As this article combines concepts and methods form multiple distinct domains, this section gives a broad overview and argues the benefits of physical and cognitive exercise, and presents related work on serious games for healthcare, AAL, and finally technology acceptance research.
Benefits of physical and cognitive exercise
Aging is “characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death” (López-Otín et al. 2013, p. 1194), and affects all aspects of human life. It is associated with an increased likelihood of chronic illnesses or disabilities such as congestive heart failures (Ho et al. 1993), stroke, diabetes mellitus (Hader et al. 2004), mild cognitive impairment (MCI), dementia, or Alzheimer’s disease (AD) (Gao et al. 1998). Besides age-related diseases, overweight and obesity are increasing risk factors and are threatening life expectancy (Mann 2005).
Physical inactivity is associated with several diseases including cancer, diabetes, hypertension, and coronary or cerebrovascular diseases, and reduces life expectancy (Knight 2012). However, health and life expectancy can be increased by maintaining a healthy body weight, a healthy diet, not smoking, low alcohol intake, and regular exercise, as shown by a 35-year long-term study (Elwood et al. 2013). People who showed all or almost all habits had a 73% lower likelihood of diabetes, 60% lower likelihood of dementia, 60% lower risk for stroke, and an increase in life expectancy by 6 years. Physical exercise increases pulmonary function, oxygen consumption, cardiovascular function, standing and walking balance, bone stability, and heart and other muscle strength, and improves the immune system (Aldwin et al. 2006). In combination with a healthy diet, it reduces overweight and obesity and thus associated diseases such as diabetes. Regular physical exercise reduces the likelihood or intensity of cardiovascular diseases, and patients suffering from chronic syndromes benefit from regular and lifelong exercise at home, such as a combination of aerobic activities and resistance, flexibility, and balance exercises (Perez‐Terzic 2012). One hundred and fifty minutes of medium-intensity aerobic exercise per week is sufficient for a significant positive impact on health (Mendes et al. 2011), especially for children, older adults, and people with overweight or obesity. Although the frequency, intensity, and duration of exercise can be optimized, the key is that some physical activity is always better than none (Saint-Maurice et al. 2018).
Physical exercise also has benefits that are less apparent. It reduces the probability of silent (unnoticed) brain infarcts in the elderly by 40% (Willey et al. 2011) and can be as effective against the occurrence of migraines as an anti-migraine drug (Varkey et al. 2011). A more active lifestyle with social interaction, listening to music, and physical activity lowers tension, improves mood, and increases perceived energy levels (Thayer et al. 1994). Exercise can also reduce the symptoms of depression, although antidepressants and psychotherapy are found to be most effective (Cooney et al. 2013).
Physical exercise also has short-term effects on cognition. Working memory performance is directly increased after 30 minutes of exercise (McMorris et al. 2011). Regular aerobic exercise improves executive functioning for healthy people, and older adults show improved task switching ability, higher selective attention, higher working memory capacity, and better overall performance (Guiney and Machado 2013). Remarkably, physical exercise in midlife reduces the risk of cognitive decline, mild cognitive impairment, and dementia later in life (Ahlskog et al. 2011).
For the domain of cognitive training, research on the effectiveness and medical implications is less conclusive. On the one hand, computerized cognitive training (CCT) programs are postulated to be suitable, effective, and necessary for healthy brain aging by stimulating neuroplasticity (Ball et al. 2004; Heyn et al. 2004) and may contribute to healthy brain aging (Shah et al. 2017).
Serious games and serious games for healthcare
The main idea behind serious games (for healthcare and for other domains) is built on the Premack principle (Premack 1959): the likelihood of performing an unpleasant and thus infrequent activity—such as training and exercise—can be increased by linking it with a more pleasant and thus more frequently performed activity—such as playing games. It involves packaging less liked and therefore less practiced activities with games to increase the motivation and thus the likelihood of the activity being carried out. Serious games can be defined as “a game in which education (in its various forms) is the primary goal, rather than entertainment” (Michael and Chen 2006). In contrast, the related concept of gamification (Deterding et al. 2011) does not use games as packaging around activities, but certain real-world activities are incentivized by game-like elements (e.g., points, badges).
