mhealth (mobile health) in developing countries

Mobile Health in developing countries

key indicators for a SMS based design framework

PA-V-PrüfPA-III-vs01ile

MHMK Macromedia Hochschule

für Medien und Kommunikation

BACHELOR THESIS Final paper for the obtainment of the

Bachelor of Arts Degree

Mobile Health in developing countries key indicators for a SMS based design framework

in the study course Media Management,

study focus Brand Communications and Advertisement

First supervisor: Prof. Dr. Alyosh Agarwal

Submitted by:

First name and family name: Maximilian D. Heitsch

Student ID number: M-25031

Study course: Media Management

Study focus: Brand Communications and Advertisement

Munich, July 23rd, 2012

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Summary

On a global perspective, disease is one of the most severe drivers of poverty (Chilundo, 2004; Bennett et al., 2008; Poggenpohl, Sato, 2009). This was already recognized in 1978, when the Declaration of Alma-Ata defined human health as one of the most important social goods worldwide (World Health Organisation (WHO), 1978). As a result, the aim to equally facilitate the access to healthcare in low- and middle-income countries (LMIC) has become an essential component of the development process (Chetley et al., 2006). Between 2006 and 2011, the number of worldwide cellular subscriptions more than doubled from less than three billion to almost six billion (see Figure 2). Thinking about the possibility to reach close to every individual

on the planet via a wireless phone connection, enrols unforeseen advantages in the fight against disease and death (Heeks, 2008). The acknowledgement of those opportunities has lead to the emergence of a relatively young field within healthcare: Mobile Healthcare.

According to a study published recently, the worldwide Mobile Health (mHealth) market is going to have a volume of approximately 23 billion USD by 2017 (Vishwanath, 2012). This research focused on the academic examination of 62 mHealth initiatives in developing countries to derive relevant innovation potentials. A qualitative and quantitative content analysis was conducted and further supplemented by a qualitative trend investigation. The most important findings were the lack of additional intrinsic or monetary incentives for both the patient and the community health worker, the deficit of consistent transmittance standards, which contributes to the profusion of unconnected pilot projects, the absence of networks, and the dominance of SMS as the major communication gateway to minimize technological inhibition thresholds as well as one-way communication initiatives, which thus are not patient centric. Besides, the trend analysis identified “Open Data”, “Data Intelligence” and “Low- and High-Technology Sensors and Services” to be an influencing aspect for the contemporary mHealth industry. All those results were used to form the design framework, which basically consist of a SMS based service, connecting the relevant stakeholders of mHealth.

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Table of Contents

Summary ............................................................................................................ 1

Table of Contents ............................................................................................... 2

Figures ................................................................................................................ 5

Tables ................................................................................................................. 7

Abbreviations ..................................................................................................... 8

Acknowledgements ......................................................................................... 10

1. Chapter – Introduction .............................................................................. 11 1.1. Research Aims .............................................................................................. 11

1.2. Thesis Outline ............................................................................................... 12

2. Chapter – Conceptual Framework: The Contribution of Mobile

Technologies to the Healthcare Sector .......................................................... 13 2.1. Introduction ................................................................................................... 13

2.2. Mobile for Health .......................................................................................... 13

2.2.1. The Context of Healthcare in Developing Countries .................................... 13 2.2.2. Definition of Mobile and Electronic Healthcare ............................................ 16 2.2.3. Promotion, Potentials and Challenges ........................................................ 17

2.3. Review of Technologies ............................................................................... 20

2.3.1. Tools Being Used for Mobile Health ............................................................ 20 2.3.2. Most Important Wireless Technologies ....................................................... 24

2.4. Mobile Health Applications .......................................................................... 27

2.4.1. Awareness and Education (mLearning) for Patients .................................... 27 2.4.2. Remote Data Collection for Caregiver ......................................................... 28 2.4.3. Remote Monitoring as a Two-Way-Communication Tool ............................ 30 2.4.4. Communication and Training for CHWs ...................................................... 30

2.4.5. Disease and Epidemic Outbreak Adherence ............................................... 31 2.4.6. Telemedicine for Mobile Health ................................................................... 32

2.5. Conclusions .................................................................................................. 33

Table of Contents

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3. Chapter – Methodology ............................................................................ 34 3.1. Introduction ................................................................................................... 34

3.2. General Framework ...................................................................................... 34

3.2.1. Object of Research ..................................................................................... 34 3.2.2. Hypotheses ................................................................................................ 35 3.2.3. Research Criteria ........................................................................................ 36

3.3. Analysis Methods ......................................................................................... 37

3.3.1. Vision in Product Design ............................................................................. 37 3.3.2. Content and Trend Analysis ........................................................................ 38 3.3.3. Timing ........................................................................................................ 39

4. Chapter – Analysis ..................................................................................... 40 4.1. Introduction ................................................................................................... 40

4.2. General Reflections ...................................................................................... 41

4.2.1. Geographic Distribution .............................................................................. 41 4.2.2. Emphasis and Actuality of Categories ......................................................... 42

4.3. Discussion of Hypotheses ............................................................................ 44

4.3.1. Hypothesis A: Current Initiatives Mainly Focus on the Treatment of Existent

Diseases, Instead of Preventing the Original Cause. ................................... 44 4.3.2. Hypothesis B: Most of the Available Approaches are not Patient Centric or do

not Include Qualitative Insights out of the People’s Lives and Therefore are

not Adapted Adequately. ............................................................................ 46 4.3.3. Hypothesis C: There is a Profusion of Unconnected Pilot Projects. ............. 48

4.3.4. Hypothesis D: Current Mega-Trends Will Affect the Technical MHealth

Environment and Will Lead to new Potentials. ............................................. 50 4.4. Trend Analysis of Upcoming Technological Solutions ............................... 52

4.4.1. Open Data for Mobile Health in Developing Countries ................................. 53 4.4.2. Data Intelligence ......................................................................................... 54 4.4.3. Low- and High-Technology Sensors and Services ...................................... 55

4.5. Conclusions .................................................................................................. 58

Table of Contents

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5. Chapter – Design ....................................................................................... 62 5.1. Introduction ................................................................................................... 62

5.2. Text to Number – Concept ........................................................................... 63

5.3. Briefing .......................................................................................................... 65

5.3.1. Framework ................................................................................................. 65 5.3.2. Behavioural Change ................................................................................... 66

5.4. Conclusions .................................................................................................. 67

6. Chapter – Future Directions ...................................................................... 68 6.1. Restrictions of This Study and Further Research ....................................... 68

Appendix I ........................................................................................................ 70

References ....................................................................................................... 76

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Figures

Figure 1: Mobile Health as a Critical Domain Within Electronic Health. Own elaboration. (2012). ........................................................................................................................... 16 Figure 2: The Comparison Between Mobile Cellular Subscriptions in Developed

Countries and Developing Countries (ITU, 2011). ........................................................ 18 Figure 3: The GSMA mHealth Tracker counts 625 mHealth products and services worldwide (GSMA, 2012e). .......................................................................................... 27 Figure 4: Design Approach. Own Elaboration. (2012). ................................................. 40 Figure 5: Regions of Deployment. Own Elaboration. (2012). ....................................... 41 Figure 6: Countries of Deployment. Own Elaboration. (2012). ..................................... 42 Figure 7: Categories of the Examined Initiatives. Own Elaboration. (2012). ................. 43 Figure 8: Number as well as Percentages of Active and Inactive Initiatives. Own

Elaboration. (2012). ...................................................................................................... 43 Figure 9: Number of Initiatives in the Respective Treatment Phase. Own Elaboration. (2012). ........................................................................................................................... 44 Figure 10: Percentages of Professional and Patient Centric Triggers. Own Elaboration. (2012). ........................................................................................................................... 46 Figure 11: Amount of Initiatives, Which had Incentives or did not. Own Elaboration.

(2012). ........................................................................................................................... 47 Figure 12: Number of Initiatives, Which are Connected to Other Mobile Health

Approaches. Own Elaboration. (2012). ......................................................................... 48 Figure 13: Variety of Transmission Channels Being Used by the Initiatives. Own Elaboration. (2012). ...................................................................................................... 49 Figure 14: Percentages of the Respective Devices, Which Were Used. Own Elaboration. (2012). ...................................................................................................... 51 Figure 15: Three Mega Trends for Three Major Sectors: Open Data (System), Data

Intelligence (Content) and Sensors (Devices). Own Elaboration. (2012). ...................... 52 Figure 16: The Different Patterns of the Examined LHTSS and Their Trends. Own Elaboration. (2012). ...................................................................................................... 56 Figure 17: General Overview of the Design Framework. Own Elaboration. (2012). ...... 62 Figure 18: Networking the World Community With Health Care Delivery. Own

Elaboration. (2012). ...................................................................................................... 63

Figures

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Figure 19: Behavioural Change Through Right Triggers (Fogg, 2007). ........................ 66 Figure 20: Examined mHealth Initiatives 1 / 6. Own Elaboration. (2012). ..................... 70 Figure 21: Examined mHealth Initiatives 2 / 6. Own Elaboration. (2012). ..................... 71 Figure 22: Examined mHealth Initiatives 3 / 6. Own Elaboration. (2012). ..................... 72 Figure 23: Examined mHealth Initiatives 4 / 6. Own Elaboration. (2012). ..................... 73 Figure 24: Examined mHealth Initiatives 5 / 6. Own Elaboration. (2012). ..................... 74 Figure 25: Examined mHealth Initiatives 6 / 6. Own Elaboration. (2012). ..................... 75

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Tables

Table 1: Seven Groups of Devices Being Used for Mobile Healthcare in Developing Countries (Mechael, 2008). .......................................................................................... 23 Table 2: An overview about the most important wireless networks (Garg, 2007). ....... 24 Table 3: Design Framework Briefing. Own Elaboration. (2012). ................................... 66

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Abbreviations

3D = Three dimensional AESSIMS = Acute Encephalitis Syndrome Surveillance Information System AIDS = Acquired immunodeficiency syndrome CHW = Community health worker eHealth = Electronic health EDGE = Enhanced Data rates for GSM Evolution E.g. = Exempli gratia (Latin; for example) EHD = Electronic health data EHR = Electronic health records DIY = Do-It-Yourself GDP = Gross domestic product GPRS = General Packet Radio Service GSM = Global System for Mobile communications GSMA = Groupe Speciale Mobile Association HIV = Human immunodeficiency virus HSDPA = High Speed Downlink Packet Access ICT = Information and Communication Technologies ICT4D = Information and Communication Technologies for Development IEEE = Institute of Electrical and Electronics Engineers OLPC = One Laptop Per Child PDA = Personal digital assistant Kbps = Kilobytes per second LHTSS = Low- and High-Technology Sensors & Services (chapter 4.4.3)

LMIC = Low and middle income countries Mbps = Megabytes per second MDGs = Millennium Development Goals MHealth = Mobile health SMS = Short Message Service UK = United Kingdom UMTS = Universal Mobile Telecommunications System USA = United States of America

Abbreviations

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USD = United States Dollars USSR = Union of Soviet Socialist Republics ViP = Vision in Product Design

WHO = World Health Organization WLAN = Wireless Local Area Network WPAN = Wireless Personal Area Network WWAN = Wireless Wide Area Network WCDMA = Wideband Code Division Multiple Access

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1. Chapter – Introduction

1.1. Research Aims

The main aim of my research was to form a framework, which has the ability to enhance a broad scope of current mHealth initiatives in developing countries. Therefore, I had to critically analyse to date approaches to consequently attribute such a leitmotiv. In particular, the roles of the patient, economic sustainability and upcoming mega-trends interested me. To reach this aim the following six objectives

were formed:

• To understand the main stakeholders’ needs of healthcare and their abilities of health delivery within developing countries.

