Mobile health supported multi-domain recovery trajectories after major arthroplasty or spine surgery: a pilot feasibility and usability study

BACKGROUND

Recovery after surgery intersects physical, psychological, and social domains. In this study we aim to assess the feasibility and usability of a mobile health application called PositiveTrends to track recovery in these domains amongst participants undergoing hip, knee arthroplasty or spine surgery. Our secondary aim was to generate procedure-specific, recovery trajectories within the pain and medication, psycho-social and patient-reported outcomes domain.

METHODS

Prospective, observational study in participants greater than eighteen years of age. Data was collected prior to and up to one hundred and eighty days after completion of surgery within the three domains using PositiveTrends. Feasibility was assessed using participant response rates from the PositiveTrends app. Usability was assessed quantitatively using the System Usability Scale. Heat maps and effect plots were used to visualize multi-domain recovery trajectories. Generalized linear mixed effects models were used to estimate the change in the outcomes over time.

RESULTS

Forty-two participants were enrolled over a four-month recruitment period. Proportion of app responses was highest for participants who underwent spine surgery (median = 78, range = 36-100), followed by those who underwent knee arthroplasty (median = 72, range = 12-100), and hip arthroplasty (median = 62, range = 12-98). System Usability Scale mean score was 82 ± 16 at 180 days postoperatively. Function improved by 8 and 6.4 points per month after hip and knee arthroplasty, respectively. In spine participants, the Oswestry Disability Index decreased by 1.4 points per month. Mood improved in all three cohorts, however stress levels remained elevated in spine participants. Pain decreased by 0.16 (95% Confidence Interval: 0.13-0.20, p < 0.001), 0.25 (95% CI: 0.21-0.28, p < 0.001) and 0.14 (95% CI: 0.12-0.15, p < 0.001) points per month in hip, knee, and spine cohorts respectively. There was a 10.9-to-40.3-fold increase in the probability of using no medication for each month postoperatively.

CONCLUSIONS

In this study, we demonstrate the feasibility and usability of PositiveTrends, which can map and track multi-domain recovery trajectories after major arthroplasty or spine surgery.

Data analysis supports monitoring and surveillance of goat health and welfare in the Netherlands

Monitoring and surveillance systems have an increasingly important role in contemporary society ensuring high levels of animal health and welfare, securing export positions, and protecting public health by ensuring animal health and product safety. In the Netherlands, a voluntary monitoring and surveillance system is in place since 2003 to provide a broad overview of livestock trends in addition to disease-specific surveillance systems, including insight into the occurrence and prevalence of new and emerging non-notifiable diseases and disorders. Being a major surveillance component of this monitoring and surveillance system for small ruminant health in the Netherlands, an annual data analysis on routine census data is performed to retrospectively monitor trends and developments regarding goat health and welfare. This paper aims to describe the process of the data analysis on goat farms in the Netherlands in 2020 and subsequent results are discussed. The data analysis provides key monitoring indicators such as animal and farm density, mortality, animal movements, and numbers and origin of imported small ruminants. Trends were analysed over a five-year, period and associations between herd characteristics and herd health are evaluated. Results showed that in 2020 the Dutch goat population consisted of 670,842 goats, distributed over 14,730 unique herds and increased by 2.3 % compared to 2019. Between 2016 and 2020, although probably underestimated, recorded mortality rates showed a decline on both small-scale and professional farms, with a strongest decrease on farms with herd sizes over more than 200 animals. Seventy-five percent of all professional farms registered animal introductions, in addition to 63 % of small-scale farms, including 2439 imported goats. Performing risks analyses requires demographic knowledge of the goat industry. During and after several disease outbreaks, such as bluetongue and Schmallenberg virus disease, the data analysis proved to function as a valuable tool, however, appeared just as important for recording outbreak-free data. Since its start in 2006, the concept of the data-analysis has continuously been improved, and will in the future be further developed, especially if more complete data sets become available. Subsequently, data analysis will increasingly support monitoring and surveillance of goat health and welfare.