Examples of serious games for healthcare are numerous and address various medical domains such as mental health (Fleming et al. 2017), rehabilitation (Nap and Diaz-Orueta 2015; Bonnechère et al. 2016), physical exercise for the elderly (Konstantinidis et al. 2016), and even training of medical professionals (Gentry et al. 2019).
Also, there are already many available commercial game titles for physical exercise or cognitive training (e.g., a variety of mobile games and console titles such as Wii Fit and Brain Boost for Nintendo, Body and Brain Connection for Microsoft Xbox, or Smart As… for Sony PlayStation). There is evidence that exercise gaming at home has positive effects on health, pain perception, and overall well-being (Warburton et al. 2007; Whitehead et al. 2010; Franco et al. 2012; Brauner and Ziefle 2020), but the effectiveness and transferability of cognitive training games is not as well established. A meta-analysis found small but significant positive effects on processing speed, working memory, and verbal and nonverbal memory, but no effect on attention or executive function (Lampit et al. 2014). Also, home-based and too frequent training was found to be ineffective. A subsequent meta-analysis found that commercial video games increased cognitive performance, such as attention and problem-solving skills. However, the performance gains were limited to the specific task in the games and had no further positive effects (Choi et al. 2020). Consequently, more research on the effectiveness of CCT is needed.
Ambient assisted living
Apart from serious games for healthcare, AAL environments are another active research area aimed at mitigating the negative effects of the changing demographics: by augmenting the living environments with smart sensors and actuators, elderly (or chronically ill) people can stay independent in their own living environments for longer (Raisinghani et al. 2006; Kleinberger et al. 2007; Rashidi and Mihailidis 2013). These environments reduce the costs of healthcare systems while increasing individuals’ independent living and self-determination. However, despite its immense potential and the increasing use of smart home components in the homes of a few technology enthusiasts, the widespread penetration of AAL is still a bold vision of the future, and many fundamental questions regarding acceptance, perception, use, and interaction styles are still open (Cook and Das 2007; Lindley et al. 2008; Cook et al. 2009).
To study how AAL environments are perceived by future inhabitants and how these environments need to be designed, and to evaluate how these will shape future living, researchers construct prototypical living labs to simulate the envisioned models. These labs are built to test functional and nonfunctional properties of innovative technologies and evaluation by potential users. Potential residents thus not only have the opportunity to be part of the evaluation of the technology, but also can play an active role by contributing feedback, fears, and ideas regarding the modern technology, and the residents can thereby shape their future technology-augmented living environments. We will present the test environment used for this work in more detail in the Method section.
Which factors influence the use of technology?
The main goal of technology acceptance research is predicting whether and how a given technology will be adopted by users and which technological (capabilities, design, interfaces, etc.) and user factors (age, gender, attitude towards technology) are relevant to the actual use (Davis 1993; Rogers 2003).
A cornerstone of this research domain is Davis’ technology acceptance model (TAM) aimed at predicting the actual use of a technology through the intention to use that can be measured in advance (Davis 1993). TAM has revealed a strong relationship between the perceived usefulness and ease of use of the technology, and the projected and the later actual use.
Over time, numerous other models have emerged and have integrated additional explanatory factors, increased the predictive power, and opened new areas of application. However, this has led to a variety of highly specific models that are rarely transferable to other areas (such as TAM2, TAM3, and unified theory of acceptance and use of technology [UTAUT]) (Venkatesh et al. 2003). Regarding our work, there is no established model for studying serious games for healthcare or for evaluating AAL environments. Nevertheless, the existing and established models provide at least some guidance as to which factors are suitable for studying the social acceptance of serious games for healthcare in AAL environments. In this work, we use the UTAUT2 model (Venkatesh et al. 2012), as it is the first model that integrates the hedonic evaluation of a technology. In this model, later use is predicted through the intention to use, which is explained by the seven factors of performance expectancy (perceived benefits of the technology), effort expectancy (how complicated it is to use), facilitating conditions (is the technology compatible with my environment, can others help me in case of trouble), social influence (how do my peers rate my use of the technology), price–value trade-off (is the technology worth the money), hedonic motivation (how much fun is the technology to use), and habit (can I integrate the use into my daily routine).
