Realizing the potential of mobile mental health: new methods for new data in psychiatry

Digital phenotyping of depression during pregnancy using self-report data

BACKGROUND

Depression is a common pregnancy complication yet is often under-detected and, subsequently, undertreated. Data collected through mobile health tools may be used to support the identification of depression symptoms in pregnancy.

METHODS

An observational cohort study of 2062 pregnancies collected self-reports of patient history, mood, pregnancy-specific symptoms, and written language using a prenatal support app. These app inputs were used to model depression risk in subsequent 30- and 60-day periods throughout pregnancy. A selective inference lasso modeling approach examined the individual and additive value of each type of patient-reported app input.

RESULTS

Depression models ranged in predictive power (AUC value of 0.64-0.83), depending on the type of inputs. The most predictive model included personal history, daily mood, and acute pregnancy-related symptoms (e.g., severe vomiting, cramping). Across models, daily mood was the strongest indicator of depression symptoms in the following month. Models that retained natural language inputs typically improved predictive accuracy and offered insight into the lived context associated with experiencing depression.

LIMITATIONS

Our findings are not generalizable beyond a digitally literate patient population that is self-motivated to report data during pregnancy.

CONCLUSIONS

Simple patient reported data, including sparse language, shared directly via digital tools may support earlier depression symptom identification and a more nuanced understanding of depression context.

Efficacy of mental health smartphone apps on stress levels: a meta-analysis of randomised controlled trials

The management of stress has evolved in recent years due to widespread availability of mobile-device applications (apps) and their capacity to deliver psychological interventions. We evaluated the efficacy of mental health apps on stress and sought to identify characteristics associated with effect size estimates. Sixty-nine randomised controlled trials (RCTs) were included. Random effects meta-analyses were performed and putative moderators were examined at univariate and multivariate (combinations and interactions) levels. From 78 comparisons, we observed a small but significant pooled effect of apps over control conditions on perceived stress levels (g = 0.27; 95% CI = 0.20, 0.34; I2 = 68%). This effect weakened after taking into account small-study bias according to the trim-and-fill procedure (g = 0.10; 95% CI = 0.02, 0.19; I2 = 78%). Delivery of apps with stress monitoring features produced smaller efficacy estimates, although this association interacted with other trial features (small sample size and inactive control group) in multivariate analyses, suggesting that this effect may have been explained by features characteristic of low-quality trials. Mental health apps appear to have small, acute effects on reducing perceived stress. Future research should shift focus towards identifying change mechanisms, longitudinal outcomes, features that facilitate sustained app usage, and tangible pathways to integrating apps into real-world clinical settings.

Speech Features as Predictors of Momentary Depression Severity in Patients With Depressive Disorder Undergoing Sleep Deprivation Therapy: Ambulatory Assessment Pilot Study

BACKGROUND

The use of mobile devices to continuously monitor objectively extracted parameters of depressive symptomatology is seen as an important step in the understanding and prevention of upcoming depressive episodes. Speech features such as pitch variability, speech pauses, and speech rate are promising indicators, but empirical evidence is limited, given the variability of study designs.

OBJECTIVE

Previous research studies have found different speech patterns when comparing single speech recordings between patients and healthy controls, but only a few studies have used repeated assessments to compare depressive and nondepressive episodes within the same patient. To our knowledge, no study has used a series of measurements within patients with depression (eg, intensive longitudinal data) to model the dynamic ebb and flow of subjectively reported depression and concomitant speech samples. However, such data are indispensable for detecting and ultimately preventing upcoming episodes.

METHODS

In this study, we captured voice samples and momentary affect ratings over the course of 3 weeks in a sample of patients (N=30) with an acute depressive episode receiving stationary care. Patients underwent sleep deprivation therapy, a chronotherapeutic intervention that can rapidly improve depression symptomatology. We hypothesized that within-person variability in depressive and affective momentary states would be reflected in the following 3 speech features: pitch variability, speech pauses, and speech rate. We parametrized them using the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) from open-source Speech and Music Interpretation by Large-Space Extraction (openSMILE; audEERING GmbH) and extracted them from a transcript. We analyzed the speech features along with self-reported momentary affect ratings, using multilevel linear regression analysis. We analyzed an average of 32 (SD 19.83) assessments per patient.

RESULTS

Analyses revealed that pitch variability, speech pauses, and speech rate were associated with depression severity, positive affect, valence, and energetic arousal; furthermore, speech pauses and speech rate were associated with negative affect, and speech pauses were additionally associated with calmness. Specifically, pitch variability was negatively associated with improved momentary states (ie, lower pitch variability was linked to lower depression severity as well as higher positive affect, valence, and energetic arousal). Speech pauses were negatively associated with improved momentary states, whereas speech rate was positively associated with improved momentary states.

CONCLUSIONS

Pitch variability, speech pauses, and speech rate are promising features for the development of clinical prediction technologies to improve patient care as well as timely diagnosis and monitoring of treatment response. Our research is a step forward on the path to developing an automated depression monitoring system, facilitating individually tailored treatments and increased patient empowerment.

Rapid, reliable mobile assessment of affect-related motor processing

Mobile technologies can be used for behavioral assessments to associate changes in behavior with environmental context and its influence on mental health and disease. Research on real-time motor control with a joystick, analyzed using a computational proportion-derivative (PD) modeling approach, has shown that model parameters can be estimated with high reliability and are related both to self-reported fear and to brain structures important for affective regulation, such as the anterior cingulate cortex. Here we introduce a mobile version of this paradigm, the rapid assessment of motor processing (RAMP) paradigm, and show that it provides robust, reliable, and accessible behavioral measurements relevant to mental health. A smartphone version of a previous joystick sensorimotor task was developed in which participants control a virtual car to a stop sign and stop. A sample of 89 adults performed the task, with 66 completing a second retest session. A PD modeling approach was applied to compute Kp (drive) and Kd (damping) parameters. Both Kp and Kd exhibited high test-retest reliabilities (ICC .81 and .78, respectively). Replicating a previous finding from a different sample with the joystick version of the task, both Kp and Kd were negatively associated with self-reported fear. The RAMP paradigm, a mobile sensorimotor assessment, can be used to assess drive and damping during motor control, which is robustly associated with subjective affect. This paradigm could be useful for examining dynamic contextual modulation of affect-related processing, which could improve assessment of the effects of interventions for psychiatric disorders in a real-world context.

Daily Pain Prediction Using Smartphone Speech Recordings of Patients With Spine Disease

BACKGROUND

Pain evaluation remains largely subjective in neurosurgical practice, but machine learning provides the potential for objective pain assessment tools.

OBJECTIVE

To predict daily pain levels using speech recordings from personal smartphones of a cohort of patients with diagnosed neurological spine disease.

METHODS

Patients with spine disease were enrolled through a general neurosurgical clinic with approval from the institutional ethics committee. At-home pain surveys and speech recordings were administered at regular intervals through the Beiwe smartphone application. Praat audio features were extracted from the speech recordings to be used as input to a K-nearest neighbors (KNN) machine learning model. The pain scores were transformed from a 0 to 10 scale to low and high pain for better discriminative capacity.

RESULTS

A total of 60 patients were enrolled, and 384 observations were used to train and test the prediction model. Using the KNN prediction model, an accuracy of 71% with a positive predictive value of 0.71 was achieved in classifying pain intensity into high and low. The model showed 0.71 precision for high pain and 0.70 precision for low pain. Recall of high pain was 0.74, and recall of low pain was 0.67. The overall F1 score was 0.73.

CONCLUSION

Our study uses a KNN to model the relationship between speech features and pain levels collected from personal smartphones of patients with spine disease. The proposed model is a stepping stone for the development of objective pain assessment in neurosurgery clinical practice.

Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study

BACKGROUND

Personal sensing may improve digital therapeutics for mental health care by facilitating early screening, symptom monitoring, risk prediction, and personalized adaptive interventions. However, further development and the use of personal sensing requires a better understanding of its acceptability to people targeted for these applications.

OBJECTIVE

We aimed to assess the acceptability of active and passive personal sensing methods in a sample of people with moderate to severe alcohol use disorder using both behavioral and self-report measures. This sample was recruited as part of a larger grant-funded project to develop a machine learning algorithm to predict lapses.

METHODS

Participants (N=154; n=77, 50% female; mean age 41, SD 11.9 years; n=134, 87% White and n=150, 97% non-Hispanic) in early recovery (1-8 weeks of abstinence) were recruited to participate in a 3-month longitudinal study. Participants were modestly compensated for engaging with active (eg, ecological momentary assessment [EMA], audio check-in, and sleep quality) and passive (eg, geolocation, cellular communication logs, and SMS text message content) sensing methods that were selected to tap into constructs from the Relapse Prevention model by Marlatt. We assessed 3 behavioral indicators of acceptability: participants' choices about their participation in the study at various stages in the procedure, their choice to opt in to provide data for each sensing method, and their adherence to a subset of the active methods (EMA and audio check-in). We also assessed 3 self-report measures of acceptability (interference, dislike, and willingness to use for 1 year) for each method.

