The overview effect is the commonly reported experience of astronauts viewing planet Earth from space and the subsequent reflection on and processing of this experience. The overview effect is associated with feelings of awe, self-transcendence, and a change of perspective and identity that manifest themselves in taking steps toward protecting the fragile ecosystem. In the current study, we investigated whether the overview effect can be obtained in school children when simulated using virtual reality (VR) and whether the effect has a positive impact on learning gains. Using questionnaires and attention data in an existing simulation environment used in the school system, we showed that the VR simulation elicits an overview effect experience. Moreover, the experience yields learning gains in the domain of astrophysics. These findings are in line with past evidence regarding the positive impact of awe on learning and can be used to support further investigations of the relation between the overview effect and behavioral changes, specifically for educational purposes.

BACKGROUND Advanced sensor, measurement and analytics technologies enable entirely new ways to deliver care. Increased availability of digital data can be used for data-driven personalization of care. Data-driven personalization can complement expert-driven personalization by providing support for decision making, or even automating some parts of decision making in relation to the care process. OBJECTIVE The aim of this study is to analyze how digital data acquired from posture scanning can enhance physiotherapy services and enable a more personalized delivery of physiotherapy. METHODS A Case study is conducted with a company that has designed a Posture Scan Recording System (PSRS), which is an Information System (IS) that can record, measure and report human movement digitally to be used in physiotherapy. Data is collected through interviews with different stakeholders – healthcare professionals, healthcare users and the IS provider – and is analyzed thematically. RESULTS As the result of our thematic analysis, we propose three different types of support the posture scanning data can provide to enhance and enable more personalized delivery of physiotherapy. These types are: (1) Modeling the condition, which is about the use of posture scanning data for detecting and understanding the healthcare user’s condition and the root cause of the possible pain. (2) Visualization for a shared understanding, which is about the use of posture scanning data to inform and involve the healthcare user in more collaborative decision-making regarding care. (3) Evaluating the impact of the intervention, which is about the use of posture scanning data to evaluate the care progress and impact of the intervention. CONCLUSIONS The adoption of digital tools has remained low in physiotherapy. Physiotherapy has also lacked the digital tools and means that can be used to inform and involve the healthcare user in care in a person-centered manner. With the present study, we gathered insights from different stakeholders to provide understanding on how the availability of posture scanning digital data can enhance physiotherapy and enable more personalized physiotherapy. CLINICALTRIAL

BACKGROUND Advanced sensor, measurement and analytics technologies enable entirely new ways to deliver care. Increased availability of digital data can be used for data-driven personalization of care. Data-driven personalization can complement expert-driven personalization by providing support for decision making, or even automating some parts of decision making in relation to the care process. OBJECTIVE The aim of this study is to analyze how digital data acquired from posture scanning can enhance physiotherapy and enable more personalized delivery of physiotherapy. METHODS A Case study is conducted with a company that has designed a Posture Scan Recording System (PSRS), which is an Information System (IS) that can record, measure and report human movement digitally to be used in physiotherapy. Interviews are used to explore the viewpoints of different stakeholders involved in physiotherapy. The data is analyzed thematically. RESULTS As the result of our thematic analysis, we identified three different support types the posture scanning can provide to enable more personalized delivery of physiotherapy. The types are: (1) Modeling the condition, which is about the use of posture scanning data for detecting and understanding the healthcare user’s condition and the root cause of the possible pain. (2) Visualization for a shared understanding, which is about the use of posture scanning data to inform and involve the healthcare user in more collaborative decision-making regarding care. (3) Evaluating the impact of the intervention, which is about the use of posture scanning data to evaluate the care progress and impact of the intervention. CONCLUSIONS Current care models in healthcare emphasize the importance to put the healthcare user at the center of the care. However, physiotherapy has lacked data driven solutions to inform and involve the healthcare user in care in a person-centered manner. The present study analyzes how posture scanning can enhance physiotherapy and presents three different types of support that posture scanning can provide for data-driven personalization of physiotherapy.