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 Table of Contents  
Year : 2021  |  Volume : 65  |  Issue : 1  |  Page : 51-56  

Efficacy of an online course in developing competency for prescribing balanced diet by medical students: A non - inferiority trial

1 Additional Professor, Department of Community Medicine, Government Medical College, Kollam, Kerala, India
2 Scientist E, Centre for Development of Advanced Computing, Thiruvananthapuram, Kerala, India
3 Assistant Professor, Department of Community Medicine, Government Medical College, Kollam, Kerala, India
4 Professor and Head, Department of Community Medicine, Government Medical College, Kollam, Kerala, India

Date of Submission09-Oct-2020
Date of Decision23-Dec-2020
Date of Acceptance24-Feb-2021
Date of Web Publication20-Mar-2021

Correspondence Address:
Zinia T Nujum
Department of Community Medicine, Government Medical College, Kollam, Kerala
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijph.IJPH_1248_20

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Background: In the COVID era, medical education has been hit hard. Paradoxically, the need for health professionals has increased. Online methods are being widely used, but its efficacy is rarely measured. Objectives: This study was conducted to find the efficacy of an online course in developing competency among medical students to prescribe balanced diet. Methods: An online module was hosted at A noninferiority trial was conducted among voluntary participants of the third MBBS students, in 2019. Stratified block randomization was done, so that ten students were allocated to the intervention arm of online sessions and ten students were allocated to the control arm of classroom sessions. Pretest assessments, seven assessments related to sessions conducted, and a postassessment were done. Generalized estimating equations were done to adjust for the effects of other confounders and see whether the intervention was a significant determinant of ability to prescribe balanced diet. Results: Baseline variables were comparable in the two groups. The pretest scores were not significantly different in the two groups. The mean total marks scored by the online group (47.33/70) was not significantly different (t=0.68; p=0.50) from that of the class room group (45.70/70). The posttest scores were significantly higher than the pretest scores. Ninety-percent of students in the online course agreed that they could effectively learn through an online course. Conclusion: Online teaching is effective to learn the prescription of balanced diet. Similar efforts in other domains can make medical education evidence based in the current scenario.

Keywords: Diet, distance education, education, medical students, random allocation

How to cite this article:
Nujum ZT, Devanand P, Remya G, Anuja U. Efficacy of an online course in developing competency for prescribing balanced diet by medical students: A non - inferiority trial. Indian J Public Health 2021;65:51-6

How to cite this URL:
Nujum ZT, Devanand P, Remya G, Anuja U. Efficacy of an online course in developing competency for prescribing balanced diet by medical students: A non - inferiority trial. Indian J Public Health [serial online] 2021 [cited 2021 Sep 20];65:51-6. Available from:

   Introduction Top

COVID-19 has changed human life dramatically. Education has been badly affected. Medical education is no exception, although the need for medical graduates is on the rise. Physical distancing is one of the most prioritized preventive strategies which makes conventional teaching challenging. Many universities have resorted to online teaching, but the efficacy of the methods used in achieving the desired specific objectives is rarely measured. The study is an attempt in this direction.

The ability to plan and recommend a suitable diet based on the local availability of foods and economic status in a simulated environment is an essential competency required by a Medical Graduate.[1] Prescribing diet and physical activity in a similar fashion to prescribing drugs have become a proposed strategy by national programs for the prevention and control of noncommunicable diseases. In medical schools, it is now taught through a series of classroom sessions.

Massive Open Online Course (MOOC) is an innovative educational method which could provide an opportunity in this scenario. The use of MOOC has risen as a medium of delivering quality educational material hosted on an Internet site accessible to the learners through their devices.[2],[3],[4] Due to their convenience and appeal, some of these courses have attracted thousands of students.[5] MOOC has been successfully used for teaching subjects such as statistics.[6] Some courses are fully anytime courses, whereas others are restricted to being offered at particular times.[7]

It is envisaged that a MOOC will enable medical students to learn to prescribe balanced diet. Toward this goal, an online course was developed. This project was done with the objective of finding the efficacy of an Online Course in developing competency to prescribe balanced diet by medical students.

