METs as Gamified Health Indicator to Promote Elderly Active Lifestyle and Technology Acceptance in Ambient Assisted Living

Abstract

This paper focuses on ambient assisted living (AAL) scenarios and proposes the use of location-based games (LBGs) as engaging applications for (1) the promotion of an active lifestyle in healthy senior adults (+65) and (2) the enhancement of current acceptance rates of technology used in these scenarios. It offers a high-level software architecture that can be used to integrate health indicators produced from gameplay data of LBGs with AAL healthcare systems, thus serving as data sources capable of contributing to better professional healthcare support. The proposed concept enables care providers in AAL settings to recommend gaming exercises that can be done through LBGs; in turn, such professionals have access to health indicators (metabolic expenditure) of the gameplay, which can then be compared to the WHO recommendations for an active lifestyle of older adults. This concept enables the use of digital LBGs running on commonly available smartphones without the need for extra hardware, as applications that are more engaging and motivational than traditional technologies by design. A test of concept for the proposed architecture is presented, whereby the health indicator METs are offered from multiple gameplay data provided by an LBG and where such indicator is compared to dedicated hardware.

1. Introduction

Ambient assisted living (AAL) explores technology to support the ageing process of the elderly and chronically ill. This is a concept that contributes to a more sustainable national healthcare system, through which high costs and stress in the healthcare infrastructure can be alleviated [1]. Within AAL, technology is designed and used to turn ageing into an easier and (to some extent) self-dependent process to individuals, all the while aiming to prevent, heal, and improve overall wellness. Even though being a promising concept, researchers have focused on the technical evolution of AAL systems [2, 3, 4] (e.g., sensors [5, 6], IoT-based devices and architectural designs [7], dependability analysis [8], complex systems engineering [9], modeling of AAL-related factors [4, 10], and artificial intelligence in AAL [11]). Less effort has been put on user acceptance of AAL technology [2, 12]. This, together with the status quo of legacy systems of formalized care institutions that tend to be substantially sized, complex, and closed [13, 14, 15], prolongs the lack of adoption of AAL systems in the larger-scale national healthcare systems [3].

Pervasive serious games are applications that are designed early on to engage players and that can serve as an alternative approach to user acceptance. The serious games discussed in this paper are digital location based, that is, games that run on location-aware smartphones connected to the internet and that are designed to provide an outdoor pervasive gaming experience. Even though argued to be lacking overall social acceptance themselves as “serious” applications for healthcare [2], serious games in general are established as valid options for multiple health-related purposes such as rehabilitation [16], physical exercise [17, 18, 19], and cognitive training [20]. Serious location-based games (LBGs) serve as a potential means for a more sustainable and engaging health improvement approach to AAL settings, as they expose healthy players to their physical surroundings and motivate them to go outside and do fun game activities to advance gameplay. When the serious game is an active LBG, movement (and maybe even physical exercise) must be made to progress through the gameplay. The activities promoted by LBGs can be purposefully designed to be beneficial for the elderly living in AAL scenarios and must be researched further to have their potential properly unlocked in AAL settings.

This paper presents a concept of LBGs for healthy senior individuals living in AAL locations, a concept that informs researchers and practitioners on how to integrate LBGs in health and wellness applications used by healthcare professionals in these locations. The presented concept proposes the benefit of combining LBGs with AAL technology, by proposing LBGs as data sources for health indicators that are usable in AAL scenarios. It offers an instantiation of this new architectural concept with the existing LBG “Secrets of the South,” which offers one health indicator that is relevant for AAL scenarios: the estimated metabolic expenditure (METs) of the gameplay sessions of that LBG.

The following section of this paper provides a brief background on the evolution of AAL systems and their current maturity level and the use of engagement and LBGs as key research avenues to improve current technological approaches in AAL scenarios. The next section proposes the health indicator MET as one appropriate indicator for older adults, all the while proposing a conceptual architecture, where LBGs work together with AAL systems to inform them on the metabolic expenditure during gameplay sessions throughout the week. The section after that instantiates this concept with the LBG Secrets of the South. The following section discusses the presented proof of concept and highlights implications for game development and computer science. The last section concludes with future work.

