Advancing Health Psychology Through Ecological Bio-Psycho-Social Assessments

Real-life and real-time data collection methods offer unprecedented opportunities to advance the understanding and promotion of physical and mental health. Everyday assessments offer valuable insights into the behavior of individuals in their natural environment, providing a more complete picture than experimental studies conducted in the laboratory. Thus, real-world bio-psycho-social assessments allow the investigation of the links between psychological, biological, situational, and social influences on the one hand and mental and physical well-being on the other, while taking into account not only interindividual but also intraindividual variation. They also allow for the development and evaluation of interventions that provide tailored support where needed. In this context, the focus also shifts to intervention effects, where deliberate changes in behavior are intended. It is clear that the effectiveness of interventions often depends on individual factors.

This special issue shows that person-level moderators play a pivotal role in this regard. These individual characteristics, such as personality, motivation, or past experience, can influence the intraindividual and interindividual variation in the various variables studied and the success of an intervention. Taking such moderators into account allows for a personalized approach to understanding health and disease processes, and designing interventions, as what works for one person may not be as effective for another.

Digital device-based assessments provide objective indicators of a range of behaviors, including social media use. However, they are not always feasible to implement, as they require tracking applications to be installed on the participants’ smartphones and data to be collected over long periods of time. Irmer and Schmiedek (2023, this issue) tested whether children’s reported intensity of social media use was consistent with objective measures. Indeed, the two measures were highly positively correlated, indicating a relative accuracy of self-report measures at both the between-subject and within-subject levels. Importantly, accuracy declined with use intensity, highlighting the usefulness of digital objective measures of social media use particularly for heavy users.

Digital technology can also be used to evaluate the effectiveness of interventions in everyday life. Talic and colleagues (2023, this issue) provide an example of this in the context of positive psychology interventions. They show that intensive digital longitudinal data collection allows researchers to examine within-day effects of the intervention on a range of psychobiological measures, as well as to study whether these effects are moderated by trait variables such as personality traits.

Physical (in)activity and social participation were in the focus of the study by Rinn and colleagues (2023, this issue). The authors examined engagement in social activities following a physical activity intervention in older inactive adults (60 years and older). The results showed an increase in the frequency of social leisure time activities, especially among those with higher self-reported physical performance. The individual’s stage of change to be physically active appeared to be a significant mediator in this link. Thus, both a positive physical self-concept and the intention to become physically active are needed to benefit from targeted interventions. Finally, participants’ engagement in the intervention predicted their stage of change to be physically active over a 3-month period. The results of this study confirm previous evidence that theory-based interventions that are designed to be engaging and have high levels of participation are particularly successful in inducing behavior change.

In addition to individual factors, it is equally important to consider contextual factors. The social environment, cultural differences, and environmental conditions can have a significant impact on (un)healthy behaviors or the effectiveness of an intervention. This idea is supported by Elling and colleagues (2023, this issue), who followed smokers, who were motivated to quit within 3 months, during the first two weeks of a quit attempt, to examine who relapses and when. Lapses, that is, temptations that could not be overcome without smoking, were more likely to occur when participants were social, when they were with friends, when they saw people smoking, and when they were outdoors. These findings highlight the importance of considering contextual factors when developing relapse prevention interventions for people trying to quit smoking. The authors suggest that future research should focus on addressing these factors in just-in-time adaptive interventions to better support individuals in their quitting efforts, particularly by providing support in (social) contexts associated with increased risk. This study also demonstrated the applicability of ecological momentary assessments in the simultaneous collection of different relevant factors, e.g., lifestyle factors. For future studies, the use of digitally supported momentary assessments may also provide new insights into interrelated health behaviors, such as alcohol (ab)use, (unhealthy) diet, or physical (in)activity.

As research and technological advances, predictive algorithms are becoming increasingly important. These algorithms utilize digitally collected data to forecast future behavior and biological responses. Similarly, they can be used to infer psychological states from physiological measures. Rominger and Schwerdtfeger (2023, this issue) present an algorithm that attempts to predict stress from heart rate variability. While such an algorithm may in the future be useful in a just-in-time adaptive intervention to provide support in stressful situations, the authors provide suggestions on how to improve the testing of algorithms that predict psychological states in real-life studies so that they can reach their full potential. For example, both individual characteristics and contextual factors can be incorporated. These predictive algorithms hold the potential for even more tailored intervention design, aiming to promote physical and mental health.

In summary, the study of everyday behavior, the incorporation of person-level moderators and contextual factors, and the use of predictive algorithms are interrelated. A holistic approach, taking into account individual differences and environmental factors, offers a promising way to develop effective interventions to promote positive behavioral change and improve physical and mental health.