Theoretical background

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A forcefield analysis on the use of eHealth and mHealth monitoring in healthcare: A systematic review

Jelle (J.J.) Logcher S2615983

j.j.logcher@student.rug.nl Supervisor: prof. Dr. Ir. D. Langley Co-assessor: assistant prof. M. Hanisch

July 2020 Wordcount: 27822

Faculty of Economics and Business MSC BA Change Management

Master Thesis

University of Groningen

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Abstract

Purpose: eHealth and mHealth monitoring could be used to improve healthcare outcomes and general health while possibly also reducing the costs of healthcare. However, there seemed to be a lack of literature on the problems that could emerge when a slow-clockspeed industry, like healthcare, tries to implement the use of rapidly changing and innovating technologies into their practices. The purpose of this paper is to map the driving and restraining forces of using eHealth and mHealth monitoring in healthcare, a slow-clockspeed industry, and to see if some of the issues that arise could be explained through the slow-clockspeed nature of the healthcare industry.

Methods: This study used a systematic literature review approach and contains articles on eHealth and mHealth monitoring from 2015 to 2020. In total, 30 articles have been included of which 22 through database searching and 8 through snowballing.

Findings: This study identified a total of 21 forces that are described and visually presented in a force field analysis. 9 of these forces were identified as being driving forces while 12 were identified as restraining. This yielded some interesting findings and contradictions that are discussed and attempted to be explained.

Conclusion: It seems like the potential of using eHealth and mHealth monitoring within healthcare is big and the potential is expected to increase even further in the years to come. However, it also seems like the ability of healthcare organisations to grasp the full potential of using eHealth and mHealth monitoring in healthcare has been limited in recent years. This could possibly be explained through the presence of strategic focus, stability and status quo behaviour, usually positively influencing the performance of slow-clockspeed industries. However, in the case of healthcare trying to take advantage of rapidly changing and innovating technologies, it might limit their ability to grasp the full potential and more strategic complexity and flexibility and less status quo behaviour, usually more suitable for fast-clockspeed industries, might possibly improve this situation.

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Introduction

Last decades healthcare costs have been rising and financial resources are becoming more scarce.

Paradoxically, developments in information technology have proven to reduce costs and at the same time improve healthcare outcomes (Devaraj et al, 2000). Recent literature showed that this so called digital revolution in healthcare improves efficiency (Weenk et al, 2020) and can lead to wider accessible, more differentiated healthcare with health outcomes that are more accurate and less costly, therefore decreasing healthcare costs (Denicolai et al, 2020). The transition towards more digital health seems to be facilitated by the adoption of rapidly changing and innovating technologies. An example is health monitoring through the use of smartphone sensors (Trifan et al, 2019), smartphone apps (Ross et al, 2020; Dounavi & Tsoumani, 2019), wearables (Loncar-Turukalo et al, 2019; Weenk et al, 2020) and text-messages (Schulze et al, 2019; Campbell et al, 2018) which are all part of the relatively new domain of eHealth (electronic healthcare) and mHealth (mobile healthcare).

The healthcare industry naturally has been an industry that can be classified as having a relatively ‘slow- clockspeed’. In slow-clockspeed industries, rates of competitive and technological change is relatively slow and, in general, strategic actions are durable (Nadkarni & Narayanan, 2007). Traditionally, investments in technologies would take a long time to recoup and therefore high rates of (new) product introduction and innovations can have a negative impact on the performance of organizations in slow- clockspeed industries (Jones, 2003). This being said, organisations showing strategic focus, strategic stability and status quo behaviour are likely to succeed in slow-clockspeed industries (Nadkarni &

Narayanan, 2007). By starting to use smartphone sensors, apps, wearables and other relatively new and fast changing technologies for health monitoring, it seems like healthcare organisations are getting involved and starting to work with rapidly innovating and changing products originating from a totally different industry: one that is considered to be fast-clockspeed. In fast-clockspeed industries processes, products and competitive actions are prone to rapid changes that happen often and at a high pace (Nadkarni & Narayanan, 2007). Here, stability and low rates of product innovation and introduction can have a negative effect on firm performance (Nerkar, 2004; Jones, 2003). Rather, organisations operating in a fast-clockspeed industry benefit from strategic flexibility and complexity, enabling them to create new, situation specific knowledge by, for example, experimenting (Nadkarni & Narayanan, 2007).

According to Lewin’s field theory (Burnes, 2013), it could be argued that fast-clockspeed industries are being impacted by different internal and external forces in their life space as compared to slow changing- clockspeed industries and therefore demanding different levels of status quo behaviour and strategic focus.

There appears to be a lack of literature on the complexities and ambiguities that influence the change process concerning the implementation of rapidly changing technologies in slow-clockspeed industries.

This is relevant because, for organizations to be successful in either slow- or fast-clockspeed industries, different decision making speeds (Brown & Eisenhardt, 1997), organisational structures, strategic

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5 responses (D’Aveni, 1994; Williams, 1994), environmental scanning processes (Garg et al, 2003) and overall capabilities (Eisenhardt, 2000) are needed. In the healthcare context this, for example, might imply that, when a certain rapidly evolving technology (such as smartphone sensors, mobile apps or wearables) is officially deemed approved and fully useable, it might already be obsolete and a new and better technology might already be available and in use by the targeted consumer/patient.

In this study I will do a systematic literature review to the most recent developments in mHealth and eHealth technologies in the healthcare domain and shed light on the current possibilities of eHealth and mHealth health monitoring technologies, while identifying driving and restraining forces that are influencing the implementation of rapidly changing technologies in healthcare (a slow-clockspeed industry). The findings will be presented in a force field analysis based on Lewin’s field theory, of which the purpose is twofold (Burnes, 2013): The construction and mapping of force fields allows one to understand the forces influencing the implementation of rapidly changing technologies in healthcare (a slow-clockspeed industry), while at the same time opening possibilities in understanding how change could be realized by altering one or more of these forces. Following from the above, the research question of this study will be: ‘What are the driving and restraining forces of eHealth and mHealth monitoring in healthcare and how does the slow-clockspeed nature of the healthcare industry influence the change process and implementation of these rapidly evolving technologies?’

This study will add to the current literature by getting a better understanding of how a change process involving rapidly changing technologies unfolds in an industry that is rather characterized by having a slow-clockspeed. It will do so by identifying the driving and restraining forces that seem to have an impact on either the successful implementation, or failure, of using eHealth and mHealth monitoring in healthcare. This could lead to further research on how exactly these forces influence the success of the change initiative and to further research on how the change could possibly be realised by altering one or more of the identified forces.

