1. Demir K, Akpınar E. The effect of mobile learning applications on students' academic achievement and attitudes toward mobile learning. MOJET. 2018;6:48–59. doi:10.17220/mojet.2018.02.004.
2. Osailan A. The relationship between smartphone usage duration (using smartphone's ability to monitor screen time) with hand-grip and pinch-grip strength among young people: an observational study. BMC Musculoskelet Disord. 2021;22:186. doi:10.1186/s12891-021-04054-6.
3. Osorio-Molina C, Martos-Cabrera MB, Membrive-Jiménez MJ, Vargas-Roman K, Suleiman-Martos N, Ortega-Campos E, Gómez-Urquiza JL. Smartphone addiction, risk factors and its adverse effects in nursing students: A systematic review and meta-analysis. Nurse Educ Today. 2021;98:104741. doi:10.1016/j.nedt.2020.104741.
4. Konok V, Liszkai-Peres K, Bunford N, Ferdinandy B, Jurányi Z, Ujfalussy DJ, et al. Mobile use induces local attentional precedence and is associated with limited socio-cognitive skills in preschoolers. Computers in Human Behavior. 2021;120:106758. doi:10.1016/j.chb.2021.106758.
5. Joo E, Kononova A, Kanthawala S, Peng W, Cotten S. Smartphone Users' Persuasion Knowledge in the Context of Consumer mHealth Apps: Qualitative Study. JMIR Mhealth Uhealth. 2021;9:e16518. doi:10.2196/16518.
6. Mergany NN, Dafalla A-E, Awooda E. Effect of mobile learning on academic achievement and attitude of Sudanese dental students: a preliminary study. BMC Med Educ. 2021;21:121. doi:10.1186/s12909-021-02509-x.
7. Wilkerson GB, Acocello SN, Davis MB, Ramos JM, Rucker AJ, Hogg JA. Wellness Survey Responses and Smartphone App Response Efficiency: Associations With Remote History of Sport-Related Concussion. Percept Mot Skills. 2021;128:714–30. doi:10.1177/0031512520983680.
8. Kwon SE, Kim YT, Suh H, Lee H. Identifying the mobile application repertoire based on weighted formal concept analysis. Expert Systems with Applications. 2021;173:114678. doi:10.1016/j.eswa.2021.114678.
9. Villota Enríquez JA, editor. Tecnología, sociedad y educación: desafíos de las Tic en el desarrollo social y sus implicaciones en la práctica educativa: Editorial Universidad Santiago de Cali; 2019.
10. Shaygan M, Jaberi A. The effect of a smartphone-based pain management application on pain intensity and quality of life in adolescents with chronic pain. Sci Rep. 2021;11:6588. doi:10.1038/s41598-021-86156-8.
11. Sohn SY, Krasnoff L, Rees P, Kalk NJ, Carter B. The Association Between Smartphone Addiction and Sleep: A UK Cross-Sectional Study of Young Adults. Front Psychiatry. 2021;12:629407. doi:10.3389/fpsyt.2021.629407.
12. Hitti E, Hadid D, Melki J, Kaddoura R, Alameddine M. Mobile device use among emergency department healthcare professionals: prevalence, utilization and attitudes. Sci Rep. 2021;11:1917. doi:10.1038/s41598-021-81278-5.
13. Thornton L, Gardner LA, Osman B, Green O, Champion KE, Bryant Z, et al. A Multiple Health Behavior Change, Self-Monitoring Mobile App for Adolescents: Development and Usability Study of the Health4Life App (Preprint); 2020.
14. Abadiyan F, Hadadnezhad M, Khosrokiani Z, Letafatkar A, Akhshik H. Adding a smartphone app to global postural re-education to improve neck pain, posture, quality of life, and endurance in people with nonspecific neck pain: a randomized controlled trial. Trials. 2021;22:274. doi:10.1186/s13063-021-05214-8.
15. Maharjan SM, Poudyal A, van Heerden A, Byanjankar P, Thapa A, Islam C, et al. Passive sensing on mobile devices to improve mental health services with adolescent and young mothers in low-resource settings: the role of families in feasibility and acceptability. BMC Med Inform Decis Mak. 2021;21:117. doi:10.1186/s12911-021-01473-2.
16. Freitas BHBM de, Gaíva MAM, Bernardino FBS, Diogo PMJ. Smartphone Addiction in Adolescents, part 2: Scoping Review—Prevalence and Associated Factors. Trends in Psychol. 2021;29:12–30. doi:10.1007/s43076-020-00040-4.
17. Warsaw RE, Jones A, Rose AK, Newton-Fenner A, Alshukri S, Gage SH. Mobile Technology Use and Its Association With Executive Functioning in Healthy Young Adults: A Systematic Review. Front Psychol. 2021;12:643542. doi:10.3389/fpsyg.2021.643542.
