Background:
The rapid advancement of digital technology has profoundly transformed educational practices in medical and allied health sciences. Mobile applications have emerged as widely used learning tools among healthcare students. However, their awareness, usage patterns, and perceived educational value among Medical Laboratory Technology (MLT) students, a distinct allied health population reliant on procedural and laboratory skill acquisition, remain underexplored, particularly in resource-constrained settings such as Pakistan. This study aimed to assess the awareness, utilization, and perceived effectiveness of mobile learning applications among MLT students in Hyderabad, Sindh, Pakistan.
Methods:
A descriptive cross-sectional study was conducted among MLT students (n=384) at educational institutions in Hyderabad, Sindh, Pakistan. Data were collected using a structured, self-administered, pilot-tested questionnaire with documented internal consistency (Cronbach’s alpha: Section B=0.82, Section C=0.80, Section D=0.78). Data was analyzed using descriptive statistics and chi-square tests; p<0.05 was considered statistically significant.
Results:
A total of 384 students participated (54.20% female; 92.19% B.S. MLT). The majority (85.2%) were aware of mobile learning applications. YouTube was the most frequently used platform (71.1%). Perceived improvement in understanding of laboratory procedures was reported by 79.7%, and perceived improvement in practical class performance by 70.8%. The only statistically significant finding was a gender-based difference in barriers to use (χ²=8.13, df=3, p=0.043).
Conclusion:
MLT students in Hyderabad demonstrate high awareness and frequent use of mobile learning applications with predominantly positive perceptions of their educational value. These findings reflect perceived rather than objectively demonstrated benefits. Integration of mobile learning tools into the formal MLT curriculum and rigorous outcome-based research are warranted.
Keywords: Mobile-based education; Student awareness; Mobile applications; Digital learning tools; Pakistan.
1. Introduction
The proliferation of smartphones and high-speed mobile internet has fundamentally reshaped learning environments in health sciences education worldwide. Mobile learning, the use of portable digital devices to access educational content at any time and location, has been adopted broadly across medical, nursing, dental, and pharmacy programs, with mounting evidence of its potential to enhance knowledge retention, procedural comprehension, and learner engagement.
Within this expanding landscape, Medical Laboratory Technology represents a discipline with uniquely demanding educational requirements: students must acquire proficiency in specimen processing, reagent preparation, instrument operation, quality assurance, and result interpretation competencies that are fundamentally technique-dependent and difficult to rehearse outside the physical laboratory. Traditional MLT pedagogy, reliant on textbooks and scheduled laboratory sessions, is frequently constrained by equipment limitations, large class sizes, and restricted contact hours. In low- and middle-income countries such as Pakistan, where laboratory infrastructure in teaching institutions may be inadequate relative to student enrolment, these constraints are particularly pronounced.
Mobile applications, especially those offering step-by-step procedure demonstrations, high-resolution video content, and interactive assessments, represent a potentially high-value supplementary learning modality that extends educational access beyond the physical laboratory and beyond scheduled teaching hours.
2. Methods
2.1 Study Design
A descriptive cross-sectional design was employed to evaluate awareness, utilization, and perceived effectiveness of mobile learning applications among MLT students.
2.2 Study Setting and Period
The study was conducted in Hyderabad, Sindh, Pakistan. Participants were recruited from MLT programs at the University of Sindh, Jamshoro and affiliated teaching institutions in the Hyderabad district. Data collection was conducted between January and March 2024.
2.3 Participants
The required sample size was determined using Cochran’s formula, yielding n = 384. All 384 distributed questionnaires were returned in full (response rate: 100%). Non-probability convenience sampling was employed.
2.4 Data Collection Instrument
A structured, self-administered questionnaire comprised four sections covering sociodemographic characteristics, awareness, utilization patterns, and perceived effectiveness. Internal consistency was documented with Cronbach’s α values of 0.82, 0.80, and 0.78 for Sections B, C, and D respectively.
