Measuring Quality of Life (QoL)

WHO defines QoL as an “individual’s perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” [1]. Four areas of life that QoL describes includes physical health, psychological state, social relationships and relationship with the environment. QoL is often mentioned with related term “subjective well-being” (SWB), recently defined as overall evaluation of the quality of a person’s life from her or his own perspective [2]. Since concepts that relate to quality of life have been defined in numerous ways [3], many instruments and tools are available for measuring quality of life. For example, French MAPI Research Institute offers access to more than 1000 QoL instruments available through a database [4]. Linton et al. [5] did a review of 99 self-report measures for assessing well-being in adults, and concluded with a warning about major variability between instruments and the need to pay close attention to what is being assessed under the concept of “well-being”.

Although many of these instruments show strong psychometric properties, there is much debate over self-report as a method to measure quality of life concepts. People are, consciously or unconsciously, deceiving themselves or others about what really is the truth regarding their own well-being. On the other side, some? research show that reaching a decision about someone overall quality of life is not based on careful and systematic analysis of all personal experiences, but on emotionally intensive peak-end moments in a person’s life [6]. In contrast to many self-report measures of QoL concepts, there are some measures emerging that exclude self-report and rely on patterns of behavior such as intensity of smiling in Facebook photos [7] or the content of tweets posted on Twitter [8]. By analyzing the words and topics from collection of tweets, researchers were able to improve accuracy in predicting life satisfaction over and above standard demographic and socio-economic controls such as age, income or education.

This brings us to the trend of “quantified self (QS)”, developed due to increased availability of wearables and tracking applications. According to Lee [9], QS involves extended tracking and analysis of personally relevant data. For example, results of Gfk’s global study [10] on health and fitness self-tracking reported that one in three Internet users track their fitness health via mobile apps or other wearable technology. Many of these self-trackers report that tracking changed how they approach to maintaining their health. Some researchers suggest that mobile technologies and wearables accompanied by Internet of Things (IoT) will revolutionize the way we understand ourselves and live our lives [11]. Besides the value of QS movement in learning sciences [9], a line of research that has been recently developed proposes a new term—“QoL Technologies (QoLT)”, and gives rich insight into the ways QS can improve quality of life [12]. QolT collects data from hardware and/or software technologies, provides objective and minimally intrusive assessments of QoL, and via feedback mechanisms aims at improving individual’s QoL [13]. As such, QoLT gives great promise of providing greater self-awareness and opportunities for better quality of life.

Leisure Engagement and Quality of Life

WHO defined leisure within QoL model as “Participation in and opportunities for recreation/leisure activities as “a person’s ability, opportunities and inclination to participate in leisure, pastimes and relaxation. The questions include all forms of pastimes, relaxation and recreation.” [14, 15, p. 66]. Leisure engagement, more objectively, is defined by the amount of time, diversity, or frequency of person’s participation in leisure activities [16]. Defining features of leisure activities is intrinsic motivation and freedom to engaging in them, and as such, they predict subjective well-being [17, 18]. There are many studies that report on significant relationship between engaging in leisure activities and improving quality of life. Recent meta-analysis of 37 effect sizes, with more than 11,000 participants, reported on strong evidence for the moderately positive association between leisure engagement and SWB [19]. Moreover, the relationship was mediated by leisure satisfaction, while measures of the frequency and diversity of leisure engagement were more strongly associated with SWB than measures of time spent in leisure.