• To explore the possibilities of connecting those needs and abilities through a mobile network. This includes the evaluation of actual flows of information and communication within those areas.

• To understand the main processes of regular data collection in developing countries and the continuously growing role of mobile devices within this field.

• To identify strengths, weaknesses, opportunities and threats for current mobile data collection initiatives.

• To identify current economic and behavioural incentives being used and thus

formulating a proposal for future approaches.

• To examine which measures should be prioritized, so health delivery in developing countries can be enhanced.

Those research aims do not represent the hypotheses of this thesis, which

were articulated in the section 3.2.2, but can rather be understood as my personal questionnaire, defining the guideline of the framework, at the same time.

Introduction

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1.2. Thesis Outline

This study contains six chapters. While the first one is simply introducing the whole thesis, the second one already focuses on the examination of the conceptual framework for mHealth initiatives in developing countries. It describes the value of mobile devices for healthcare, the technical environment of the industry and defines six categories for mHealth applications and services. The knowledge gathered through that chapter, is considered as the basis for the research aims formulated above and the methodology, used to examine the field. In the following chapter, the required measurement tools are defined. Mainly concentrating on a mixture of a qualitative and quantitative content analysis, a trend analysis was also performed. Also the object of research, namely 62 mHealth approaches, the hypotheses and research criteria are articulated in that section.

The fourth chapter was dedicated to the execution of the different analyses. After a passage about the general attributes of the examined initiatives, the formerly stated hypotheses are discussed. Besides, multiple macro trends have been collected and were summarized to four major trends, influencing the current and future mHealth field in emerging countries.

The last two chapters describe the developed design framework and conclude with future directions for both the work of this thesis and the academic research of the Mobile Health industry. Moreover, the Appendix I covers all 62 mHealth initiatives, which were used during the time of this study, as well as the spread sheet compiled for the measurements.

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2. Chapter – Conceptual Framework: The Contribution of Mobile

Technologies to the Healthcare Sector

2.1. Introduction

The subsequent paragraphs and pages describe the conceptual framework of mHealth in emerging countries. First of all, the context of healthcare in developing countries and the role of mHealth in those regions are evaluated. Mobile health is then identified as a part of the electronic healthcare field and further defined. After the promotion of mobile devices and the potentials as well as the challenges of mHealth are examined and explained, this chapter conducts with the technical environment of Mobile Health applications and services. The last section finally manifests the six most important mHealth categories.

2.2. Mobile for Health

2.2.1. The Context of Healthcare in Developing Countries

On a global perspective, disease is one of the most severe drivers of poverty (Chilundo, 2004; Bennett et al., 2008; Poggenpohl, Sato, 2009). This was already

recognized in 1978, when the Declaration of Alma-Ata defined human health as one of the most important social goods worldwide (World Health Organisation (WHO), 1978). The declaration evoked the principle of Primary Health Care, which has been at the heart of health service delivery systems throughout the globe ever since (WHO, 1978). As a result, the aim to equally facilitate the access to healthcare in low- and middle-income countries (LMIC) has become an essential component of the development process (Chetley et al., 2006). Recently, the United Nations published the Millennium Development Goals (MDGs) to emphasize the cooperation of all stakeholders within a given range of macro level targets. Whereas mostly all targets can be addressed to the context of broader human wellbeing, especially three aims are directly associated with health: To reduce child mortality, improve maternal health and to combat HIV / AIDS, malaria and other major diseases such as tuberculosis (United Nations, 2000).

Conceptual Framework

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Relating to the World Bank’s Annual Report 2011 the efforts by governments, international agencies and Non-Governmental Organizations (NGOs) showed some substantial effects in many countries and regions, before the peak of the financial crisis in 2008. In developing countries all over the world, regular life-expectancy rose, the enrolement of primary education programs increased, maternal mortality rates dropped and deaths of children under five years declined by almost 28 percentage from 1990 to 2008. However, health delivery in rural areas developed much slower than elsewhere (The World Bank, 2011). But the contemporary society is still facing major inequalities, which evoke a variety of effects on healthcare. With the occurrence of the financial crisis, the global economic system was weakened, causing massive irregularities in agricultural trade. Highly volatile nutrition prices have been the consequence, affecting the development towards the MDGs immensely (Willem te Velde, 2009). Leaving millions without food supply again, represents a huge setback for the former achievements. At this point of time health delivery in developing countries has only improved marginally and therefore a lot of the nominal aims seem to be unmanageable until the end of the MDG-cycle in 2015 (United Nations, 2011).

The organizations on the ground face several severe problems, which basically

have their origin in the lack of a profound infrastructure. This includes not only the unavailability of cyberinfrastructe or access to public transport systems and ways, but also the shortage of trained healthcare personnel. There are 57 countries, which acknowledge a shortage in health work force density. With a global deficit of 2.4 million physicians, midwives and nurses, several communities cannot even rely on minimum standards of healthcare. Whereas in Africa only 2.3 healthcare workers have to cover the diseases of 1000 people, in America the population is treated by ten times as much professionals. On a global perspective, this means that every fourth disease world-wide has to be managed by 1.3 percentage of the world’s health workers (Naicker et al., 2009). Those facts are leading to devastating results for communities and patients. Children born in emerging countries and under the age of five are over 33 times more likely to die than children born in developed countries, even though the most common causes of deaths are predictable and preventable through the adaption of standard medical procedures. Every day 1.500 women die as a consequence of pregnancy complications or childbirth. Further more, approximately 10 million women suffer from health issues evoked by mistreatment during birth, each


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year. 2.5 million people have been infected with HIV in 2007. Additionally completely communicable and therefore avoidable illnesses like malaria or tuberculosis are still responsible for millions of deaths, due to the absence of access to medical treatment (Vital Wave Consulting, 2009). Since developing countries are lacking a reasonable amount of health personnel and are facing such distinct and interrelated health challenges at the same time, all stakeholders recognize the need for new tools and technological solutions, which could leverage the power of community health workers to provide the people with basic healthcare. Focussing thereby on enhancing the possibilities of the real work force, is considered as one of the most promising objectives to improve health (WHO, 2006).

Information and Communication Technologies (ICTs) can help to improve actual processes by simply enabling a digitalized data exchange. The collection and distribution of (patient-) data, which is one of the most important tasks in developing countries, can be achieved more efficiently with a higher grade of quality and lower cost (Chetley et al., 2006). The ubiquity of ICTs throughout the industrialized world

already helped to steadily maximize the output of economical and humanitarian efforts. Still emerging countries are struggling to build a sufficient coverage of those new technologies (Castells, 1996). Five billion people still lack an access to the Internet, while the broad majority is retrieving their information from radio or television. Only 15 percentage of all households within the sub-Saharan area are connected to a stable provision of electricity and therefore are not able to charge electronic devices (Heeks, 2008). “One Laptop Per Child” (OLPC), an organization dedicated to furnish minimalized laptops to children in developing countries and enabling them to get education, has already deployed 2.5 million laptops worldwide (OLPC, 2012). And still this is not enough to connect five billion people, especially when it comes to healthcare. Additionally, health staff often is not trained enough to use computers productively or maintain them correctly (Idowu et al, 2003). The instruction of health workers and the construction of wide internet-infrastructure could take decades. Rather than to wait for those changes, a lot of organizations are focusing their research on the capabilities of low-cost mobile phones. During the last years the distribution of cell phones in emerging countries grew faster than any ICT before and has reached almost absolute ubiquity. Thinking about the possibility to reach nearly

every individual on the planet via a wireless phone connection, enrols unforeseen


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advantages in the fight against disease and death (Heeks, 2008). The acknowledgement of those opportunities has lead to the emergence of a relatively young field within healthcare: Mobile healthcare.

2.2.2. Definition of Mobile and Electronic Healthcare

The term “Mobile Healthcare“ (mHealth or Mobile Health) describes the general practise of medicine and public health assisted by mobile devices. Those include not only low-technology handsets, but also personal digital assistants (PDAs), smartphones, tablet computers or other small, portable and wireless computing and communication devices. Mobile Health helps to meet the demands of patients and healthcare providers through the exchange of critical health information and services (Kuhn et al, 2007). More precisely, mHealth is associated within the context of electronic healthcare (eHealth or electronic health).

Figure 1: Mobile Health as a Critical Domain Within Electronic Health. Own elaboration. (2012).

Electronic health can be understood as an overlapping domain, which uses all

information and communication technologies for health services and information. Gunther Eysenbach, an editor from the Journal of Medical Internet Research, defines the term as following:

“eHealth is an emerging field in the intersection of medical informatics, public health and business, referring to health services and information delivered or enhanced through the Internet and related technologies. In a broader sense, the term


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characterizes not only development, but also a state-of-mind, a way of thinking, an attitude, and a commitment for networked, global thinking, to improve healthcare locally, regionally and worldwide by using information and communication technology.” (Eysenbach, 2001).

Thus mHealth and eHealth have both the same objectives, namely to leverage the outcome of healthcare work and to use technology as a key factor, they have become inseparably linked. For instance, a lot of eHealth initiatives enable healthcare providers to build a web-based backend for the aggregation of standardized and digitalized patient data, like electronic health records (EHRs). Such data can then be accessed nationally or internationally, whereas mHealth programs are being used to function as the point for entering and accessing patient or other health related datasets (Vital Wave Consulting, 2009). However the technological capabilities and limitations of mobile devices in developing countries play a huge role for the design of new and sufficient initiatives or ecosystems.

2.2.3. Promotion, Potentials and Challenges

The field of mHealth applications and services in developing countries has not only

seen an extensive growth in the variety of initiatives, but was also promoted by the wide adoption of mobile telephony. Between 2006 and 2011, the number of worldwide cellular subscriptions more than doubled from less than three billion to almost six billion (see Figure 2). Mainly LMICs are responsible for this rapid advancement.

In particular, there are six factors, which essentially contributed to the progress of cellular spread. Previously unconnected people value the possibility of distant communication and at the same time can benefit from prepaid systems, which allow them to control their expenditure as well as enable them to participate – even with very low investment or obligations. Besides, mobile devices are easy to use and SMS as well as the use of “beeping” (inviting an recipient to call back) represent extremely affordable communication opportunities. On the side of the network providers, necessary mobile network infrastructure is less expensive than the establishment of land-lines. Additionally, the liberation of the mobile phone market promoted the competition among operators and therefore vividly decreased prices (World Bank, 2010).


