Horses’ behavior can provide valuable insight into their subjective state and is thus a good indicator of welfare. However, its complexity requires objective, quantifiable, and unambiguous evidence-based assessment criteria. As healthy, stress-free horses exhibit a highly repetitive daily routine, temporal quantification of their behavioral activities (time budget analysis) can assist in equine welfare assessment. Therefore, the present systematic review aimed to provide an up-to-date analysis of equine time budget studies. A review of the literature yielded 12 papers that fulfilled the inclusion criteria: assessment of equine time budgets for eating, resting and movement for a minimum of 24 continuous hours. A total of 144 horses (1–27 years old), 59 semi-feral and 85 domesticated horses, are included in this review. The 24 h time budgets for foraging or eating (10–6.6%), resting (8.1–66%), lying (2.7–27.3%), and locomotion (0.015–19.1%) showed large variance between studies, which can largely be attributed to differences in age and environmental conditions. Management interventions in domesticated horses (ad libitum access to food, increased space, decreased population density) resulted in time budgets similar to their (semi-)feral conspecifics, emphasizing the importance of environmental conditions and the ability of time budgets to assist in monitoring horses’ welfare.

Housing and management conditions strongly influence the health, welfare and behaviour of horses. Consequently, objective and quantifiable comparisons between domestic environments and their influence on different equine demographics are needed to establish evidence-based criteria to assess and optimize horse welfare. Therefore, the present study aimed to measure and compare the time budgets (=percentage of time spent on specific activities) of horses with chronic orthopaedic disease and geriatric (≥20 years) horses living in different husbandry systems using an automated tracking device. Horses spent 42% (range 38.3–44.8%) of their day eating, 39% (range 36.87–44.9%) resting, and 19% (range 17–20.4%) in movement, demonstrating that geriatric horses and horses suffering from chronic orthopaedic disease can exhibit behaviour time budgets equivalent to healthy controls. Time budget analysis revealed significant differences between farms, turn-out conditions and time of day, and could identify potential areas for improvement. Horses living in open-air group housing on a paddock had a more uniform temporal distribution of feeding and movement activities with less pronounced peaks compared to horses living in more restricted husbandry systems.

Objective : In EIT applications to the thorax, a single electrode plane has typically been used to reconstruct a transverse 2D ‘slice’. However, such images can be misleading as EIT is sensitive to contrasts above and below the electrode plane, and ventilation and aeration inhomogeneities can be distributed in complex ways. Using two (or more) electrode planes, 3D EIT images may be reconstructed, but 3D reconstructions are currently little used in thoracic EIT. In this paper, we investigate an incremental pathway towards 3D EIT reconstructions, using two electrode planes to calculate improved transverse slices as an intermediate step. We recommend a specific placement of electrode planes, and further demonstrate the feasibility of multi-slice reconstruction in two species. Approach : Simulations of the forward and reconstructed sensitivities were analysed for two electrode planes using a ‘square’ pattern of electrode placement as a function of two variables: the stimulation and measurement ‘skip’, and the electrode plane separation. Next, single- versus two-plane measurements were compared in a horse and in human volunteers. We further show the feasibility of 3D reconstructions by reconstructing multiple transverse and, unusually, frontal slices during ventilation. Main results : Using two electrode planes leads to a reduced position error and improvement in off-plane contrast rejection. 2D reconstructions from two-plane measurements showed better separation of lungs, as compared to the single plane measurements which tend to push contrasts in the center of the image. 3D reconstructions of the same data show anatomically plausible images, inside as well as outside the volume between the two electrode planes. Significance : Based on the results, we recommend EIT electrode planes separated by less than half of the minimum thoracic dimension with a ‘skip 4’ pattern and ‘square’ placement to produce images with good slice selectivity.

The arterial to end-tidal CO₂ difference (P_(a-ET)CO₂) and alveolar dead space fraction (VDalv_frac = P_(a-ET)CO₂/PaCO₂), are used to estimate Enghoff’s “pulmonary dead space” (V/Q_Eng), a factor which is also influenced by venous admixture and other pulmonary perfusion abnormalities and thus is not just a measure of dead space as the name suggests. The aim of this experimental study was to evaluate which factors influence these CO₂ indices in anesthetized spontaneously breathing horses. Six healthy adult horses were anesthetized in dorsal recumbency breathing spontaneously for 3 h. Data to calculate the CO₂ indices (response variables) and dead space variables were measured every 30 min. Bohr’s physiological and alveolar dead space variables, cardiac output (CO), mean pulmonary pressure (MPP), venous admixture $(\dot{Q}s\text{}/\text{}\dot{Q}t)$, airway dead space, tidal volume, oxygen consumption, and slope III of the volumetric capnogram were evaluated (explanatory variables). Univariate Pearson correlation was first explored for both CO₂ indices before V/Q_Eng and the explanatory variables with rho were reported. Multiple linear regression analysis was performed on P_(a-ET)CO₂ and VDalv_frac assessing which explanatory variables best explained the variance in each response. The simplest, best-fit model was selected based on the maximum adjusted R² and smallest Mallow’s p (C_p). The R² of the selected model, representing how much of the variance in the response could be explained by the selected variables, was reported. The highest correlation was found with the alveolar part of V/Q_Eng to alveolar tidal volume ratio for both, P_(a-ET)CO₂ (r = 0.899) and VDalv_frac (r = 0.938). Venous admixture and CO best explained P_(a-ET)CO₂ (R² = 0.752; C_p = 4.372) and VDalv_frac (R² = 0.711; C_p = 9.915). Adding MPP (P_(a-ET)CO₂) and airway dead space (VDalv_frac) to the models improved them only marginally. No “real” dead space variables from Bohr’s equation contributed to the explanation of the variance of the two CO₂ indices. P_(a-ET)CO₂ and VDalv_frac were closely associated with the alveolar part of V/Q_Eng and as such, were also influenced by variables representing a dysfunctional pulmonary perfusion. Neither P_(a-ET)CO₂ nor VDalv_frac should be considered pulmonary dead space, but used as global indices of V/Q mismatching under the described conditions.