The application of mHealth to mental health: opportunities and challenges

Implementation of an Ecological Momentary Assessment (EMA) in Naturalistic Psychotherapy Settings: Qualitative Insights from Patients, Therapists, and Supervisors Perspectives

Ecological momentary assessment (EMA) allows measuring intra-individual processes moment by moment, identifying and modeling, in a naturalistic way, individual levels and changes in different psychological processes. However, active EMA requires a high degree of adherence, as it implies a significant burden for patients. Moreover, there is still no consensus on standardized procedures for implementation. There have been few results in detecting desirable characteristics for the design and implementation of an EMA device. Studies that address these issues from the perspectives of participants in psychotherapeutic processes are needed. To analyze the perspectives of patients, therapists and supervisors on the implementation of an EMA device in a psychotherapeutic treatment for depression. The sample will include eight patients, eleven therapists and five supervisors, taken from a research project that implemented an EMA system for monitoring the dynamics of affectivity at the beginning of psychotherapies for depression. Semi-structured interviews specific to each group are being conducted and analyzed from a qualitative approach based on consensual qualitative research (CQR). Participants reported having a positive evaluation of the study's informational resources and implementation. Difficulties were expressed in responding in the morning hours and the importance of having a customized EMA that is tailored to the needs of the patients was expressed. Furthermore, patients and therapists agreed that the impact of the use of the monitoring system on treatment was neutral or positive. In contrast, patients considered the EMA to be positive for their daily life.

Developing prediction algorithms for late-life depression using wearable devices: a cohort study protocol

INTRODUCTION

Despite the high prevalence of major depressive disorder (MDD) among the elderly population, the rate of treatment is low due to stigmas and barriers to medical access. Wearable devices such as smartphones and smartwatches can help to screen MDD symptoms earlier in a natural setting while forgoing these concerns. However, previous research using wearable devices has mostly targeted the younger population. By collecting longitudinal data using wearable devices from the elderly population, this research aims to produce prediction algorithms for late-life depression and to develop strategies that strengthen medical access in community care systems.

METHODS AND ANALYSIS

The current cohort study recruited a subsample of 685 elderly people from the Korean Genome and Epidemiology Study-Cardiovascular Disease Association Study, a national large cohort established in 2004. The current study has been conducted over a 3-year period to explore the development patterns of late-life depression. Participants have completed three annual face-to-face interviews (baseline, the first follow-up and the second follow-up) and 2 years of app-based surveys and passive sensing data collection. All the data collection will end at the second follow-up interview. The collected self-report, observational and passive sensing data will be primarily analysed by machine learning.

ETHICS AND DISSEMINATION

This study protocol has been reviewed and approved by the Yonsei University Mirae Campus Institutional Review Board (1041849-2 02 111 SB-180-06) in South Korea. All participants provided written informed consent. The findings of this research will be disseminated by academic publications and conference presentations.

Digital Lifestyle Interventions for Young People With Mental Illness: A Qualitative Study Among Mental Health Care Professionals

BACKGROUND

Given the physical health disparities associated with mental illness, targeted lifestyle interventions are required to reduce the risk of cardiometabolic disease. Integrating physical health early in mental health treatment among young people is essential for preventing physical comorbidities, reducing health disparities, managing medication side effects, and improving overall health outcomes. Digital technology is increasingly used to promote fitness, lifestyle, and physical health among the general population. However, using these interventions to promote physical health within mental health care requires a nuanced understanding of the factors that affect their adoption and implementation.

OBJECTIVE

Using a qualitative design, we explored the attitudes of mental health care professionals (MHCPs) toward digital technologies for physical health with the goal of illuminating the opportunities, development, and implementation of the effective use of digital tools for promoting healthier lifestyles in mental health care.

METHODS

Semistructured interviews were conducted with MHCPs (N=13) using reflexive thematic analysis to explore their experiences and perspectives on using digital health to promote physical health in youth mental health care settings.