   Materials and Methods Top

Design, setting, and study population

A randomized controlled trial – Noninferiority trial was conducted in a medical college in the government sector in Kerala. Third semester students of MBBS course were the study population.

Sample size and sampling technique

Minimum sample size of 20, 10 in each group was required for a standardized noninferiority limit of 1.5 and a percentage mean difference of 25%.[8] Sample size formula = (21σ2)/([m1 − m2]−dNI) 2, where σ is standard deviation, (μA−μB) is the difference in means which is 0.25; dN1 is the inferiority limit. The program was planned as a one-day workshop and announced in class. Participation was voluntary, so no sampling technique was involved for the selection of participants.


Online course is based on the Balanced Diet Prescription Module. The details of the module and guide to use it are provided in Supplementary Material 1. It included six sessions with lecture videos (seven) and power points (three). A diet survey was also included. The setting up of the online course was done with the help of an expert from Center for the Development of Advanced Computing, India.

To setup and host the e-learning course, the cloud services provided by been used. New account was created in the moodle cloud for the free hosting plan which provides support for 50 student users to access the course. Once an account was created, e-Learning site was ready with an address ( Each enrolled student received an email with the course site link and details to access the course. Students logged into the E-learning site and learned the course contents by following the presentations and lecture videos. To clarify the doubts, students posted the questions to the discussion forum and the instructor responded with answers.


Same module was administered as class room sessions. The six sessions were delivered in the classroom by the same teacher using the same power points. A diet survey was also taken by the students who were assigned to the control group.

Implementation and data collection

The data collection was done as a one-day workshop under the name of “u87-Nutrishop.” A detailed description of the course was announced in class. An information sheet regarding the study was circulated in the WhatsApp group of the class. A list of participants willing to attend the workshop was taken. The twenty participants who came for the workshop were given an orientation once more. Stratified block randomization was done after taking written informed consent.


Stratified block randomization was done. Male and female were the strata identified. A sequence of eight blocks with block size four was determined before the day of the workshop. As the participants were recruited, they were given codes by a lottery method, each time opening up only one block till recruitment in the block was completed. We assigned three complete male block codes and two more codes from the fourth block. For the females, we assigned six codes from complete block one and two randomly picked codes from block two. The block identifier, block size, sequence within block, and group name (Group A – classroom and Group B – online) were obtained online.[9]

The participants assigned to the classroom session were administered the module in the “demonstration room” of the community medicine department. The group assigned to online session was taken to the library. The participants thereafter did not see each other except at the time of the common presentation for an assessment. A pretest was administered followed by the sessions. Each student in the online course had an individual laptop, earphone, and Internet facility. Two faculties from other departments who were unfamiliar with subject content but have technical expertise in computers were available as monitors for the online course. The monitors were previously trained on how to carry forth the sessions. During the session hours, access to any other sites was not allowed. After each session, the student did the assessments.

Measures to minimize and take care of contamination in design

The time of the intervention and control was the same. Access to the online course content was opened only at 8:00 a.m. on the day of the workshop. After all assessments and completion of workshop, all students could access the site.

Study variables

Primary outcome measure

Ability to prescribe balanced diet as measured by the scores of assessment exercises 1 to 7 [Supplementary Materials 2 and 3]. Students' ability to adapt the prescription according to affordability, availability, and likes and dislikes were assessed using standardized patients. A posttest was also done using the same questions administered during pretest (assessment 8). The outcome assessments were done by two faculty of community medicine, from another Government Medical College, who were not involved at any stage of the design and conduct of the project, for the purpose of blinding outcome measurement. All answer sheets were coded using the same codes assigned during randomization.

Baseline variables such as age and potential confounders likely to affect outcome: rank in entrance, percentage scored in MBBS, and hours of learning [Supplementary Material 4] were also collected. Student perceptions regarding the sessions were captured on a five-point Likert scale.