The managerial implication of this study is that it might aid managers and decision makers of healthcare organisations attempting to implement mHealth or eHealth health monitoring techniques enabled by current rapidly developing technologies, by providing a starting direction for the changes needed in an organisation operating in a slow-clockspeed industry. As stated before, slow-clockspeed industries need different decision-making speeds, structures, strategic responses, environmental scanning processes and overall capabilities. This study might add to the managerial understanding of the changes needed in the abovementioned slow-clockspeed organisational characteristics to be able to work successfully with fast-clockspeed industry products and their implementation.

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Theoretical background

Force Field Analysis – For understanding force field analysis we need to go back to the theory that is at its core: Kurt Lewin’s field theory. Kurt Lewin was a researcher par excellence and has probably added more to management literature, management practice and organisational consulting than anyone else that has been active in the field (Schein, 1997). Although Kurt Lewin died almost 75 years ago, his work is still considered central in change management and OD (Organizational development) (Burnes 2007; Marion, 2002). It is even stated that, through his field theory, Lewin laid the foundations of organizational development (OD) (Burnes & Cooke, 2012; Boje et al, 2011; Cummings & Worley, 2005) and is even called ‘the intellectual father of planned change’ (Schein, 1988). The core idea of Lewin’s field theory is that, if you want to understand and predict how and why change is happening, a

‘life space’ can be constructed (Burnes & Cooke, 2012). The basic argument for his field theory was that, if you do not understand the forces that brought and keeps an organization in the current equilibrium, you are not able to bring about sustainable and effective change. If you want to bring about change, you have to understand which forces need to be changed in a certain life space and what the effects of changing these forces would be. This is also how Kurt Lewin laid the foundations of planned change, as without the understanding of the abovementioned forces, it is impossible to understand the specific forces that maintain the current status quo, explain current behaviour, and identify the forces that would have to be altered in order to successfully change (Burnes& Cooke, 2012). As Lewin described it himself: behaviour flows from the totality of interdependent and coexisting forces that impinge on organisations and individuals, and jointly make up the life space where the behaviour is taking place (Lewin, 1942). It is important to understand that the life space is a subjective perception of the forces that impinge on individuals and organizations (Marion, 2002; Rock & Palmer, 1990). This gives room for ambiguity and contradictions in either the driving or restraining forces perceived by individuals, organizations and industries. The formula at the core of field theory is:

B= f(p,e) where behaviour (B) is a function of a group, person or organization (p) and their environment (e). Lewin defined the life space also as (p,e). (Marrow, 1969)

Force Field Analysis is the variant of field theory generally used these days. It is a rather straightforward approach for identifying driving and restraining forces of change (Burnes, 2012) and has been applied in a wide range of general management, change management and organizational issues including IT implementation in healthcare (Bozan, 2003) and medicine (Kathan-Selck & van Offenbeek 2011). It is, however, important to emphasise that force field analysis does not fully cover the true complexity of human behaviour. Force Field Analysis rather can be seen as a stimuli-response formula that gives a good starting point in understanding an organisation’s or industry’s life space and its accompanying restraining and driving forces for change, while human behaviour and the overall intention of field theory is more complex than this (Burnes, 2009).

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7 Dezsca et al (2019) describe force field analysis as being a tool to identify the forces against and for change. They state that, in equilibrium, the forces are balanced, and, to create sustainable change, the restraining and driving forces could be altered or new pressures could be added. The authors state that the forces can come in many shapes and forms including both external, as well as internal sources.

Examples of external forces mentioned are: changing regulations, benchmark data and various market forces. Also, future opportunities for growth and development are mentioned. Examples of internal pressures are employee attitudes, norms, habits, internal systems and organisational systems not being in line with the change. An example specifically on the systems not being in line is that, if innovation is part of the preferred direction for change, systems that minimalize experimentation and focus on efficiency will serve as a restraining force. Dezsca et al reference to two articles concerning their content on Force Field analysis: Field theory in social science by Kurt Lewin himself (1951) and Force field analysis: A new way to evaluate your strategy by J. Thomas (1985).

In recent years, it seems that high speed technological innovations are triggering a change in healthcare.

To keep it within Lewin’s words: high speed technological innovations are entering the life space of the healthcare industry through changing the environment, and is even called a ‘revolution in healthcare’

(Rönkko, 2018). This change in the environment is driven among others by the use of mobile technology (Lupton, 2013) and it seems like mHealth technology can bring huge benefits to a wide population (Dounavi & Tsoumani, 2019). The technological advancement of smartphones, combined with a decrease in size and price in the large variety of sensors, has turned them into powerful health monitoring devices (Trifan et al, 2019) and the mHealth market has grown rapidly because of increased internet accessibility and smartphones being massively adopted worldwide, with the expectation of 1 billion telephone subscriptions in 2022 (Dounavi & Tsoumani, 2019), increasing the possibilities for eHealth and mHealth monitoring. Simultaneously, the developments in mobile technology have triggered the development of wearables that are increasingly influencing healthcare as well (Weenk, 2020) and are aggressively marketed with exaggerated health claims (Düking, 2018). In 2016, 15% of all consumers in the United States used these wearable technologies and it was expected that 110 million wearables would be sold in 2018 (Piwek, 2016). Also, the mHealth application market has exploded with over 160.000 mHealth applications available in the Apple Appstore (Stec, 2019). These developments combined have the potential to improve and change the current healthcare systems. It is stated that health practitioners need to prepare themselves for patients bringing in their own digitally generated health data (Piwek, 2016). All these changes in the environment of healthcare, triggered by these rapidly changing and innovating technologies, seem to apply new forces on the life space of healthcare organizations which might result in a shifting equilibrium, where change seems inevitable (Loncar- Turukalo, 2019).

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Methodology

As argued by multiple authors, a literature review should gather, combine, critically appraise and summarise existing studies in a specific field (Wolfswinkel et al 2013; Zurynski, 2014) and are important because of their ability to transform large quantities of information into smaller pieces, making them more suitable for literary digestion (Mulrow, 1994). This review includes academic articles that have been published between 2015 and now. The motivation behind not reviewing older articles is that the technologies enabling eHealth and mHealth monitoring possibilities have changed, developed and innovated drastically over this period of time and this study attempts to analyse the forces that are influencing the current transition towards implementing rapidly evolving and innovating technologies in healthcare.

As it seems, narrative reviews lack thoroughness frequently (Tranfield et al. 2003). Therefore, the decision has been made to write a systematic literature review, which, according to Tranfield et al, means that the reviewer describes the decisions, procedures and conclusions used and made during the process.

Thereby, systematic reviews diverge from traditional narrative reviews because they adopt a more replicable, transparent and scientific process (Tranfield et al. 2003). This is also confirmed by more recent authors who argue that data in a systematic literature review should be coded, analysed and synthesized transparently to reach conclusions (Ridley, 2012). To do this, this literature review uses the article ‘Using grounded theory as a method for rigorously reviewing literature’ by Wolfswinkel et al.

