Mood instability in patients with unipolar depression measured using smartphones and the association with measures of wellbeing, recovery and functioning.

OBJECTIVE: While mood instability is strongly linked to depression, its ramifications remain unexplored. In patients diagnosed with unipolar depression (UD), our objective was to investigate the association between mood instability, calculated based on daily smartphone-based patient-reported data on mood, and functioning, quality of life, perceived stress, empowerment, rumination, recovery, worrying and wellbeing. METHODS: Patients with UD completed daily smartphone-based self-assessments of mood for 6 months, making it possible to calculate mood instability using the Root Mean Squared Successive Difference (rMSSD) method. A total of 59 patients with UD were included. Data were analyzed using mixed effects regression models. RESULTS: There was a statistically significant association between increased mood instability and increased perceived stress (adjusted model: B: 0.010, 95% CI: 0.00027; 0.021, p = 0.044), and worrying (adjusted model: B: 0.0060, 95% CI: 0.000016; 0.012, p = 0.049), and decreased quality of life (adjusted model: B: -0.0056, 95% CI: -0.011; -0.00028, p = 0.039), recovery (adjusted model: B: -0.032, 95% CI: -0.0059; -0.00053, p = 0.019) and wellbeing. There were no statistically significant associations between mood instability and functioning, empowerment, and rumination (p's >0.09). CONCLUSION: These findings underscore the significant influence of mood instability on patients' daily lives. Identification of mood fluctuations offer potential insights into the trajectory of the illness in these individuals.

Perceptions and effectiveness of episodic future thinking as digital micro-interventions based on mobile health technology.

Objective: Delay discounting denotes the tendency for humans to favor short-term immediate benefits over long-term future benefits. Episodic future thinking (EFT) is an intervention that addresses this tendency by having a person mentally "pre-experience" a future event to increase the perceived value of future benefits. This study explores the feasibility of using mobile health (mHealth) technology to deliver EFT micro-interventions. Micro-interventions are small, focused interventions aiming to achieve goals while matching users' often limited willingness or capacity to engage with interventions. We aim to explore whether EFT delivered as digital micro-interventions can reduce delay discounting, the users' perceptions, and if there are differences between regular EFT and goal-oriented EFT (gEFT), a variant where goals are embedded into future events. Method: A randomized study was conducted with 208 participants allocated to either gEFT, EFT, or a control group for a 21-day study. Results: Results indicate intervention groups when combined achieved a significant reduction of Δlogk=-.80 in delay discounting (p=.017) compared to the control. When split into gEFT and EFT separately only the reduction of Δlogk=.96 in EFT delay discounting was significant (p=.045). We further explore and discuss thematic user perceptions. Conclusions: Overall, user perceptions indicate gEFT may be slightly better for use in micro-interventions. However, perceptions also indicate that audio-based EFT micro-interventions were not always preferable to users, with findings suggesting that future EFT micro-interventions should be delivered using different forms of multimedia based on user preference and context and supported by other micro-interventions to maintain interest.

Using digital phenotyping to classify bipolar disorder and unipolar disorder - exploratory findings using machine learning models.

The aims were to investigate 1) differences in smartphone-based data on phone usage between bipolar disorder (BD) and unipolar disorder (UD) and 2) by using machine learning models, the sensitivity, specificity, and AUC of the combined smartphone data in classifying BD and UD. Daily smartphone-based self-assessments of mood and same-time passively collected smartphone data on smartphone usage was available for six months. A total of 64 patients with BD and 74 patients with UD were included. Patients with BD during euthymic states compared with UD in euthymic states had a lower number of incoming phone calls/ day (B: -0.70, 95%CI: -1.37; -0.70, p=0.040). Patients with BD during depressive states had a lower number of incoming and outgoing phone calls/ day as compared with patients with UD in depressive states. In classification by using machine learning models, 1) overall (regardless of the affective state), patients with BD were classified with an AUC of 0.84, which reduced to 0.48 when using a leave-one-patient-out crossvalidation (LOOCV) approach; similarly 2) during a depressive state, patients with BD were classified with an AUC of 0.86, which reduced to 0.42 with LOOCV; 3) during a euthymic state, patients with BD were classified with an AUC of 0.87, which reduced to 0.46 with LOOCV. While digital phenotyping shows promise in differentiating between patients with BD and UD, it highlights the challenge of generalizing to unseen individuals. It should serve as an complement to comprehensive clinical evaluation by clinicians.