Usually, leisure activities are classified into two categories: relaxed leisure activities (more passive, e.g., sedentary activities) and serious leisure activities (more active, e.g., physical activities) [20]. While Passmore [21] talks about three types of leisure activities—active, social, and time-out, Silverstein and Parker [22] propose 6 domains: culture-entertainment, productive-personal growth, outdoor-physical, recreation-expressive, friendship, and formal-group. Not all leisure activities lead to positive outcomes. Studies have shown that relaxed and passive leisure activities correlate to less satisfaction and well-being, compared to more active or physical leisure activities [21,22,23,26]. Kahneman and Kruger [27] offer a list of activities with accompanying self-reported measures of positive emotions and proportion of time with negative affect. Three activities that lead to most positive and least negative affect are intimate relations, socializing after work and relaxing. At the bottom of the list, with up to 30% of time feeling negative emotions, are activities of commuting and working. Oishi, Diener and Lucas [28] report that happiest people are most successful in terms of close relationships and volunteer work. On the other side, most commonly chosen leisure activity, watching TV, offers only limited enjoyment and needs satisfaction [29]. Overall, it seems that physical leisure activities and social leisure time rank high in predicting quality of life aspects.

Results of research on leisure activities and quality of life measures have important implications for developing alternative and less intrusive measures of QoL, backed by modern technologies. Although research on this topic is extremely limited, there are some research paving the way through the emerging field of QoL technologies.

Quality of Life Technologies

If we assume that there is a strong relationship between engaging in leisure activities and benefits for individual quality of life, especially in the long term, then it is also legitimate to assume that alternative measures for leisure activities could point to outcomes related with quality of life. This leads us back to quality of life technologies (QoLT). Widely available and affordable tools such as personal smartphones or wearable technology provide us with ample of data that can be correlated with how individuals spend their leisure activities. For example, smartphones can be seen both as tools that provide valuable information about overall activities individuals engage in, as well as tools that influence usage of leisure time.

Because smartphones have become quite pervasive [30], they are associated with many different behaviors and behavioral patterns such as social interactions, daily activities, and mobility patterns. Smartphone devices are becoming behavioral data-collection tools, and thanks to their computational power, logs and sensors, they provide us with unprecedented access to people’s social interactions, daily activities, and mobility patterns [31, 32]. Research on this topic has only started to accumulate. Although there are number of studies that explore the relationship between smartphone usage, leisure activities and affective states [31,32,33,36], they all rely on self-reports. One interesting study, that also used self-reports, asked a representative sample of participants to report on the type of communication (text message of voice call) and physical proximity from individuals’ whom the participants was in contact with [37]. Authors reported that geographic proximity of individuals was related with mobile communication patterns and social leisure activities between people who communicate. Let us now image how this study would look like if all the data was coming from smartphone devices. If backlog communication and geolocation data would be available for participants and their contacts from this study, we could determine the type and length of mobile communication (including recently popular use of messaging apps, beyond standard texting or calls), as well as geolocation of, both, caller and receiver. Social leisure activity could be, also, identified by geolocation data (e.g. restaurants, movies, theaters, concerts, sport games, bars and clubs). Thus, relying only on backlog smartphone data, we could learn about communication patterns and make assumptions about quality of life of these individuals, as well. These assumptions could take form of key predictors of important life outcomes, such as subjective well-being.

To test whether smartphone data can be predictive of subjective assessments, de Masi and Wac [38] used smartphone logger data to predict quality of experience assessed by in-situ quality of experience survey. Authors reported that predictive model for “good” and “bad” quality of experience can be build using quality of service information, mobile application name, user task (e.g., consuming or producing content) data within an app and physical activity of user. By combining self-reports and quantitative data, authors were able to determine alternative measure of quality of experience. Using similar study design, alternative measures for leisure activities can be built, as well. For each alternative leisure activity measure, self-reported satisfaction and positive/negative affect should be obtained. In Table 22.1, we offer a possible list of leisure activities and potential objective indicators, without affective indication, i.e., if the leisure activity indeed was enjoyable for the individual (which would require more physiological assessment with respect to the lower levels of stress, more calmness and happiness).