(2013) and uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist for reporting and structure. The article of Wolfswinkel et al (2013) recommends using five stages to make the process of literature reviewing as systematic, replicable and transparent as possible.

The five stages are: define, search, select, analyse and present (Wolfswinkel et al. 2013). Although strict planning of a literature review is common in other fields of research, management reviews are regarded as being a process of discovery, exploration and development. It therefore is considered unacceptable to closely plan literature-review activities (Tranfield et al. 2003).

Define and search. The database ‘Web of Science’ has been the database that is chosen for use in this literature review. Web of Science is a database with extensive search filters that can enhance the results found significantly and incorporates papers from numerous journals and different fields of science while automatically filtering out low quality journals. For this literature review, three search terms have been used as can be seen in Table 2. This resulted in a total of 68 articles.

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Search term Total number of results

TOPIC: (digitalization OR digital) AND TOPIC: (healthcare OR "health care") AND TOPIC: (monitor*)

21

'TOPIC: (Mobile AND phone AND monitor* AND (healthcare OR 'health care')) or TOPIC: (Smart phone OR smartphone AND monitoring AND (healthcare OR 'health care')'

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'TOPIC: (Ehealth OR Mhealth) AND TOPIC: monitor*)' 36 Table 2: specific search terms and their respective results

Select. After this, the title and abstract has been read to see if the article was related to new eHealth or mHealth monitoring possibilities. Articles that did not discuss the subject of eHealth or mHealth monitoring or did not describe the success or failure have been excluded. This resulted in a set of 30 eligible articles. Four articles of this set were excluded as the full text articles were not accessible in the English language, three articles were deemed not in line with the scope of this study after reading more than the abstract and one article wasn’t accessible through the databases available without payment.

This resulted in a set of 22 articles retrieved through the database. For gathering more articles, the process of ‘snowballing’ has been used. Snowballing identifies relevant papers which help to further develop a study (Wohlin, 2014). Snowballing here is defined as: ‘using the reference list of a paper or the citations to the paper to identify additional papers’ (Wohlin, 2014). For the process of snowballing, the article ‘Guidelines for Snowballing in Systematic Literature Studies and a Replication in Software Engineering’ by Wohlin will be used. In this article, Wholin developed an extensive snowballing procedure diagram. See figure 1.

Figure 1: Wohlin’s snowballing procedure

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10 Through snowballing, 8 articles had been identified that seemed to be of importance to this literature review and therefore have been added to the article set. The total number of articles included in this study therefore ended up to be 30. No more articles have been added after this as it seemed that the point of literature saturation had been reached and no more insights were gained. The described process is visually presented below in the flow diagram in figure 2.

Figure 2: Flow diagram of articles included

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11 Of the articles included, 13 were literature reviews (of which 7 were systematic, 4 were narrative and 2 were scoping reviews), 6 were randomized controlled trials, one was a repeated measures effectiveness study, one was a retrospective analysis and one was an evaluation study, and 8 did not specifically mention what kind of studies they were, but they seem to be somewhere between field studies, experiments and pilot studies.

Analyse. Here the articles have been coded in three sequential steps: open-coding identified themes and categories, which resulted in a total amount of 539 used open codes; Axial coding explored sub- categories in the open codes resulting in a total amount of 71 axial codes and lastly, selective coding to explore relationships between the (sub-)categories, which resulted in 22 selective codes: the driving and restraining forces as presented in the force field analysis. The selective codes, accompanied by some sample quotes are visible in appendix 1. The axial codes used are presented in appendix 2 and the open codes are presented in appendix 3.

Present. This literature review endeavoured to present its findings, conclusions and decisions made during the process as clearly and replicable as possible. For example, the coding program Atlas TI has been used to code the articles and the codes used have been included in the appendix, a flow diagram has been added and the process of snowballing has been described. Other tables and figures depicting the results and processes as clearly as possible have been included wherever possible.

Findings

The findings section will consist of 2 parts. Firstly, the identified driving forces will be described and presented and secondly the restraining forces will be described and presented. Based on their descriptions, the forces are either classified as immediate, longer-term forces or both and an estimation has been made about their strengths. Immediate forces are acting now where longer-term forces have a less immediate effect, but might increase in the future (Dezsca et al, 2019). For rating their strengths, the same rating scale as Depanfilis (1996) has been used: High, low or uncertain.

In appendix 4, an overview of the possibilities of eHealth and mHealth monitoring based on the technologies used in the literature has been added.

Driving forces

In this systematic literature study, 9 driving forces for the change towards using eHealth and mHealth monitoring techniques have been identified. Combined, these driving forces have been recognized 253 times throughout the articles included in this review. Figure 3 shows the driving forces identified through this literature review. Below the figure, the driving forces will be presented one by one starting with the one most brought up in the literature and ending with the least mentioned one. This chapter will conclude with a combined visualisation of the driving and restraining forces in one figure.

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12 Figure 3: the identified driving forces of using eHealth and mHealth monitoring technologies in

healthcare

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13 Improved healthcare- By far the most recognised driving force of the change towards using new (distant) monitoring techniques in healthcare is the potential positive effect it will have on healthcare outcomes, health interventions, and public health in general. The improvement of healthcare results is mentioned 114 times in 25 of the articles reviewed. Improvements of healthcare mentioned include:

earlier identification of diseases , improved efficiency, safety and quality leading to reduced mortality rate and hospital stay (Weenk et al, 2020), improved insights in health outcomes, performance, risks and the possibility to take prescriptive action (Tana et al, 2017), increased treatment adherence (Ames et al, 2019; Campbell et al, 2018; Dounavi & Tsoumani, 2019; Ingersoll et al, 2015; Schiaffini et al, 2016;

Schulze et al, 2019; Stenzel et al, 2015) and the possibility to detect nonadherence in time to engage in adherence interventions (Ingersoll et al, 2015), the ability to see trends in vital signs and thereby being able to earlier recognise clinical deterioration leading to earlier interventions (Weenk et al, 2020), increased self-management (Eisner et al, 2019; Ammerlaan, et al 2015; Castensøe-Seidenfaden et al, 2018 Rönkkö et al, 2018; Stec et al, 2019), mHealth apps improved the health outcomes of patients living with chronic diseases and offer care in a natural environment (Whitehead & Seaton, 2016), a significant reduction of anxiety and depression symptoms was achieved through telephone symptom monitoring (Van den Berg et al, 2015). and more.

This force seems to have an effect on healthcare immediately (e.g. through reducing anxiety and depression symptoms (Van den Berg et al, 2015)) as well as over the longer-term (e.g. improving healthcare through better insights in health outcomes (Tana et al, 2017) and improved efficiency, safety and quality (Weenk, 2020)) and therefore has been classified as both an immediate as longer-term force.