The effect of smartphone-based monitoring and treatment including clinical feedback versus smartphone-based monitoring without clinical feedback in bipolar disorder: the SmartBipolar trial-a study protocol for a randomized controlled parallel-group trial.

INTRODUCTION: A substantial proportion of patients with bipolar disorder experience daily subsyndromal mood swings, and the term "mood instability" reflecting the variability in mood seems associated with poor prognostic factors, including impaired functioning, and increased risk of hospitalization and relapse. During the last decade, we have developed and tested a smartphone-based system for monitoring bipolar disorder. The present SmartBipolar randomized controlled trial (RCT) aims to investigate whether (1) daily smartphone-based outpatient monitoring and treatment including clinical feedback versus (2) daily smartphone-based monitoring without clinical feedback or (3) daily smartphone-based mood monitoring only improves mood instability and other clinically relevant patient-related outcomes in patients with bipolar disorder. METHODS AND ANALYSIS: The SmartBipolar trial is a pragmatic randomized controlled parallel-group trial. Patients with bipolar disorder are invited to participate as part of their specialized outpatient treatment for patients with bipolar disorder in Mental Health Services in the Capital Region of Denmark. The included patients will be randomized to (1) daily smartphone-based monitoring and treatment including a clinical feedback loop (intervention group) or (2) daily smartphone-based monitoring without a clinical feedback loop (control group) or (3) daily smartphone-based mood monitoring only (control group). All patients receive specialized outpatient treatment for bipolar disorder in the Mental Health Services in the Capital Region of Denmark. The trial started in March 2021 and has currently included 150 patients. The outcomes are (1) mood instability (primary), (2) quality of life, self-rated depressive symptoms, self-rated manic symptoms, perceived stress, satisfaction with care, cumulated number and duration of psychiatric hospitalizations, and medication (secondary), and (3) smartphone-based measures per month of stress, anxiety, irritability, activity, and sleep as well as the percentage of days with presence of mixed mood, days with adherence to medication and adherence to smartphone-based self-monitoring. A total of 201 patients with bipolar disorder will be included in the SmartBipolar trial. ETHICS AND DISSEMINATION: The SmartBipolar trial is funded by the Capital Region of Denmark and the Independent Research Fund Denmark. Ethical approval has been obtained from the Regional Ethical Committee in The Capital Region of Denmark (H-19067248) as well as data permission (journal number: P-2019-809). The results will be published in peer-reviewed academic journals, presented at scientific meetings, and disseminated to patients' organizations and media outlets. TRIAL REGISTRATION: Trial registration number: NCT04230421. Date March 1, 2021. Version 1.

Reaching the Frail Elderly for the Diagnosis and Management of Atrial Fibrillation-REAFEL.

BACKGROUND: Frail elderly patients are exposed to suffering strokes if they do not receive timely anticoagulation to prevent stroke associated to atrial fibrillation (AF). Evaluation in the cardiological ambulatory can be cumbersome as it often requires repeated visits. AIM: To develop and implement CardioShare, a shared-care model where primary care leads patient management, using a compact Holter monitor device with asynchronous remote support from cardiologists. METHODS: CardioShare was developed in a feasibility phase, tested in a pragmatic cluster randomization trial (primary care clinics as clusters), and its implementation potential was evaluated with an escalation test. Mixed methods were used to evaluate the impact of this complex intervention, comprising quantitative observations, semi-structured interviews, and workshops. RESULTS: Between February 2020 and December 2021, 314 patients (30% frail) were included, of whom 75% had AF diagnosed/not found within 13 days; 80% in both groups avoided referral to cardiologists. Patients felt safe and primary care clinicians satisfied. In an escalation test, 58 primary-care doctors evaluated 93 patients over three months, with remote support from four hospitals in the Capital Region of Denmark. CONCLUSIONS: CardioShare was successfully implemented for AF evaluation in primary care.

