Exploring sex differences in cardiac interoceptive accuracy using the phase adjustment task.

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.

Disentangling change across the time and true stability of employees' resilience using latent state model.

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.

Deep learning with wearable based heart rate variability for prediction of mental and general health.

The ubiquity and commoditisation of wearable biosensors (fitness bands) has led to a deluge of personal healthcare data, but with limited analytics typically fed back to the user. The feasibility of feeding back more complex, seemingly unrelated measures to users was investigated, by assessing whether increased levels of stress, anxiety and depression (factors known to affect cardiac function) and general health measures could be accurately predicted using heart rate variability (HRV) data from wrist wearables alone. Levels of stress, anxiety, depression and general health were evaluated from subjective questionnaires completed on a weekly or twice-weekly basis by 652 participants. These scores were then converted into binary levels (either above or below a set threshold) for each health measure and used as tags to train Deep Neural Networks (LSTMs) to classify each health measure using HRV data alone. Three data input types were investigated: time domain, frequency domain and typical HRV measures. For mental health measures, classification accuracies of up to 83% and 73% were achieved, with five and two minute HRV data streams respectively, showing improved predictive capability and potential future wearable use for tracking stress and well-being.

Heightened interoception in adults with fibromyalgia.

Previous research suggests that the processing of internal body sensations (interoception) affects how we experience pain. Some evidence suggests that people with fibromyalgia syndrome (FMS) - 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. Here, we used an alternative measure - the Phase Adjustment Task (PAT) - to examine cardiac interoceptive accuracy in adults with FMS. 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 sample (N = 94); and (iii) if subgroups of participants with FMS are identifiable according to interoceptive accuracy levels. We found the PAT was tolerable in the FMS sample, with additional task breaks and a recommended hand posture. The FMS sample were more likely to be classified as 'interoceptive' on the PAT, and had significantly higher self-reported interoception compared to the pain-free sample. Within the FMS sample, we identified a subgroup who demonstrated very strong evidence of being interoceptive, and concurrently had lower fibromyalgia symptom impact (although the effect size was small). Conversely, self-reported interoception was positively correlated with FMS symptom severity and impact. Overall, interoception may be an important factor to consider in understanding and managing FMS symptoms. We recommend future longitudinal work to better understand associations between fluctuating FMS symptoms and interoception.

Trial protocol for the Building Resilience through Socio-Emotional Training (ReSET) programme: a cluster randomised controlled trial of a new transdiagnostic preventative intervention for adolescents.

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.

App Characteristics and Accuracy Metrics of Available Digital Biomarkers for Autism: Scoping Review.

BACKGROUND: Diagnostic 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. OBJECTIVE: The 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. METHODS: We 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. RESULTS: We 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. CONCLUSIONS: Despite 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.

Feasibility of using intermittent active monitoring of vital signs by smartphone users to predict SARS-CoV-2 PCR positivity.

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.

Smartphone movement sensors for the remote monitoring of respiratory rates: Technical validation.

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.

Measuring Interoception: The CARdiac Elevation Detection Task.

Interoception has increasingly been the focus of psychiatric research, due to its hypothesized role in mental health. Existing interoceptive tasks either suffer from important methodological limitations, impacting their validity, or are burdensome and require specialized equipment, which limits their usage in vulnerable populations. We report on the development of the CARdiac Elevation Detection (CARED) task. Participants' heart rate is recorded by a wearable device connected to a mobile application. Notifications are sent to participants' mobile throughout the day over a period of 4 weeks. Participants are asked to state whether their heart rate is higher than usual, rate their confidence and describe the activity they were involved in when the notification occurred. Data (N = 30) revealed that 1/3 of the sample was classified as interoceptive and that participants presented overall good insight into their interoceptive abilities. Given its ease of administration and accessibility, the CARED task has the potential to be a significant asset for psychiatric and developmental research.

Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population.

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.

Accelerometer-derived sleep onset timing and cardiovascular disease incidence: a UK Biobank cohort study.

