MachDep-PhysX: a survey dataset on automation dependency and physical fitness decline among university students

Md Fahim Ferdous, Fardia Akter Omi, Md. Mehedi Hasan

This dataset contains responses from a structured questionnaire-based survey designed to investigate the relationship between automation/technology dependency and physical fitness decline among university undergraduate students in Bangladesh. The survey was administered as a bilingual (English/Bengali) Google Form to capture authentic, unbiased responses from a demographic actively transitioning through increasingly automated academic and daily-life environments. The dataset comprises 3000 raw responses across 25 questionnaire items: 1. What is your age group? 2. What is your gender? 3. What is your current level of education? 4. Do you walk or exercise in the morning? 5. Do you do any physical tasks daily? 6. How has your physical activity changed after automation? 7. Are you now more dependent on machines? 8. How many hours do you sit for work daily? 9. Is your work entirely computer-based? 10. Do you use elevators more or stairs? 11. Do you cook manually or using machines? 12. Do you think automation has made people comfort-seeking? 13. Do you use technology beyond your needs (e.g., lazy scrolling)? 14. Have you ever tried doing tasks manually instead of using automation? 15. Has your physical fitness declined in the past 2 years? 16. Are you aware of the drawbacks of automation? 17. Do you agree that people are becoming lazier due to technology? 18. Is it possible to have both technology and physical fitness? 19. Will automation eliminate human labor in the future? 20. Is machine dependency increasing health risks? 21. Does automation reduce human efficiency? 22. Is technology reducing the use of the human brain? 23. Are you satisfied with the nature of your work? 24. Is it possible to spend a day without technology? 25. What kind of society do you want to see in the future – human-dependent or machine-dependent? All responses were collected using a mix of binary (Yes/No), ternary (Yes/Somewhat/No, or similar ordinal scales), and Likert-style categorical answer options, making the dataset suitable for both descriptive statistical analysis and supervised machine learning classification tasks (e.g., predicting physical fitness decline from lifestyle and automation-dependency features). Data were collected from Bangladeshi university undergraduate students via an anonymous, voluntary, self-administered online questionnaire (Google Forms), distributed digitally among students across multiple academic years and departments. Participation was voluntary, and no personally identifiable information was collected. Responses were recorded in both English and Bengali to ensure clarity and comprehension for all participants.