Exploring Patient Needs and Designing Concepts for Digitally Supported Health Solutions in Managing Type 2 Diabetes: Cocreation Study.

BACKGROUND: Self-management of the progressive disease type 2 diabetes mellitus (T2DM) becomes part of the daily life of patients starting from the time of diagnosis. However, despite the availability of technical innovations, the uptake of digital solutions remains low. One reason that has been reported is that digital solutions often focus purely on clinical factors that may not align with the patient's perspective. OBJECTIVE: The aim of this study was to develop digital solutions that address the needs of patients with T2DM, designed from the user's perspective. The goal was to address the patients' expressed real-world needs by having the users themselves choose the scope and format of the solutions. METHODS: Using participatory methods, we conducted 3 cocreation workshops in collaboration with the Danish Diabetes Association, with 20 persons with T2DM and 11 stakeholders across workshops: user experience designers, researchers, and diabetes experts including a diabetes nurse. The overall structure of the 3 workshops was aligned with the 4 phases of the double diamond: initially discovering and mapping out key experienced issues, followed by a workshop on thematic mapping and definition of key concepts, and succeeded by an exploration and development of 2 prototypes. Subsequently, high-fidelity interactive prototypes were refined as part of the delivery phase, in which 7 formative usability tests were conducted. RESULTS: The workshops mapped experiential topics over time from prediagnosis to the current state, resulting in a detailed exploration and understanding of 6 themes related to and based on the experiences of patients with T2DM: diabetes care, diabetes knowledge, glucose monitoring, diet, physical activity, and social aspects of diabetes. Two prototypes were developed by the participants to address some of their expressed needs over time related to the 6 themes: an activity-based continuous glucose monitoring app and a web-based guide to diabetes. Both prototypes emphasize periods of structured self-measurements of blood glucose to support evolving needs for self-exploration through distinct phases of learning, active use, and supporting use. Periods of low or intermittent use may thus not reflect a failure of design in a traditional sense but rather be a sign of evolving needs over time. CONCLUSIONS: Our results indicate that the needs of patients with T2DM differ between individuals and change over time. As a result, the suggested digitally supported empowering health prototypes can be personalized to support self-exploration, individual preference in long-term management, and changing needs over time. Despite individuals experiencing different journeys with diabetes, users perceive the self-measurement of blood glucose as a universally useful tool to empower everyday decision-making.

Mood instability and activity/energy instability in patients with bipolar disorder according to day-to-day smartphone-based data - An exploratory post hoc study.

BACKGROUND: Alterations and instability in mood and activity/energy has been associated with impaired functioning and risk of relapse in bipolar disorder. The present study aimed to investigate whether mood instability and activity/energy instability are associated, and whether these instability measures are associated with stress, quality of life and functioning in patients with bipolar disorder. METHODS: Data from two studies were combined for exploratory post hoc analyses. Patients with bipolar disorder provided smartphone-based evaluations of mood and activity/energy levels from day-to-day. In addition, information on functioning, perceived stress and quality of life was collected. A total of 316 patients with bipolar disorder were included. RESULTS: A total of 55,968 observations of patient-reported smartphone-based data collected from day-to-day were available. Regardless of the affective state, there was a statistically significant positive association between mood instability and activity/energy instability in all models (all p-values < 0.0001). There was a statistically significant association between mood and activity/energy instability with patient-reported stress and quality of life (e.g., mood instability and stress: B: 0.098, 95 % CI: 0.085; 0.11, p < 0.0001), and between mood instability and functioning (B: 0.045, 95 % CI: 0.0011; 0.0080, p = 0.010). LIMITATIONS: Findings should be interpreted with caution since the analyses were exploratory and post hoc by nature. CONCLUSION: Mood instability and activity/energy instability is suggested to play important roles in the symptomatology of bipolar disorder. This highlight that monitoring and identifying subsyndromal inter-episodic fluctuations in symptoms is clinically recommended. Future studies investigating the effect of treatment on these measures would be interesting.

Remote Assessment in healthcare-Technologies, methods, benefits, and challenges.