RESULTS

Three overarching themes from the qualitative analysis are reported: (1) motivation will affect implementation, (2) patients' readiness and capability, and (3) reallocation of staff roles and responsibilities. The subthemes within, and supporting quotes, are described.

CONCLUSIONS

The use of digital means presents many opportunities for improving the provision of physical health interventions in mental health care settings. However, given the limited experience of many MHCPs with these technologies, formal training and additional support may improve the likelihood of implementation. Factors such as patient symptomatology, safety, and access to technology, as well as the readiness, acceptability, and capability of both MHCPs and patients to engage with digital tools, must also be considered. In addition, the potential benefits of data integration must be carefully weighed against the associated risks.

A review of risks, adverse effects and mitigation strategies when delivering mental health services using telehealth

BACKGROUND

This paper presents a scoping review of the peer-reviewed literature regarding reported risks, adverse effects and mitigation factors related to providing mental health services using telehealth.

AIMS

The paper aims to describe risks and risk management strategies.

METHODS

Publications were included if they reported upon risks, adverse events or mitigation factors experienced, hypothesised or discussed for: any population (any country, any age), service (any mental health services), intervention (telehealth), English language, 2010 to 10 July 2021, any publication type (commentary, research, policy), excluding protocol papers, and self-help tools. The following databases were searched: PsycINFO (from 2010 to 10 July 2021), MEDLINE (2010 to 10 July 2021) and the Cochrane Database from 2010 to 10 July 2021.

RESULTS

The search strategy resulted in 1,497 papers and after exclusions a final 55 articles were selected. Results of this scoping review are presented in terms of types of risk, risk by client population, risk by modality (eg group therapy using telehealth) and risk management.

CONCLUSIONS

Recommendations for future research include gathering and publishing more detailed information regarding near-miss and actual adverse events when delivering mental health assessment and care using telehealth. In clinical practice, training is required for potential adverse events, and to prevent them and reporting mechanisms in place to collate and learn from these.

Characterizing Longitudinal Patterns in Cognition, Mood, And Activity in Depression With 6-Week High-Frequency Wearable Assessment: Observational Study

BACKGROUND

Cognitive symptoms are an underrecognized aspect of depression that are often untreated. High-frequency cognitive assessment holds promise for improving disease and treatment monitoring. Although we have previously found it feasible to remotely assess cognition and mood in this capacity, further work is needed to ascertain the optimal methodology to implement and synthesize these techniques.

OBJECTIVE

The objective of this study was to examine (1) longitudinal changes in mood, cognition, activity levels, and heart rate over 6 weeks; (2) diurnal and weekday-related changes; and (3) co-occurrence of fluctuations between mood, cognitive function, and activity.

METHODS

A total of 30 adults with current mild-moderate depression stabilized on antidepressant monotherapy responded to testing delivered through an Apple Watch (Apple Inc) for 6 weeks. Outcome measures included cognitive function, assessed with 3 brief n-back tasks daily; self-reported depressed mood, assessed once daily; daily total step count; and average heart rate. Change over a 6-week duration, diurnal and day-of-week variations, and covariation between outcome measures were examined using nonlinear and multilevel models.

RESULTS

Participants showed initial improvement in the Cognition Kit N-Back performance, followed by a learning plateau. Performance reached 90% of individual learning levels on average 10 days after study onset. N-back performance was typically better earlier and later in the day, and step counts were lower at the beginning and end of each week. Higher step counts overall were associated with faster n-back learning, and an increased daily step count was associated with better mood on the same (P<.001) and following day (P=.02). Daily n-back performance covaried with self-reported mood after participants reached their learning plateau (P=.01).

CONCLUSIONS

The current results support the feasibility and sensitivity of high-frequency cognitive assessments for disease and treatment monitoring in patients with depression. Methods to model the individual plateau in task learning can be used as a sensitive approach to better characterize changes in behavior and improve the clinical relevance of cognitive data. Wearable technology allows assessment of activity levels, which may influence both cognition and mood.

