Despite concerns regarding its validity, the two-alternative forced-choice heartbeat detection task (2AFC-HDT) is a frequently used measure of cardiac interoceptive accuracy. In this task, participants must decide whether a series of tones occur synchronously with their heartbeats. One series of tones is predefined by the researcher as synchronous with heartbeats, and one series is predefined as asynchronous. The 2AFC-HDT may result in individuals judged to be not interoceptive when they are, either if participants perceive their heartbeats as occurring synchronously with tones predefined as asynchronous rather than synchronous with their heartbeats, or if they do not perceive either set of tones as synchronous. Currently, there is little data on the proportion of participants this may affect. We addressed this using data from the Phase Adjustment Task (PAT) - a measure of cardiac interoceptive accuracy that determines if, and when in the cardiac cycle, a participant can perceive their heartbeat. The timing of heartbeat perception in 43 interoceptive participants was compared to the timing of synchronous and asynchronous tones used in the 2AFC-HDT, assuming temporal precision of 50, 100, and 150 ms. Results suggest that between 53.5%-97.7% of delay-based interoceptive individuals perceive heartbeats at a delay that does not correspond to the typical asynchronous or synchronous delays used to present tones on the 2AFC-HDT. These issues suggest that the 2AFC-HDT (or other measures that make assumptions about perceived timing of heartbeats) should not be used to measure cardiac interoceptive accuracy, or cardiac interoceptive insight (also known as awareness or metacognition).

In 2021 a new measure for the assessment of cardiac interoceptive accuracy—the Phase Adjustment Task (PAT)—was developed that overcomes several limitations of existing methods and can be administered using a smartphone application. In this report, we describe several refinements to the PAT. These include: (1) triggering tones from the detection of the heartbeat via the smartphone camera, rather than using an algorithm for predicting the occurrence of the next heartbeat; (2) technical amendments to enable implementation on iPhones with multiple cameras; (3) changes to instructions and (4) changes to the collection of confidence ratings to improve participant understanding and the utility of confidence ratings for interpretation of results; (5) the introduction of new practice trials to improve clarity; (6) changes to the analysis approach to identify cardiac phase-based heartbeat perceivers; (7) a review of the use of continuous scores; (8) a reanalysis of data comparing PAT performance in supervised laboratory settings and unsupervised remote settings using the new analysis method, and (9) the implementation of additional measures to discourage participants from checking their pulse. In this paper we outline our justification for these changes and provide details of where researchers can access the materials required for implementing the PAT and analyzing data. Finally, we provide further recommendations for implementation.

Commonly used methods for assessing cardiac interoceptive accuracy have been criticized for assuming that all individuals perceive their heartbeat at the same delay following contraction of the heart, despite evidence for notable variability across individuals. However, it remains unclear whether some individuals perceive their heartbeat at a particular phase of their cardiac cycle-that is, at a relative point in the cycle that may vary in absolute timing depending on heart rate-rather than at a specific delay. Identification of all heartbeat perceivers is critical for accurate measurement of cardiac interoceptive accuracy; individual differences in which are theorized to play a role in several aspects of higher-order cognition as well as health and wellbeing. In the current study, data from 526 participants who completed the Phase Adjustment Task (PAT) as a measure of cardiac interoceptive accuracy were examined. In this task, participants are asked to adjust a virtual dial until tones appear synchronous with their heartbeats. Data were analyzed using a novel framework that allows differentiation between delay-based and phase-based response patterns. Of 76 interoceptive individuals identified, 21% (N = 16) demonstrated response patterns consistent only with phase-based responding. These novel findings challenge current assumptions regarding individual differences in the perception of heartbeats, and suggest that many commonly used measures may underestimate the true proportion of heartbeat perceivers.

Adolescence is a developmental period during which an estimated 75

Interoceptive accuracy, the ability to correctly perceive internal body signals such as heartbeats, has been empirically and theoretically linked to stress. However, issues with the measurement of both interoceptive accuracy and stress have led to lack of clarity regarding this relationship. This systematic review and meta-analysis aimed to clarify whether interoceptive accuracy is associated with different facets of stress, including - physical, cognitive and self-reported stressors and the physiological stress response. A systematic search identified 2014 abstracts. Twenty-eight authors were contacted to request data for eligible studies, which yielded a final sample of 20 studies. Results revealed a positive association between heartbeat counting task (HCT) performance and acute physical stressors, and a negative association between HCT performance and physiological stress responses. No significant relationships were observed between stress and interoceptive accuracy assessed by the heartbeat discrimination task. While these findings offer tentative support for stress-interoceptive accuracy associations, they must be interpreted with caution given substantial heterogeneity in stress measures, limited use of interoception tasks beyond the HCT, and ongoing concerns regarding task validity. Implications for future research and methodological recommendations are discussed.

