Artificial Intelligence in Healthcare Innovation

Running head: POSITION PAPER 2 1

Position Paper 2

Arizona State University

HCI 544 - Information Technology (IT) for Health Care Innovation

September 07, 2020

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Artificial Intelligence in Healthcare Innovation

Introduction

Description of the technology and give your explanation for why it will be so important

Artificial intelligence (AI) is still reclaiming the frontiers of the healthcare field by

inculcating analytical and predictive services into mobile and wearable devices. Researchers

state that the accessibility of AI is explained by its ability to bring raw data to actionable

understanding so that individuals could be more conscious about their wellness (Iversen and

Eierman, 2018). The transformation changes healthcare as a clinical service to a service based on

collaboration between users and intelligent systems. As Mishra (2015) elaborates, the fact that

wearable gadgets can detect and analyze physiological patterns has made AI a key premise in the

current preventive medicine. The implication of such developments is that health ceases to be

reliant on institutional knowledge but is more personalized, building upon personal interaction

with adaptive digital care. Landeweerd, Spil, and Klein (2013) conclude that the best uses

involve establishing an easy interaction between user and technology, in terms of forming trust

and long-term involvement. This kind of dynamic is an indicator of a new healthcare paradigm in

which feedback and motivation are real-time and generate awareness and behaviour change

(Iversen & Eierman, 2018). This healthcare accountability redirection will mark a significant

change in social direction to the use of data-driven self-management. It preconditions the

investigation of concrete instruments, such as Google Fit, that represent a new model of digital

wellness.

The history of AI-based healthcare technologies is conditioned by the experience of early

experimental failures. Such a case can be Google Health, which provided a lot of storage, but it

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did not keep users interested, as it was not personalized enough and poorly integrated into the

design (Spil & Klein, 2014). The failures of these preliminary solutions demonstrate that the

technological development is not sufficient in itself to be successful unless people comprehend

how to interact with digital realms. Landeweerd, Spil, and Klein (2013) concur that health

technological innovation has to be comprehensive in an approach that combines technical

complexity with emotional usability so as to be applicable to daily life. This observation explains

the success of newer applications such as GoogleFit; they have benefited by understanding how

to overcome the limitations of the older models to offer flexible and user-friendly functionality.

When a technology fits well with the routines of its users, Iversen and Eierman (2018) note that

the adoption will be habitual, not forced. Mishra (2015) continues by stating that passive tracking

becomes intelligent companionship through seamless sensor integration in the wearable world,

and this is what connects emotion with functionality. These trends show that advancements in

technology within the healthcare sector rely on factor to do with empathy and design as much as

they do on calculating. They also disclose how corporate organization such as Google evolved to

fit innovation with the real life worlds of users.

The definition of health literacy is also reinvented by artificial intelligence in the format

of meaningful and accessible information based on complex medical data. Such power of

interpretation enables users to make rational judgments regarding their physical activity and

overall well-being (Lai, Huang, and Chiou, 2017). Mishra (2015) states that AI-based mobile

solutions customize feedback to match personal objectives and behavioral patterns, which

supports a healthy or sustainable behavior. This kind of personalization enables preventive

healthcare to be more interesting and achievable outside the hospital environment. Landeweerd,

Spil, and Klein (2013) observe that the availability of user data makes people less passive in the

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management of their health, as it leads to a culture of active engagement. Such systems facilitate

continuous communication between technology and human behavior, in which accountability

and spurring are promoted due to unremitting feedback (Iversen and Eierman, 2018). This

suggests that AI does not just analyze information but also develops cognitive interest through

assisting users to identify trends in their respective lifestyles. With the development of digital

tools, technology has become more symbiotic towards self-care, which represents a more general

shift in the direction of smarter user-empowered health systems. These ecosystems inform

people to make health consciousness in everyday life decisions. Their other point is that

platforms such as Google Fit are the sources of entry to engaging and more informed vision of

personal health management.

The growth of artificial intelligence across the globe is quite evidential as numerous

companies design artificially intelligent software, machines, and mobile phone applications with

voice assistance among others that are currently revolutionizing the health sector. Levine, at al.,

(2013) states that although we might not be able to realize or see it, artificial intelligence is truly

making changes in the way we deal with our health either indirectly or directly. It is because,

across the world, there is a large scale application of these mobile applications, software, and

machines among healthcare centers, enterprises, and consumers. Despite the development in

other sectors and industries, the sector has genuinely and largely adapted to the use of AI in

healthcare and this adoption has made a difference in the lives of millions of people by making it

better (Levine, et al., 2013). This paper will assess Google Fit Application, one of these artificial

intelligence technologies by explaining its importance and its potential impacts on healthcare

spending among consumers, providers, and payers.

Evolution of Artificial Intelligence in Healthcare

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The emergence of artificial intelligence (AI) in healthcare is attributed to the

advancements in technology, social development, and clinical need interplay. According to Lu

(2019) the early computational systems in the medical healthcare domain were rule based, and

followed set structures of algorithms, and not learning systems. This shift, termed as the

‘breakthrough shift’, changes the health practitioner’s approach towards diagnosis and predictive

modeling. AI as a technology greatly benefited from the expansion of big data, as it was able to

find complex overlapping associations that were missed by human analysts (Bohr and

Memarzadeh, 2020). This shift in paradigm with the use of computers in the field of medicine,

argues AI was was not automating human activities, but rather changing the very face of

medicine. As Jones et al (2018) mention, more advanced AI and computer systems bring about

development, but to quote the authors, “with great power comes great responsibility.” This

responsibility in the the domain of ethics, is the oversight in the lack of human touch to replace

loss of emotion with precise computation. Thus, the evolution of AI and its use in healthcare, is

not only a sign of technology advancement, but rather a shift in the core philosophy and then

governance, where clinical responsibility, autonomy and AI use.

The implementation of AI in global healthcare research and practice has been shaped by

global academic and industrial collaboration. According to Tran et al. (2019), bibliometric

analyses associated with AI in medicine have indicated breakthrough growth since 2010,

suggesting a cross-disciplinary and cross-regional convergence of interest. This phenomenon

demonstrates not only interest in technological advancement but also a strategic consensus on

AI’s potential to alleviate healthcare inequities. The surge in research output from various

institutions has been driven by recognition of the potential of computational models in

supporting precision medicine and efficient resource allocation. However, as Chen and Decary

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(2020) argue, the AI technology diffusion needs well-informed leaders who can shift strategies

from innovative thinking to sustainable policy reasoning. AI technology diffusion needs well-

informed leaders who can shift focus from innovative thinking to sustainable policy reasoning.

Their work underscores the critical sophistication of management in the ethical and operational

challenges of AI implementation. Such evidence demonstrates the technological evolution is, in a

great part, an institutional phenomenon. As healthcare systems increasingly adopt AI tools, they

reshape their governance systems, allocation of research efforts, and the identities of their

personnel. With this understanding, the globalization of AI in healthcare is about as much shared

technological promise as global technological and institutional disparity.

The way values have shaped and built up the credibility of the field, is well demonstrated

by the past and present of the use of AI in the healthcare system. According to Tekkein (2019),

the use of AI within the med field can be traced back to the decision-support systems on the

MYCIN platform; these systems were among the first to lay down the bases of reasoning in

computers. the systems were far from perfect and had many unresolved issues, but they brought

to light the debates surrounding trust and system transparency that remain unresolved. According

to Bohr and Memarzadeh (2020), every new generation of AI technologies within the field of

medicine have had the ability to augment the system's ability to diagnose and predict; however,

concerns such as accountability and system's transparency always arise. These issues

demonstrate that progress in AI requires a form of ethical negotiation to progress. These issues

demonstrate that progress in AI requires a form of ethical negotiation in order to progress. In the

words of Briganti and Le Moine (2020), AI’s new generation reflects the optimal balance of the

ability to think and the ability to control, the ability to automate and the ability to explain. There

is an observable shift from systems that are purely algorithmic, to systems that are intelligent,

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and from systems that are devoid of accountability, to systems that are responsible. This shift

illustrates how the AI technologies used in healthcare have justified the moral and ethical

advancement of society. This demonstrates how the developments in technology is directly

dependent on the level of ethics a society has attained. The case of AI illustrates how the success

of a system is not a result of the system’s ability to reason only, but also the socio-ethical context

surrounding the system.

The increasing integration of AI into clinical settings signifies a shift in the very nature of

the practice of medicine. In the past, medicine was practiced based on experience and

observation; now, AI prescribes a new way of knowing through inference and data. With the

observation of Lu (2019), AI changes the boundaries of diagnostic reasoning by enabling the

formation of patterns from complex and interrelated data that are beyond the grasp of the human

mind. This in turn, widens clinical vision, enabling practitioners to discern patterns that were

previously unseen. The shift articulated by Jones et al. (2018) suggests that clinicians pending on

AI will possess a new form of interpretative skills, as clinicians are required to interlace the

suggestions from algorithms into human context. The analytic AI paradigm that is not mentioned

and that stands outside the frameworks set by professional judgment is the reconfiguration of its

epistemic boundaries. This is what Bohr and Memarzadeh (2020) term as a “hybrid intelligence”

model, where the professional element and the data analytic element are in a mutually beneficial

interdependence. This move towards hybridity, to Bohr and Memarzadeh, is the “focus of a new

story in the evolution of health care technology:” the interdependence of human and machine

thinking in order to provide more accurate, equitable and adaptable care.

The interconnection of innovation and perception has been just as important as

innovation in shaping the trajectory of AI in healthcare. Laï et al. (2020) discovered that the

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attitude of professionals towards AI in the French context is characterized by optimism but also

ambivalence, emphasizing trust as a matter of perceived safety and transparency. This attitude

appears to be a reflection of a global sentiment that trust in AI systems is not so much a function

of their technical efficiency and more on their social acceptability. Trust is described by Chen

and Decary (2020) as a product of leadership communication and clinician as well as patient

participatory implementation. This implies that trust is not a given, but an achievement that is

created through processes of engagement rather than top-down approaches. In the view of Bohr

and Memarzadeh (2020), the successful adoption of AI is likely in instances when there is

alignment with the institutional culture as well as the identity of the intended users. These

viewpoints offer context to why the growth of AI in healthcare hinges as much on socio-

psychological phenomena as it does on computation. The process of normalization, transforming

AI algorithms into clinical resources, depends on the process of cultural translation and ethical

responsibility. To put it more simply, the progress of AI is not determined by technical

milestones alone as is often perceived, but rather the relation ethics that surround its use in

patient-centered care.

The application of AI in healthcare is no different than AI in other domains in how the

foundations of data science is integrated in primitive clinical breakthroughs. Tran et al. (2019)

traces the phenomena of AI research and how it coincides with the growth and proliferation of

data and computer technology, especially with the advent of cloud analytics. This marriage has

made it possible for healthcare organizations to utilize gargantuan data repositories for narrows

bands of predictive and diagnostic analytics. Still, Jones et al. (2018) highlights the problems

associated with heavy reliance on data, the problems of data quality, representativeness, and bias.

This suggests that the emergence of AI in healthcare is concomitant with the emerging discourse

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of data ethics. As pointed out by Bohr and Memarzadeh (2020), the word data and the term

patient protection are orthogonal in modern health data science, which is to say the balancing act

of data exploitation and patient healthcare has become the primary issue. The meaning of this is

that it is AI which is the weakest when it comes to data, the data must be exploited. The history

of AI in healthcare is henceforth an unfolding story of contradictions, conflict, and opposing

forces. The other side of the story is the absence of conflict. In this continuum discourse, the

growth of the field mirrors the relentless conflict between the expanding frontiers of the

imagination and the cautious walls of accountability.

The expansion of domain-specific AI interfaces has also influenced its refinement. Bohr

and Memarzadeh (2020) claim that certain areas of medicine, such as radiology, cardiology, and

genomics, where pattern recognition and precise modeling are powerful, are now the focus of

most contemporary AI systems. This evolution suggests functionality domain focus rather than

general-purpose AI systems. Briganti and Le Moine (2020) contend that such movement has

resulted in more clinically reliable devices that can enhance the professionals’ decision-making

process while still preserving the autonomy of the user. They propose that specialization builds

trust based on the premise that the AI system is integrated within the established workflows of

diagnosis rather than as an external system. Lu (2019) also argues that the scalability of such

domain focused algorithms is dependent on real-time data refinement and continuous validation

of the systems to enhance the accuracy in step with medical knowledge. This is a case where

technology and discipline inform and improve upon each other. It is also evident that the

incorporation of AI within certain fields is based on more than just computational effectiveness.

It should also reflect the medical knowledge brought forth by the domain. Such systems re-

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conceptualize specialization as collaborative rather than competitive Integrating human and

Artificial Intelligence enhances technical accuracy while improving the clinical insight.

Additional defining traits of AI’s development in healthcare is the rise of interpretability

as a criterion of validation. Jones et al. (2018) argue that professionally assured, marginalized

predictive outcomes, even with accurate results, are a consequence of the cognitive dissonance

perpetuated by opaque, “black box” models. This paradox is worrisome as AI is becoming more

advanced, it seems its reasoning is more opaque to humans. Decary and Chen (2020) argue,

therefore, that ethical AI adoption hinges on having some transparency mechanisms, which

include, amongst other things, the fundamental systems of explainability, traceability, and

feedback loops. Their research shows interpretability is more than a technical, procedural

requirement; it is a relational practice that nurtures synergies between clinicians and algorithms.