The PLOS ONE Collection on "Remote Assessment" brings together a series of studies on how remote assessment methods and technologies can be used in health and behavioral sciences. At the time of writing (October 2022), this collection has accepted and published 10 papers, which address remote assessment in a wide range of health topics including mental health, cognitive assessment, blood sampling and diagnosis, dental health, COVID-19 infections, and prenatal diagnosis. The papers also cover a wide range of methodological approaches, technology platforms, and ways to utilize remote assessment. As such, this collection provides a broad view into the benefits and challenges of remote assessment, and provides a lot of detailed knowledge on how to make it work in practice This paper provides an overview of the included studies, and presents and discusses the different benefits as well as challenges associated with remote assessment.

Irritability in bipolar disorder and unipolar disorder measured daily using smartphone-based data: An exploratory post hoc study.

OBJECTIVE: To investigate (i) the proportions of time with irritability and (ii) the association between irritability and affective symptoms and functioning, stress, and quality of life in patients with bipolar disorder (BD) and unipolar depressive disorder (UD). METHODS: A total of 316 patients with BD and 58 patients with UD provided self-reported once-a-day data on irritability and other affective symptoms using smartphones for a total of 64,129 days with observations. Questionnaires on perceived stress and quality of life and clinical evaluations of functioning were collected multiple times during the study. RESULTS: During a depressive state, patients with UD spent a significantly higher proportion of time with presence of irritability (83.10%) as compared with patients with BD (70.27%) (p = 0.045). Irritability was associated with lower mood, activity level and sleep duration and with increased stress and anxiety level, in both patient groups (p-values<0.008). Increased irritability was associated with impaired functioning and increased perceived stress (p-values<0.024). In addition, in patients with UD, increased irritability was associated with decreased quality of life (p = 0.002). The results were not altered when adjusting for psychopharmacological treatments. CONCLUSIONS: Irritability is an important part of the symptomatology in affective disorders. Clinicians could have focus on symptoms of irritability in both patients with BD and UD during their course of illness. Future studies investigating treatment effects on irritability would be interesting.

CACHET-CADB: A Contextualized Ambulatory Electrocardiography Arrhythmia Dataset.

ECG is a non-invasive tool for arrhythmia detection. In recent years, wearable ECG-based ambulatory arrhythmia monitoring has gained increasing attention. However, arrhythmia detection algorithms trained on existing public arrhythmia databases show higher FPR when applied to such ambulatory ECG recordings. It is primarily because the existing public databases are relatively clean as they are recorded using clinical-grade ECG devices in controlled clinical environments. They may not represent the signal quality and artifacts present in ambulatory patient-operated ECG. To help build and evaluate arrhythmia detection algorithms that can work on wearable ECG from free-living conditions, we present the design and development of the CACHET-CADB, a multi-site contextualized ECG database from free-living conditions. The CACHET-CADB is subpart of the REAFEL study, which aims at reaching the frail elderly patient to optimize the diagnosis of atrial fibrillation. In contrast to the existing databases, along with the ECG, CACHET-CADB also provides the continuous recording of patients' contextual data such as activities, body positions, movement accelerations, symptoms, stress level, and sleep quality. These contextual data can aid in improving the machine/deep learning-based automated arrhythmia detection algorithms on patient-operated wearable ECG. Currently, CACHET-CADB has 259 days of contextualized ECG recordings from 24 patients and 1,602 manually annotated 10 s heart-rhythm samples. The length of the ECG records in the CACHET-CADB varies from 24 h to 3 weeks. The patient's ambulatory context information (activities, movement acceleration, body position, etc.) is extracted for every 10 s interval cumulatively. From the analysis, nearly 11% of the ECG data in the database is found to be noisy. A software toolkit for the use of the CACHET-CADB is also provided.

Deepaware: A hybrid deep learning and context-aware heuristics-based model for atrial fibrillation detection.