Previous evidence suggests males and females differ with respect to interoception-the processing of internal bodily signals-with males typically outperforming females on tasks of interoceptive accuracy. However, interpretation of existing evidence in the cardiac domain is hindered by the limitations of existing tools. In this investigation, we pooled data from several samples to examine sex differences in cardiac interoceptive accuracy on the phase adjustment task, a new measure that overcomes several limitations of the existing tools. In a sample of 266 individuals, we observed that females outperformed males, indicative of better cardiac interoceptive accuracy, but had lower confidence than males. These results held after controlling for sex differences in demographic, physiological and engagement factors. Importantly, these results were specific to the measure of cardiac interoceptive accuracy. No sex differences were observed for individuals who completed the structurally identical screener task, although a similar pattern of results was observed in relation to confidence. These surprising data suggest the presence of a female advantage for cardiac interoceptive accuracy and potential differences in interoceptive awareness (metacognition). Possible reasons for mixed results in the literature, as well as implications for theory and future research, are discussed.

Purpose: Online autism assessments are an effective way to help tackle the ever growing waitlist to be assessed. However, research has shown that families maintain a preference for face-to-face assessments. We examined families' anxieties both before and after having an online assessment to determine whether any initial concerns are due to a lack of exposure and information surrounding online assessments, as opposed to the quality of the assessment itself. We also aimed to understand how referral services can help families choose an online autism assessment for their child. Methods: 358 families with a child who received an online autism assessment through Healios rated their satisfaction with the service from the point of referral through to when they received the diagnostic outcome. Results: Upon entering the online assessment service, families tended to worry about the quality they expected to receive. However, after exiting the service, families held significantly higher expectations about the quality of future online autism assessments. Furthermore, families were highly satisfied throughout the entire service, except for at the point of referral. Conclusions: Our research shows families initially expect lower quality from online autism assessments, which is contrary to their high satisfaction levels upon completion. We emphasise the need for referral services to better educate families about the quality and the benefits of online autism assessments, and provide specific recommendations about what information families would like to receive to help ease their anxieties about their child receiving an online assessment.

Background Adolescence is a period of heightened vulnerability to developing mental health problems, and rates of mental health disorder in this age group have increased in the last decade. Preventing mental health problems developing before they become entrenched, particularly in adolescents who are at high risk, is an important research and clinical target. Here, we report the protocol for the trial of the ‘Building Resilience through Socioemotional Training’ (ReSET) intervention. ReSET is a new, preventative intervention that incorporates individual-based emotional training techniques and group-based social and communication skills training. We take a transdiagnostic approach, focusing on emotion processing and social mechanisms implicated in the onset and maintenance of various forms of psychopathology. Methods A cluster randomised allocation design is adopted with randomisation at the school year level. Five-hundred and forty adolescents (aged 12–14) will be randomised to either receive the intervention or not (passive control). The intervention is comprised of weekly sessions over an 8-week period, supplemented by two individual sessions. The primary outcomes, psychopathology symptoms and mental wellbeing, will be assessed pre- and post-intervention, and at a 1-year follow-up. Secondary outcomes are task-based assessments of emotion processing, social network data based on peer nominations, and subjective ratings of social relationships. These measures will be taken at baseline, post-intervention and 1-year follow-up. A subgroup of participants and stakeholders will be invited to take part in focus groups to assess the acceptability of the intervention. Discussion This project adopts a theory-based approach to the development of a new intervention designed to target the close connections between young people’s emotions and their interpersonal relationships. By embedding the intervention within a school setting and using a cluster-randomised design, we aim to develop and test a feasible, scalable intervention to prevent the onset of psychopathology in adolescence. Trial registration ISRCTN88585916. Trial registration date: 20/04/2023.