Reinforcing this position, Bohr and Memarzadeh (2020) argue explainable AI quintessence

provides healthcare professionals a basis to self-validate their cognition and reasoning to the

outcomes, which in turn strengthens accountability. The interpretive contextualization is that

there is a sustained regression towards explainability in AI, which in turn, innovation does not

overstep ethical obligations. In this regard, the paradox of internal accountability AI systems

possess toward logic provides a greater understanding of how interpretability and trust functions

as a hinge within a continuum of autonomy. Hence, the application of AI shifts from primary

exploration to an integral segment within a clinician’s reasoning framework.

The Tran and Bohr et al. texts sheds light on the differing patterns of AI innovation

distribution on cultural and socioeconomic aspects of the given territories. Tran et al. (2019)

continues to highlight the AI research and development disparity asserting that AI research

concentration is the highest in high-income nations. Developing countries are no exception as

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they increasingly engage in AI development to serve localized healthcare needs. Such

differentiation is likely to be very problematic in the understanding of the development phase of

AI as it is no longer perceived as being single and linear. Bohr and Memarzadeh (2020) states

that the regionally relative data architectural frameworks and regulations establish individual

innovation cultures, each of which striking a diverse balance of liberty and restriction. Such

distinctions indicate that AI in healthcare is not a monolithic structure, rather, a mosaic tailored

to the specific needs, aspirations, and available resources of each individual country. The Laï,

Brian, and Mamzer (2020) document also supports the argument of AI disaggregation, in which

the differences come from the region’s political, socioeconomic, and infrastructural disposition,

as well as the active attitudes about privacy, trust in the community, corporate confidentiality,

and social behavioral patterns. Such statements enforce the understanding that the prospects of

technology in healthcare are subject to the geospatial disposition of the society. Hence, AI in its

very essence needs to be viewed as the interrelation of cross-cultural frameworks with local

cultural paradigms, the difference being the understanding of cultural diversity and inequality in

the penetrability of innovation.

Ethics, regulation and technical advancement attributes of AI, are said to have evolved

together. In the view of Chen and Decary (2020), the absence of harmonization between policies

and technology development can lead to regulatory stagnation, which poses potential risk to

patients’ safety. Their analysis highlights the absence of governance frameworks which are

designed to deal with new emerging issues. With regards to algorithm bias, emergent issues

include consent and liability. Briganti and Le Moine (2020) argue that ethical governance should

not only be exercised for the final product, but should be incorporated in the development

process through design to control major ethical issues. Ethics, in this proactive position, is

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treated as an engine of development and not a barrier of progress. Jones et al. (2018), state that

healthcare’s reputation is hinged on the balance between advancement and safety. This balance

defines the position of AI in medical culture. The AI ethical governance is interpreted to advance

the position that the more AI technology develops, the more difficult it becomes to separate the

technology from the institutional development in healthcare. The evolution of AI systems is fact

to consider: it development should not be anchored only on precision, but rather the ability to

draw governance frameworks which are ethical, clear, and trustworthy.

Eventually, the current evolution of AI in healthcare indicates an ongoing negotiation

between potential and caution. Bohr and Memarzadeh (2020) described this as a shift from

optimistic experimentation to progressive stratification where the focus changes from proof of

concept to real-world viability. This shift signals AI’s emergence as a determining factor in a

system, not as a speculative concept. Lu (2019) posits the next stage of development will focus

on increasing interoperability so that AI can systematically communicate with an array of other

technologies and healthcare systems. This type of interaction integrates previous inventions as

part of the larger digital health framework. Briganti and Le Moine (2020) hypothesize that this

systemic framework may elevate global health standards through increased data integration,

shared insights, and flexible policy formulation. However, as Chen and Decary (2020) points

out, the integration of technology into healthcare systems and structures must accompany a form

of institutional modesty as a governing principle, one that balances the pace of algorithm

evolution with governance adaptability. The insights AI has evolved with emphasize the fact that

progress in technology within healthcare is not isolated from ethical contemplation, cultural

change, and systemic adaptability. In this context, it is not so much an evolution of instruments

but, rather, a deepening change in the philosophy behind care that is uncovered.

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Overview of Google Fit Technology

Google Fit Application is one of the best smartphone applications available in the market

today that tracks one’s health. This application from the giant technology company, Google, has

many users across the world praising for its excellent services since its inception. According to

Menaspà (2015), this application is praised for good performance through its mint

responsiveness and prices. It is an android application that comes with Wear OS by Google’s

smartwatches. The application can be installed on a smartphone or one can purchase a Google

smartwatch that has it pre-installed. It tracks running, cycling and walking automatically. It also

has an option where users can track their workouts manually, analyzing various activities, which

may range from curling, boxing, strengthening training and even circuit training (Menaspà,

2015). Google Fit in wearable such as smartwatches have the heart rate monitors that users can

use to track their heart rates. To achieve all these goals, Mishra (2015), asserts that this

application uses artificial intelligence technology that allows it to engage with the advanced

sensors in user’s smartphones and the smartwatches to work out when users are running, walking

cycling, jogging among other activities.

Among many applications that utilize AI to monitor human health, Google Fit is so

important and in the next 5-10years, this application will be famous and quite useful among may

users across the world. One of the reasons why this application will be very important is because

research shows that heart problems due to lack of exercise is on the increase. According to

Mishra (2015), human health is important and while there is a need to follow a healthy diet, it is

essential for one to regularly look after their hearts through exercises. Lai, Huang, and Chiou

(2017) add that lack of exercise is one of the worst risk factor that causes heart diseases. With

this increased need exercise, a majority of the people across the world will require an application

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that will monitor their heart rate. This will make them look for smart intelligence applications

that will autonomously help them achieve this goal. Given the reputation of this application,

more people will choose to use it.

When people choose the types of technology to use, they always look for technologies

that make their work easier, that are easy to use, easy to access and install. Google Fit, according

to Lai, Huang, and Chiou (2017) is easily available through the Google Play store and a user

only requires a Wi-Fi to download it and install it. Also, this application works automatically

which means that one does not require to give it attention all the time. Additionally, this app will

also be important because one has to create an account within it to be able to use it. In the end,

one will have a set of data such as graphs showing their fitness progress for a period of time.

This kind of information is useful because one will know how he or she has been fairing

(Farshchian & Vilarinho, 2017). Finally, the application can be connected to other third-party

applications and it does not require one to tell Google Fit who they are which assures users of

their privacy.

The subsequent iterations of Google Fit showcase the integration of cloud computing,

sensor technology, and artificial intelligence into an adaptive system which individualizes user’s

health engagement. Mishra (2015) notes that the app’s architecture is a complex, multi-layered

fusion of accelerometer, GPS and biometric sensor data streams which provide real-time

feedback about a user’s health. Google Fit’s infrastructure allows the app to discern intricate

contextual details of user engagement, such as activity intensity, pace, and surrounding

conditions, instead of relying on motion tracking alone. The analytical implication here is that

the application, by virtue of AI, has transcended static data collection and entered the realm of

real-time interpretation. “Task-technology fit” is said to be higher when there is greater

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alignment between the technology and user’s routines and cognitive processes (Iversen &

Eierman, 2018). Google Fit’s design is a clear showcase of this principle, as it allows users to

capture data passively, which is later integrated into daily routines. This kind of flexibility

changes data capturing from a cumbersome activity into an effortless one, thereby promoting

long-term compliance. In further studies user feedback hee is of utmost importance, as it has

been demonstrated by Lai, Huang, & Chiou (2017) that the main elements of Google Fit’s

success lie within the simplicity and perceived accuracy of the system. These findings exemplify

that mere advancement in technology is not enough; there must be a thoughtful human-centered

design to back it up. Google Fit is an example of how technical integration is only meaningful

when placed in the context of user psychology and behavioral conditioning.

Google Fit's success comes, in part, from its ability to integrate disparate pieces of health

data into an actionable format. Mishra (2015) states that user-defined data silos, such as steps

taken, heart rates, and calories burned, will be transformed into data visualizations that reflect

patterns of overall wellness. This paradigm shift in user data consumption is part of a wider trend

in digital health towards interoperable devices and applications that communicate based on

common protocols. Landeweerd, Spil, and Klein (2013) argue that previous attempts at health

data integration, such as Google Health, focused on seamless integration at the data source,

rather than data context and user workflows. By contrast, Google Fit overcomes that limitation

by permitting automatic synchronization of information from third-party fitness and nutrition

applications. The unique value of this integration is that it reconstitutes personal health from a

disjointed series of events into a singular, flowing narrative. Users of Google Fit encounter a

context also described by Iversen and Eierman (2018) in which the use of technology and the

desired outcomes are intertwined, which increases the potential for technology adoption. The

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reconfiguration of the data into an intuitive framework enhances user engagement as it aids in

the transformation of the data into self-reflection. Google Fit's ability to integrate with third-

party services is an illustration of the new paradigm in digital healthcare, in which collective

intelligence, rather than siloed data, is the primary driver for personal health decisions.

A prominent aspect of the overall design of Google Fit is the application behavioral

change motivation by means of algorithms predicated on goals. Mishra (2015) says Google Fit’s

activity-tracking function employs adaptive feedback loops to change goals based on recent

activities. Such feedback systems capture more than the progress made; they become behavioral

modifiers to sustain the motivation needed. In an app-store survey, Lai et al. (2017) found users’

responses to this adaptive goal-setting positively framed the experience, characterizing it as

encouraging and personally relevant. This shows the positive impact AI technology does in

changing passive observation to active engagement where participants are rewarded for gradual

improvement. According to Iversen and Eierman (2018), a desirable quality in digital

technologies is their ability to balance automation and user controllable engagement. Google Fit

implements this balance by setting personalized goals and maintaining goal selection freedom. A

more poignant interpretation is that the technology succeeds in capturing user attention when

health-tracking devices invoke a feeling of partnership rather than constant monitoring. Tekkeşin

(2019) relates this change to the broader development in preventive medicine where individual

self-tracking serves as a means of to help to anticipate health emergencies. Viewed through this

perspective, Google Fit is not only a data collector but also a partner in health transformation.

Google Fit machine learning Take AI as an example of how images and sensors work. In

the case of the mobile application, Mishra (2015) argues, the algorithm is capable of recognizing

patterns and discerning between walking, cycling, and running, even in certain “difficult”

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contexts. This adaptability AI possesses is an important breakthrough, especially considering

what it can do in noisy data environments. Jones et al. (2018) argue that the focus of any

algorithm in need of health care should also focus on the need for accuracy and ease of

understanding, which must always remain a priority on how data is captured. This focus Google

Fit has on AI is in line with the rest of the design principles of the app, where users can change

and see levels of automation applied to folders. By maintaining this capability, more strides are

made in algorithmic and user control. Tran et al. (2019) suggest that health technology that is

more automated in nature has a better reception globally, which Google Fit has for allowing

users to change and annotate data. The inclination is that the two are still highly and

interdependently computerized, yet the system allows for valuable human insight to transform

data into more understandable content. The dialogue created here is the uniquely interdependent

blend of system and user that positions Google Fit as a world-first innovative system in AI

healthcare.

Wear OS’s incorporation of Google Fit moves even further on the sophistication

technological front by expanding health tracking beyond smartphones into wearables. Mishra

(2015) states that this ability of the system to function across different platforms increases

portability since the users are able to access the health information in real time. The real time

notifications that users receive as a result of the flawless connection between devices help to

maintain the users’ cognitive activity level by providing continuous monitoring of their activity

patterns. The continuity of experience that users have, as discussed by Landeweerd, Spil, and

Klein (2013), is crucial for the sustenance of technology as it is an expectation that users will

move seamlessly across different digital spaces. Google has, therefore, integrated Google Fit in

the wearable operating systems to construct a mobile health system that is easily carried by users.

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Lai, Huang, and Chiou (2017) point out that there is a strong positive relationship between user

satisfaction and the perceived level of the system’s convenience and immediacy, both of which

are wearable systems. The deeper point is that the monitoring of a user’s health becomes most

effective when it is integrated seamlessly and invisibly into the user’s physical activity of the

day, converting the user from passively observing to actively engaged. Iversen and Eierman

(2018) further supports this phenomenon when they argue that the fit between the task and the

technology is enhanced when the systems are formally out of sight, yet everywhere, which

Google Fit is able to achieve through the wrist interfaces. The combination of these two elements

in the application exemplifies a new health infrastructure where the technology becomes a

natural and continuous part of the user’s life.

The platform remains a reflection of Google learning from past projects like Google

Health which failed at engaging users and privacy issues. Google Health and how it failed in part

due to a, “clinical design which left many users from general public due to medical terminology

and jargon. Unlike Google Health however, Google Fit focuses on ease of use and

personalization by turning complicated data into simple graphics.” This exemplifies the

company’s ability to balance the ambition of technology with a human centered design. “Trust

and transparency,” says healthcare experts like Landeweerd, Spil, and Klein, who argue Google

Fit is centered around these principles in its interface and data governance. Mishra (2015) says

the app’s privacy model is best described as the low personal disclosure model, which permits

the use of anonymous data while preserving operational. In the takeaway interpretation, it

becomes very apparent, Google’s move from being a medical data holder to a medical lifestyle

companion represents a change from information centralization to sympathetic design. This

change is a manifestation of the technology’s ability to relate emotionally and ethically with

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users on the matter as the change drives the point home. This is what drives Google Fit to expand

the framework of digital wellness to be more inclusive, adaptable, and considerate of users.

