Development and evaluation of a 3D mobile application for learning ...

Computers & Education 69 (2013) 96–108

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Development and evaluation of a 3D mobile application for learning manual therapy in the physiotherapy laboratory José M. Noguera a, *, Juan J. Jiménez a, M. Catalina Osuna-Pérez b a b

Department of Computer Sciences, High Polytechnic School of Jaén, University of Jaén, Spain Department of Health Sciences, Faculty of Health Sciences, University of Jaén, Spain

a r t i c l e i n f o

a b s t r a c t

Article history: Received 7 May 2013 Received in revised form 4 July 2013 Accepted 8 July 2013

Teaching manual therapy is one of the most relevant issues in a physiotherapy course. However, knowledge of anatomy is considered fundamental for an effective instruction of this topic. Students should be able to refresh this knowledge while practicing manipulative techniques in the teaching laboratory in an efficient way. In this paper, we present a novel anatomy learning application for mobile devices and describe the successful embedding of such tool into a practical manual therapy course in a laboratory. This application provides students with interactive 3D visualizations of medical imaging on easy-to-carry hand-held devices. We also report two evaluations of this tool. The first one was conducted to evaluate the students’ and professors’ grade of satisfaction when using our tool. The second one was carried out to determine whether the tool has better learning outcomes than standard methods of teaching anatomy. Students and professors rated the proposed m-learning tool very positively. We also found significant relevance in learning outcomes through the use of this tool. We can conclude that the developed application could be a useful tool for studying manual therapy, and that it could be integrated into the existing physiotherapy curricula. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Applications in subject areas M-learning 3D volume rendering Health care professional education Physical therapy

1. Introduction The ability to identify musculoskeletal architectures by palpation is a basic skill of the physiotherapist professional. This manual skill is extensively used for identification and location of symptomatic diseases, evaluation of patient progress and selection of appropriate treatment techniques (Billis, Foster, & Wright, 2003). Classical physiotherapist curricula teach anatomy through 2D cross-sections images of the human body, as seen in Fig. 1a. Although these techniques can be sufficient when the student has adequate time to internalize and transform this information into 3D understanding, they fall short when the student is exposed to living patients in the teaching laboratory. Under these time-constrained circumstances, the student has to remember the anatomy lessons previously received, integrate this 2D content into a 3D mental model, and relate this model with the body of the corresponding patient. Inaccurate identification of key anatomical landmarks is a crucial source of error when assessing and treating pathologies. Therefore, innovative computer-assisted learning tools capable of providing students with interactive 3D visualizations as shown in Fig. 1b are especially valuable to enhance learning of the complex spatial relationships found in the musculoskeletal system. From an educational point of view, interactive 3D medical imaging offers several potential advantages compared to traditional methods of teaching anatomy (Keedy et al., 2011). These advantages include (1) a directly recognizable visualization of anatomical structures; (2) reduction of the cognitive load as students do not need to construct their own mental visualization of the model; (3) infinite anatomical perspectives and the ability to interactively move the model for additional depth cues; and (4) the ability to include 3D models acquired from living human imaging datasets, eliminating the potential inaccuracies of stylized 2D drawings.

* Corresponding author. University of Jaén, Campus Las Lagunillas s/n, Dependencia 102, Edificio A-3, 23071 Jaén, Spain. Tel.: þ34 953 21 28 53; fax: þ34 953 212 472. E-mail addresses: [email protected], [email protected] (J.M. Noguera). 0360-1315/$ – see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.compedu.2013.07.007

J.M. Noguera et al. / Computers & Education 69 (2013) 96–108

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Fig. 1. a) Some 2D cross-sections images acquired with CT scan. (b) Volume visualization of the complete set of images on a tablet device using our proposed educational tool.