2. Background

2.1 Ambient assisted living and its maturity level

Several factors, such as innovation in technology, the demand of citizens for easier and updated access to healthcare, and a justified concern over rising costs and stress in existent formalized care infrastructure, have driven the pursuit of new healthcare solutions and models [21, 22]. These have sought to transform the present reactive healthcare system centered on treating patients’ ailments, into a user-centered care provision centered on what users (instead of patients) want. Among multiple transitions, the concept of AAL emerged as a model based on the connected devices over the internet (IoT), which aims at promoting an active and assisted self-centered ageing process to the elderly and chronically ill primarily in their home [23]. AAL is the use of IT technologies (AALTs) for the enhancement of the quality of life of the elderly via active and healthy applications. The AAL concept (1) refers to an ecosystem that (2) is enabled by cloud and IoT technologies in particular [24] and that (3) promotes health and wellness applications for a high-quality ageing process. This ecosystem includes the elderly’s relatives, social services, health workers, and care agencies [25].

Current state of the art on AAL is most solidly based on the technological aspects of AAL scenarios, leaving other aspects such as user acceptance of such new technology largely unaddressed [25]. Over 95% of the research published within the last 3 decades on AAL [26] falls within 12 clinical themes [27]: routine action monitoring (42.7%), fall detection (13.7%), physiological parameters tracking (8.6%), presence detection (7.7%), gait analysis (6%), assessment of environment (6%), sleep monitoring (5.8%), estimation of level of activity (4.3%), routine support (1.7%), gesture recognition (1.5%), indoor localization (1.5%), and wandering study (0.4%). This body of research has different focal points, mainly on the health status (e.g., asymptomatic seniors, dementia, or Parkinson’s), use case settings (home, smart homes, nursing homes, hospitals, public places, and other settings), population (elderly, younger target groups, caregivers, and healthcare professionals), different technologies (e.g., sensors for contact, monitoring, motion, presence, pressure, or temperature), and methodological perspectives (it is predominantly quantitative [27]).

The purpose of AAL is to improve and promote an active quality of life enjoyed by the elderly, but it is largely lacking the measurement of the impact of these new technologies in the user’s life. Current aspects such as user acceptance, based on perceived usability, ease of use, intention to use, and actual use [28], show that seniors currently perceive AALTs as useful, but they are hesitant to accept and adopt them [28, 29, 30]. This means that there is a mismatch between proposed AALTs and what works for the elderly and care personnel alike [30] and shows that AAL as a concept is still materialized as a set of prototypes that do not offer extensive and much needed qualitative insights for AAL to become a consolidated reality.

2.2 User acceptance of AAL technology: engagement and the contribution of location-based games

Given the presented foci on technological aspects, one potentially beneficial but largely missing research direction in AAL literature is the design for engagement. Engagement will hardly solve structural and technical problems hindering the daily use of AALT such as data security issues or the extra workload imposed on care professionals [31, 32, 33]. Yet engagement provides an ever-evolving set of design strategies that can lead to engaging user experiences and higher adoption rates of technology [34, 35]. These strategies vary at least per target group, as each has distinct capabilities, needs, and preferences [36]. The elderly in particular have a set of conditions that younger generations do not: beyond the expected lack of IT proficiency, they experience a decline in cognitive, physical and sensorial functions, and may face some impairment that is unique from the already diverse age group they belong to [37].

Numerous strategies exist to influence the level of engagement of the elderly with interactive technology in AAL settings, such as the allowance of creative expression, emotional attachment, knowledge sharing, and activities with a purpose they identify with [17, 38, 39]. It is worth noting, however, that by “the elderly”, this paper refers to healthy independent senior individuals and not to those seriously impaired mentally or physically. This is an important positioning when arguing about engagement because the observed lack of engagement of seniors with technology in AAL settings may be due to issues far deeper than the potentially benevolent lack of motivation to use technology [28, 40].

