Fatigue Symptom, Game Addiction, Video, Inactivity, Physical, Musculoskeletal Pain Disorder, Sleep, Smartphone Addiction
Conditions
Keywords
Smartphone addiction, Digital game addiction, Physical activity, Sleep quality, Stress, Fatigue, Musculoskeletal pain, University students, Self-reported questionnaire, Screen time
Brief summary
This study aims to examine the relationship between smartphone and digital game addiction and various health indicators, including physical activity level, sleep quality, stress, fatigue, and musculoskeletal pain among young people. University students often spend prolonged time on digital devices, which may affect their mental and physical well-being. By collecting self-reported data through validated questionnaires, the study will explore how these addictive behaviors are associated with lifestyle and health outcomes. The findings may help guide future interventions to promote healthier technology use among youth.
Detailed description
This cross-sectional, observational study investigates the associations between smartphone and digital game addiction and physical activity level, sleep quality, perceived stress, fatigue, and musculoskeletal pain among undergraduate students in the Department of Physiotherapy and Rehabilitation at Istanbul University-Cerrahpaşa. The study targets a minimum of 193 participants, with sample size determined via G\*Power analysis. Data will be collected using online survey forms distributed to students who provide informed consent. The study employs a range of validated assessment tools including the Smartphone Addiction Scale-Short Form (SAS-SF), Digital Game Addiction Scale for University Students (DGAS-U), International Physical Activity Questionnaire-Short Form (IPAQ-SF), Pittsburgh Sleep Quality Index (PSQI), Perceived Stress Scale (PSS), Multidimensional Fatigue Inventory (MFI-20), Cornell Musculoskeletal Discomfort Questionnaire (CMDQ), and the Numerical Rating Scale (NRS) for pain intensity. Sociodemographic data and screen usage patterns will also be gathered. Statistical analysis will be conducted using IBM SPSS Statistics (Version 24). Appropriate statistical tests such as correlation analysis, independent samples t-test or Mann-Whitney U test, ANOVA or Kruskal-Wallis test, and multiple regression analysis will be applied to assess relationships and differences among subgroups. Comparisons will be made based on variables such as gender, academic year, screen time, and addiction severity. This study is expected to reveal important links between digital addiction and decreased physical activity, poor sleep quality, elevated stress levels, fatigue, and increased musculoskeletal pain, particularly in the neck, back, shoulder, elbow, and wrist. It offers a comprehensive evaluation of both psychosocial and physical health outcomes related to digital addiction in physiotherapy students, a population with future responsibilities in musculoskeletal health care. By highlighting the potential occupational health risks associated with students' digital habits, the study underscores the importance of preventive strategies, including ergonomics education, digital health literacy, and self-care practices. It also contributes uniquely to the literature by addressing how prolonged device use relates to musculoskeletal symptoms and by examining how daily step counts and physical activity levels may be influenced by digital addiction. Ultimately, the findings are expected to inform future interventions aimed at promoting healthier digital behaviors and supporting the well-being of students in health-related academic programs.
Interventions
None listed
Sponsors
Istanbul University - Cerrahpasa
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to 30 Years
Healthy volunteers
Yes
Inclusion criteria
* Inclusion Criteria Aged between 18 and 30 years Currently enrolled as a student in a physiotherapy and rehabilitation undergraduate program Willing to voluntarily participate and able to provide signed informed consent Capable of providing self-reported data regarding smartphone or digital game use *
Exclusion criteria
Diagnosis of any neurological, psychiatric, or chronic pain disorder Presence of a serious musculoskeletal condition or a history of major orthopedic surgery Having a medical condition (e.g., sleep disorder) that could affect the study outcomes Previous participation in a treatment program for smartphone or digital game addiction
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Smartphone Addiction Level Measured by SAS-SF | At enrollment | The Smartphone Addiction Scale-Short Form (SAS-SF) is a 10-item self-report instrument scored on a 6-point Likert scale (1 = Strongly disagree, 6 = Strongly agree). The total score ranges from 10 to 60, with higher scores indicating higher levels of smartphone addiction. The Turkish version of the scale has shown high reliability (Cronbach's α = 0.88). All participants will complete the questionnaire online or in print. |
| Digital Game Addiction Level Measured by DGAS-U | At enrollment | The Digital Game Addiction Scale for University Students (DGAS-U) consists of 21 items rated on a 5-point Likert scale (1 = Never, 5 = Always). Total scores range from 21 to 105, with higher scores indicating greater digital game addiction. The scale has demonstrated strong psychometric properties in university populations. In this study, the questionnaire will be completed online or in paper form. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Physical Activity Level Assessed by IPAQ-Short Form | At enrollment | The International Physical Activity Questionnaire - Short Form (IPAQ-SF) will be used to assess the physical activity level over the last 7 days. It includes questions on walking, moderate and vigorous activities, and sitting time. Results are expressed in MET-min/week; higher values indicate greater physical activity. |
| Sleep Quality Assessed by Pittsburgh Sleep Quality Index (PSQI) | At enrollment | Sleep quality over the past month will be measured using the PSQI, which includes 19 items grouped into 7 components (e.g., duration, latency, disturbances). The global score ranges from 0 to 21; scores \>5 indicate poor sleep quality. |
| Perceived Stress Measured by Perceived Stress Scale (PSS) | At enrollment | The PSS-10 will be used to evaluate participants' perceived stress levels over the past month. It contains 10 items rated on a 5-point Likert scale (0-4), with total scores ranging from 0 to 40. Higher scores indicate greater perceived stress. |
| Fatigue Level Measured by Multidimensional Fatigue Inventory (MFI-20) | At enrollment | Fatigue will be assessed with the MFI-20, a 20-item instrument measuring five dimensions: general fatigue, physical fatigue, mental fatigue, reduced motivation, and reduced activity. Each item is scored on a 5-point Likert scale, total score ranging from 20 to 100. Higher scores indicate more fatigue. |
| Pain Intensity Measured by Numeric Rating Scale (NRS) | At enrollment | Pain intensity in specific anatomical regions (e.g., neck, back, shoulder, elbow, wrist) will be rated using the NRS. Participants score their current pain from 0 (no pain) to 10 (worst possible pain). Widely used in clinical and survey research. |
| Musculoskeletal Discomfort Measured by Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) | At enrollment | CMDQ assesses discomfort frequency, severity, and functional interference for over 20 body regions. Participants report symptoms experienced in the past 7 days. Higher scores reflect greater musculoskeletal impact. The tool is validated for use in ergonomic and student populations. |
Countries
Turkey (Türkiye)
Contacts
PRINCIPAL_INVESTIGATORAyşe Zengin Alpözgen, Assoc. Prof.
Istanbul University - Cerrahpasa
Outcome results
None listed