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Healthcare

Paper on Integrated Technologies of Care published in Nature Scientific Reports

    Our paper titled “Integrated technologies of care: proof-of-concept study on an integrated physiological and activity monitoring system to enhance independence and care” has just been published by Nature Scientific Reports. You can access it here. The showcases work done as part of the Advanced Care Research Centre (ACRC).

    Abstract

    This study presents a multi-sensor activity monitoring system designed to verify daily behaviours and detect physiological anomalies within a controlled home environment. Seven subjects participated in a scripted routine of Activities of Daily Living carried out in a home-like environment. The proposed system is designed for single-occupancy environments (e.g. individuals living alone). Accordingly, this proof-of-concept evaluation was conducted under controlled, single-participant conditions using sequential, non-concurrent activities and excluding overlapping sensor changes. Within the routine, different sensors capture physiological hydration levels and breathing rates of the participant as sensor events, to check whether the measurements were within normal levels. In addition, camera, contact, motion and pressure sensors are used to capture action triggered events within the routine. Sensor data is processed and translated into a time-ordered trace of events. A model is constructed to capture the layout of the controlled environment and the trace of events. Expected behaviours are specified as properties encoded in Linear Temporal Logic and model checking is used to assess whether the sensor-captured events align with these expectations. Through model checking, the captured behaviour can be verified against a set of logical formulae representing properties. The identified deviations from expected behaviours demonstrate the viable application of model checking in the verification of Activities of Daily Living. Cross-sensor data aggregation compensates for occasional sensor inaccuracy, ensuring reliability. The initial results support the system’s potential for use in behaviour and physiological monitoring of people living independently, where accurate and unobtrusive monitoring is crucial.

    New pre-print out on the pitfalls in understanding the clustering of multiple long-term conditions

      We have a new pre-print entitled “Pitfalls in understanding how multiple long-term conditions cluster: whole population and age-stratified associations in 7,490,874 people in England”.

      Abstract

      Studies of how multiple long-term conditions (MLTC) cluster together in individuals vary in the populations studied, and whether they age and/or sex stratify, which limits comparison between studies and reproducibility. This study uses a large, UK primary-care dataset to examine how pairwise strength of association between 74 conditions varies by age in both men and women aged 30-99 years, and to explore implications for MLT cluster analyses. Joint prevalence of conditions was lowest in younger age-groups and progressively increased with age, whereas Association Beyond Chance (ABC) was highest in younger age-groups and progressively decreased with age. Condition clustering based on ABC identified different clusters in all men and all women aged 30-99 years, and these clusters differed from those identified in each age-group. Researchers examining how MLTC cluster should consider whether age and sex stratification is appropriate given their study aims and/or would improve comparability and reproducibility, and explicitly justify their choices.

      More details can be found here.

      Our new paper on chronic illnesses and depression featured in UKRI news

        Our new paper, Cluster and survival analysis of UK biobank data reveals associations between physical multimorbidity clusters and subsequent depression, has just been published in Nature Communications Medicine. The results have been highlighted by the MRC on the UKRI website and featured in its newsletter. See:

        UKRI News: https://www.ukri.org/news/multiple-chronic-illnesses-linked-to-higher-risk-of-depression
        Edinburgh University post: https://www.ed.ac.uk/news/multiple-chronic-illnesses-could-double-risk-of-depression

        and

        Full paper at: https://doi.org/10.1038/s43856-025-00825-7
        Code at: https://github.com/laurendelong21/clusterMed

        Fiona Smith’s exhibition “The BOX” premieres at the Edinburgh Science Festival!

          We are proud to announce that AI Modelling Lab PhD Student Fiona Smith’s exhibition “The BOX” is running from the 6th to the 19th of April 2024 as part of the Edinburgh Science Festival. Fiona is funded by our Centre for Doctoral Traning in Biomedical AI and was selected to be the Creator in Residence at Fraunhofer MEVIS.

          The live exhibition can be visited at Inspace, Crichton St, University of Edinburgh.

           

           

          Our second analysis of the impact of COVID-19 on Scotland’s care-homes published in Age and Ageing

            Following from our previous analysis of care-home outbreaks of COVID-19 in Scotland in 2020, which appeared in Age and Ageing in 2021, our paper on the “Analysis of the impact of COVID-19 on Scotland’s care-homes from March 2020 to October 2021: national linked data cohort analysis” has just been published in the same journal.

            This shows that, in total 296 (27.1%) care-homes had one outbreak, 220 (20.1%) had two, 91 (8.3%) had three, and 68 (6.2%) had four or more. A general conclusion is that COVID-19 mitigation measures appear to have been beneficial, although the impact on residents remained severe until early 2021.

            Pre-print on Associations between Morbidities in Small But Important Subgroup using a Bayesian approach

              Our paper on the “Associations between Morbidities in Small But Important Subgroups: A Novel Bayesian Approach for Robust Multimorbidity Analysis with Small Sample Sizes”  is out.

              Abstract:

              Background: Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when data is limited. We have developed a Bayesian inference framework that is robust to sparse data and have used it to quantify morbidity associations in the oldest old, a population with limited available data.

              Methods: We conducted a retrospective cross-sectional cohort study of a representative dataset of primary care patients in Scotland. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex, to study the effect of small sample sizes in the estimation of associations between long-term conditions. We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with a new measure of associations, Associations Beyond Chance (ABC), that utilises a Bayesian framework. To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC.

              Findings: Our Bayesian framework was appropriately more cautious in attributing association when evidence is small, as it dismissed six of the top ten associations reported by RR, most of which relate to uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex, for which ABC only reported as significant about one-fifth of those reported by RR. Last, the presence of potentially inaccurate associations by RR also affected the aggregated measures of multimorbidity and network representations.

              Interpretation: Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when data is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity.

              More information at SSRN: https://ssrn.com/abstract=4515875 or http://dx.doi.org/10.2139/ssrn.4515875.