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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).

BackgroundReproductive health conditions such as endometriosis, uterine fibroids and polycystic ovary syndrome affect a large proportion of women and people who menstruate worldwide. Prevalence estimates for these conditions range from 5-40% of women of reproductive age. Long diagnostic delays, up to 12 years, are common and contribute to health complications and increased healthcare costs. Symptom checker apps provide users with information and tools to better understand their symptoms and thus have the potential to reduce the time to diagnosis for reproductive health conditions.ObjectiveThis study aims to evaluate the accuracy of three symptom checkers developed by Flo Health assessing symptoms of endometriosis, uterine fibroids and polycystic ovary syndrome (PCOS) against current medical guidelines.MethodsIndependent general practitioners were recruited to create clinical case vignettes of simulated users with and without the conditions of interest. Vignettes were reviewed, modified and approved by separate general practitioners. A further independent panel of general practitioners reviewed the cases and designated a final classification. Vignettes were entered into the symptom checkers and the outcomes were compared with the final classification from the panel using accuracy metrics including percent agreement, sensitivity and specificity.ResultsA total of 24 cases were created per condition. Overall, exact matches between the vignette classification and the symptom checker outcome was 83.3% for endometriosis and uterine fibroids, and 87.5% for PCOS. While sensitivity was high for all conditions (>81%) and very high (100%) for PCOS, specificity was >81% for endometriosis and uterine fibroids and 75% for PCOS.ConclusionThe single condition symptom checkers have high levels of accuracy for endometriosis, uterine fibroids and PCOS. Given long delays in diagnosis for many reproductive health conditions, which lead to increased medical costs and potential health complications for individuals and healthcare providers, innovative health apps and symptom checkers hold the potential to improve care pathways.

BACKGROUND Reproductive health conditions such as endometriosis, uterine fibroids and polycystic ovary syndrome affect a large proportion of women and people who menstruate worldwide. Prevalence estimates for these conditions range from 5-40% of women of reproductive age. Long diagnostic delays, up to 12 years, are common and contribute to health complications and increased healthcare costs. Symptom checker apps provide users with information and tools to better understand their symptoms and thus have the potential to reduce the time to diagnosis for reproductive health conditions. OBJECTIVE This study aims to evaluate the accuracy of three symptom checkers developed by Flo Health assessing symptoms of endometriosis, uterine fibroids and polycystic ovary syndrome (PCOS) against current medical guidelines. METHODS Independent general practitioners were recruited to create clinical case vignettes of simulated users with and without the conditions of interest. Vignettes were reviewed, modified and approved by separate general practitioners. A further independent panel of general practitioners reviewed the cases and designated a final classification. Vignettes were entered into the symptom checkers and the outcomes were compared with the final classification from the panel using accuracy metrics including percent agreement, sensitivity and specificity. RESULTS A total of 24 cases were created per condition. Overall, exact matches between the vignette classification and the symptom checker outcome was 83.3% for endometriosis and uterine fibroids, and 87.5% for PCOS. While sensitivity was high for all conditions (>81%) and very high (100%) for PCOS, specificity was >81% for endometriosis and uterine fibroids and 75% for PCOS. CONCLUSIONS The single condition symptom checkers have high levels of accuracy for endometriosis, uterine fibroids and PCOS. Given long delays in diagnosis for many reproductive health conditions, which lead to increased medical costs and potential health complications for individuals and healthcare providers, innovative health apps and symptom checkers hold the potential to improve care pathways.

ObjectiveTo investigate symptomatology throughout the menstrual cycle, including the frequency of symptoms reported for each phase of the cycle and the relationship between symptoms and individual characteristics.MethodsThis study included self-reported cycle information and symptoms during ovulatory menstrual cycles in mobile application users. Symptom frequency was described overall, by phase, and by day. Relationships between symptoms and individual characteristics (age, body mass index) were tested with logistic regression.Results224,676 unique users and 498,126 unique cycles were included in the analysis. Somatic symptoms were most frequently reported, logged in 85.3% of cycles. Of total symptoms logged, somatic, gastrointestinal, and negative mood symptoms were most prevalent in the late luteal and bleeding days, while positive mood and discharge were most common in the fertile window. Symptom experience differed by age.ConclusionsMenstrual cycle-related symptoms are very common and vary throughout the cycle phases. While most women report negative symptoms including pain, bloating, and negative mood, women also report positive symptoms such as energetic and happy mood. Self-reported data from cycle tracking and health apps can improve our understanding of the prevalence and variability of menstrual-related symptoms.Funding SourceFlo Health UK LimitedPrecisMenstrual symptoms include both positive and negative physical and mood symptoms and differ by age.