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Figure 2: The Comparison Between Mobile Cellular Subscriptions in Developed Countries and Developing Countries (ITU, 2011).

According to a study published recently, the worldwide mHealth market is going to have an approximate amount of 23 billion USD in 2017 (Vishwanath, 2012). It is estimated that about 750 million Indians still have no access to proper health services (Ganapathy, 2008). Regarding the fact that every citizen in the developing world is about 210 times more likely to have access to a mobile phone than to a hospital bed, underlines the immense potential mHealth is holding (Vital Wave Consulting, 2009). Although there exists a global trend to urbanisation, which does not stop before LMCIs, mHealth is particularly interesting for people living in remote areas where health delivery is even scarcer. In those regions, cellular communication for the purpose of health enables communities to bridge information gaps and leapfrog towards integrated national eHealth platforms (Ganapathy, 2008). Further, patients and health workers benefit from the remote access to information and communication from anywhere with network coverage at anytime and for low costs (Mechael et al, 2009).

But mHealth in developing countries also faces some difficulties and challenges. For instance, a lot of projects lack economic sustainability and heavily rely on funding systems. Fundamental business models have not been developed or established, although the market of bio- and wearable sensors promises new directions in that field (Mechael, 2008). Due to the current ubiquitous coverage with low-technology handsets or feature phones, technical limitations become clear. SMS and voice transmittance may not exhaust the full capabilities of the field, but future


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evolvements point to a lowering in cost for more advanced devices, which then are considered to be adapted more frequently in emerging countries (Vishwanath, 2012). Until then the organic use of mobile phones, for example a call during an emergency, still remains as the most valued capability of the device. To date, products and services may have not been investigated enough on how consumer’s needs could be met more precisely and how behavioural change can be rewarded more sufficiently. Further, most initiatives work solitarily and unconnected. Network effects, experiences and valuable insights thereby cannot be shared or exchanged, which slows the whole innovation and development process (Mechael, 2008).


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2.3. Review of Technologies

Since the beginning of the year 2012, worldwide mobile subscription numbers have surpassed the milestone of six billion delivered devices. According to the Groupe Speciale Mobile Association (GSMA), the Asia Pacific region is therefore the major driving force. By 2015, it will have four times more connections than Europe and North America combined, accounting for 40 percentage of the global mobile data traffic. The GSMA calculates with about 4.1 billion cell phones, only in that area (GSMA, 2011). Those figures are an indicator for the feasibility of networking the whole world to the benefits of ICT and consequently form a realistic approach to achieve the MDGs. To understand the technological side of mHealth initiatives, the capabilities and limitations of used tools and wireless networks as well as upcoming technological solutions and trends have to be described.

2.3.1. Tools Being Used for Mobile Health

Regular low technology handsets and feature phones are the most common communication channels in emerging markets. Although 488.5 million smartphones have been shipped so far, their penetration is mainly focused on the countries of the industrialized world. This does not exclude that penetration rates cannot rise up in the near future. However, this depends heavily on the evolution of smartphone prices, consumer benefits and future value creation chains of smartphone suppliers (Vakulenko, 2011). For the same reasons newer technologies, like Multimedia Messaging Service (MMS), direct emailing or other web-based solutions, have not been able to unfold their full potential until now. In the case of online concepts, this may be attributed to the comparatively high upfront costs for devices, relatively low bandwidth and connectivity in developing countries, to the point that online time is more being used for Social Media than for anything else and to the possibility to use those concepts with other, more impersonal hardware, like laptops or PCs (Popescu-Zeletin et al, 2011). Consequentely, impersonal tools are not as much suitable for health purposes as more personal ones. Regarding those indicators, mainstream mobile communication for health purposes should be restricted to the capabilities of regular cell phones. As a result, Short Message Service (SMS) and voice calls remain as key technologies (Vital Wave Consulting, 2009) in order to reach the highest


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amount of people through one channel in addition with the lowest possible inhibition threshold. SMS is considered as a diverse transmitting method for data. Primarily used to visualize static text, it can be utilized for several other intentions. For example, FrontlineSMS has developed a system, which enables anyone with a laptop and a mobile phone to set up a communication gateway, which allows mass texting for surveys or community awareness campaigns (Miguel, 2009). Other initiatives use SMS to extend interactive decision support algorithms, transfer other visual information or to bridge the gap to the lack of Internet through the integration of SMS to email systems (Mechael, 2008). Whereas voice is usually used as a two-way-communication tool, it currently suffers from an insufficient capability to be machine-readable. At the moment, voice driven initiatives are very time and resource consuming, concerning the mass market. Consequently, some research teams have begun to think about using automated speech recognition and spoken language systems for mHealth, but technology issues hold innovations back (Benzeghiba, 2007). Another project, called

Masiluleke, which is initiated by the global design and innovation company Frogdesign, used SMS to communicate with HIV / Aids patients in South Africa. Patients received a message, which made them aware of the project and the possibility to order a free, at-home HIV test. Besides, they implemented a cost-free helpline to receive calls from HIV positive patients. Within days, hundreds of thousands of calls had been processed (Frogdesign, 2008). Although there is still very little evidence for the true effects of text messaging on human health and healthcare objectives (Kaplan, 2006), the opportunity to finally build a connection between consumers and carers seems to be extremely promising to lower the burden of bad health in developing countries. Apart from those handsets, which only can use text and voice, the mHealth field has seen several other groups of tools. Table 1 summarizes all five groups that have been used in the field.


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Group Description

Personal Devices

1. Low technology handsets and feature phones.

• Extremely low cost and vast distribution.

• Only low capacity data transfer and voice.

• Easy to use, intuitive and user friendly.

• Two-way communication tool, but input-oriented.

• No geographical data transmittable, no or low

multimedia capacities.

• Example: Nokia 3310.

2. Personal Digital Assistants (PDAs).

• High cost and specified distribution.

• High capacity data transfer, voice and Internet.

• Training and higher maintenance necessary.

• True two-way communication tool.

• Highly preferred for data collection, GPS and multimedia capacities.

• Example: Dell Axim X51v.

3. Smartphones.

• High cost and growing distribution.

• High capacity data transfer, voice and Internet.

• Lower training and high maintenance necessary.

• True two-way communication tool, but being

used predictably in hospitals or urban areas.

• Highly preferred for data collection, GPS and multimedia capacities.

• Open application platforms, point-of-care support.

• Example: iPhone 4s.


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Devices With Ecosystems

4. Mobile patient monitoring and telemedicine devices (biosensors / wearable sensors).

• Cost is very variable, distribution very specific.

• Gather / asks for patient data and transmits it through text, PDAs or smartphones.

• Training intensive, tools for carer.

• Machine to machine communication only.

• Specialized on determined health subjects.

• Example: EyeNetra.

5. Mobile computing and convergent technologies.

• High cost, but the potential to work for millions.

• (Open Source) software / applications and every hardware mentioned before allows e.g. the request for medical advise.

• Needed training depends on user interface design, tools for patients and carer.

• Wide range of communication possibilities.

• Examples: OpenMRS, Cell-Life Project South

Africa.

Table 1: Seven Groups of Devices Being Used for Mobile Healthcare in Developing Countries (Mechael, 2008).

The first three groups mainly divide themselves through cost and capabilities and thus through the way of distribution. High frontend costs for devices, such as PDAs and smartphones, provoke a low adaption in LMICs. Although the technologic advantages could help to immensely affect the quality of Mobile Healthcare initiatives (Kulkarni et al, 2008). But, as mentioned before, the costs for smartphones are

expected to drop, as they become more and more ubiquitous in developed countries and demand grows in other parts of the world. This would allow smartphones to gradually gain enough market share to unveil their full potentials for the emerging world.


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The last two groups are very dependent on the devices mentioned in the first three points and are therefore representing the evolving field of ecosystems. They are steadily enhancing their capabilities to gather or process a specific part of patient data. As a result, they build the cutting point between hardware and hardware or hardware and software. In the future we are going to need further initiatives focusing on the connection of existing projects, rather than creating new pilots (Mechael, 2008). At the same time those approaches deliver reasonable business models, which often are the centre of criticism or the reason, why a pilot project does not last until after the testing phase (Sandhu, 2011).

2.3.2. Most Important Wireless Technologies

The most important wireless technologies can be clustered into three different domains: Low and middle range networks, as well as long distance transmitting technologies (Garg, 2007).

Table 2: An overview about the most important wireless networks (Garg, 2007).

Short-range-networks, or Wireless Personal Area Networks (WPANs), mostly

focus on the exchange of smaller amounts of data within a well-known environment and relatively restricted numbers of peers. It enables Machine-to-Machine (M2M) communication within locally restricted points-of-interest and therefore plays a role in the design of events, which ought to be triggered by a certain state of position and


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time (of a device). Bluetooth, Ultra Wideband and the other technologies, listed under WPAN in Table 2, enable the different devices to interact among themselves, building each a standard for the way data can be sent or received (Garg, 2007).

Middle-range-networks, or Wireless Local Area Networks (WLANs), have the task to connect a wider range of devices and hardware to a local system. Servers can function as commonly used data storages and sharing-platforms. Besides, WLANs can share as well further communication channels, such as Internet, and consequently enable all users to participate at the communication channel. Although the excessively utilisation of one channel will predictably slow down connection speed, sharing channels is an important possibility to facilitate workflows and lower costs. Televisions and entertainment systems normally use the Home RF standard, whereas PCs, laptops or other WLAN-capable hardware relies on IEEE 802.11a, b or g (Garg, 2007).

Long-distance-networks, or Wireless Wide Area Networks (WWANs), use antennas and satellites to transmit a variable set and form of data to another precise

identifiable user or computer-system. The other networks are working within smaller or medium ranges and groups, thus are restricted to a regionally defined areas. On the contrary, WWANs bridge long distances. For this they need a variety of different system and transmittance technologies.

The Global System for Mobile Communications (GSM) has been implemented with the second generation of mobile phones. It is an openly accessible technology, designed for the exchange of cellular data. Transmitting voice calls, text messages via SMS and data services with a transfer speed of up to 9.6 Kbps (GSMA, 2012a). GSM is available worldwide and the most popular standard for cell phones (Worldtimezone, 2010).

The General Packet Radio Service (GPRS) offers up to four times higher data rates than regular GSM and is therefore the cutting point for the use of rudimentary online services. Thus, users are enabled to benefit from feature-rich services, such as email, MMS, Social Media or geospatial services. GPRS is one of the most widely deployed data services in the world and now accessible in the biggest parts of GSM networks (GSMA, 2012b).


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If there is a need for bigger bandwidth, GSM networks can be upgraded with

Enhanced Data rates for GSM Evolution (EDGE). EDGE is a transmission-technology out of the 2.5 generation (2.5G) and has a maximal capacity of 120 Kbps and was created to fulfil users’ demand for higher and quality improved data exchanges. Network provider can implement EDGE, which is based on the structure of GSM networks, with a simple software update and without intensive switching costs. Hence monetary inhibition thresholds for providers are lowered tremendously (GSMA, 2012c).