BACKGROUND: State-of-the-art automatic atrial fibrillation (AF) detection models trained on RR-interval (RRI) features generally produce high performance on standard benchmark electrocardiogram (ECG) AF datasets. These models, however, result in a significantly high false positive rates (FPRs) when applied on ECG data collected under free-living ambulatory conditions and in the presence of non-AF arrhythmias. METHOD: This paper proposes DeepAware, a novel hybrid model combining deep learning (DL) and context-aware heuristics (CAH), which reduces the FPR effectively and improves the AF detection performance on participant-operated ambulatory ECG from free-living conditions. It exploits the RRI and P-wave features, as well as the contextual features from the ambulatory ECG. RESULTS: DeepAware is shown to be very generalizable and superior to the state-of-the-art models when applied on unseen benchmark ECG AF datasets. Most importantly, the model is able to detect AF efficiently when applied on participant-operated ambulatory ECG recordings from free-living conditions and has achieved a sensitivity (Se), specificity (Sp), and accuracy (Acc) of 97.94%, 98.39%, 98.06%, respectively. Results also demonstrate the effect of atrial activity analysis (via P-waves detection) and CAH in reducing the FPR over the RRI features-based AF detection model. CONCLUSIONS: The proposed DeepAware model can substantially reduce the physician's workload of manually reviewing the false positives (FPs) and facilitate long-term ambulatory monitoring for early detection of AF.

What's Up With These Conversational Health Agents? From Users' Critiques to Implications for Design.

Recent advancements in speech recognition technology in combination with increased access to smart speaker devices are expanding conversational interactions to ever-new areas of our lives - including our health and wellbeing. Prior human-computer interaction research suggests that Conversational Agents (CAs) have the potential to support a variety of health-related outcomes, due in part to their intuitive and engaging nature. Realizing this potential requires however developing a rich understanding of users' needs and experiences in relation to these still-emerging technologies. To inform the design of CAs for health and wellbeing, we analyze 2741 critical reviews of 485 Alexa health and fitness Skills using an automated topic modeling approach; identifying 15 subjects of criticism across four key areas of design (functionality, reliability, usability, pleasurability). Based on these findings, we discuss implications for the design of engaging CAs to support health and wellbeing.

Software Architecture Patterns for Extending Sensing Capabilities and Data Formatting in Mobile Sensing.

Mobile sensing­that is, the ability to unobtrusively collect sensor data from built-in phone and attached wearable sensors­have proven to be a powerful approach to understanding the behavior, well-being, and health of people in their everyday life. Different platforms for mobile sensing have been presented and significant knowledge on how to facilitate mobile sensing has been accumulated. However, most existing mobile sensing platforms only support a fixed set of mobile phone and wearable sensors which are `built into' the platform's generic `study app'. This creates some fundamental challenges for the creation and approval of application-specific mobile sensing studies, since there is little support for adapting the sensing capabilities to what is needed for a specific study. Moreover, most existing platforms use their own proprietary data formats and there is no standardization in how data are collected and in what formats. This poses some fundamental challenges to realizing the vision of using mobile sensing in health applications, since mobile sensing data collected across different phones and studies cannot be compared, thus hampering generalizability and reproducibility across studies. This paper presents two software architecture patterns enabling (i) dynamic extension of mobile sensing to incorporate new sensing capabilities, such as collecting data from a wearable sensor, and (ii) handling real-time transformation of data into standardized data formats. These software patterns are derived from our work on CARP Mobile Sensing (CAMS), which is a cross-platform (Android/iOS) software architecture providing a reactive and unified programming model that emphasizes extensibility. This paper shows how the framework uses the two software architecture patterns to add sampling support for an electrocardiography (ECG) device and support data transformation into the new Open mHealth (OMH) data format. The paper also presents data from a small study, demonstrating the robustness and feasibility of using CAMS for data collection and transformation in mobile sensing.

Differences in mobility patterns according to machine learning models in patients with bipolar disorder and patients with unipolar disorder.

BACKGROUND: It is essential to differentiate bipolar disorder (BD) from unipolar disorder (UD) as the course of illness and treatment guidelines differ between the two disorders. Measurements of activity and mobility could assist in this discrimination. AIMS: 1) To investigate differences in smartphone-based location data between BD and UD, and 2) to investigate the sensitivity, specificity, and AUC of combined location data in classifying BD and UD. METHODS: Patients with BD and UD completed smartphone-based self-assessments of mood for six months, along with same-time passively collected smartphone data on location reflecting mobility patterns, routine and location entropy (chaos). A total of 65 patients with BD and 75 patients with UD were included. RESULTS: A total of 2594 (patients with BD) and 2088 (patients with UD) observations of smartphone-based location data were available. During a depressive state, compared with patients with UD, patients with BD had statistically significantly lower mobility (e.g., total duration of moves per day (eB 0.74, 95% CI 0.57; 0.97, p = 0.027)). In classification models during a depressive state, patients with BD versus patients with UD, there was a sensitivity of 0.70 (SD 0.07), a specificity of 0.77 (SD 0.07), and an AUC of 0.79 (SD 0.03). LIMITATIONS: The relative low symptom severity in the present study may have contributed to the magnitude of the AUC. CONCLUSION: Mobility patterns derived from mobile location data is a promising digital diagnostic marker in discriminating between patients with BD and UD.