Given these advantages, interactive 3D visualizations have received considerable interest among educators (Brown, Hamilton, & Denison, 2012). Unfortunately most reported learning tools require the use of a desktop computer, which precludes them from being useful in a physiotherapy teaching laboratory. In our University, these facilities are mainly equipped with treatment stretchers and other clinical equipment. However, due to space limitations the number of computing stations is very reduced. Given that students have to constantly consult anatomical information in the course of their manual activities, computer-based learning tools prove to be too inefficient as they force students to move away from the patient in order to operate the computer. This article reports our experience developing a mobile learning tool for mobile devices (such as smartphones and tablets) that overcomes these issues. Our application permits students to visualize patients’ images (e.g., computerized tomography [CT] or magnetic resonance imaging [MRI] scanning) as vivid and fully interactive 3D objects on a handheld device. Fig. 1b depicts an actual screenshot of our proposal running on an Apple iPad. Mobile devices are easy to use when performing surface palpation to actual living patients in the physiotherapy teaching laboratory, and are also easy to carry when moving from one patient to another. Our proposal is intended for use as an assisting tool so the lessons can focus on physical therapy techniques in a more efficient way because students no longer need to move away from the patient to consult anatomy. The article ends with an evaluation of the education value of this 3D m-learning tool among second-year physiotherapy students at the Faculty of Health Sciences of the University of Jaén. First, a preliminary study was carried out to determine the satisfaction value of students and professors when using our tool in an actual learning environment. Then, we conducted a second study that used a crossover posttest study to compare whether our tool had better learning outcomes than the traditional methods of teaching physical therapy. 2. Background In the context of medical 3D visualization, medical images are usually composed of a set of images, each one representing a slice of the patient’s body. The set of slices can be acquired by a number of methods, e.g., CT or MRI scanning. These slices are sampled in a regular pattern, typically following a parallel and equidistant distribution across the body, as shown in Fig. 1a. Unfortunately, the resulting set of slices is difficult to understand for the untrained student, as it requires an evident cognitive effort to visualize the actual 3D structures of the body from a set of 2D images. In this study we use volume rendering techniques in order to visualize these slices as true 3D objects. Volume rendering is a vast topic with innumerable applications in a number of fields such as for scientists, engineers, physicists, and medical imaging professionals. For a comprehensive study of this topic we refer the reader to (Hadwiger, Kniss, Rezk-salama, Weiskopf, & Engel, 2006). In what follows we revise the literature concerning the general use of the emerging technologies (namely, interactive 3D graphics and m-learning) in anatomy education. Then we focus on the use of such technologies in manual therapy education, which is the main topic of interest of this study. 2.1. Emerging technologies in anatomy education Three-dimensional graphics are not new in computer-assisted medical learning tools. Over the years, several studies have shown that 3D medical visualizations improve the understanding of anatomy and provide a distinct educational benefit: (Beermann et al., 2010; Butson, Tamm, Jain, Fogal, & Kruger, 2013; Estevez, Lindgren, & Bergethon, 2010; Keedy et al., 2011; Nguyen & Wilson, 2009; Petersson, Sinkvist, Wang, & Smedby, 2009).

