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The role of interdisciplinary research team in the impact of health apps in health and computer science publications: a systematic review

Abstract

Background

Several studies have estimated the potential economic and social impact of the mHealth development. Considering the latest study by Institute for Healthcare Informatics, more than 165.000 apps of health and medicine are offered including all the stores from different platforms. Thus, the global mHealth market was an estimated $10.5 billion in 2014 and is expected to grow 33.5 percent annually between 2015 and 2020s. In fact, apps of Health have become the third-fastest growing category, only after games and utilities.

Methods

This study aims to identify, study and evaluate the role of interdisciplinary research teams in the development of articles and applications in the field of mHealth. It also aims to evaluate the impact that the development of mHealth has had on the health and computer science field, through the study of publications in specific databases for each area which have been published until nowadays.

Results

Interdisciplinary nature is strongly connected to the scientific quality of the journal in which the work is published. This way, there are significant differences in those works that are made up by an interdisciplinary research team because of they achieve to publish in journals with higher quartiles. There are already studies that warn of methodological deficits in some studies in mHealth, low accuracy and no reproducibility. Studies of low precision and poor reproducibility, coupled with the low evidence, provide low degrees of recommendation of the interventions targeted and therefore low applicability.

Conclusions

From the evidence of this study, working in interdisciplinary groups from different areas greatly enhances the quality of research work as well as the quality of the publications derived from its results.

Background

Several studies have estimated the potential economic and social impact of the mHealth development. mHealth is an abbreviation for mobile health, a term used for the practice of medicine and public health supported by mobile devices. According to WHO [1], nearly 90 % of the world population could benefit from the opportunities offered by mobile technologies and with a relatively low cost. Considering the latest study by Institute for Healthcare Informatics (IMS) [2], more than 165.000 apps of health and medicine are offered including all the stores from different platforms. Thus, the global mHealth market was an estimated $10.5 billion in 2014 and is expected to grow 33.5 percent annually between 2015 and 2020s [3].

Also, the IMS Institute indicates that 70 % of health apps is focused on general population, offering tools to reach and maintain wellness and to improve physical activity. The remaining 30 %, were designed to more concrete areas such as professionals or people affected by specific diseases.

Despite this situation, it is important to note that more than 50 % of the available apps received less than 500 downloads and only five of them comprise 15 % of all those in the health category. The IMS attributed this situation to different causes, which include: poor quality in many of them, the lack of guidance on the usefulness of the app and a low level of support from health professionals.

However, it is well-known that health apps, solving the problems detailed above, could represent a very useful tool for monitoring chronic diseases will account for 65 % of the global market for mHealth in 2017 [3].

This fact will represent revenue of 15.000 million dollars. The pathologies with a higher potential to increase business are in order: diabetes and cardiovascular disease. They will also play an important role related to diagnostic services (they will reach 15 % and will generate 3.400 million of dollars) and medical treatments (10 % of the market and revenues of 2.300 million). By the other hand, it is estimated that business will increase from 4.500 million in 2013, to 23.000 million in 2017. Continents with largest market share are, in descending order, Europe and Asia (30 %), United States of America and Canada (28 %) [3].

However, we do not know if the apps available to the population are based on scientific knowledge and therefore, it is difficult to assess the real impact of this spectacular development on the health of populations. On the other hand, we do not know how the great spread of the phenomenon of Health 2.0 (that is a term presented in the mid-2000s, as the subset of health technologies mirroring the wider Web 2.0 movement, offering possibilities for changing health care which started with the introduction of eHealth following the emergence of the World Wide Web [4, 5] ) that is reaching the scientific field (medical or computer), which should occur in parallel in order to offer products that positively affect the health of citizens.

Therefore, this study aims to identify, study and evaluate the role of interdisciplinary research teams in the development of articles and applications in the field of mHealth and the impact that the development of mHealth has had on the health and computer science field, through the study of publications and the composition of the research teams in specific databases for each area, which have been published until nowadays. According to Yadros et al. [6] interdisciplinary research seems to be a supplier of creative and innovative approaches. It is able to produce new lines of research and renew scientific field. In this sense, the justification of using interdisciplinary research is thus particularly strong and crucial in scientific programmes addressing grand societal issues or challenges that require an holistic approach including biological, physical and social factors.

Methods

This work is extended and based on the previous work [7]. A systematic review was conducted in two stages during November 2014. The first one was focused on locating papers available in databases from Health Sciences. After this step, we repeat the search but in the Computer Science field because we wanted to find the different penetration in each area. As recommended in the PRISMA Statement [8] for systematic reviews. The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The aim of the PRISMA Statement is to help authors improve the reporting of systematic reviews and meta-analyses. We describe the search strategy and the number of papers located, discarded and finally selected for review using a for-phase flow diagram. In the first stage, we have consulted the PubMed database. We used “mHealth” and “mHealth AND app” terms as search strategies. Finally 79 items were selected for reviewing (see Fig. 1). We also consulted “Science Direct” and “Scopus” using “mHealth” but reducing the search to “Computer Science” area. Having initially located 375 publications, only 27 were chosen for our study (see Fig. 2). Thus, a total of 106 items were reviewed. The impact factor of the journals that published the papers selected was consulted using the journal citation report from web of science (WoS). Since this impact factor usually varies for each year, we took the corresponding to the year when the article was published.

Fig. 1
figure1

Review and selection criteria of papers (health science)

Fig. 2
figure2

Review and selection criteria of papers (computer science)

As noted previously, the execution of this work has been carried out by means of the recommendations given by the PRISMA [8] statement. So, this work includes a study through the following information:

  • The summaries and results of all reviewed papers after performing his complete reading.

  • The departments that participate in the development of the works and their categorization for subsequent statistical analysis.