18. Choi S, Kim M, Kim E, Shin G. Changes in Low Back Muscle Activity and Spine Kinematics in Response to Smartphone Use During Walking. Spine (Phila Pa 1976). 2021;46:E426-E432. doi:10.1097/brs.0000000000003808.
19. Yue H, Zhang X, Sun J, Liu M, Li C, Bao H. The relationships between negative emotions and latent classes of smartphone addiction. PLoS One. 2021;16:e0248555. doi:10.1371/journal.pone.0248555.
20. Annoni AM, Petrocchi S, Camerini A-L, Marciano L. The Relationship between Social Anxiety, Smartphone Use, Dispositional Trust, and Problematic Smartphone Use: A Moderated Mediation Model. Int J Environ Res Public Health 2021. doi:10.3390/ijerph18052452.
21. Conlin M-C, Sillence E. Exploring British Adolescents’ Views and Experiences of Problematic Smartphone Use and Smartphone Etiquette. JGI 2021. doi:10.4309/jgi.2021.46.14.
22. Chen P-L, Pai C-W. Pedestrian smartphone overuse and inattentional blindness: an observational study in Taipei, Taiwan. BMC Public Health. 2018;18:1342. doi:10.1186/s12889-018-6163-5.
23. Gamero K, Flores C, Arias WL, Ceballos KD, Román A, Marquina E. Estandarización del Test de Dependencia al Celular para estudiantes universitarios de Arequipa. Persona. 2017;0:179. doi:10.26439/persona2016.n019.979.
24. Azizi A, Pleimling M. A cautionary tale for machine learning generated configurations in presence of a conserved quantity. Sci Rep. 2021;11:6395. doi:10.1038/s41598-021-85683-8.
25. Hohman F, Srinivasan A, Drucker SM. TeleGam: Combining Visualization and Verbalization for Interpretable Machine Learning; 2019.
26. Lu Y, Lu G, Li J, Zhang Z, Xu Y. Fully shared convolutional neural networks. Neural Comput & Applic. 2021;33:8635–48. doi:10.1007/s00521-020-05618-8.
27. Sarker IH, Kayes ASM, Watters P. Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. J Big Data 2019. doi:10.1186/s40537-019-0219-y.
28. Chóliz M. Mobile-phone addiction in adolescence: The Test of Mobile Phone Dependence (TMD). Prog Health Sci 2012, Vol 2, No1 Test Mobile Phone Addiction. 2012;2.
29. Verónica Villanueva Silvestre. PROGRAMA DE PREVENCIÓN DEL ABUSO Y LA DEPENDENCIA DEL TELÉFONO MÓVIL EN POBLACIÓN ADOLESCENTE [Tesis Doctoral]. España: UNIVERSITAT DE VALÈNCIA-Departamento de Psicología Básica; 2012.
30. Dimate AE, Rodríguez DC, Rocha AI. Percepción de desórdenes musculoesqueléticos y aplicación del método RULA en diferentes sectores productivos: una revisión sistemática de la literatura. revsal. 2017;49:57–74. doi:10.18273/revsal.v49n1-2017006.
31. Tolles J, Meurer WJ. Logistic Regression: Relating Patient Characteristics to Outcomes. JAMA. 2016;316:533–4. doi:10.1001/jama.2016.7653.
32. Vapnik V. Statistical learning theory. New York: John Wiley and Sons; 1998.
33. Anuja Priyama and Saurabh Srivastava. Comparative Analysis of Decision Tree Classification Algorithms: International Journal of Current Engineering and Technology; 2013.
34. Breiman L. Using Iterated Bagging to Debias Regressions. Machine Learning. 2001;45:261–77. doi:10.1023/A:1017934522171.
35. Duda RO, Hart PE, Stork DG. Pattern classification. 2nd ed. New York: Wiley; 2001.
36. Bishop CM. Pattern recognition and machine learning. New York: Springer; 2006.
37. Kumar R, Indrayan A. Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatr. 2011;48:277–87. doi:10.1007/s13312-011-0055-4.
38. Fletcher RH, Fletcher SW, Fletcher GS. Clinical epidemiology: The essentials. 5th ed. Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins Health; 2019.
39. RESOLUCIÓN Nº 008430 DE 1993.
40. Laurence PG, Busin Y, Da Cunha Lima HS, Macedo EC. Predictors of problematic smartphone use among university students. Psicol Reflex Crit. 2020;33:8. doi:10.1186/s41155-020-00147-8.
41. Ivanova A, Gorbaniuk O, Błachnio A, Przepiórka A, Mraka N, Polishchuk V, Gorbaniuk J. Mobile Phone Addiction, Phubbing, and Depression Among Men and Women: A Moderated Mediation Analysis. Psychiatr Q. 2020;91:655–68. doi:10.1007/s11126-020-09723-8.