The chronic and acute effects of stress can have divergent effects on health; long-termeffects are associated with detrimental physical and mental health sequelae, while acute effectsmay be advantageous in the short-term. Stress-induced analgesia, the attenuation of painperception due to stress, is a well-known phenomenon that has yet to be systematicallyinvestigated under ecological conditions. Using Flo, a women’s health app with a world-widemonthly active usership of more than 48 million, women in Ukraine were monitored for theirreporting of stress, pain and affective symptoms before, and immediately after, the onset of theRussian-Ukrainian conflict. To avoid potential selection (attrition) or collider bias, we rely on asample of 87,315 users who were actively logging multiple symptoms before and after the startof the war. We found an inverse relationship between stress and pain, whereby higher reports ofstress predicted lower rates of pain. Stress didn’t influence any other physiological symptomswith a similar magnitude, nor any other symptom had a similar effect on pain. This relationshipgeneralised with decreasing magnitude to countries neighbouring Ukraine, with Ukraine servingas the epicentre. These findings help characterise the relationship between stress and health in areal-world setting.

BACKGROUND Research shows that poor knowledge and awareness of menstrual and pregnancy health among women are associated with adverse reproductive health and pregnancy outcomes. Menstrual cycle– and pregnancy-tracking mobile apps are promising tools for improving women’s awareness of and attitudes toward their reproductive health; however, there is little information about subscribers’ perceptions of app functionality and its impact on their knowledge and health. OBJECTIVE This study aimed to explore knowledge and health improvements related to menstrual cycle and pregnancy, as well as improvements in general health among Flo app users. We also investigated what components of the Flo app were associated with the abovementioned improvements and evaluated whether those improvements differed based on education level, country of residence (low- and middle-income vs high-income countries), free or premium subscription to the app, short- or long-term use of the app, and frequency of use. METHODS Flo subscribers who had been using the app for no less than 30 days, completed a web-based survey. A total of 2212 complete survey responses were collected. The survey included demographic questions and questions about motivations guiding the use of the Flo app and which components of the app improved their knowledge and health, as well as to what extent. RESULTS Most study participants reported improvements in menstrual cycle (1292/1452, 88.98%) and pregnancy (698/824, 84.7%) knowledge from Flo app use. Participants with higher levels of education and those from high-income countries reported using the app predominantly for getting pregnant (χ21=4.2, P=.04; χ21=52.3, Pχ21=19.3, Pχ21=20.9, P=.001, respectively). Participants with less education reported using the app to avoid pregnancy (χ21=4.2; P=.04) and to learn more about their body (χ21=10.8; P=.001) and sexual health (χ21=6.3; P=.01), while participants from low- and middle-income countries intended to mainly learn more about their sexual health (χ21=18.2; P CONCLUSIONS This study suggests that menstrual health apps, such as Flo, could present revolutionary tools to promote consumer health education and empowerment on a global scale.

Introduction: Low health literacy among girls, women, and people who menstruate is associated with worse reproductive health outcomes. Mobile apps promise tools for improving women’s health literacy, however, there is little information about subscribers’ perceptions of app functionality and its impact on their knowledge and health. Objective: This study aimed to explore knowledge and health improvements related to menstrual cycle and pregnancy, as well as improvements in general health in Flo app users. We also investigated what components of the Flo app were associated with the above mentioned improvements, and evaluated whether those improvements differed based on education level, free or a premium subscription to the app, short or long term use of the app, and frequency of use. Methods: Flo subscribers who had been using the app for no less than 30 days completed an online survey. A total of 2214 of complete survey responses were collected. The survey included demographic questions, questions about motivations guiding usage of the Flo app, to what extent and what components of the app may have helped improve their knowledge and health (i.e., reproductive, sexual, physical, mental health, health behaviours, and communication with healthcare providers).Results: Most study participants reported menstrual cycle (89.0%) and pregnancy (84.6%) knowledge improvements from Flo app use. 62.3% of Flo users reported ‘period predictions’ and 72.2% reported ‘reading and/or watching articles and video sources in the app’ helped them feel the most informed about their cycle and pregnancy health. Higher educated, Flo Premium, frequent, and short term app users reported using the app mainly for getting pregnant and pregnancy tracking. Lower educated and long time users reported using the app to learn more about their body, sexual health, irregular cycles and related conditions, and to avoid pregnancy. Importantly, intended uses of the app for both lower and higher educated users matched areas in which they have gained knowledge and achieved their health goals upon the Flo app use. Flo Premium, frequent, and long term users were more likely to report improvements in knowledge and health in all areas they chose to use the app for and even beyond.Conclusion: There is a need to increase reproductive health literacy in the global female population. This study has shown that the use of the period tracking app, Flo, as self-reported by its users, improved their menstrual cycle and pregnancy knowledge and health. Additionally, more frequent, long-term, and Flo Premium users were more likely to report knowledge and health improvements across multiple health areas. Thus, the use of digital health apps, such as the Flo app, should be considered for integration into healthcare systems to increase women’s health literacy in the wider population.