The sociotechnical evolution of Google Fit also portends future implications for AI use in

healthcare. Tran et al. (2019) posit that AI-based healthcare applications are increasingly

functioning within a distributed network of smart devices that harness collective intelligence

beyond single silos. This networked paradigm improves prediction models and facilitates

context-independent learning across different populations. Jones et al. (2018) contend that

distributed intelligence systems can widen access to personalized healthcare data and insights,

thereby mitigating the gulf between professional medical practice and self-care. From an

economic perspective, the interpretive problem is how to rest balance the conflict between the

automation of a process and the erosion of a user’s agency. Tekkeşin (2019) argues that

automated systems in sustainable digital health must retain an element of human judgment and

discretion to balance technological efficiencies that algorithms offer. Google Fit provides a case

in point, for it situates AI technology within a framework of balance: guiding them, rather than

centrally controlling them. Beyond its theoretical implications, the app's practical implication is

that it is a portrayal of a philosophical turn in AI equipped healthcare, contextual, participatory,

and morally sensitive intelligence. Google Fit is able, in particular, to advance the discourse on

the ethical paradox of technological sophistication and behavioral empathy. Automation is

rationally designed to promote user agency, a component of the long-term wellness that it also

aims to achieve at the same time.

Functionality and User Experience

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The functionalities offered by Google Fit are a practical demonstration of how a

sophisticated health tracking interface can conduct a complex analytical exercise while

remaining user friendly. Suzianti, Minanga, & Fitriani, (2017) reports that users of health

trackers appreciate ease of use and prompt recognition of process completion that is apparent

from the user interface. This user interface design makes a strong and direct case for improved

user engagement. These observations explain how Google Fit's design simplicity, coupled with

color-coded metrics, builds user trust while simultaneously reducing cognitive load. Mishra

(2015) argues that the app's circular progress indicators, along with other metrics the app refers

to as "Move Minutes," are designed with a deliberate intent to soften the track exercise daunting.

Visual nudges draws on behavioral design principles that promote exercise activity. From the

user-experience corner, the appreciation of visual beauty and functional clarity attains the

engagement purpose. Türkyilmaz et al. (2015) underpin the notion of sustainable UX when

visual and functional value diverges from concentration. Google Fit's use of data to derive signs

of minimalism demonstrates the need to operationalize the data shift from performance to

comprehension. This practical dilemma underscores the need to reconcile interface design with

interface psychology. Having noted this, it can be appreciated that emotional touch, as much as

computational accuracy, determines effective UX in health applications.

Google Fit is adaptable in functioning as a platform that deals with different behavioral

and contextual user styles. Flexibility and interoperability across devices pervade and

significantly influence perceived usefulness in mobile health applications (Astrup, Jansen, &

Aksic, 2016). The associated Google Fit user experience is frictionless and promotes habitual

engagement as the app seamlessly syncs across Wear OS and Android devices, as well as myriad

third-party apps. The app’s machine learning models, per Mishra (2015), identify variations in

POSITION PAPER 2 21

motion patterns and distinguish activities that are seemingly similar, such as walking uphill and

climbing stairs. This responsiveness enhances user trust in the data as it reduces false readings.

People disengage quickly from devices that produce erroneous data, and so perceived accuracy

has important retention implications (Suzianti et al., 2017). The insight in this context is that

relatability serves as technique functionality. Users are able to track data reliably, as Google Fit

has become more than just a data tracker. Users are able to form a bond with the app as it adjusts

to their routines. This bond highlights the synergy of Google Fit with user experience. The user

experience sensitivity highlights the need to use data in designing algorithms. This algorithmic

responsiveness aligns with the functionality that emerges from empathy driven design.

An important aspect of user experience for Google Fit is the onboarding process that sets

the first impression of value and usability. The first quarter of an hour of using an application is

paramount for its adoption in the long run (Michaelis et al., 2016). Google Fit aims to tackle this

challenge by providing an easy setup flow that offers a guided and personalized experience

without causing cognitive overload. Mishra (2015) clarifies that the unnecessary insertion of

height, weight, and activity level is to the user’s advantage, as it lowers the barrier for entry to

the system. This corresponds with the findings of Türkyilmaz et al. (2015) that overly complex

designs that technology users perceive as unfair because of the level of intimidation it evokes do

not serve the intended purpose. The onboarding process is simple and as such lowers

intimidation to offer a sense of control. Mishra (2015) clarifies that onboarding should be

designed with objectives that include the user’s trust. Google Fit’s response to this is the

feedback loops that help the user to understand the system’s responses to his or her inputs. The

feedback provided in the onboarding sequence is invaluable, as it is designed to shape the user’s

emotions towards the system’s responses. Therefore, the onboarding is not only emotional, but it

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is designed to encourage the user to engage with the system over a long period. Thus, the novelty

of the onboarding system is that it emotionally elevates the user’s experience to stimulate

habitual use.

Social and motivational elements are becoming increasingly important for user

interactions mobile health applications. Pagano and Maalej (2013) showed that engagement is

driven by user feedback loops and peer comparisons that cater to intrinsic social drives. Google

Fit incorporates this understanding with functionalities that promote sharing progress as well as

tying progress to other applications that provide community challenge activities. Mishra (2015)

argues that even though Google Fit is not a social platform, the absence of a proprietary model

allows integration with competing apps via the open API. Users’ psychological needs of

autonomy and relatedness are both fulfilled by this ecosystemic thinking. Michaelis et al. (2016)

indicate that users seek and appreciate recognition and accountability, even though it is in the

form of data and not directly spoken. Google Fit as a platform emphasizes facilitation of social

integration and does not impose a single social paradigm, thereby catering to a variety of

motivational factors. Integration in Google Fit health apps is meant to provide an encouraging

community setting that enhances personal autonomy. Google Fit exemplifies how device

gamification should be controlled to enhance user self-regulation, rather than overwhelming

them with external social pressures.

The user experience of Google Fit still encompasses reasonable accessibility. Wang et al.

(2018) claim that the use of an app which is able to read, understand the interface, and heavily

interact with the app by users of different countries is vital to its use and adoption globally.

Google Fit’s support of cultural diversity vis-a-vis different languages and metric systems is

illustrative of UX inclusiveness and responsive UX design. Astrup et al. (2016) noted that

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usability scores of an app were higher when the app had some sort of geo-features and adaptive

localization. This is an instance of how the ability to scale globally is tied to the ability to

understand the underlying local conditions. Mishra (2015) argues how Google Fit embraces

users with different levels of literacy, as well as other users with physical challenges, through the

use of voice assistance and visual simplifications. Suzianti et al. (2017) explains the perception

of accessibility as the notion of emotional accessibility through which the users feel that the

product is designed to accommodate all users. This way, Google Fit is positioned as not just a

device to keep track of health and fitness of users in the West, but rather, a device intended to

foster health and fitness of all users around the world. The conclusion from this is that with the

right technology, accommodating diversity in society is the way to attaining universal relevance.

This means that in terms of equity, design intelligence in UX is as a result of social exclusion.

The case of Google Fit appears to function as a case study in which trained professionals

understand the role of effective application design in promoting health literacy. Mishra (2015)

discusses the application as one which emphasizes the notions of controlled access. These access

controls emphasize the importance of obtaining user permission before engaging in the

automated cross application sharing of data which user has to share to use the application. Li et

al. (2015) noted that mobile ecosystems, privacy protection emerges as one of the key concerns

of users. Google Fit has the functionalities which allow users to determine the types of data

permissions which are to be allocated to the application and the decision whether to share data

with other apps. The features which allow users to determine the type and level of control which

they possess with respect to data sharing and user consenting are the design features which mark

the current state of user experience design.

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Astrup et al. (2016) highlights that the ability to implement and control privacy enhances

the user’s experience in the application. Users have the ability to implement control

functionalities as opposed to having to comply with privacy mandates. Users are able to exercise

personal privacy and data protection control in a manner which exceeds regulatory compliance.

Whether willingly or unwillingly are uplifted above individual control and are exercised over in

the Google Fit application. They also experience the Google Fit application and personally

interpret to themselves that the application has been built to control and protect the user’s

individual privacy in the manner which increases user dignity and psychological ease. Suzianti et

al. (2017) encapsulate these sentiments by arguing that privacy design features tend to increase

comfort and thus drive the psychological stressful feeling of being monitored, which enhances

ease of mind.

Assuming the perspective of a developer, Google Fit has functionality showing the

effectiveness of design based on data. Deka et al. (2017) point out that interaction behaviors help

designers interface in more natural ways. Google Fit utilises Google’s broad analytics system,

which improves UX in real time from actual engagement data. Mishra (2015) argues that updates

on user behaviour obtained from analytics are more productive than prior updates. Developers

like Astrup et al. (2016) document that Google Fit’s SDK is highly rated for its versatility.

Documents describe consistent user experiences across devices, which are the result of unending

adaptive development. Pagano and Maalej (2013) defend user feedback integration as critical for

development to construct retention and satisfaction tailored co-designed ecosystems. The interest

here is in shifting the conventional view that functionality is static to a more complete one that

captures the performative dimension of the interplay between data, design, and lived experience.

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Google Fit is a case in point to demonstrate how sustained relevance is obtained from UX

devices that quickly change across digital landscapes.

The effect of aesthetics on user experience, although understated, has a significant impact

on emotional involvement. The ease with which users interact with an interface, even if the

functionality remains unchanged, is perceived to be easier because of the interface's aesthetic

harmony which enhances cognitive fluency (Türkyilmaz et al., 2015). Google Fit uses this

psychological principle with its well-spaced typography, motivationally appropriate colors, and

clean color combinations. The intent of the design used with soft gradients and motion cues

which discord the user is to help facilitate feelings of calmness and encouragement (Mishra,

2015). The findings of Michaelis et al. (2016) about emotional aesthetics provide visceral

enjoyment which professional polish to satisfaction and emotional aesthetics. Clearly, the

evidence suggests the pragmatic purpose of beauty, which in this case, user engagement friction

is minimized while trust is maximized. This means that, in the case of health tracking apps,

functionality and aesthetics work together. The example set by Google Fit suggests that visual

coherence serves a purpose other than decoration: to provide clarity, emotion, and continuity to

user interaction. When the purpose of beauty is to achieve an aim, design is no longer an

accessory, it is a means to achieving well-being.

Feedback mechanisms and error recovery functions in Google Fit illustrate the balance

between functionality and resilience. In health applications, feedback loops are critical for

maintaining user engagement (Suzianti et al., 2017). If an app detects an anomaly, such as a

period of inactivity, it will pause the user and offer explanatory messages and corrective

feedback. According to Mishra (2015), the system of real-time corrections eases frustration by

turning a break in the workflow into a chance to learn, not a failure. Mishra et al. (2016)

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demonstrated that applications that allow users to recover from technical failures easily tend to

achieve greater user loyalty. The interpretive value is that recovery from errors fosters a sense of

reliability, and thus, strengthens the emotional attachment users have to the system. Responsive

feedback, as argued by Michaelis et al. (2016), is an element of perceived empathy, which

enhances user trust. Google Fit transforms error maintenance into psychological comfort by

designing it as a built-in factor of learning. This is an application of social focus engineering,

where the scope of functionality exceeds fulfillment to embrace the elegant management of

failure.

The case of Google Fit illustrates the convergence of function, design, and emotion in

user experience as it relates to positive change and wellness over time. Mishra (2015) asserts it is

best value is in converting intermittent historical lifestyle change to tracking. Suzianti et al.

(2017) point out to the fact that chronic users of the app are not interested in new features

(successful UX) as much as in the app’s ability to track the passage of time, and offer relevant

services. Astrup et al. (2016) attribute the sustainability of an app to the balance of functional

dependability, beauty, and the feedback given. This suggests that Google Fit is user friendly

because it balances data expertise with user pleasure. Türkyilmaz et al. (2015) terms this

“aesthetic functionality,” where the design of an app works on the emotions of the users as a

result of it sensory components. Google Fit offers the best of both worlds in UX design because

it merges systems that operate on sophisticated logic with those that exhibit stripped down

humanity. Its features represent the apex in synthesis of cognitive ease, moral architecture, and

feeling availability, which is the desired state in digital health experience.

Google Fit-Impacts on Healthcare Spending

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Integrating Google Fit with personal health management systems hinges on individual

health and macroeconomic health system productivity. As noted by Bourreau et al. (2020), when

technology companies monetize health data, they uncover hidden cost potentials alongside

consumer welfare threats. Though the analysis of aggregated fitness data can optimize

preventive-care approaches, it raises the problem of the influence of profit interests on healthcare

budget distribution. Such digital systems, from the economic perspective, can minimize the

number of unnecessary medical consultations by reinforcing the value of early behavioral

changes (Gallet & Doucouliagos, 2017). Guided physical activity monitoring helps individuals

accumulate physical activity, and chronic disease management long-term costs are likely

reduced. The innovation and responsibility dichotomy offers a distinctive interpretive challenge.

Pellegrini, Rodriguez-Monguio, and Qian (2014) assert that technological changes influence

healthcare labor demands and economic productivity, resulting in workforce and system-level

productivity changes, and exemplifying the cross-sectional impacts of digital health integration.

Google Fit, on the other hand, has the potential to diminish consumer direct expenditures,

although, in my opinion, the financial cross-sectional ramifications depend on the cross-the-

board savings reallocated into the healthcare system.

Technological devices such as the Google Fit app have shifted the focus of health spending from

treatment to prevention. Louw and Von Solms (2015) point out that due to the wearable devices’

capability, users can track their physical activities almost in real-time, allowing them to make

changes to their behaviors before their health deteriorates. This model of health care, If applied

universally, Levy and Thorndike (2019) suggest that artificial intelligence techniques applied to

company health programs, similar to Google Fit, can in the short-term minimize health spending.

The ability to monitor and measure behaviors that drive risk of injury or diseases make these

POSITION PAPER 2 28

devices accessible to more users. The digital health devices, in the premise of Gallet and

Doucouliagos (2017), can make the expenditure resolving goals of more users economically

visible. Google Fit is consistent with the premise that populations with more expenditure on

healthcare are more economically sustainable. Google Fit, along with other similar devices,

represents a drastic departure from expenditure on health care to expenditure on health

preservation.