Digital health competencies in medical school education: a scoping review and Delphi method study.

INTRODUCTION: In order to fulfill the enormous potential of digital health in the healthcare sector, digital health must become an integrated part of medical education. We aimed to investigate which knowledge, skills and attitudes should be included in a digital health curriculum for medical students through a scoping review and Delphi method study. METHODS: We conducted a scoping review of the literature on digital health relevant for medical education. Key topics were split into three sub-categories: knowledge (facts, concepts, and information), skills (ability to carry out tasks) and attitudes (ways of thinking or feeling). Thereafter, we used a modified Delphi method where experts rated digital health topics over two rounds based on whether topics should be included in the curriculum for medical students on a scale from 1 (strongly disagree) to 5 (strongly agree). A predefined cut-off of ≥4 was used to identify topics that were critical to include in a digital health curriculum for medical students. RESULTS: The scoping review resulted in a total of 113 included articles, with 65 relevant topics extracted and included in the questionnaire. The topics were rated by 18 experts, all of which completed both questionnaire rounds. A total of 40 (62%) topics across all three sub-categories met the predefined rating cut-off value of ≥4. CONCLUSION: An expert panel identified 40 important digital health topics within knowledge, skills, and attitudes for medical students to be taught. These can help guide medical educators in the development of future digital health curricula.

Discriminating between patients with unipolar disorder, bipolar disorder, and healthy control individuals based on voice features collected from naturalistic smartphone calls.

BACKGROUND: It is of crucial importance to be able to discriminate unipolar disorder (UD) from bipolar disorder (BD), as treatments, as well as course of illness, differ between the two disorders. AIMS: To investigate whether voice features from naturalistic phone calls could discriminate between (1) UD, BD, and healthy control individuals (HC); (2) different states within UD. METHODS: Voice features were collected daily during naturalistic phone calls for up to 972 days. A total of 48 patients with UD, 121 patients with BD, and 38 HC were included. A total of 115,483 voice data entries were collected (UD [n = 16,454], BD [n = 78,733], and HC [n = 20,296]). Patients evaluated symptoms daily using a smartphone-based system, making it possible to define illness states within UD and BD. Data were analyzed using random forest algorithms. RESULTS: Compared with BD, UD was classified with a specificity of 0.84 (SD: 0.07)/AUC of 0.58 (SD: 0.07) and compared with HC with a sensitivity of 0.74 (SD: 0.10)/AUC = 0.74 (SD: 0.06). Compared with BD during euthymia, UD during euthymia was classified with a specificity of 0.79 (SD: 0.05)/AUC = 0.43 (SD: 0.16). Compared with BD during depression, UD during depression was classified with a specificity of 0.81 (SD: 0.09)/AUC = 0.48 (SD: 0.12). Within UD, compared with euthymia, depression was classified with a specificity of 0.70 (SD 0.31)/AUC = 0.65 (SD: 0.11). In all models, the user-dependent models outperformed the user-independent models. CONCLUSIONS: The results from the present study are promising, but as reflected by the low AUCs, does not support that voice features collected during naturalistic phone calls at the current state of art can be implemented in clinical practice as a supplementary and assisting tool. Further studies are needed.

Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states.

