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Physical inactivity is one of the core risk factors for non-communicable diseases. Scaling up health interventions using theoretically-driven and smart-enabled approaches is critical to strengthening prevention strategies. This study evaluated the efficacy of an ad-hoc developed mobile health (mHealth) intervention, which adopts a theory-based and tailored communication approach and targets walking behavior change. A convenience sample of physically inactive adults (n = 193) participated in a 30-day randomized trial. Before the intervention, psychological variables selected from the Health Action Process Approach (HAPA) were evaluated to profile participants and develop tailored materials. Allocation (1:1:1) was performed to one of three conditions: (i) tailored communication based on the HAPA model (HAPA-T group); (ii) non-tailored communication focused on wellbeing (Wellbeing-NT group); (iii) no-communication group. All participants were also exposed to goal-setting (i.e., reaching 7,000 steps daily) and self-monitoring. A repeated measures ANOVA was performed to monitor the mean weekly steps over the 30-days trial period within and among the three conditions. Overall, participants reported significant improvements in walking behavior over the trial period. Although no significant outcome differences were observed among the three experimental conditions, differential patterns of walking emerged from the study groups, with the HAPA-T group showing a wider increase at the final time-point. The intervention showed a significant overall impact, unveiling crucial procedural strengths and limitations. These were discussed to orient optimized implementation for future digital full-scale trials. Trial Protocol Registration ClinicalTrials.gov registration ID NCT05620888 (17 th November 2022; https://clinicaltrials.gov/study/NCT05620888 ).

(1) Background: Identifying groups with a misaligned physical capacity (PC) and physical activity (PA) is potentially relevant for health promotion. Although an important health determinant, deeper knowledge of underlying walking behavior patterns in older adults is currently missing. We aim to identify specific PA signatures of misaligned groups and determine PA variables discriminating between groups. (2) Methods: In total, 294 community-dwelling older adults (≥70 years) were divided into four quadrants based on thresholds for PA (≥ or <5000 steps/day) and PC (≤ or >12 s, Timed Up and Go test). Kruskal–Wallis and effect sizes were calculated to compare quadrants’ PA variables and to determine the discriminative power of PA parameters on walking duration, frequency, and intensity. (3) Results: We identified quadrant-specific PA signatures. Compared with “can do–do do”, the “cannot do–do do” group performs shorter continuous and lower-intensity walks; the “can do–do not do” group takes fewer steps and walks with less intensity. The “cannot do–do not do” group presents lower values in all PA variables. “Walking duration greater or equal 3 METs” was the strongest discriminative PA variable. (4) Conclusion: We provide distinct PA signatures for four clinically different groups of older adults. Walking intensity is most useful to distinguish community-dwelling older adults, which is relevant for developing improved customized health promotion interventions.

(1) Background: Almost all trips include a walking leg. Pedestrian flow dynamics are an essential input to infrastructure design as well as efficient and safe operations. Pedestrian walking speed is the most influential traffic flow variable. This study examines the factors influencing pedestrian walking speed, categorizing them into pedestrian flow characteristics, pedestrian attributes, layout configuration, ambient conditions, and pedestrian behavioral patterns. (2) Methods: A comprehensive literature review was conducted, aggregating studies that investigate pedestrian walking speed across various environments and conditions. The identified factors were systematically categorized, and a meta-analysis was employed to synthesize the results. (3) Results: Speed measurements seem to be dependent on the method and technique employed, with experiments systematically overestimating speed and video recordings systematically underestimating it. Pedestrian density strongly influences speed as in motorized traffic. Being female, being of older age, walking in a group, engaging in social interactions or phone-related tasks, and moving under noise conditions are reported to have a negative impact on walking speed. Carrying baggage and moving under adverse weather conditions are also reported to have a statistically significant impact, but the direction of the impact is not always the same and seems to be very context dependent. (4) Conclusions: The findings highlight the significance of physiological, psychological, and environmental elements in shaping pedestrian behavior and thus speed. Valuable insights from this review can assist researchers, designers, and operators in providing safer, more inclusive, and reliable infrastructures for pedestrians. Future investigations should broaden the scope of data collection methods, particularly indoors.