This last categorization has been performed including the departments in ten large groups, which are: ‘Research Center’, ‘Nursing and other health professionals’, ‘Engineering and Technology’, ‘Finance and Statistics’, ‘General Medicine and Specialties’, ‘Agencies and Institutions’, ‘Health’, ‘Health Care and Community’, ‘Physiotherapy Associates’, ‘Pharmacy and Associates’.

In order to analyze and evaluate the impact of health apps in health and computer science publications in a precise way, additional features which are not used to be considered into systematic reviews, have been included in this study. All the information taken into account has been clustered into two main categories:

  1. 1.

    Publication characteristics:

    • Journal name.

    • Type of journal (According to 22 categories taken from ISI Web of Knowledge).

    • Journal ranking (quartile).

    • Journal impact factor.

    • Article publication date.

    • Type of study.

    • Number of received citations.

  2. 2.

    Interdisciplinary nature:

    • Departments working on the contribution.

The evaluation of the last item (interdisciplinary nature) has been performed using the Rao–Stirling index as explained by Rafolds and Meyer [9], among others. Interdisciplinary research has been defined as a mode of research that integrates techniques, tools, and/or theories from two or more disciplines to advance fundamental understanding or to solve problems whose solutions are beyond the scope of a single discipline or area of research practice [10]. The advantage of the Rao–Stirling measure is that it takes into account the distribution of references across disciplinary categories on journals in the WoS for 220 WoS categories or subject categories (SC) and also considers how cognitively distant these categories are.

For statistical analysis of the data we have used own descriptive statistics (frequency tables, measures of central tendency and dispersion, Pearson correlation coefficient as well as graphic representation) and analytical techniques, using as evidence contrast hypothesis Chi square, t Student, ANOVA and Kruskal–Wallis as non-parametric version. Processing and analysis of data was performed using the SPSS version 22.0.0 [11].

Results and discussion

A brief summary of the main features, topics and contents in the reviewed papers is shown in Tables 1 and 2. We found papers focused on mHealth in 51 different journals, being most of them (68.6 %) indexed in the ISI web of Knowledge. The higher proportion of papers published in journal indexed in JCR, is located in four WoS categories: medical informatics (17.9 %), Healthcare Sciences & Services (12.8 %), computer science interdisciplinary applications (11.1 %) and mathematical and computational biology (8.5 %), showing a great concern about developing mHealth research in two fields of ISI that we think that must be strongly linked to this topic: clinical medicine and computer science. Taking into account not indexed journals, we found same development patterns, because most of the researches belong to different departments and institutions, including professionals from health and computer science. This fact could be explained by the concentration of articles in just three journals, journal of medical internet research mHealth and uHealth, (JMU) containing 21.7 % of the published papers, journal of medical internet research (JMIR), covering 7.5 % and international journal of medical informatics, reaching 4.7 % papers. Thus, for example, journal of medical internet research is classified by journal citation report (JCR) into several categories (“Health Care Sciences & Services” and “Medical Informatics”) and the international journal of medical informatics is listed by JCR into three categories, the two previously mentioned, as well as in “Computer Science, Information Systems” that clearly belongs to a non-health area. Another important result is the great impact factor of these publications. This way, in any category where they could be classified of these two journals belong to the first quartile, excepting JMU which is not indexed because it was created in 2013 like an spin-off from JMIR. This may also explain that the average impact factor of the found publications was 1.54 (±1898) including all the papers, and 3.0248 (±1.6014) if we select just the papers published in indexed journals. Moreover, 27.4 % of the articles are located in first quartile journals, 13.2 % in the second, 7.5 % in the third and 2.8 % in the fourth quartile. The 49.1 % of the papers appear in not indexed journals, specially in JMU. From our point of view, this fact highlights the high impact and newness that scientific work based on the use of mHealth technology represents for editors, allowing researchers to access to high impact publications and how new categories of are opening progressively.

Table 1 Main features of reviewed papers
Table 2 Main results and conclusions of reviewed papers

However, the number of citations received of these articles is relatively small and widely dispersed. The sample shows an average of 6.35 (±13,56) references, excluding 2015 and 4.71 (±8.77) counting this year. Two papers highlight with 74 [12] and 63 [13] citations, facing many that have never been referenced. Interestingly, these two articles were published in 2011 and 2012 (respectively), when the exponential growth in the number of publications on mHealth began. Thus, we can find just 2 papers in 2011 (1.9 % of total found) while we locate 45 in 2014 (42.5 %). Similarly, only four articles were found in 2012 (3.8 %) and 26 in 2013 (24.8 %). Until October of 2015 we located 29 (27.4 %) papers published in 2015. In 2010 there were no relevant papers included in the search. This could be a sure sign that the scientific community is increasingly aware of the potential and the need to focus some research into this field.

Similarly, the mean number of citations received for years (excluding 2015), shows a clear downward trend (F = 36,72, 3 df; p < 0.001). As the years pass, the average is down from the 62 items on average in 2011 till 1.53 in 2014. Temporal matters could explain this, i.e., it stands to reason that more recent articles have had less time to be referenced (and that is the main reason because we decided to exclude 2015 from this analysis), but it is a trend that is also seen in 2012 (with an average of 23 references) and 2013 (7.85). Post hoc tests found significant differences between every year (p < 0.01).