High-income countries' spending patterns on healthcare have the kinds of inefficiencies

which opportunities technologies like Google Fit could address. As noted by Papanicolas et al.

(2018), in the US, spending per person is higher than that of other countries, yet the overall

health outcomes remain poorer than the rest of the countries. This is why the use of mobile

health platforms is a potential solution in closing this gap through personalized, data-informed

approaches. As noted by Dieleman et al. (2020), a large part of the health spending is associated

with chronic conditions that are, in a significant number of cases, preventable, with prevailing

inactivity being a major contributing factor. Google Fit can help delay the self-monitoring and

engagement level that people rise to, therefore indirectly reducing the need to manage chronic

illnesses. The interpretive conclusion is that tools for self-care at a distance serve to lower costs

and, in doing so, widen access to fundamental preventive procedures. Nevertheless, Bourreau et

al. (2020) warns that where an emphasis is placed on a freemium model with data monetization

strategies, private costs and inequitable access becomes a significant concern, along with a

potential loss of overall economic efficiency. Hence, the policy approach is to make certain that

the savings in newly monetized health data, AI health monitoring, and other policies do not

increase inequities in health outcomes.

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Google Fit impacts expenditure in healthcare in part by changing consumer practices.

Knight et al. (2015) state that wellness apps are “bridges” during the gap between the

recommendation of the public health and the practice of an individual. This practice reduces

“over the gap” information that is the cause of medical overuse. Levy and Thorndike (2019)

show the association between digital engagement in self-wellness activities and decreased health

expenditure by employers as the digital wellness engagement moderation has a positive impact

on self-health management. These results suggest a digital form of health. free from institutional

care. The decrease of health literacy about the care of self through the use of technologies Gallet

and Doucouliagos (2017) suggests further, has an explanatory system of cost lowering through

prevention of hospitalization on which the system spends large amounts. This new behavior, the

explanatory importance of which is in the cost in the aggregate, is the result using a new form of

care and self-monitoring. The use of these technologies by millions of people changing their

behavior leads to individual spending behavior that results in institutional expenditure. The

individual tracks using Google Fit and the institution pays, giving Google Fit a dual role as a

propelling change in behavior and an expenditure tool.

The refinement of the workflows of health professionals is often called the “Google Fit

optimization” and refers the fitness applications of google technology functionalities from the

point of view of Pellegrini et al. (2014). Healthcare personnel adjustments sensitive to patient

turnover and alterations in the volume of care demand is a commonplace phenomenon (the so

called Pellegrini et al. (2014) effect). Moderation of demand for health services is possible

through lifestyle changes induced by portable technologies. Further to this, Louw and Von Solms

(2015) highlight the value “wearables” affords healthcare providers in terms of additional

analytics to achieve better, primary, diagnostics without surplus, counterproductive testing lower

POSITION PAPER 2 30

overall operational costs and thus positively impact on resource spending. Use of resources and

overall operational costs of healthcare are positively interrelated as articulated by Gallet and

Doucouliagos (2017) who reinforced the link between spatially and temporally health spending

allocated to a population and the health outcomes from that spending. Thus, serving as a marker

to measure the relationship between healthcare spending and population health. What can be

construed from this literature is that self-care, in this instance, through technology, can

redistribute clinical attention in a more effective manner, as additional digital health technologies

can redistribute the attention of already weary clinicians in a more sustainable way in a strained

system. These benefits of google fit functionalities are not limited to personal advantages, but

rather are systemically favorable and reflect optimally on public health. It is a perfect

amalgamene of technology and economics and furthers google fit technologies value innovations

in healthcare.

The healthcare performance assessment impacts also include the use of user-generated

digital health data from Google Fit. Population health analytics derive from Nuti et al. (2014),

where the study found data trends from Google platforms can successfully forecast health

interest topics. These data analyses can aid traditional epidemiological techniques and serve as

inexpensive methods for surveillance. Dieleman et al. (2020) suggest the analysis of expenditure

on healthcare is enhanced through real-time analysis of everyday actions by people that supports

better expenditure targeting. From this perspective, Google Fit is a resource for policy that has

low market value but is able to aid the development of preventive policy expenditure

frameworks. Data commodification, according to Bourreau et al. (2020), may shatter the trust of

consumers if pragmatically applied. Data policy rationalization could be utilized to construct

summary statements on the basis of the phylogenetic principle that policy use of data improves

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efficiency but needs to be sensitive to the fact that the interests of the patients come first, as that

would determine the data use for profit. Therefore, Google Fit’s analytics value would not just

rely on predictive accuracy but also the intention of the analytics in the context of public health

policy, as illustrated by the suggestion that spending Google Fit’s analytics is public centered,

profit created.

The economic impacts of digital fitness platforms, as discussed in the previous

subsection, are in parallel to the global modernization initiatives of health systems in the world.

Care beyond the walls of institutionalized settings, made possible with technology, is something

countries with sustainable health financing rely on, as Papanicolas et. al. (2018) explain. Google

Fit decentralizes preventive monitoring to make participation more economically accessible, and

the overall health costs lower. This use of technology allows Louw and Von Solms (2015) to

conclude that the use of predictive wearables aids in the disaggregation of health inequities by

enabling resource-poor individuals to access advanced health analytics. This form of

technological inclusiveness leads to a more equitable allocation of costs and, as argued by Gallet

and Doucouliagos (2017), efficient spending on the diffusion of health technology yields ever-

increasing population health benefits. Interpreted, Google Fit acts as an equalizer in the digital

health and economic systems by reducing the disparity between poorly and well-resourced

countries. Because it integrates prevention and daily routines, the economics of wellness

becomes achievable to all as opposed to an elite privilege.

Consequently, the economic and ethical issues involved in the commercialization of

health information generated with Google Fit are quite nuanced. As stated by Bourreau et al.

(2020), the purchase of Fitbit, along with its valuable data, signifies Google’s attempt at

formulating new ways of monetizing user information, which in turn could lead to adverse

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impacts on the market and the healthcare sector. Such information monetization strategies will

lead to prioritizing expenditures on marketing analytics to the detriment of health improvement.

Today, Nuti et al. (2014) demonstrated how the digital search and usage patterns are already

determining the direction of healthcare research and, in turn, the private corporate control of data

could overtly or covertly shape the public health. On the contrary, as noted by Dieleman et al.

(2020), data analytics under the proper modern governance can help eliminate system

inefficiencies within payers and improve centralized cost control. Adopting a social welfare

frame seems to be the most appropriate way of tackling the issues raised. Whether platforms like

Google Fit improve or undermine spending efficiency hinges on the extent to which data value is

reinvested towards improvement in healthcare system quality as opposed to pure corporate gain.

Thus, in digital health, the economic rationality of data opacity paired with unfair data

governance is misplaced. Rationality brings governance which directs economic outcomes.

The affordability of employer-sponsored healthcare for organizations has been attributed,

in part, to the adoption of corporate wellness programs that use Google Fit for activity tracking.

According to Levy and Thorndike (2019), programs that use technology to track physical activity

and exercise focus on reducing healthcare costs in the short-term, and healthcare expenditures in

the long-term for organizations. Louw and Von Solms (2015) refer to this technology as part of a

feedback economy at the behavioral level, in which data sharing encourages behavioral change.

Such corporate policies have been shown to have a “multiplier effect” by decreasing absenteeism

and associated costs as a result of improved health behaviors. According to Gallet and

Doucouliagos (2017), healthcare cost reductions due to increased productivity typically result in

a greater return on investment than the costs of the productivity. The implication in this case is

that the use of digital fitness tools promotes the synergy between business productivity and

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health advocacy. Google Fit, by tracking physical activity, gives organizations the ability to

show a direct return on investment for wellness programs and organizational productivity. The

increased adoption of personal technology has direct implications on organizational spending

patterns, demonstrating the use of health economics.

The behavioral psychology pertaining to the cost benefits of Google Fit most

fundamentally focuses on the relationship between self-efficacy and economic reasoning. Knight

et al. (2015) demonstrate that applications which reward small wins and sustained efforts over

time tap into intrinsic motivation and therefore foster sustained engagement. This high level of

participation ensures ongoing seamless maintenance of health and fitness and therefore reduces

the need for expensive maintenance health care interventions. Louw and Von Solms (2015)

portray wearable devices as behavioral mirrors which provide immediate feedback on physical

activity and therefore enhance the habit-reward system. Gallet and Doucouliagos (2017) argue

that the psychological framed within the context of the population level profoundly impacts the

level of health care spend as it shifts the burden of avoidable illness within the health system.

The interpretive insight is that the financial dividends accruing from applications of AI to fitness

trackers drive from behavioral economics as much as they do from predictive and prescriptive

analytics. Google Fit is a case study in the impact of self-regulation on self-control at the level of

digital experiences and the resulting cross border economic impact.

The anticipated evolution of spending in integrated technological health care systems

hinges on the evolution of social accountability of systems such as Google Fit. Dieleman et al.

(2020) predict that expenditure on digital health will progressively dominate the strategies of

payers and providers, enforcing the need for siloed, transparent equitably accessible frameworks.

Bourreau et al. (2020) argue that lack of regulation on the monetization of data can lead to the

POSITION PAPER 2 34

erosion of trust by consumers and the misallocation of resources. In contrast, Papanicolas et al.

(2018) point out that a proportionate investment in preventive technologies yields positive fiscal

and human outcomes. It is this interpretive conclusion that illustrates the duality of Google Fit as

an economic innovation and a governance dilemma. It can undoubtedly curtail spending on

healthcare, but its enduring success hinges on ethical data stewardship and adaptive, just policy

frameworks. More Integrated digital fitness tools will continue to be associated with national

health systems, and the impact of such tools will transcend cost to use and the investment in an

equity-oriented, transparent economic system for preventive healthcare.

The potential impact of this technology on health care spending, from the perspective of

consumer, payers, and providers

The Google Fit application will positively impact healthcare spending from the

perspectives of providers, players, and consumers. First, in regard to consumers, this application,

according to will cut on consumer spending on healthcare because it provides them with

necessary details on their health through its analyzed data which does to require one to visit a

physician which is quite costly (Singh, et al., 2016). Singh, et al., (2016) found out that

consumers normally spend a lot of money for consultations regarding their heart issues and the

presence of such an application will save consumers the need for such tests. Therefore, this

application is of great importance to consumers across the globe. The payers will experience

increased spending on healthcare because they will have to fund such technologies for their

clients. However, Bradley, et al., (2016) affirms that payers in the United States, despite the

likelihood of new technologies increasing their spending on healthcare, they will experience

improved health outcomes for their clients.

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On the other hand, providers will benefit from reduced spending on healthcare because it

will cut spending on physicians specialized on heart issues since this application will have the

required data from their patients (Bradley, et al., 2016). It will also help health care providers by

reducing their huge spending on research. According to Bradley, et al., (2016) the US health

providers spend billions of dollars each year on research on heart issues caused by lack of

exercise. This spending will reduce since Google Fit will have useful data that could have been

collected through research from various patients. From these data, providers can create other

healthcare applications that analyze such information from a large number of clients.

Appending Google Fit to existing healthcare infrastructures marks another critical stage

of reforming consumer-driven expenditure. Atluri et al. (2016) reiterate that with the increasing

adoption of technology, consumers are increasingly becoming demanders of healthcare services

and are focused on convenience, transparency, and personalized care. This behavioral shift

entails a migration of spending power from traditional institutions to digital platforms for self-

service management. Electronic devices and Fasano (2013) argues that they minimize the

overhead cost by way of redundant testing and manual recordkeeping. Such efficiencies bear on

the overarching digital augmentation that continues to reduce cost per patient per visit. Marino

and Lorenzoni (2019) outline that changes in technology increase expenditure in the short run

and save in the long run as gains in efficiencies are realized. Such application of devices like

Google Fit illustrates investment front-loading with long-term paybacks. There are evolved

dynamics of paying within the system. There are decreased spending in unregulated, delayed,

and more proactive glance chronic care systems. The system, however, realigns spending in the

continuous care systems. This reflects another shift in the economics of spending. The expenses

are now spent on continuous monitoring rather than reactive treatment.

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For consumers, digital health platforms redefine value by creating information savings.

Almalki and Simsim (2020) claim that technology improves savings by allowing users to

identify and resolve health issues. Google Fit demonstrates this role by using real-time analytics

to steer exercise and wellness practices that avert the need for health care. Wu et al. (2016)

studied how wearable devices merged with big data analytics produce predictive information that

lowers unnecessary health care expenditures. These observations support Atluri et al. 2016

conclusion that informed consumers spend differently and shift market demand towards value for

money. The broad conclusion to be drawn is that this type of consumer power shifts cost

management from a system policy to a personal discipline. In this regard, the technologies

creating opportunities for patients to manage their health system “Google Fit” shifts the notion of

financial responsibility from passive inaction to active co-participation, and this is a formative

example of the economics of participatory healthcare.

The proliferation of digital fitness technologies has fostered the development of

performance-based spending models among payers, including insurers and employers. Moro

Visconti and Morea (2020) pointed out that digitalization makes it possible for payers to

reimburse using the pay-for-performance method, which assess reimbursements based on health

outcomes instead of the volume of services. Ethically aggregated Google Fit data could fuel such

systems by calculating the attainment of prescribed wellness goals. Long, Mortimer, and

Sanzenbacher (2014) underline that innovative payment structures are based on the precise

measurement of outcomes, which is something that wearable technology provides. Also,

emerging analytics, as proposed by Agarwal et al. (2020), reconceptualize value-based marketing

which balances spending on healthcare with improvement of quantified health outcomes. This

spending reallocation suggests that the behavior of payers is changing from reactive coverage to

POSITION PAPER 2 37

proactive investment in wellness activities. Insurers, by rewarding prevention instead of

treatment, use digital technology to minimize claims, stabilize premiums, and provide cross-the-

board healthcare incentives.