This paper defends that engagement is a missing design goal for technological adoption in AAL scenarios and proposes serious games as one type of application that must be engaging by design to benefit AAL systems. Serious games should be designed to be engaging [39, 40, 41, 42], and the study of the impact of serious games in the elderly’s engagement can contribute to the adoption of AALTs, for example, on subjective qualitative experiences commonly not accounted for in the AAL literature, physiological reactions, motives for playing, game usage, time spent on playing, and the impact of playing on life satisfaction [43]. Existent research on serious games for health is abundant [19, 44, 45, 46, 47, 48], yet research on the combination of serious games for AAL scenarios is rarely done [2, 49]. The only works on serious games and AAL are the ones developed by the human-computer interaction center at Aachen since 2015 [2, 49, 50, 51]. Brauner, Wittland, and Ziefle have researched the use of serious games for cognitive training [2, 51], physical exercise [2, 49, 50, 52], and social acceptance [2] and focus on providing design guidelines for the successful introduction of serious games for health purposes to residents of AAL environments. They discuss the social acceptance of serious games and argue that this is an emergent field with challenges at (1) the relationship level between game performance and intention of use, (2) user diversity, and (3) the lack of IT literacy of seniors. Thus, with this exception, serious games for AAL settings are hardly researched [2, 49].

This paper argues that LBGs, as a specific type of digital serious games that run on devices with locative features and data connection, contribute to the state of the art of applications for AAL settings and influence user acceptance of AALTs. LBGs as complement applications to AAL environments are an unexplored research avenue, and if they are properly designed for a prolonged and sustainable user engagement, they (1) promote an active gameplay experience that is commonly outside AAL settings and (2) offer a user experience that can serve as potential motivator for a more active lifestyle to healthy seniors. LBGs are a game genre that promotes outdoor play around public space, and this is largely not considered in AAL research possibly because researchers have the assumption that the elderly must be assisted and hardly have autonomy. LBGs offer clear benefits when compared to traditional AAL applications (and even regular serious games): when the elderly are healthy and have the autonomy to walk outside their living location in an unrestricted way, they can actively move around while engaged in play. When the game design matches the knowledge of what the elderly prefer playing, and have the physical ability to play, LBGs may serve as an extra motivator to a more active lifestyle when compared to the scenarios where they are permanently located within the AAL facility.

3. Concept proposal for healthy elderly (65+) in AAL locations

For serious games to be relevant in AAL scenarios, they must contribute to the overall AAL technical system in place, and this paper proposes a conceptual model, where LBGs provide health indicators that complement AAL systems. This paper proposes the use of metabolic equivalents (METs) as an estimate of the amount of physical activity done by the elder. With this health indicator (derived from multiple data sources from the smartphone where the LBG runs), a conceptual architecture is presented where LBGs are integrated with an AAL technological environment and where they work together to promote an active lifestyle with technology.

3.1 Metabolic equivalents as a health indicator of active lifestyle for the elderly

The metric MET is a physiologic estimation of absolute exercise intensity and bases itself on the amount of energy that an individual’s body consumes per minute while resting [53, 54, 55, 56]. It varies based on the intensity of the activity performed: light activities (<3 METs/min) include slow walking or light gardening; moderate activities (3–6 METs/min) include faster walking or leisure bicycle; and vigorous activities (>6 METs/min) include running or faster active sports [57]. METs are an important estimate measure because they are a reliable estimate of effort that can be used to understand how the individual is doing regarding the recommendations of the world health organization (WHO). WHO recommends several guidelines to children, adolescents, adults, and older adults on physical activity, which, in general, recommend a minimum amount of time for moderate to vigorous-intensity physical activity and a reduction of sedentary lifestyle.