Data entry was done in Excel. Analysis was performed using the Statistical Package for the Social Sciences (SPSS) software trial version 24 (IBM Corp, Armonk, NewYork, United States). The scores were checked for the distribution of normality by the Kolmogorov–Smirnov test. The qualitative variables were compared using the Chi-square test. The quantitative variables including the scores/marks of assessment were compared by either t-test or Mann–Whitney U-test for normally and not normally distributed variables, respectively. Pretest and posttest scores were compared by the paired t-test. Generalized estimating equations (GEEs) were done to determine whether the intervention was an independent determinant of marks/scores after adjusting for the potential confounders.[10]

Compliance with ethical standards

The proposal was submitted to the research and ethics committee (IEC No. 2/EC-2/2019/GMCKLM dated 7/03/2019) of the institutions and clearance was obtained before starting the study. It was registered with the Clinical Trial Registry of India (CTRI reg. no. CTRI/2019/04/018809). Informed written consent was obtained from the students. Crossover of the intervention and control arm was planned if the results were negative. The workshop was planned on holiday so that the routine classes were not affected.

   Results Top

Baseline comparison of the two groups

Age was comparable in the two groups with a mean age of 20.10 (standard deviation [SD]-1.10) in the online group and 19.90 (SD-0.74) in the group given classroom sessions. The duration of use of computer and mobile was not significantly different in the two groups. The learning pattern in terms of use of online resources and e-learning duration was also comparable [Table 1].
Table 1: Comparison of baseline variables

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Efficacy of online compared to classroom sessions

There were marks for seven assessments other than the pretest and posttest. The outcome variables were normally distributed.

The pretest scores were not significantly different in the two groups. It shows that the two groups were comparable with regard to their knowledge on nutrition before the workshop. The mean total marks scored by the online group (47.33) were not significantly different from that of the class room group (45.70). The posttest marks were also comparable. Both groups had a mean score of 8.9 out of 10 [Table 2]. The marks scored in almost all sessions and assessments were comparable across the two groups, except the answering questions after presentation of diet survey [Marks 5, [Table 2]] and exercise on writing prescription [Marks 6 in [Table 2]]. The mark scored by the students of the online group was significantly lower than the classroom group for these two sessions. This could be pointing toward the fact that online sessions were not as effective as classroom for stimulating higher cognitive levels involving the analysis and synthesis. However, online sessions are effective in imparting knowledge. The posttest scores were significantly higher than the pretest scores [Table 3].
Table 2: Comparison of marks scored by the two groups

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Table 3: Comparison of pre- and posttest scores

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Multivariable analysis using generalized estimating equations

The scores were not significantly associated with the educational intervention type. The marks scored in first MBBS (P = −0.005), hours of use of mobile, (P = 0.001) and the pretest score (P = 0.001) significantly determined the marks scored [Table 4].
Table 4: Multivariable analysis using generalized estimating equations

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Perceptions about the online and classroom teaching

The perceptions of the students of teachers, learning environment, self-perception of academic skills, and computer skills were assessed on a five-point Likert scale ranging from very poor to very good. There was no significant difference in the perception scores of the two groups in any domain. Ninety percent of students in online courses agreed that they could effectively learn through an online course and 70% of students in classroom also thought so. These differences were, however, not significant (Chi-square value − 1.67; P = −0.64).

   Discussion Top

The use of e-learning has increased among health professionals worldwide. Studies show large positive effects when compared to no intervention and small positive effects when compared with traditional learning. However, the results are conflicting and not conclusive. A review on e-learning versus traditional learning in 5679 licensed health professionals on patient outcomes or health professionals' behaviors, skills and knowledge, showed little or no difference for patient outcomes.[11] This study has sought to look at the efficacy of an e-learning module for a difficult area like diet prescription.