RESULTS

Of the 192 eligible individuals screened, 191 consented to personal sensing. Most of these individuals (169/191, 88.5%) also returned 1 week later to formally enroll, and 154 participated through the first month follow-up visit. All participants in our analysis sample opted in to provide data for EMA, sleep quality, geolocation, and cellular communication logs. Out of 154 participants, 1 (0.6%) did not provide SMS text message content and 3 (1.9%) did not provide any audio check-ins. The average adherence rate for the 4 times daily EMA was .80. The adherence rate for the daily audio check-in was .54. Aggregate participant ratings indicated that all personal sensing methods were significantly more acceptable (all P<.001) compared with neutral across subjective measures of interference, dislike, and willingness to use for 1 year. Participants did not significantly differ in their dislike of active methods compared with passive methods (P=.23). However, participants reported a higher willingness to use passive (vs active) methods for 1 year (P=.04).

CONCLUSIONS

These results suggest that active and passive sensing methods are acceptable for people with alcohol use disorder over a longer period than has previously been assessed. Important individual differences were observed across people and methods, indicating opportunities for future improvement.

Digitally Assisted Peer Recovery Coach to Facilitate Linkage to Outpatient Treatment Following Inpatient Alcohol Withdrawal Treatment: Proof-of-Concept Pilot Study

BACKGROUND

Alcohol use disorder (AUD), associated with significant morbidity and mortality, continues to be a major public health problem. The COVID-19 pandemic exacerbated the impact of AUD, with a 25% increase in alcohol-related mortality from 2019 to 2020. Thus, innovative treatments for AUD are urgently needed. While inpatient alcohol withdrawal management (detoxification) is often an entry point for recovery, most do not successfully link to ongoing treatment. Transitions between inpatient and outpatient treatment pose many challenges to successful treatment continuation. Peer recovery coaches-individuals with the lived experience of recovery who obtain training to be coaches-are increasingly used to assist individuals with AUD and may provide a degree of continuity during this transition.

OBJECTIVE

We aimed to evaluate the feasibility of using an existing care coordination app (Lifeguard) to assist peer recovery coaches in supporting patients after discharge and facilitating linkage to care.

METHODS

This study was conducted on an American Society of Addiction Medicine-Level IV inpatient withdrawal management unit within an academic medical center in Boston, MA. After providing informed consent, participants were contacted by the coach through the app, and after discharge, received daily prompts to complete a modified version of the brief addiction monitor (BAM). The BAM inquired about alcohol use, risky, and protective factors. The coach sent daily motivational texts and appointment reminders and checked in if BAM responses were concerning. Postdischarge follow-up continued for 30 days. The following feasibility outcomes were evaluated: (1) proportion of participants engaging with the coach before discharge, (2) proportion of participants and the number of days engaging with the coach after discharge, (3) proportion of participants and the number of days responding to BAM prompts, and (4) proportion of participants successfully linking with addiction treatment by 30-day follow-up.

RESULTS

All 10 participants were men, averaged 50.5 years old, and were mostly White (n=6), non-Hispanic (n=9), and single (n=8). Overall, 8 participants successfully engaged with the coach prior to discharge. Following discharge, 6 participants continued to engage with the coach, doing so on an average of 5.3 days (SD 7.3, range 0-20 days); 5 participants responded to the BAM prompts during the follow-up, doing so on an average of 4.6 days (SD 6.9, range 0-21 days). Half (n=5) successfully linked with ongoing addiction treatment during the follow-up. The participants who engaged with the coach post discharge, compared to those who did not, were significantly more likely to link with treatment (83% vs 0%, χ²=6.67, P=.01).

CONCLUSIONS

The results demonstrated that a digitally assisted peer recovery coach may be feasible in facilitating linkage to care following discharge from inpatient withdrawal management treatment. Further research is warranted to evaluate the potential role for peer recovery coaches in improving postdischarge outcomes.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05393544; https://www.clinicaltrials.gov/ct2/show/NCT05393544.

Mobile health in the specific management of first-episode psychosis: a systematic literature review

Purpose

The purpose of this systematic literature review is to assess the therapeutic efficacy of mobile health methods in the management of patients with first-episode psychosis (FEP).

Method

The participants are patients with FEP. The interventions are smartphone applications. The studies assess the preliminary efficacy of various types of application.

Results

One study found that monitoring symptoms minimized relapses, visits to A&E and hospital admissions, while one study showed a decrease in positive psychotic symptoms. One study found an improvement in anxiety symptoms and two studies noted an improvement in psychotic symptoms. One study demonstrated its efficacy in helping participants return to studying and employment and one study reported improved motivation.

Conclusion

The studies suggest that mobile applications have potential value in the management of young patients with FEP through the use of various assessment and intervention tools. This systematic review has several limitations due to the lack of randomized controlled studies available in the literature.

Using Digital phenotyping to understand health-related outcomes: A scoping review

BACKGROUND

Digital phenotyping may detect changes in health outcomes and potentially lead to proactive measures to mitigate health declines and avoid major medical events. While health-related outcomes have traditionally been acquired through self-report measures, those approaches have numerous limitations, such as recall bias, and social desirability bias. Digital phenotyping may offer a potential solution to these limitations.

OBJECTIVES

The purpose of this scoping review was to identify and summarize how passive smartphone data are processed and evaluated analytically, including the relationship between these data and health-related outcomes.

METHODS

A search of PubMed, Scopus, Compendex, and HTA databases was conducted for all articles in April 2021 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines.

RESULTS

A total of 40 articles were included and went through an analysis based on data collection approaches, feature extraction, data analytics, behavioral markers, and health-related outcomes. This review demonstrated a layer of features derived from raw sensor data that can then be integrated to estimate and predict behaviors, emotions, and health-related outcomes. Most studies collected data from a combination of sensors. GPS was the most used digital phenotyping data. Feature types included physical activity, location, mobility, social activity, sleep, and in-phone activity. Studies involved a broad range of the features used: data preprocessing, analysis approaches, analytic techniques, and algorithms tested. 55% of the studies (n = 22) focused on mental health-related outcomes.

CONCLUSION

This scoping review catalogued in detail the research to date regarding the approaches to using passive smartphone sensor data to derive behavioral markers to correlate with or predict health-related outcomes. Findings will serve as a central resource for researchers to survey the field of research designs and approaches performed to date and move this emerging domain of research forward towards ultimately providing clinical utility in patient care.

Monitoring Australian parents' shifting receptiveness to digital mental health interventions during the COVID-19 pandemic

BACKGROUND

Nascent evidence indicates that the mental health of parents and children has markedly declined during the COVID-19 pandemic. Considering disruptions to traditional face-to-face mental health services resultant from stay-at-home orders, the potential value of digital mental health interventions has become extremely apparent. Despite this, uptake of digital interventions remains poor, indicating that a better understanding is needed of factors that determine a willingness to use digital platforms.

METHOD

The present multi-wave, longitudinal study of 2365 Australian parents explored between-person and within-person predictors of intentions to use digital interventions during the pandemic.

RESULTS

More than one-third of parents reported likely use of a self-guided and therapist-guided digital intervention, with the most endorsed reason for use being to support their child's mental health. Between-person baseline predictors of higher intention ratings were parent's prior mental illness, not living with a partner and recent environmental stressors. Within-person predictors of higher intention ratings were endorsement of mindful parenting strategies, child access to the Internet, better perceived management of child's education, lower social support and financial hardship.

CONCLUSION

Findings demonstrate that willingness to engage in digital interventions fluctuates in response to changing circumstances. Identifying novel ways to increase acceptance and uptake of digital interventions based on modifiable predictors established here is needed to realize the full potential of these modes of care in times of need.

Meeting Young Adults' Social Support Needs across the Health Behavior Change Journey: Implications for Digital Mental Health Tools

In pursuit of mental wellness, many find that behavioral change is necessary. This process can often be difficult but is facilitated by strong social support. This paper explores the role of social support across behavioral change journeys among young adults, a group at high risk for mental health challenges, but with the lowest rates of mental health treatment utilization. Given that digital mental health tools are effective for treating mental health conditions, they hold particular promise for bridging the treatment gap among young adults, many of whom, are not interested in - or cannot access - traditional mental healthcare. We recruited a sample of young adults with depression who were seeking information about their symptoms online to participate in an Asynchronous Remote Community (ARC) elicitation workshop. Participants detailed the changing nature of social interactions across their behavior change journeys. They noted that both directed and undirected support are necessary early in behavioral change and certain needs such as informational support are particularly pronounced, while healthy coping partnerships and accountability are more important later in the change process. We discuss the conceptual and design implications of our findings for the next generation of digital mental health tools.