Evaluating the effectiveness of a mobile app-based self-guided psychological interventions to reduce relapse in substance use disorder: protocol for a randomized controlled trial

Background

Substance Use Disorder (SUD) persists as a significant public health challenge worldwide, with an estimated prevalence of approximately 10-15% across the global populace. This condition is characterized by a notably high risk of lapses and relapses, even subsequent to treatment interventions. Mobile health interventions, owing to their widespread accessibility, emerge as a promising approach to diminish the risk of relapse post-treatment and to broaden the scope of care, especially in regions with a scarcity of trained medical professionals.

Method

This study is designed to assess the effectiveness of mobile interventions in mitigating cravings and preventing lapses among individuals diagnosed with SUD. Employing a two-armed, randomized controlled trial framework, the study will evaluate a self-administered psychological intervention delivered through a mobile application, Nałogometr 2.0. Over a period of three months, participants will engage with intervention modules that primarily incorporate mindfulness techniques and Cognitive Behavioral Therapy (CBT) principles. Ecological Momentary Assessment (EMA) will be utilized to gather longitudinal data on a range of variables that are indicative of craving intensity and the risk of lapse. In addition to this, a monthly-administered battery of questionnaires will be employed to gauge the severity of substance dependence, as well as to measure levels of anxiety, depression, and overall life satisfaction.

Results

Results will be submitted for publication in peer-reviewed journals.

Clinical trial registration

https://clinicaltrials.gov/, identifier [NCT05730504].

User engagement with organizational mHealth stress management intervention - A mixed methods study

Mobile health (mHealth) demonstrates great promise for providing effective and accessible interventions within an organizational context. Compared with traditional workplace interventions, mHealth solutions may be significantly more scalable and easier to standardize. However, inadequate user engagement is a major challenge with mHealth solutions that can negatively impact the potential benefits of an intervention. More research is needed to better understand how to ensure sufficient engagement, which is essential for designing and implementing effective interventions. To address this issue, this study employed a mixed methods approach to investigate what factors influence user engagement with an organizational mHealth intervention. Quantitative data were collected using surveys (n = 1267), and semi-structured interviews were conducted with a subset of participants (n = 17). Primary findings indicate that short and consistent interactions as well as user intention are key drivers of engagement. These results may inform future development of interventions to increase engagement and effectiveness.

Digital health technologies and major depressive disorder

There is an urgent need to improve the clinical management of major depressive disorder (MDD), which has become increasingly prevalent over the past two decades. Several gaps and challenges in the awareness, detection, treatment, and monitoring of MDD remain to be addressed. Digital health technologies have demonstrated utility in relation to various health conditions, including MDD. Factors related to the COVID-19 pandemic have accelerated the development of telemedicine, mobile medical apps, and virtual reality apps and have continued to introduce new possibilities across mental health care. Growing access to and acceptance of digital health technologies present opportunities to expand the scope of care and to close gaps in the management of MDD. Digital health technology is rapidly evolving the options for nonclinical support and clinical care for patients with MDD. Iterative efforts to validate and optimize such digital health technologies, including digital therapeutics and digital biomarkers, continue to improve access to and quality of personalized detection, treatment, and monitoring of MDD. The aim of this review is to highlight the existing gaps and challenges in depression management and discuss the current and future landscape of digital health technology as it applies to the challenges faced by patients with MDD and their healthcare providers.

The effectiveness of CBT-based daily supportive text messages in improving female mental health during COVID-19 pandemic: results from the Text4Hope program

Introduction

The COVID-19 pandemic has significantly exacerbated gender disparities in mental health, particularly impacting women. To address this, Alberta, Canada, launched Text4Hope, a Cognitive Behaviour Therapy-based text messaging intervention, to provide support and resources for psychological challenges during the pandemic. This study aimed to assess the effectiveness of Text4Hope in reducing stress, anxiety, depression, sleeping disturbances, and suicidal ideation among female subscribers during the COVID-19 pandemic.