The strength is estimated to be high, based on the wide variety of possible improvements mentioned.

Technological advancements- The second most mentioned driving force is the technological advancement and rapid adoption of new technologies making the new monitoring techniques possible in the first place. Technological advancements driving the change towards using new monitoring techniques in healthcare is mentioned 48 times in 17 of the articles reviewed. The technological advancements that seemingly are pushing researchers and health practitioners towards using new monitoring techniques in healthcare include, among other things: the development of the broad capabilities of smartphones nowadays (Trifan et al, 2019) combined with their widespread adoption with over 1 billion smartphone subscriptions expected in 2022 and increased internet access resulting in a rapid growth in the mHealth market (Dounavi & Tsoumani, 2019). The decrease in size, variety and price of the sensors used in both smartphones and wearables (Trifan et al, 2019). The ability of smartphones to monitor behaviour using multi-tasking tools thanks to a processor that is much faster than processors used by PC’s 10 years ago (Zapata-Lamana et al, 2020) making it possible for smartphones to passively collect data (Trifan et al, 2019). The development and advancement of wearables that can be used for health monitoring that are getting more mobile, accurate and reliable (Weenk et al, 2020) making it possible to provide detailed and longitude data effortlessly, without

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14 having to use uncomfortable, expensive and more sophisticated alternatives (Piwek et al, 2016). The rapid growth in numbers of available mHealth apps (Castensøe-Seidenfaden et al, 2018; Ross et al, 2020) with over 160.000 mHealth applications available in the Apple Appstore (Stec et al, 2019) and a larger population adopting the use of health-related applications (Tana et al, 2017).

Besides the current possibilities recent technological advancements have yielded, future technological developments have also been mentioned as driving forces in the change process towards implementing the new health monitoring techniques in healthcare. These future developments include the development of sensors to be ingestible or implantable and integrated in garments on a fibre level (Loncar-Turukalo et al, 2019); 5G increasing the communication speed of wearables allowing for faster communication and real-time control (Loncar-Turukalo et al, 2019); the further development of big data analytics to store and process the big amounts of data generated, and machine-learning algorithms to prevent overtreatment and unnecessary diagnostic procedures resulting from the large sums of data retrieved (Weenk et al, 2020).

The technological advancements seem to have an immediate effect on the possibilities of eHealth and mHealth monitoring through the possibilities that have already been enabled by developments in recent years, but also seems to be a longer-term force taking the future developments into account. This strength is estimated to be high as the possibilities enabled are widespread and seem to be growing and further develop over coming years.

Increased health accessibility- The third most mentioned driving force towards implementing new monitoring techniques is the increased access to health that can be enabled by these technologies. The increased access to health is mentioned 23 times in 15 of the articles reviewed. The abilities of increasing the access to healthcare enabled by these technologies include: the increased access to care and healthcare information in low- and middle-income countries (van den Heuvel et al, 2018). Making it possible and more convenient to deliver healthcare services through transmitting relevant data and telemonitoring for patients with difficulties accessing healthcare facilities because they live in rural and remote areas or communities (Schiaffini et al, 2016; Zaidi et al, 2020). mHealth makes it possible to close long-lasting inequity gaps and engage underserved populations (Nittas et al, 2018). The possibility to deliver interventions at any place or time for an enlarged period makes access to healthcare universal (Dounavi & Tsoumani, 2019) and makes healthcare totally unrestricted by place and time (Eisner et al, 2019; van den Heuvel et al, 2018). The use of text messages to monitor patients can reach remote places, even places where cellular signals are weak (Ingersoll et al, 2015). It also increases the access of health in the sense that it enables patients unwilling to speak to share their issues or concerns about worsening symptoms, as it decreases the feeling of inconvenience (Eisner et al, 2019) and increases the willingness of patients to share issues relating privacy and shyness (Ames et al, 2019). mHealth self-monitoring also

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15 seemed to reduce stigmas of mental health issues such as depression, which motivated patients to seek appropriate professional help (Scherr et al, 2019).

Even though future developments and trends could increase the accessibility of health even further, the increased access to health seems to be an immediate force, as the possibility to increase health accessibility seems already to be widely available and many of the advantages could already be gained.

The strength is estimated to be high as this force represents a relatively easy and efficient way to improve and increase the health and healthcare accessibility of many people.

Positive attitude of patients- The next most mentioned force that is pushing healthcare providers towards implementing more digital and mHealth monitoring techniques is the positive attitude and willingness of patients to use these technologies. The positive attitude of patients is mentioned 18 times in 10 of the articles included in this study. The studies for example showed that individuals regular monitoring their health using their smartphones expected it could improve their habits and lifestyles (Trifan et al, 2019). Surgical patients had a positive attitude towards using mobile apps and wearable devices and almost all patients in a randomized controlled trial encouraged the idea of wearables measuring vital signs continuously (Weenk et al, 2020). All participants of an experiment with an online portal enabling self-monitoring in combination with the possibility to get an e-consult were positive and said they intended to use the portal again (Ammerlaan et al, 2015). And using text messages for drug adherence monitoring was highly accepted by the study population, although the text messages were automated, they felt happy that someone seemed to care about them (Ingersoll et al, 2015). A literature review on eHealth in perinatal care states that patient’s satisfaction of eHealth is generally good, with satisfaction rates reaching up to 95% (van den Heuvel et al, 2018) and mHealth application use is widely accepted by patients and regarded useful (Dounavi & Tsoumani, 2019; Eisner et al, 2019; Stec et al, 2019). Also, most of the participants in an experiment using a diabetes monitoring app would recommend it to others (Castensøe-Seidenfaden et al, 2018).

The positive attitude of patients seems to be an immediate force as it shows the willingness of patients to use eHealth and mHealth monitoring at this point in time. Patients and consumers seem to adopt the idea and seem currently ready to engage in using these new technologies. The strength of the force seems to be uncertain, as the studies showing the positive attitude were relatively short, and the positive attitude of patients could be nothing more than a fad with their positive attitude and willingness fading away over time.