Measuring Daily Activity Rhythms in Young Adults at Risk of Affective Instability Using Passively Collected Smartphone Data: Observational Study

BACKGROUND

Irregularities in circadian rhythms have been associated with adverse health outcomes. The regularity of rhythms can be quantified using passively collected smartphone data to provide clinically relevant biomarkers of routine.

OBJECTIVE

This study aims to develop a metric to quantify the regularity of activity rhythms and explore the relationship between routine and mood, as well as demographic covariates, in an outpatient psychiatric cohort.

METHODS

Passively sensed smartphone data from a cohort of 38 young adults from the Penn or Children's Hospital of Philadelphia Lifespan Brain Institute and Outpatient Psychiatry Clinic at the University of Pennsylvania were fitted with 2-state continuous-time hidden Markov models representing active and resting states. The regularity of routine was modeled as the hour-of-the-day random effects on the probability of state transition (ie, the association between the hour-of-the-day and state membership). A regularity score, Activity Rhythm Metric, was calculated from the continuous-time hidden Markov models and regressed on clinical and demographic covariates.

RESULTS

Regular activity rhythms were associated with longer sleep durations (P=.009), older age (P=.001), and mood (P=.049).

CONCLUSIONS

Passively sensed Activity Rhythm Metrics are an alternative to existing metrics but do not require burdensome survey-based assessments. Low-burden, passively sensed metrics based on smartphone data are promising and scalable alternatives to traditional measurements.

Alliance With an Unguided Smartphone App: Validation of the Digital Working Alliance Inventory

The working alliance may be relevant in unguided smartphone-based interventions, but no validated measure exists. We evaluated the psychometric properties of the six-item Digital Working Alliance Inventory (DWAI) using a cross-sectional survey of meditation app users (n = 290) and the intervention arm of a randomized trial testing a smartphone-based meditation app (n = 314). Exploratory factor analysis suggested a single-factor solution which was replicated using longitudinal confirmatory factor analysis. The DWAI showed adequate internal consistency and test-retest reliability. Discriminant validity was supported by a lack of association with social desirability, psychological distress, and preference for a waitlist condition. Convergent validity was supported by positive associations with perceived app effectiveness and preference for an app condition. Supporting predictive validity, DWAI scores positively predicted self-reported and objective app utilization. When assessed at Weeks 3 or 4 of the intervention, but not earlier, DWAI scores predicted pre-post reductions in psychological distress.

Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities

Importance

Identifying neurobiological differences between patients with major depressive disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for decades. However, recent meta-analyses have raised concerns regarding the replicability and clinical relevance of brain alterations in depression.

Objective

To quantify the upper bounds of univariate effect sizes, estimated predictive utility, and distributional dissimilarity of healthy individuals and those with depression across structural magnetic resonance imaging (MRI), diffusion-tensor imaging, and functional task-based as well as resting-state MRI, and to compare results with an MDD polygenic risk score (PRS) and environmental variables.

Design, Setting, and Participants

This was a cross-sectional, case-control clinical neuroimaging study. Data were part of the Marburg-Münster Affective Disorders Cohort Study. Patients with depression and healthy controls were recruited from primary care and the general population in Münster and Marburg, Germany. Study recruitment was performed from September 11, 2014, to September 26, 2018. The sample comprised patients with acute and chronic MDD as well as healthy controls in the age range of 18 to 65 years. Data were analyzed from October 29, 2020, to April 7, 2022.

Main Outcomes and Measures

Primary analyses included univariate partial effect size (η2), classification accuracy, and distributional overlapping coefficient for healthy individuals and those with depression across neuroimaging modalities, controlling for age, sex, and additional modality-specific confounding variables. Secondary analyses included patient subgroups for acute or chronic depressive status.

Results

A total of 1809 individuals (861 patients [47.6%] and 948 controls [52.4%]) were included in the analysis (mean [SD] age, 35.6 [13.2] years; 1165 female patients [64.4%]). The upper bound of the effect sizes of the single univariate measures displaying the largest group difference ranged from partial η2 of 0.004 to 0.017, and distributions overlapped between 87% and 95%, with classification accuracies ranging between 54% and 56% across neuroimaging modalities. This pattern remained virtually unchanged when considering either only patients with acute or chronic depression. Differences were comparable with those found for PRS but substantially smaller than for environmental variables.

Conclusions and Relevance

Results of this case-control study suggest that even for maximum univariate biological differences, deviations between patients with MDD and healthy controls were remarkably small, single-participant prediction was not possible, and similarity between study groups dominated. Biological psychiatry should facilitate meaningful outcome measures or predictive approaches to increase the potential for a personalization of the clinical practice.

Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence

BACKGROUND

This PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], digital biomarkers, passive sensing, mobile sensing, ambulatory assessment, and time-series analysis), emphasizing on digital phenotyping (DP) to study depression. DP is defined as the use of digital data to profile health information objectively.

AIMS

Four distinct yet interrelated goals underpin this study: (a) to identify empirical research examining the use of DP to study depression; (b) to describe the different methods and technology employed; (c) to integrate the evidence regarding the efficacy of digital data in the examination, diagnosis, and monitoring of depression and (d) to clarify DP definitions and digital mental health records terminology.

RESULTS

Overall, 118 studies were assessed as eligible. Considering the terms employed, "EMA", "ESM", and "DP" were the most predominant. A variety of DP data sources were reported, including voice, language, keyboard typing kinematics, mobile phone calls and texts, geocoded activity, actigraphy sensor-related recordings (i.e., steps, sleep, circadian rhythm), and self-reported apps' information. Reviewed studies employed subjectively and objectively recorded digital data in combination with interviews and psychometric scales.

CONCLUSIONS

Findings suggest links between a person's digital records and depression. Future research recommendations include (a) deriving consensus regarding the DP definition and (b) expanding the literature to consider a person's broader contextual and developmental circumstances in relation to their digital data/records.

Smartphones for musculoskeletal research - hype or hope? Lessons from a decennium of mHealth studies

BACKGROUND

Smartphones provide opportunities for musculoskeletal research: they are integrated in participants' daily lives and can be used to collect patient-reported outcomes as well as sensor data from large groups of people. As the field of research with smartphones and smartwatches matures, it has transpired that some of the advantages of this modern technology are in fact double-edged swords. BODY: In this narrative review, we illustrate the advantages of using smartphones for data collection with 18 studies from various musculoskeletal domains. We critically appraised existing literature, debunking some myths around the advantages of smartphones: the myth that smartphone studies automatically enable high engagement, that they reach more representative samples, that they cost little, and that sensor data is objective. We provide a nuanced view of evidence in these areas and discuss strategies to increase engagement, to reach representative samples, to reduce costs and to avoid potential sources of subjectivity in analysing sensor data.

CONCLUSION

If smartphone studies are designed without awareness of the challenges inherent to smartphone use, they may fail or may provide biased results. Keeping participants of smartphone studies engaged longitudinally is a major challenge. Based on prior research, we provide 6 actions by researchers to increase engagement. Smartphone studies often have participants that are younger, have higher incomes and high digital literacy. We provide advice for reaching more representative participant groups, and for ensuring that study conclusions are not plagued by bias resulting from unrepresentative sampling. Costs associated with app development and testing, data storage and analysis, and tech support are substantial, even if studies use a 'bring your own device'-policy. Exchange of information on costs, collective app development and usage of open-source tools would help the musculoskeletal community reduce costs of smartphone studies. In general, transparency and wider adoption of best practices would help bringing smartphone studies to the next level. Then, the community can focus on specific challenges of smartphones in musculoskeletal contexts, such as symptom-related barriers to using smartphones for research, validating algorithms in patient populations with reduced functional ability, digitising validated questionnaires, and methods to reliably quantify pain, quality of life and fatigue.

Geolocation features differentiate healthy from remitted depressed adults

Depression recurrence is debilitating, and there is a pressing need to develop clinical tools that detect the reemergence of symptoms with the aim of bridging patients to treatment before recurrences. At baseline, remitted depressed adults (n = 22) and healthy controls (n = 24) were administered clinical interviews and completed self-report symptom measures. Then, smartphone apps were installed on personal smartphones to acquire geolocation data over 21 days and ecological momentary assessment of positive and negative affect during the initial 14-day period. Compared with healthy controls, remitted depressed adults exhibited reduced circadian routine (regularity of one's daily routine) and lower average daily distance traveled. Further, reduced distance traveled associated with greater daily negative affect after controlling for depression severity; however, this effect was not more pronounced among remitted adults. A least absolute shrinkage and selection operator (LASSO) regression indicated that a linear combination of circadian routine, average distance traveled, and baseline depression severity classified remitted depressed individuals with 72% accuracy; outperforming models restricted to either geolocation or clinical measures alone. Mobile sensing approaches hold enormous promise to improve clinical care for depressive disorders. Although barriers remain, leveraging technological advancements related to real-time monitoring can improve treatment for depressed patients and potentially, reduce high rates of recurrence. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Predicting perinatal health outcomes using smartphone-based digital phenotyping and machine learning in a prospective Swedish cohort (Mom2B): study protocol

INTRODUCTION

Perinatal complications, such as perinatal depression and preterm birth, are major causes of morbidity and mortality for the mother and the child. Prediction of high risk can allow for early delivery of existing interventions for prevention. This ongoing study aims to use digital phenotyping data from the Mom2B smartphone application to develop models to predict women at high risk for mental and somatic complications.