Table 22.1 List of leisure activities and potential objective measures
Full size table

Having identified some of the objective sources of data for measuring leisure activities, we need to mention that future alternative measures of QoL will need to include not only the type of activity, but also its frequency, duration and diversity, as well as resulting affective state of the individual after the activity is finished (e.g., lower stress levels). This calls for different index measures that will have greater validity in assessing QoL. Once relevant alternative measures are identified, QoLT could help improve quality of life via feedback mechanisms such as notifications, reminders or motivational messages, leading the individuals to managing their leisure activity nest matching their momentary needs and context (e.g., if there are resources or opportunities for leisure in location where they are, with whom they could meet). With this heightened self-awareness, individuals will be able to change their behavioral patterns by overcoming self-regulation barriers that arise due to lack of planning or lack of goal progress. This way, individuals will be able to have direct impact into their quality of life.

Possible Negative Impact of Screen-Time on the Quality of Life

Since 2008, daily hours spent with digital media per adult user have risen from 2.7 h to 5.9 h in 2018, according to Meeker [39]. In fact, we rely more on devices for leisure time but not all of us (hence it’s important to collect diversity of data to quantify leisure) [40, 41].

A notable rise in technology use in the American youth from in recent years, according to Pew research center (2018) is related to the increase in the smartphone ownership. 95% of teens reporting owning a smartphone, with the majority using some online social networks such as YouTube, Instagram, Snapchat or Facebook. Although the mobile phone technology and instant Internet connectivity is a somewhat new phenomenon, it has opened up new opportunities for improvements on various aspects of our lives and the quality of life. Also, it created new and superior ways of tracking and studying human behaviour [31, 42] there are already some research point out possible negative aspects of this hyper connectivity. Most of the studies done so far have been focusing on possible negative impact information communication technologies and social networks such as Facebook have on children and teenagers while some research that linked recently observed decrease in well-being and happiness in adolescent populations to the increase in screen activities facilitated mostly by widespread use of smartphones [43]. In many ways, focus on the negative impacts of new technologies has been a rule in psychological research as fears of detrimental impact of television as well as video games spreads through the population. For the most part our intuitions on the catastrophic effects of for instance video games and their link to violence have for the most part been dispelled [44, 45], however it seems that that there is reason for justifiable concern regarding this new technological trend.

As Pew research study suggested, although the participation in social media is almost ubiquitous in the teen population, the impact of it is not straightforward [46]. About a third of the teens report mostly positive effect of social media such helping them connect with friends and family, find information and meet people with whom they share interest, as much as a quarter report mostly negative effects of social media, with bullying, reducing in person contact, imposing unrealizing views of others’ lives and distraction being poised as biggest issues. In a recent review of paper dealing with the impact of online social media on adolescent mental health Keles, McCrae and Grealish [47] found a positive relation between the use of social media and mental health problems. More precisely, the exposure to social media in the context of the time spent on the social media networks, type of activity (i.e. frequency of checking the profile, number of “selfies”), participant investment and addiction to social media were implied to be risk factors for the development of depression, anxiety and general psychological distress. High frequency users are, especially, under great risk from decreasing their quality of life due to less physical activity and poorer physical condition, and having greater connection to their smartphones in spending leisure time [48]. High frequency users report that smartphones make leisure more enjoyable, increase personal freedom, are intrinsically rewarding, make it easier to engage in and experience leisure [49].

There are some caveats in these findings. Amongst others, the main issues with studies conducted so far include small samples and reliance on self-report measures [47]. Also, the majority of these studies are correlational and have a hard time establishing the direction of a causal relationship as it could be also likely that people with pre-existing psychological disorders spend more time on social networks. However, there are some experimental studies that do imply how abstaining from social networks can have a positive impact on our well-being. For instance, Tromholt [50] conducted an experiment on more than one thousand participants where the experimental group was made to abstain from using Facebook for a week. The results showed abstaining from Facebook had a positive effect on life satisfaction and emotional life, especially for heavy Facebook users and people prone to experiencing envious feelings while using Facebook. Another similar study conducted in organizational setting [51] showed that Facebook use and effects happiness in a way that it promotes comparison which has negative effect on happiness and that this effect was stronger for younger users. Another problem would also be the depth of analysis. The quality of interactions that people have on these networks also seems to influence the outcomes. In an extensive review of 70 studies dealing with social network sites and well-being depression and anxiety showed that the impact of social network use marked with positive interactions, social support and connectedness is related to lower levels of both depression and anxiety, while negative interactions, social comparison and addictive behavior has the opposite effect [52]. Also, the positive interactions on social networks might benefit those that otherwise struggle with face to face communication.