In recent years, there has been an increased interest in remote testing methods for quantifying individual differences in interoception, the perception of the body’s internal state. Hampering the adoption of remote methods are concerns as to the quality of data obtained remotely. Using data from several studies, we sought to compare the performance of individuals who completed the Phase Adjustment Task—a new measure of cardiac interoceptive accuracy that can be administered via a smartphone application—supervised in a laboratory against those who completed the task remotely. Across a total sample of 205 individuals (119 remote and 86 laboratory), we observed no significant differences in task performance between the two groups. These results held when matching groups on demographic variables (e.g., age) and considering only individuals who had successfully completed a screener task. Overall, these data attest to the suitability of the Phase Adjustment Task for remote testing, providing an opportunity to collect larger and more diverse samples for future interoception research.

Background: Previous research suggests that the processing of internal body sensations (interoception) affects how we experience pain. There is some evidence that people with fibromyalgia syndrome (FMS), which is a condition characterised by chronic pain and fatigue, may have altered interoceptive processing. However, extant findings are inconclusive, and some tasks previously used to measure interoception are of questionable validity. We used a task which overcomes problems with previous tasks – the Phase Adjustment Task (PAT) – to measure interoception in adults with FMS. Methods: We examined: (i) the tolerability of the PAT in an FMS sample (N = 154); (ii) if there are differences in facets of interoception (PAT performance, PAT-related confidence, and scores on the Private Body Consciousness Scale) between an FMS sample and an age- and gender-matched pain-free control sample (N = 94); and (iii) if subgroups of participants with FMS could be identified according to interoceptive accuracy levels. Results: After including additional task breaks and a recommended hand posture, we found that the PAT was tolerable in the FMS sample. Participants in the FMS sample were more likely to be classified as ‘interoceptive’ on the PAT, and had significantly higher self-reported interoception and interoceptive beliefs compared to participants in the pain-free sample. Within the FMS sample, participants who were classified as interoceptive on the PAT had significantly lower symptom impact than the unclassified participants. Conversely, self-reported interoception was positively correlated with FMS symptom severity and impact. Conclusions: The present findings suggest that interoception may be an important factor to consider in understanding and managing FMS symptoms, and that the PAT is a useful tool for assessing interoception in this population. We recommend future longitudinal work to better understand associations between fluctuating FMS symptoms and interoceptive processing.

Early detection of highly infectious respiratory diseases, such as COVID-19, can help curb their transmission. Consequently, there is demand for easy-to-use population-based screening tools, such as mobile health applications. Here, we describe a proof-of-concept development of a machine learning classifier for the prediction of a symptomatic respiratory disease, such as COVID-19, using smartphone-collected vital sign measurements. The Fenland App study followed 2199 UK participants that provided measurements of blood oxygen saturation, body temperature, and resting heart rate. Total of 77 positive and 6339 negative SARS-CoV-2 PCR tests were recorded. An optimal classifier to identify these positive cases was selected using an automated hyperparameter optimisation. The optimised model achieved an ROC AUC of 0.695 ± 0.045. The data collection window for determining each participant’s vital sign baseline was increased from 4 to 8 or 12 weeks with no significant difference in model performance (F(2) = 0.80, p = 0.472). We demonstrate that 4 weeks of intermittently collected vital sign measurements could be used to predict SARS-CoV-2 PCR positivity, with applicability to other diseases causing similar vital sign changes. This is the first example of an accessible, smartphone-based remote monitoring tool deployable in a public health setting to screen for potential infections.

