Amit Birenboim

Gamification is often used to promote walking through mobile applications. One strategy to improve the efficacy of these interventions is user-type-based personalization, tailoring game design to individual personality traits. While this approach is applied across gamification domains, empirical evidence linking personality-based matching to actual walking behavior remains scarce, as most studies have relied on stated preferences or laboratory settings. This study used a randomized controlled field experiment to test whether HEXAD, the most common personality framework in gamification, predicts walking behavior in response to different game designs. Using the Flutter software development kit, we developed Stepit, a mobile application designed to promote urban walking. The application included two game modes, Challenge and Influence. Participants were randomly allocated to one of these modes; thus, each participant experienced only one game condition during the intervention. Results from 93 participants in a two-week experiment, in which the first week served as a baseline measurement, showed that the Challenge game increased daily steps by 29% on average, while the Influence game increased steps among sedentary participants, but not across the entire sample. The Influence game also enhanced civic awareness among Philanthropist-oriented participants. A longitudinal mixed-effects model was used to examine the relationship between HEXAD personality dimensions, game type, and daily step counts, revealed a significant interaction between the Player dimension and game type, indicating that the Challenge game was more effective for high-Player participants, whereas the Influence game was more effective for encouraging walking among participants with low Player scores.

Background: Exposure to circadian entrainers, such as sunlight, positively impacts sleep architecture, while exposure before bedtime to circadian disruptors, such as artificial light and smartphone use, can negatively affect sleep. However, real-world evidence from longitudinal observational studies that simultaneously capture these factors alongside electroencephalography-derived sleep stages remains limited. Objective: This study aimed to investigate the effects of specific environmental and behavioral factors on sleep metrics and architecture by using sensor-based measurements over 7 consecutive days. Specifically, it examined day-to-day associations between (1) daytime sunlight exposure and (2) prebedtime artificial light exposure and smartphone use with selected sleep outcomes on the following night. Methods: A total of 21 participants from the Jerusalem metropolitan area were monitored continuously using the Dreem wearable electroencephalography for sleep staging, HOBO data loggers for light exposure, the wGT3X+ triaxial accelerometer for physical activity, and a dedicated mobile app to record smartphone usage. Sleep outcomes included total sleep time (TST), sleep onset latency (SOL), and the proportions of light sleep (N1) and deep sleep (N3). Sunlight exposure was defined as the number of hours above 1000 lux during daytime, and artificial light and smartphone use before bedtime were quantified as the duration of exposure accumulated in the 2 hours preceding sleep onset. Linear mixed-effects models with a random intercept at the individual level estimated the associations between these exposures and next-night sleep outcomes, adjusting for step count and other individual covariates. Results: The average TST was 420 (SD 85) minutes, and SOL averaged 17.6 (SD 18) minutes. Light sleep (N1) represented 6.6% (SD 2.1%) of sleep, and deep sleep (N3) accounted for 20.1% (SD 7.6%). Each additional hour of daytime sunlight exposure was associated with an increase of 10.67 (95% CI 0.6-20.7) minutes in TST the following night and with a 0.3 (95% CI –0.6 to –0.0) percentage-point decrease in light sleep (N1) percentage. No associations were found between evening artificial light exposure and sleep outcomes, while each minute of smartphone use before bedtime was linked to an increase in SOL of 0.2 (95% CI 0.0-0.4) minutes. Conclusions: These findings emphasize the importance of daylight exposure for circadian alignment and the potential sleep-disruptive effects of evening digital engagement. This study demonstrates the feasibility and value of integrating wearable electroencephalography and environmental and behavioral sensors in naturalistic settings to uncover behavioral and environmental correlates of sleep architecture.

Introduction: Stress is a growing public health concern, with urban environments strongly influencing stress levels. Experimental approaches simulating human motion (i.e., walking) in virtual urban environments may constitute a promising avenue to assess environmental effects on stress. This study aims to systematically examine the ability to induce and measure psychological and physiological stress responses during walking in immersive virtual urban environments. It includes two sub-experiments: one comparing average stress levels between an urban park and a street, and one assessing momentary stress responses to a siren stimulus. Methods: Fifty adults residing in Luxembourg experienced virtual walks through a park and a street, in a randomized crossover design, as well as a walk through the street with a siren stimulus. Physiological responses (electrodermal activity (EDA), heart rate (HR), and pupil diameter) were recorded continuously, while self-reported stress and emotional perceptions were assessed after each session via in-VR questionnaires. Results: Participants reported significantly lower self-reported stress and more positive emotional engagement in the park compared to the street. The standard deviation of HR was higher in the street, possibly indicating higher stress levels, while average pupil diameter was larger in the park, reflecting heightened emotional arousal. Other biomarkers showed no significant differences. Pupil diameter and EDA effectively detected momentary stress after the siren stimulus. Conclusions: Combining virtual environments with a walking simulator and biosensors provides a novel, effective method to study environmental impacts on stress. Findings contribute to identifying stress-inducing urban environments and developing stress reduction interventions.

BACKGROUND Exposure to circadian entrainers, such as sunlight, positively impacts sleep architecture, while exposure before bedtime to circadian disruptors, such as artificial light and smartphone use, can negatively affect sleep. However, real-world evidence from longitudinal observational studies that simultaneously capture these factors alongside electroencephalography-derived sleep stages remains limited. OBJECTIVE This study aimed to investigate the effects of specific environmental and behavioral factors on sleep metrics and architecture by using sensor-based measurements over 7 consecutive days. Specifically, it examined day-to-day associations between (1) daytime sunlight exposure and (2) prebedtime artificial light exposure and smartphone use with selected sleep outcomes on the following night. METHODS A total of 21 participants from the Jerusalem metropolitan area were monitored continuously using the Dreem wearable electroencephalography for sleep staging, HOBO data loggers for light exposure, the wGT3X+ triaxial accelerometer for physical activity, and a dedicated mobile app to record smartphone usage. Sleep outcomes included total sleep time (TST), sleep onset latency (SOL), and the proportions of light sleep (N1) and deep sleep (N3). Sunlight exposure was defined as the number of hours above 1000 lux during daytime, and artificial light and smartphone use before bedtime were quantified as the duration of exposure accumulated in the 2 hours preceding sleep onset. Linear mixed-effects models with a random intercept at the individual level estimated the associations between these exposures and next-night sleep outcomes, adjusting for step count and other individual covariates. RESULTS The average TST was 420 (SD 85) minutes, and SOL averaged 17.6 (SD 18) minutes. Light sleep (N1) represented 6.6% (SD 2.1%) of sleep, and deep sleep (N3) accounted for 20.1% (SD 7.6%). Each additional hour of daytime sunlight exposure was associated with an increase of 10.67 (95% CI 0.6-20.7) minutes in TST the following night and with a 0.3 (95% CI –0.6 to –0.0) percentage-point decrease in light sleep (N1) percentage. No associations were found between evening artificial light exposure and sleep outcomes, while each minute of smartphone use before bedtime was linked to an increase in SOL of 0.2 (95% CI 0.0-0.4) minutes. CONCLUSIONS These findings emphasize the importance of daylight exposure for circadian alignment and the potential sleep-disruptive effects of evening digital engagement. This study demonstrates the feasibility and value of integrating wearable electroencephalography and environmental and behavioral sensors in naturalistic settings to uncover behavioral and environmental correlates of sleep architecture.