BACKGROUND: Voice features have been suggested as objective markers of bipolar disorder (BD). AIMS: To investigate whether voice features from naturalistic phone calls could discriminate between (1) BD, unaffected first-degree relatives (UR) and healthy control individuals (HC); (2) affective states within BD. METHODS: Voice features were collected daily during naturalistic phone calls for up to 972 days. A total of 121 patients with BD, 21 UR and 38 HC were included. A total of 107.033 voice data entries were collected [BD (n = 78.733), UR (n = 8004), and HC (n = 20.296)]. Daily, patients evaluated symptoms using a smartphone-based system. Affective states were defined according to these evaluations. Data were analyzed using random forest machine learning algorithms. RESULTS: Compared to HC, BD was classified with a sensitivity of 0.79 (SD 0.11)/AUC = 0.76 (SD 0.11) and UR with a sensitivity of 0.53 (SD 0.21)/AUC of 0.72 (SD 0.12). Within BD, compared to euthymia, mania was classified with a specificity of 0.75 (SD 0.16)/AUC = 0.66 (SD 0.11). Compared to euthymia, depression was classified with a specificity of 0.70 (SD 0.16)/AUC = 0.66 (SD 0.12). In all models the user dependent models outperformed the user independent models. Models combining increased mood, increased activity and insomnia compared to periods without performed best with a specificity of 0.78 (SD 0.16)/AUC = 0.67 (SD 0.11). CONCLUSIONS: Voice features from naturalistic phone calls may represent a supplementary objective marker discriminating BD from HC and a state marker within BD.

Public Attitudes to Digital Health Research Repositories: Cross-sectional International Survey.

BACKGROUND: Digital health research repositories propose sharing longitudinal streams of health records and personal sensing data between multiple projects and researchers. Motivated by the prospect of personalizing patient care (precision medicine), these initiatives demand broad public acceptance and large numbers of data contributors, both of which are challenging. OBJECTIVE: This study investigates public attitudes toward possibly contributing to digital health research repositories to identify factors for their acceptance and to inform future developments. METHODS: A cross-sectional online survey was conducted from March 2020 to December 2020. Because of the funded project scope and a multicenter collaboration, study recruitment targeted young adults in Denmark and Brazil, allowing an analysis of the differences between 2 very contrasting national contexts. Through closed-ended questions, the survey examined participants' willingness to share different data types, data access preferences, reasons for concern, and motivations to contribute. The survey also collected information about participants' demographics, level of interest in health topics, previous participation in health research, awareness of examples of existing research data repositories, and current attitudes about digital health research repositories. Data analysis consisted of descriptive frequency measures and statistical inferences (bivariate associations and logistic regressions). RESULTS: The sample comprises 1017 respondents living in Brazil (1017/1600, 63.56%) and 583 in Denmark (583/1600, 36.44%). The demographics do not differ substantially between participants of these countries. The majority is aged between 18 and 27 years (933/1600, 58.31%), is highly educated (992/1600, 62.00%), uses smartphones (1562/1600, 97.63%), and is in good health (1407/1600, 87.94%). The analysis shows a vast majority were very motivated by helping future patients (1366/1600, 85.38%) and researchers (1253/1600, 78.31%), yet very concerned about unethical projects (1219/1600, 76.19%), profit making without consent (1096/1600, 68.50%), and cyberattacks (1055/1600, 65.94%). Participants' willingness to share data is lower when sharing personal sensing data, such as the content of calls and texts (1206/1600, 75.38%), in contrast to more traditional health research information. Only 13.44% (215/1600) find it desirable to grant data access to private companies, and most would like to stay informed about which projects use their data (1334/1600, 83.38%) and control future data access (1181/1600, 73.81%). Findings indicate that favorable attitudes toward digital health research repositories are related to a personal interest in health topics (odds ratio [OR] 1.49, 95% CI 1.10-2.02; P=.01), previous participation in health research studies (OR 1.70, 95% CI 1.24-2.35; P=.001), and awareness of examples of research repositories (OR 2.78, 95% CI 1.83-4.38; P<.001). CONCLUSIONS: This study reveals essential factors for acceptance and willingness to share personal data with digital health research repositories. Implications include the importance of being more transparent about the goals and beneficiaries of research projects using and re-using data from repositories, providing participants with greater autonomy for choosing who gets access to which parts of their data, and raising public awareness of the benefits of data sharing for research. In addition, future developments should engage with and reduce risks for those unwilling to participate.

Automatically Generated Smartphone Data in Young Patients With Newly Diagnosed Bipolar Disorder and Healthy Controls.