In addition, considering the types of papers, we found that original researches are more prevalent (63.2 %), Besides this, we evaluated the type of the research carried out. We found descriptive studies (23.6 %), researches focused on technological development (21.7 %) and reviews (16 %) highlighting over the rest (see Fig. 3). This fact happens because most of the articles are focused on the user data collection and evaluation from apps designed to assess adherence or modification of habits to healthier lifestyles (studies with larger sample sizes). Another percentage of the original researches is represented by small pilot studies or clinical trials with smaller sample sizes (5.7 %). As we mentioned above, reviews are also frequent, something justifiable given the exponential growth of publications. This situation force researchers to conduct periodically synthesis and evaluation of trends in the development of apps and main findings, as well as the errors most frequently committed (especially as the methodological design is concerned). Another significant point was to find nine papers from qualitative research which represent 9.4 % of the types of research. It is understandable if we comprehend that this type of studies is very helpful in trying to expose the different realities of the new or unknown phenomena which we have a very little information or experience. A good example could be the expected results of interactions between mHealth apps and populations who want to improve their health. Furthermore, we were surprised because two of these articles had been published in computer science journals, where papers highly focused on applied and/or quantitative research are traditionally accepted. No statistically significant relation between the type of research and the number of citations received (p = 0.297) were found. Similarly, the type of research and the impact factor of the journal where the article is published did not show to be related (p = 0.094).

Fig. 3
figure3

Type of research

We also wanted to know the link between the field of knowledge where the publishing journal is classified and the impact factor (see Fig. 4). We found a clear relation (p < 0.05), highlighting “Environment/Ecology”, “Chemistry” and “Multidisciplinary” with higher average of impact factor. In any case, it is very important to evidence how fields of knowledge, very far from health, a priori, accept papers focused on mHealth, which, from our point of view, demonstrate the deep impact that this new topic is generating in many fields of knowledge, related or not to health sciences.

Fig. 4
figure4

Impact factor average related to ISI 22 Fields

Research teams and the role of interdisciplinarity

The results show that more than half of the reviewed papers (67.9 %) had the participation of at least two departments/institutions focused on different topics, reaching the maximum amount of five different topics (see Fig. 5). Only 32.1 % [1341] of the reviewed papers had participants focused on just one topic.

Fig. 5
figure5

Number or departments/institutions focused on different areas involved in the research

Besides this, we considered the interdisciplinarity, calculating the degree of diversity through Rao–Stirling (multidimensional index which includes the analysis of variety, balance and disparity). We found a very low and dispersed rates (0.07 ± 0.05). From our point of view, and related to the different topics where the departments involved in each paper are focused, these results mean that, although the diversity of the participants could seem high because they belong to different areas, finally they do not use the specific knowledge of each one, finding many references in each paper that belong to the same WoS categories (causing a low Rao–Stirling diversity).

About interdisciplinary research teams, interdisciplinary publications, impact factor and number of citations received

Although the visual examination of Fig. 6 shows a clear tendency for researches carried out by more than two departments/institutions from other fields of knowledge, are published in major journals, statistical analysis shows that these differences are not significant (p = 0.33), obtaining an average impact factor of 1.81 (±1.93) for researches carried out by more than two teams focusing on different fields, versus 1.42 (±1.88) for teams with two or fewer different fields of knowledge. However, we evidenced a light but positive and significant relation between the Rao–Stirling diversity and the impact factor (r = 0.182; p = 0.031) which means that a higher degree of interdisciplinarity increase of impact factor of the paper. Anyway, as we mentioned above, it is important to remember that the participation of multidisciplinary research teams does not necessarily means the presence of interdisciplinarity. This fact is supported by the analysis of the relation between the number of departments focused on different research areas for each paper and the value of Rao–Stirling diversity. In this case, we can not assume that the participation of research teams from different fields lead performing a multidisciplinary study. (p = 0.108) (see Fig. 7).

Fig. 6
figure6

Relation between teams of more than 2 different departments/institutions focused on different areas and impact factor

Fig. 7
figure7

Relation between number of departments/institutions involved in the research and Rao–Stirling diversity

Another method to evaluate the impact of a paper consists in evaluating the number of citations received. Neither the Rao–Stirling (p = 0.063), the field where the publishing journal is classified according to 22 ISI web of knowledge categories (p = 0.811), or the number of departments focused on different fields of knowledge (p = 0.869), seem to be related to the number of citations received. The average number of citations for teams with more than two departments focused on different fields of knowledge is 6.52 (±9.9), versus an average of 6.14 (±9.6) citations for teams composed of two or less. From our point of view, this could be explained by the novelty of the research on mHealth, which seems to take precedence over the quality or focus of each investigation. We think that this could be also confirmed by the absence of statistical relations between the impact factor of the journals in which the papers were published, the number of citations received and the research design. That is, it would be logical to think that clinical trials, technological developments, and other quasi-experimental studies with higher levels of evidence, should be published in major journals or be more referenced. However, the result described above, indicates that this type of research designs is not better valued by publishers or other research investigators than others with lower levels of evidence (descriptive studies, qualitative researches, etc.).

A priori, it would seem that the interdisciplinary nature of the research teams is directly linked with access to publication in journals indexed in higher quartiles. This fact is confirmed by finding significant relations (p = 0.027) between the quartile of the journal in which the articles were published and the number of departments focused on different areas involved in the investigation (see Fig. 8). By contrast, we did not find relation with Rao–Stirling diversity (p = 0.475); showing that the fact of accessing higher quartiles may be related to the presence of multidisciplinary teams in the investigation, which does not necessarily mean that researches show an interdisciplinary approach.

Fig. 8
figure8

Relation between the quartile of publication and the number of departments/institutions focused in different areas involved in the research

Another aspect to take into account is referent to the number of articles that are included in our study, which covers until October of 2015. Because of that, it is difficult to stablish the progression in the number of papers in this year. We considered that the number of papers in the last quarter of this year (2015) continue with a increasing tendency, but, it seems that in a lineal level (not exponential as in previous years). This fact could be only estimated in studies that will be carried out in 2016.

Limitations

The results should be taken with caution given various limitations. In this sense, Yadros et al. [6] stated some limitations as: “the inaccuracies in the WoS categories used to define subdisciplinary categories may create biases in the indicators of citation impact (since citation impact is highly affected by normalisation) and may have an important effect as well in diversity measures.”, or “the inclusion or not of some control variables such as number of co-authors, institutions or article length is open to debate and these may have an effect on results.”, among others.