2.5 Data Analysis
Data was entered and verified in Microsoft Excel 2019. Descriptive statistics were computed for all variables. Exploratory chi-square tests examined gender-based differences. A significance threshold of p < 0.05 was applied.
2.6 Ethical Considerations
The study was conducted in accordance with the Declaration of Helsinki. Formal institutional ethical approval was not obtained prior to data collection. Participation was voluntary, written informed consent was obtained, and no personally identifiable information was collected.
3. Results
3.1 Participant Characteristics
A total of 384 MLT students participated. The sample comprised predominantly female students (54.2%, n = 208) enrolled in the B.S. MLT program (92.2%, n = 354). The largest age group was 20–22 years (48.7%, n = 187), and fourth-year students constituted the largest year-of-study category (33.9%, n = 130).
Table 1: Sociodemographic characteristics of participating MLT students (n = 384).
| Characteristic | Category | n | % |
| Age group | 17–19 years | 27 | 7.0 |
| Age group | 20–22 years | 187 | 48.7 |
| Age group | 23–25 years | 142 | 37.0 |
| Age group | >25 years | 28 | 7.3 |
| Subtotal | 384 | 100.0 | |
| Gender | Male | 176 | 45.8 |
| Gender | Female | 208 | 54.2 |
| Subtotal | 384 | 100.0 | |
| MLT programme | Diploma MLT | 30 | 7.8 |
| MLT programme | B.S. MLT | 354 | 92.2 |
| Subtotal | 384 | 100.0 | |
| Year of study | 1st year | 19 | 4.9 |
| Year of study | 2nd year | 60 | 15.6 |
| Year of study | 3rd year | 85 | 22.1 |
| Year of study | 4th year | 130 | 33.9 |
| Year of study | Graduated | 90 | 23.4 |
| Subtotal | 384 | 100.0 |
B.S. = Bachelor of Science; MLT = Medical Laboratory Technology.
3.2 Awareness of Mobile Learning Applications
Most students (85.2%, n = 327) reported awareness of mobile learning applications. Self-rated knowledge was generally positive, with 86.0% rating their knowledge as good or very good. Internet/YouTube was the primary source of awareness (40.6%), and YouTube was the most-used platform (71.1%).
Table 2: Awareness, self-rated knowledge, sources of awareness, and platform utilization (n = 384).
| Variable | Category | n | % | p-value |
| Awareness of mobile apps | Yes | 327 | 85.2 | 0.36 ns |
| Awareness of mobile apps | No | 57 | 14.8 | 0.36 ns |
| Self-rated knowledge | Very good | 125 | 32.6 | — |
| Self-rated knowledge | Good | 205 | 53.4 | — |
| Self-rated knowledge | Poor | 38 | 9.9 | — |
| Self-rated knowledge | No knowledge | 16 | 4.2 | — |
| Primary source of awareness | Internet / YouTube | 156 | 40.6 | — |
| Primary source of awareness | Social media | 120 | 31.3 | — |
| Primary source of awareness | Friends / Classmates | 56 | 14.6 | — |
| Primary source of awareness | Teachers | 52 | 13.5 | — |
| Most-used platform | YouTube | 273 | 71.1 | — |
| Most-used platform | Google / Medical websites | 57 | 14.8 | — |
| Most-used platform | Other platforms | 32 | 8.3 | — |
| Most-used platform | Lab Tests Guide app | 22 | 5.7 | — |
P-values are gender-stratified exploratory chi-square results. — = not subjected to inferential testing; ns = not statistically significant.
Figure 2: Awarenessand platform preferences among MLT students.(A) Primary sources of awareness about mobile learning applications (n = 384). (B) Most frequently used mobile learning platform. MLT = Medical Laboratory Technology.
3.3 Utilization Patterns
The combined proportion of students who used mobile applications regularly or sometimes was 82.3%. Weekly use was the most common pattern (33.9%). The majority (73.4%) used applications across all laboratory subjects. Understanding practical procedures was the leading stated purpose of use (36.2%).