From an economic and operational point of view, Google Fit interacts with providers in

various ways. According to Fasano (2013), technology reduces transaction costs and duplication

of administrative work by digitizing data and workflows. When patients engage in activity and

biometric data sharing via Google Fit, providers are able to verify diagnoses with less resource

expenditure. Mithas et al (2020) argues even further, claiming technology lessens the burden of

Baumol’s cost disease, whereby productivity plummets and healthcare labor costs continue to

increase. By reducing the need for manual monitoring that clinicians have to engage in, routine

tasks are automated, freeing up the clinician’s time to attend to more complicated tasks that have

greater value. Wu et al (2016) also emphasize the value of wearable devices in improving

outcomes and decreasing waste through precision medicine. The interpretive conclusion is that

the AI-integrated applications within the clinical setting increase Sustainability, with the focus

being on the ability to provide more while needing less. Structural efficiency like this provides

lasting effects in decreasing the rate of expenditure increase while maintaining the quality of care

provided.

Google Fit and other digital tools also affect how providers and consumers make choices,

though these effects are subtler. Zhang et al. (2020) found that greater market transparency

improves competition since patients are able to assess the costs and outcomes of different

treatments. Increased visibility may lead providers to more competitive service pricing and the

elimination of superfluous procedures. According to Agarwal et al. (2020), there is higher

consumer trust and loyalty in systems where there is greater transparency over the dispensation

POSITION PAPER 2 38

of information. Atluri et al. (2016) go even further and argue that the information technology

reevaluates the consumer’s premise of the worth of the service and the worth of the service. The

interpretive consequence is that within the context of digitalization of healthcare, transparency

behaves like a setter of external economic parameters. Patients fitted with tools like Google Fit

are able to visualize how much they are spending in context with health the metrics, and are

therefore able to improve spending health spending. Spending shifts the imbalance of power that

favors institutions to a more shared mutual accountability over cost control.

Analyzed from the lens of macroeconomics, the emergence of AI-powered wearables

signifies a fundamental change in the pattern of expenditures in the areas of health care. Gruber

and Sommers (2019) note that the Affordable Care Act is an example of a policy that “adapts” to

balance the spending of technologically driven preventive care. In a similar fashion, Marino and

Lorenzoni (2019) contend that innovations in the early stages of development place a strain on

the budget, but, at a later stage, improve productivity as a result of systemic learning effects. In

addition, Fasano (2013) claims, with regard to the population health initiatives, that

“digitization” has to be “scaled” in order to produce exponential returns. The interpretive

implication is that applications such as Google Fit act as accelerators of sustainable expenditure

growth, through the frontloaded digital investment that produces compounding downstream

savings. These technologies rest at the center of a wider and deeper transformation in health care

economics, along with the decoupling and redefinition of “spending” as the investment on the

maintenance of continuous and active “wellness” as opposed to episodic consumption.

The integration of digital technologies and health systems by consumers in emerging

markets hinges on many unique economics and socio cultural factors. Almalki and Simsim

(2020) state that mobile health tools of minimal cost are vital in remote areas as they can shift

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preventive monitoring from health professionals (i.e physicians) to the patients themselves. Wu

et al. (2016) point out that the use of wearables in developing countries addresses health inequity

by addressing the over dependence on highly priced clinical facilities. When discussing the role

of big data in these scenarios, Groves et al. (2013) argues that the value of big data is enhanced

when digital networks replace institutional scaffolding in an economically favorable manner.

This is relevant because disbursing the monitoring systems of Google Fit strengthens the

scenarios where Global Health expenditures greatly decrease. The reason for this is that Google

Fit offers the users inexpensive tools for remote monitoring, thereby reducing the users’ reliance

on other systems of healthcare support. When users monitor their own health more

autonomously, the system becomes more interconnected, and subsequently, the expenditures in

healthcare is reduced. This phenomenon is driven more from users’ initiative than from set

healthcare policies.

Technological adoption influences in what ways markets within the healthcare industry

allocate capital. As suggested by Mithas et al. (2020), investing in information technology has

the potential to offset the inflation of labor costs and consequently stabilize the growth in

expenditure within high-skill sectors. Providers utilizing platforms such as Google Fit derive

predictive analytics that optimize workflow, thus minimizing idle capacity and other scheduling

inefficiencies. Neon Fasano (2013) claims that the increase in financial performance within

certain hospital systems stems from the digital optimization of interfacility processes and the

workflow. Atluri et al. (2016) note that consumer technologies shift the outer boundary of the

system and accelerate the trend by partially offloading health maintenance to the individual. The

interpretive insight is that digital consumerization expands the boundary of control over costs to

every stakeholder, implanting cost discipline at every level. In this case, technology is both

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deflationary and democratizing in the economics of healthcare, as it shifts the locus of

accountability for efficiency to every level of the care continuum.

The extent to which tools such as Google Fit will affect spending in healthcare depends

on tools which are currently being integrated as well as changes in legal regulations. Marino and

Lorenzoni (2019) explain that for savings to be realized, consumer technologies need to be

integrated with the country’s health management infrastructure. According to Agarwal et al.

(2020), the policies in place today must construct an incentive system that encourages the

development of data-sharing systems which are advanced in analytics and privacy. In the views

of Atluri et al. (2016), the evolution of digital health care will still be driven by consumers who

will require that wellness application systems be integrated with health care insurance

companies. The salient point to take away from this is that collaboration between developers of

the technology, the payers, the health care providers, and the patients will largely determine the

evolution of fiscal healthcare in the future. If ethically designed, Google Fit and similar systems

can redefine health care costs not in terms of belt-tightening, but rather “smart”, spending less

averagely through shared data, intelligent insights, and reinforced behavioral changes.

Importance of Google Fit in Promoting Preventive Health

Google Fit is gaining importance in incorporating preventive healthy behaviour in

everyday life via data-driven reinforcement. As Carey et al. (2015) suggest, eHealth tools

contribute to better preventive care, since the tools introduce continuous monitoring and

feedback loops into the lifestyle of the patient, which basically decentralizes clinical

interventions. This shift enables the preventive practices to make it a habit, not a sentimental

occasion, and eliminates the need to submit to an expensive medical supervision. On the same

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note, Knight et al. (2015) found that the use of fitness apps that convert public health information

into digitizable outcomes enhances physical activity advice compliance. According to their

findings, digitization transforms targets that are abstract into tasks that have to be accomplished,

hence reducing the motivational barriers. Farshchian and Vilarinho (2017) also add that the

presence of interoperability with other health platforms enhances the preventive ecosystem of

Google Fit with different types of biometric data aggregation. The interpretive implication is

both that Google Fit is not only facilitation of individual self-regulation, but also a conduit

structure in the relation of technology-preventive policy. Its advantage is that it turns prevention

into quantifiable participation, which effectively makes awareness into long-term involvement.

Wearable and mobile technologies have a natural ally in the behavioral psychology of

preventive health. Liang et al. (2019) also showed that the higher the level of interaction with

online fitness content, including Google search and Twitter, the smaller the prevalence of obesity

reported in the area. This fact highlights the wider influence of the digital signals in defining the

collective health standards. Bert et al. (2014) found that mobile phones were found to be a

permanent assistant in health promotion, with ability to provide personalized intervention in real-

time. Their work points out how accessibility builds consistency -prerequisite to the successful

preventive results. Kampmeijer et al. (2016) observed that mHealth apps contribute to active

aging by sustaining motivation and progress monitoring toward older adults. The association of

behavioral reinforcement and usability makes Google Fit a behavioral catalyst and not just a data

repository. It integrates self-discipline and convenience by incorporating feedback structures into

the daily behavior. The practical implication of this is that digital health is not simply an

augmentation of preventative care, but a behavioral framework which reconsiders the actions of

its users in a perpetual form of feedback.

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Sustainability of preventive health interventions is also determined by technological

infrastructure. Farshchian and Vilarinho (2017) note that the use of Google Fit in conjunction

with third-party APIs guarantees scalability and continuity, which is essential to long-term

preventative measures. This type of interoperability will enable users to coordinate nutrition,

activity and sleep information between applications, making it a self-assessment system in a

whole. Henriksen et al. (2017) demonstrated that population-level research accuracy increases

with the data provided by the cloud-based system in monitoring physical activity, which enriches

the preventive health analytics. Such a massive amalgamation offers policymakers practical

information on the behavioral pattern and health disparities. Astrup et al. (2016) made a

comparison between Google Fit and other health platforms and discovered that Google Fit is

especially useful in the cases when the developer is interested in the flexible usage and access to

the data. The implicative implication is that technological fluidity solidifies the adaptability of

preventive care within demographics and equipment. With seamless communication between

data ecosystems, preventive healthcare will no longer be seen as a disjointed project, but as a

constantly developing network of continuous learning that can respond dynamically to the needs

of the population.

Google Fit is not solely a device used to track the individual, but it can add to the overall

epidemiology. Nuti et al. (2014) emphasize the ability of digital search data and app use to point

to the trend in public health and implement early interventions. This predictive possibility turns

out to be a preventive type of intelligence developed in cyber interactions. Henriksen et al.

(2017) confirm that a combination of app-based data in activity data with health surveys can

dramatically increase the epidemiological accuracy and lower the tendency to reporting bias. In a

technological perspective, this cloud-based synergy is a gap between the micro-level of user

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behaviour and the macro-level of health planning. According to Landeweerd et al. (2013), the

data analytics system that Google has the potential to reach what previous projects such as

Google Health failed to accomplish, not due to the lack of user value alignment, but because it

was managed irresponsibly. The message meant by this interpretive insight is that the future of

preventive health is in its dual role; as an instrument of personal empowerment and as a

population level surveillance machine that can be run in an ethical manner to drive the proactive

intervention. Google Fit can therefore serve as a crucial data broker of the transformation

between personal activity and group protection.

The other important Google Fit prevention model outcome is the democratization of

health information. Ragusa and Crampton (2019) disclose that the use of "Doctor Google"

indicates the cultural change toward self-directed education and independence in making health

choices. And this empowerment, when combined with the believable applications such as

Google Fit, becomes less a speculation to action. According to Bert et al. (2014), smartphones

provide affordable avenues to deliver preventive information at a large scale. This particular

democratization reduces access barriers especially among marginalized groups. According to

Carey et al. (2015), prevention is inclusive because eHealth platforms remove the gap between

health literacy and clinical application. The interpretive implication is that access is

multiplicative, so when preventive knowledge is made “experienceable” via mobile interfaces,

health awareness will become daily behavior. A good example of this evolution is Google Fit, as

it is taking preventive knowledge not as simply ingested, but as acted upon, given that self-

education should be part of self-tracking.

Preventive strategies are also redesigned into digital health applications in clinical

workflows. Kampmeijer et al. (2016) discovered that clinicians are becoming more inclined to

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adopt the mHealth tools interested in extending preventive care beyond clinical sessions

especially in chronic disease management. Such applications promoted in primary care promote

continuity and accountability, according to Carey et al. (2015). According to Farshchian and

Vilarinho (2017), the openness of the Google Fit platform allows its developers to integrate

Google Fit with clinical software, allowing a doctor to see not only the lifestyle pattern but also

the clinical values. These findings have an interpretive worth, in that patients are co-producers of

preventive health: patients create data and provider interprets it, creating meanings that are acted

upon. This model narrows the temporal gap between intercession and intervention moving

prevention to continuous care. By definition, Google Fit is a tracking tool and a means of

communication, which changes prevention medicine into a partnership of data and mutual

understanding.

Preventive healthcare participates in the change psychology aspect of gamification of

goal-setting, and this is seen in applications like Google Fit. According to Middelweerd et al.

(2014), the recorded behavioral feedback using visuals (such as progress bars) significantly

increases adherence to exercise prescriptions. In a similar fashion, Abroms et al. (2013)

scrutinized the use of mobile applications and found that they offer rewards and reminders which

buttresses participation in smoking cessation programs. Knight et al. (2015) stress that fitness

applications that convert health policies and information to simple target-setting take away the

know-do gap. The interpretive synthesis is that gratification is the fuel for digital prevention--

sustained motivation is the route where information fails. Google Fit’s user interface, capable of

intelligent motivation through progress visualization, transforms self-care to a conquerable

challenge. It reframes prevention as a self-determined goal, which is essential for adherence and

self-regulation, the public health lock-in.

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Utilizing Google Fit for preventive healthcare intersects with some environmental and

social determinants of health. As noted by Liang et al. (2019), digital fitness participation is

associated with community awareness regarding obesity and associated health risks and diseases.

These associations imply that app-supported actions can galvanize the awareness of health-

related issues on a broader scale. Within the broader context of Google’s digital ecosystem,

Landeweerd et al. (2019) integrate behavioral, spatial and contextual elements with digital fitness

apps. According to Bert et al. (2014), mobile health (mHealth) programs enable instant health

promotion and disease prevention (HPDP) in the community. The interpretative implication is

that the objectives of digital networks transform preventive actions into an individual pathway,

framing it as a social network. There is a phenomenon that when users observe their performance

and aggregated data on the progress of their goals, social norms shift to integrate health-centered

communities. Google Fit has the the ability to act as an individual coach while simultaneously

fulfilling the role of a social motivator by promoting the preventive health with shared actions.