The third generation (3G) of mobile phones and networks, is essentially formed through the usage of Universal Mobile Telecommunications System (UMTS) and the further development of UMTS to 3.5G: High Speed Downlink Packet Access (HSDPA). Both represent mobile data standards with bandwidth capacities up to 14 Mbps, although such high amounts of data-transfer only can be achieved within the context of a scientific test. Wideband Code Division Multiple Access (WCDMA) is used as the interface for both, UMTS and HSDPA networks. WCDMA is able to furnish data

intensive multimedia applications in high speed, equipping mobile phones with broadband Internet access (GSMA, 2012d). CDMA2000 is another standard like WCDMA, only with an alternative regional deployment (Worldtimezone, 2010).


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2.4. Mobile Health Applications

At the moment, the GSMA mHealth Tracker counts 625 mHealth products and services worldwide (GSMA, 2012e). Particularly, in developing countries a lot of initiatives can be seen, which canalize their work on the maintenance of basic and health related human needs. However, a view on the literature unveils different categorizations of mHealth sectors (e.g. Freng, 2011; Vital Wave Consulting, 2009; Mechael, 2008). As there is no coherent opinion, the amount of mHealth applications and services in the respective category vary, depending on the report considered. In this thesis six sectors have been defined. Those include awareness and education, remote data collection, medication monitoring, communication and networking, disease and epidemic outbreak surveillance and telemedicine. The following paragraphs summarize the most important aspects about those fields and create the necessary theoretic framework for the research-part of this thesis. Additionally, to the examples given in the summaries, a list of all researched initiatives have been attached to Appendix I.

Figure 3: The GSMA mHealth Tracker counts 625 mHealth products and services worldwide (GSMA, 2012e).

2.4.1. Awareness and Education (mLearning) for Patients

The most common way of using “Awareness and Education” initiatives within the field of mHealth is the dissemination of health related information through SMS. Text messages offer thereby a cost-effective, efficient and scalable method to educate the


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population about hygiene and nutrition concerns, sexual diseases like HIV / AIDS, the maintenance of clean water or the importance of immunization and finally about current programs in the receiver’s area (Mechael, 2009). Although such initiatives were very limited to the technical capabilities of SMS and were not always able of making a two-way-communication possible, first evidence is gathered that SMS alerts have relevant impact on patients’ behaviour. Further, information send through text messages is more efficient to change disease concerned behavioural patterns than other mass media channels, like radio or television campaigns. In comparison with those channels, SMS is cost-effective, scalable and widely spread, convenient and very popular among cell phone subscribers. Besides, it is more private and less obtrusive, which is very important for the communication of stigmatized topics such as HIV / AIDS, sex, and sexually transmitted infections (Vital Wave Consulting, 2009). Some initiatives use interactive quizzes or Gamification (the use of virtual or intrinsic incentives to motivate the user) to make the services more appealing. Text to Change, for example, sends an SMS quiz about HIV / AIDS to a targeted audience, asking it on their knowledge about the illness and the local medical infrastructure. Based on their

individual responses, further educational material is distributed (Text to Change, 2012). The service of mPedigree is a patient centric example for Awareness and Education initiatives. It gives consumers the possibility to check on the authenticity of medicaments through a simple SMS request and as a result of that helps to fight counterfeiting drugs (mPedigree, 2012).

Nevertheless, current approaches lack personalization and suffer from the anonymity of mass-texting. Additionally, the data capacity of SMS limits the transmittable amount of data immensely (Mechael et al, 2009). With the fast emergence of web-based solutions, new opportunities occur. Neither they were validated yet, nor is the penetration of EDGE networks in developing countries high enough to reach relevance. Therefore SMS and voice remain as the main channels to this domain (Heeks, 2008).

2.4.2. Remote Data Collection for Caregiver

The second category contains all mHealth approaches, which are concerned with the collection of health data in the field, and the access to EHRs through mobile devices.


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This bi-directional exchange of information helps health workers to supervise, evaluate and research statistical data. GPS attributes to localize certain occurrences, like emergencies or disease outbreaks. Moreover, Remote Data Collection is used to control and validate the performance of public health projects. Due to cost of transportation or the lack of travel infrastructure in emerging countries, the inability of the population to visit health centres is a critical component. Consequently, gathering field information at the point-of-care and having access to updated health data is particularly important for leveraging the outcome of health workers’ operations (Burasa, 2005).

Traditional paper-based information systems can only provide incomplete, sometimes inaccurate and unreliable data, which leads to equally inadequate decisions (Wessels, 2005). Digital information systems – fostered through smartphones, PDAs or mobile phones – articulate a huge improvement in the data collection process (Vital Wave Consulting, 2009). Whereas reports of epidemiological spreads, the provisions of health records as well as the status and location of

available drug stocks are communicated insufficiently during the absence of such technologies (Burasa, 2005). But mobile devices, such as PDAs, have technical limitations, which have to be considered: Duration of batteries, memory capacity, cost for training and maintenance as well as their dependence on connectivity (Ganesan et al, 2011). RapidSMS, a free and open framework for SMS-based data collection, even suggests to maintain both systems, paper and digital, simultaneously, to prevent a loss of data in the case of intermittence of the telecommunications network or governmental intervention (RapidSMS, 2012).

Aside from this statement, RapidSMS is a perfect case for Remote Data Collection tools. It uses technologies, which are mostly ubiquitous available for all cell phone users, and is openly accessible for other initiatives. The operation ChildCount, for example, has been developed by the Earth Institute to measure the progress of the MDG “child-mortality”. Community health workers (CHWs) are given the opportunity to monitor child mortality rates in specific areas and report those via SMS. Further, patients can be registered and health reports formed. An automated alert system keeps track of treatment goals (RapidSMS, 2012).


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2.4.3. Remote Monitoring as a Two-Way-Communication Tool

Remote Monitoring applications and services focus their work on the monitoring of patients’ chronic diseases and the adherence of prescribed medication. Hence, such solutions make sense in the context with chronic diseases. Such illnesses demand complex treatment, compliance of individual medication plans and close monitoring of the patient’s body reactions. It is common practise that in developing countries CHWs have to take care of drug compliance and delivery, which implies huge efforts for patients as well as carers (Ramey, 2008).

Whereas emerging countries mainly fight with the dominance of communicable diseases, three big conditions exist with the need for long-term treatment. Those are HIV / AIDS, tuberculosis and malaria. Remote Monitoring faces these widely spread illnesses with applications, which communicate caregiver appointments, remind patients to take their prescribed drugs and may integrate inpatient or outpatient sensors to overlook relevant body information. Besides, they attribute to diminish treatment interruptions, which can increase disease incidents. Although SMS reminders do not guarantee medication adherence, they have shown already great effects to improve survival rates (Vital Wave Consulting, 2009). A promising project in this sector is X Out TB. It addresses tuberculosis patients, who regularly have to be treated on a daily basis between four and six months, and helps them to reduce medication interruptions by reminding them. In the case of tuberculosis, an interruption can cause an even severe form of the disease, called multi-drug-resistant tuberculosis. X Out TB stands out, because it uses a new type of incentive to motivate the patients. Their documented ingestion of pills is rewarded with free airtime, building a monetary stimulus.

2.4.4. Communication and Training for CHWs

Establishing a communication and training network for community based health workers is the major objective for this category. Not only that the current shortage of health personnel is challenging the developing world, but also that a deficit of communication networks leads to a loss for professional capabilities. Therefore, networking CHWs and knowledge resources through the utilization of mobile technology is an effective strategy to bridge communicational gaps and to motivate


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them through the intercomradely exchange of experiences (Vital Wave Consulting, 2009).

Mobile devices are for this task a more as satisfying solution. They enable their users to gain information from anywhere anytime for comparatively low costs. In the case of a massive overload of the provider network, coverage can vanish, though. This happened during the last immense natural disasters, like the earthquakes in Haiti and Chile in 2010. But still, SMS was a convenient tool to send and receive personalized messages, despite overburdened cellular networks, which were not able anymore to interconnect voice calls (Harvard Humanitarian Initiative, 2011). Also, during the regular exercise of health work in developing countries, Communication and Training for CHWs is adapted. For example the Primary Healthcare Nursing Program in Guatemala used ICT and especially mobile phones to teach nurses, who are operating in the remoteness of the rainforest (Vital Wave Consulting, 2009).

2.4.5. Disease and Epidemic Outbreak Adherence

The past has shown several times how the uncontrolled spread of epidemic diseases, such as H1N1 or H5N1, can become pandemics and suddenly not only affect the well-being of local communities but the health of the world population. This fifth sector of mHealth applications in emerging countries is assigned to adherence of such events. The first man, who ever tracked the source of an epidemic disease, was John Snow. He discovered the contamination of a dwell in London, which infected its neighbours with Cholera (Snow, 1855). He built the first milestone for today’s early warning systems, being deployed in Rwanda, Peru or India.

In this context, mobile phones are used to identify and report incidents to national compliance centres. The aggregated data can then be transformed into valuable actions, to prevent the further outbreak of the disease or to forecast epidemics early enough to even evade them from becoming reality. For example, the Acute Encephalitis Syndrome Surveillance Information System (AESSIMS) in India addresses the surveillance of Japanese Encephalitis – a devastating illness, which spreads through mosquitos and can be prevented by simple vaccination. CHWs use mobile phones to report incidences to the central system where decision makers


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access, assess and analyse the collected data in real time (Vital Wave Consulting, 2009).

2.4.6. Telemedicine for Mobile Health

Traditionally telemedicine is to understand as a subdomain of eHealth, but nowadays gains more and more importance in the field of mHealth applications. It can be described as the remote supply of health related services with the anticipation of clinical personal transmitted through electronic communication networks. Formerly, performed through the usage of standard ICTs, like personal computers, completed through further health sensors, mobile phones have the advantage of low-costs, build-in cameras and large-distance communication access as well as mobility and therefore being at the point-of-care (Freng, 2011). Services in that field basically help CHWs and patients to examine an illness or wound. Indications, like text notes, photographs or, when available, GPS data, can be transmitted via SMS or the Internet and then be analysed by a remote specialist. Furthermore, sensors, such as the EyeNetra, evolve, sustaining the examination process through more accurate and precise measurements. Those new ways of diagnostics and treatment support give people the opportunity to get healthcare, without leaving their home and spending money on transportation or accommodation (Vital Wave Consulting, 2009). Nevertheless, treatment and access to prescribed drugs require travelling, even if the examination has taken place remotely.

The whole category can be divided into two sections: First the processing equipment and second the communication technology. Previously, different devices have covered these tasks, now they can be handled by the capabilities of one device; the cell phone. Although the technical constraints of the most common mobile devices in developing countries, such as battery consumption or the lack of coverage and bandwidth, limit this potential. Teledoc, for example, is an award-winning initiative, which was initiated in India. Established in 2003, it deployed a Java-enabled mobile phone service, enabling health workers to diagnose patients through a multiple-choice questionnaire. The collected health data then could be transferred to a central clinic, where Ayurvedic physicians analysed the gathered information and formed an individual treatment and medication advise. Based on that advice, CHWs could take


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action and finally treat the patient in the field (Center for Health Market Innovations, 2012).