Specific to older adults (+65), the WHO argues that the benefits of physical activity are critical in many situations such as adverse events, all-cause and specific cause mortality, cognitive outcomes, falls and fall-related injuries, functional ability, frailty, osteoporosis, and mental health. As part of their weekly physical activity, seniors should do varied physical activity of moderate to higher intensity (>= 3 METs/min) for at least 3 times a week to enhance functional capacity, and it is recommended to all older adults and to not just those with reduced mobility [58]. In terms of METs, the minimum recommended by WHO is 450 METs per week.

Given that the MET is an estimate measure of physical effort, it is appropriate for scenarios where dedicated sensing hardware is not deployed (e.g., electrodes attached to the person’s body), like those where only a smartphone is used. In LBGs, it is important to provide a playful experience that is fluid and does not offer a big barrier to start playing (like the setup of body sensing hardware) [18, 59], which makes the estimation of METs a compatible approach.

3.2 Location-based games as media to Gamify METs and promote usage of technology in AAL settings

LBGs can serve multiple purposes in AAL settings, such as serving as engaging digital applications to seniors and serving as applications that source relevant data to both AAL settings and formalized healthcare systems. In terms of the sort of data this paper refers to, LBGs can calculate the amount of METs that players consumed every time they played the LBG and give back the aggregated weekly amount of METs to the AAL system. This can inform healthcare professionals on the seniors’ level of physical activity outside the AAL location.

To that end, this paper proposes the conceptual architecture of Figure 1. This conceptual architecture assumes a 3-tier AAL system as what is commonly proposed as future healthcare systems: with edge and fog computing as architectural concepts needed to adopt new IoT sensors/actuators and handle the consequentially huge amounts of data with a short reaction time [24, 60]. The architectural proposal for the LBGs is based on what these games require to work in terms of infrastructure and third-party services (e.g., map provision, gaming accounts, and geolocated points of interest) [17, 61].

Figure 1.

Conceptual architecture to use LBGs as data sources for AAL settings.

Such architectural concept for LBGs further specifies a separate storage for health indicators processed by the game, and this is a security measure by design to only allow access to this database by the AAL system (instead of the whole game data). The conceptual systems architecture in Figure 1 presents a connection between the AAL location, other healthcare institutions responsible for the electronic health record data of the elderly (e.g., multiple hospitals or clinics), and the LBGs. AAL systems should have access to the personal EHR of care recipients because each person is unique from the clinical perspective, and this information, recorded over a lifetime, should be considered by AAL entities. It is admissible for AAL systems to pass data onto formalized healthcare systems not related to LBGs, so the bidirectionality is represented.

Parallel to formalized healthcare systems, AAL systems are also integrated at the cloud level with LBGs, with two main data flows. The first one is the establishment of patient recommended activity criteria by the AAL care staff, which can inform LBGs of the desired goals from the AAL perspective. The second one pertains to the access of relevant gameplay indicators from the AAL care personnel that contribute to the overall health data collection that AAL systems collect.

Regarding the 1st identified dataflow, the proposed concept establishes the use of criteria for activities that each older adult can/should perform. These criteria are defined by the care staff of the AAL environments and are, in turn, consumed by LBGs to propose differentiated gameplay experiences to seniors. Every time the LBG is played, the game consults such criteria defined and stored in AALTs and recommends gaming activities based on the updated criteria. The admissible criteria are not specified in this paper but may include: (1) the type of physical activity allowed or level of risk the senior has; (2) a scale recommending more movement (in METs); (3) more cognitive training (e.g., memory exercises); and (4) more activities promoting emotional gain (e.g., giving back to society, having random social interactions, or transmitting knowledge).

Regarding the 2nd identified dataflow, LBGs store and process data from the game state and gameplay. This goes from GPS coordinates to game-related events that players encountered during gameplay. Much of this data can be post-processed in the game cloud into valuable health indicators that are in turn available to be included in the AAL care providers’ assessment on how active and healthy each OA has been. Specifically on health indicators, LBGs can calculate the amount of METs spent through the following game indicators:

• Activity type (Activity): The computing of energy expenditure is directly associated with the type of activity performed (e.g., walking, fast pace, running, or jumping).