Reduced healthcare costs- the fifth most mentioned driving force is that implementing eHealth and mHealth monitoring techniques could reduce overall short- and long-term healthcare costs. Reduced healthcare costs as a positive effect has been mentioned 15 times in 10 of the articles included in this study. Examples mentioned in the papers are: The use of symptom monitoring apps could facilitate the move towards more preventative care rather than reactionary care, where deterioration is only noticed

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16 when a crisis intervention is needed which is relatively more expensive (Eisner et al, 2019). mHealth applications for monitoring weight and weight loss seemed to be cost-effective (Dounavi & Tsoumani et al, 2019) and mHealth monitoring applications were deemed able to replace weekly visits from clinicians and medical researchers, which are more time consuming and resource intensive interventions (Eisner et al, 2019). mHealth technology is also mentioned to be of great help in delivering healthcare in developing countries, as they reduce costs compared to more traditional interventions (Zapata- Lamana et al, 2020). Telemedicine also allowed detailed monitoring of patients while providing social support, without the need for more costly face-to-face appointments (Schulze et al, 2019). In one extreme example, the potential ability of mHealth monitoring to reduce long-term healthcare costs is illustrated vigorously. In this study, text messages and individual calls were used to monitor drug adherence of HIV patients. They state that, if the monitoring enhances the medicine adherence, and therefore reduces the risk of HIV transmission, costs up to $1,3 million on a single prevented newly diagnosed HIV patient can be saved (Campbell et al, 2018).

eHealth and mHealth monitoring seems to be able to decrease healthcare costs on the longer-term. At this moment, it is not yet able to fully replace traditional interventions, but rather is facilitating the move towards preventative care (Eisner et al, 2019). It seems to have enormous potential to decrease healthcare costs, but it seems not realistic that this can be achieved right away. It rather seems to be a slow transition were costs could be expected to rise initially, as traditional interventions will be combined with new eHealth and mHealth monitoring initiatives. However, the potential is huge, amounting to millions saved through prevention. This force therefore has been classified as a high strength, longer-term force.

Possibility to obtain relevant data to further improve healthcare- eHealth and mHealth monitoring techniques can yield relevant health data that can be used to improve healthcare in the long run. The possibility to retrieve relevant data has been mentioned 12 times in 6 Of the articles reviewed in this paper. Some examples are: the possibility to use the big amounts of generated data to prevent, predict and treat individual health and public health (Tana et al, 2017); it can enable healthcare professionals to obtain evidence-based information that they can use in clinical practice (Stec et al, 2019); it enables clinicians to review and access relevant data in real time rather than after the event, meaning the data is

‘momentary’ (Guillodo et al, 2020; Zapata-Lamana et al, 2020); the data obtained is also extra valuable as the data captured can be ‘ecological’- captured while in the natural environment of the patient, and the data is not singular, but rather consisting of multiple ‘assessments’ that could be used to make a profile of patient’s behaviour (Zapata-Lamana et al, 2020). Also, sleep data gained through wearables could reveal or strengthen links between health and sleep behaviour, which can be useful for a broad span of medical conditions (Guillodo et al, 2020). It is even stated that these novel opportunities to monitor health transform customers and their lives into important sources for health information (Nittas et al, 2018).

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17 The possibility to obtain relevant data to further improve healthcare seems to be a longer-term force.

The articles mentioned the possibilities of using the relevant data, rather than the current possibilities based on the data that is actually accessible. This also becomes clear in the restraining force ‘inaccessible data’ mentioned later on. However, the potential possibilities the data can be used for are wide and could be of big importance in the future of healthcare. Therefore the strength of this force has been rated high.

Positive attitude of healthcare providers- besides the positive attitude of patients, the healthcare providers also seem to be positive about using more eHealth and mHealth monitoring techniques. The attitude of healthcare providers is the sixth most mentioned driving force of the change process towards using new eHealth and mHealth monitoring techniques in healthcare and is mentioned 9 times in 3 of the articles included in this review. In a randomized controlled trial of using wearables to continuously monitor the vital signs of patients in the general ward in between medical check-ups all nurses (n= 20) involved mentioned that they encourage the implementation of wearable devices. One nurse even mentioned that it was the future of healthcare and that using wearables should be implemented as soon as possible as that would maximize the potential profit that could be achieved (Weenk et al, 2020). In another study, in rural Pakistan, vaccinators and their work were monitored through an Android app using GPS and photos of children they vaccinated. All vaccinators (n=26) were willing to continue to use the app in the future and 23 preferred the digital records over manual ones for monitoring their routine immunization efforts. Most of the vaccinators also stated that the use of the application increased their motivation as it showed their performance level and some even stated that using the app made them take their job seriously for the first time (Zaidi et al, 2020). Besides the vaccinators being positive, also the managers of the overarching immunization program were positive, as through monitoring the vaccinators and their work, they could increase coordination and publicly recognize the best performing vaccinators (Zaidi, 2020). A literature study on eHealth in perinatal care also found that care provider satisfaction was generally high, achieving rates up to 95%. (van den Heuvel et al, 2018).

The positive attitude of healthcare providers seems to be an immediate force as the three studies mentioned above showed that healthcare providers seem to be willing to use and experiment with new eHealth and mHealth monitoring possibilities. However, the strength has been rated weak, as just three articles mentioned the positive attitude and the attitudes were observed after an experiment. It therefore does not show that most or the average healthcare providers are willing to use these new technologies, rather it shows that, after engaging in an experiment, they showed positive attitudes towards the specific initiatives used during these experiments.

Enhanced healthcare provider – patient interaction- Using new eHealth and mHealth monitoring technologies can enhance the interaction between healthcare providers and patients. This driving force has been mentioned 8 times in 5 of the articles reviewed. One study found that using a skilled nurse to monitor drug adherence may further strengthen the healthcare provider – patient relationships as it

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18 increased their interactions. (Campbell et al, 2018). Another study stated that text messages enabled patients to talk about issues they wouldn’t talk about because of shyness or privacy issues (Ames et al, 2019). One study using a web portal with self-monitoring tools mentioned that all patients using the available e-consult possibility thought that it improved their communication with their healthcare providers and made it easier. It also enabled them to think more about the questions they were asked and they were better able to explain their concerns (Ammerlaan et al, 2015). Another study using wearables to continuously monitor vital signs of patients in the general ward mentioned that all nurses interviewed said they would use the time saved by using this technique on the patient by simply offering a listening ear, providing information, and more (Weenk et al, 2020). It is also mentioned that the roles of healthcare providers might switch to a more supportive role, which might also positively influence the relationship between patients and healthcare providers (Nittas et al, 2018).

eHealth and mHealth monitoring seems to be able to enhance healthcare provider – patient interaction immediately with the currently present possibilities. Therefore the force can be classified as immediate.

The strength, however, is harder to assess as an identified restraining force in this study pointed at the concerns that eHealth and mHealth monitoring might decrease healthcare provider – patient interaction.

Therefore the strength of the force has been rated uncertain.

Reduced workload- The least mentioned driving force found in this literature review was that using these new monitoring techniques might reduce the workload of healthcare professionals. This was mentioned 6 times in 3 of the articles reviewed. For example, it was stated that the use of mHealth apps used for symptom monitoring can help to shift medical services towards a more preventative rather than a reactive practise, preventing, psychosis relapse and therefore the admission in a psychiatric institution (Eisner et al, 2019). It is also mentioned that the use of wearables for monitoring vital signs can increase efficiency, can result in shorter hospital stays and prevent intensive care admission and reduced workload (Weenk et al, 2020). Monitoring vaccinators and their work also drastically reduced tracking and monitoring time, increasing the time availability of supervisors, which then could be used for other activities (Zaidi et al, 2020). Using an mHealth app could also replace weekly visits from researchers or clinicians (Eisner et al, 2019).