So far, only a limited number of researchers reflected on the relationship between mobile technologies use and leisure activities, drawing from similarly limited number studies on the use of mobile phones and their impact on our behavior and emotions. Lepp [34, 35] sees two major domains of overlap between mobile communicating technologies and leisure. First one is facilitation of leisure activities through enhancing communication and coordination. Access to this technology can help in planning efforts for various outdoor activities but also in creating opportunities for spending leisure time in sedentary behaviors for individuals who are less prone to face to face interactions. The second is related to the depth of our experiences whilst engaged in leisure activities. In one hand taking away from our ability to isolate ourselves from outside influences and being in the moment, but also enhancing our outdoor experiences trough access to information providing navigational aid. A creative experiment by Kushlev et al. [53] showed exactly how mobile technology can take away meaningful experiences from our everyday activities. Participants were asked to navigate through campus and find a particular building either with or without using smartphones, and whilst individuals using smartphones were more efficient at completing the task the ones that were left to their own devices—looking at signs, asking for direction, etc.—felt more socially connected. Dwyer, Kushlev and Dunn [54] reported similar findings in a study where participants either had their smartphones on the table or put away during a meal. The results pointed toward phone use taking away the enjoyment experienced in real world interactions, and in a follow up study the negative effect of smartphone presence was found in other types of face-to-face interactions. Mostly, the phone was seen as a distraction, preventing people to fully engage with their environments. A negative relationship between smartphone addiction and productivity has also been reported, with spending time on smartphones taking considerable amount of both work and leisure—off time that could have been used on more meaningful pursuits [55]. This hyperconnected, interruptive and addiction forming quality of smartphones seems to be a cause of psychological distress. However, there is some proof that some of the content available on smartphones, such as health apps aimed at promoting health lifestyles and increased physical activity can produce positive outcomes (i.e. [56]).

Conclusive Remarks

This chapter focuses on the possibilities of combining behavioral patterns collected through use of smartphone and other technologies in predicting leisure time and quality of life. With the constant rise of internet users and ever-developing technology, the abundance of data gives new opportunities for behavioral research that goes beyond traditional methods and its deficiencies. One such opportunity includes using behavioral lifestyle data to recognize leisure activities and outcomes of engaging in such activities. Research has shown that life outcomes, such as quality of life and related constructs (i.e. SWB), are highly correlated with engaging in leisure activities. Throughout this chapter we offered numerous ways of using technology and data proxies for assessing leisure activities. We, also, imply that QoLT has a major opportunity to impact individual lives through feedback mechanisms that they offer to its users.

There are great advantages to using personal mobile technologies in research on leisure activities, and plethora of data it collects is in many ways more reliable than standard self-report measures we relied on so far. Especially since there is reason to believe that much of our self-report data on our mobile technology use is flawed, as recent studies comparing actual smartphone use to self-reports demonstrated people grossly underestimate the time spent using our smartphones (i.e. [57]). These trends will not only affect individual lives and their quality of life, but will also strengthen interdisciplinary research, and possibly transform field of psychology and its research methods [42, 58].

However, just the extent to which information and communication technologies should be fully incorporated in leisure activities is left to be determined. A recent interesting step away from digitalization, and toward the trend of so called “Digital detox” was reported by MIT Technology review [59]. The so-called Google Paper Phone, a product of Google creative lab is basically a piece of paper where a person would print out the relevant information for the day—telephone numbers, to do list, shopping list, a map, and use it to go about the day whilst leaving phone at home. That proves that maybe QoLT is not the only answer, and the person’s ability, opportunities and inclination to participate in leisure, pastimes and relaxation may be when technology doesn’t reach.