Table 3: Utilization patterns of mobile learning applications among MLT students (n = 384).
| Variable | Category | n | % |
| Frequency of use | Regularly | 136 | 35.4 |
| Frequency of use | Sometimes | 180 | 46.9 |
| Frequency of use | Rarely | 57 | 14.8 |
| Frequency of use | Never | 11 | 2.9 |
| Periodicity | Weekly | 130 | 33.9 |
| Periodicity | Daily | 105 | 27.3 |
| Periodicity | During examinations only | 87 | 22.7 |
| Periodicity | Monthly | 62 | 16.1 |
| Subject area | All laboratory subjects | 282 | 73.4 |
| Subject area | Biochemistry | 37 | 9.6 |
| Subject area | Microbiology | 34 | 8.9 |
| Subject area | Hematology | 31 | 8.1 |
| Primary purpose of use | Understanding practical procedures | 139 | 36.2 |
| Primary purpose of use | Theory learning | 99 | 25.8 |
| Primary purpose of use | Examination preparation | 96 | 25.0 |
| Primary purpose of use | Revision of topics | 50 | 13.0 |
Figure 3: Utilizationpatterns of mobile learning applications among MLT students.(A) Frequency of use (n = 384). (B) Primary purpose of mobile application use. MLT = Medical Laboratory Technology.
3.4 Perceived Effectiveness and Preferred Application Features
A considerable majority of students reported perceived improvement in understanding laboratory procedures (79.7%) and practical class performance (70.8%). Video content (51.3%) and step-by-step procedure demonstrations (38.3%) were the preferred application features. Interest in future advanced applications was high (92.2%), and 95.3% reported willingness to recommend mobile applications to peers. These are self-reported perceptions rather than objectively measured learning outcomes.
Table 4: Perceived effectiveness, preferred features, recommendation behavior, and interest in advanced applications (n = 384).
| Variable | Category | n | % | p-value |
| Perceived improvement in procedural understanding | Yes, considerably | 306 | 79.7 | 0.14 ns |
| Perceived improvement in procedural understanding | To some extent | 46 | 12.0 | 0.14 ns |
| Perceived improvement in procedural understanding | No perceived improvement | 32 | 8.3 | 0.14 ns |
| Perceived improvement in practical performance | Yes | 272 | 70.8 | 0.69 ns |
| Perceived improvement in practical performance | Not sure | 63 | 16.4 | 0.69 ns |
| Perceived improvement in practical performance | No | 49 | 12.8 | 0.69 ns |
| Most useful application feature | Videos | 197 | 51.3 | — |
| Most useful application feature | Step-by-step procedures | 147 | 38.3 | — |
| Most useful application feature | Images / Diagrams | 34 | 8.9 | — |
| Most useful application feature | MCQs / Quizzes | 6 | 1.6 | — |
| Would recommend to peers | Yes | 366 | 95.3 | 0.90 ns |
| Would recommend to peers | No | 18 | 4.7 | 0.90 ns |
| Interest in advanced applications | Yes | 354 | 92.2 | 0.79 ns |
| Interest in advanced applications | Not sure | 17 | 4.4 | 0.79 ns |
| Interest in advanced applications | No | 13 | 3.4 | 0.79 ns |
Figure 4: Perceived effectivenessand preferred application features among MLT students.(A) Stacked percentage bar chart of self-reported perceived learning outcomes. (B) Most useful mobile application features. All values are self-reported perceptions
3.5 Barriers to Mobile Application Use
Internet connectivity was the most frequently reported barrier (48.7%), followed by lack of appropriate content (26.0%), no barriers reported (18.2%), and difficulty with English-language content (7.0%).