• MET level of Activity (MET Level): The computing of energy expenditure is directly associated with Activity and the level of effort applied. This is the MET per full minute of activity of a given activity type, and the different METs/min per type of activity (and the vigorousness involved) are found here.¹

• Distance (GPS): what distance was covered while doing the activity.

• Time of Play (ToP): within which amount of time the given activity was done.

The two mentioned data flows of the presented concept happen across different systems on the cloud (those of the AAL and the LBG). These systems work completely autonomously by definition: LBGs can work without an integration with AAL systems, and vice versa. The flow of data must therefore be explicitly agreed upon between each elder of the AAL location and their LBG account: only they may decide to enable their gameplay as a data source for the analysis and monitoring of their health and wellness in the AAL settings.

With these game indicators, the amount of METs can be computed first by finding the speed with which the type of activity was done:

$$\text{Speed}\left(\text{Km}/\text{h}\right)=\text{GPS}/\text{ToP}$$E1

With Activity and the speed from Eq. (1), the MET Level can be found here¹. With that information, the amount of METs of each LBG gameplay can be found with the following equation:

$$\text{METs}=\text{MET}\text{Level}\text{x}\text{ToP}$$E2

All these indicators (Activity, MET Level, GPS, ToP, and METs) are to be stored in “Health Indicators DB” of Figure 1 and can then be used to inform healthcare professionals on the weekly amount of METs spent by each patient of an AAL location.

4. Test on MET conceptual validity

From the conceptual architecture presented in Figure 1, this paper presents a test of concept where an existent LBG is used to provide an estimate of the amount of METs from a gameplay session and where such estimate is compared to the effort measured through a dedicated smartwatch. This is to reflect on the soundness of the health indicators estimated through LBGs played on a smartphone, not to validate the concept.

4.1 Context: the LBG secrets of the South

Even though not designed specifically for users aged 65+, the SotS is chosen to further explore the proposed concept. It is an LBG that the authors have information and control over its implementation; it fits the proposed concept and can be used to generate and test the estimation of METs as a health indicator.

The LBG Secrets of the South (SotS) is a game created for the promotion of meaningful social interaction in public space and has been validated with both adolescents and adults in both Rotterdam and the Hague, the Netherlands [17, 18, 39, 42]. It is a game that proposes gaming activities (named challenges) spread across the city, and these activities require different sort of actions to be solved: Quizzes with open and closed answers, taking pictures and voting on other players’ pictures, doing physical activities together in a group of players (e.g., playing music or football in the middle of the street), or performing given actions within a time limit [18, 36, 41, 62]. Players can play this game by opening it on their smartphones and looking on the map to see the nearest ones. Given that the game is originally designed to promote social interaction with passers-by, the game activities proposed usually invite players to engage with other people on the streets, such as interviewing them, inviting them for a dare challenge, or asking for their collaboration toward a given task (e.g., high fiving a given number of people within 2 minutes). On top of the described gameplay, players can also go to the online game portal and propose game challenges for other players to do in their own neighborhood. The game architecture of the SotS LBG is shown in Figure 2.

Figure 2.

The SotS software architecture [17].

Figure 2 provides an example on how to implement Figure 1’s LBG system: the SotS gaming application requires many services such as the recording of the game state, the recording of the position of players, account authorization, and the usage of 3^rd party services such as the geographical data with points of interest of the surrounding area of the player. On the cloud of the game, a custom private server with storage capabilities (“SotS Custom Server”) is implemented, where both the “Game DB” and “Health Indicators DB” from the presented conceptual systems architecture in Figure 1 are built in.