METHODS AND ANALYSIS

All Swedish-speaking women over 18 years, who are either pregnant or within 3 months postpartum are eligible to participate by downloading the Mom2B smartphone app. We aim to recruit at least 5000 participants with completed outcome measures. Throughout the pregnancy and within the first year postpartum, both active and passive data are collected via the app in an effort to establish a participant's digital phenotype. Active data collection consists of surveys related to participant background information, mental and physical health, lifestyle, and social circumstances, as well as voice recordings. Participants' general smartphone activity, geographical movement patterns, social media activity and cognitive patterns can be estimated through passive data collection from smartphone sensors and activity logs. The outcomes will be measured using surveys, such as the Edinburgh Postnatal Depression Scale, and through linkage to national registers, from where information on registered clinical diagnoses and received care, including prescribed medication, can be obtained. Advanced machine learning and deep learning techniques will be applied to these multimodal data in order to develop accurate algorithms for the prediction of perinatal depression and preterm birth. In this way, earlier intervention may be possible.

ETHICS AND DISSEMINATION

Ethical approval has been obtained from the Swedish Ethical Review Authority (dnr: 2019/01170, with amendments), and the project fully fulfils the General Data Protection Regulation (GDPR) requirements. All participants provide consent to participate and can withdraw their participation at any time. Results from this project will be disseminated in international peer-reviewed journals and presented in relevant conferences.

The relationship between text message sentiment and self-reported depression

BACKGROUND

Personal sensing has shown promise for detecting behavioral correlates of depression, but there is little work examining personal sensing of cognitive and affective states. Digital language, particularly through personal text messages, is one source that can measure these markers.

METHODS

We correlated privacy-preserving sentiment analysis of text messages with self-reported depression symptom severity. We enrolled 219 U.S. adults in a 16 week longitudinal observational study. Participants installed a personal sensing app on their phones, which administered self-report PHQ-8 assessments of their depression severity, collected phone sensor data, and computed anonymized language sentiment scores from their text messages. We also trained machine learning models for predicting end-of-study self-reported depression status using on blocks of phone sensor and text features.

RESULTS

In correlation analyses, we find that degrees of depression, emotional, and personal pronoun language categories correlate most strongly with self-reported depression, validating prior literature. Our classification models which predict binary depression status achieve a leave-one-out AUC of 0.72 when only considering text features and 0.76 when combining text with other networked smartphone sensors.

LIMITATIONS

Participants were recruited from a panel that over-represented women, caucasians, and individuals with self-reported depression at baseline. As language use differs across demographic factors, generalizability beyond this population may be limited. The study period also coincided with the initial COVID-19 outbreak in the United States, which may have affected smartphone sensor data quality.

CONCLUSIONS

Effective depression prediction through text message sentiment, especially when combined with other personal sensors, could enable comprehensive mental health monitoring and intervention.

Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives

Depression is a serious medical condition and is a leading cause of disability worldwide. Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations, lack of objective assessments, and assessments that rely on patients' perceptions, memory, and recall. Digital phenotyping (DP), especially assessments conducted using mobile health technologies, has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes. DP includes two primary sources of digital data generated using ecological momentary assessments (EMA), assessments conducted in real-time, in subjects' natural environment. This includes active EMA, data that require active input by the subject, and passive EMA or passive sensing, data passively and automatically collected from subjects' personal digital devices. The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients' clinical status. Preliminary investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression status. These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients. Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines. The current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations. A clinically-relevant model for incorporating DP in clinical setting is presented. This model, based on investigations conducted by our group, delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making process. Benefits, challenges, and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.

The digital transformation of hepatology: The patient is logged in

The rise in innovative digital health technologies has led a paradigm shift in health care toward personalized, patient-centric medicine that is reaching beyond traditional brick-and-mortar facilities into patients' homes and everyday lives. Digital solutions can monitor and detect early changes in physiological data, predict disease progression and health-related outcomes based on individual risk factors, and manage disease intervention with a range of accessible telemedicine and mobile health options. In this review, we discuss the unique transformation underway in the care of patients with liver disease, specifically examining the digital transformation of diagnostics, prediction and clinical decision-making, and management. Additionally, we discuss the general considerations needed to confirm validity and oversight of new technologies, usability and acceptability of digital solutions, and equity and inclusivity of vulnerable populations.

A Smartphone-Based Self-management Intervention for Individuals With Bipolar Disorder (LiveWell): Empirical and Theoretical Framework, Intervention Design, and Study Protocol for a Randomized Controlled Trial

Background

Bipolar disorder is a severe mental illness with high morbidity and mortality rates. Even with pharmacological treatment, frequent recurrence of episodes, long episode durations, and persistent interepisode symptoms are common and disruptive. Combining psychotherapy with pharmacotherapy improves outcomes; however, many individuals with bipolar disorder do not receive psychotherapy. Mental health technologies can increase access to self-management strategies derived from empirically supported bipolar disorder psychotherapies while also enhancing treatment by delivering real-time assessments, personalized feedback, and provider alerts. In addition, mental health technologies provide a platform for self-report, app use, and behavioral data collection to advance understanding of the longitudinal course of bipolar disorder, which can then be used to support ongoing improvement of treatment.

Objective

A description of the theoretical and empirically supported framework, design, and protocol for a randomized controlled trial (RCT) of LiveWell, a smartphone-based self-management intervention for individuals with bipolar disorder, is provided to facilitate the ability to replicate, improve, implement, and disseminate effective interventions for bipolar disorder. The goal of the trial is to determine the effectiveness of LiveWell for reducing relapse risk and symptom burden as well as improving quality of life (QOL) while simultaneously clarifying behavioral targets involved in staying well and better characterizing the course of bipolar disorder and treatment response.

Methods

The study is a single-blind RCT (n=205; 2:3 ratio of usual care vs usual care plus LiveWell). The primary outcome is the time to relapse. Secondary outcomes are percentage time symptomatic, symptom severity, and QOL. Longitudinal changes in target behaviors proposed to mediate the primary and secondary outcomes will also be determined, and their relationships with the outcomes will be assessed. A database of clinical status, symptom severity, real-time self-report, behavioral sensor, app use, and personalized content will be created to better predict treatment response and relapse risk.

Results

Recruitment and screening began in March 2017 and ended in April 2019. Follow-up ended in April 2020. The results of this study are expected to be published in 2022.

Conclusions

This study will examine whether LiveWell reduces relapse risk and symptom burden and improves QOL for individuals with bipolar disorder by increasing access to empirically supported self-management strategies. The role of selected target behaviors (medication adherence, sleep duration, routine, and management of signs and symptoms) in these outcomes will also be examined. Simultaneously, a database will be created to initiate the development of algorithms to personalize and improve treatment for bipolar disorder. In addition, we hope that this description of the theoretical and empirically supported framework, intervention design, and study protocol for the RCT of LiveWell will facilitate the ability to replicate, improve, implement, and disseminate effective interventions for bipolar and other mental health disorders.

Trial Registration

ClinicalTrials.gov NCT03088462; https://www.clinicaltrials.gov/ct2/show/NCT03088462

International Registered Report Identifier (IRRID)

DERR1-10.2196/30710

Passive Data Use for Ethical Digital Public Health Surveillance in a Postpandemic World

There is a fundamental need to establish the most ethical and effective way of tracking disease in the postpandemic era. The ubiquity of mobile phones is generating large amounts of passive data (collected without active user participation) that can be used as a tool for tracking disease. Although discussions of pragmatism or economic issues tend to guide public health decisions, ethical issues are the foremost public concern. Thus, officials must look to history and current moral frameworks to avoid past mistakes and ethical pitfalls. Past pandemics demonstrate that the aftermath is the most effective time to make health policy decisions. However, an ethical discussion of passive data use for digital public health surveillance has yet to be attempted, and little has been done to determine the best method to do so. Therefore, we aim to highlight four potential areas of ethical opportunity and challenge: (1) informed consent, (2) privacy, (3) equity, and (4) ownership.

Using Smartphones to Reduce Research Burden in a Neurodegenerative Population and Assessing Participant Adherence: A Randomized Clinical Trial and Two Observational Studies

Background

Smartphone studies provide an opportunity to collect frequent data at a low burden on participants. Therefore, smartphones may enable data collection from people with progressive neurodegenerative diseases such as amyotrophic lateral sclerosis at high frequencies for a long duration. However, the progressive decline in patients’ cognitive and functional abilities could also hamper the feasibility of collecting patient-reported outcomes, audio recordings, and location data in the long term.