The reduced workload that could be achieved through eHealth and mHealth monitoring clearly is a longer-term force. It should not be expected that the implementation of eHealth and mHealth monitoring could immediately reduce the workload. Rather, this would happen over time, as it facilitates the shift towards more preventative care. This is also emphasised by the restraining force ‘increased workload’

mentioned later on in this study. It could be expected that the workload rather increases initially, as new eHealth and mHealth monitoring technologies are implemented and complement traditional proceedings and health interventions at first, slowly facilitating the shift towards preventative care and reducing the

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19 workload over time. Because of the observed contradictions of the effect on the workload, the force has been rated uncertain.

Restraining forces

In this systematic literature study, 12 restraining forces for the change towards using new eHealth and mHealth monitoring techniques in healthcare have been identified. Combined, these restraining forces have been recognized 183 times throughout the articles included in this review. Figure 4 shows the restraining forces identified through this literature review. Below the figure, the restraining forces will be presented one by one starting with the one most brought up in the literature and ending with the least mentioned one.

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20 Figure 4: the identified restraining forces of using eHealth and mHealth monitoring technologies

in healthcare

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21 Technology dependency and (un)reliability- The most mentioned restraining force is the dependency on and (un)reliability of the technologies used. This restraining force is mentioned 43 times in 11 of the articles reviewed in this study. Most mentioned are concerns about dependability on the battery used in the devices and battery technology lagging behind compared to other technologies used (Ames et al, 2019; Loncar-Turukalo et al, 2019; Trifan et al, 2019; Weenk et al, 2020; Zaidi et al, 2020). Also, sensors used can wear out and deteriorate with extended use, and, unlike laboratory equipment, they are not checked routinely and therefore the trustworthiness and quality levels of the date may decrease with use (Düking et al, 2018). Another important issue is the large variation in measurement accuracies, with wearables showing error margins up to 25% in monitoring physical activity, and a reported 30% failure rate for an application for melanoma detection using the camera (Piwek et al, 2016). Also, the software used in devices influences the quality of the data and the data itself and it seems that in multi-sensor devices the data quality depends on the interplay between the sensors rather than on the quality and measurements of individual ones, also sensors can be influenced by other devices or even the photosensitivity of the skin (Düking et al, 2018). Other issues include the inability to directly differentiate health status changes and system related errors leading to false alarms and possibly causing

‘alarm fatigue’ in healthcare professionals (Loncar-Turukalo et al, 2019; Weenk et al, 2020), internet dependability (Ames et al, 2019; Zaidi et al, 2020), dependability on mobile phones in general in case they are stolen or damaged (Ames et al, 2019; Zaidi et al, 2020), data and memory use (Trifan et al, 2019; Zaidi et al, 2020) short life expectancy of mobile phones and apps (Ross et al, 2020; Zaidi et al, 2020), wearables providing unreliable (or unreliable interpretation of) data (Düking et al, 2018) and users becoming over-reliant on their devices (Tana et al, 2017; Piwek et al, 2016).

The technology dependency and (un)reliability seems to be an immediate force, as it currently limits the applicability of eHealth and mHealth monitoring. The potential of the technology still seems to be enormous and it could be expected that, as time passes, the technologies will evolve and become more reliable and improved internet access, software and battery quality will probably make the technologies more suitable for use in healthcare. However, the current state, regarding the unreliability and dependency on the technologies, make them unsuitable because of the huge risks in healthcare associated with errors and measurement mistakes. Therefore, the force has been rated to be of high strength.

Lack of scientific evidence- The second most mentioned restraining force is the perceived lack of scientific evidence. This was mentioned 26 times in 16 different articles. Some examples are researchers addressing the need for more studies on how mobile phone self-monitoring applications can provide motivation (rönkkö et al, 2018). It is mentioned that present literature on eHealth and mHealth monitoring is scarce and their positive outcomes are deemed controversial with complications not well described (Schiaffini et al, 2016). Just a few out of hundreds of thousands health apps have been tested, research quality is deemed low and evidence of mHealth apps effectiveness is mixed (Dounavi &

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22 Tsoumani, 2019). Wearables are aggressively marketed with exaggerated health claims lacking scientific evidence (Düking et al, 2018). Very limited research is conducted on the impact of wearables on user’s behaviour and the majority of manufacturers provide no evidence to support claims made about their ability to improve general health (Piwek et al, 2016). The wide variation in the methodologies used complicate comparison (Düking et al, 2018). Also, it is mentioned that many studies use small sample groups and were conducted over a relatively short amount of time, too short to provide reasonable results (Trifan et al, 2019). This was also visible in some of the studies included in this literature review, where, for example, one study only included 13 participants for testing an online portal (Ammerlaan et al, 2015) and another study using an activity tracker to monitor physical movement only had 8 participants (Rönkkö et al, 2018) while a literature review only included 9 articles in their study (Whitehead & Seaton, 2016). It is specifically mentioned that healthcare professionals need more scientific evidence in order to make well-grounded decisions of using mHealth apps in their practices (Dounavi & Tsoumani, 2019) and that testing and evaluation of eHealth technologies is crucial before implementation (Ammerlaan et al, 2015). The lack of evidence based and evaluated mHealth apps leads to the situation that clinicians hesitate to recommend the use of these applications, despite their patients requesting for such information (Stec et al, 2019).

The lack of scientific evidence seems to be an immediate force. It is currently limiting the applicability of eHealth and mHealth monitoring. However, as time passes, it could be expected that more and better research will lead to more useable scientific evidence on the use of eHealth and mHealth monitoring.

There also seems to be one exception, the lack of scientific evidence on mHealth applications could be moderated through the Mobile Application Rating Scale Instrument (MARS): a reliable and valid instrument to examine the functionality, engagement, aesthetics and quality of mHealth app information that can be used to make app suggestions to clients which could improve health outcomes (Stec et al, 2019). However, due to the low-risk nature of healthcare and the limited use of MARS, the lack of scientific evidence has been rated to be of high strength.