UNSTRUCTURED Interoception has increasingly been the focus of psychiatric research, due to its hypothesised role in mental health. Existing interoceptive tasks are often burdensome and require specialised equipment, limiting 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 ⅓ 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.

Interoception, perception of one's bodily state, has been associated with mental health and socio-emotional processes. However, several interoception tasks are of questionable validity, meaning associations between interoception and other variables require confirmation with new measures. Here we describe the novel, smartphone-based Phase Adjustment Task (PAT). Tones are presented at the participant's heart rate, but out of phase with heartbeats. Participants adjust the phase relationship between tones and heartbeats until they are synchronous. Data from 124 participants indicates variance in performance across participants which is not affected by physiological or strategic confounds. Associations between interoception and anxiety, depression and stress were not significant. Weak associations between interoception and mental health variables may be a consequence of testing a non-clinical sample. A second study revealed PAT performance to be moderately stable over one week, consistent with state effects on interoception.

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.

AimCOVID-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.MethodsThe systematic review was conducted using standardized methodology, searching two electronic databases (PubMed and SCOPUS) for relevant literature published between 1stJanuary 2020 and 9thJuly 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.ResultsSeventy-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.ConclusionsBased 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.

BackgroundEven though established links exist between individuals behaviours and potentially adverse health outcomes, to date either univariate, simpler models or multivariate, yet difficult to employ ones, have been developed. Such models are unlikely to be successful at capturing the wider determinants of health in the broader population. Hence, there is a need for a multidimensional, yet widely employable and accessible, way to obtain a comprehensive health metric.ObjectiveTo develop and validate a novel, easily interpretable points-based health score (“C-Score”) derived from metrics measurable using smartphone components, and iterations thereof that utilise statistical modelling and machine learning approaches.MethodsComprehensive literature review to identify suitable predictor variables for inclusion in a first iteration 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/machine learning models to assess the balance between expediency and ease of interpretability versus model complexity. Primary and secondary outcome measures: Discrimination of a points-based score for all-cause mortality within 10 years (Harrell’s c-statistic). Discrimination and calibration of Cox proportional hazards models and machine learning models that incorporate C-Score values (or raw data inputs) and other predictors to predict risk of all-cause mortality within 10 years.ResultsThe 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: 0.69, 95% CI: 0.663 to 0.675). A Cox model integrating age and C-Score had improved discrimination (8% percentage points, c-statistic = 0.74) and good calibration. Machine learning approaches did not offer improved discrimination over statistical modelling.ConclusionsThe novel health metric (‘C-Score’) has good predictive capabilities for all-cause mortality within 10 years. Embedding C-Score within a smartphone application may represent a useful tool for democratised, individualised health risk prediction. A simple Cox model using C-Score and age optimally balances parsimony and accuracy of risk predictions and could be used to produce absolute risk estimations for application users.

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.

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 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.