Objective

The aim of this study is to investigate the completeness of survey data, audio recordings, and passively collected location data from 3 smartphone-based studies of people with amyotrophic lateral sclerosis.

Methods

We analyzed data completeness in three studies: 2 observational cohort studies (study 1: N=22; duration=12 weeks and study 2: N=49; duration=52 weeks) and 1 clinical trial (study 3: N=49; duration=20 weeks). In these studies, participants were asked to complete weekly surveys; weekly audio recordings; and in the background, the app collected sensor data, including location data. For each of the three studies and each of the three data streams, we estimated time-to-discontinuation using the Kaplan–Meier method. We identified predictors of app discontinuation using Cox proportional hazards regression analysis. We quantified data completeness for both early dropouts and participants who remained engaged for longer.

Results

Time-to-discontinuation was shortest in the year-long observational study and longest in the clinical trial. After 3 months in the study, most participants still completed surveys and audio recordings: 77% (17/22) in study 1, 59% (29/49) in study 2, and 96% (22/23) in study 3. After 3 months, passively collected location data were collected for 95% (21/22), 86% (42/49), and 100% (23/23) of the participants. The Cox regression did not provide evidence that demographic characteristics or disease severity at baseline were associated with attrition, although it was somewhat underpowered. The mean data completeness was the highest for passively collected location data. For most participants, data completeness declined over time; mean data completeness was typically lower in the month before participants dropped out. Moreover, data completeness was lower for people who dropped out in the first study month (very few data points) compared with participants who adhered long term (data completeness fluctuating around 75%).

Conclusions

These three studies successfully collected smartphone data longitudinally from a neurodegenerative population. Despite patients’ progressive physical and cognitive decline, time-to-discontinuation was higher than in typical smartphone studies. Our study provides an important benchmark for participant engagement in a neurodegenerative population. To increase data completeness, collecting passive data (such as location data) and identifying participants who are likely to adhere during the initial phase of a study can be useful.

Trial Registration

ClinicalTrials.gov NCT03168711; https://clinicaltrials.gov/ct2/show/NCT03168711

Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study

BACKGROUND

The assessment of behaviors related to mental health typically relies on self-report data. Networked sensors embedded in smartphones can measure some behaviors objectively and continuously, with no ongoing effort.

OBJECTIVE

This study aims to evaluate whether changes in phone sensor-derived behavioral features were associated with subsequent changes in mental health symptoms.

METHODS

This longitudinal cohort study examined continuously collected phone sensor data and symptom severity data, collected every 3 weeks, over 16 weeks. The participants were recruited through national research registries. Primary outcomes included depression (8-item Patient Health Questionnaire), generalized anxiety (Generalized Anxiety Disorder 7-item scale), and social anxiety (Social Phobia Inventory) severity. Participants were adults who owned Android smartphones. Participants clustered into 4 groups: multiple comorbidities, depression and generalized anxiety, depression and social anxiety, and minimal symptoms.

RESULTS

A total of 282 participants were aged 19-69 years (mean 38.9, SD 11.9 years), and the majority were female (223/282, 79.1%) and White participants (226/282, 80.1%). Among the multiple comorbidities group, depression changes were preceded by changes in GPS features (Time: r=-0.23, P=.02; Locations: r=-0.36, P<.001), exercise duration (r=0.39; P=.03) and use of active apps (r=-0.31; P<.001). Among the depression and anxiety groups, changes in depression were preceded by changes in GPS features for Locations (r=-0.20; P=.03) and Transitions (r=-0.21; P=.03). Depression changes were not related to subsequent sensor-derived features. The minimal symptoms group showed no significant relationships. There were no associations between sensor-based features and anxiety and minimal associations between sensor-based features and social anxiety.

CONCLUSIONS

Changes in sensor-derived behavioral features are associated with subsequent depression changes, but not vice versa, suggesting a directional relationship in which changes in sensed behaviors are associated with subsequent changes in symptoms.

Sociodemographic characteristics of missing data in digital phenotyping

The ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect longitudinal, diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphones for "digital phenotyping," the collection and analysis of raw phone sensor and log data to study the lived experiences of subjects in their natural environments using their own devices. While digital phenotyping has shown promise in fields such as psychiatry and neuroscience, there are fundamental gaps in our knowledge about data collection and non-collection (i.e., missing data) in smartphone-based digital phenotyping. In this meta-study using individual-level data from six different studies, we examined accelerometer and GPS sensor data of 211 participants, amounting to 29,500 person-days of observation, using Bayesian hierarchical negative binomial regression with study- and user-level random intercepts. Sensitivity analyses including alternative model specification and stratified models were conducted. We found that iOS users had lower GPS non-collection than Android users. For GPS data, rates of non-collection did not differ by race/ethnicity, education, age, or gender. For accelerometer data, Black participants had higher rates of non-collection, but rates did not differ by sex, education, or age. For both sensors, non-collection increased by 0.5% to 0.9% per week. These results demonstrate the feasibility of using smartphone-based digital phenotyping across diverse populations, for extended periods of time, and within diverse cohorts. As smartphones become increasingly embedded in everyday life, the insights of this study will help guide the design, planning, and analysis of digital phenotyping studies.

AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units

Introduction

Growing demand for mental health services, coupled with funding and resource limitations, creates an opportunity for novel technological solutions including artificial intelligence (AI). This study aims to identify issues in patient flow on mental health units and align them with potential AI solutions, ultimately devising a model for their integration at service level.

Method

Following a narrative literature review and pilot interview, 20 semi-structured interviews were conducted with AI and mental health experts. Thematic analysis was then used to analyse and synthesise gathered data and construct an enhanced model.

Results

Predictive variables for length-of-stay and readmission rate are not consistent in the literature. There are, however, common themes in patient flow issues. An analysis identified several potential areas for AI-enhanced patient flow. Firstly, AI could improve patient flow by streamlining administrative tasks and optimising allocation of resources. Secondly, real-time data analytics systems could support clinician decision-making in triage, discharge, diagnosis and treatment stages. Finally, longer-term, development of solutions such as digital phenotyping could help transform mental health care to a more preventative, personalised model.

Conclusions

Recommendations were formulated for NHS trusts open to adopting AI patient flow enhancements. Although AI offers many promising use-cases, greater collaborative investment and infrastructure are needed to deliver clinically validated improvements. Concerns around data-use, regulation and transparency remain, and hospitals must continue to balance guidelines with stakeholder priorities. Further research is needed to connect existing case studies and develop a framework for their evaluation.

Measuring and Quantifying Collateral Information in Psychiatry: Development and Preliminary Validation of the McLean Collateral Information and Clinical Actionability Scale

BACKGROUND

The review of collateral information is an essential component of patient care. Although this is standard practice, minimal research has been done to quantify collateral information collection and to understand how collateral information translates to clinical decision making. To address this, we developed and piloted a novel measure (the McLean Collateral Information and Clinical Actionability Scale [M-CICAS]) to evaluate the types and number of collateral sources viewed and the resulting actions made in a psychiatric setting.

OBJECTIVE

This study aims to test the feasibility of the M-CICAS, validate this measure against clinician notes via medical records, and evaluate whether reviewing a higher volume of collateral sources is associated with more clinical actions taken.

METHODS

For the M-CICAS, we developed a three-part instrument, focusing on measuring collateral sources reviewed, clinical actions taken, and shared decision making between the clinician and patient. To determine feasibility and preliminary validity, we piloted this measure among clinicians providing psychotherapy at McLean Hospital. These clinicians (n=7) completed the M-CICAS after individual clinical sessions with 89 distinct patient encounters. Scales were completed by clinicians only once for each patient during routine follow-up visits. After clinicians completed these scales, researchers conducted chart reviews by completing the M-CICAS using only the clinician's corresponding note from that session. For the analyses, we generated summary scores for the number of collateral sources and clinical actions for each encounter. We examined Pearson correlation coefficients to assess interrater reliability between clinicians and chart reviewers, and simple univariate regression modeling followed by multilevel mixed effects regression modeling to test the relationship between collateral information accessed and clinical actions taken.

RESULTS

The study staff had high interrater reliability on the M-CICAS for the sources reviewed (r=0.98; P<.001) and actions taken (r=0.97; P<.001). Clinician and study staff ratings were moderately correlated and statistically significant on the M-CICAS summary scores for the sources viewed (r=0.24, P=.02 and r=0.25, P=.02, respectively). Univariate regression modeling with a two-tailed test demonstrated a significant association between collateral sources and clinical actions taken when clinicians completed the M-CICAS (β=.27; t₈₇=2.47; P=.02). The multilevel fixed slopes random intercepts model confirmed a significant association even when accounting for clinician differences (β=.23; t₅₇=2.13; P=.04).