On the other hand, from the revised bibliography it is possible to extract that for calculating certain bibliometric indexes (as Rao–Stirling diversity), it is usual to consider a higher quantity of the number of papers than the number of them that we have considered in this work. This fact is because the main objective in our paper was to realize a systematic review and no the calculation of bibliometric indexes. The number of revised papers is considerably minor, so, it would be advisable to take with caution the results of this bibliometric index.

Conclusions

There are mHealth papers of all kinds (trials, analytical, descriptive, reviews, etc.) and its number grows at an exponential rate. This could show how technologies related to mHealth are reaching the scientific field. These technologies are reaching the population even faster. Therefore, mHealth is becoming an interesting research topic in both fields, health and computer sciences (hasta 2014 siendo más moderado en 2015).

These articles are published in high impact journals. We have found them in specific journals (focused on eHealth) and in generalist health and computing journals. Generalist journals have just begun to accept research based on the application of mHealth technologies. This highlights the growing importance of this topic.

There are already studies that warn of methodological deficits in some studies in mHealth, low accuracy and no reproducibility. Studies of low precision and poor reproducibility, coupled with the low evidence, provide low degrees of recommendation of the interventions targeted and therefore low applicability. The market apps, mostly lack of scientific evidence or participation of health professionals. This should compel publishers and researchers to be more stringent on the design of experiments and in the publication of results.

Fundamentally, it is possible to extract two main conclusions from our study. First, it is evident the increased interest that publishers of scientific journals are showing to mHealth, given the steady increase in publications focusing on this issue, regardless of the subject area. Second, it seems clear that the participation of multidisciplinary teams (with a variety of professionals focused on different areas of knowledge) is not necessarily linked to the presence of interdisciplinary approaches. Finally, this interdisciplinarity plays a limited role in the impact of the papers (measured by the impact factor of the journal where they are published and the number of citations that they received), this fact could be more related to the novelty of the topic of research.

References

  1. 1.

    The App Date. Informe 50 Mejores Apps de Salud en Español. Madrid. 2014. http://www.theappdate.es/static/media/uploads/2014/03/Informe-TAD-50-Mejores-Apps-de-Salud.pdf. Accessed 21 Dec 2015.

  2. 2.

    IMS Institute for Healthcare Informatics: patient adoption of mHealth: use, evidence and remaining barriers to mainstream acceptance. 2015.

  3. 3.

    Deloitte Center for Health Solutions: mobilizing MedTech for mHealth: market trends and potential opportunities. 2015.

  4. 4.

    Economist The. Health 2.0: technology and society: is the outbreak of cancer videos, bulimia blogs and other forms of “user generated” medical information a healthy trend? Econ. 2007;6:73–4.

  5. 5.

    Giustini D. How Web 2.0 is changing medicine: editorial. Br Med J. 2006;333:1283–4.

  6. 6.

    Yegros-Yegros A, Rafols I, D’Este P. Does interdisciplinary research lead to higher citation impact? The different effect of proximal and distal interdisciplinarity. PLoS One. 2015;10(8):e0135095.

  7. 7.

    Molina-Recio G, García-Hernández L, et al. Impact of health apps in health and computer science publications. A systematic review from 2010 to 2014. Bioinformatics and biomedical engineering. Lecture notes in computer science. Berlin: Springer; 2015. p. 24–34.

  8. 8.

    Moher D, Liberati A, Tetzlaff J, Altman DG. The PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(6):e1000097. doi:10.1371/journal.pmed1000097.

  9. 9.

    Rafols I, Meyer M. Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience. Scientometrics. 2010;82(2):263–87.

  10. 10.

    Porter AL, Roessner JD, Cohen AS, Perreault M. Interdiscipinary research: meaning, metrics and nurture. Res Eval. 2006;15:187–96.

  11. 11.

    IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk: IBM Corp.

  12. 12.

    Liu C, Zhu Q, Holroyd KA, Seng EK. Status and trends of mobile-health applications for iOS devices: A developer’s perspective. J Syst Softw. 2011;84(11):2022–33.

  13. 13.

    Cafazzo JA, Casselman M, Hamming N, Katzman DK, Palmert MR. Design of an mHealth app for the self-management of adolescent type 1 diabetes: a pilot study. J Med Internet Res. 2012;14(3):e70.

  14. 14.

    Hundert AS, Huguet A, McGrath PJ, Stinson JN, Wheaton M. Commercially available mobile phone headache diary apps: a systematic review. JMIR MHealth Uhealth. 2014;2(3):e36.

  15. 15.

    Wang A, An N, Lu X, Chen H, Li C, Levkoff S. A classification scheme for analyzing mobile apps used to prevent and manage disease in late life. JMIR MHealth Uhealth. 2014;2(1):e6.

  16. 16.

    Ploderer B, Smith W, Pearce J, Borland R. A mobile app offering distractions and tips to cope with cigarette craving: a qualitative study. JMIR MHealth Uhealth. 2014;2(2):e23.

  17. 17.

    Grindrod KA, Gates A, Dolovich L, Slavcev R, Drimmie R, Aghaei B, et al. ClereMed: lessons learned from a pilot study of a mobile screening tool to identify and support adults who have difficulty with medication labels. JMIR MHealth Uhealt. 2014;2(3):e35.

  18. 18.

    O’Malley G, Dowdall G, Burls A, Perry IJ, Curran N. Exploring the usability of a mobile app for adolescent obesity management. JMIR MHealth Uhealt. 2014;2(2):e29.

  19. 19.

    Dunford E, Trevena H, Goodsell C, Ng KH, Webster J, Millis A, et al. FoodSwitch: a mobile phone app to enable consumers to make healthier food choices and crowdsourcing of national food composition data. JMIR MHealth Uhealth. 2014;2(3):e37.