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Mobile learning has also fostered a considerable interest among practitioners and educators. For example, Wu et al. (2012) reviewed mlearning studies published between 2003 and 2010 and found that 86% of them presented positive outcomes for learning. Regarding health education, several academic studies also report that these devices are an adequate anatomy learning resource (Lindquist, Johansson, Petersson, Saveman, & Nilsson, 2008; Ranson, Boothby, Mazmanian, & Alvanzo, 2007; Trelease, 2008). In fact, in 2011 23.75% of the top80 most relevant applications in the Medical category of iOS’s AppStore were educational tools (Liu, Zhu, Holroyd, & Seng, 2011). As a result, mobile devices are starting to make their way to the medical schools. For example, the School of Medicine of the University of California (USA) is pioneering implementing a fully digital curriculum based on web-enabled tablets (Apple iPad), see (Youm et al. 2011a, 2011b). However, and despite the interest of educators in using interactive 3D graphics and mobile devices for educational purposes, solutions that combine both technologies are very scarce in the literature. This stems from the hardware and software limitations associated with mobile computing platforms. As a result, most existing mobile learning proposals are limited to displaying only text and 2D data, such as images and videos, 3D solutions still being limited to the desktop scenario. Looking at the available m-learning tools for anatomy (Mosa, Yoo, & Sheets, 2012), we observe that the most common approach consists of imitating the classical “atlas of anatomy” concept, featuring synthetic diagrams and/or 2D annotated medical images. User interaction is typically limited to browsing the images provided and, at best, zooming and panning them. Among this kind of educational tools we can cite (Ec-Europe, 2010; ERAC, 2013; Netter, 2012), all of them focusing on displaying annotated diagrams. Similarly, applications such as (Epocrates, 2011) and (IMAIOS, 2013) complement the synthetic diagrams with real 2D medical imaging obtained through magnetic resonances. More complex tools like (Pocket Anatomy, 2012) also provide a limited 3D experience over synthetic pre-rendered 3D anatomic models, consisting of discrete rotations around one axis. Fortunately, with the advent of low energy programmable Graphics Processing Units (GPUs) (Noguera & Torres, 2012) true medical 3D image visualization is being incorporated into m-learning tools in a minor degree. Nowinski et al. (2009) proposed a 3D digital brain atlas, but it was not evaluated with actual students. In (Butson et al., 2013) an application for rendering volumes on mobile devices (ImageVis3D Mobile) is used to visualize and simulate models of Parkinson’s disease patients who receive therapy. 2.2. Emerging technologies in manual therapy education Regarding manual therapy teaching, most physical therapy professors typically try to present realistic patient scenarios to their students. But given the usual resource limitations, professors are continuously forced to seek new effective and efficient teaching methodologies to enhance their classes (Veneri, 2011). Not surprisingly, the complexity of teaching a hands-on profession in an academic setting has already been recognized in the literature (Perry, 1999). And unfortunately, computer assisted learning for physical therapy is still a largely under-researched area in comparison to other health professions (Smith, Jones, Cavanaugh, Venn, & Wilson, 2006; Veneri, 2011). Notwithstanding, a small number of studies have recently proposed learning applications for physical therapy education (Bowley & Holey, 2009). For example, (Kanai & Shimada, 2006) compared computer-based and traditional methods of teaching observational gait analysis. Their study showed that test scores increased significantly whereas test duration decreased significantly with the computer method. Similarly, (Ford, Mazzone, & Taylor, 2005) compared computer-based and traditional methods of teaching musculoskeletal special tests. They proved that their proposed computer-based method was more effective than standard textbooks, particularly at facilitating psychomotor skill acquisition and retention. Following this line of research, a study by (Smith et al., 2006) supports the assumption that the use of multimedia instructional CDs of clinical orthopedic techniques is an effective strategy to instruct clinical skills related to the knee and ankle. Their results were backed by an experiment with 45 physical therapy students. (van Zoest, Staes, & Stappaerts, 2007) proposed a method based on 3D force components to measure passive manual contact forces exerted during a manual procedure in a learning environment. They concluded that the 3D force feedback helped students to improve their palpation and force delivery skills. Finally, (Cantarero-Villanueva et al., 2012) compared two groups. The e-learning group had free access to a website about musculoskeletal palpation and ultrasound assessment, whereas the control group only had access to classical documents and books about the topic. They concluded that clinical evaluation scores were significantly higher in the e-learning group for skills related to the palpation ability. 3. Materials In this section we introduce our proposed m-learning tool. First, software design and implementation details are outlined. Then we describe the innovative features of our proposed application and describe its user interface. 3.1. Technical aspects Volume rendering on desktop computers and workstations is a relatively mature field, and several commercial and open source solutions are available for the general public (Hadwiger et al., 2006). However, given the spatial and practical limitations of the teaching laboratories we are only interested in solutions that can be efficiently used on small hand-held devices. Unfortunately, existing solutions for these devices are still very sparse and limited in terms of visual quality. This stems from the fact that mobile devices must rely on batteries, and therefore their architecture favors energy efficiency rather than pure graphics performance. As a result, existing techniques and algorithms designed for desktop computers do not perform well on these constrained devices (Campoalegre, Brunet, & Navazo, 2012; Noguera, Jiménez, Ogáyar, & Segura, 2012). Given these limitations, we implemented our own volume renderer specifically designed for mobile devices. We adopted a volume ray casting algorithm originally proposed by (Levoy, 1988). This method defines the color of each screen pixel by tracing a ray into the volume and solving the ray integral along it. This computation was entirely performed by the device’s GPU as explained in (Hadwiger, Ljung, Salama, & Ropinski, 2008). In order to store the slices of the volume in GPU memory, we applied the mosaic texture approach described in (Congote


