Food site residence time and female competitive relationships in wild gray-cheeked mangabeys (Lophocebus albigena)

Authors of socioecological models propose that food distribution affects female social relationships in that clumped food resources, such as fruit, result in strong dominance hierarchies and favor coalition formation with female relatives. A number of Old World monkey species have been used to test predictions of the socioecological models. However, arboreal forest-living Old World monkeys have been understudied in this regard, and it is legitimate to ask whether predominantly arboreal primates living in tropical forests exhibit similar or different patterns of behavior. Therefore, the goal of our study was to investigate female dominance relationships in relation to food in gray-cheeked mangabeys (Lophocebus albigena). Since gray-cheeked mangabeys are largely frugivorous, we predicted that females would have linear dominance hierarchies and form coalitions. In addition, recent studies suggest that long food site residence time is another important factor in eliciting competitive interactions. Therefore, we also predicted that when foods had long site residence times, higher-ranking females would be able to spend longer at the resource than lower-ranking females. Analyses showed that coalitions were rare relative to some other Old World primate species, but females had linear dominance hierarchies. We found that, contrary to expectation, fruit was not associated with more agonism and did not involve long site residence times. However, bark, a food with a long site residence time and potentially high resource value, was associated with more agonism, and higher-ranking females were able to spend more time feeding on it than lower-ranking females. These results suggest that higher-ranking females may benefit from higher food and energy intake rates when food site residence times are long. These findings also add to accumulating evidence that food site residence time is a behavioral contributor to female dominance hierarchies in group-living species

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

Background Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. Results We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms’ performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. Conclusions Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms’ performances

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device’s walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson’s Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and − 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application. Trial registration: ISRCTN – 12246987