Evidence indicates that bodily perception is negatively related to Parkinson’s disease (PD); in particular, people with Parkinson’s (PwP) feel dissatisfaction in their physical abilities and appearance. While established treatments exist to ameliorate motor symptoms in PD, research has yet to explore the effects of well-being-focused interventions in relation to the subjective experience of bodily concerns of PwP. This mixed methods exploratory study investigated the constructs of body appreciation in relation to well-being in PwP and the impact of participation in a dance class on body appreciation and well-being, comparing PwP with age-matched controls. Participants (n = 27 PwP, n = 14 controls) completed the Warwick Edinburgh Mental Well-Being Scale and the Body Appreciation Scale before and after taking part in a dance class. Well-being was positively associated with body appreciation in PwP (rs = 0.64, p < 0.001) but not in controls. Following participation in a dance class, all participants’ well-being scores increased; a greater increase in well-being scores was observed for controls. A pilot qualitative study explored bodily awareness with PwP who attended dance classes (n = 4) and other movement-based activities (n = 4). Analysis of the interview data indicated that PwP who danced showed heightened bodily awareness, including bodily limitations, in comparison with PwP who did not dance. These preliminary findings provide initial insight explaining the lack of improvements in body appreciation in PwP following a dance class. The current study highlights the need for dance interventions for PwP to consider incorporating elements that encourage a body positive attitude alongside fostering perceived well-being.

Background: Interoception, the perception of one’s internal bodily state, has been repeatedly linked to mental health, and atypical interoception proposed as a transdiagnostic risk factor. Despite the clinical importance of interoception, existing measures are suboptimal as they are susceptible to physiological and psychological confounds. Furthermore, existing measures are lab-based, limiting both scalability and accessibility.Methods: Here we describe a novel measure, the Phase Adjustment Task (PAT), administered via smartphone. During the task tones are presented at the participant’s heartrate (recorded via smartphone camera), but out of phase with heartbeats. Participants are required to adjust the phase relationship between tones and heartbeats until they are synchronous.Results: Data from 124 participants indicates both variance in performance across participants and that performance is not affected by physiological or strategic variables. Furthermore, results are consistent with previous data on the temporal characteristics of cardiac interoception. Conclusions: Given its validity, accessibility and scalability, the PAT task is suitable for large-scale clinical interoception research.

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.

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.

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.

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; Pd=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.

BACKGROUND University students in the United Kingdom are experiencing increasing levels of anxiety. A program designed to increase awareness of one’s present levels of well-being and suggest personalized health behaviors may reduce anxiety and improve mental well-being in students. The efficacy of a digital version of such a program, providing biofeedback and therapeutic content based on personalized well-being metrics, is reported here. OBJECTIVE The aim of this study was to test the efficacy and sustained effects of using a mobile app (BioBase) and paired wearable device (BioBeam), compared with a waitlist control group, on anxiety and well-being in university students with elevated levels of anxiety and stress. METHODS The study employed a randomized, waitlist-controlled trial with assessments at baseline, 2 weeks, postintervention (4 weeks), and follow-up (6 weeks). Participants were eligible if they were current full-time undergraduate students and (1) at least 18 years of age, (2) scored >14 points on the Depression, Anxiety, and Stress Scale-21 items (DASS-21) stress subscale or >7 points on the DASS-21 anxiety subscale, (3) owned an iOS mobile phone, (4) did not have any previous psychiatric or neurological conditions, (6) were not pregnant at the time of testing, and (7) were able to read and understand English. Participants were encouraged to use BioBase daily and complete at least one course of therapeutic content. A P value ≤.05 was considered statistically significant. RESULTS We found that a 4-week intervention with the BioBase program significantly reduced anxiety and increased perceived well-being, with sustained effects at a 2-week follow-up. Furthermore, a significant reduction in depression levels was found following the 4-week usage of BioBase. CONCLUSIONS This study shows the efficacy of a biofeedback digital intervention in reducing self-reported anxiety and increasing perceived well-being in UK university students. Results suggest that digital mental health interventions could constitute a novel approach to treat stress and anxiety in students, which could be combined or integrated with existing therapeutic pathways. CLINICALTRIAL Open Science Framework (OSF.io) 2zd45; https://osf.io/2zd45/

