student sample supported psychometric testing
The study recruited 658 undergraduate students, a sample size large enough to support a formal psychometric evaluation. That matters because scale validation requires more than face validity or informal acceptance; it depends on whether the items behave coherently when tested across a real population. Both exploratory and confirmatory factor analyses were used, allowing the investigators to examine how the items clustered and whether the observed structure held up under a second analytic lens. Test-retest reliability was also assessed to evaluate whether the scale remained stable over time rather than fluctuating randomly from one administration to the next.
One sentence captures the practical point: this was not a casual translation exercise, but a structured examination of measurement integrity.
Because the cohort came from a single university, the findings cannot automatically be extended to all Turkish young adults, all students, or all age groups. University life carries its own rhythms, stressors, and sleep patterns, and those conditions can shape how often students use smartphones in bed. Online self-report data add another layer of uncertainty, since response bias and selection bias can affect who participates and how honestly they answer. Those limitations do not erase the value of the work, but they do define the boundary of what the scale can claim at this stage.
Why bedtime smartphone behavior matters for sleep physiology
Sleep procrastination linked to smartphone use is not a trivial habit. Once a person is in bed, the expectation is usually transition into sleep, but smartphone engagement can interrupt that transition at exactly the wrong moment. The brain remains cognitively activated, the sleep window narrows, and the behavior can become repetitive enough to look like a stable pattern rather than an isolated lapse. In public health terms, this is important because sleep loss in college populations can influence concentration, mood, academic performance, and overall well-being.
A standardized scale helps researchers ask sharper questions. Instead of asking whether students “use phones too much at night,” investigators can examine associations with insomnia symptoms, daytime sleepiness, stress, bedtime routines, and mental health markers. The measure can also support cross-cultural work, since the Turkish version offers a common language for comparisons with other populations using the same construct. For clinicians and behavioral scientists, that opens the door to more precise screening and intervention planning.
Smartphone prevalence gives the instrument real-world relevance
The introduction places the study in a digital context that is hard to ignore. Around 60.42% of the world’s population uses smartphones, and the rate in Türkiye has reached 95.4%. Those figures make bedtime smartphone behavior more than an isolated student issue; they suggest a near-universal exposure environment in which device use can infiltrate sleep routines across the lifespan. When smartphone ownership is this widespread, a validated scale gains value not only in research but also in prevention work aimed at sleep hygiene and digital behavior counseling.
The relevance is especially strong for young adults, who often keep their phones within arm’s reach and integrate them into communication, entertainment, and study. Bedtime becomes a vulnerable point because the device is both a tool and a distraction. A person may intend to sleep, yet continue scrolling, messaging, or watching content until sleep is delayed. The scale is built to capture exactly that pattern.
Measurement quality, not diagnosis, is the study’s central contribution
This validation work does not diagnose insomnia, screen for a psychiatric disorder, or test an intervention. Its value lies in measurement quality. A good psychometric instrument gives future studies a more dependable foundation, and dependable measurement is often the difference between noise and actionable evidence. By combining exploratory and confirmatory factor analyses with test-retest reliability assessment, the study approached the scale from several angles instead of relying on a single statistical check.
For sleep researchers, that kind of methodological care matters because bedtime technology use often overlaps with other behaviors and symptoms. Without a reliable scale, it becomes difficult to distinguish late-night smartphone engagement from broader insomnia complaints or general digital overuse. With a validated Turkish measure, the field can better isolate the specific phenomenon of sleep procrastination induced by while-in-bed smartphone use.
Limits of generalisability remain tied to the sample and the data source
The study’s own boundaries are clear. Data came from one university, and the online self-report design may have introduced bias at both the participation and response stages. Those constraints are common in scale validation studies, but they still limit how confidently the findings can be generalized beyond the sampled students. The next step will depend on whether the instrument performs similarly in other Turkish populations, including non-student adults, younger adolescents, and clinical groups with more severe sleep complaints.
For now, the scale provides a useful Turkish-language framework for studying a behavior that is already embedded in daily life. As smartphone use continues to dominate pre-sleep routines, the need for precise measurement will only grow. This study supplies that measurement framework, while leaving broader questions about intervention, long-term sleep outcomes, and population-wide applicability for future research.