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et al., 2011). Note that most of today’s high end mobile devices include low-power programmable GPUs. As a result, our solution can be deployed on a wide range of existing devices while ensuring good image quality and a decent performance (Balsa & Vázquez, 2012; Noguera & Jiménez, 2012). As software tools to implement our software, we selected the Cþþ programming language and the OpenGL ES 2.0 graphics library. These software tools are an industry-standard, which guarantees us maximum portability between different mobile platforms. Nevertheless, and given the popularity and availability of Apple’s iOS devices among our students, we targeted our first prototype to this platform, which includes devices such as the iPhone, iPod Touch and iPad. As the specifications of these devices reflect the design of most modern mobile devices, our findings in this paper should be applicable to a wide range of mobile platforms. 3.2. Application overview Our mobile learning application presents a clear interface that emphasizes the visualization of the anatomical object of study. This way most of the screen area is devoted to displaying the medical imaging. The bottom part of the screen features a toolbar that provides a quick access to all the features of the application. Fig. 2 depicts an actual screenshot of the application. Students can interact with the 3D model by means of a natural multi-touch interface that implements the typical interaction techniques that smartphone/tablet users already know well (Telkenaroglu & Capin, 2012). In this way students can rotate, pan and zoom the volumetric models according to a virtual trackball metaphor (Henriksen, Sporring, & Hornbaek, 2004) by simply touching the screen. Fig. 2 illustrates the multi-touch gestures that our software implements. Our proposed application provides a number of key features such as 2D/3D interactive visualizations, sliding planes to configure the visualization and a visual transfer function editor. We describe all these features below. 3.2.1. 2D/3D visualization Two different ways of visualizing the volumetric models are available: 2D and 3D view, as illustrated by Fig. 3: The 2D view provides an interactive tool to browse through axial, sagittal and coronal views of the 3D model. In this mode the application displays three orthogonal planes on the screen, each one depicting the intersection of the plane with the volume data, see

Fig. 2. User interface of the mobile application and multi-touch gestures.

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Fig. 3. 2D and 3D views of two different joints obtained from CT scan. The 2D view allows the student to move the three orthogonal planes (axial, sagittal and coronal) at will using the three sliders.

Fig. 3a. The user can freely slide each plane along its corresponding axis by simply moving the slider control with a finger. A color code (red, green or blue) is used to easily associate each plane with a slider. When a plane is moving, the image visualized on the plane is continuously updated in real time to show the volume data that overlaps with the current position of the plane. The 3D view presents an explicit, high-quality 3D representation of the anatomical model, as shown in Fig. 3b. The students can use this mode to study the 3D object from any angle in real-time, according to their needs.

Fig. 4. In the 3D view, the three orthogonal planes are used to remove unwanted parts of the model. In the example, the internal side of the cranium becomes visible after removing the top part.


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Fig. 5. The curve of the transfer function editor controls the color and opacity in the volume rendering of the 3D model. In (b), everything but the skeleton has been made transparent. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

3.2.2. Clipping planes Note that images obtained from a CT/MRI scan typically include some unwanted parts that can obstruct the vision of the model when rendered in 3D. Also, although 3D views provide an effective way to study the reliefs of the models, the visibility of the inner parts is limited. In order to overcome these issues our mobile application includes three configurable orthogonal planes that can be used to remove unwanted parts of the 3D model. By default these clipping planes are located outside of the model, so they do not affect the visualization, see Fig. 4a. In Fig. 4b however, the axial plane has been moved down in order to remove the top part of the cranium, thus revealing its inner side. 3.2.3. Transfer function editor The 3D view mode of our application includes an additional feature, known as the transfer function editor. Transfer functions (TFs) are fundamental in order to make the volume visible. Their role is to assign optical properties like color and opacity to the image data based on the grey-tone of the original images (Hadwiger et al., 2006). We have implemented a simple editor that allows students to intuitively explore the volumetric data, as shown in Fig. 5. This editor is composed of a curve that the user can modify with his finger in real time to identify interesting parts of the mode. The curve is used to map a color and a transparency value (horizontal axis) to each grey tone of the original image (vertical axis). Fig. 5 shows the same model rendered with two different TFs. We clearly see that the TF can be designed to just visualize the skin, the bones, or even both at the same time. We should remark that defining a good transfer function is a time consuming, trial-and-error process. In order to save time in class, our tool allows the professor to create a set of functions beforehand. For a given model, students are provided with a set of predefined transfer functions that highlights those parts that are interesting for the lesson. These functions are automatically loaded each time the student opens the model. 4. Methods This section describes the educational methodology we developed to introduce our 3D mobile application into physical therapy education. The study was conducted at the Faculty of Health Sciences of the University of Jaén (Spain) during the academic years 2011/12 and 2012/13. In Spain the physical therapy curriculum consists of four years of undergraduate education, including approximately 2400 h of basic education composed of biologic (anatomy, pathology.) and clinical sciences. During the first year of the degree students receive several ECTS credits of anatomy instruction that provides them with an in-depth knowledge of the human body. In the second year students begin to learn hands-on physical therapy techniques. At this level students are supposed to remember in detail the anatomy concepts they received in the previous year. However in practice these concepts need to be refreshed as they are basic for achieving the required palpation skills. The proposed mobile application was introduced into the existing curricula of the second-year undergraduate physiotherapy programs. The target users are students learning hands-on physical therapy in the teaching laboratory. Its goal was to serve as an assisting tool for refreshing the anatomy lessons that students received one year ago.