Aims: Growing evidence suggests that poor sleep health is associated with cardiovascular risk. However, research in this area often relies upon recollection dependent questionnaires or diaries. Accelerometers provide an alternative tool for measuring sleep parameters objectively. This study examines the association between wrist-worn accelerometer-derived sleep onset timing and cardiovascular disease (CVD). Methods and results: We derived sleep onset and waking up time from accelerometer data collected from 103 712 UK Biobank participants over a period of 7 days. From this, we examined the association between sleep onset timing and CVD incidence using a series of Cox proportional hazards models. A total of 3172 cases of CVD were reported during a mean follow-up period of 5.7 (±0.49) years. An age- and sex-controlled base analysis found that sleep onset time of 10:00 p.m.-10:59 p.m. was associated with the lowest CVD incidence. An additional model, controlling for sleep duration, sleep irregularity, and established CVD risk factors, did not attenuate this association, producing hazard ratios of 1.24 (95% confidence interval, 1.10-1.39; P < 0.005), 1.12 (1.01-1.25; P = 0.04), and 1.25 (1.02-1.52; P = 0.03) for sleep onset <10:00 p.m., 11:00 p.m.-11:59 p.m., and ≥12:00 a.m., respectively, compared to 10:00 p.m.-10:59 p.m. Importantly, sensitivity analyses revealed this association with increased CVD risk was stronger in females, with only sleep onset <10:00 p.m. significant for males. Conclusions: Our findings suggest the possibility of a relationship between sleep onset timing and risk of developing CVD, particularly for women. We also demonstrate the potential utility of collecting information about sleep parameters via accelerometry-capable wearable devices, which may serve as novel cardiovascular risk indicators.

Development of an accessible 10-year Digital CArdioVAscular (DiCAVA) risk assessment: a UK Biobank study.

Aims: Cardiovascular diseases (CVDs) are among the leading causes of death worldwide. Predictive scores providing personalized risk of developing CVD are increasingly used in clinical practice. Most scores, however, utilize a homogenous set of features and require the presence of a physician. The aim was to develop a new risk model (DiCAVA) using statistical and machine learning techniques that could be applied in a remote setting. A secondary goal was to identify new patient-centric variables that could be incorporated into CVD risk assessments. Methods and results: Across 466 052 participants, Cox proportional hazards (CPH) and DeepSurv models were trained using 608 variables derived from the UK Biobank to investigate the 10-year risk of developing a CVD. Data-driven feature selection reduced the number of features to 47, after which reduced models were trained. Both models were compared to the Framingham score. The reduced CPH model achieved a c-index of 0.7443, whereas DeepSurv achieved a c-index of 0.7446. Both CPH and DeepSurv were superior in determining the CVD risk compared to Framingham score. Minimal difference was observed when cholesterol and blood pressure were excluded from the models (CPH: 0.741, DeepSurv: 0.739). The models show very good calibration and discrimination on the test data. Conclusion: We developed a cardiovascular risk model that has very good predictive capacity and encompasses new variables. The score could be incorporated into clinical practice and utilized in a remote setting, without the need of including cholesterol. Future studies will focus on external validation across heterogeneous samples.

Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis.

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 1st January 2020 and 9th 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.

Development and Feasibility of a Digital Acceptance and Commitment Therapy-Based Intervention for Generalized Anxiety Disorder: Pilot Acceptability Study.

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.

Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study.

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.

Development and Validation of Risk Scores for All-Cause Mortality for a Smartphone-Based "General Health Score" App: Prospective Cohort Study Using the UK Biobank.