One such frontier is personalized behavioral analytics, which creates recommendations

specific to a user’s physiology and conduct. As adaptive algorithms developed by Farshchian and

Vilarinho in 2017 have shown, platforms such as Google Fit can adjust uploaded user-goals in

real-time to reflect actual performance numbers. As noted by Henriksen and colleagues in 2017,

systems can archive data in a manner that supports personalized individual health journeys for

prompt identification of assertion strays. As suggested by Carey et al. in 2015, personally

tailored eHealth feedback systems can significantly boost motivational levels by temporally

associating provided behavioral health inputs with real-time health issues. The data driven

personalization integrate behavioral medicine with data science, whereby users infer that instead

of passive, blanket prescriptions, prevention is offered in a manner that is distinctively pertinent.

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From this angle, personalization is a way of humanizing the technology: it gives context, strips

metrics of their abstraction, and places users within their own preventive story arch. Feedback

provided by Google Fit serves as an example of integrated data analytics whereby the user is

provided with real time intimate insights as a way of dynamic preventative feedback.

At last, Google Fit's enduring impact on preventive healthcare is its data ethics and user

confidence integration. Landeweerd et al. (2013) explains that the downfall of past technologies

of health is a consequence of the negligence of privacy and user value alignment. Astrup et al.

(2016) explains that the mHealth industry users’ enduring participation is determined, in part, by

the relevance of the platform’s usability and transparency. On the other hand, Bert et al. (2014)

argues that trust augments a person’s willingness to share data which, in turn, enhances the

overall effectiveness of preventive systems. The interpretive conclusion from these works is that

as much as prevention depends on technological architecture, it also hinges on ethical design.

The effectiveness of Google Fit is a measure of its precision but also a measure of its ethical

value--users must feel safe in the data-for-insight exchange which hinges on balance. If this

balance is achieved, technology in preventive healthcare will develop from a mere tool to a

trusted partner, fostering personal health and digital responsibility symbiosis.

Future Prospects and Technological Advancements

The future of healthcare technology will be most likely determined by the integration of

artificial intelligence and wearables, using systems such as Google Fit as adaptive ecosystems.

Mishra (2015) points out that systems Android based wearables are metamorphosing from

passive biometric data recorders and collectors to real time biometric signal interpreters and

assistants. This change relates to the increasing sophistication of contextual computing and

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sensor fusion. Jones et al. (2018) proposes that the next decade will feature AI medical systems

that are capable of prediction and prescription, closing the gap between monitoring and

intervention. Tran et al. (2019) also points out the rapid expansion of AI health research, which

indicates the fast spread of machine learning to mobile wellness systems. The interpretive insight

is that future versions of Google Fit will most likely evolve from descriptive tracking and

progress to primary diagnostic capabilities. With the continued refinement of learning

algorithms, health apps will monitor and analyze data to identify anomalies, and preemptively

recommend medical consultations, thereby transforming smartphones into every day, easily

accessible, predictive health agents.

Cooperation across digital ecosystems will be another front for tech development. Iversen

and Eierman (2018) declare that shifts in technology change the task-technology fit and require

an alignment of adaptive integration between who uses the system and what the system can do.

This change brings to the fore the need for wearables, healthcare professionals, and insurers to

liaise with each other. Mishra (2015) underscores that the evolution of Android Wear is due to its

open framework system, which embraces modular modifications that cater to sustaining and

evolving technologies without full system redesigns. Spil and Klein (2014) warn that past

blunders like Google Health prove the value of system user interdisciplinary relationships for

integration which does user experience and not complication. This suggests that the healthcare of

the future will be a system of systems as opposed to a range of separated applications. With the

growth of cloud computing, Google Fit has a potential to become a personal health network

integration hub that seamless exchanges data between the user, healthcare providers and AI

diagnostic systems.

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The incorporation of sophisticated AI technology into Google Fit is probable and depends

on the context-sensitive learning adaptive algorithms of the future. Tekkeşin (2019), for instance,

imagines new models of AI for healthcare that evolve from being static to learning systems that

adjust to new medical innovations. According to Lai et al. (2017), user’s comments and

suggestions are pivotal to the evolution of the app’s algorithms and the personalization of the

app. Tran et al. (2019) further reinforce this assertion by arguing that AI in healthcare is shifting

towards a greater emphasis on feedback in practice rather than purely on theoretical

optimization. The feedback-rich systems, including Google Fit, are seen as living systems that

evolve based on the contributions from a multitude of users. These self-learning systems are

projected to have the capacity to respond to new health trends, real time, by designing and

deploying dynamic, self-optimizing, personalized preventive health strategies that are aligned

with the prevailing global health trends.

The development of new materials and sensor miniaturization will draw new capabilities

for wearables. Mishra (2015) stresses that advances in hardware design fosters a new wave of

health applications for everyday items such as watches and garments. Jones et al. (2018) note

that more precise biosensors may measure complex physiological indices of health such as stress

and oxygen saturation, thereby enhancing health monitoring. Tekkeşin (2019) notes that non-

burdening sensors will, as the sensors become more precise and less intrusive, offer an

uninterrupted stream of clinically relevant data. The implication is startling: the medical device

and lifestyle accessory will no longer be distinct, thus making preventive health available to

everyone. The unobtrusive monitoring that Google Fit intends to employ is the intelligence of

healthcare woven into the daily fabric of life, not confined to the silos of clinical practice.

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In the future emphasis would be laid on predictive analytics and individualized

forecasting. Tran et al. (2019) note the expanding focus of research on predicting modeling that

forecast disease pathways using multi-source data. Spil and Klein (2014) contend that the

previous failed attempts on healthcare technologies was the absence of etiology and actionable

data interpretation resulting to disengagement of users. The contrasting situation is that emerging

AI methods now offer predictive reasoning that engages contextualized probability of risk at the

individual level. Jones et al. (2018) posit that this ability to predict might change the trajectory of

healthcare towards timely diagnosis and timely intervention with the view of transforming

healthcare to a continuously improving system. The resultant interpretive change is a

foundational change: Future versions of Google Fit may go beyond the conventional metric of

wellness to offer real-time assessments with probability estimates for potential medical

conditions such as hypertension or arrhythmia. Transformative foresight integrates prevention as

a predictive proactive technology-user algorithmic partnership.

Technological advancement in the health sector will be supported by edge computing and

cloud infrastructure. Centralized responsiveness and adaptive responsive computation must be

balanced, according to Iversen and Eierman (2018). According to Mishra (2015), Within the

hybrid architecture, Android Wear sustems bone-distributed cloud computing so that the

computing resources are offloaded to the cloud and immediate feedback is provided on the

device. Tran et al. (2019) assert that the availability of cloud computing has greatly increased

participation in and collaboration on healthcare research, enabling large-scale federated learning.

interpreting this, health applications will certainly rely more on distributed, and therefore, cloud

systems for global analytics, and edge processors for personal responsiveness. In this context,

Google Fit will not passively aggregate health data but will actively integrate and contribute to

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the network globally and locally, providing real-time insights to users and storing data for instant

research collaboration.

Integration with artificial intelligence will surely progress to multimodal sensing and

awareness of context. Lai et al. (2017) remarks that there is already user feedback app

optimization involving context variables such as place and time. Jones et al. (2018) show how AI

can add vision to hearing and biometric feedback to make better decisions. Tekkeşin (2019)

imagines the ability of health care systems to deduce behavioral states like stress and fatigue

from interactivity with the environment. A plausible interpretation is that future updates of

Google Fit can turn into ambient health companions. These systems will have the ability to

comprehend the user’s context, mood and environment to provide subtle suggestions. The ability

to provide suggestions and advice on health based on data collected is a leap in health technology

and predictive care. This also indicates that data presentation is no longer the end goal, it is what

the user experiences that truly matters.

The lack of ethical data practices and transparency with algorithms will be of critical

concern in regards to the credibility of future healthcare technology. Landeweerd et al. (2013)

indicate that user distrust is a risk of unethical technological innovation, as seen in the historic

failures of Google Health. Spil and Klein (2014) emphasize that personal health records will be

workable only if user control and privacy protection are baked in. Tran et al. (2019) further assert

that the development of AI in healthcare must be governed by conditions that will ensure

accountability and responsible stewardship. The key takeaway is that accountability in the

development of such AI systems is a critical factor in building trust. As Google Fit adds more

complex analytics to its systems, the ability to employ explainable AI will be a competitive

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advantage, enabling users to understand health recommendations and rationale. An ethical

framework in this area is thus a prerequisite to technological advancement.

The enhancements machine learning models undergo will lead to a transformation of the

operation of healthcare ecosystems through collaborative innovation. Iversen and Eierman

(2018) observe that the environments in which change in technology takes place have a greater

chance of success if collaboration and learning at different levels and across different groups is

encouraged. Tran et al. (2019) note that the growth of open AI and medicine research is

associated with shared innovation cycles. Tekkeşin (2019) notes that the collaborative work of

engineers with clinicians boosts the clinical relevance and reliability of the work in question. In

this case, the interpretive consequence is that the future of Google Fit will not only depend on

private algorithms advanced by Google, but also a strong global collaborative network

combining public datasets, academia, and private enterprises. AI technologies in healthcare will

undergo continual development, collaboration will foster greater inclusiveness, and the

associated ethical and scientific transparency will be addressed.

Like other digital platforms, Google Fit is likely to evolve towards all-encompassing

interaction ecosystems, where human interaction, algorithms, and data intermesh seamlessly. In a

future imagined by Jones et al. (2018), artificial intelligence continuously assimilates data from

various sensory inputs and constructs a coherent model of human well-being. As Mishra (2015)

points out, passive wearable devices running on the Android OS are already exhibiting a

convergence of technologies, incorporating real-time adaptive feedback provided in the

interfaces. According to Tran et al. (2019), the next ten years are likely to entrench artificial

intelligence as the cognitive frontier of targeted health. This evolution of Google Fit can be

interpreted as a clear signal of the larger digital transformation of the health field: the shift of

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intelligence from institutions to individuals and the consequent shift in users from patients to

collaborators in intelligent health ecosystems.

Ethical, Privacy, and Security Considerations

The first challenge with the ethical issues of technology such as Google Fit revolves

around the question of who owns the data. Bohr and Memarzadeh (2020) claim that ownership

structures need to be established for AI health platforms because they rely on sensitive data to

safeguard the patient’s autonomy. The current state of data protection laws is outdated; as Lu

(2019) puts it, sensitive data may be misused to the extent that a data processor wants and,

somehow, that is not applicable in the AI era. As Bourreau et al. (2020) note, the unrestrained

monetization of health and fitness data by large technology corporations is accompanied by a

decline in consumer welfare, and trust is treated like a disposable commodity. One interpretation

of ownership is that it also comes with ethical responsibilities. Refined in that way, the question

of health data, wherein the user becomes the product, is a situation where the users as data are

stripped of their civil dignity. The notion of digital health is hardly accommodated within Google

Fit’s policy; it should go beyond user privacy to include user-managed consent frameworks that

enhance data agency.

Mobile device health systems manage informed consent and the right to privacy within

ethics and pragmatic boundaries. Chen and Decary (2020) observe that, with mobile health

applications, users often click permission buttons without understanding the true extent of the

data that gets collected and what gets done beyond the primary intent. Inter-component privacy

leaks documented by Li et al. (2015) on Android applications are said to often, and most of the

time without consent, disclose sensitive information. Suzianti et al. (2017) show that interface

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ethical dilemmas can be solved by ensuring data-sharing UX is designed to be comprehension

friendly. Privacy that is ethical and unexploited can no longer be a regulative and abstract

concern. Rather, it is a concern that interface designers need to deal with. Contextualized

sequences of permission requests, explanatory data-rich visual dashboards, controlled privacy

refrains, and reminiscences can implant proactively interfaced user proactivity over privacy to

elevate privacy from post- interface design thoughts on privacy. UX ethical design is thus

preemptive, and users do not have to rely on exercising their autonomy to gain usability.

The ethical dilemma posed by cyber risks is much more nuanced when considering

digital health and health technology. Lu (2019) observes that AI systems can be configured so

that their interconnections create weak points that can be leveraged to execute unauthorized

inferences and breaches. Astrup et al. (2016) found marked differences in the mobile health

toolkits in their approaches to securing data encryption and data transfer. Their findings suggest

that, at least in some respects, Google Fit and Apple HealthKit differ. Advocates of digital health

concentrate on more fundamental and forward-looking issues. Their efforts are geared towards

digital health standards that prioritize clinical data with equally strong defenses. The Ethics of

Artificial Intelligence mainly rests on the observation that good intentions are not good enough.

Systems that offer no encryption, strict unauthorized access control, multi-factor access control,

and the real-time monitoring of workflows cannot be considered the gold standard, or even the

baseline, in ethically sound systems. Trust is unavoidable in both situations. Systems designed to

protect the users but are considered unencrypted lapse into user spying systems.

Transgressions of trust are fundamental breaches and in response, user systems and

health apps will invariably nudge users toward counterproductive actions. Proactive

recommendations will not ethically justify the engagement of AI systems. Security needs to be

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embedded in a way that is adaptable in relation to the sophisticated, multi-faceted risks. There

needs to be a fundamental shift in the posture of cyber security to focus on proactive, ethical, and

primary care approaches to global digital health. Another important dimension decision fairness

and algorithmic bias. Bohr and Memarzadeh (2020) specify that inequity in healthcare outcomes

can result from datasets that are unrepresentative or incomplete. Laï et al. (2020) noted that in the

New AI functionalities in medicine and healthcare class profession AI perception in health care

is often the most articulated regarding issues of fairness and explainability of the AI algorithm.

Briganti and Le Moine (2020) claim that fairness is also a determinant of clinical trust but in

addition there has to be explainability of the reasoning underlying the algorithm outputs. The

fairness here concern relates to the ethical constriction on the AI and not its functional workings.