2.5. Conclusions

The new ubiquity of ICTs in developing countries opens unforeseen opportunities for the well-being of the poorest and most disadvantaged people the 21st century has to offer (see point 2.2). The capabilities of those technologies, and in particular of those, which are the most common ones (see point 2.3), definitely have the potential to leverage the power of community health workers to deliver care faster, cheaper and more effectively. But also patients suddenly can be educated about their health and for the very first time can anticipate at the process of and the discussion about health care. At the same time, mobile phones help to overcome resource shortages. Products and services evolved, which provide almost instant and reliable data gathered in the field, at the heart of the problem. Creating awareness for illnesses, collecting and monitoring data, training CHWs, adhering diseases or epidemics and using telemedicine, all for the good of health and with minimizing efforts (see point 0). In contrast, to date initiatives often lack economic sustainability. Consequently, dozens of approaches were initiated and pilots tested, but gained experiences often stayed with the project. Even when papers were published, a vivid exchange of information between the different initiatives does not exist. Further, there is an immense lack of standards, facilitating or promoting such interchanges. Additionally, current projects need to focus more on patient centric ideas and the possibilities of new, technologic advancements as well as the impact of global trends on the field of mHealth (Vital Wave Consulting, 2009; Mechael, 2008). Besides, the density of academic publications within this field is very thin (Mechael et al, 2009). Therefore this

thesis aims to supplement current releases through the exploration of the aims raised in 1.1 and the hypotheses in 3.2.2.

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3. Chapter – Methodology

3.1. Introduction

Current literature has pointed out that the vast fragmentation of mHealth initiatives in developing countries and the lack of connections between those will become one of the major challenges and most important factors for further development (Vital Wave Consulting, 2009; Mechael et al, 2009). Hence, the main aim for this study was to

research a significant amount of projects and to make meaningful statements, which then could be transformed into an innovative design approach, trying to solve the formerly identified issue. To reach this goal an empirical framework has been set up. Proceeding with the philosophy of the “Vision in Product Design” approach, a content and trend analysis was conducted. Sixty-two international Mobile Health initiatives out of the developing world were examined. Aside from the expectations regarding the design approach, the results of this study should meet the requirements to satisfyingly discuss the, in point 3.2.2, predefined hypotheses.

3.2. General Framework

3.2.1. Object of Research

According to the GSMA mHealth Tracker currently 625 mHealth initiatives are active worldwide (GSMA, 2012e) and about 75 of those are situated in developing countries. As this thesis focuses its’ work on LMIC, only initiatives in the corresponding environment have been considered. Equally, the research part has to reach a certain

quantitative validity. Therefore, this investigation covers 62 initiatives, which aims to represent approximately 82 percentages of mHealth approaches in LMICs as well as about 10 percentages of the initiatives worldwide. Consequently, further statements and conclusions, which are articulated in the 4th chapter, meet the conditions of mostly all mHealth products and services in emerging countries. Besides, trends and guidelines for the developed world can be evaluated vaguely. Reliability and

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objectivity are achieved through the methods described in 3. A full list and description of the researched initiatives can be found in Appendix I.

3.2.2. Hypotheses

Additionally to the research aims formulated in 1.1 and the background described in 3.1, the hypotheses articulated in this section are understood as the research guideline. Thereby, each of those is supplemented through subcategorized assumptions, validating or detailing their respective main hypothesis. They were formulated out of the comprehension for the mHealth topic, which was gained during literature review. They concentrated in particular on the elaboration of the mHealth environment and thus are in line with the Vision in Product Design approach, described in 3.3.1. Accordingly, this thesis has been built, based on the following four hypotheses:

3.2.2.A. Current initiatives mainly focus on the treatment of existent diseases, instead of preventing the original cause. 3.2.2.A.1. There exists a market opportunity for scouting or sensing communicable illnesses.

3.2.2.B. Most of the available approaches are not patient centric or do not include qualitative insights out of the people’s lives and therefore are not adapted adequately. 3.2.2.B.1. Caregiver’s needs are placed above consumer’s needs. 3.2.2.B.2. Initiatives using incentives are better off than others.

3.2.2.C. There is a profusion of unconnected pilot projects. 3.2.2.C.1. The lack of standards is a reason for this deficit of connectedness.

3.2.2.D. Current mega-trends will affect the technical mHealth environment and will lead to new potentials. 3.2.2.D.1. The benefits of more advanced handsets, like smartphones, are appealing enough to enforce a deployment of those devices at caregiver centres.

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3.2.3. Research Criteria

The research criteria describe the terms and values under which the predefined object of research will be explored. Those have been evaluated according to the points described in 1.1, 3.1 and 3.2.2. Methods of analysis were determined through the point 3.3. The following listing mentions all criteria, which were adapted in this thesis. The more detailed codebook, including the definite terms researched, can be found in Appendix I. Quantitative criteria

• Application or service category in compliance to 0,

• Region of deployment,

• Country of deployment,

• In compliance to 3.2.2.A.: Phase of intervention / treatment,

• In compliance to 3.2.2.B.: Presence of incentives and triggers,

• In compliance to 3.2.2.C.: Connectedness and transmission standards,

• In compliance to 3.2.2.E.: Deployment of devices. Qualitative criteria

• Name of mHealth initiative,

• In compliance to 3.2.2.B.: Patients and caregivers needs as well as special

issues, which occur in the event of humanitarian disasters,

• In compliance to 3.2.2.C.: Business models,

• In compliance to 3.2.2.D.: Connection of stakeholders,

• In compliance to 3.2.2.E.: Megatrends and innovation through destruction.

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3.3. Analysis Methods

The following section is dedicated to the analysis methods, which were used in this study. As the main aim of this research is to create a new, meaningful product, service or similar, the Vision in Product Design (ViP) approach developed by Hekkert and van Dijk was conducted. It merely focuses on problem solving, rather on understanding the whole surrounding and ecosystem of a subject – whereas, for this study, the object of research, which was formulated in 3.2.1, is considered as the subject (Hekkert & van Dijk, 2011). Further, ViP was filled with the insights gathered through a traditional qualitative and quantitative content analysis and future directions were sought through an additional trend analysis.

3.3.1. Vision in Product Design

In 1995, this method was first applied at the Delft University of Technology and then later, in 2011, manifested with a first publication. Basically, the process of ViP is initiated by a structured aggregation of relevant content, stakeholders or statements, which occur to describe the environment of the subject and have an according relevance for it. In this thesis, quantitative as well as qualitative content analysis and trend research have been used to foster such key indicators. Further, those indicators were combined to a new, unified context in order to formulate a statement or goal, which then functions as the leading idea for the design process (see 5th chapter). An example for such a statement would be: “People prefer to achieve something with a minimum of effort”. Consequently, the design principle based on that finding should be easily accessible or utilized (Hekkert & van Dijk, 2011).

ViP differentiates itself thereby from other design approaches, by not translating results of the research phase directly into product features. It focuses more on the interaction between users and products. This is because ViP claims that products are created to accomplish a predefined aim, whereas in interaction with people, products finally manifest their meaning. Therefore, during the design process, a vision for the product has to be described. This vision includes a concept, which is in line with the statements made through research. It delivers ideas for the way a product ought to be viewed, used, experienced or understood (Hekkert & van Dijk,

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2011).

3.3.2. Content and Trend Analysis

The data collection was conducted, as mentioned before, through a content and trend analysis. Whereas the content analysis is considered as an empirical tool to, firstly, investigate the hypotheses from point 3.2.2 and to, secondly, evaluate formal and contextual factors. The content analysis thereby enjoys several advantages compared to other data collection methods. For instance, it is reproducible and consequently independent of scientist bias or experimental subjects (Brosius, 2009). To accomplish a virtually objective and valid investigation, the subsequent criteria should be taken into consideration (Mayring & Gläser-Zikuda, 2008):

• System,

• Intersubjective traceability,

• Lead by theoretic context and predefined rules.

Generally, there has to be differentiated between qualitative and quantitative

content analysis. Whereas the qualitative version delivers a variety of statements for groups and areas of application, it does not allow detailed information about individual projects. On the contrary, qualitative measurement evaluates certain, maybe individual, aspects of a content to derive statements or conclusions, which represent the whole context. The qualitative approach, for example, is restricted to count numbers, words, appearances or other numeric factors. Qualitative approaches lead to more detailed results, regarding the response of research requests, but also are more susceptible for bias (Brosius, 2009).

The following research uses both, qualitative and quantitative content analysis, whereas for the qualitative research trend analysis was added to the regular content examination. Trend analysis is essentially based on the accumulation of smaller indicators, such as early stage applications, products and services, which are called micro-trends. Those micro-trends can be bundled to bigger, more sophisticated streams called macro-trends. Which again are brought together to global mega-trends, possibly affecting certain social or economic developments in the entire world.

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3.3.3. Timing

The 62 Mobile Health initiatives mentioned in 3.2.1 have been determined, evaluated and analysed during April the 15th and July the 23rd, 2012.

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4. Chapter – Analysis

4.1. Introduction

The following chapter covers the qualitative and quantitative measurements of this thesis. Further, those measurements were examined and assessed, providing empirical evidence for the design framework articulated in the 5th chapter.

Figure 4: Design Approach. Own Elaboration. (2012).

The first part of the analysis discloses the general reflections, which could be

derived from the investigation. This includes, on the one hand, the regional and national distribution of the mHealth initiatives in developing countries and, on the other hand, shows the division within the different categories as well as the amount of active approaches. In the second section, all four hypotheses, including their subtopics, which all were formulated in 3.2.2, were discussed. Thereby, each hypothesis with its respective factors was considered individually as well as possible potentials or problems were extracted and assessed. The last hypothesis then is additionally discussed through a qualitative trend analysis, manifested in point 4.4. The conclusions of this chapter finally summarize all potentials and opportunities of the discussion of hypotheses and provide the basis for the following design framework.

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4.2. General Reflections

4.2.1. Geographic Distribution

More than half of the examined mHealth initiatives in developing countries are located in Africa and the Middle East. This may base on the wide distribution of cell phones in this area, but can also be related to an innovative leadership within this field. Exercising 58 percentages of the initiatives, will have positive implications for the development of a consistent mHealth strategy on a long term.

Figure 5: Regions of Deployment. Own Elaboration. (2012).

Further, through this over proportioned amount of approaches in that area, a real and definite will to promote mHealth can be derived. Besides, necessary network infrastructure and mobile phone coverage is most probably available. Latin American and Caribbean regions still seem to beware patience about the Mobile Health topic and therefore are missing out opportunities. Figure 5 describes those facts.