BACKGROUND Resilience is thought to be associated with how individuals perceive, experience, and react to stressful situations. Most previous research has focused on the psychosocial and behavioral aspects of resilience. More recently, researchers have started to investigate potential biological markers of resilience e.g., heart rate variability (HRV) and blood pressure (BP). OBJECTIVE The main objective of this research study was to investigate whether resilience influences cardiovascular recovery following exposure to acute stress. METHODS Physiological markers of stress (BP, heart rate (HR) and HRV) were collected to evaluate whether resilience affects stress recovery. Participants (N=72) completed a series of questionnaires (resilience, stress, psychological distress, stressful life events, anxiety, and affect) and two mental stress tasks: a mock interview task and an arithmetic task. A recovery period followed immediately after. HRV and HR were assessed continuously throughout the research study and participants BP was assessed at three separate instances. The physiological markers were divided into three sections of at least 5 minutes of recordings (HRV and HR) for this study including: baseline (before mental stress tasks), stress period (during the mental stress tasks) and the recovery period (after mental stress tasks). RESULTS The experimental manipulation was successful as participants rated their feelings of pressure higher after stress exposure (M=4.2) than before (M=3.0, P=.04). There were also significant increases in HR between baseline (M=79.9) and the last three minutes of the mental stress tasks (M=84.5, P< .001). Following stress exposure, the high resilience group demonstrated enhanced BP and HRV recovery (Root Mean Square of the Successive Differences, RMSSD) relative to the low resilience group (P CONCLUSIONS The findings suggested that the effects of resilience are only exhibited during recovery, following a stress episode. Those with high resilience showed enhanced BP and RMSSD recovery after acute stress. The results suggested that HRV (RMSSD) could be an indicator of resilience and a protective factor for both mental and physical health. This has implications for both interventions, future research and shows how important resilience is in physiological stress recovery. CLINICALTRIAL The research was pre-registered on the Open Science Framework (osf.io/rh8dw).

BackgroundDiagnostic delays in autism are common, with the time to diagnosis being up to 3 years from the onset of symptoms. Such delays have a proven detrimental effect on individuals and families going through the process. Digital health products, such as mobile apps, can help close this gap due to their scalability and ease of access. Further, mobile apps offer the opportunity to make the diagnostic process faster and more accurate by providing additional and timely information to clinicians undergoing autism assessments. ObjectiveThe aim of this scoping review was to synthesize the available evidence about digital biomarker tools to aid clinicians, researchers in the autism field, and end users in making decisions as to their adoption within clinical and research settings. MethodsWe conducted a structured literature search on databases and search engines to identify peer-reviewed studies and regulatory submissions that describe app characteristics, validation study details, and accuracy and validity metrics of commercial and research digital biomarker apps aimed at aiding the diagnosis of autism. ResultsWe identified 4 studies evaluating 4 products: 1 commercial and 3 research apps. The accuracy of the identified apps varied between 28% and 80.6%. Sensitivity and specificity also varied, ranging from 51.6% to 81.6% and 18.5% to 80.5%, respectively. Positive predictive value ranged from 20.3% to 76.6%, and negative predictive value fluctuated between 48.7% and 97.4%. Further, we found a lack of details around participants’ demographics and, where these were reported, important imbalances in sex and ethnicity in the studies evaluating such products. Finally, evaluation methods as well as accuracy and validity metrics of available tools were not clearly reported in some cases and varied greatly across studies. Different comparators were also used, with some studies validating their tools against the Diagnostic and Statistical Manual of Mental Disorders criteria and others through self-reported measures. Further, while in most cases, 2 classes were used for algorithm validation purposes, 1 of the studies reported a third category (indeterminate). These discrepancies substantially impact the comparability and generalizability of the results, thus highlighting the need for standardized validation processes and the reporting of findings. ConclusionsDespite their popularity, systematic evaluations and syntheses of the current state of the art of digital health products are lacking. Standardized and transparent evaluations of digital health tools in diverse populations are needed to assess their real-world usability and validity, as well as help researchers, clinicians, and end users safely adopt novel tools within clinical and research practices.

Background Mobile health (mHealth) offers potential benefits to both patients and healthcare systems. Existing remote technologies to measure respiratory rates have limitations such as cost, accessibility and reliability. Using smartphone sensors to measure respiratory rates may offer a potential solution to these issues. Objective The aim of this study was to conduct a comprehensive assessment of a novel mHealth smartphone application designed to measure respiratory rates using movement sensors. Methods In Study 1, 15 participants simultaneously measured their respiratory rates with the app and a Food and Drug Administration-cleared reference device. A novel reference analysis method to allow the app to be evaluated ‘in the wild’ was also developed. In Study 2, 165 participants measured their respiratory rates using the app, and these measures were compared to the novel reference. The usability of the app was also assessed in both studies. Results The app, when compared to the Food and Drug Administration-cleared and novel references, respectively, showed a mean absolute error of 1.65 ( SD = 1.49) and 1.14 (1.44), relative mean absolute error of 12.2 (9.23) and 9.5 (18.70) and bias of 0.81 (limits of agreement = –3.27 to 4.89) and 0.08 (–3.68 to 3.51). Pearson correlation coefficients were 0.700 and 0.885. Ninety-three percent of participants successfully operated the app on their first use. Conclusions The accuracy and usability of the app demonstrated here in individuals with a normal respiratory rate range show promise for the use of mHealth solutions employing smartphone sensors to remotely monitor respiratory rates. Further research should validate the benefits that this technology may offer patients and healthcare systems.