  20. 20.

    Pulman A, Taylor J, Galvin K, Masding M. Ideas and enhancements related to mobile applications to support type 1 diabetes. JMIR MHealth UHealth. 2013;1(2):e12.

  21. 21.

    Becker S, Miron-Shatz T, Schumacher N, Krocza J, Diamantidis C, Albrecht U-V. mHealth 2.0: experiences, possibilities, and perspectives. JMIR MHealth Uhealth. 2014;2(2):e24.

  22. 22.

    Lopez C, Ramirez DC, Valenzuela JI, Arguello A, Saenz JP, Trujillo S, et al. Sexual and reproductive health for young adults in Colombia: teleconsultation using mobile devices. JMIR MHealth UHealth. 2014;2(3):e38.

  23. 23.

    Mann DM, Kudesia V, Reddy S, Weng M, Imler D, Quintiliani L. Development of DASH Mobile: a mHealth lifestyle change intervention for the management of hypertension. Stud Health Technol Inform. 2013;192:973.

  24. 24.

    Lyons EJ, Lewis ZH, Mayrsohn BG, Rowland JL. Behavior change techniques implemented in electronic lifestyle activity monitors: a systematic content analysis. J Med Internet Res. 2014;16(8):e192.

  25. 25.

    Iwaya LH, Gomes MA, Simplício MA, Carvalho TCMB, Dominicini CK, Sakuragui RRM, et al. Mobile health in emerging countries: a survey of research initiatives in Brazil. Int J Med Inf. 2013;82(5):283–98.

  26. 26.

    Lee SSS, Xin X, Lee WP, Sim EJ, Tan B, Bien MPG, et al. The feasibility of using SMS as a health survey tool: an exploratory study in patients with rheumatoid arthritis. Int J Med Inf. 2013;82(5):427–34.

  27. 27.

    Labrique A, Vasudevan L, Chang LW, Mehl G. Hope for mHealth: more “y” or “o” on the horizon? Int J Med Inf. 2013;82(5):467–9.

  28. 28.

    Alnanih R, Ormandjieva O, Radhakrishnan T. Context-based and rule-based adaptation of mobile user interfaces in mHealth. Procedia Comput Sci. 2013;21:390–7.

  29. 29.

    Menezes J Jr, Gusmão C, Machiavelli J. A proposal of mobile system to support scenario-based learning for health promotion. Procedia Technol. 2013;9:1142–8.

  30. 30.

    Sezgin E, Yıldırım SÖ. A literature review on attitudes of health professionals towards health information systems: from e-Health to m-Health. Procedia Technol. 2014;16:1317–26.

  31. 31.

    Van der Heijden M, Lucas PJF, Lijnse B, Heijdra YF, Schermer TRJ. An autonomous mobile system for the management of COPD. J Biomed Inform. 2013;46(3):458–69.

  32. 32.

    Balsam J, Rasooly R, Bruck HA, Rasooly A. Thousand-fold fluorescent signal amplification for mHealth diagnostics. Biosens Bioelectron. 2014;51:1–7.

  33. 33.

    Van Drongelen A, Boot CR, Hlobil H, Twisk JW, Smid T, van der Beek AJ. Evaluation of an mHealth intervention aiming to improve health-related behavior and sleep and reduce fatigue among airline pilots. Scand J Work Environ Health. 2014;40(6):557–68.

  34. 34.

    Eskenazi B, Quirós-Alcalá L, Lipsitt JM, Wu LD, Kruger P, Ntimbane T, et al. mSpray: a mobile phone technology to improve malaria control efforts and monitor human exposure to malaria control pesticides in Limpopo, South Africa. Environ Int. 2014;68:219–26.

  35. 35.

    Hao W-R, Hsu Y-H, Chen K-C, Li H-C, Iqbal U, Nguyen P-A, et al. LabPush: a pilot study of providing remote clinics with laboratory results via short message service (SMS) in Swaziland, Africa—a qualitative study. Comput Methods Programs Biomed. 2015;118(1):77–83.

  36. 36.

    Kuo M-C, Lu Y-C, Chang P. A newborn baby care support app and system for mHealth. Nurs Inform Proc Int Congr Nurs Inform. 2012;2012:228.

  37. 37.

    Turner-McGrievy GM, Tate DF. Are we sure that mobile health is really mobile? An examination of mobile device use during two remotely-delivered weight loss interventions. Int J Med Inf. 2014;83(5):313–9.

  38. 38.

    King C, Hall J, Banda M, Beard J, Bird J, Kazembe P, et al. Electronic data capture in a rural African setting: evaluating experiences with different systems in Malawi. Glob Health Action. 2014;7:25878.

  39. 39.

    Bricker JB, Mull KE, Kientz JA, Vilardaga R, Mercer LD, Akioka KJ, et al. Randomized, controlled pilot trial of a smartphone app for smoking cessation using acceptance and commitment therapy. Drug Alcohol Depend. 2014;143:87–94.

  40. 40.

    Jibb LA, Stevens BJ, Nathan PC, Seto E, Cafazzo JA, Stinson JN. A smartphone-based pain management app for adolescents with cancer: establishing system requirements and a pain care algorithm based on literature review, interviews, and consensus. JMIR Res Protoc. 2014;3(1):e15.

  41. 41.

    Ribu L, Holmen H, Torbjørnsen A, Wahl AK, Grøttland A, Småstuen MC, et al. Low- intensity self-management intervention for persons with type 2 diabetes using a mobile phone-based diabetes diary, with and without health counseling and motivational interviewing: protocol for a randomized controlled trial. JMIR Res Protoc. 2013;2(2):e34.

  42. 42.

    Brown W, Yen P-Y, Rojas M, Schnall R. Assessment of the health IT usability evaluation model (Health-ITUEM) for evaluating mobile health (mHealth) technology. J Biomed Inform. 2013;46(6):1080–7.