4.2 Gameplay test session and health indicators with the SotS

A gameplay test session was prepared with the SotS to exemplify how, from an LBG gameplay, the indicators GPS (distance) and ToP (time of play) are made available to then estimate the amount of METs from a gameplay session. This test session served to test the idea of using LBGs in AAL settings as proposed in the concept above and not to validate the presented concept. It started with the design and set up of several gaming activities around a random public place accessible by an older adult, which were defined in the SotS online platform as shown below:

The gameplay test session was executed with one person to cover most of the challenges placed around a given public space in Porto, Portugal, and see how health indicators could be derived from the SotS game data. Figure 3 shows several 2D icons on the map representing the different types of gaming activities created in the SotS game. The test session lasted around 25 minutes in a walking pace, with a senior male individual weighing 78kg, and the resulting gameplay data that were logged by the LBG are plotted in both Figures 4 and 5.

Figure 3.

Setup of game challenges on public space.

Figure 4.

Data collected from the gameplay test session with the SotS. Yellow pins represent collected GPS coordinates by the SotS; green arrows make the locations of the gaming activities previously defined in Figure 3.

Figure 5.

Snippet of the entire LBG game data collected by the SotS: latitude and longitude (GPS coordinates), timestamp (data/time of record), and message (game event logged).

Figure 5 shows a small snipped of the entire game data logged by the SotS, which are needed for the functioning of the SotS (other LBGs may define other data). From these data, the indicator time of play (ToP) and GPS (distance) are directly calculable by reading the game database (column “timestamp” in Figure 5 for ToP and columns “latitude” and “longitude” for distance). In this game test session, ToP was 25 minutes, and total distance walked was 1.68 kms. This distance was walked (this is the type of activity).

From the collected data, we can use formula (1) to calculate the speed of walk, which results on an average 4 km/h. That means that, by checking the MET level of walking at an average walking speed of 4 km/h¹, it is possible to find that the MET level is 2.9 METs/min. Using the formula (2), this results in 72.5 METs total spent walking during the gameplay session (around 99 kcal, based on the formula provided and the weight of the participant).

A wearable was used² to compare real data from this gameplay test session (Figure 6) with the values estimated by the game SotS (from Figure 5). Based on the smartwatch, the distance traveled is 1.84 km instead of 1.68 km (observable by the blue line- Figure 6a), with the details of the walking session in Figure 6b. Of these, it is relevant for the comparison with the LBG gameplay the distance walked, average speed, duration measured, and the kilocalories measured by the equipment.

Figure 6.

Data collected from the wearable during the game test session: (a) path recorded as 1.84 km, (b) detailed view of the physical activity.

Comparably to the data gathered by the game itself, the distance captured is more detailed. This means that we can find a speed of 4.4 km/h with formula (1), which translates into MET Level of 2.97 and results on a total amount of 74.25 METs. This closely compares to the 72.5 METs/min estimated by the LBG SotS. In terms of calories, the 99 kcal estimated through the data of the gameplay compare to 122 kcal estimated through the wearable.

5. Discussion

This paper presented the concept of location-based games (LBGs) for the promotion of an active lifestyle in AAL scenarios, a concept that informs researchers and practitioners of not only how to benefit from LBGs in AAL settings but also on how to integrate LBGs as data sources in existent AAL systems. This integration refers to the use of LBGs as producers of health indicators, specifically the use of gameplay data such as the type of Activity, MET Level, distance (GPS), and time of play (ToP) to calculate the quantity of metabolic equivalents (METs) spent during such gameplay. These METs estimated by LBGs are an estimate measure of absolute exercise intensity that can help care providers invite older adults to be more active and allow them to follow up on exactly how well seniors respond to an increased active lifestyle. Parallel to the extraction of health indicators from LBGs, the proposed concept aims at contributing to a higher acceptance rate of technology in AAL settings in healthy seniors, when compared to traditional applications. This may be the case when the employed LBGs are engaging and well designed for this target group.

The test on METs validity was made with one senior individual and with an existent LBG not originally designed for this scenario or target group. Given that, and the number of players used in the test, the previous section cannot and is not meant to validate the concept. It serves to illustrate that the metabolic estimate calculated by the LBG SotS is close (≈ 19 kcal difference) to what can be derived from dedicated hardware used to produce the equivalent estimate. This indicates that the use of LBGs to promote physical activity can also serve to provide an informative and reliable estimate of the metabolic expenditure of the gameplay. In this case, the proposed conceptual framework would be able to inform the AAL healthcare professionals that this senior participant spent exactly 72.5 METs over 25 minutes in a potentially engaging application, which could then be compared to the World Health Organization’s set of recommendations (or others) at the end of the week to further support the wellbeing of the older adult.

