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Health self-tracking is an ongoing trend as software and hardware evolve, making the collection of personal data not only fun for users but also increasingly interesting for public health research. In a quantitative approach we studied German health self-trackers (N = 919) for differences in their data disclosure behavior by comparing data showing and sharing behavior among peers and their willingness to donate data to research. In addition, we examined user characteristics that may positively influence willingness to make the self-tracked data available to research and propose a framework for structuring research related to self-measurement. Results show that users' willingness to disclose data as a "donation" more than doubled compared to their "sharing" behavior (willingness to donate = 4.5/10; sharing frequency = 2.09/10). Younger men (up to 34 years), who record their vital signs daily, are less concerned about privacy, regularly donate money, and share their data with third parties because they want to receive feedback, are most likely to donate data to research and are thus a promising target audience for health data donation appeals. The paper adds to qualitative accounts of self-tracking but also engages with discussions around data sharing and privacy.

Various mHealth/eHealth services play an increasingly important role in healthcare systems and personal lifestyle management. Yet, the relative popularity of these services among the young population of the Czech Republic was not known. Therefore, we carried out an on-line survey with a convenience sample ( n = 299) of young adults aged 18-29 and living in the Czech Republic. To this end, we adapted the survey instrument which was previously used in a similar study conducted in a different cultural context (Hong Kong). In our study, we found out that health tutorial activities (i.e., acquiring information on diet, exercise, fitness) were the most common among our respondents (M = 2.81, SD = 1.14). These were followed by health information seeking activities (i.e., acquiring information on medical problems) (M = 2.63, SD = 0.89) and medical services (i.e., the eHealth/mHealth services that provide infrastructural support, such as ePrescription and doctor appointment organizers) (M = 2.18, SD = 0.97). Based on the grouping according to gender and existing health condition, pairwise comparisons showed statistically significant differences. We also briefly analyzed the influence of the COVID-19 pandemic on the examined activities. Based on their relative popularity, we suggest leveraging the potential of health tutorial activities to improve public health.

Since the emergence of the quantified self movement, users aim at health behavior change, but only those who are sufficiently motivated and competent with the tools will succeed. Our literature review shows that theoretical models for quantified self exist but they are too abstract to guide the design of effective user support systems. Here, we propose principles linking theory and implementation to arrive at a hierarchical model for an adaptable and personalized self-quantification system for physical activity support. We show that such a modeling approach should include a multi-factors user model (activity, context, personality, motivation), a hierarchy of multiple time scales (week, day, hour), and a multi-criteria decision analysis (user activity preference, user measured activity, external parameters). This theoretical groundwork, which should facilitate the design of more effective solutions, has now to be validated by further empirical research.

More and more people use self-tracking technologies to track their psychological states, physiology, and behaviors to gain a better understanding of themselves or to achieve a certain goal. Ecological Momentary Assessment (EMA) also offers an excellent opportunity for restorative environments research, which examines how our physical environment (especially nature) can positively influence health and wellbeing. It enables investigating restorative health effects in everyday life, providing not only high ecological validity but also opportunities to study in more detail the dynamic processes playing out over time on recovery, thereby bridging the gap between laboratory (i.e., short-term effects) and epidemiological (long-term effects) research. We have identified four main areas in which self-tracking could help advance restoration research: (1) capturing a rich set of environment types and restorative characteristics; (2) distinguishing intra-individual from inter-individual effects; (3) bridging the gap between laboratory and epidemiological research; and (4) advancing theoretical insights by measuring a more broad range of effects in everyday life. This paper briefly introduces restorative environments research, then reviews the state of the art of self-tracking technologies and methodologies, discusses how these can be implemented to advance restoration research, and presents some examples of pioneering work in this area.