Background:Many patients with chronic skin diseases lack regular access to dermatologists in the United States and suffer poor clinical outcomes.Introduction:We performed a 12-month randomized controlled trial to evaluate the impact of an online, collaborative connected health (CCH) model for psoriasis management on access to specialty care.Materials and Methods:The 300 enrolled patients were randomized to online or in-person care. We compared distance traveled as well as transportation and in-office waiting time between the two groups and obtained patient and provider perspectives on CCH.Results:At baseline, no differences existed between the groups in difficulties obtaining specialty care. Over 12 months, the mean (standard deviation [SD]) distance traveled to and from appointments was 174.8 (±577.4) km/person for the in-person group and 2.2 (±14.2) km/person for the online group (p = 0.0003). The mean (SD) time spent on transportation and in-office waiting for in-person appointments was 4.0 (±4.5) h/person for the in-person group and 0.1 (±0.4) h/person for the online group (p = 0.0001). Patients found CCH to be safe, accessible, equitable, efficient, effective, and patient-centered. Providers found CCH to be useful for providing psoriasis care.Discussion:The CCH model resulted in significantly less distance traveled as well as transportation and in-office waiting time compared to in-person care. Both patients and providers were highly satisfied with CCH.Conclusions:The CCH model resulted in increased access to specialty care and enabled patient-centered, safe, and effective management of psoriasis patients.

This 12-month, pragmatic, randomized controlled equivalency trial evaluated whether an online, collaborative connected-health model results in equivalent improvements in quality of life compared with in-person care for psoriasis. Overall, 296 adults with physician-diagnosed psoriasis from ambulatory clinics were randomly assigned to either online or in-person care; all were analyzed for outcomes. In the online group, patients and primary care providers sought dermatologists' care directly and asynchronously online. The in-person group sought care face to face. Interventions did not allow blinding of participants; investigators were blinded during analysis. Across 12 months, for the online group, the mean ± standard deviation decline in Skindex-16 from baseline across follow-up visits was 9.02 ± 20.67 compared with 10.55 ± 23.50 for the in-person group. The difference in Skindex-16 between the two groups was -0.83 (95% confidence interval = -5.18 to 3.51), and this was within the equivalence margin (±7.0). For the online group, the mean ± standard deviation decline in Dermatology Life Quality Index was 1.64 ± 4.34 compared with 1.18 ± 4.77 for the in-person group. The difference in Dermatology Life Quality Index between the two groups was -0.45 (95% confidence interval = -1.29 to 0.38) and was within the equivalence margin (±2.5). In conclusion, the online model was as effective as in-person care in improving quality of life among psoriasis patients. This study was funded by the Patient-Centered Outcomes Research Institute and is registered on clinicaltrials.gov (NCT02358135).

Importance

Innovative, online models of specialty-care delivery are critical to improving patient access and outcomes.

Objective

To determine whether an online, collaborative connected-health model results in equivalent clinical improvements in psoriasis compared with in-person care.

Design, setting, and participants

The Patient-Centered Outcomes Research Institute Psoriasis Teledermatology Trial is a 12-month, pragmatic, randomized clinical equivalency trial to evaluate the effect of an online model for psoriasis compared with in-person care. Participant recruitment and study visits took place at multicenter ambulatory clinics from February 2, 2015, to August 18, 2017. Participants were adults with psoriasis in Northern California, Southern California, and Colorado. The eligibility criteria were an age of 18 years or older, having physician-diagnosed psoriasis, access to the internet and a digital camera or mobile phone with a camera, and having a primary care physician. Analyses were on an intention-to-treat basis.

Interventions

Participants were randomized 1:1 to receive online or in-person care (148 randomized to online care and 148 randomized to in-person care). The online model enabled patients and primary care physicians to access dermatologists online asynchronously. The dermatologists provided assessments, recommendations, education, and prescriptions online. The in-person group sought care in person. The frequency of online or in-person visits was determined by medical necessity. All participants were exposed to their respective interventions for 12 months.

Main outcomes and measures

The prespecified primary outcome was the difference in improvement in the self-administered Psoriasis Area and Severity Index (PASI) score between the online and in-person groups. Prespecified secondary outcomes included body surface area (BSA) affected by psoriasis and the patient global assessment score.

Results

Of the 296 randomized participants, 147 were women, 149 were men, 187 were white, and the mean (SD) age was 49 (14) years. The adjusted difference between the online and in-person groups in the mean change in the self-administered PASI score during the 12-month study period was -0.27 (95% CI, -0.85 to 0.31). The difference in the mean change in BSA affected by psoriasis between the 2 groups was -0.05% (95% CI, -1.58% to 1.48%). Between-group differences in the PASI score and BSA were within prespecified equivalence margins, which demonstrated equivalence between the 2 interventions. The difference in the mean change in the patient global assessment score between the 2 groups was -0.11 (95% CI, -0.32 to 0.10), which exceeded the equivalence margin, with the online group displaying greater improvement.