Table 5: Barriers to mobile application use (n = 384).
| Barrier | n | % |
| Internet connectivity issues | 187 | 48.7 |
| Lack of appropriate content | 100 | 26.0 |
| No barriers reported | 70 | 18.2 |
| Difficulty with English content | 27 | 7.0 |
| Total | 384 | 100.0 |
The overall barrier-response distribution was statistically significant by gender (χ² = 8.13, df = 3, p = 0.043). Gender-disaggregated frequencies for individual barrier categories were not separately recorded.
3.6 Summary of Inferential Analyses
Seven exploratory comparisons were performed. Two reached statistical significance: program enrolment (χ² = 16.83, df = 1, p < 0.001) and barrier distribution (χ² = 8.13, df = 3, p = 0.043).
Table 6: Summary of exploratory chi-square analyses: gender as the independent variable.
| Outcome variable | χ² | df | p-value | Significant? |
| Program enrolment (B.S. vs. Diploma) | 16.83 | 1 | < 0.001 | Yes |
| Awareness of mobile applications | 0.57 | 1 | 0.36 | No (ns) |
| Recommendation of apps to peers | 0.01 | 1 | 0.90 | No (ns) |
| Perceived improvement in procedural understanding | 3.95 | 2 | 0.14 | No (ns) |
| Perceived improvement in practical performance | 0.71 | 2 | 0.69 | No (ns) |
| Interest in future advanced applications | 0.46 | 2 | 0.79 | No (ns) |
| Barriers to app use | 8.13 | 3 | 0.043 | Yes |
p < 0.05. All analysis is exploratory. χ² = chi-square statistic; df = degrees of freedom; ns = not statistically significant.
4. Discussion
The study demonstrates high awareness, sustained and purposeful engagement, and broadly positive perceived benefits of mobile learning among MLT students. Internet connectivity emerged as the predominant structural barrier. The authors emphasize that perceived improvement does not demonstrate objectively improved laboratory competency because the cross-sectional design precludes causal inference.
YouTube dominated platform use (71.1%), while understanding practical laboratory procedures was the leading purpose of use (36.2%). Video content and step-by-step demonstrations accounted for 89.6% of stated feature preferences. The study highlights the potential value of offline-accessible, locally cached, MLT-specific applications.
Limitations include the single-city setting, lack of formal institutional ethical approval, self-reported data, cross-sectional design, convenience sampling, and unavailable gender-stratified raw frequencies for individual barrier categories.
5. Conclusion
MLT students in Hyderabad, Pakistan, exhibit high awareness and regular engagement with mobile learning applications and broadly perceive them as beneficial adjuncts to clinical laboratory education. Video-based content and procedure demonstrations are the most valued modalities. Internet connectivity is the principal structural barrier. Formal curricular integration of validated mobile learning tools, offline-capable and MLT-specific applications, equitable digital infrastructure, and longitudinal outcome-based research are recommended.
6. Declarations
Ethical approval: Formal institutional ethical review board approval was not obtained prior to data collection, which the authors acknowledge as a limitation.
Consent: All participants provided voluntary written informed consent and no personally identifiable data were collected.
Sources of funding: This research received no external funding.
Conflicts of interest: The authors declare no conflicts of interest.
Data availability: The anonymized dataset and questionnaire instrument are available from the corresponding author upon reasonable request.
AI Statement: The authors declare that generative AI (ChatGPT plus) was used solely for Figure preparation and language polishing. All scientific content, interpretation, and final approval of the manuscript were performed by the authors.
References
- Khan H, Waqas S, Golani S, Kadir MM, Ashraf M. Perceived usefulness of Smartphone Medical apps as theoretical and clinical learning aids among medical students in Pakistan: a cross-sectional study. Cureus. 2023 Jul 11;15(7).
- Hisam A, Shafique MU, Khurshid MN, Hamza A, Asad MB, Shakeel T. Usage and types of mobile medical applications amongst medical students of Pakistan and its association with their academic performance. Pakistan Journal of Medical Sciences. 2019 Mar;35(2):432.