The problem with Google Fit is if the suggestions are the result of training on biased data the

outcome will be no equilibrium and more disparity than health. There lacks the absence of trust

that will be maintained in gradient diverse populations and within model artefacts there is the

absence of ethical scrutiny on data source diversity.

User trust and legitimacy can be earned and maintained through transparency and ethical

transparency. To close this understanding gap, Chen and Decary (2020) propose XAI.

Complexity and interpretability balance, Lu (2019) argues, with OptAI’s ability to advance

meaningful human-algorithm engagement. Suzianti et al. (2017) found users’ trust in a health

application rests on a clear data policy and feedback transparency. The teleological

understanding is ethical transparency is not only about disclosure but about that which fosters

understanding. Proprietary health tools such as Google Fit can enhance that trust. Users can be

provided with comprehensible explanations as to the logic behind data-driven insights and

recommendations, which users can reason. Users can appreciate the rationale for the outcomes,

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thus, shifting the outcomes of transparency from compliance to engagement. This fosters trust,

sustained involvement and ethical confidence.

Healthcare applications are ethical and innovative at the same time which brings

complexity to the concepts of design. Türkyilmaz et al. (2015) states user trust can be greatly

reliant on the aspects of coherent pattern design and appreciate the applications which are

designed with a sense of credibility. Michaelis et al. (2016) emphasized the attitudes and feelings

of the users and how they are directed with the help of the devices which are easy to operate and

dependable and the same goes with wearable fitness devices. Deka et al. (2017) claim and

strongly suggest that user-oriented and data-driven design concepts and frameworks are

beneficial to having ethical design and functionality. The crucial view is design is not just a

matter of surface treatment. It is also a carrier of ethical stains and signals. A user interface that

is uncluttered, coherent, and deferential to user imposition hugely signals confident trust.

Therefore, later versions of Google Fit need to regard design as an ethical debate and treat it as a

form of ethical discourse where the design framework and ethics of the system interface can

easily express the sentiments of user safety, dignity, and reliability.

Merging external elements with data monetization involves complex ethical issues. To

illustrate, Bourreau et al. (2020) point out how Google’s acquisition of Fitbit demonstrates

corporate mergers can transform personal health data into commodities. According to Bohr and

Memarzadeh (2020), monetization of AI-driven insights constitutes profit-driven malpractice.

Also, Lu (2019), technology ethics concerning marketing AI ecosystems highlight the need for

equilibrium between agile AI and social responsibility. The interpretive consequence is that data

monetization is in perpetual struggle with the ethical limit of technological exploitation. Google

Fit’s approach to monetization affords user benefit in the form of anonymized analytics, shared

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decision making, and opt-in data sharing, which alleviates ethical tension. When users perceive

the monetized service as reciprocally valuable, the transaction is a collaborative exchange rather

than exploitative, thereby upholding the ethical principles of a digital health monetization.

Within the AI health systems, user feedback loops are invaluable for maintaining ethical

implementation. Pagano and Maalej (2013) state that within app ecosystems, structured feedback

enhances accountability since the users can affect ongoing development. Lai et al. (2017)

showed how text mining user reviews can identify ethical gaps, including privacy and data

accuracy issues. There are those, like Chen and Decary (2020), who argue that ethical refinement

in AI feedback loops is underscored by the ethics embedded in the feedback analytics. The

feedback changes ethics from a principle to a living process. The potential ethical sustainability

of Google Fit in the future will depend upon its capacity to listen, that is, change design in

response to user feedback. These changes in ethics that participatory to the users will shift the

governance of digital health from the developers to the users who are impacted by the

technology.

The intersection of AI with healthcare entails an often neglected dimension of ethics:

cross-country ethics. Different societies tend to morally perceive the acceptability of AI

technology differently because of the prevailing cultural attitudes toward privacy and autonomy.

Wang et al. (2018) point out that regulations on the app market and the market in general differ

remarkably in the various countries, making the protection of global data more complex. The

global power relations in the cloud computing industry also add to the complexity of de facto

data privacy and data protection laws (Kitchin et al., 2019). Laï et al. (2020) argue that the app

market is more fragmented than ever because of domestic regulations, which makes the

protection of global data more complex. The culturally sensitive ethics frameworks proposed by

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Bohr and Memarzadeh (2020) pertain to the ethics of data-driven global health technologies with

specific reference to platforms like Google Fit. All these elements point to the importance of the

ethics of design being responsive, not static. Much of these contexts are ones where the

developers need to appreciate the relative sensitivities to data, consent, privacy, and norms of the

region. Global health technologies will only be relevant and responsive if they are designed with

local consent frameworks and compliance mechanisms.

Balanced accountability and innovation are equally two sides of the same coin for our

purposes. According to Chen and Decary (2020), in developing countries, the public healthcare

sector’s declining ability and willingness to embrace innovation are rooted in unsubstantiated

techno-optimism and its ethical void. Responsible innovation, as Bohr and Memarzadeh (2020)

insist, is rooted in ethical governance, which, in turn, is based on precisely articulated

accountability structures that tie the actions of algorithms to particular people. This level of

accountability is the type of thinking Lu (2019) refers to as AI accountability frameworks, which

include audit trails and decision logs that are accessible and retraceable. Accountability reframes

the scope of innovation and shifts the burden from guesswork to certification. For Google Fit, the

responsibility frameworks of Nouze is to smoothen the ethical slumber of progress.

Accountability envelops the user’s trust, which is mostly unspoken and, in turn, is the price of

the rapid growth of digital health technologies.

Stewardship of data also encompasses retention for long periods of time. According to

Briganti and Le Moine (2020), in contrast to regular consumer data, health data has an enduring

and pervasive degree of sensitivity and thus, as a matter of record, has to be protected and

preserved for an entire lifetime. According to Lu (2019), even in the years to come,

contemporary AI systems and models may be able to extrapolate and derive value from data that

POSITION PAPER 2 58

has been accumulated long after it has been collected. This has a direct impact on the deletion

and consent processes. Bourreau et al. (2020) posit that the absence of adequate stewardship of

data in the long term might be the cause for the vicious secondary data utilization. The outcome

of this interpretive insight suggests that ethics of data must extend temporally as a legacy time

and the stewardship must extend beyond the present use. Frameworks for the automated renew

procedures, data retention and expiration protocols, and consent systems for archive

anonymization that has been kept for a long time can help keep the information for research

purposes while also enhancing privacy. Thus, the long term stewardship of data provides a

guarantee for innovation in the present and the future.

Within digital health care, maintaining some form of control by a human operator must

be a key area of focus for any form of ethical innovation. Chen and Decary (2020) argues that it

is the system designer who bears the responsibility to ensure that, integration is done in

alignment with human ethos. Briganti and Le Moine (2020) `s assertion is that the greatest

ethical AI is the one that augments with hybrid intelligence- the machine's precision and the

human's empathy. This brings us to the interpretive conclusion that technology must serve, not

erode, human dignity. For Google Fit, it denotes the advancement of the system in a way that

still accommodates clinicians and users of all tiers. By intertwining ethical control with your

digital precision, the aim of enhancing humane wellbeing with the reach of digital health is

achieved and the ethical limits of humanity are observed.

Conclusion and Recommendations

Innovation in healthcare hinges on the successful assimilation of digital technologies that

promote operational efficiency while maintaining standards of care. Adoption of technology has

POSITION PAPER 2 59

already changed the operational and financial dynamics of healthcare by facilitating the

automation of processes that were previously stymied by administrative barriers (Fasano, 2013).

As noted by Atluri et al. (2016), the “value- focused” evolution of healthcare in response to

consumer demand for transparency and personalization, a phenomenon referred to as

“consumerization of healthcare,” has further accelerated this evolution. “Although technological

innovation may lift costs, in the long run it increases efficiency by automating and optimizing the

use of data” (Marino and Lorenzoni, 2019). In this case it is only reasonable to assume that the

health sector will continue to view technology as a long term investment in adaptability, rather

than as a short term cost to cut. Integrating systems like Google Fit provides operational

resilience, while closing the gap between technological investment and its concrete outcomes in

population health equity.

The current trend in healthcare expenditures points to the need to match outcome-based

policies with digital innovation. Dieleman et al. (2020) show that in the U.S., healthcare

spending is still reasonably inequitable across payers and conditions, implying the inallocation

instead of the underinvestment in overall healthcare spending is the issue. According to Gallet

and Doucouliagos (2017), improvements in health outcomes as a result of increased spending on

health are only noted when the expenditures are on preventive, coordinated, and effectively

targeted health programs. Papanicolas et al. (2018) further illustrate that countries with integrated

digital system healthcare infrastructures have a higher performance to cost ratio than countries

that have only traditional care infrastructure. The interpretative understanding in this case is that

spending more is not the solution, rather, innovation is the answer. In this sense, the economist

who uses the strategy scientifically understands that technology is not a spending issue, but

POSITION PAPER 2 60

rather a spending optimizing tool that redesigns cost reflexes away from ineffective to preventive

efficiency.

Combining data analytic techniques with wearable technologies offers a unique

opportunity to facilitate chronic disease management. Wu et al. (2016) state that big data and

wearables together provide real-time analytics on health behavior, which addresses the gap

between clinical and lifestyle changes. Louw and Von Solms (2015) state that wearable

technologies will be critical in forecasting and averting the advancement of a disease in real-

time. Knight et al. (2015) argue that the most effective applications are those which align with

the public health approach as they are able to transform behavioral understanding to real impact.

The interpretive conclusion is that ability to incorporate these technologies into scalable

preventive measures will define future health care ecosystems. This change marks a shift from

episodic care systems to value-based data-driven wellness systems that maximize cost and

patient value, and in the process, underscores the new paradigm of preventive healthcare

ecosystems.

The innovations economically strengthen payment methods, and thus, payment systems

have to be updated to accommodate such innovations to ostensibly beneficial technology (Long,

Mortimer and Sanzenbacher, 2014). Also, some technology systems should be advanced enough

to accommodate pay-for-performance systems (Moro Visconti and Morea 2020). These systems

have techno-ethical accountability frameworks and appreciate AI-powered digital health, which

aligns with Agarwal 2020, who emphasizes the importance of advanced analytics systems in

enhancing performance evaluation and data transparency within health institutions. The dual

frameworks of academia and policy have come to militarize incentive-driven reforms to capture

and address the inequity stemming from technology’s passive silencing. Innovation must be

POSITION PAPER 2 61

embedded alongside AI, data analytics, and payment systems. In such systems, the promoted

performance or economic equity is not purely incidental but definably deliberate.

The importance of collaboration across different sectors when implementing cross-

industry collaboration in the digitalization of the health care sector is well recognized.

Integration of big data is, according to Groves et al. (2013). Almalki and Simsim (2020) go so far

as to say that systems of collaborative governance as a construct are more efficient in leveraging

digital technologies. The lessons from these articles suggest that innovation is not a solitary

activity. The focus of research in this case should be on the intersection of health data

governance, the education of the workforce, and health systems. The ability to achieve digital

transformation will be dependent on alignment from all these constituents to ensure that the

benefits of technology are social, rather than confined to proprietary benefits of the private

sector.

An academic understanding of reforming healthcare involves considering global

consequences of digital evolution as well. Papanicolas et al. (2018) depicts how America’s

spending lags its peers in achieving preventive outcomes, indicating how spending cannot

replace systemic efficiency. Marino and Lorenzoni (2019) point out that high-income countries

with high levels of AI use in clinical and administrative workflow outperform others on health-

adjusted life expectancy. Gallet and Doucouliagos (2017) further provide evidence that

systemically efficient spending is coupled with spending on systemic digital learning. The

interpretative explanation is that any future reform in healthcare must strive towards mounting

expenditure and how advanced the system is in terms of innovations. Cross-border academic

frameworks portray that healthcare inequities on a global scale can be solved by embracing

technology in divergent economies.

POSITION PAPER 2 62

The integrated practical and conceptual examination of policy changes driven by

technological diffusion in healthcare has come to the attention of intellectuals attributed to the

work of Levy and Thorndike (2019). At the same time, policy changes do not only depend on

legislatory modification, changes in behavior prompted by changes in policy also facilitates the

shift in reaction to market instead of regulation as mentioned by Atluri et al in 2016.\ Fasano in

2013 has also talked about the shift policymakers need to adopt, suggesting they need to permit

changes instead of working as regulators. There is an interpret explicit argument proposed in

these research works suggested by scholars is there is the absence of regulation devices.

Governing innovation surfaces as central to the idea rather than prescriptive devices. This shift is

what the scholars have called policy agility, a phenomenon necessary to be able to attain success

in public health infrastructure.

The economic sustainability of digital healthcare relies on its bolstering innovation and

effective workforce management. According to Pellegrini et al. (2014), workforce changes have

a direct influence on efficiency of spending, especially in high-tech industries. Automation and

digital optimization, according to Mithas et al. (2020), mitigate Schmidt and Estelin’s cost

disease in the level of productivity provided to service-oriented digital-like systems. Almalki and

Simsim (2020) further argue that the diffusion of technology in the economy eliminates excesses

in the healthcare system, leading to better resource utilization. The interpretative conclusion is

that the future academic discourse on health care will have to merge health care labor economics

and technology adoption as interrelated. The net benefit of digital transformation, as routine

tasks get automated, hinges on effective workforce retraining and restructuring as the focus will

be on harnessing the potential of new technologies to enhance human capabilities.