The context of COVID-19 has increased the relevance of this study. In the country, regulatory bodies have issued advisories regarding the suspension of classes in medical schools.[12] Medical students have expressed their concerns and related anxieties in the public domain.[13] The conduct of classroom sessions for theory, practical demonstrations, and clinical teaching has been compromised.[14],[15] Disciplines such as anesthesia, which play a vital role in this pandemic, have recorded difficulties in training.[16]

To keep the medical education moving, institutions have heavily depended on technology-based innovations. Web castings, video vignettes, audio recordings, online chats, flash multimedia, digitalized images, simulators, etc., have become the norm for medical education.[17],[18] The barriers to e-learning include resource constraints, lack of technical skills, and negative attitudes.[19],[20],[21] In low-income countries such as India, barriers to the use of these new methods are higher. Besides the social distancing demands hampers the relationship between a teacher and student which is also important in enhancing learning. In this study, we have made the use of a relatively simple method of videos of usual classes for teaching. Interaction between teacher and student has been facilitated through the interaction platform of Moodle.

Softwares for e-learning can be Open Source Software Linux, apache, Mozilla, and open office.[22] Most of the learning management systems incorporate a Learning Content Management System (LCMS) that enables the storage, use, and reuse of the content. Choice of LCMS is based on the open-source nature of software, platform independence, strength of user base, availability of communication tools such as discussion forum, file sharing, E-mail, chat/whiteboard, productivity tools such as Bookmarks, Link database, Visual editor, Generate reports, Localization, Help and student involvement tools, Documentation, and Multilingual support. An analysis done based on the above criteria suggested that MOODLE is the best choice.[23]

Noninferiority trials assess whether a new intervention is not much worse when compared to a standard treatment or care.[24] The GEE approach facilitates the analysis of data collected in longitudinal, nested, or repeated measures designs.[25]

The online package developed is as effective as traditional classroom teaching in developing overall competency to prescribe a balanced diet. However, in certain domains involving higher level of cognition such as analysis and synthesis, it is inferior to class room teaching. This could be probably because the online package was delivered in a controlled environment which enabled less of interaction because of the stipulated timeframe. The development of e-learning packages with the aim of igniting higher levels of cognition may probably help reduce this discrepancy.

The use of a sound methodology to provide evidence for this teaching methodology is one of the strengths of this work. The limitations are the open nature, the restriction of time, and number of participants. However, this was essential to prove the efficacy of the intervention before moving on to effectiveness.

   Conclusion Top

Online learning is as effective as traditional classroom teaching in developing competency to prescribe a balanced diet. More studies are required to prove the consistency of our findings. Research needs to be done on effectiveness in the practice of prescription by long-term follow-up studies which can predict the validity of such tools. It may be carried forward to the development of a MOOC program. Such studies can be done in other specific areas of learning to make medical education more evidence based. These are inevitable for medical education in the COVID era.


I would like to express my heartfelt gratitude to our Principal, Dr. Sara Varghese for supporting the implementation of the project in our institution. I place on record my gratitude to all the faculty at the MCI Nodal Centre for faculty development for inspiring us to do the project and providing the necessary support and guidance, especially Dr. Sajith Kumar. This project was done as part of the requirements of the Advanced Course in Medical Education (ACME) of the Medical Council of India. We thank Dr Alexander G., CDAC for arranging the necessary support for hosting the online course. My colleagues from the department of Biochemistry (Dr. Roshni H. Babu), Tranfusion Medicine (Dr. Chitra James) helped me as mentors for the online session. I extend my gratitude to them. Dr. Devika J., from the department of Physiology, Dr. Vipin K. Ravi and Dr. Betsy from the department of Community Medicine, Government Medical College Thiruvananthapuram came as assessors. I thank them for the support extended. I would like to thank Mr. Anandu and Mrs. Kavitha, project staff of RNTCP unit for helping in the development of videos and other clerical work.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