- Alsharif MA, Bahbah AA, Ghonaim MM, Omran JA, Rihan MM, Zayed MA, Tabour MS, Alwardany MH, Aboshady OA. Usage and attitude of medical students towards mobile medical applications during and after COVID-19 lockdown: repeated cross-sectional study. BMC Medical Education. 2024 Mar 4;24(1):233.
- Chandran VP, Balakrishnan A, Rashid M, Pai Kulyadi G, Khan S, Devi ES, Thunga G. Mobile applications in medical education: A systematic review and meta-analysis. PLoS One. 2022 Mar 24;17(3):e0265927.
- Medina Aguerrebere P, Medina E, Gonzalez Pacanowski T. Promoting health education through Mobile apps: a quantitative analysis of American hospitals. Healthcare. 2022 Nov 8;10(11):2231.
- Song Z, Wen X, Li Z, Han X, Lai L, Yang J, Yu F, Zhao Z, Li P. Behaviors and attitudes towards mobile learning during course work and scientific research among dental, medical and non-medical undergraduates: a cross-sectional study. BMC Medical Education. 2024 Dec 18;24(1):1455.
- Ehteshami A, Hachesu PR, Esfahani MK, Rezazadeh E. Awareness and using of medical students about mobile health technology in clinical areas. Acta Informatica Medica. 2013 Jun;21(2):109.
- Jabour AM, Rehman W, Idrees S, Thanganadar H, Hira K, Alarifi MA. The adoption of mobile health applications among university students in health colleges. Journal of Multidisciplinary Healthcare. 2021 May 31:1267-73.
- Owrangi S, Zarifsanaiey N, Rezaei M. Mobile apps to enhance student learning in medical education: a systematic search in app stores and evaluation using the mobile app rating scale. Computers in Biology and Medicine. 2025 Sep 1;196:110740.
- Mergany NN, Dafalla AE, Awooda E. Effect of mobile learning on academic achievement and attitude of Sudanese dental students: a preliminary study. BMC Medical Education. 2021 Feb 22;21(1):121.
- Sayedalamin Z, Alshuaibi A, Almutairi O, Baghaffar M, Jameel T, Baig M. Utilization of smart phones related medical applications among medical students at King Abdulaziz University, Jeddah: a cross-sectional study. Journal of Infection and Public Health. 2016 Nov 1;9(6):691-7.
- Latif MZ, Hussain I, Saeed R, Qureshi MA, Maqsood U. Use of smart phones and social media in medical education: trends, advantages, challenges and barriers. Acta Informatica Medica. 2019 Jun;27(2):133.
- Haque MA, Baroi L, Koly IA, Hasan MS, Mahmud F, Eva SA, Labib MK, Nafisa HT, Islam S, Eva II, Islam MR. Effects of Android phone vs. iPhone use on BlackBerry thumb symptoms among university students in Bangladesh. Bulletin of Faculty of Physical Therapy. 2024 Jun 21;29(1):39.
- World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013 Nov 27;310(20):2191-4.
- van der Storm SL, Jansen M, Meijer HA, Barsom EZ, Schijven MP. Apps in healthcare and medical research; European legislation and practical tips every healthcare provider should know. International Journal of Medical Informatics. 2023 Sep 1;177:105141.
- Doherty I, Sharma N, Harbutt D. Contemporary and future eLearning trends in medical education. Medical Teacher. 2015 Jan 2;37(1):1-3.
- Papadakis S, Kalogiannakis M. Mobile educational applications for children: what educators and parents need to know. International Journal of Mobile Learning and Organisation. 2017;11(3):256-77.
- Kirkwood EK, Clymer C, Imbulana K, Mozumder S, Dibley MJ, Alam NA. The role of mHealth interventions in changing gender relations: systematic review of qualitative findings. JMIR Human Factors. 2022 Jul 21;9(3):e32330.