  43. 43.

    Balsam J, Bruck HA, Rasooly A. Capillary array waveguide amplified fluorescence detector for mHealth. Sens Actuators B Chem. 2013;186(7):11–7.

  44. 44.

    Akter S, D’Ambra J, Ray P. Development and validation of an instrument to measure user perceived service quality of mHealth. Inf Manag. 2013;50(4):181–95.

  45. 45.

    Datta AK, Sumargo A, Jackson V, Dey PP. mCHOIS: an application of mobile technology for childhood obesity surveillance. Procedia Comput Sci. 2011;5:653–60.

  46. 46.

    Surka S, Edirippulige S, Steyn K, Gaziano T, Puoane T, Levitt N. Evaluating the use of mobile phone technology to enhance cardiovascular disease screening by community health workers. Int J Med Inf. 2014;83(9):648–54.

  47. 47.

    Cornelius CT, Kotz DF. Recognizing whether sensors are on the same body. Pervasive Mob Comput. 2012;8(6):822–36.

  48. 48.

    Turner-McGrievy GM, Beets MW, Moore JB, Kaczynski AT, Barr-Anderson DJ, Tate DF. Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program. J Am Med Inform Assoc JAMIA. 2013;20(3):513–8.

  49. 49.

    Kizakevich PN, Eckhoff R, Weger S, Weeks A, Brown J, Bryant S, et al. A personal health information toolkit for health intervention research. Stud Health Technol Inform. 2014;199:35–9.

  50. 50.

    Sunyaev A, Dehling T, Taylor PL, Mandl KD. Availability and quality of mobile health app privacy policies. J Am Med Inform Assoc JAMIA. 2015;22(e1):e28–33.

  51. 51.

    Martínez-Pérez B, de la Torre-Díez I, López-Coronado M, Herreros-González J. Mobile apps in cardiology: review. JMIR mhealth and uhealth. 2013;1(2):e15.

  52. 52.

    Carter T, O’Neill S, Johns N, Brady RRW. Contemporary vascular smartphone medical applications. Ann Vasc Surg. 2013;27(6):804–9.

  53. 53.

    Abel O, Shatunov A, Jones AR, Andersen PM, Powell JF, Al-Chalabi A. Development of a smartphone app for a genetics website: the amyotrophic lateral sclerosis online genetics database (ALSoD). JMIR MHealth UHealth. 2013;1(2):e18.

  54. 54.

    Chen L, Wang W, Du X, Rao X, van Velthoven MH, Yang R, et al. Effectiveness of a smart phone app on improving immunization of children in rural Sichuan Province, China: study protocol for a paired cluster randomized controlled trial. BMC Public Health. 2014;14:262.

  55. 55.

    Bierbrier R, Lo V, Wu RC. Evaluation of the accuracy of smartphone medical calculation apps. J Med Internet Res. 2014;16(2):e32.

  56. 56.

    Brooke MJ, Thompson BM. Food and Drug Administration regulation of diabetes- related mHealth technologies. J Diabetes Sci Technol. 2013;7(2):296–301.

  57. 57.

    Masters K. Health professionals as mobile content creators: teaching medical students to develop mHealth applications. Med Teach. 2014;36(10):883–9.

  58. 58.

    Parmanto B, Pramana G, Yu DX, Fairman AD, Dicianno BE, McCue MP. iMHere: a novel mHealth system for supporting self-care in management of complex and chronic conditions. JMIR MHealth UHealth. 2013;1(2):e10.

  59. 59.

    Vriend I, Coehoorn I, Verhagen E. Implementation of an app-based neuromuscular training programme to prevent ankle sprains: a process evaluation using the RE- AIM Framework. Br J Sports Med. 2015;49(7):484–8.

  60. 60.

    Pérez-Cruzado D, Cuesta-Vargas AI. Improving adherence physical activity with a smartphone application based on adults with intellectual disabilities (APPCOID). BMC Public Health. 2013;13:1173.

  61. 61.

    Fiordelli M, Diviani N, Schulz PJ. Mapping mHealth research: a decade of evolution. J Med Internet Res. 2013;15(5):e95.

  62. 62.

    Lewis TL, Wyatt JC. mHealth and mobile medical apps: a framework to assess risk and promote safer use. J Med Internet Res. 2014;16(9):e210.

  63. 63.

    De la Vega R, Miró J. mHealth: a strategic field without a solid scientific soul. a systematic review of pain-related apps. PLoS One. 2014;9(7):e101312.

  64. 64.

    Shishido HY, de Alves da Cruz Andrade R, Eler GJ. mHealth data collector: an application to collect and report indicators for assessment of cardiometabolic risk. Stud Health Technol Inform. 2014;201:425–32.

  65. 65.

    Slaper MR, Conkol K. mHealth tools for the pediatric patient-centered medical home. Pediatr Ann. 2014;43(2):e39–43.

  66. 66.

    Martínez-Pérez B, de la Torre-Díez I, López-Coronado M, Sainz-De-Abajo B. Comparison of mobile apps for the leading causes of death among different income zones: a review of the literature and app stores. JMIR MHealth UHealth. 2014;2(1):e1.

  67. 67.

    Martínez-Pérez B, de la Torre-Díez I, López-Coronado M, Sainz-de-Abajo B, Robles M, García-Gómez JM. Mobile clinical decision support systems and applications: a literature and commercial review. J Med Syst. 2014;38(1):4.

  68. 68.

    Martínez-Pérez B, de la Torre-Díez I, López-Coronado M. Mobile health applications for the most prevalent conditions by the World Health Organization: review and analysis. J Med Internet Res. 2013;15(6):e120.

  69. 69.

    Yang YT, Silverman RD. Mobile health applications: the patchwork of legal and liability issues suggests strategies to improve oversight. Health Aff Proj Hope. 2014;33(2):222–7.