POSITION PAPER 2 63

When evaluating innovation in healthcare, academic assessments must focus on value

creation over efficiency. Dieleman et al. (2020) maintain that piecemeal approaches to cost

control mask systemic problems that only full-spectrum data analysis can illuminate. Marino and

Lorenzoni (2019) maintain that process expansion, rather than first-level implementation, is

where real economic value from technology is generated. Wu et al. (2016) argue that the ongoing

assimilation of big data with wearables generates multiplicative value at the individual and

organizational levels for health. The interpretive synthesis is that digital health systems are an

evolving phenomenon, rather than policy outcomes with a clear endpoint. For both academics

and policymakers, the logic is inescapable: these are systems that must be built to adapt in

parallel with technology. Otherwise, digital innovation in public health could rapidly become a

stubbornly obsolete phenomenon.

POSITION PAPER 2 64

References

Abroms, L. C., Westmaas, J. L., Bontemps-Jones, J., Ramani, R., & Mellerson, J. (2013). A

content analysis of popular smartphone apps for smoking cessation. American journal of

preventive medicine, 45(6), 732-736.

Agarwal, R., Dugas, M., Gao, G., & Kannan, P. K. (2020). Emerging technologies and analytics

for a new era of value-centered marketing in healthcare. Journal of the Academy of

Marketing Science, 48(1), 9-23.

Almalki, Z. S., & Simsim, D. A. (2020). The role of health technology in transforming

healthcare delivery and enhancing spending efficiency. Glob J Med Res, 2(3), 11-15.

Astrup, P., Jansen, E. G., & Aksic, N. (2016). Usability of Commercial mHealth Toolkits From a

Developer Perspective-An Empirical Evaluation of Google Fit, Apple HealthKit and

Samsung Digital Health (Master's thesis, NTNU).

Atluri, V., Cordina, J., Mango, P., Rao, S., & Velamoor, S. (2016). How tech-enabled consumers

are reordering the healthcare landscape. McKinsey & Company.

Bert, F., Giacometti, M., Gualano, M. R., & Siliquini, R. (2014). Smartphones and health

promotion: a review of the evidence. Journal of medical systems, 38(1), 9995.

Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications.

In Artificial Intelligence in healthcare (pp. 25-60). Academic Press.

Bourreau, M., Caffarra, C., Chen, Z., Choe, C., Crawford, G. S., Duso, T., ... & Vergé, T.

(2020). Google/Fitbit will monetise health data and harm consumers. Centre for

Economic Policy Research.

POSITION PAPER 2 65

Bradley, E. H., Canavan, M., Rogan, E., Talbert-Slagle, K., Ndumele, C., Taylor, L., & Curry, L.

A. (2016). Variation in health outcomes: the role of spending on social services, public

health, and health care, 2000–09. Health Affairs, 35(5), 760-768.

Briganti, G., & Le Moine, O. (2020). Artificial intelligence in medicine: today and

tomorrow. Frontiers in medicine, 7, 509744.

Carey, M., Noble, N., Mansfield, E., Waller, A., Henskens, F., & Sanson-Fisher, R. (2015). The

role of eHealth in optimizing preventive care in the primary care setting. Journal of

medical internet research, 17(5), e3817.

Chen, M., & Decary, M. (2020, January). Artificial intelligence in healthcare: An essential guide

for health leaders. In Healthcare management forum (Vol. 33, No. 1, pp. 10-18). Sage

CA: Los Angeles, CA: Sage Publications.

Deka, B., Huang, Z., Franzen, C., Hibschman, J., Afergan, D., Li, Y., ... & Kumar, R. (2017,

October). Rico: A mobile app dataset for building data-driven design applications.

In Proceedings of the 30th annual ACM symposium on user interface software and

technology (pp. 845-854).

Dieleman, J. L., Cao, J., Chapin, A., Chen, C., Li, Z., Liu, A., ... & Murray, C. J. (2020). US

health care spending by payer and health condition, 1996-2016. Jama, 323(9), 863-884.

Farshchian, B. A., & Vilarinho, T. (2017, April). Which mobile health toolkit should a service

provider choose? A comparative evaluation of Apple HealthKit, Google Fit, and

Samsung Digital Health Platform. In European Conference on Ambient Intelligence (pp.

152-158). Springer, Cham.

POSITION PAPER 2 66

Farshchian, B. A., & Vilarinho, T. (2017, April). Which mobile health toolkit should a service

provider choose? A comparative evaluation of Apple HealthKit, Google Fit, and

Samsung Digital Health Platform. In European Conference on Ambient Intelligence (pp.

152-158). Cham: Springer International Publishing.

Fasano, P. (2013). Transforming health care: The financial impact of technology, electronic

tools and data mining. John Wiley & Sons.

Gallet, C. A., & Doucouliagos, H. (2017). The impact of healthcare spending on health

outcomes: A meta-regression analysis. Social Science & Medicine, 179, 9-17.

Groves, P., Kayyali, B., Knott, D., & Kuiken, S. V. (2013). The'big data'revolution in healthcare:

Accelerating value and innovation.

Gruber, J., & Sommers, B. D. (2019). The Affordable Care Act's effects on patients, providers,

and the economy: what we've learned so far. Journal of Policy Analysis and

Management, 38(4), 1028-1052.

Henriksen, A., Hopstock, L. A., Hartvigsen, G., & Grimsgaard, S. (2017). Using cloud-based

physical activity data from google fit and apple healthkit to expand recording of physical

activity data in a population study. In MEDINFO 2017: Precision Healthcare through

Informatics (pp. 108-112). IOS Press.

Iversen, J. H., & Eierman, M. A. (2018). The Impact of Experience and Technology Change on

Task-Technology Fit of a Collaborative Technology. Journal of Education and

Learning, 7(3), 56-75.

POSITION PAPER 2 67

Jones, L. D., Golan, D., Hanna, S. A., & Ramachandran, M. (2018). Artificial intelligence,

machine learning and the evolution of healthcare: A bright future or cause for

concern?. Bone & joint research, 7(3), 223-225.

Kampmeijer, R., Pavlova, M., Tambor, M., Golinowska, S., & Groot, W. (2016). The use of e-

health and m-health tools in health promotion and primary prevention among older

adults: a systematic literature review. BMC health services research, 16(Suppl 5), 290.

Knight, E., Stuckey, M. I., Prapavessis, H., & Petrella, R. J. (2015). Public health guidelines for

physical activity: is there an app for that? A review of android and apple app stores. JMIR

mHealth and uHealth, 3(2), e4003.

Laï, M. C., Brian, M., & Mamzer, M. F. (2020). Perceptions of artificial intelligence in

healthcare: findings from a qualitative survey study among actors in France. Journal of

translational medicine, 18(1), 14.

Lai, Y. H., Huang, F. F., & Chiou, P. Y. (2017, November). Analysis of user feedback in the

mobile app store using text mining: A case study of Google Fit. In 2017 IEEE 8th

International Conference on Awareness Science and Technology (ICAST) (pp. 50-54).

IEEE.

Landeweerd, M., Spil, T., & Klein, R. (2013, June). The success of Google search, the failure of

Google health and the future of Google plus. In International Working Conference on

Transfer and Diffusion of IT (pp. 221-239). Berlin, Heidelberg: Springer Berlin

Heidelberg.

POSITION PAPER 2 68

Levine, A. I., DeMaria Jr, S., Schwartz, A. D., & Sim, A. J. (Eds.). (2013). The comprehensive

textbook of healthcare simulation. Springer Science & Business Media.

Levy, D. E., & Thorndike, A. N. (2019). Workplace wellness program and short-term changes in

health care expenditures. Preventive medicine reports, 13, 175-178.

Li, L., Bartel, A., Bissyandé, T. F., Klein, J., Le Traon, Y., Arzt, S., ... & McDaniel, P. (2015,

May). Iccta: Detecting inter-component privacy leaks in android apps. In 2015

IEEE/ACM 37th IEEE International Conference on Software Engineering (Vol. 1, pp.

280-291). IEEE.

Liang, B. O., Wang, Y. E., & Tsou, M. H. (2019). A “fitness” theme may mitigate regional

prevalence of overweight and obesity: Evidence from Google search and tweets. Journal

of Health Communication, 24(9), 683-692.

Long, G., Mortimer, R., & Sanzenbacher, G. (2014). Evolving provider payment models and

patient access to innovative medical technology. Journal of Medical Economics, 17(12),

883-893.

Louw, C., & Von Solms, S. (2015). Game, settings, match–the impact and future of wearable

technology in fitness and healthcare.

Lu, Y. (2019). Artificial intelligence: a survey on evolution, models, applications and future

trends. Journal of management analytics, 6(1), 1-29.

Marino, A., & Lorenzoni, L. (2019). The impact of technological advancements on health

spending: A literature review.

POSITION PAPER 2 69

Menaspà, P. (2015). Effortless activity tracking with Google Fit. Br J Sports Med, 49(24), 1598-

1598.

Michaelis, J. R., Rupp, M. A., Kozachuk, J., Ho, B., Zapata-Ocampo, D., McConnell, D. S., &

Smither, J. A. (2016, September). Describing the user experience of wearable fitness

technology through online product reviews. In Proceedings of the Human Factors and

Ergonomics Society Annual Meeting (Vol. 60, No. 1, pp. 1073-1077). Sage CA: Los

Angeles, CA: SAGE Publications.

Middelweerd, A., Mollee, J. S., van der Wal, C. N., Brug, J., & Te Velde, S. J. (2014). Apps to

promote physical activity among adults: a review and content analysis. International

journal of behavioral nutrition and physical activity, 11(1), 97.

Mishra, S. M. (2015). Wearable Android: android wear and google fit app development. John

Wiley & Sons.

Mithas, S., Hofacker, C. F., Bilgihan, A., Dogru, T., Bogicevic, V., & Sharma, A. (2020).

Information technology and Baumol's cost disease in healthcare services: a research

agenda. Journal of Service Management, 31(5), 911-937.

Moro Visconti, R., & Morea, D. (2020). Healthcare digitalization and pay-for-performance

incentives in smart hospital project financing. International journal of environmental

research and public health, 17(7), 2318.

Muessig, K. E., Pike, E. C., LeGrand, S., & Hightow-Weidman, L. B. (2013). Mobile phone

applications for the care and prevention of HIV and other sexually transmitted diseases: a

review. Journal of medical Internet research, 15(1), e2301.

POSITION PAPER 2 70

Nuti, S. V., Wayda, B., Ranasinghe, I., Wang, S., Dreyer, R. P., Chen, S. I., & Murugiah, K.

(2014). The use of google trends in health care research: a systematic review. PloS

one, 9(10), e109583.

Pagano, D., & Maalej, W. (2013, July). User feedback in the appstore: An empirical study.

In 2013 21st IEEE international requirements engineering conference (RE) (pp. 125-

134). IEEE.

Papanicolas, I., Woskie, L. R., & Jha, A. K. (2018). Health care spending in the United States

and other high-income countries. Jama, 319(10), 1024-1039.

Pellegrini, L. C., Rodriguez-Monguio, R., & Qian, J. (2014). The US healthcare workforce and

the labor market effect on healthcare spending and health outcomes. International

Journal of Health Care Finance and Economics, 14(2), 127-141.

Ragusa, A. T., & Crampton, A. (2019). Doctor Google, health literacy, and individual behavior:

a study of university employees’ knowledge of health guidelines and normative

practices. American Journal of Health Education, 50(3), 176-189.

Singh, K., Drouin, K., Newmark, L. P., Lee, J., Faxvaag, A., Rozenblum, R., ... & Bates, D. W.

(2016). Many mobile health apps target high-need, high-cost populations, but gaps

remain. Health Affairs, 35(12), 2310-2318.

Spil, T., & Klein, R. (2014, January). Personal health records success: why Google Health failed

and what does that mean for Microsoft HealthVault?. In 2014 47th Hawaii International

Conference on System Sciences (pp. 2818-2827). IEEE.

POSITION PAPER 2 71

Sutikno, T., Handayani, L., Stiawan, D., Riyadi, M. A., & Subroto, I. M. I. (2016). WhatsApp,

viber and telegram: Which is the best for instant messaging?. International Journal of

Electrical & Computer Engineering (2088-8708), 6(3).

Suzianti, A., Minanga, R. P., & Fitriani, F. (2017). Analysis of user experience (UX) on health-

tracker mobile apps. International Journal of Computer Theory and Engineering, 9(4),

262-267.

Tekkeşin, A. İ. (2019). Artificial intelligence in healthcare: past, present and future. Anatol J

Cardiol, 22(Suppl 2), 8-9.

Tran, B. X., Vu, G. T., Ha, G. H., Vuong, Q. H., Ho, M. T., Vuong, T. T., ... & Ho, R. C. (2019).

Global evolution of research in artificial intelligence in health and medicine: a

bibliometric study. Journal of clinical medicine, 8(3), 360.

Türkyilmaz, A., Kantar, S., Bulak, M. E., & Uysal, O. (2015). User experience design:

Aesthetics or functionality. Managing Intellectual Capital and Innovation for Sustainable

and Inclusive Society: Managing Intellectual Capital and Innovation, 559-565.

Wang, H., Liu, Z., Liang, J., Vallina-Rodriguez, N., Guo, Y., Li, L., ... & Xu, G. (2018,

October). Beyond google play: A large-scale comparative study of chinese android app

markets. In Proceedings of the Internet Measurement Conference 2018 (pp. 293-307).

Wu, J., Li, H., Cheng, S., & Lin, Z. (2016). The promising future of healthcare services: When

big data analytics meets wearable technology. Information & Management, 53(8), 1020-

1033.

POSITION PAPER 2 72

Zhang, A., Prang, K. H., Devlin, N., Scott, A., & Kelaher, M. (2020). The impact of price

transparency on consumers and providers: a scoping review. Health Policy, 124(8), 819-

825.

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