CONCLUSIONS

This pilot study established the feasibility and preliminary validity of the M-CICAS in assessing collateral sources and clinical decision making in psychiatry. This study also indicated that reviewing more collateral sources may lead to an increased number of clinical actions following a session.

Digital Technologies for Schizophrenia Management: A Descriptive Review

While the implementation of digital technology in psychiatry appears promising, there is an urgent need to address the implications of the absence of ethical design in the early development of such technologies. Some authors have noted the gap between technology development and ethical analysis and have called for an upstream examination of the ethical issues raised by digital technologies. In this paper, we address this suggestion, particularly in relation to digital healthcare technologies for patients with schizophrenia spectrum disorders. The introduction of digital technologies in psychiatry offers a broad spectrum of diagnostic and treatment options tailored to the health needs and goals of patients' care. These technologies include wearable devices, smartphone applications for high-immersive virtual realities, smart homes, telepsychiatry and messaging systems for patients in rural areas. The availability of these technologies could increase access to mental health services and improve the diagnostics of mental disorders. In this descriptive review, we systematize ethical concerns about digital technologies for mental health with a particular focus on individuals suffering from schizophrenia. There are many unsolved dilemmas and conflicts of interest in the implementation of these technologies, such as (1) the lack of evidence on efficacy and impact on self-perception; (2) the lack of clear standards for the safety of their daily implementation; (3) unclear roles of technology and a shift in the responsibilities of all parties; (4) no guarantee of data confidentiality; and (5) the lack of a user-centered design that meets the particular needs of patients with schizophrenia. mHealth can improve care in psychiatry and make mental healthcare services more efficient and personalized while destigmatizing mental health disorders. To ensure that these technologies will benefit people with mental health disorders, we need to heighten sensitivity to ethical issues among mental healthcare specialists, health policy makers, software developers, patients themselves and their proxies. Additionally, we need to develop frameworks for furthering sustainable development in the digital technologies industry and for the responsible usage of such technologies for patients with schizophrenia in the clinical setting. We suggest that digital technology in psychiatry, particularly for schizophrenia and other serious mental health disorders, should be integrated into treatment with professional supervision rather than as a self-treatment tool.

The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae

Personalized medicine is among the most exciting innovations in recent clinical research, offering the opportunity for tailored screening and management at the individual level. Biomarker-enriched clinical trials have shown increased efficiency and informativeness in cancer research due to the selective exclusion of patients unlikely to benefit. In acute stress situations, clinically significant decisions are often made in time-sensitive manners and providers may be pressed to make decisions based on abbreviated clinical assessments. Up to 30% of trauma survivors admitted to the Emergency Department (ED) will develop long-lasting posttraumatic stress psychopathologies. The long-term impact of those survivors with posttraumatic stress sequelae are significant, impacting both long-term psychological and physiological recovery. An accurate prognostic model of who will develop posttraumatic stress symptoms does not exist yet. Additionally, no scalable and cost-effective method that can be easily integrated into routine care exists, even though especially the acute care setting provides a critical window of opportunity for prevention in the so-called golden hours when preventive measures are most effective. In this review, we aim to discuss emerging machine learning (ML) applications that are promising for precisely risk stratification and targeted treatments in the acute care setting. The aim of this narrative review is to present examples of digital health innovations and to discuss the potential of these new approaches for treatment selection and prevention of posttraumatic sequelae in the acute care setting. The application of artificial intelligence-based solutions have already had great success in other areas and are rapidly approaching the field of psychological care as well. New ways of algorithm-based risk predicting, and the use of digital phenotypes provide a high potential for predicting future risk of PTSD in acute care settings and to go new steps in precision psychiatry.

Screening accuracy of a 14-day smartphone ambulatory assessment of depression symptoms and mood dynamics in a general population sample: Comparison with the PHQ-9 depression screening

Introduction

Major depression affects over 300 million people worldwide, but cases are often detected late or remain undetected. This increases the risk of symptom deterioration and chronification. Consequently, there is a high demand for low threshold but clinically sound approaches to depression detection. Recent studies show a great willingness among users of mobile health apps to assess daily depression symptoms. In this pilot study, we present a provisional validation of the depression screening app Moodpath. The app offers a 14-day ambulatory assessment (AA) of depression symptoms based on the ICD-10 criteria as well as ecologically momentary mood ratings that allow the study of short-term mood dynamics.

Materials and methods

N = 113 Moodpath users were selected through consecutive sampling and filled out the Patient Health Questionnaire (PHQ-9) after completing 14 days of AA with 3 question blocks (morning, midday, and evening) per day. The psychometric properties (sensitivity, specificity, accuracy) of the ambulatory Moodpath screening were assessed based on the retrospective PHQ-9 screening result. In addition, several indicators of mood dynamics (e.g. average, inertia, instability), were calculated and investigated for their individual and incremental predictive value using regression models.

Results

We found a strong linear relationship between the PHQ-9 score and the AA Moodpath depression score (r = .76, p < .001). The app-based screening demonstrated a high sensitivity (.879) and acceptable specificity (.745). Different indicators of mood dynamics covered substantial amounts of PHQ-9 variance, depending on the number of days with mood data that were included in the analyses.

Discussion

AA and PHQ-9 shared a large proportion of variance but may not measure exactly the same construct. This may be due to the differences in the underlying diagnostic systems or due to differences in momentary and retrospective assessments. Further validation through structured clinical interviews is indicated. The results suggest that ambulatory assessed mood indicators are a promising addition to multimodal depression screening tools. Improving app-based AA screenings requires adapted screening algorithms and corresponding methods for the analysis of dynamic processes over time.

Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

Background: Depression and anxiety are leading causes of disability worldwide but often remain undetected and untreated. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods. Objective: The current study aimed to explore the extent to which data from smartphone and wearable devices could predict symptoms of depression and anxiety. Methods: A total of N = 60 adults (ages 24-68) who owned an Apple iPhone and Oura Ring were recruited online over a 2-week period. At the beginning of the study, participants installed the Delphi data acquisition app on their smartphone. The app continuously monitored participants' location (using GPS) and smartphone usage behavior (total usage time and frequency of use). The Oura Ring provided measures related to activity (step count and metabolic equivalent for task), sleep (total sleep time, sleep onset latency, wake after sleep onset and time in bed) and heart rate variability (HRV). In addition, participants were prompted to report their daily mood (valence and arousal). Participants completed self-reported assessments of depression, anxiety and stress (DASS-21) at baseline, midpoint and the end of the study. Results: Multilevel models demonstrated a significant negative association between the variability of locations visited and symptoms of depression (beta = -0.21, p = 0.037) and significant positive associations between total sleep time and depression (beta = 0.24, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression. Conclusion: The current findings demonstrate that wearable devices may provide valuable sources of data in predicting symptoms of depression and anxiety, most notably data related to common measures of sleep.

The Future of Telepsychiatry in India

Technology is bringing about a revolution in every field and mental health care is no exception. The ongoing COVID-19 pandemic has provided us with both a need and an opportunity to use technology as means to improve access to mental health care. Hence, it is imperative to expand and harness the tremendous potential of telepsychiatry by expanding the scope of its applications and the future possibilities. In this article, we explore the different avenues in digital innovation that is revolutionizing the practice in psychiatry like mental health applications, artificial intelligence, e-portals, and technology leveraging for building capacity. Also, we have also visualized what the future has in store for our practice of psychiatry, considering how rapid technological advances can occur and how these advances will impact us. There will be challenges on the road ahead, especially for a country like India for instance; the digital divide, lack of knowledge to utilize the available technology and the need for a quality control and regulation. However, it is safe to presume that telepsychiatry will evolve and progress beyond these roadblocks and will fulfill its role in transforming health care. Telepsychiatry will improve the health care capacity to interact with patients and family. The blurring of national and international borders will also open international opportunities to psychiatrist in India, heralding a new wave of virtual health tourism.

Artificial Intelligence and Posttraumatic Stress Disorder (PTSD)

Abstract. Posttraumatic stress disorder (PTSD) is a debilitating disease that can occur after experiencing a traumatic event. Despite recent progress in computational research, it has not yet been possible to identify precise and reliable risk factors that enable predictive models of individual risk for posttraumatic stress after trauma. In this overview, we discuss recent advances in the use of Machine Learning (ML) and Artificial Intelligence (AI) for risk stratification and targeted treatment allocation in the context of stress pathologies and we critically review the benefits and challenges of emerging approaches. The vast heterogeneity in the manifestation and the etiology of PTSD is discussed as one major reason for the need to deploy ML-based computational models to better account for individual differences between patients. Striving for personalized medicine is one of the most important goals of current clinical research and is of great potential for the field of posttraumatic stress research. The use of ML is a promising and necessary approach for reaching more personalized treatments and to make further progress in the field of precision psychiatry.

Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: Exploratory Study

BACKGROUND

Emotional state in everyday life is an essential indicator of health and well-being. However, daily assessment of emotional states largely depends on active self-reports, which are often inconvenient and prone to incomplete information. Automated detection of emotional states and transitions on a daily basis could be an effective solution to this problem. However, the relationship between emotional transitions and everyday context remains to be unexplored.

OBJECTIVE

This study aims to explore the relationship between contextual information and emotional transitions and states to evaluate the feasibility of detecting emotional transitions and states from daily contextual information using machine learning (ML) techniques.

METHODS

This study was conducted on the data of 18 individuals from a publicly available data set called ExtraSensory. Contextual and sensor data were collected using smartphone and smartwatch sensors in a free-living condition, where the number of days for each person varied from 3 to 9. Sensors included an accelerometer, a gyroscope, a compass, location services, a microphone, a phone state indicator, light, temperature, and a barometer. The users self-reported approximately 49 discrete emotions at different intervals via a smartphone app throughout the data collection period. We mapped the 49 reported discrete emotions to the 3 dimensions of the pleasure, arousal, and dominance model and considered 6 emotional states: discordant, pleased, dissuaded, aroused, submissive, and dominant. We built general and personalized models for detecting emotional transitions and states every 5 min. The transition detection problem is a binary classification problem that detects whether a person's emotional state has changed over time, whereas state detection is a multiclass classification problem. In both cases, a wide range of supervised ML algorithms were leveraged, in addition to data preprocessing, feature selection, and data imbalance handling techniques. Finally, an assessment was conducted to shed light on the association between everyday context and emotional states.

RESULTS

This study obtained promising results for emotional state and transition detection. The best area under the receiver operating characteristic (AUROC) curve for emotional state detection reached 60.55% in the general models and an average of 96.33% across personalized models. Despite the highly imbalanced data, the best AUROC curve for emotional transition detection reached 90.5% in the general models and an average of 88.73% across personalized models. In general, feature analyses show that spatiotemporal context, phone state, and motion-related information are the most informative factors for emotional state and transition detection. Our assessment showed that lifestyle has an impact on the predictability of emotion.

CONCLUSIONS

Our results demonstrate a strong association of daily context with emotional states and transitions as well as the feasibility of detecting emotional states and transitions using data from smartphone and smartwatch sensors.

Connected Mental Health: Systematic Mapping Study

BACKGROUND

Although mental health issues constitute an increasing global burden affecting a large number of people, the mental health care industry is still facing several care delivery barriers such as stigma, education, and cost. Connected mental health (CMH), which refers to the use of information and communication technologies in mental health care, can assist in overcoming these barriers.

OBJECTIVE

The aim of this systematic mapping study is to provide an overview and a structured understanding of CMH literature available in the Scopus database.

METHODS

A total of 289 selected publications were analyzed based on 8 classification criteria: publication year, publication source, research type, contribution type, empirical type, mental health issues, targeted cohort groups, and countries where the empirically evaluated studies were conducted.

RESULTS

The results showed that there was an increasing interest in CMH publications; journals were the main publication channels of the selected papers; exploratory research was the dominant research type; advantages and challenges of the use of technology for mental health care were the most investigated subjects; most of the selected studies had not been evaluated empirically; depression and anxiety were the most addressed mental disorders; young people were the most targeted cohort groups in the selected publications; and Australia, followed by the United States, was the country where most empirically evaluated studies were conducted.

CONCLUSIONS

CMH is a promising research field to present novel approaches to assist in the management, treatment, and diagnosis of mental health issues that can help overcome existing mental health care delivery barriers. Future research should be shifted toward providing evidence-based studies to examine the effectiveness of CMH solutions and identify related issues.

Designing a Clinician-Facing Tool for Using Insights From Patients' Social Media Activity: Iterative Co-Design Approach

BACKGROUND

Recent research has emphasized the need for accessing information about patients to augment mental health patients' verbal reports in clinical settings. Although it has not been introduced in clinical settings, computational linguistic analysis on social media has proved it can infer mental health attributes, implying a potential use as collateral information at the point of care. To realize this potential and make social media insights actionable to clinical decision making, the gaps between computational linguistic analysis on social media and the current work practices of mental health clinicians must be bridged.

OBJECTIVE

This study aimed to identify information derived from patients' social media data that can benefit clinicians and to develop a set of design implications, via a series of low-fidelity (lo-fi) prototypes, on how to deliver the information at the point of care.

METHODS

A team of clinical researchers and human-computer interaction (HCI) researchers conducted a long-term co-design activity for over 6 months. The needs-affordances analysis framework was used to refine the clinicians' potential needs, which can be supported by patients' social media data. On the basis of those identified needs, the HCI researchers iteratively created 3 different lo-fi prototypes. The prototypes were shared with both groups of researchers via a videoconferencing software for discussion and feedback. During the remote meetings, potential clinical utility, potential use of the different prototypes in a treatment setting, and areas of improvement were discussed.

RESULTS

Our first prototype was a card-type interface that supported treatment goal tracking. Each card included attribute levels: depression, anxiety, social activities, alcohol, and drug use. This version confirmed what types of information are helpful but revealed the need for a glanceable dashboard that highlights the trends of these information. As a result, we then developed the second prototype, an interface that shows the clinical state and trend. We found that focusing more on the changes since the last visit without visual representation can be more compatible with clinicians' work practices. In addition, the second phase of needs-affordances analysis identified 3 categories of information relevant to patients with schizophrenia: symptoms related to psychosis, symptoms related to mood and anxiety, and social functioning. Finally, we developed the third prototype, a clinical summary dashboard that showed changes from the last visit in plain texts and contrasting colors.

CONCLUSIONS

This exploratory co-design research confirmed that mental health attributes inferred from patients' social media data can be useful for clinicians, although it also revealed a gap between computational social media analyses and clinicians' expectations and conceptualizations of patients' mental health states. In summary, the iterative co-design process crystallized design directions for the future interface, including how we can organize and provide symptom-related information in a way that minimizes the clinicians' workloads.

A Combined Digital and Biomarker Diagnostic Aid for Mood Disorders (the Delta Trial): Protocol for an Observational Study

Background

Mood disorders affect hundreds of millions of people worldwide, imposing a substantial medical and economic burden. Existing diagnostic methods for mood disorders often result in a delay until accurate diagnosis, exacerbating the challenges of these disorders. Advances in digital tools for psychiatry and understanding the biological basis of mood disorders offer the potential for novel diagnostic methods that facilitate early and accurate diagnosis of patients.

Objective

The Delta Trial was launched to develop an algorithm-based diagnostic aid combining symptom data and proteomic biomarkers to reduce the misdiagnosis of bipolar disorder (BD) as a major depressive disorder (MDD) and achieve more accurate and earlier MDD diagnosis.

Methods

Participants for this ethically approved trial were recruited through the internet, mainly through Facebook advertising. Participants were then screened for eligibility, consented to participate, and completed an adaptive digital questionnaire that was designed and created for the trial on a purpose-built digital platform. A subset of these participants was selected to provide dried blood spot (DBS) samples and undertake a World Health Organization World Mental Health Composite International Diagnostic Interview (CIDI). Inclusion and exclusion criteria were chosen to maximize the safety of a trial population that was both relevant to the trial objectives and generalizable. To provide statistical power and validation sets for the primary and secondary objectives, 840 participants were required to complete the digital questionnaire, submit DBS samples, and undertake a CIDI.

Results

The Delta Trial is now complete. More than 3200 participants completed the digital questionnaire, 924 of whom also submitted DBS samples and a CIDI, whereas a total of 1780 participants completed a 6-month follow-up questionnaire and 1542 completed a 12-month follow-up questionnaire. The analysis of the trial data is now underway.

Conclusions

If a diagnostic aid is able to improve the diagnosis of BD and MDD, it may enable earlier treatment for patients with mood disorders.

International Registered Report Identifier (IRRID)

DERR1-10.2196/18453

Universal mental health screening with a focus on suicidal behaviour using smartphones in a Mexican rural community: protocol for the SMART-SCREEN population-based survey

INTRODUCTION

Mental disorders represent the second cause of years lived with disability worldwide. Suicide mortality has been targeted as a key public health concern by the WHO. Smartphone technology provides a huge potential to develop massive and fast surveys. Given the vast cultural diversity of Mexico and its abrupt orography, smartphone-based resources are invaluable in order to adequately manage resources, services and preventive measures in the population. The objective of this study is to conduct a universal suicide risk screening in a rural area of Mexico, measuring also other mental health outcomes such as depression, anxiety and alcohol and substance use disorders.