   References Top

Supe A, Seshadri KG, Singh P, Kumar RS, Chalam PV, Maulik SK, et al. Competency Based Undergraduate Curriculum For The Indian Medical Graduate. New Delhi: Medical Council of India; 2018. p. 98.  Back to cited text no. 1
Hoy MB. MOOCs 101: An introduction to massive open online courses. Med Ref Serv Q 2014;33:85-91.  Back to cited text no. 2
Gyles C. Is there a MOOC in your future? Can Vet J 2013;54:721-4.  Back to cited text no. 3
Billings DM. Understanding massively open online courses. J Contin Educ Nurs 2014;45:58-9.  Back to cited text no. 4
Breslow L, Pritchard DE, DeBoer J, Stump GS, Ho AD, Seaton DT. Studying learning in the worldwide classroom: Research into edX′s first MOOC. Res Pract Assess 2013;8:13-25.  Back to cited text no. 5
Sarkar S, Bharadwaj B. Adapting massive open online courses for medical education. Int J Adv Med Health Res 2015;2:68-7.  Back to cited text no. 6
Johnston TC. What makes a MOOC? Massive Open Online Courses (MOOCs) Compared to Mainstream Online University Courses. J Learn Higher Educ 2014;10:7-22.  Back to cited text no. 7
Flight L, Julious SA. Practical guide to sample size calculations: Non-inferiority and equivalence trials. Pharm Stat 2016;15:80-9.  Back to cited text no. 8
Sealedenvelope.Com. Create A Blocked Randomisation List | Sealed Envelope; 2020. Available from: [Last accessed on 2020 Oct 05].  Back to cited text no. 9
Hanley JA, Negassa A, Edwardes MD, Forrester JE. Statistical analysis of correlated data using generalized estimating equations: An orientation. Am J Epidemiol 2003;157:364-75.  Back to cited text no. 10
Vaona A, Banzi R, Kwag KH, Rigon G, Cereda D, Pecoraro V, et al. E-learning for health professionals. Cochrane Database Syst Rev 2018; 1:CDO11736.  Back to cited text no. 11
Medical Council of India. Advisory Regarding UG Classes in View of COVID-19 Epidemic. Medical Council of India; 2020. Available from: [Last accessed on 2020 Jul 04].  Back to cited text no. 12
Chatterjee S. The COVID-19 pandemic through the lens of a medical student in India. Int J Med Stud 2020;8:82-3.  Back to cited text no. 13
Rose S. Medical student education in the time of COVID-19. JAMA 2020;323:2131-2.  Back to cited text no. 14
Del Rio C, Malani PN. Novel coronavirus – Important information for clinicians. JAMA 2020;323:1039-40.  Back to cited text no. 15
Anwar A, Seger C, Tollefson A, Diachun CA, Tanaka P, Umar S. Medical education in the COVID-19 Era: Impact on anesthesiology trainees. Clin Anesth 2020;66:109949.  Back to cited text no. 16
Abrahamson SD, Canzian S, Brunet F. Using simulation for training and to change protocol during the outbreak of severe acute respiratory syndrome. Crit Care 2006;10:R3.  Back to cited text no. 17
Gillett B, Peckler B, Sinert R, Onkst C, Nabors S, Issley S, et al. Simulation in a disaster drill: Comparison of high-fidelity simulators versus trained actors. Acad Emerg Med 2008;15:1144-51.  Back to cited text no. 18
O'Doherty D, Dromey M, Lougheed J, Hannigan A, Last J, McGrath D. Barriers and solutions to online learning in medical education – An integrative review. BMC Med Educ 2018;18:130.  Back to cited text no. 19
Dhir SK, Verma D, Batta M, Mishra D. E-learning inmedical education in India. Indian Pediatr 2017;54:871-7.  Back to cited text no. 20
Sahi PK, Mishra D, Singh T. Medical education amid the COVID-19 pandemic. Indian Pediatr 2020;57:652-7.  Back to cited text no. 21
E Learning and Learning Management Software Tools. Available from: [Last accessed on 2019 Mar 29].  Back to cited text no. 22
Effectiveness of Moodle on E-Learning Platform in Medical Education – A Review. Available from: [Last accessed on 2019 Mar 29].  Back to cited text no. 23
Rehal S, Morris TP, Fielding K, Carpenter JR, Phillips PP. Non-inferiority trials: Are they inferior? A systematic review of reporting in major medical journals. BMJ Open 2016;6:e012594.  Back to cited text no. 24
Ballinger GA. Using Generalized Estimating Equations for Longitudinal Data Analysis. Available from: [Last accessed on 2019 Mar 29].  Back to cited text no. 25


  [Table 1], [Table 2], [Table 3], [Table 4]


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