  70. 70.

    Boulos MNK, Brewer AC, Karimkhani C, Buller DB, Dellavalle RP. Mobile medical and health apps: state of the art, concerns, regulatory control and certification. Online J Public Health Inform. 2014;5(3):229.

  71. 71.

    Ahtinen A, Mattila E, Välkkynen P, Kaipainen K, Vanhala T, Ermes M, et al. Mobile mental wellness training for stress management: feasibility and design implications based on a one-month field study. JMIR MHealth UHealth. 2013;1(2):e11.

  72. 72.

    Barwais FA, Cuddihy TF, Tomson LM. Physical activity, sedentary behavior and total wellness changes among sedentary adults: a 4-week randomized controlled trial. Health Qual Life Outcomes. 2013;11:183.

  73. 73.

    Tsui I, Drexler A, Stanton AL, Kageyama J, Ngo E, Straatsma BR. Pilot study using mobile health to coordinate the diabetic patient, diabetologist, and ophthalmologist. J Diabetes Sci Technol. 2014;8(4):845–9.

  74. 74.

    Goldenberg T, McDougal SJ, Sullivan PS, Stekler JD, Stephenson R. Preferences for a Mobile HIV prevention app for men who have sex with men. JMIR MHealth UHealth. 2014;2(4):e47.

  75. 75.

    Mobasheri MH, Johnston M, King D, Leff D, Thiruchelvam P, Darzi A. Smartphone breast applications—what’s the evidence? Breast Edinb Scotl. 2014;23(5):683–9.

  76. 76.

    Zmily A, Mowafi Y, Mashal E. Study of the usability of spaced retrieval exercise using mobile devices for Alzheimer’s disease rehabilitation. JMIR MHealth Uhealth. 2014;2(3):e31.

  77. 77.

    Mirkovic J, Kaufman DR, Ruland CM. Supporting cancer patients in illness management: usability evaluation of a mobile app. JMIR MHealth Uhealth. 2014;2(3):e33.

  78. 78.

    Klonoff DC. The current status of mHealth for diabetes: will it be the next big thing? J Diabetes Sci Technol. 2013;7(3):749–58.

  79. 79.

    Van der Weegen S, Verwey R, Spreeuwenberg M, Tange H, van der Weijden T, de Witte L. The development of a mobile monitoring and feedback tool to stimulate physical activity of people with a chronic disease in primary care: a user-centered design. JMIR MHealth UHealth. 2013;1(2):e8.

  80. 80.

    Leal Neto OB, Albuquerque CM, Albuquerque JO, Barbosa CS. The schisto track: a system for gathering and monitoring epidemiological surveys by connecting geographical information systems in real time. JMIR MHealth Uhealth. 2014;2(1):e10.

  81. 81.

    Albrecht U-V, Behrends M, Schmeer R, Matthies HK, von Jan U. Usage of multilingual mobile translation applications in clinical settings. JMIR MHealth UHealth. 2013;1(1):e4.

  82. 82.

    Hilliard ME, Hahn A, Ridge AK, Eakin MN, Riekert KA. User preferences and design recommendations for an mhealth app to promote cystic fibrosis self-management. JMIR MHealth UHealth. 2014;2(4):e44.

  83. 83.

    Arnhold M, Quade M, Kirch W. Mobile applications for diabetics: a systematic review and expert-based usability evaluation considering the special requirements of diabetes patients age 50 years or older. J Med Internet Res. 2014;16(4):e104.

  84. 84.

    Breton ER, Fuemmeler BF, Abroms LC. Weight loss-there is an app for that! But does it adhere to evidence-informed practices? Transl Behav Med. 2011;1(4):523–9.

  85. 85.

    BinDhim NF, McGeechan K, Trevena L. Who uses smoking cessation apps? A feasibility study across three countries via smartphones. JMIR MHealth Uhealth. 2014;2(1):e4.

  86. 86.

    Breland JY, Yeh VM, Yu J. Adherence to evidence-based guidelines among diabetes self-management apps. Transl Behav Med. 2013;3(3):277–86.

  87. 87.

    Silva BM, Rodrigues JJPC, Canelo F, Lopes IC, Zhou L. A data encryption solution for mobile health apps in cooperation environments. J Med Internet Res. 2013;15(4):e66.

  88. 88.

    Mann DM, Quintiliani LM, Reddy S, Kitos NR, Weng M. Dietary approaches to stop hypertension: lessons learned from a case study on the development of an mhealth behavior change system. JMIR MHealth UHealth. 2014;2(4):e41.

  89. 89.

    Aguilera A, Schueller SM, Leykin Y. Daily mood ratings via text message as a proxy for clinic based depression assessment. J Affect Disord. 2015;175:471–4.

  90. 90.

    Almunawar MN, Anshari M, Younis MZ. Incorporating customer empowerment in mobile health. Health Policy Technol. 2015;4(4):312–9.

  91. 91.

    Anwar M, Joshi J, Tan J. Anytime, anywhere access to secure, privacy-aware healthcare services: issues, approaches and challenges. Health Policy Technol. 2015;4(4):299–311.

  92. 92.

    Azzazy HME, Elbehery AHA. Clinical laboratory data: acquire, analyze, communicate, liberate. Clin Chim Acta. 2015;438:186–94.

  93. 93.

    Boissin C, Laflamme L, Wallis L, Fleming J, Hasselberg M. Photograph-based diagnosis of burns in patients with dark-skin types: the importance of case and assessor characteristics. Burns. 2015;41(6):1253–60.

  94. 94.

    Bradway M, Årsand E, Grøttland A. Mobile Health: empowering patients and driving change. Trends Endocrinol Metab. 2015;26(3):114–7.

  95. 95.