METHODS AND ANALYSIS

A population-based cross-sectional study with a temporary sampling space of 9 months will be performed between September 2019 and June 2020. We expect to recruit a large percentage of the target population (at least 70%) in a short-term survey of Milpa Alta Delegation, which accounts for 137 927 inhabitants in a territorial extension of 288 km².They will be recruited via an institutional call and a massive public campaign to fill in an online questionnaire through mobile-assisted or computer-assisted web app. This questionnaire will include data on general health, validated questionnaires including Well-being Index 5, Patient Health Questionnaire-9, Generalized Anxiety Disorder Scale 2, Alcohol Use Disorders Identification Test, selected questions of the Drug Abuse Screening Test and Columbia-Suicide Severity Rating Scales and Diagnostic and statistical manual of mental disorders (DSM-5) questions about self-harm.We will take into account information regarding time to mobile app response and geo-spatial location, and aggregated data on social, demographical and environmental variables. Traditional regression modelling, multilevel mixed methods and data-driven machine learning approaches will be used to test hypotheses regarding suicide risk factors at the individual and the population level.

ETHICS AND DISSEMINATION

Ethical approval (002/2019) was granted by the Ethics Review Board of the Hospital Psiquiátrico Yucatán, Yucatán (Mexico). This protocol has been registered in ClinicalTrials.gov. The starting date of the study is 3 September 2019. Results will serve for the planning and healthcare of groups with greater mental health needs and will be disseminated via publications in peer-reviewed journal and presented at relevant mental health conferences.

TRIAL REGISTRATION NUMBER

NCT04067063.

Can Acceptance, Mindfulness, and Self-Compassion Be Learned by Smartphone Apps? A Systematic and Meta-Analytic Review of Randomized Controlled Trials

The potential health benefits of acceptance, mindfulness, and self-compassion are well-documented. However, interventions that teach these principles typically rely on face-to-face delivery, which can limit their dissemination. Delivering these interventions through smartphone apps could help overcome this. This meta-analysis examined whether principles of acceptance, mindfulness, and self-compassion can be learned through smartphone apps. Twenty-seven randomized controlled trials were included. Smartphone apps that included acceptance and/or mindfulness components resulted in significantly higher levels of acceptance/mindfulness than comparison conditions (k = 33; g = 0.29; 95% CI = 0.17, 0.41). These effects were moderated by the type of comparison and whether reminders to engage were offered. Smartphone apps also resulted in significantly lower levels of psychological distress than comparisons (k = 22; g = -0.32; 95% CI = -0.48, -0.16). Meta-regression revealed a negative relationship between the effect sizes for mindfulness/acceptance and the effect sizes for distress. Smartphone apps produced significantly greater increases in self-compassion than comparisons (k = 9; g = 0.31; 95% CI = 0.07, 0.56), although the quality of RCTs in this analysis was poor. Findings suggest that principles of acceptance, mindfulness, and self-compassion may be learned through cheap, easily accessible, and low-intensity interventions delivered via smartphone apps. However, the quality of available evidence is poor, as low risk of bias was noted in few trials (18%) and the observed effects were likely explained by a digital placebo.

Embedding Mobile Health Technology into the Nurses' Health Study 3 to Study Behavioral Risk Factors for Cancer

BACKGROUND

Physical activity and sleep are behavioral risk factors for cancer that may be influenced by environmental exposures, including built and natural environments. However, many studies in this area are limited by residence-based exposure assessment and/or self-reported, time-aggregated measures of behavior.

METHODS

The Nurses' Health Study 3 (NHS3) Mobile Health Substudy is a pilot study of 500 participants in the prospective NHS3 cohort who use a smartphone application and a Fitbit for seven-day periods, four times over a year, to measure minute-level location, physical activity, heart rate, and sleep.

RESULTS

We have collected data on 435 participants, comprising over 6 million participant-minutes of heart rate, step, sleep, and location. Over 90% of participants had five days of ≥600 minutes of Fitbit wear-time in their first sampling week, and this percentage dropped to 70% for weeks 2 to 4. Over 819 sampling weeks, we observed an average of 7,581 minutes of heart rate and step data [interquartile range (IQR): 6,651-9,645] per participant-week, and >2 million minutes of sleep in over 5,700 sleep bouts. We have recorded location data for 5,237 unique participant-days, averaging 104 location observations per participant-day (IQR: 103-107).

CONCLUSIONS

This study describes a protocol to incorporate mobile health technology into a nationwide prospective cohort to measure high-resolution objective data on environment and behavior.

IMPACT

This project could provide translational insights into interventions for urban planning to optimize opportunities for physical activity and healthy sleep patterns to reduce cancer risk.See all articles in this CEBP Focus section, "Modernizing Population Science."

"Energy is a Finite Resource": Designing Technology to Support Individuals across Fluctuating Symptoms of Depression

While the HCI field increasingly examines how digital tools can support individuals in managing mental health conditions, it remains unclear how these tools can accommodate these conditions' temporal aspects. Based on weekly interviews with five individuals with depression, conducted over six weeks, this study identifies design opportunities and challenges related to extending technology-based support across fluctuating symptoms. Our findings suggest that participants perceive events and contexts in daily life to have marked impact on their symptoms. Results also illustrate that ebbs and flows in symptoms profoundly affect how individuals practice depression self-management. While digital tools often aim to reach individuals while they feel depressed, we suggest they should also engage individuals when they are less symptomatic, leveraging their energy and motivation to build habits, establish plans and goals, and generate and organize content to prepare for symptom onset.

A Machine Learning and Integration Based Architecture for Cognitive Disorder Detection Used for Early Autism Screening

About 15% of the world’s population suffers from some form of disability. In developed countries, about 1.5% of children are diagnosed with autism. Autism is a developmental disorder distinguished mainly by impairments in social interaction and communication and by restricted and repetitive behavior. Since the cause of autism is still unknown, there have been many studies focused on screening for autism based on behavioral features. Thus, the main purpose of this paper is to present an architecture focused on data integration and analytics, allowing the distributed processing of input data. Furthermore, the proposed architecture allows the identification of relevant features as well as of hidden correlations among parameters. To this end, we propose a methodology able to integrate diverse data sources, even data that are collected separately. This methodology increases the data variety which can lead to the identification of more correlations between diverse parameters. We conclude the paper with a case study that used autism data in order to validate our proposed architecture, which showed very promising results.

Artificial intelligence and the future of psychiatry: Qualitative findings from a global physician survey

BACKGROUND

The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics.

OBJECTIVE

This study aimed to explore psychiatrists' opinions about the potential impact innovations in artificial intelligence and machine learning on psychiatric practice.

METHODS

In Spring 2019, we conducted a web-based survey of 791 psychiatrists from 22 countries worldwide. The survey measured opinions about the likelihood future technology would fully replace physicians in performing ten key psychiatric tasks. This study involved qualitative descriptive analysis of written responses ("comments") to three open-ended questions in the survey.

RESULTS

Comments were classified into four major categories in relation to the impact of future technology on: (1) patient-psychiatrist interactions; (2) the quality of patient medical care; (3) the profession of psychiatry; and (4) health systems. Overwhelmingly, psychiatrists were skeptical that technology could replace human empathy. Many predicted that 'man and machine' would increasingly collaborate in undertaking clinical decisions, with mixed opinions about the benefits and harms of such an arrangement. Participants were optimistic that technology might improve efficiencies and access to care, and reduce costs. Ethical and regulatory considerations received limited attention.

CONCLUSIONS

This study presents timely information on psychiatrists' views about the scope of artificial intelligence and machine learning on psychiatric practice. Psychiatrists expressed divergent views about the value and impact of future technology with worrying omissions about practice guidelines, and ethical and regulatory issues.

The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials

Although impressive progress has been made toward developing empirically-supported psychological treatments, the reality remains that a significant proportion of people with mental health problems do not receive these treatments. Finding ways to reduce this treatment gap is crucial. Since app-supported smartphone interventions are touted as a possible solution, access to up-to-date guidance around the evidence base and clinical utility of these interventions is needed. We conducted a meta-analysis of 66 randomized controlled trials of app-supported smartphone interventions for mental health problems. Smartphone interventions significantly outperformed control conditions in improving depressive (g=0.28, n=54) and generalized anxiety (g=0.30, n=39) symptoms, stress levels (g=0.35, n=27), quality of life (g=0.35, n=43), general psychiatric distress (g=0.40, n=12), social anxiety symptoms (g=0.58, n=6), and positive affect (g=0.44, n=6), with most effects being robust even after adjusting for various possible biasing factors (type of control condition, risk of bias rating). Smartphone interventions conferred no significant benefit over control conditions on panic symptoms (g=-0.05, n=3), post-traumatic stress symptoms (g=0.18, n=4), and negative affect (g=-0.08, n=5). Studies that delivered a cognitive behavior therapy (CBT)-based app and offered professional guidance and reminders to engage produced larger effects on multiple outcomes. Smartphone interventions did not differ significantly from active interventions (face-to-face, computerized treatment), although the number of studies was low (n≤13). The efficacy of app-supported smartphone interventions for common mental health problems was thus confirmed. Although mental health apps are not intended to replace professional clinical services, the present findings highlight the potential of apps to serve as a cost-effective, easily accessible, and low intensity intervention for those who cannot receive standard psychological treatment.