42. Zhao SZ, Luk TT, Guo N, Wang MP, Lai AYK, Wong BYM, et al. Association of Mobile Instant Messaging Chat Group Participation With Family Functioning and Well-Being: Population-Based Cross-sectional Study. J Med Internet Res. 2021;23:e18876. doi:10.2196/18876.
43. Xie YJ, Cheung DS, Loke AY, Nogueira BL, Liu KM, Leung AY, et al. Relationships Between the Usage of Televisions, Computers, and Mobile Phones and the Quality of Sleep in a Chinese Population: Community-Based Cross-Sectional Study. J Med Internet Res. 2020;22:e18095. doi:10.2196/18095.
44. Jeong Y-W, Han Y-R, Kim S-K, Jeong H-S. The frequency of impairments in everyday activities due to the overuse of the internet, gaming, or smartphone, and its relationship to health-related quality of life in Korea. BMC Public Health. 2020;20:954. doi:10.1186/s12889-020-08922-z.
45. Romero-Rodríguez J-M, Aznar-Díaz I, Marín-Marín J-A, Soler-Costa R, Rodríguez-Jiménez C. Impact of Problematic Smartphone Use and Instagram Use Intensity on Self-Esteem with University Students from Physical Education. Int J Environ Res Public Health 2020. doi:10.3390/ijerph17124336.
46. Balogun FM, Olatunde OE. Prevalence and predictors of problematic smart phone use among pre-varsity young people in Ibadan, Nigeria. Pan Afr Med J. 2020;36:285. doi:10.11604/pamj.2020.36.285.18858.
47. Vaterlaus JM, Aylward A, Tarabochia D, Martin JD. “A smartphone made my life easier”: An exploratory study on age of adolescent smartphone acquisition and well-being. Computers in Human Behavior. 2021;114:106563. doi:10.1016/j.chb.2020.106563.
48. Forster M, Rogers C, Sussman SY, Yu S, Rahman T, Zeledon H, Benjamin SM. Adverse childhood experiences and problematic smartphone use among college students: Findings from a pilot study. Addict Behav. 2021;117:106869. doi:10.1016/j.addbeh.2021.106869.
49. Tangmunkongvorakul A, Musumari PM, Tsubohara Y, Ayood P, Srithanaviboonchai K, Techasrivichien T, et al. Factors associated with smartphone addiction: A comparative study between Japanese and Thai high school students. PLoS One. 2020;15:e0238459. doi:10.1371/journal.pone.0238459.
50. Merma-Molina G, Gavilán-Martín D, Álvarez-Herrero J-F. Education for Sustainable Development: The Impact of the Values in Mobile Phone Addiction. Sustainability. 2021;13:1479. doi:10.3390/su13031479.
51. Cha S-S, Seo B-K. Smartphone use and smartphone addiction in middle school students in Korea: Prevalence, social networking service, and game use. Health Psychol Open. 2018;5:2055102918755046. doi:10.1177/2055102918755046.
52. Mustafaoglu R, Yasaci Z, Zirek E, Griffiths MD, Ozdincler AR. The relationship between smartphone addiction and musculoskeletal pain prevalence among young population: a cross-sectional study. Korean J Pain. 2021;34:72–81. doi:10.3344/kjp.2021.34.1.72.
53. Al-Hadidi F, Bsisu I, AlRyalat SA, Al-Zu'bi B, Bsisu R, Hamdan M, et al. Association between mobile phone use and neck pain in university students: A cross-sectional study using numeric rating scale for evaluation of neck pain. PLoS One. 2019;14:e0217231. doi:10.1371/journal.pone.0217231.
54. Thapa K, Lama S, Pokharel R, Sigdel R, Rimal SP. Mobile Phone Dependence among Undergraduate Students of a Medical College of Eastern Nepal: A Descriptive Cross-sectional Study. JNMA J Nepal Med Assoc. 2020;58:234–9. doi:10.31729/jnma.4787.
55. Baabdullah A, Bokhary D, Kabli Y, Saggaf O, Daiwali M, Hamdi A. The association between smartphone addiction and thumb/wrist pain: A cross-sectional study. Medicine (Baltimore). 2020;99:e19124. doi:10.1097/MD.0000000000019124.
56. Ismaeel FT. The Impact of Smart Phones on Musculoskeletal Pain on Students in Tikrit University. Ind. Jour. of Publ. Health Rese. & Develop. 2019;10:553. doi:10.5958/0976-5506.2019.01629.2.
57. Toh SH, Coenen P, Howie EK, Smith AJ, Mukherjee S, Mackey DA, Straker LM. A prospective longitudinal study of mobile touch screen device use and musculoskeletal symptoms and visual health in adolescents. Appl Ergon. 2020;85:103028. doi:10.1016/j.apergo.2019.103028.