BACKGROUND: Given the established links between an individual's behaviors and lifestyle factors and potentially adverse health outcomes, univariate or simple multivariate health metrics and scores have been developed to quantify general health at a given point in time and estimate risk of negative future outcomes. However, these health metrics may be challenging for widespread use and are unlikely to be successful at capturing the broader determinants of health in the general population. Hence, there is a need for a multidimensional yet widely employable and accessible way to obtain a comprehensive health metric. OBJECTIVE: The objective of the study was to develop and validate a novel, easily interpretable, points-based health score ("C-Score") derived from metrics measurable using smartphone components and iterations thereof that utilize statistical modeling and machine learning (ML) approaches. METHODS: A literature review was conducted to identify relevant predictor variables for inclusion in the first iteration of a points-based model. This was followed by a prospective cohort study in a UK Biobank population for the purposes of validating the C-Score and developing and comparatively validating variations of the score using statistical and ML models to assess the balance between expediency and ease of interpretability and model complexity. Primary and secondary outcome measures were discrimination of a points-based score for all-cause mortality within 10 years (Harrell c-statistic) and discrimination and calibration of Cox proportional hazards models and ML models that incorporate C-Score values (or raw data inputs) and other predictors to predict the risk of all-cause mortality within 10 years. RESULTS: The study cohort comprised 420,560 individuals. During a cohort follow-up of 4,526,452 person-years, there were 16,188 deaths from any cause (3.85%). The points-based model had good discrimination (c-statistic=0.66). There was a 31% relative reduction in risk of all-cause mortality per decile of increasing C-Score (hazard ratio of 0.69, 95% CI 0.663-0.675). A Cox model integrating age and C-Score had improved discrimination (8 percentage points; c-statistic=0.74) and good calibration. ML approaches did not offer improved discrimination over statistical modeling. CONCLUSIONS: The novel health metric ("C-Score") has good predictive capabilities for all-cause mortality within 10 years. Embedding the C-Score within a smartphone app may represent a useful tool for democratized, individualized health risk prediction. A simple Cox model using C-Score and age balances parsimony and accuracy of risk predictions and could be used to produce absolute risk estimations for app users.

Implementation of a mHealth solution to remotely monitor patients on a cardiac surgical waiting list: service evaluation.

BACKGROUND: The emergence of COVID-19 resulted in postponement of nonemergent surgical procedures for cardiac patients in London. mHealth represented a potentially viable mechanism for highlighting deteriorating patients on the lengthened cardiac surgical waiting lists. OBJECTIVE: To evaluate the deployment of a digital health solution to support continuous triaging of patients on a cardiac surgical waiting list. METHOD: An NHS trust utilized an app-based mHealth solution (Huma Therapeutics) to help gather vital information on patients awaiting cardiac surgery (valvular and coronary surgery). Patients at a tertiary cardiac center on a waiting list for elective surgery were given the option to be monitored remotely via a mobile app until their date of surgery. Patients were asked to enter their symptoms once a week. The clinical team monitored this information remotely, prompting intervention for those patients who needed it. RESULTS: Five hundred and twenty-five patients were on boarded onto the app. Of the 525 patients using the solution, 51 (9.71%) were identified as at risk of deteriorating based on data captured via the remote patient monitoring platform and subsequently escalated to their respective consultant. 81.7% of patients input at least one symptom after they were on boarded on the platform. DISCUSSION: Although not a generalizable study, this change in practice clearly demonstrates the feasibility and potential benefit digital remote patient monitoring can have in triaging large surgical wait lists, ensuring those that need care urgently receive it. We recommend further study into the potential beneficial outcomes from preoperative cardiac mHealth solutions.

Exploring the Effects of a Brief Biofeedback Breathing Session Delivered Through the BioBase App in Facilitating Employee Stress Recovery: Randomized Experimental Study.