Conclusions and relevance

The online, collaborative connected-health model was as effective as in-person management in improving clinical outcomes among patients with psoriasis. Innovative telehealth delivery models that emphasize collaboration, quality, and efficiency can be transformative to improving patient-centered outcomes in chronic diseases.

Trial registration

ClinicalTrials.gov Identifier: NCT02358135.

Aim

Macroecological studies that require habitat suitability data for many species often derive this information from expert opinion. However, expert-based information is inherently subjective and thus prone to errors. The increasing availability of GPS tracking data offers opportunities to evaluate and supplement expert-based information with detailed empirical evidence. Here, we compared expert-based habitat suitability information from the International Union for Conservation of Nature (IUCN) with habitat suitability information derived from GPS-tracking data of 1,498 individuals from 49 mammal species.

Location

Worldwide.

Time period

1998-2021.

Major taxa studied

Forty-nine terrestrial mammal species.

Methods

Using GPS data, we estimated two measures of habitat suitability for each individual animal: proportional habitat use (proportion of GPS locations within a habitat type), and selection ratio (habitat use relative to its availability). For each individual we then evaluated whether the GPS-based habitat suitability measures were in agreement with the IUCN data. To that end, we calculated the probability that the ranking of empirical habitat suitability measures was in agreement with IUCN's classification into suitable, marginal and unsuitable habitat types.

Results

IUCN habitat suitability data were in accordance with the GPS data (> 95% probability of agreement) for 33 out of 49 species based on proportional habitat use estimates and for 25 out of 49 species based on selection ratios. In addition, 37 and 34 species had a > 50% probability of agreement based on proportional habitat use and selection ratios, respectively.

Main conclusions

We show how GPS-tracking data can be used to evaluate IUCN habitat suitability data. Our findings indicate that for the majority of species included in this study, it is appropriate to use IUCN habitat suitability data in macroecological studies. Furthermore, we show that GPS-tracking data can be used to identify and prioritize species and habitat types for re-evaluation of IUCN habitat suitability data.

Accurately quantifying species' area requirements is a prerequisite for effective area-based conservation. This typically involves collecting tracking data on species of interest and then conducting home-range analyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated. Based on the previous work, we hypothesized the magnitude of underestimation varies with body mass, a relationship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals, we estimated home-range areas with global positioning system (GPS) locations from 757 individuals across 61 globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied block cross-validation to quantify bias in empirical home-range estimates. Area requirements of mammals <10 kg were underestimated by a mean approximately15%, and species weighing approximately100 kg were underestimated by approximately50% on average. Thus, we found area estimation was subject to autocorrelation-induced bias that was worse for large species. Combined with the fact that extinction risk increases as body mass increases, the allometric scaling of bias we observed suggests the most threatened species are also likely to be those with the least accurate home-range estimates. As a correction, we tested whether data thinning or autocorrelation-informed home-range estimation minimized the scaling effect of autocorrelation on area estimates. Data thinning required an approximately93% data loss to achieve statistical independence with 95% confidence and was, therefore, not a viable solution. In contrast, autocorrelation-informed home-range estimation resulted in consistently accurate estimates irrespective of mass. When relating body mass to home range size, we detected that correcting for autocorrelation resulted in a scaling exponent significantly >1, meaning the scaling of the relationship changed substantially at the upper end of the mass spectrum.

Wildlife tagging provides critical insights into animal movement ecology, physiology, and behavior amid global ecosystem changes. However, the stress induced by capture, handling, and tagging can impact post-release locomotion and activity and, consequently, the interpretation of study results. Here, we analyze post-tagging effects on 1585 individuals of 42 terrestrial mammal species using collar-collected GPS and accelerometer data. Species-specific displacements and overall dynamic body acceleration, as a proxy for activity, were assessed over 20 days post-release to quantify disturbance intensity, recovery duration, and speed. Differences were evaluated, considering species-specific traits and the human footprint of the study region. Over 70% of the analyzed species exhibited significant behavioral changes following collaring events. Herbivores traveled farther with variable activity reactions, while omnivores and carnivores were initially less active and mobile. Recovery duration proved brief, with alterations diminishing within 4-7 tracking days for most species. Herbivores, particularly males, showed quicker displacement recovery (4 days) but slower activity recovery (7 days). Individuals in high human footprint areas displayed faster recovery, indicating adaptation to human disturbance. Our findings emphasize the necessity of extending tracking periods beyond 1 week and particular caution in remote study areas or herbivore-focused research, specifically in smaller mammals.