On a country level, in particular Kenya, Uganda and India are making the pace. Together they are responsible for more than every third project being launched. India is directing to a huge potential to reach vast majorities of its’ population. An estimated

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700 million Indians have access to cellular phones (see point 2). From an overall perspective, the remaining mHealth initiatives are not very densely populated in certain countries, but rather spread over a huge diversity of nationalities. Figure 6 illustrates those statements.

Figure 6: Countries of Deployment. Own Elaboration. (2012).

4.2.2. Emphasis and Actuality of Categories

As shown in Figure 7, the category “Awareness and Education (mLearning) for Patients” receives, with almost a third of the applications, the most attention. Traditionally, initiatives in that field are one-way communication oriented, which does not always meet the needs of patients. Though a clear guidance of one categorical field cannot be proved, as the mHealth community has not manifested a clear definition of categories yet.

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Figure 7: Categories of the Examined Initiatives. Own Elaboration. (2012).

Figure 8 illustrates the number of active and inactive initiatives investigated during this study. Whereas 42 mHealth projects still ran during research, already 32 percentages of those were stopped. The main reason for this is that they were initiated as pilots only and had accomplished testing phase already. That is an indicator for a profusion of nascent trials and even leads to the claim that many initiatives may lack economic sustainability. However, this shows the fast dynamics of the industry, which is a potential concerning innovation.

Figure 8: Number as well as Percentages of Active and Inactive Initiatives. Own Elaboration. (2012).

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4.3. Discussion of Hypotheses

4.3.1. Hypothesis A: Current Initiatives Mainly Focus on the Treatment of

Existent Diseases, Instead of Preventing the Original Cause.

Developing countries mainly have to fight communicable diseases (see 1. chapter), when they already have appeared. Enforcing prevention and early detection is the only reasonable solution to minimize those occurrences and therefore might hold a huge potential to reduce healthcare costs and lower disease burden on a long term. This hypothesis aims to verify this assumption.

Figure 9: Number of Initiatives in the Respective Treatment Phase. Own Elaboration. (2012).

To collect relevant evidence for this assumption, the 62 initiatives were

clustered into three different phases. Those phases, “Sensing”, “Intervention” and “Reestablishment” (respective examples can be found in Appendix I), thereby represent the chronological stages of Mobile Healthcare treatment. “Sensing” contains all approaches, which try to gather knowledge about regional or national occurrences of disease or health related topics. Initiatives in that field attempt to predict and prevent diseases or epidemics in an early state. Therefore, they find and fight the original cause of in particular communicable illness. “Intervention” covers all initiatives, which e.g. approach diseases directly through medication, focus on vaccination or long term treatment. “Reestablishment”, the last phase, assists e.g. in

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communicating with patients after they have overcome their diseases or addictions, coordination of health goods in the aftermath of a bigger health crisis or disaster reliefs.

As Figure 9 illustrates, this hypothesis still applies to the current status, but prevention (and thus “Sensing”) becomes equally relevant. “Sensing” has already gained a market share of 44 percentages, whereas “Intervention” occupies 53 percentages and therefore has only a slight tendency for initiatives in the “Intervention” section remains. At the same time, only two mHealth initiatives were suitable for the sector “Reestablishment”.

Further research needs to be conducted to discover the development of the different phases during time, but according to Vital Wave Consulting (2009) there is going to be a shift from late treatment to prevention and consequently towards “Sensing”. Another fact, which speaks for that assumption, is that already 36 % of the initiatives in the “Intervention” section were inactive compared to the 26 % inactive

approaches in “Sensing”. Regarding this, “Sensing” initiatives may have greater benefits for consumers and carers or simply can achieve economical sustainability more easily, as “communication” only is hard to monetize. The growing number of private business cases, which use additional body sensors as their central product for revenue creation, even sustains that theory. Also mentionable is the fact that 52 % of the “Sensing” projects use the Web or a combination of SMS and Web technology to transmit data. That is more than every second one. Again only 36 % of the “Intervention” field uses online interfaces or transmittance. This may indicate that applications and services in the “Sensing” sector benefit from a better cyberinfrastructure as well as conduct more complex networks, since they have an enforced utilization of online capacities. Nevertheless, “Intervention” holds with 66 % a majority of SMS based approaches, which may be more suitable for patient centric initiatives, as those have to have the lowest technological inhibition threshold possible. According to all of those characteristics, the hypothesis still can be proclaimed, but soon will be out-dated. Current developments and the programmatic of the private healthcare industry will most certainly lead to a further enhancement of disease prevention and therefore represents a reasonable market opportunity for sensors (referring to 3.2.2.A.1.).

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Very noteworthy is also the fact that almost no attention has been paid to the

phase “Reestablishment” so far. Due to the small amount of initiatives in that field, qualitative statements cannot be given. Further research in that field is advised.

4.3.2. Hypothesis B: Most of the Available Approaches are not Patient

Centric or do not Include Qualitative Insights out of the People’s Lives

and Therefore are not Adapted Adequately.

During literature review it was mentioned that most of the approaches had not planned to involve patients actively in the project. Opening initiatives to a broad audience would possibly maximize national attendance and help to focus on the treatment of emergencies, rather than on a limited circle of project members.

Figure 10: Percentages of Professional and Patient Centric Triggers. Own Elaboration. (2012).

To prove or negate this presumption, the user orientation of the initiatives had to be examined. Therefore all initiatives were either assigned to the field of professionals or classified as “patient centric”. Crucial for the process of categorization was thereby, by whom a service or an application had to be initiated (triggered). Further, the amount of inactive initiatives was counted to measure the rate of adaption. Qualitative content analysis was pursued to examine whether consumer insights were used or not.

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As illustrated in Figure 10, professionals trigger the vast majority of initiatives,

claiming 63 % of all mHealth approaches in developing countries. Besides, 40 % of all patient centric projects were already inactive, which indicates either insufficient user benefits or lack of profound design and leads to the assumption that this field is still in an early development stage, initiating a lot of pilots. But also it may appear that capital investments had been undertaken mainly in the professional sector, which occurs to match with the traditional flow of money in healthcare. Rather healthcare centres, NGOs or governmental organizations are able to collect monetary resources for health purposes than individual persons. Unfortunately, content analysis has resulted in the conclusion that most of the approaches are done top-down and are only considering solving an acute problem instead of researching real patients needs. The project Masiluleke (see 2.3.1) is a good contra example. It took consumer insights into the design process and therefore created a product from bottom-up. Consequently, also “3.2.2.B.1.” is verified.

Figure 11: Amount of Initiatives, Which had Incentives or did not. Own Elaboration. (2012).

Figure 11 was designed to prove “3.2.2.B.2.”. It clearly describes the number

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of initiatives1 with incentives found. Whereas seven projects use incentives to keep up user’s motivation, 55 do not give any additional reward than the essential benefit of the product. Every project, which was examined and used such incentives was still active and successfully went through pilot phase. Thus, incentives seem to have a positive effect on the outcome of mHealth initiatives in emerging countries.

4.3.3. Hypothesis C: There is a Profusion of Unconnected Pilot Projects.

Based on the statement that connected projects benefit from shared knowledge about technology, consumer needs, transmittance standards or simply scale, a majority of unconnected initiatives would hold a potential to improve the industry. Therefore this hypothesis was formulated and measured through the appearances of connected initiatives.

Figure 12: Number of Initiatives, Which are Connected to Other Mobile Health Approaches. Own Elaboration. (2012).

To approve the hypothesis, all initiatives were examined on basis of their

integration with other projects. As Figure 12 shows, 38 approaches and thus 61 % are still not connected to other initiatives. But further 21 mHealth applications and services have linked each other. Representing 34 %, it has to be mentioned that every 1 In this thesis the term ‘incentives’ is defined as an additional intrinsic or monetary reward, which motivates a user to consistently use an application or a service. For example, one project rewarded participation with mobile credit (search Appendix I for X Out TB). An example for intrinsic incentives are “social competition” or “highscore lists”.

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link between two projects consequently also counts for two initiatives, although not all couples were within the research group of mHealth projects. To gain a more relevant result, only the links, which connect the approaches, should be considered. By applying this concept, 11 links remain within this group, covering 18 %, compared with 38 unconnected initiatives.

Figure 13: Variety of Transmission Channels Being Used by the Initiatives. Own Elaboration. (2012).

The subcategorized presumption “3.2.2.C.1. The lack of standards is a reason

for this deficit of connectedness”, which is referring to hypothesis “C”, demands a different measurement to be examined adequately. As a consequence, all initiatives were investigated on their data transmittance standards.

Interestingly, almost all of them, namely 45 (73 %), used SMS as at least one possible communication technology. This may be referred to the will for low technological inhibition thresholds. By using SMS, not a single cell phone is technically excluded to become a participant. Further, 10 projects were identified, which use only online interfaces of transmittance. Those were all professional approaches (see 4.3.2), relying on PDAs or Smartphones distributed by the respective organization of the project. Two initiatives used voice as their solitaire communication gateway. This comparatively small amount may be related to the immense effort in personnel and monetary resources, which has to be undertaken to provide hotlines or similar services. Besides, it is remarkable that 22 approaches established a

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combination of SMS and voice- or web-technologies. In particular those with online interfaces show first cases of integrated network solutions. They basically provide very low-technology frontend solutions like SMS to their users, who can be patients or CHWs, and aggregate collected data in an intelligent management system online. Operations can be coordinated there, data requests sent and data sets managed. With the further development of EDGE-capable networks in emerging countries or other Internet enabling technologies, such approaches will become more and more relevant, as they combine the advantages of both, online (complex applications) and SMS (broad scope within the population).

Regarding “3.2.2.C.1.”, standards for transmittance, like SMS, voice or traditional Web files, could be derived, but there neither exist guidelines for formatting nor for coding data. Also there were not any standards for the development of interchangeable databases. This might function within closed ecosystems, but evicts complications in the exchange of data between different mHealth organizations or companies. Therefore standardization of data is strongly recommended to open the

transfer of knowledge and data.

4.3.4. Hypothesis D: Current Mega-Trends Will Affect the Technical

MHealth Environment and Will Lead to new Potentials.

This hypothesis was intentionally held more abstract to allow a variety of deeper research in various directions. Still, the discussion of the hypothesis remains in focus. Therefore, a qualitative measurement, which was based on the acknowledgement of the different devices used in the examined initiatives, and a more extensive trend analysis were conducted. The trend analysis was divided into three major sections, each defined by one mega trend. Those sections are: “System”, “Content” and “Devices”. Due to the more detailed examination of trends, it was given an own section, which is considered to be a part of this hypothesis discussion. Therefore, in this section only “3.2.2.D.1. The benefits of more advanced handsets, like smartphones, are appealing enough to enforce a deployment of those devices at caregiver centres” was investigated.

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Figure 14: Percentages of the Respective Devices, Which Were Used. Own Elaboration. (2012).