BACKGROUND: Recovery from stress is a predictive factor for cardiovascular health, and heart rate variability (HRV) is suggested to be an index of how well people physiologically recover from stress. Biofeedback and mindfulness interventions that include guided breathing have been shown to be effective in increasing HRV and facilitating stress recovery. OBJECTIVE: This study aims to assess the effectiveness of a brief app-based breathing intervention (BioBase) in enhancing physiological recovery among employees who were induced to cognitive and emotional stress. METHODS: In total, we recruited 75 full-time employees. Interbeat (RR) intervals were recorded continuously for 5 min at baseline and during cognitive and emotional stress induction. The session ended with a 5-min recovery period during which participants were randomly allocated into 3 conditions: app-based breathing (BioBase), mindfulness body scan, or control. Subjective tension was assessed at the end of each period. RESULTS: Subjective tension significantly increased following stress induction. HRV significantly decreased following the stress period. In the recovery phase, the root mean square of successive RR interval differences (P=.002), the percentage of successive RR intervals that differed by >50 ms (P=.008), and high frequency (P=.01) were significantly higher in the BioBase breathing condition than in the mindfulness body scan and the control groups. CONCLUSIONS: Biofeedback breathing interventions digitally delivered through a commercially available app can be effective in facilitating stress recovery among employees. These findings contribute to the mobile health literature on the beneficial effects of brief app-based breathing interventions on employees' cardiovascular health.

Testing the independence of self-reported interoceptive accuracy and attention.

It has recently been proposed that measures of the perception of the state of one's own body ("interoception") can be categorised as one of several types depending on both how an assessment is obtained (objective measurement vs. self-report) and what is assessed (degree of interoceptive attention vs. accuracy of interoceptive perception). Under this model, a distinction is made between beliefs regarding the degree to which interoceptive signals are the object of attention and beliefs regarding one's ability to perceive accurately interoceptive signals. This distinction is difficult to test, however, because of the paucity of measures designed to assess self-reported perception of one's own interoceptive accuracy. This article therefore reports on the development of such a measure, the Interoceptive Accuracy Scale (IAS). Use of this measure enables assessment of the proposed distinction between beliefs regarding attention to, and accuracy in perceiving, interoceptive signals. Across six studies, we report on the development of the IAS and, importantly, its relationship with measures of trait self-reported interoceptive attention, objective interoceptive accuracy, confidence in the accuracy of specific interoceptive percepts, and metacognition with respect to interoceptive accuracy. Results support the distinction between individual differences in perceived attention towards interoceptive information and the accuracy of interoceptive perception.

Effectiveness of a Smartphone App (BioBase) for Reducing Anxiety and Increasing Mental Well-Being: Pilot Feasibility and Acceptability Study.

BACKGROUND: The prevalence of workplace-related stress and anxiety is high, resulting in stress-related physical and mental illness. Digital self-guided interventions aimed at key areas of workplace design may be able to provide remote anxiolytic effects. OBJECTIVE: The aim of this feasibility study is to assess changes in anxiety and mental well-being after use of the BioBase programme, a mobile phone platform for psycho-educational modules, tools, and real-time feedback of physiological data. METHODS: A 4-week observational study was carried out in 55 healthy adults who were screened for stress with the Depression Anxiety Stress Scale (DASS) Stress subscale. Participants completed anxiety (6-item State-Trait Anxiety Inventory [STAI]) and mental well-being (Warwick-Edinburgh Mental Well-being Scale [WEMWBS]) questionnaires at baseline and at 4 weeks. Feedback questionnaires were administered after 4 weeks. RESULTS: After 4 weeks of using the programme and controlling for any effect of being paid to take part in the study, STAI significantly decreased (baseline mean 45.52 [SD 13.2]; 4-week mean 39.82 [SD 11.2]; t54=-3.51; P<.001; CI -8.88 to -2.52; Cohen d=0.96) and WEMWBS significantly increased (baseline mean 48.12 [SD 6.4]; 4-week mean 50.4 [SD 6.9]; t53=2.41; P=.019; CI 0.44-4.23; Cohen d=0.66). Further, higher baseline stress was significantly associated with a greater decrease in STAI (t53=-3.41; P=.001; CI -8.10 to -2.10; R2=0.180) and a greater increase in WEMWBS (t52=2.41; P=.019; CI 0.38-4.11, R2=0.101). On feedback, participants found the programme easy to use/navigate, with the content being acceptable and relevant to workplace-related stressors; 70% (21/30) of participants would recommend the programme to a friend. CONCLUSIONS: The BioBase programme is a potentially effective intervention in decreasing anxiety and increasing mental well-being, with larger changes in those with higher baseline levels of stress.