Privacy issues- the third most mentioned restraining force concerns privacy. Privacy issues are recognized 23 times in 10 of the articles reviewed in this study. It is stated that mHealth apps are regulated poorly and often exclude guidelines about privacy (Stec et al, 2019), a lack of control of the data collected and lack of knowledge on third parties using the collected data (van den Heuvel et al, 2018). It is stated that privacy issues have been neglected in prior research in favour of validity and reliability challenges (Tana et al, 2017) and if privacy issues are not fulfilling user’s expectations, the solutions might not be used in the first place (Trifan et al, 2019). Also, patients were afraid that their confidential health information could be revealed and their identity traced (Ames et al, 2019). Users often agree with the mobile app and social media terms of use while not aware of the implications this has on their privacy (Mejova et al, 2018). Data generated by devices is not owned by the user’s but rather is owned and stored by the manufacturer (Piwek et al, 2019) while the user is just presented with

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23 a summary of the results based on this data (Tana et al, 2017). Mobile phones were also, in some cases, controlled by others (Ames et al, 2019) and concerns about increases in the number of data breaches in healthcare organisations is also mentioned (Loncar-Turukalo et al, 2019).

Privacy issues could be considered both an immediate, as well as longer-term force. It seems to be limiting the current use of mHealth and eHealth monitoring and it is not observed that the mentioned privacy issues will decrease in the future. However, despite it being mentioned as an issue, not much is mentioned about the individual’s considerations between improved health and privacy. Therefore the force has been rated to be of uncertain strength.

Demographic issues- the fourth most mentioned force restraining the widespread use of eHealth and mHealth monitoring concern demographic issues and their effect on the use(ability) of these monitoring possibilities. Demographic issues were mentioned 19 times 8 of the articles. First of all, and most mentioned, are issues concerning age. It is mentioned that, although smartphones are used worldwide, the young generations feel most comfortable using them, while, for elders, smartphones may not be a tool they can easily use. Paradoxically, the older populations might benefit most from the health monitoring possibilities they enable (Trifan et al, 2019). Also, most studies seemed to use relatively young samples, which are considered to be ‘digital natives’ (Eisner et al, 2019) and mainly young audiences seem to be willing to engage in research in this area, resulting in inaccurate results when used by other populations (Trifan et al, 2019). Also, participants mentioned using text messages to be more appropriate for the younger population (ames et al, 2019). Other demographic issues include: using Facebook data for health monitoring excludes populations less likely to use the social medium (Mejova et al, 2018) . Monitoring apps might attract people with already diagnosed diseases and specific goals in their minds rather than the ‘average’ individual (Trifan et al, 2019), this was also confirmed in a randomized controlled trial of a pain-monitoring app, where people with greater pain-disability tended to use the app more compared to less disabled (Ross et al, 2020) while the opposite seemed to be true for wearables as it seems that wearable consumers are already likely to lead a healthier lifestyle (Piwek et al, 2016). The last demographic issue is that, however digital health services might be especially helpful for clients living far from health facilities and poorer people, access to digital health is also limited for individuals speaking minority language and have low literacy and digital skills (Ames et al, 2019; Eisner et al, 2019).

The demographic issues could be considered an immediate force. The limited research using mixed or older samples could possibly increase over time, as the volume of research increases and left-out populations are identified. Also, the elderly and rural populations might feel more comfortable using eHealth and mHealth monitoring technologies as the adoption of e.g. smartphones increases throughout the layers of society over the years to come, and the numbers of people speaking solely minority languages and have low literacy are probably going to decrease. Even though some populations are

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24 currently excluded, mHealth and eHealth monitoring seems to be of value to the majority of individuals, and because of future trends, these relative numbers are expected to grow. Therefore the strength of this force has been rated low.

Lack of cooperation- Lack of cooperation between the different organizations and industries involved is the fifth most mentioned restraining force. It is mentioned 14 times in 9 articles. There seems to be a lack of cooperation between information technology scientists, clinical researchers and device industries for implementing commercially available wearables (Guillodo et al, 2020). mHealth apps are selected without clinician guidance and require more evaluation by clinicians as highly rated applications may not contain evidence-based information (Stec et al, 2019). Supplementary support by qualified healthcare professional is needed for mHealth apps monitoring sleep and eHealth services often seem to miss professional healthcare provider engagement during the development (Rönkkö et al, 2018); a review of 224 pain monitoring apps found little to no evidence of healthcare professionals involvement in the creation and development (Ross et al, 2020). It is also recommended that researchers and practitioners should try to work together and start a dialogue that is constructive (Tana et al, 2017) and some participants in a study on the feasibility of a web portal incorporating health monitoring stated they would only use the tools when their healthcare provider would ask it, or if he would actually use the generated information during consultations. (Ammerlaan et al, 2015). Also, insurance coverage is extremely fragmented, varying from country to country, between hospitals within a single country and for different specialities within a single hospital and insurance companies are only tempted to cover well-researched eHealth services with well-founded economic evaluations (van den Heuvel et al, 2018).

The lack of cooperation seems to be an immediate force. The lack of healthcare professional engagement in the development of eHealth and mHealth monitoring services are currently preventing healthcare professionals to use them or recommend them to patients that are willing to use them, and therefore patients seem to select them without clinician guidance. However, research suggested that cooperation should be increased and therefore the force is expected to decrease over the years to come. Because of the assumption that the use of eHealth and mHealth services would be much higher if cooperation and healthcare professional involvement increased, the force is rated of high strength.

Healthcare providers lack relevant IT knowledge- The sixth most mentioned restraining force is that healthcare providers lack the relevant IT knowledge to work with these new initiatives. This is mentioned 11 times in 6 Of the articles reviewed. It is stated that the rise of digital healthcare brings new demand for specialized healthcare skills and a new type of employee. It would require an enhanced or even new set of skills, attitudes and knowledge (Tana et al, 2017). The current lack of knowledge seems to slow down the adoption of relevant professional knowledge related to IT (Rönkkö et al, 2018) and the shortage of healthcare personnel with the proper IT competences is partly the reason for the slow uptake of mHealth and eHealth technology (Tana et al, 2017). Training for healthcare providers is

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25 needed to further optimize their abilities to use new eHealth and mHealth initiatives (Castensøe- Seidenfaden et al, 2018) and it seemed like nurses did not have the required knowledge and training to interpret the continuous data produced by using wearables for monitoring (Weenk et al, 2020). Further training in using social media for health-related issues was also recommended in the literature (Mejova et al, 2018) and it is even mentioned that security breaches and hacks were partly caused by the lack of knowledge of employees working with this sensitive data. (Loncar-Turukalo et al, 2019).

The healthcare providers lack of relevant IT knowledge seems to be an immediate force. It is clear in the literature that the current lack of IT knowledge is limiting the possible use of eHealth and mHealth monitoring, but as these initiatives are gaining more interest and are gradually being tested and implemented, it could be expected that the IT knowledge of healthcare professionals will increase over time. For some eHealth and mHealth monitoring initiatives, IT knowledge of healthcare providers can be relevant, such as continuously monitoring vital signs using wearables. While for others, such as the use of self-monitoring apps by patients or using text messages and telephone calls, this appears to be less the case. Therefore this force has been rated to be of low strength.