As Figure 14 illustrates, the respective devices of the examined mHealth initiatives were observed. Whereas the vast majority uses ordinary cell phones for their projects, already 22 % of all approaches implemented Smartphones or PDAs. As mentioned above in this chapter, all of those initiatives are focused on professional health care workers. Consequently, there exists a relevant amount of those devices in the hands of CHWs, providing several advantages. Digital photography helps to diagnose patients and to collect data, GPS enables the localization of events while being in the field, personalized applications suit the individual preferences and demands, additional sensors can aggregate health data and communicate with the device via Bluetooth or Zigbee. Disadvantages might be the high consumption of battery energy, high upfront cost compared to traditional cell phones or the lack of nationwide network coverage. In particular the rise of diverse sensors holds huge potentials to transform such mobile devices into mobile hospitals. Thus a broader coverage of healthcare delivery in remote areas could be achieved, but also the evolvement of micro businesses is thinkable. By supplying the right software and

additional sensors, also private persons could collect centrally requested health data or act as connecting link between CHWs and organizations. As articulated earlier, the following section will discuss three mega-trends and assigned to this hypothesis.

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4.4. Trend Analysis of Upcoming Technological Solutions

New and upcoming technologies or ecosystems can influence the existing field of applications in various ways. For the one side, they may substitute, improve or remodel current ways of procedure, where for the other side, they may never be adapted on a mass scale due to high cost or complexity, because they have not taken consumer’s or carer’s needs into account or simply are not noticed.

Figure 15: Three Mega Trends for Three Major Sectors: Open Data (System), Data Intelligence (Content) and Sensors (Devices). Own Elaboration. (2012).

The following technologies have been summarized through overlapping macro-

trends. Once formed to such clusters, potentials for new business models can be assessed and influence on mHealth applications can be predicted. Thereby, macro-trends do not necessarily have to have a direct relation to the healthcare sector, but in particular they have to have relevant impact on the current development of society, technology or business landscape. The subsequent trends “Open Data”, “Data Intelligence” and “Low- and High-Technology Sensors and Services” are relevant for the influence on the system, content and devices of the mHealth landscape.

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4.4.1. Open Data for Mobile Health in Developing Countries

Open Data describes the open and free access to information and data. It invokes the human right for information. Thereby, regular legislative conditions, such as copyright, patents or privacy, are neglected for the good of information exchange. Thus copyright holders and innovation companies see their individual right to commercialize their products in danger. Further, Open Data collections stand in direct conflict with offers of the private industries. Open Data is extremely helpful for scientific studies, education purposes and nationwide health initiatives, which focus for example on the research of pandemic outbreaks or links between social and health patterns. It can help to diminish the Digital Divide between industrialized and emerging markets. The following trends manifest the assumption that Open Data is going to have a strong impact on Mobile Health in developing countries. Bridging the Digital Divide is a core objective to deliver healthcare through ICT and were the substance of many publications (e.g. United Nations, 2005; Warschauer, 2004; Sciadas, 2003; Norris, 2001). On a global basis, only easy, free and open access to knowledge through available and ubiquitous devices can help to fight the Digital Divide. In developing countries this means, enabling access to knowledge through mobile phones.

There is an intensive need for nationwide and international cooperation, including the fields of epidemic and pandemic research, electronic health data (EHD) as well as new forms of data aggregation. Diseases like A/H1N1 (Swine Influenza) or A/H5N1 (Avian Influenza) can only be stopped fast or even become predictable by the utilization of international warning systems. The development of new medicaments for the treatment of younger illnesses always seeks for as many as possible insights from EHD. Open Source Systems, like OpenMRS, which can match those requirements are already emerging and therefore building relevant confirmation for the Open Data trend. The wide population of communications channels, within the mHealth field and especially in emerging countries, lead to new demands for automatic data aggregation and analysis. Particularly, in the event of humanitarian or ecological crisis the past have shown a vast requirement for new approaches, since communication channels have been flooded with a variety of different help inquiries. During the Haitian

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earthquake in 2010, the Red Cross and the present network provider implemented a SMS helpline to manage and canalize incoming text messages. At the moment, first web-based tools, such as Swift River by Ushahidi or Geofeedia, appear on the surface, enabling users to search, sort and manage keywords throughout different Social Media channels. A combination of both data streams, namely SMS and Social Media messages, should be considered. Finally such aggregation tools have to have open access to big data sets and consequently are enforcing the Open Data trend.

4.4.2. Data Intelligence

The macro-trend Data Intelligence has been formed by a further spectrum of data applications and services, which build new forms of Social and Artificial Intelligence. They process data, make it more accessible and easier to read. At the same time, machines and software solutions undertake more and more tasks automatically. Through parameters – like time, quality and verification – data sets become more comparable and urgent tasks or instructions, which were articulated through the evaluation of consistent data sets, can be handled with higher preference. But the development of Data Intelligence is partly linked to the further evolvement of Open Data. Data sets are only then really valid, reliable and objective, if they reach a representative volume. Nevertheless, already nowadays they attribute a growing part to strategic decision-making in the field of healthcare and among others. The following trends represent several indicators for Data Intelligence and it’s influence on mHealth. The Global Positioning System (GPS), a former military system, which can determine the geospatial position of a GPS receiver through the communication with at least 4 of the 32 GPS-satellites, was opened to the public in 1994. Therefore, it is not longer a new trend, but forming in the combination with upcoming other applications and services a vital sign for Data Intelligence. One outstanding example is the mapping of data through a crowd, also known as Crowdmapping. Companies like Ushahidi or Crisis Mappers provide web-based maps of areas of interest. Those then can be modified and enriched with data, like events, and GPS coordinates gathered through mobile devices. Such events can be specified with notes, stories, keywords, categories and the particular location, where it took place. Having a filterable map with localized events makes aggregated data more accessible as well as faster to evaluate.

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Besides, it is a fundamental component for the establishment of a Geographic Information System (GIS). Humanitarian disasters, like the earthquake in Haiti, have already shown how useful such tools can be to capture, store, manage and analyse health related data. This can include for instance food- or medication-supply or healthcare delivery.

At the same time, the international world community – the crowd – is given the ability to participate at the whole process of disaster relief, through the handling of micro-tasks. Such tasks are formed by health staff in the field, e.g. asking for the translation, categorization and mapping of incoming text messages or other cries for help. The processing of such tasks and data through the crowd, creates on the one hand swarm intelligence lead by the factor “time” and covers on the other hand, within this swarm, smaller intelligent processing-cells. Crowdsourcing micro-tasks is a powerful tool to make big data issues easier and faster to solve, by splitting them up into tiny pieces and sharing those pieces with thousands.

But Data Intelligence is not only pushed in the web-environment, it also has reached the devices on the ground. With the emergence of the Internet of Things devices become inevitably connected with the Internet. Additionally, short-range technologies like Bluetooth or Zigbee enable automatic machine-to-machine communication. The self-managed exchange of data through the Internet as well as between the devices itself, consequently forms new, intelligent networks with the ability to automatically contribute to national or international sensor-platforms and measurement demands. Further devices gain the capability to actually acknowledge their geographic and “social” environment. Geographical specified information, like warnings for pandemic diseases or other dangers, can be distributed intelligently and standard sense requests, using the features of each individual device, can complement existing data sets – all for the good of health.

4.4.3. Low- and High-Technology Sensors and Services

While the previous trends described the influence of new technological solutions on the system and content of mHealth applications or services, Low- and High-Technology Sensors and Services (LHTSS) are covering the innovative role of devices.

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Promoted through fields like Remote Disease Surveillance, Self-Diagnosis as well as the need for extremely cheap and mobile body-sensors, LHTSS can assist or even substitute current methods. They enable remotely situated patients to get a medical examination at the point-of-care. As a result of that, they reduce costs for travelling and time. Further, the existent mobile communication network is used to report the gathered health information to hospitals or other professional headquarters. It is significant that this trend separates itself into two divisions: Low-technology solutions for consumers or patients and high-technology solutions for carer, especially centres of care – such as hospitals. Whereby “high-technology” has to be understood in the context of developing countries.

Figure 16: The Different Patterns of the Examined LHTSS and Their Trends. Own Elaboration. (2012).

On the side of low-technology we can detect first examples of Subscriber-Identity-Module-applications (SIM-apps). They explore the possibilities to enhance the software-functionalities of regular cell phones and may open a new market for Java-based tools. But until now, there are not any bigger development- or distribution-platforms. Besides, future ubiquitous availability of EDGE networks and devices may eliminate this market. Although this process could take years and still leaves some space for niche-products. Cheap and specified sensors appear to shape the low-technology side as well. They leverage the power of health workers or even patients to make more precise statements about the current well-being of the examined person.

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EyeNetra, a project based on research conducted at the MIT Media Lab, tries to disrupt the $75 billion eyecare market. It’s sensor is a simple device, which can be adapted onto any cell phone with an integrated camera. The device determines the patient’s needed vision correction and therefore could potentially serve about 2.4 billion people, who have not had any access to eyecare before (EyeNetra, 2012). On the side of high-technology most of the time the devices’ computing power is used to simulate three-dimensional (3D) models for certain medical procedures or to augment real-time data with additional information from the Internet. Therefore, the line between software and hardware solutions cannot be drawn so clearly anymore. It is more like an ecosystem, including several software, hardware and even social components, such as Crowdsourcing (see the example of Haiti in 2.4.4, 4.4.1 and 4.4.2) and Social Media. An examination of a patient can therefore be enhanced by the benefit of the diverse and decentralized knowledge of the network itself. This does not only incorporate the Internet, but also the elements mentioned before. Each component is thereby commissioned with a defined task, like sensing, reporting or

taking action. At the moment, still at the level of Augmented Reality, this trend is already evolving to become the vast field of Computing. Computing, not to be confused with Computer Science, describes an intelligent environment. Mirrors, which are enabled to measure your blood pressure through hyper-sensitive cameras, Google Glasses, which augment the reality and therefore as well surgeries or patient examinations, mobile devices and their software, which capture and monitor all basic health information. Further, scientific teams have initiated first approaches to print organs or other bio medical objects. Based on the technology of industrial 3D-printers, Bio Medical Printing could already be implemented in the near future. There exist more trends, which concern both domains – low- and high-technology solutions. Do-It-Yourself (DIY) Diagnosis, Temporary Communities and Telemedicine are some examples. The further deployment of EDGE, 3G and 4G communication networks in developing countries, sustains moreover the evolvement of high-technology approaches, which normally relies on a consistent broadband connectivity.

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4.5. Conclusions

The analysis of the predefined hypotheses and the further examination of the current influence of macro trends on the field of mHealth initiatives in developing countries, showed several innovation potentials. Those were extracted in the following section and supplemented through additional statements, articulating the outline for the design framework. The general reflections disclosed that the current innovative leadership within the mHealth field for emerging countries lies in Africa, the Middle East and Asia. In particular, Kenya, Uganda and India hold the majority of projects. Setting up new pilots or even commercial products in the field demands a reliable infrastructure, thus it is advised to focus on those countries or regions first. The category “Awareness and Education (mLearning)” had the biggest market share, but as described in 4.4.3 may not hold enough potential for monetization. In contrast, the development of mobile body sensors in combination with an intelligent and web based management system offers various cutting points for value creation. For example, the distribution of sensors to patients and especially CHWs could lead to the impetus of new micro businesses. Besides, governmental organizations or NGOs could establish a network of trusted data collectors, which trade their gathered information. Thereby, transmittance should use SMS only to verify the lowest possible technological inhibition threshold and as a result of that a large participatory audience. Gaining economic sustainability respectively evaluating reasonable business models is assumed to be one of the main reasons, why 32 % of all initiatives were inactive already (4.2.2). That presumption is consistent with the dominance of professional approaches. Those mainly obtain funding through donors and therefore are, in the beginning, not reliant on any income, which evicts pilots without a concept for monetary profits.