There is debate within the literature about whether resilience should be considered a stable character trait or a dynamic, changeable process (state). Two widely used measures to assess resilience are the Connor-Davidson Resilience Scale (CD-RISC) and the Resilience Scale for Adults (RSA). The aim of this study was to evaluate the true stability (invariance) and change across time in resilience captured by these two measures. Using the perspective of Latent State-Trait theory, the aim was to decipher if the CD-RISC and the RSA are more trait-like or more state-like and to address whether true differences in resilience between participants increased (or decreased) across time. In this longitudinal study, UK-based employees (N = 378) completed the CD-RISC (10-item version) and the RSA (33-item version, aggregated and analyzed under six parcels) at three occasions over six months. A latent-state model and latent-state model with indicator specific residual factors were utilized. The analysis suggested that both questionnaires capture trait and state components of resilience. These results contribute to the discussion about how resilience scales are measuring change and stability, and how we define resilience as a more trait-like or state-like phenomena. The findings also highlight the issue of what resilience scales are measuring and whether resilience is a quantifiable construct.

Abstract IntroductionMobile health applications are increasingly being used in health and clinical research. SARS-CoV-2 has proven to have high infectivity, making outbreaks difficult to contain. Early detection can help prevent spread, but there is a need to develop easy-to-use screening tools that can help identify potential infection as early as possible. Here, we describe the development of a machine learning classifier that can predict SARS-CoV-2 PCR positivity using smartphone-submitted vital sign measurements.MethodsThe Fenland App study followed 2,199 UK participants using a smartphone application from August 2020 and for a minimum of six months. Participants completed a baseline questionnaire and then monthly questionnaires about SARS-CoV-2 status and vaccinations. Three times a week, participants provided measurements of their blood oxygen saturation, body temperature, and resting heart rate via a pulse oximeter, digital thermometer, and their smartphone. The participants participated in self initiated SARS-CoV-2 testing as per concurrent public health guidelines.We built predictive models SARS-CoV-2 PCR positivity status as obtained from national surveillance PCR test results.ResultsA total of 77 positive and 6,339 negative SARS-CoV-2 tests were recorded during the study. The final model achieved an ROC AUC of 0.695 ± 0.045. There was no difference in model performance when using 4, 8 or 12 weeks of baseline data before a SARS-CoV-2 test (F(2) = 0.80, p = 0.472). Addition of demographic or symptom information had no impact on model performance.ConclusionsUsing only three smartphone collected vital sign measurements, it is possible to predict SARS-CoV-2 PCR positivity, using a four week baseline period. Smartphone based remote monitoring of patient vital signs could allow for earlier screening for potential infections. This method could be applicable to any infectious disease that causes physiological changes in vital signs.

The burden of depression and anxiety in the world is rising. Identification of individuals at increased risk of developing these conditions would help to target them for prevention and ultimately reduce the healthcare burden. We developed a 10-year predictive algorithm for depression and anxiety using the full cohort of over 400,000 UK Biobank (UKB) participants without pre-existing depression or anxiety using digitally obtainable information. From the initial 167 variables selected from UKB, processed into 429 features, iterative backward elimination using Cox proportional hazards model was performed to select predictors which account for the majority of its predictive capability. Baseline and reduced models were then trained for depression and anxiety using both Cox and DeepSurv, a deep neural network approach to survival analysis. The baseline Cox model achieved concordance of 0.7772 and 0.7720 on the validation dataset for depression and anxiety, respectively. For the DeepSurv model, respective concordance indices were 0.7810 and 0.7728. After feature selection, the depression model contained 39 predictors and the concordance index was 0.7769 for Cox and 0.7772 for DeepSurv. The reduced anxiety model, with 53 predictors, achieved concordance of 0.7699 for Cox and 0.7710 for DeepSurv. The final models showed good discrimination and calibration in the test datasets. We developed predictive risk scores with high discrimination for depression and anxiety using the UKB cohort, incorporating predictors which are easily obtainable via smartphone. If deployed in a digital solution, it would allow individuals to track their risk, as well as provide some pointers to how to decrease it through lifestyle changes.