5.1 Implications for computer science and game development

This case study delves into the complex information systems that are deployed both at formal (hospitals) and informal healthcare sites (e.g., home or elder houses) and argues toward a specific health indicator through gameplay mediated by LBGs. The first implication of this is that health-relevant indicators such as metabolic equivalents can be extracted from gameplay-related indicators. This not only poses as a guideline for game designers of such games but also takes a step forward toward reducing the documented lack of social acceptance of serious games as valid applications for healthcare. Going even beyond the social acceptance of serious games, health indicators that are produced by systems such as LBGs may be seen by hospital staff as being worth less than that which is produced by certified hospital equipment. This may breed the need to create mechanisms in IT healthcare systems of formal care institutions to legitimate and accommodate data from third-party sources such as LBGs. It may also imply an extra rigor from the software development’s perspective to guarantee that health indicators produced by LBGs in AAL settings are a source of information that healthcare physicians can rely on.

A second implication is the use of LBG as a driver toward the evolution of the current healthcare IT systems. The future of healthcare is argued to go from a reactive “I’ve got a disease” care system to a proactive/preventive “I want to take care of my own health” care system [22]. This means that healthcare information systems will be different in the future: (1) they will be centered around what the user (instead of patient) wants and (2) they will enable more personalized healthcare by potentially accounting for information produced by third-party non-medical software or somehow release part of the patient’s information to other systems controlled by the user. This serves to guide the evolution of current healthcare IT systems toward higher interoperability and flexibility. At the same time, the use of METs by LBGs as posited by this chapter contributes to enabling this future of healthcare while at the same time considering user acceptance of technology.

Lastly, another implication for game development and healthcare systems is that there is the possibility of having to certify location-based games as medical devices in the future so that their data can in fact be accounted for in wider healthcare IT systems. This means designing and developing location-based games where health-related data produced is secure, private, and locked behind mechanisms; only the user can articulate. Such a mechanism can be the release of information (METs produced by an LBG) only when the doctor asks the patient to or when the user explicitly gives order for the game to upload the information to the AAL information system. Even though this may pose as a usability challenge for the elder, it may enable the integration of LBGs as data sources in existing healthcare IT systems and support the user-centered care paradigm.

6. Future work

Regarding future research, the conceptual architecture should be further explored with use cases that can implement LBGs and AAL systems in AAL locations. First, engagement levels that LBGs can achieve in older adults must be researched and how to design for a sustainable and prolonged gameplay that most likely occurs around the same places over time. This is a known research issue in games in general, which are particularly relevant for LBGs. Second, future research needs to further the state of the art on technology adoption: do engaging LBGs influence user acceptance of AAL technologies (AALTs)? Third, future research should assess whether the health indicator considered in this paper (METs) is the only or the most useful metric for the discussed scenario, which breeds the need to perform lengthier studies on how well/reliable are the METs produced form LBGs. Fourth, future integrations of LBGs and AALTs must enforce privacy (and pseudo-anonymity) by design. It is conceivable to imagine scenarios where seniors do not want their health care providers to access their gameplay information but still want to play the LBG. Other topics are more concerned to games themselves, such as the sort of content that is appropriate to this target group and who manages and maintains such application in the longer run. Lastly, the literature on AAL ecosystems must be further developed from the user-centered design perspective, as the elderly are usually not expected to be fully literate IT users. Seniors should have a dignified ageing process, and technology must seamlessly aid in the process and serve as neither an impediment to health and wellness nor as a tool excluding individuals from social interactions mediated by technology.

Insights on these issues can have positive implications that far outreach AAL systems and have implications at the larger-scale national healthcare systems of current societies.

Conflict of interest

No conflicts of interest are declared.