Methods

The study employed both an uncontrolled longitudinal design and a controlled cohort design. The uncontrolled longitudinal study analyzed a one-year dataset (n = 9,545) of clinical outcomes, comparing mean differences in mental health symptoms from baseline to 6 weeks after subscription. The controlled cohort design compared two groups, with (n = 1,763) and without (n = 567) intervention exposure during the same period. Data were collected through self-administered online surveys completed at baseline and six weeks after subscription. Sociodemographic information and validated scales (e.g., 10-item Perceived Stress Scale (PSS-10), Generalized Anxiety Disorder (GAD-7), and Patient Health Questionnaire (PHQ-9)) were used to assess mental health outcomes.

Results

The results from the longitudinal study indicated a significant reduction in anxiety prevalence and anxiety symptoms, with a 19.63% decrease in GAD-7 mean score and a 32.02% decrease in likely anxiety from baseline to six weeks. Depressive symptoms and perceived stress also showed a significant reduction (p < 0.001), albeit to a lesser extent. In the controlled cohort study, the intervention group had significantly (p < 0.001) lower PHQ-9 [19.5 (SD 7.05)], GAD-7 [7.5 (SD 5.27)], and CMH [35.53 (SD 18.45)] scores. Additionally, the study found substantial differences (p < 0.001) in suicidal ideation (26.1 vs. 15.7) between groups but no significant differences in sleep disruption.

Discussion

These findings suggest that Text4Hope could be an effective intervention for reducing stress, depression, suicidal ideation, and particularly anxiety symptoms among women during public emergencies. The study provides valuable insights into the potential benefits of text messaging interventions in supporting mental health during crisis situations.

From the Individual to the Group: Using Idiographic Analyses and Two-Stage Random Effects Meta-Analysis to Obtain Population Level Inferences for within-Person Processes

In psychology, the use of portable technology and wearable devices to ease participant burden in data collection is on the rise. This creates increased interest in collecting real-time or near real-time data from individuals within their natural environments. As a result, vast amounts of observational time series data are generated. Often, motivation for collecting this data hinges on understanding within-person processes that underlie psychological phenomena. Motivated by the body of Dr. Peter Molenaar's life work calling for analytical approaches that consider potential heterogeneity and non-ergodicity, the focus of this paper is on using idiographic analyses to generate population inferences for within-person processes. Meta-analysis techniques using one-stage and two-stage random effects meta-analysis as implemented in single-case experimental designs are presented. The case for preferring a two-stage approach for meta-analysis of single-subject observational time series data is made and demonstrated using an empirical example. This provides a novel implementation of the methodology as prior implementations focus on applications to short time series with experimental designs. Inspired by Dr. Molenaar's work, we describe how an approach, two-stage random effects meta-analysis (2SRE-MA), aligns with recent calls to consider idiographic approaches when making population-level inferences regarding within-person processes.

Effectiveness of an app-based intervention to improve well-being through cultivating positive thinking and positive emotions in an adult sample: study protocol for a randomized controlled trial

Introduction

Interventions to promote health and well-being based on the construction of psychological resources can positively impact the daily life of users and foster human flourishing. Nowadays, mobile health represents a safe way to support health research and implement evidence-based psychological interventions. The present study aims to evaluate the effectiveness of a mobile app-based intervention program (OneUS) designed to cultivate positive emotions and positive thinking to improve overall well-being.

Methods

The study is designed as a randomized controlled trial. Two hundred participants will be randomly assigned to either a mobile health intervention condition (OneUS App) or an active control condition. The intervention consists of a program to cultivate positive emotions and positive thoughts. The active control intervention will comprise a set of mental imagery exercises and daily routine recording. The primary outcome comprises optimal well-being assessed using the multidimensional PERMA-Profiler scale. The sample will include adult people from the general population, who will be assessed at 8 time points: baseline (t1), intervention (t2, t3, t4, t5, t6) post-intervention (t7) and 3-month follow-up (t8).