Adaptive interventions are an emerging class of behavioral interventions that allow for individualized tailoring of intervention components over time to a person’s evolving needs. The purpose of this study was to evaluate an adaptive step goal + reward intervention, grounded in Social Cognitive Theory delivered via a smartphone application (Just Walk), using a mixed modeling approach. Participants (N = 20) were overweight (mean BMI = 33.8 ± 6.82 kg/m2), sedentary adults (90% female) interested in participating in a 14-week walking intervention. All participants received a Fitbit Zip that automatically synced with Just Walk to track daily steps. Step goals and expected points were delivered through the app every morning and were designed using a pseudo-random multisine algorithm that was a function of each participant’s median baseline steps. Self-report measures were also collected each morning and evening via daily surveys administered through the app. The linear mixed effects model showed that, on average, participants significantly increased their daily steps by 2650 (t = 8.25, p < 0.01) from baseline to intervention completion. A non-linear model with a quadratic time variable indicated an inflection point for increasing steps near the midpoint of the intervention and this effect was significant (t2 = −247, t = −5.01, p < 0.001). An adaptive step goal + rewards intervention using a smartphone app appears to be a feasible approach for increasing walking behavior in overweight adults. App satisfaction was high and participants enjoyed receiving variable goals each day. Future mHealth studies should consider the use of adaptive step goals + rewards in conjunction with other intervention components for increasing physical activity.

The growing urban population and traffic congestion underline the importance of building pedestrian-friendly environments to encourage walking as a preferred mode of transportation. However, a major challenge remains, which is the absence of such pedestrian-friendly walking environments. Identifying locations and routes with high pedestrian concentration is critical for improving pedestrian-friendly walking environments. This paper presents a quantitative method to map pedestrian walking behavior by utilizing real-time data from mobile phone sensors, focusing on the University of Moratuwa, Sri Lanka, as a case study. This holistic method integrates new urban data, such as location-based service (LBS) positioning data, and data clustering with unsupervised machine learning techniques. This study focused on the following three criteria for quantifying walking behavior: walking speed, walking time, and walking direction inside the experimental research context. A novel signal processing method has been used to evaluate speed signals, resulting in the identification of 622 speed clusters using K-means clustering techniques during specific morning and evening hours. This project uses mobile GPS signals and machine learning algorithms to track and classify pedestrian walking activity in crucial sites and routes, potentially improving urban walking through mapping.

Population aging has become a notable and enduring demographic phenomenon worldwide. Older adults’ walking behavior is determined by many factors, such as socioeconomic attributes and the built environment. Although a handful of recent studies have examined the influence of street greenery (a built environment variable readily estimated by big data) on older adults’ walking behavior, they have not focused on the spatial heterogeneity in the influence. To this end, this study extracts the socioeconomic and walking behavior data from the Travel Characteristic Survey 2011 of Hong Kong and estimates street greenery (the green view index) based on Google Street View imagery. It then develops global models (linear regression and Box–Cox transformed models) and local models (geographically weighted regression models) to scrutinize the average (global) and location-specific (local) relationships, respectively, between street greenery and older adults’ walking time. Notably, green view indices in three neighborhoods with different sizes are estimated for robustness checks. The results show that (1) street greenery has consistent and significant effects on walking time; (2) the influence of street greenery varies across space—specifically, it is greater in the suburban area; and (3) the performance of different green view indices is highly consistent.