Social acceptance is both shaped by the evaluation of the system and mediated by individual user factors. Numerous studies have shown that age is associated not only with less ease of use in interacting with novel technologies, but also with lower perceived usefulness and overall acceptance (Selwyn et al. 2003; Arning and Ziefle 2007; Schreder et al. 2013). Furthermore, women, on average, report lower interest and skill in dealing with technical systems. This is especially relevant for the design of technology-based interventions in the healthcare sector, as these interventions should be usable and useful for all potential users, regardless of age, gender, or (perceived) technical self-efficacy (Wilkowska et al. 2018).
Even though the two areas of AAL and serious games for healthcare are established concepts in research that are increasingly flowing into the market, the intersection has received little attention. Consequently, our research objective is to develop healthcare games in technology-augmented environments, to evaluate how elderly people—who are often reluctant to deal with innovative technology—interact with these games, and to identify the predictors for social acceptance and adoption by means of technology acceptance research. Thus, this work is at the intersection of technology development for AAL, healthcare, and user research. Here, we combine the knowledge about the benefits of exercise, serious games as a method to promote activities usually perceived as unpleasant, the invisible integration of interfaces in AAL environments, and user and acceptance modeling to understand whether this approach increases the likelihood of exercising and which factors are the largest contributors to projected acceptance and thus later use.
Designing healthcare games for the elderly is difficult, as many distinct aspects must be considered. Besides the identification of suitable training domains, the games must be fun and entertaining, adapt to the various age-related changes such as lower information processing speed and reaction times, altered vision (Fisk et al. 2009), and different interests and often less ability to interact with computers compared to younger adults.
To facilitate these numerous, often conflicting, requirements, guidelines and recommendations have evolved over the years. One of the earliest guidelines for serious games for healthcare emerged in the early 1980s when Weisman (1983) modified and evaluated games on the Apple II for institutionalized elderly.
Games should be slowed down and be focused on single tasks; on-screen objects should be well-defined and larger, and additional hints should be provided to support interaction. Playing with others has also been found to be fun, entertaining, and rewarding. Personalized messages may increase engagement with the game as well (“Linus, your score is not so good; keep on trying.”).
More recently, Ijsselsteijn et al. (2007) identified requirements, benefits, and opportunities for game design for elderly users. For example, they suggest that the boundaries of the visual game environment should adjust to the individual visual decrement. Also, information and feedback should use multiple modalities (e.g., visual and auditory feedback) to account for preferences and individual limitations. Memory load and cognitive requirements should be kept to a minimum, for example, by avoiding the need to remember information from various parts of the game). Lastly, players should be given time to get used to the game and learn the basic interactions. During this time in particular, encouraging feedback should be provided to support insecure players.
The first guidelines for exercise games that use body sensors were presented by Whitehead et al. (2010). Games should detect whether body movements were correctly executed, and shortened movements should be rejected. Activities and gestures should be designed to use larger muscles of the body, such as the legs. The games should support players gaining experience and playing the games for longer periods, for example by providing incentives, as experience and frequent use yields higher physical benefits. These incentives should have a lasting effect, not just for hours or days, but for months and years. Ideally, these incentives should not be related to the health benefits but should not be an abstraction. Furthermore, there must be regular recuperation periods within the games to allow players to rest between active phases.
Gerling et al. (2012) presented guidelines specifically for full-body motion exercise games for the elderly. For example, interactions within the game should take account of potential individual physical or cognitive impairments, games should adapt to limitations in players’ range of motion, and overexertion should be managed by alternating between physically intense and less challenging parts. Further, the difficulty and challenge of the game should not be fixed but should adapt to players’ abilities. Movement gestures should be easy to learn and easy to remember, and ideally there should be a natural mapping between gestures and real-world activities. Players should be guided by initial tutorials and later hints at required actions. Finally, games should support independent play, meaning that routines to start the game should be easy and intuitive, and no assistance should be needed.