The discussion of the four main hypotheses of this study, unveiled relevant insights about the current mHealth landscape by concentrating in particular on the interaction between users and products as well as between users and users. The field of “Sensing” thereby was considered to be very important to enhance disease

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prevention and therefore to minimize the demand of “Intervention” (see 4.3.1). However, the field of “Reestablishment” was almost not populated, thus representing a market area, which have not been occupied. This potential should be investigated more accurately, as it will become more relevant for the treatment of mental illnesses or for the accompaniment of former addicts. It also should be examined, if this field is already approached by initiatives in the developed world.

Further, hypothesis B illustrated the potential for more patient centric mHealth applications and services (see 4.3.2). Besides, additional incentives are as good as not present and the current mHealth landscape. Use cases of Gamification, like the Nike+ Fuel Band (Nike, 2012) or RunKeeper (RunKeeper, 2012), have already proved their strength in motivating users to long-term usage. This concept should be transferred into the developing world more often. But not only that incentives lead to motivation, they also promote involvement and word-of-mouth propaganda. Thus, they could represent the key factor to finally initiate an organic distribution of mHealth products, which did not occur formerly.

Hypothesis C disclosed the lack of profound connections between mHealth

initiatives and referred that among other causes to the deficit of standardized data transmittance. Both aspects contain the opportunity to stimulate the mHealth market in developing countries and to accelerate adoption. Using a machine-readable standard unified, would not only give CHWs or other caregivers the chance to access for instance EHRs more easily, over borders of regions as well as immensely faster and thus save time and lives, but also would enable patients to collect data accurately. As the demand for data grows consistently, a shift to UGC becomes inevitably.

Through the measurements undertaken to prove hypothesis D and the more extensive trend analysis, future and current implications of the technical settings on the mHealth industry were derived. On today’s perspective, advanced technology sets are particularly concentrated within the field of professional applications and services (see 4.3.4), thus clearly dividing patients and caregivers through their technical capabilities. In the future, a disruption of this model could be accomplished through the findings of the analysis of hypotheses A and B. The examined trends “Open Data”,

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“Data intelligence” and “Low- and High-Technology Sensors and Services” give further evidence for the impact of technology on the mHealth sector. Open Data for example will help to navigate nationwide or even international initiatives. But as the trend emerges, also issues like the deficit of data privacy need to get considered adequately. Data Intelligence thereby can be understood as an organic development or logical consequence of current mHealth contents, such as GPS, GIS or the Internet of Things (see 4.4.2). It will shape how we interact with information and how health data can be designed more easily to comprehend and to access. At the moment anyhow, geographic localization leads those evolvements. LHTSS enables the treatment of patients at the point-of-care, reducing costs for travelling and minimizing time efforts. This field experiences the growth of an extraordinary diversity of products and attains the attention of global investors, like the MIT Media Lab (see 4.3.4). Therefore, LHTSS will develop even at a faster pace during the next years and represents the most interesting field for commercialization. In combination with Crowdsourcing communities or Social Media in general, even the opportunity occurs to connect the broad world society with affected populations. Such efforts, as seen

during the earthquake in Haiti (see 2.4.4, 4.4.1 and 4.4.2), can open the access to micro funding private persons in developing countries for the very first time. That could be maybe a network, where users can request micro funding for a certain cause via SMS and the world community has the ability to react on that request directly. From an overall perspective, future mHealth initiatives in emerging countries should promote strong partnerships between projects to maximize efficiency, be accessible for the vast majority of people, enforce patient centric design and gain economic sustainability through consistent business models. To achieve those goals, also general and global influences, like the growing challenge of urbanisation, need to be considered carefully. The introduced measurements, hypotheses and trends shall contribute to the further development of this field, to particularly serve the poorest of the poor. Frogdesign recently published an article, which quoted the Coalition of Adolescent Girls (Frogdesign, 2012):

“The poorest, least developed countries tend to have the largest shares of young people in their populations, and it is the girls and young women who face the greatest disadvantages”.

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It is our task to deliver healthcare where it has the greatest need.

All conclusions aggregated in this section were used to formulate the design

framework manifested in the following chapter.

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5. Chapter – Design

5.1. Introduction

As the scope of a Bachelor Thesis is limited, the following chapter contains only a brief design framework, which represents the base for a new mHealth product respectively business model. Based on the analysis and the knowledge gained through literature review, the framework was developed. The realization of this conceptual leitmotiv is still in progress. Information about the status quo can be found

in point 6.

Figure 17: General Overview of the Design Framework. Own Elaboration. (2012).

Figure 17 illustrates all aspects, which were extracted for the design approach.

Generally, the four factors, “privacy”, “network”, “SMS only” and “incentives”, build the main columns, each carrying several more subcategorized topics. A more detailed description of each point was explained in the following text passage.

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5.2. Text to Number – Concept

Regarding the investigation and the environment of mHealth in developing countries, in particular the lack of standardized communication and networks, which connect all stakeholders, it builds a challenge but at the same time contains market potential. Therefore, this framework was set up, to provide a brief and basic description of the key elements a business model in this field could have and how it should be built.

Figure 18: Networking the World Community With Health Care Delivery. Own Elaboration. (2012).

The idea is to create a service, which can be accessed by almost everyone

with a cell phone. Thus, SMS was chosen to be the main channel of communication. Further, SMS does not have vast data capacities, as for a system needs to be designed, which can decode and encode text respectively any kind of data into a shorter, machine-readable language – namely numbers. The initial impetus for this concept was derived from the exploration of humanitarian disasters in the past. In such an event, immense masses of data occur within very short time and lead to overburdened provider networks. Besides, those amounts of data cannot be qualified due to the lack of resources and the inability of machines to read individual constellations of data. A standardized SMS code would lift that problem.

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Further, private users can use those SMS codes to send and receive funding requests, network with other participants in their regional area (two-way-communication) and offer products, work time or other goods. Despite of the usage of personal identification numbers, codes could be personalized as well to meet privacy concerns. Intrinsic incentives, like a regional highscore list, are applied.

Professional users can use the system to request certain health data sets, which then will be intelligently shifted to the relevant audience. Figure 18 shows how the different stakeholders can contribute and benefit from the framework. The world community can donate or get involved themselves, aid agencies ask for data and the affected population can gather this information for a monetary compensation, which can be transferred via mobile credit.

First the numeric SMS code should be developed through an Open Source approach and spread among mHealth initiatives in emerging countries. Then first issues can be examined and the system can be improved iteratively in field. After it

shows a consistent structure, the second phase, including the actual business model, should be initiated: The development of the backend and network. Later Smartphone applications and own biosensors can expand the field of income.

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5.3. Briefing

5.3.1. Framework

The subsequent table describes the briefing for the further development of the framework and consequently the product.

Phase Description

1. Develop a numeric Open Source SMS code.

• Every user gets an own ID through cell phone number.

• Needs to be individually adaptable and expandable through users (ID + ID for the new section).

• Should be based on a numeric system.

• Basic categories are firm.

• Transmittance works via SMS.

• Open Source helps to develop the standard code faster and make it more common.

• Cutting points to existent mHealth initiatives, like Open MRS.

2. Develop the backend.

• Web based backend, which has a “SMS-interface” (is readable through SMS).

• Aggregates all incoming data and makes it easier accessible.

• Requests can be formulated and managed.

• Marketplace.

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3. Additions: Smartphone / web based application & sensors.

• As technology advances fast, native applications should be developed.

• Sensors could be used to enhance data collection and create further income.

Table 3: Design Framework Briefing. Own Elaboration. (2012).

5.3.2. Behavioural Change

To achieve the best possible adaption rate among users, the behavioural change model by BJ Fogg (2007) should be applied. Figure 19 illustrates this model. Basically, Fogg says that users will only adapt a behaviour, if there is a mentionable trigger as well as the ability and motivation to execute the wished task. If the ability for a certain task is very low, behaviour will not happen. The same happens with motivation. If motivation is too low, the task will not be done. Besides, Fogg describes that a task, which can be fulfilled with low motivation is going to be executed much likelier as tasks, which demand higher motivation. Thus, products always should be designed with a trigger and a task that needs less motivation and relatively more ability.

Figure 19: Behavioural Change Through Right Triggers (Fogg, 2007).

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5.4. Conclusions

This short chapter provides the basis for a further product development. First, consumer insights in Cambodia, a country selected in consideration with point 4.5, will be gathered through the author, in the end of August of 2012. Also first pilot tests and workshops will be performed there. This additional investigation aims to gain more qualitative information about the consumer’s needs and abilities to fit in the project. Besides, first examples of incentives will be applied in the field to measure, which kinds show the best impact on motivation and involvement. Then further adaptions on the concept will be made.

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6. Chapter – Future Directions

6.1. Restrictions of This Study and Further Research

As this study was published to be awarded with the Bachelor of Arts degree, it was limited in it’s extend. The content analysis could only cover a cross section of the vast majority of mHealth applications and services in emerging countries and thus a more detailed examination based on insights collected in field and expert interviews would be advised. Exercising such an approach, would allow more detailed statements

about the current demands of citizens and CHWs of developing countries. To bridge that research gap, the author will pursue further investigations, as described in 5.4.

Through literature review, a content analysis of 62 mHealth initiatives in developing countries and the development of a brief design approach, several further research topics were found to be uncovered by current academic publications. Therefore, the following listing of specific mHealth questions was prepared:

• “How to qualify collectively aggregated health information through ICT and consequently form action tasks for field workers",

• "The effects of human failure during the stage of data entry on diagnosis",

• "The potential of mobile technologies to transform the way in which clinical, epidemiological and surveillance research is conducted – a research in the field of Remote Data Collection",

• "Mobile technologies as delivery tools for health education and training targeted towards healthcare providers and / or patients",

• "Assessment of mHealth's potential to diminish pandemics in crisis areas",

• "Assessment of health in crisis - the impact of data on human well-being",

• "Security of electronic health records – an assessment",

• "Social healthcare – the influence of Social Media on the healthcare sector",

• “The different standards of the health industry – a technological framework for interoperability”,

• "The provision of secure and reliable access and exchange of medical data

and information",

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• "Crisismappers and Ushaidi – the impact of geographical localized data on mHealth",

• “How Augmented Reality and Monitoring 2.0 is going to influence mHealth in

developing countries”.

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Appendix I

Figure 20: Examined mHealth Initiatives 1 / 6. Own Elaboration. (2012).

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