    Chang C-W, Ma T-Y, Choi M-S, Hsu Y-Y, Tsai Y-J, Hou T-W. Electronic personal maternity records: both web and smartphone services. Comput Methods Programs Biomed. 2015;121(1):49–58.

  96. 96.

    Danaher BG, Brendryen H, Seeley JR, Tyler MS, Woolley T. From black box to toolbox: outlining device functionality, engagement activities, and the pervasive information architecture of mHealth interventions. Internet Interv. 2015;2(1):91–101.

  97. 97.

    Green BB. BP here, there, and everywhere – mobile health applications (apps) and hypertension care. J Am Soc Hypertens. 2015;9(2):137–9.

  98. 98.

    Guo SH-M, Chang H-K, Lin C-Y. Impact of mobile diabetes self-care system on patients’ knowledge, behavior and efficacy. Comput Ind. 2015;69:22–9.

  99. 99.

    Helf C, Hlavacs H. Apps for life change: critical review and solution directions. Entertainment Computing, 2015.

  100. 100.

    Jain N, Singh H, Koolwal GD, Kumar S, Gupta A. Opportunities and barriers in service delivery through mobile phones (mHealth) for Severe Mental Illnesses in Rajasthan, India: a multi-site study. Asian J Psychiatry. 2015;14:31–5.

  101. 101.

    Kramer GM, Kinn JT, Mishkind MC. Legal, regulatory, and risk management issues in the use of technology to deliver mental health care. Cogn Behav Pract. 2015;22(3):258–68.

  102. 102.

    Kumar N, Khunger M, Gupta A, Garg N. A content analysis of smartphone–based applications for hypertension management. J Am Soc Hypertens. 2015;9(2):130–6.

  103. 103.

    Lucivero F, Prainsack B. The lifestylisation of healthcare? ‘Consumer genomics’ and mobile health as technologies for healthy lifestyle. Appl Transl Gen. 2015;4:44–9.

  104. 104.

    Maciel FR, Hayashi S. NOPA, usability testing of an application to help patients during the treatment of infectious, and chronic diseases in Brazil. Procedia Manufacturing. 2015;3:6388–92.

  105. 105.

    McCarroll ML, Armbruster S, Pohle-Krauza RJ, Lyzen AM, Min S, Nash DW, Roulette GD, Andrews SJ, von Gruenigen VE. Feasibility of a lifestyle intervention for overweight/obese endometrial and breast cancer survivors using an interactive mobile application. Gynecol Oncol. 2015;137(3):508–15.

  106. 106.

    Nocum AA, Baltao JM, Agustin DR, Portus AJ. Ergonomic evaluation and design of a mobile application for maternal and infant health for smartphone users among lower-income class filipinos. Procedia Manufacturing. 2015;3:5411–8.

  107. 107.

    Nunes IL, Simões-Marques MJ. Exploiting the Potential and Facing the Challenges of Mobile Devices: application Examples. Procedia Manufacturing. 2015;3:807–14.

  108. 108.

    Olla P, Tan J, Kauniskangas H. BPH laboratories: a proof-of-concept case on integrating smartphone diagnostics into clinical systems. Health Policy Technol. 2015;4(4):337–47.

  109. 109.

    Ovbiagele B. Phone-based intervention under nurse guidance after stroke: concept for lowering blood pressure after stroke in Sub-Saharan Africa. J Stroke Cerebrovasc Dis. 2015;24(1):1–9.

  110. 110.

    Paschou M, Papadimitiriou C, Nodarakis N, Korezelidis K, Sakkopoulos E, Tsakalidis A. Enhanced healthcare personnel rostering solution using mobile technologies. J Syst Softw. 2015;100:44–53.

  111. 111.

    Patterson V, Singh M, Rajbhandari H, Vishnubhatla S. Validation of a phone app for epilepsy diagnosis in India and Nepal. Seizure. 2015;30:46–9.

  112. 112.

    Schnall R, Iribarren SJ. Review and analysis of existing mobile phone applications for health care–associated infection prevention. Am J Infect Control. 2015;43(6):572–6.

  113. 113.

    Silva BMC, Rodrigues JJPC, de la Torre Díez I, López-Coronado M, Saleem M. Mobile-health: a review of current state in 2015. J Biomed Inform. 2015;56:265–72.

  114. 114.

    Sindi S, Calov E, Fokkens J, Ngandu T, Soininen H, Tuomilehto J, Kivipelto M. The CAIDE dementia risk score app: the development of an evidence-based mobile application to predict the risk of dementia. Alzheimer’s Dement: Diagn, Assess Dis Monit. 2015;1(3):328–33.

  115. 115.

    Thompson MJ, Valdez RS. Online Filipino-Americans’ perspectives on informatics-enabled health management. Health Policy Technol. 2015;4(4):320–36.

  116. 116.

    Waldman L, Stevens M. Sexual and reproductive health and rights and mHealth in policy and practice in South Africa. Reprod Health Matters. 2015;23(45):93–102.

  117. 117.

    Yang C-H, Maher JP, Conroy DE. Acceptability of mobile health interventions to reduce inactivity-related health risk in central Pennsylvania adults. Prev Med Rep. 2015;2:669–72.

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All authors have equally contributed to the development of this work as well as the writing of this manuscript. All authors read and approved the final manuscript.

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Guillermo Molina Recio, Laura García-Hernández, Rafael Molina Luque and Lorenzo Salas-Morera contributed equally to this work

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Molina Recio, G., García-Hernández, L., Molina Luque, R. et al. The role of interdisciplinary research team in the impact of health apps in health and computer science publications: a systematic review. BioMed Eng OnLine 15, 77 (2016). https://doi.org/10.1186/s12938-016-0185-y

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Keywords

  • Impact Factor
  • Medical Informatics
  • Journal Citation Report
  • Citation Impact
  • Interdisciplinary Nature