The green-belly stink bug (Diceraeus melacanthus Dallas (Hemiptera: Pentatomidae)) is a key pest of corn-soybean crop systems and the management of this pest is difficult due to behavioral characteristics. However, products that alter its behavior, such as arrestants, dislodgers, and phagostimulants, have been used in an attempt to improve the effectiveness of chemical control. In this study, the effect of these products on the walking behavior of nymphs and adults of D. melacanthus was initially evaluated through computational behavioral tracking (Ethovision system). Adults of D. melacanthus exposed to the dislodgers Creolin Pearson and Quimifol S450 (sulfur) significantly increased the distance covered and the walking speed. On the other hand, the only treatment that significantly affected the walking behavior of nymphs was the treatment with soy milk (phagostimulant). The physical–chemical analyses indicated considerable changes in the pH and electrical conductivity of mixtures of such products with insecticides from different chemical groups, as well as lack of homogeneity (physical incompatibility). Nevertheless, none of the products tested improved the control efficacy of an insecticide based on imidacloprid + beta-cyfluthrin, either in laboratory tests (contact bioassay) or in tests conducted in corn crops during 2 crop harvests. Thus, the association of these products in a tank mixture does not increase the control levels of D. melacanthus in post-emergence of maize and may have an antagonistic effect in some associations.

Concern about falling is prevalent and increases the risk of falling in people with peripheral neuropathy (PN). However, the assessment of concern about falling relies on self-report surveys, and thus continuous monitoring has not been possible. We investigated the influence of concern about falling on sensor-based daily physical activity among people with PN. Forty-nine people with PN and various levels of concern about falling participated in this study. Physical activity outcomes were measured over a period of 48 hours using a validated chest-worn sensor. The level of concern about falling was assessed using the falls efficacy scale-international (FES-I). The low concern group spent approximately 80 min more in walking and approximately 100 min less in sitting/lying compared to the high concern group. In addition, the low concern group had approximately 50% more walking bouts and step counts compared to the high concern group. Across all participants, the duration of walking bouts and total step counts was significantly correlated with FES-I scores. The duration of walking bouts and total step counts may serve as eHealth targets and strategies for fall risk assessment among people with PN.

The pre-courtship behavior of Proholopterus chilensis on Nothofagus obliqua trees was recorded for the first time, documenting a putative female “calling” behavior and the consequent male “oriented search,” enabling the description of behavioral units, sequences, frequencies, and degrees of stereotypy. Post-resting activity in both sexes began with walking and grooming during the first hour of the scotophase, following a period of daytime inactivity. Subsequently, females extended their ovipositor both horizontally and vertically, alternating between contact with the tree substrate and, simultaneously, walking the surface (=putative calling behavior), while males became active, extending their antennae perpendicularly to the longitudinal body axis. In response to the putative call, males exhibited oriented flight and hasty walking that followed the trajectory and direction previously taken by females on the substrate, ultimately leading to their location. Statistical analysis of behavioral sequences and the stereotypy index indicated that both behaviors were non-random and partially stereotyped. These findings are consistent with previous chemical analyses of female aerations and terminalia performed by our research group, which identified semiochemicals likely functioning as long-range sex pheromones guiding males to the vicinity of the female’s tree, as well as potential trail pheromones facilitating close-range localization. This dual signaling system seems necessary to the high mobility displayed by females during calling behavior, characterized by frequent and extended bidirectional vertical walks along the trunk, which may enhance signal dispersal and/or reduce predation risk. If confirmed, this would represent a novel sexual encounter mechanism within Cerambycidae.

Walking has been shown to improve physical and mental well-being, yet insufficient walking among university students has been increasingly reported. This study aimed to understand walking behavior of university students using theory of planned behavior (TPB). We recruited 169 undergraduate students by university mass email of the Chinese University of Hong Kong, and first administered a salient belief elicitation survey, which was used to design the TPB questionnaire, to a subset of the study sample. Secondly, all participants completed the TPB questionnaire and walking-oriented diary in a two-day period in December 2012. We mapped the walking behavior data obtained from the diary using geographic information system, and examined the extent to which TPB constructs explained walking intentions and walking behavior using Structural equation model (SEM). We found perceived behavioral control to be the key determinant of walking intention. Shaped by participants’ perceived behavioral control, attitude toward walking and subjective norms, and behavioral intention, in turn had a moderate explanatory effect on their walking behavior. In summary, our findings suggest that walking behavior among university students can be understood within the TPB framework, and could inform walking promotion interventions on the university campuses.