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Our teaching methodology can be summarized as follows. Each practical lesson in the laboratory lasted about five hours, and involved an anatomy review of the body area to be palpated during the session. The review was followed by a description of different manipulative techniques and a practical demonstration performed by the professor. After the professor’s explanations, students had to practice the respective manipulation technique in pairs (one of them simulating a patient). In order to do so, we provided each pair of students with a 4th generation iPod Touch device. Note that our application is still a prototype, and thus we cannot upload it to the AppStore, so it was pre-installed and pre-configured in each iPod. At the beginning of the first lesson, students received a brief introductory seminar about our project and some basic concepts of 3D medical imaging. Following, they received some instructions about the operation of the software and were given some time to familiarize themselves with the tool with the help of the professor (5–10 min). During the class, the mobile devices were placed on the stretchers next to the “simulated patients” and were configured to show the 3D model of the anatomy being manipulated by the student, as depicted in Fig. 6. The 3D images helped the professor to teach the precise hands location and the palpation direction required for each manual therapy technique. In addition, these interactive images also allowed students to refresh their knowledge of anatomy while they were practicing manipulation on the real subject. They could freely adjust the rotation, zoom and pan of the 3D models according to their needs. 4.1. Preliminary study A pilot study was carried out during the academic year 2011/12 to determine the acceptation of the proposed m-learning tool as well as user satisfaction when using it in an actual learning environment. All second-year physiotherapy students of our University took part in this study. Of the 70 students evaluated, 51 (72.9%) were female and 19 (27.1%) were male. Their ages varied from 20 to 28 years old, 21.86 1.94 being the average age. The students presented a good technological background. Most of them owned a smarphone or tablet (88.6%). They also claimed to regularly access the Internet from their mobile device (81.4%) and to play videogames (72.9%). All participants consented to take part in this study. In addition, the 14 physiotherapy professors of our University also participated in the evaluation, 8 females (57.1%) and 6 (42.9%) males. The professors’ ages varied from 27 to 60 years old, 39.14 7.46 being the average age. When asked about their technological background, most of them also owned a smartphone or tablet (85.7%) and regularly use it to access the Internet (64.3%). However, only half of them affirmed to be experienced with videogames (50.0%). In the experiment, the mobile device was used in five practical sessions at the teaching laboratory as explained above. During each of these practical sessions manual therapy was taught in relation to five different anatomical areas: knee, ankle, pelvis, elbow and skull. Most of the 3D models employed are shown in Figs. 2–5. In order to gage the students’ satisfaction about the proposal mobile application they were asked to fill in an anonymous questionnaire after the final lesson. The research questions to be answered in this study were whether students value our mobile application as a learning tool; whether mobile devices are appropriate for consulting anatomy during practical lessons in the teaching laboratory; and whether 3Dvisualizations help students to identify musculoskeletal architectures of the patient that they are palpating and applying manipulative techniques to. The questionnaire contained 22 questions including 20 limited response choice questions and 2 space limited free-text questions. These questions cover the management of the device (questions Q1–Q4), the user’s opinion of the device (questions Q5–Q9), the application utilities (questions Q10–Q17) and the tool assessment (questions Q18–Q20). The questionnaire ended with two open questions comparing 2D and 3D view modes (questions Q21–Q22). Questions Q1–Q17 were answered using a five-point Likert scale where 1 means “strongly disagree” and 5 means “strongly agree”. Questions Q18–Q20, on the other hand, were evaluated from 0 to 10, where 0 means “very negative” and 10 means “very positive” and for questions Q21–Q22, respondents were given free space to write their answers. We also presented the mobile application to all the professors of physiotherapy of the University of Jaén. They received a seminar in which the application was fully described. This seminar lasted about two hours. The first one was used to present the application in detail, including topics such as: aims of the project, device usage, visualization of images and an explanation about the role of the mobile tool in the manual therapy classes. During the second hour, the professors were provided with an iPod Touch and given some instructions to operate

Fig. 6. The mobile device can be easily placed on the stretcher while the students practice manual therapy in the teaching laboratory.