BackgroundThe vestibular system has been shown to contribute to multisensory integration by balancing conflictual sensory information. It remains unclear whether such modulation of exteroceptive (e.g. vision), proprioceptive and interoceptive (e.g. affective touch) sensory sources is influenced by epistemically different aspects of tactile stimulation (i.e. felt from within vs seen, vicarious touch).ObjectiveWe aimed to i) replicate previous findings regarding the effects of galvanic stimulation of the right vestibular network (i.e. LGVS) in multisensory integration and ii) examine vestibular contributions to multisensory integration when touch is felt but not seen (and vice-versa).MethodDuring artificial vestibular stimulation (LGVS, RGVS and Sham), healthy participants (N=36, Experiment 1; N=37, Experiment 2) looked at a rubber hand while either their own unseen hand or the rubber hand were touched by affective or neutral touch.ResultsWe found that i) LGVS led to enhancement of vision over proprioception during visual only conditions (replicating our previous findings), and ii) LGVS (vs Sham) favoured proprioception over vision when touch was felt (Experiment 1), with the opposite results when touch was vicariously perceived via vision (Experiment 2), and with no difference between affective and neutral touch.ConclusionsWe showed how vestibular signals modulate the weight of each sensory modality according to the context in which they are perceived and that such modulation extends to different aspects of tactile stimulation: felt and seen touch are differentially balanced in multisensory integration according to their epistemic relevance.HighlightsLGVS increased proprioceptive drift during vision of a rubber handTouch on participant’s hand decreased proprioceptive drift during LGVSVicarious touch on the Rubber Hand increased proprioceptive drift during LGVSVestibular signals differently balance sensory sources in multisensory integration

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.

Background University students in the United Kingdom are experiencing increasing levels of anxiety. A program designed to increase awareness of one’s present levels of well-being and suggest personalized health behaviors may reduce anxiety and improve mental well-being in students. The efficacy of a digital version of such a program, providing biofeedback and therapeutic content based on personalized well-being metrics, is reported here. Objective The aim of this study was to test the efficacy and sustained effects of using a mobile app (BioBase) and paired wearable device (BioBeam), compared with a waitlist control group, on anxiety and well-being in university students with elevated levels of anxiety and stress. Methods The study employed a randomized, waitlist-controlled trial with assessments at baseline, 2 weeks, postintervention (4 weeks), and follow-up (6 weeks). Participants were eligible if they were current full-time undergraduate students and (1) at least 18 years of age, (2) scored >14 points on the Depression, Anxiety, and Stress Scale-21 items (DASS-21) stress subscale or >7 points on the DASS-21 anxiety subscale, (3) owned an iOS mobile phone, (4) did not have any previous psychiatric or neurological conditions, (6) were not pregnant at the time of testing, and (7) were able to read and understand English. Participants were encouraged to use BioBase daily and complete at least one course of therapeutic content. A P value ≤.05 was considered statistically significant. Results We found that a 4-week intervention with the BioBase program significantly reduced anxiety and increased perceived well-being, with sustained effects at a 2-week follow-up. Furthermore, a significant reduction in depression levels was found following the 4-week usage of BioBase. Conclusions This study shows the efficacy of a biofeedback digital intervention in reducing self-reported anxiety and increasing perceived well-being in UK university students. Results suggest that digital mental health interventions could constitute a novel approach to treat stress and anxiety in students, which could be combined or integrated with existing therapeutic pathways. Trial Registration Open Science Framework (OSF.io) 2zd45; https://osf.io/2zd45/

The experience of our body as our own (i.e. body ownership) involves integrating different sensory signals according to their contextual relevance (i.e. multisensory integration). Until recently, most studies of multisensory integration and body ownership concerned only vision, touch and proprioception; the role of other modalities, such as the vestibular system and interoception, has been neglected and remains poorly understood. In particular, no study to date has directly explored the combined effect of vestibular and interoceptive signals on body ownership. Here, we investigated for the first time how Galvanic Vestibular Stimulation (left, right, sham), tactile affectivity (a reclassified interoceptive modality manipulated by applying touch at C-tactile optimal versus non-optimal velocities), and their combination, influence proprioceptive and subjective measures of body ownership during a rubber hand illusion paradigm with healthy participants (N = 26). Our results show that vestibular stimulation (left GVS) significantly increased proprioceptive drift towards the rubber hand during mere visual exposure to the rubber hand. Moreover, it also enhanced participants’ proprioceptive drift towards the rubber hand during manipulations of synchronicity and affective touch. These findings suggest that the vestibular system influences multisensory integration, possibly by re-weighting both the two-way relationship between proprioception and vision, as well as the three-way relationship between proprioception, vision and affective touch. We discuss these findings in relation to current predictive coding models of multisensory integration and body ownership., Highlights • We studied vestibular and affective contributions to body ownership. • We stimulated the vestibular system in a Rubber Hand paradigm with affective touch. • Right-hemisphere stimulation increased proprioceptive drift during vision of a RH. • Applying affective touch further increased proprioceptive drift. • Affective and vestibular signals may favour vision in multisensory integration.