Data overload- data overload was mentioned just as often as the lack of relevant IT knowledge, 8 times in 3 of the reviewed papers. The data generated by eHealth and mHealth monitoring can be huge and one doctor even mentioned that the amounts of data can make you crazy, especially when it is not of influence of the decision made about the patient’s treatment (Weenk et al, 2020); physicians do not have the time to take all raw data generated into account (Ross et al, 2020); it could lead to overtreatment and unnecessary diagnostic procedures by accidently identifying abnormalities in patients that cannot be ignored (Weenk et al, 2020) and it is mentioned that the heterogeneous and huge volumes of data have grown beyond data processing techniques that are commonly used (Loncar-Turukalo et al, 2019).

Participants in a study also brought up that there would be a shortage in personal to monitor all data generate by wearables (Weenk et al, 2020).

Data overload can be considered an immediate force. In the current implementation of eHealth and mHealth monitoring, data overload seems to occur. However, as mentioned in the driving force

‘technological advancement’, future developments of big data analytics and machine-learning algorithms have the potential to moderate this restraining force. Because of the future potential to moderate the data overload issue, the force has been rated low strength.

Regulatory/legislation issues- the next most mentioned restraining force concerns issues regarding regulations and legislation. Regulation and legislation issues are mentioned 11 times in 3 of the articles reviewed. On one hand it is stated that mHealth apps are poorly regulated (Stec et al, 2019) and in the United States, eHealth legislation lacks patient protection, while on the other hand it is said that the development of other eHealth initiatives is limited by strict legislation. For example, according to European law, eHealth can be considered simultaneously as an information service and healthcare

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26 service and therefore should correspond to regulations concerning both. Developers have to mind privacy protection legislation, e-Commerce directives, electronic identification services, liability requirements and follow safety requirements of medical devices, while at the same time follow the different rules of member states concerning privacy, liability and healthcare (van den Heuvel et al, 2018). Although, recently legislation in the US is also introduced, that would require mHealth technologies to be reviewed by the FDA (Food and Drug Administration) (Stec et al, 2019). Also, new regulations have been introduced in the European Union that requires more strict procedures for evaluation which should improve patient safety concerning the use of wearable technology and new security frameworks are being proposed to increase security and reliability (Loncar-Turukalo et al, 2019).

Looking at the trends of the regulatory and legislation issues, it could be said that this is both an immediate, as well as a long-term force. Current regulations are impacting the implementation of eHealth and mHealth monitoring and future regulations seem to restrict and limit the release of new eHealth and mHealth initiatives. However, this does not necessarily mean that it will negatively impact the implementation of eHealth and mHealth monitoring in healthcare, as it could improve the quality of the eHealth and mHealth monitoring possibilities that pass the regulations and legislation. The force has therefore been rated of uncertain strength.

Inaccessible data- Generated data through eHealth and mHealth monitoring was often not accessible.

This is the tenth most mentioned restraining force. It has been mentioned 9 times in 4 articles. As mentioned previously, users often do not own the data that is generated and therefore the raw data cannot be seen, accessed or analysed by healthcare providers. Rather, users get an overview presented of the results based on the data generated by the products (Piwek et al, 2019; Tana et al 2017) Ironically, some manufacturers even seem to be charging a monthly fee for consumers to access their own data (Piwek et al, 2019). As the analysis methods of wearables are patented access to raw data is not allowed. This leads to a major limitation for analysis purposes in healthcare (Guillodo et al, 2020). Also, access to the raw data for research purposes could improve the validity, sensitivity and reliability of the data and therefore it is recommended that the raw data access is provided by manufacturers (Düking et al, 2018).

The inaccessibility of data is an immediate force. As the raw data generated by smartphone and wearable sensors currently is inaccessible, healthcare professionals cannot work with the full potential of the data generated and opportunities for improvements are missed. However, if the cooperation between healthcare professionals and developers would increase, as mentioned in the restraining force ‘lack of cooperation’, the data could become accessible for the healthcare professionals involved which could lead to the mentioned possible improvements. Also, not all eHealth and mHealth monitoring services rely on patented analysing methods using inaccessible data. Examples are some mHealth applications,

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27 text messages and telephone calls that rather rely on the manually construction and transfer of information. Therefor the strength has been rated uncertain.

Increased workload- Although eHealth and mHealth monitoring was mentioned to reduce the workload of healthcare providers, it is also mentioned to increase the workload. This was recognized 7 times in 3 different articles included. It is a concern of healthcare professionals that there would be a lack of personnel to monitor all data created through continuous monitoring. Also, concerns about an increased workload include the work associated with false alarms and unnecessary diagnostic procedures (Weenk et al, 2020). Implementing mHealth interventions could also trigger the need to employ more Healthcare Professionals capable of giving medical advice through text messages (Campbell et al, 2018). It is also mentioned that using a monitoring application could double the amount of work, as besides making entries digitally, manual entry of records is also still needed. (Zaidi et al, 2020).

The increased workload seems to be an immediate force addressing concerns that healthcare professionals have at this moment. The mentioned concerns about the increased workload however are countered by other forces. As mentioned previously, the lack of personnel could be moderated by the employment of big data analytics. False alarms and unnecessary diagnostics could be moderated by machine learning. The double amounts of work could be decreased as digital records could replace manual ones, and the employment of extra healthcare could be moderated by a decrease in workload over time through improved and preventive healthcare. This force therefore has been rated as low strength.

Less healthcare provider – patient interaction- Even though increased interaction between healthcare providers and patients has been recognized as a driving force, it is also present in the literature as a restraining force. It was mentioned 6 times in the literature in 5 of the articles. eHealth and mHealth might interfere with the social relationships on which social work is build (Rönkkö et al, 2018). It is also mentioned that some eHealth and mHealth monitoring initiatives could lead to fewer patient- provider visits (Campbell et al, 2018) and replace weekly visits of clinicians and researchers (Eisner et al, 2019). It can result in stronger digitalization of healthcare, leading to a weakened healthcare provider involvement (Nittas et al, 2018), less healthcare provider – patient interaction with too much attention aimed at vital signs while having less attention for the individual (Weenk et al, 2020). A patient interviewed in one of the articles stated that patients need the confidence of the nurses, and that would be missed (Weenk et al, 2020).

Looking at the current state of eHealth and mHealth services, it seems unlikely that they would immediately replace standard health interventions. Rather, it could be a gradually transition towards more distant health monitoring, where the interaction shifts to more digital contact rather than personal contact. This force therefore is classified as a longer-term force. Also, even though the interactions are

Theoretical background

Abstract

Table of contents

Introduction

Theoretical background

Methodology

Findings