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the application. Finally, their impressions were collected by means of another questionnaire. This questionnaire was very similar to the one provided to the students, but some questions were omitted. In total it consisted of 15 questions (13 limited response choice questions and 2 space limited free-text questions). The results were presented using descriptive statistics. The reliability of the questionnaire was assessed by the Cronbach a coefficient. The individual questions scores in both groups were expressed as means and SD. Differences in the distribution of the responses between groups (students and professors) were analyzed with chi-squared tests for each question. The answers to the open questions were expressed as percentages. Statistical analysis was conducted considering p < 0.05 statistically significant. All analyses were carried out using SPSS (version 19) statistical software. 4.2. Comparative study According to the literature (Furió, González-Gancedo, Juan, Seguí, & Rando, 2013), comparing new m-learning proposals with traditional methods seems to be a common approach to evaluating their effectiveness. Following this trend, we conducted a controlled experiment during the next academic year (2012/13) to further evaluate our proposal from an objective point of view. It consisted of a crossover posttest to determinate whether the proposed 3D m-learning tool had a better learning outcome than the traditional methods. As in our preliminary study, all second-year physiotherapy students of our University took part in this second study. Of the 76 students evaluated, 42 (55.3%) were female and 34 (44.7%) were male. The average age was 20.55 3.2 years. All students consented to participate in this study. Our procedure was as follows. In the first place, all students were randomly assigned to two different groups. The first group was formed by 31 students, whereas the second one had 45 students. Note that all participants were 2nd year students, who had already acquired basic theoretical knowledge of anatomy during the previous academic year. Therefore, in order to access the comparability of both groups, we studied the averaged qualifications obtained by these students in several 1st year basic subjects. These subjects are related to anatomy and required for enrolling in the subject that we are evaluating. Table 1 provides this information. From this Table, we observe that there were no statistically significant differences in the knowledge tests carried out by both groups approximately two months before our intervention. Therefore, using this information as a baseline we can conclude that both groups were homogeneous and comparable in their academic performance and previous knowledge of anatomy. Once the groups were formed, we carried out our evaluation during two practical lessons of 5 h each. During the first session, manual therapy was taught in relation to the knee and the ankle. The volumetric models used in this lesson are shown in Figs. 3a and 5, respectively. For the knee the 2D view was used, whereas we employed the 3D view for the ankle. The second session was about the pelvic zone. In this case, the 3D view was used, as shown in Fig. 2. In the first practical lesson, the first group constituted the experimental group and used the mobile device during the lesson. The second group formed the control group and received the same lesson following the classical teaching methodology (i.e., without the mobile application). Afterwards, the groups interchanged their roles for the second practical lessons. That is, the first group of students became the control group and did not use the mobile application during the session. On the contrary, the second group had the opportunity to use the application. A posttest questionnaire was used to assess the anatomical knowledge after each practical session. The first questionnaire was filled out immediately after the first session. It was composed of 8 multiple-choice questions. The minimum rating was 0 if all questions were wrong and 8 if all of them were correctly answered. The second questionnaire was filled out immediately after the second session and was also composed of 8 questions. The first 4 were open questions, whereas the remaining 4 were multiple-choice. In this questionnaire, the possible ratings also ranged from 0 to 8. Data collected from the questionnaires were transferred to SPSS (version 19) for statistical analysis, and the results were presented using descriptive statistics. The mean value and standard deviation of the posttest scores were shown for each group and also the percentage of students who correctly answered each question were expressed in both examination tests. Differences in the distribution of the responses between groups were analyzed with the t-test after verifying the normal distribution of the data with the Kolmogorov Smirnov test. The alpha level was considered significant at p < 0.05. 5. Results 5.1. Preliminary study The response rate was 100%. Cronbach’s alpha was 0.87, indicating satisfactory reliability of the questionnaire used to carry out the study. Table 2 summarizes the results of our evaluation. The first column reproduces the questionnaire. The remaining columns list the average results obtained for students, the average results obtained for professors, and the differences between both groups when applicable. Statistically significant differences (p < 0.05) are marked with an asterisk.

Table 1 Means (standard deviations) for the qualifications obtained by both groups in several basic 1st year subjects related to anatomy. The ratings are expressed in the (0–10) range. p value < 0.05 was considered statistically significant.

Subject 1 Subject 2 Subject 3

Group 1 (n ¼ 31) X SD

Group 2 (n ¼ 45) X SD

Differences

7.40 1.17 8.04 0.65 8.27 0.99

7.37 1.27 8.02 0.60 7.90 1.29

p ¼ 0.925 p ¼ 0.864 p ¼ 0.183

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Table 2 Means (standard deviations) for the ratings on the survey questions from the students and professors. *p value