Daily behaviors influence an individual’s health and overall quality of life (QoL). Specific patterns of behavior such as smoking, having a poor diet, being physically inactive, or consuming alcohol influence the long-term development of chronic diseases such as type 2 diabetes, chronic obstructive pulmonary disease, or cardiovascular disease [1]. In the US alone, chronic diseases are becoming increasingly common, and about half of all deaths can be attributed to preventable behaviors and exposures [2, 3]. Figure 25.1 represents the proportional contribution of various factors to causes of death, including genetic, behavioral, and systemic aspects of one’s life [2, 3].

Fig. 25.1
figure 1

Proportional contribution of various factors to mortality

The influence of daily behavior on the development of chronic diseases and, ultimately, mortality is a stark example of how behaviors influence QoL in the long term. It is therefore important for health practitioners and researchers, as well individuals themselves to accurately to accurately quantify individuals’ behaviors, and psychological states to improve their awareness of these factors, encourage necessary habit changes, and provide more individualized treatment approaches. Currently, individuals’ behaviors and psychological states are mainly quantified via methods such as the use of self-reported measures, many of which consist of validated scales referred to as patient-reported outcomes (PROs) [4]. Among other behaviors, PROs may include self-reported measures of physical activity (e.g., IPAQ [5]), sleep (e.g., PSQI [6]), or nutritional habits (e.g., Mediterranean diet score [7]).

To characterize the current landscape of tools and methods used to quantify QoL, the chapters of this book discuss various PROs that have been used to assess behaviors relating to all 24 aspects of an individual’s QoL as defined by the World Health Organization [8]. In particular, each chapter identifies methods and instruments for assessing a particular variable of QoL; some of the methods and instruments are considered gold standards in the field of QoL assessment. A summary of the existing self-reported QoL measures that are most commonly used in adult populations can also be found on the website of the QoL lab, pros.qol.unige.ch, which will be regularly updated in the future.

As is discussed extensively throughout the book, these self-reported PROs have multiple limitations, including the infrequency with which they are collected, the subjective nature of responses, their reliance on memory recall, and their susceptibility to the influence of social norms. Additionally, self-reported PROs are typically performed outside of the context in which the assessed behavior occurs. Therefore, the use of personalized and miniaturized technological innovations, including smartphones [9], mobile applications [10], and wearables [11, 12], is proposed in this book as an alternative means of quantifying QoL that can enable the continuous and more accurate assessment of daily life behaviors that contribute to or result from an individual’s QoL.

The advantages of using these technologies include the fact that they allow more frequent measurements than PROs, provide longitudinal data, are objective, sensory-based, and non-judgmental, and permit a context-rich approach to the assessment of individual states and behaviors [13]. The behavioral and health outcomes captured by these personal technologies, the US Food and Drug Administration (FDA) calls “digital health technology tools (DHTTs) [14, 15], are referred to as technology-reported outcomes (TechROs) [4]. According to regulatory bodies such as the FDA, TechRO data is an example of real-world data (RWD). The RWD includes electronic health records, information from insurance claims and billing, registries, and data collected from medical devices used outside of clinical settings. The analysis of RWD produces a broad range of objective evidence that can be used for research and regulatory purposes, including information about individuals’ behavior and the potential advantages or risks of specific behavioral or pharmacological interventions [15, 16]. The emergence of standardized definitions of TechROs and the growing use of TechROs as a data source are important to note in the context of the present book. The work of the QoL lab defined the larger field of “quality of life technologies” (QoLTs) to which TechROs belong. These technologies encompass a broad range of tools that can be leveraged to assess, maintain, improve, prevent decline in, or compensate for one’s life quality, which can be applied at the individual, interpersonal, community, group, or population level [17].

Expanding on the research on QoLTs, the contributors to this book discuss and evaluate various TechROs that have been used to assess behaviors relating to different aspects of an individual’s QoL, as well as technologies that could be applied to the collection of TechRO data in the future. Moreover, each chapter elaborates on the role of the emerging quantified self (QS) movement in the context of the QoL variable it focuses on. The QS movementFootnote 1 involves a group of highly motivated individuals who leverage personalized technologies to assess and intervene in their behaviors with the ultimate goal of realize the QS ideal of “knowing thyself.” Engaging in the QS practices presented in this book may not be possible for the average individual today. However, the book’s discussions highlight practices that may be commonplace in the future, as they demonstrate the feasibility of various technology-enabled approaches to behavior self-management in particular areas of one’s daily life.

Before TechROs can be leveraged in clinical practice, many challenges need to be overcome. These include ensuring the accurate collection of technology-based data [18], interpreting the data in its appropriate context, and resolving potential ethical dilemmas in the ways data is collected and used [19, 20]. Ultimately, the data collected through use of a given technology must accurately represent its defined variable, and the individual must accept the use of these technologies in their daily life for their relevant purposes [21].

In spite of these, however, the book overall presents a positive outlook concerning the state of the art and current developments in the use of TechROs and the future potential for leveraging TechROs to supplement PROs in the assessment of individuals’ behaviors and health and life quality outcomes. In one such scenario, individuals may be assessed using TechROs throughout their lifetime, with the results of assessments being leveraged to improve their health and levels of care, including self-care. Figure 25.2 presents a vision of the future quantification of individuals’ behavioral data. Drawing upon comparisons to the genome (a term that denotes the entirety of an organism’s genetic material) and its common application in current genetics testing, the collection of the behavioral data is called one’s “behaviome.” Ultimately, the mapping of this behaviome through behavioral assessments could improve diagnoses, treatments, and practices for the prevention of diseases and, if applied regularly over an individual’s lifetime, improve their overall health outcomes.

Fig. 25.2
figure 2

Quality of life technologies: from genome to behaviome

The technologies used to collect TechRO data can empower individuals to become “co-producers” of and experts in their own health and QoL, both in the short and the long term [22]. Medicine may thereby be transformed into a more personalized, predictive, participatory, and preventative system. Meanwhile, quantifying one’s QoL might become a new norm for individuals and populations at large, enabling the transition from healthcare to self-care that the world needs.