The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.

Background Generalized anxiety disorder (GAD) is characterized by excessive worry that is difficult to control and has high comorbidity with mood disorders including depression. Individuals experience long wait times for diagnosis and often face accessibility barriers to treatment. There is a need for a digital solution that is accessible and acceptable to those with GAD. Objective This paper aims to describe the development of a digital intervention prototype of acceptance and commitment therapy (ACT) for GAD that sits within an existing well-being app platform, BioBase. A pilot feasibility study evaluating acceptability and usability is conducted in a sample of adults with a diagnosis of GAD, self-referred to the study. Methods Phase 1 applied the person-based approach (creation of guiding principles, intervention design objectives, and the key intervention features). In Phase 2 participants received the app-based therapeutic and paired wearable for 2 weeks. Self-report questionnaires were obtained at baseline and posttreatment. The primary outcome was psychological flexibility (Acceptance and Action Questionnaire-II [AAQ-II]) as this is the aim of ACT. Mental well-being (Warwick-Edinburgh Mental Well-being Scale [WEMWBS]) and symptoms of anxiety (7-item Generalized Anxiety Disorder Assessment [GAD-7]) and depression (9-item Patient Health Questionnaire [PHQ-9]) were also assessed. Posttreatment usability was assessed via self-report measures (System Usability Scale [SUS]) in addition to interviews that further explored feasibility of the digital intervention in this sample. Results The app-based therapeutic was well received. Of 13 participants, 10 (77%) completed the treatment. Results show a high usability rating (83.5). Participants found the digital intervention to be relevant, useful, and helpful in managing their anxiety. Participants had lower anxiety (d=0.69) and depression (d=0.84) scores at exit, and these differences were significantly different from baseline (P=.03 and .008 for GAD-7 and PHQ-9, respectively). Participants had higher psychological flexibility and well-being scores at exit, although these were not significantly different from baseline (P=.11 and .55 for AAQ-II and WEMWBS, respectively). Conclusions This ACT prototype within BioBase is an acceptable and feasible digital intervention in reducing symptoms of anxiety and depression. This study suggests that this intervention warrants a larger feasibility study in adults with GAD.

Aim COVID-19 clinical presentation is heterogeneous, ranging from asymptomatic to severe cases. While there are a number of early publications relating to risk factors for COVID-19 infection, low sample size and heterogeneity in study design impacted consolidation of early findings. There is a pressing need to identify the factors which predispose patients to severe cases of COVID-19. For rapid and widespread risk stratification, these factors should be easily obtainable, inexpensive, and avoid invasive clinical procedures. The aim of our study is to fill this knowledge gap by systematically mapping all the available evidence on the association of various clinical, demographic, and lifestyle variables with the risk of specific adverse outcomes in patients with COVID-19. Methods The systematic review was conducted using standardized methodology, searching two electronic databases (PubMed and SCOPUS) for relevant literature published between 1 st January 2020 and 9 th July 2020. Included studies reported characteristics of patients with COVID-19 while reporting outcomes relating to disease severity. In the case of sufficient comparable data, meta-analyses were conducted to estimate risk of each variable. Results Seventy-six studies were identified, with a total of 17,860,001 patients across 14 countries. The studies were highly heterogeneous in terms of the sample under study, outcomes, and risk measures reported. A large number of risk factors were presented for COVID-19. Commonly reported variables for adverse outcome from COVID-19 comprised patient characteristics, including age >75 (OR: 2.65, 95% CI: 1.81–3.90), male sex (OR: 2.05, 95% CI: 1.39–3.04) and severe obesity (OR: 2.57, 95% CI: 1.31–5.05). Active cancer (OR: 1.46, 95% CI: 1.04–2.04) was associated with increased risk of severe outcome. A number of common symptoms and vital measures (respiratory rate and SpO2) also suggested elevated risk profiles. Conclusions Based on the findings of this study, a range of easily assessed parameters are valuable to predict elevated risk of severe illness and mortality as a result of COVID-19, including patient characteristics and detailed comorbidities, alongside the novel inclusion of real-time symptoms and vital measurements.