Management and Treatment of Patients With Obstructive Sleep Apnea Using an Intelligent Monitoring System Based on Machine Learning Aiming to Improve Continuous Positive Airway Pressure Treatment Compliance: Randomized Controlled Trial.

BACKGROUND: Continuous positive airway pressure (CPAP) is an effective treatment for obstructive sleep apnea (OSA), but treatment compliance is often unsatisfactory. OBJECTIVE: The aim of this study was to assess the effectiveness and cost-effectiveness of an intelligent monitoring system for improving CPAP compliance. METHODS: This is a prospective, open label, parallel, randomized controlled trial including 60 newly diagnosed patients with OSA requiring CPAP (Apnea-Hypopnea Index [AHI] >15) from Lleida, Spain. Participants were randomized (1:1) to standard management or the MiSAOS intelligent monitoring system, involving (1) early compliance detection, thus providing measures of patient's CPAP compliance from the very first days of usage; (2) machine learning-based prediction of midterm future CPAP compliance; and (3) rule-based recommendations for the patient (app) and care team. Clinical and anthropometric variables, daytime sleepiness, and quality of life were recorded at baseline and after 6 months, together with patient's compliance, satisfaction, and health care costs. RESULTS: Randomized patients had a mean age of 57 (SD 11) years, mean AHI of 50 (SD 27), and 13% (8/60) were women. Patients in the intervention arm had a mean (95% CI) of 1.14 (0.04-2.23) hours/day higher adjusted CPAP compliance than controls (P=.047). Patients' satisfaction was excellent in both arms, and up to 88% (15/17) of intervention patients reported willingness to keep using the MiSAOS app in the future. No significant differences were found in costs (control: mean €90.2 (SD 53.14) (US $105.76 [SD 62.31]); intervention: mean €96.2 (SD 62.13) (US $112.70 [SD 72.85]); P=.70; €1=US $1.17 was considered throughout). Overall costs combined with results on compliance demonstrated cost-effectiveness in a bootstrap-based simulation analysis. CONCLUSIONS: A machine learning-based intelligent monitoring system increased daily compliance, reported excellent patient satisfaction similar to that reported in usual care, and did not incur in a substantial increase in costs, thus proving cost-effectiveness. This study supports the implementation of intelligent eHealth frameworks for the management of patients with CPAP-treated OSA and confirms the value of patients' empowerment in the management of chronic diseases. TRIAL REGISTRATION: ClinicalTrials.gov NCT03116958; https://clinicaltrials.gov/ct2/show/NCT03116958.

From coprophagy to predation: a dung beetle that kills millipedes.

The dung beetle subfamily Scarabaeinae is a cosmopolitan group of insects that feed primarily on dung. We describe the first case of an obligate predatory dung beetle and contrast its behaviour and morphology with those of its coprophagous sympatric congeners. Deltochilum valgum Burmeister killed and consumed millipedes in lowland rainforest in Peru. Ancestral ball-rolling behaviour shared by other canthonine species is abandoned, and the head, hind tibiae and pygidium of D. valgum are modified for novel functions during millipede predation. Millipedes were killed by disarticulation, often through decapitation, using the clypeus as a lever. Beetles killed millipedes much larger than themselves. In pitfall traps, D. valgum was attracted exclusively to millipedes, and preferred injured over uninjured millipedes. Morphological similarities placing D. valgum in the same subgenus with non-predatory dung-feeding species suggest a major and potentially rapid behavioural shift from coprophagy to predation. Ecological transitions enabling the exploitation of dramatically atypical niches, which may be more likely to occur when competition is intense, may help explain the evolution of novel ecological guilds and the diversification of exceptionally species-rich groups such as insects.

Understanding trait-dependent community disassembly: dung beetles, density functions, and forest fragmentation.

Anthropogenic disturbances such as fragmentation are rapidly altering biodiversity, yet a lack of attention to species traits and abundance patterns has made the results of most studies difficult to generalize. We determined traits of extinction-prone species and present a novel strategy for classifying species according to their population-level response to a gradient of disturbance intensity. We examined the effects of forest fragmentation on dung beetle communities in an archipelago of 33 islands recently created by flooding in Venezuela. Species richness, density, and biomass all declined sharply with decreasing island area and increasing island isolation. Species richness was highly nested, indicating that local extinctions occurred nonrandomly. The most sensitive dung beetle species appeared to require at least 85 ha of forest, more than many large vertebrates. Extinction-prone species were either large-bodied, forest specialists, or uncommon. These explanatory variables were unrelated, suggesting at least 3 underlying causes of extirpation. Large species showed high wing loading (body mass/wing area) and a distinct flight strategy that may increase their area requirements. Although forest specificity made most species sensitive to fragmentation, a few persistent habitat generalists dispersed across the matrix. Density functions classified species into 4 response groups on the basis of their change in density with decreasing species richness. Sensitive and persistent species both declined with increasing fragmentation intensity, but persistent species occurred on more islands, which may be due to their higher baseline densities. Compensatory species increased in abundance following the initial loss of sensitive species, but rapidly declined with increasing fragmentation. Supertramp species (widespread habitat generalists) may be poor competitors but strong dispersers; their abundance peaked following the decline of the other 3 groups. Nevertheless, even the least sensitive species were extirpated or rare on the smallest and most isolated islands.