Background: Smartphones may facilitate continuous and fine-grained monitoring of behavioral activities via automatically generated data and could prove to be especially valuable in monitoring illness activity in young patients with bipolar disorder (BD), who often present with rapid changes in mood and related symptoms. The present pilot study in young patients with newly diagnosed BD and healthy controls (HC) aimed to (1) validate automatically generated smartphone data reflecting physical and social activity and phone usage against validated clinical rating scales and questionnaires; (2) investigate differences in automatically generated smartphone data between young patients with newly diagnosed BD and HC; and (3) investigate associations between automatically generated smartphone data and smartphone-based self-monitored mood and activity in young patients with newly diagnosed BD. Methods: A total of 40 young patients with newly diagnosed BD and 21 HC aged 15-25 years provided daily automatically generated smartphone data for 3-779 days [median (IQR) = 140 (11.5-268.5)], in addition to daily smartphone-based self-monitoring of activity and mood. All participants were assessed with clinical rating scales. Results: (1) The number of outgoing phone calls was positively associated with scores on the Young Mania Rating Scale and subitems concerning activity and speech. The number of missed calls (p = 0.015) and the number of outgoing text messages (p = 0.017) were positively associated with the level of psychomotor agitation according to the Hamilton Depression Rating scale subitem 9. (2) Young patients with newly diagnosed BD had a higher number of incoming calls compared with HC (BD: mean = 1.419, 95% CI: 1.162, 1.677; HC: mean = 0.972, 95% CI: 0.637, 1.308; p = 0.043) and lower self-monitored mood and activity (p's < 0.001). (3) Smartphone-based self-monitored mood and activity were positively associated with step counts and the number of outgoing calls, respectively (p's < 0.001). Conclusion: Automatically generated data on physical and social activity and phone usage seem to reflect symptoms. These data differ between young patients with newly diagnosed BD and HC and reflect changes in illness activity in young patients with BD. Automatically generated smartphone-based data could be a useful clinical tool in diagnosing and monitoring illness activity in young patients with BD.

Mood and Activity Measured Using Smartphones in Unipolar Depressive Disorder.

Background: Smartphones comprise a promising tool for symptom monitoring in patients with unipolar depressive disorder (UD) collected as either patient-reportings or possibly as automatically generated smartphone data. However, only limited research has been conducted in clinical populations. We investigated the association between smartphone-collected monitoring data and validated psychiatric ratings and questionnaires in a well-characterized clinical sample of patients diagnosed with UD. Methods: Smartphone data, clinical ratings, and questionnaires from patients with UD were collected 6 months following discharge from psychiatric hospitalization as part of a randomized controlled study. Smartphone data were collected daily, and clinical ratings (i.e., Hamilton Depression Rating Scale 17-item) were conducted three times during the study. We investigated associations between (1) smartphone-based patient-reported mood and activity and clinical ratings and questionnaires; (2) automatically generated smartphone data resembling physical activity, social activity, and phone usage and clinical ratings; and (3) automatically generated smartphone data and same-day smartphone-based patient-reported mood and activity. Results: A total of 74 patients provided 11,368 days of smartphone data, 196 ratings, and 147 questionnaires. We found that: (1) patient-reported mood and activity were associated with clinical ratings and questionnaires (p < 0.001), so that higher symptom scores were associated with lower patient-reported mood and activity, (2) Out of 30 investigated associations on automatically generated data and clinical ratings of depression, only four showed statistical significance. Further, lower psychosocial functioning was associated with fewer daily steps (p = 0.036) and increased number of incoming (p = 0.032), outgoing (p = 0.015) and missed calls (p = 0.007), and longer phone calls (p = 0.012); (3) Out of 20 investigated associations between automatically generated data and daily patient-reported mood and activity, 12 showed statistical significance. For example, lower patient-reported activity was associated with fewer daily steps, shorter distance traveled, increased incoming and missed calls, and increased screen-time. Conclusion: Smartphone-based self-monitoring is feasible and associated with clinical ratings in UD. Some automatically generated data on behavior may reflect clinical features and psychosocial functioning, but these should be more clearly identified in future studies, potentially combining patient-reported and smartphone-generated data.