Discussion

Mobile apps seem to be promising tools to promote health and well-being. This study will evaluate the effectiveness of a mobile app (OneUS) aiming to cultivate positive emotions and positive thinking to improve well-being. The main strength of this study is the development of an evidence-based mobile health app, based on intentional mental training, to promote well-being. The limitations of this study relate to potential participant drop-out and the non-generalizability of the results to clinical populations.

Clinical trial registration

https://ensaiosclinicos.gov.br/rg/RBR-43hpwqk, Identifier RBR-43hpwqk.

Provision of Digital Health Technologies for Opioid Use Disorder Treatment by US Health Care Organizations

Importance

Digital health technologies may expand organizational capacity to treat opioid use disorder (OUD). However, it remains unclear whether these technologies serve as substitutes for or complements to traditional substance use disorder (SUD) treatment resources in health care organizations.

Objective

To characterize the use of patient-facing digital health technologies for OUD by US organizations with accountable care organization (ACO) contracts.

Design, Setting, and Participants

This cross-sectional study analyzed responses to the 2022 National Survey of Accountable Care Organizations (NSACO), collected between October 1, 2021, and June 30, 2022, from US organizations with Medicare and Medicaid ACO contracts. Data analysis was performed between December 15, 2022, and January 6, 2023.

Exposures

Treatment resources for SUD (eg, an addiction medicine specialist, sufficient staff to treat SUD, medications for OUD, a specialty SUD treatment facility, a registry to identify patients with OUD, or a registry to track mental health for patients with OUD) and organizational characteristics (eg, organization type, Medicaid ACO contract).

Main Outcomes and Measures

The main outcomes included survey-reported use of 3 categories of digital health technologies for OUD: remote mental health therapy and tracking, virtual peer recovery support programs, and digital recovery support for adjuvant cognitive behavior therapy (CBT). Statistical analysis was conducted using descriptive statistics and multivariable logistic regression models.

Results

Overall, 276 of 505 organizations responded to the NSACO (54.7% response rate), with a total of 304 respondents. Of these, 161 (53.1%) were from a hospital or health system, 74 (24.2%) were from a physician- or medical group-led organization, and 23 (7.8%) were from a safety-net organization. One-third of respondents (101 [33.5%]) reported that their organization used at least 1 of the 3 digital health technology categories, including remote mental health therapy and tracking (80 [26.5%]), virtual peer recovery support programs (46 [15.1%]), and digital recovery support for adjuvant CBT (27 [9.0%]). In an adjusted analysis, organizations with an addiction medicine specialist (average marginal effect [SE], 32.3 [4.7] percentage points; P < .001) or a registry to track mental health (average marginal effect [SE], 27.2 [3.8] percentage points; P < .001) were more likely to use at least 1 category of technology compared with otherwise similar organizations lacking these capabilities.

Conclusions and Relevance

In this cross-sectional study of 276 organizations with ACO contracts, organizations used patient-facing digital health technologies for OUD as complements to available SUD treatment capabilities rather than as substitutes for unavailable resources. Future studies should examine implementation facilitators to realize the potential of emerging technologies to support organizations facing health care practitioner shortages and other barriers to OUD treatment delivery.

AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health

In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework's design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.

Effectiveness of mental health website intervention on stress and depression for women with recurrent miscarriage: A randomized controlled trial

We aimed to investigate the effect of a mental health website intervention on perceived stress, depression, sleep quality, and social support in women with recurrent miscarriage (RM). Performing a randomized controlled trial, the participants in the experimental group (n = 31) received a 12-week mental health website intervention; the participants in the control group (n = 31) received RM standard medical care only. The paired t-tests results for the mean posttest scores for depression (p = .023) and perceived stress (p = .041) in the experimental group showed a significant decrease, but did not in the control group.

Preliminary effectiveness of an evidence-based mobile application to promote resilience among working adults in Singapore and Hong Kong: Intensive longitudinal study

Evidence-based mobile health (mHealth) applications on smartphones are a cost-effective way for employees to take proactive steps to improve well-being and performance. However, little is known about what sustains engagement on these applications and whether they could dynamically improve occupational outcomes such as resilience and mood. Using real-world data, this intensive longitudinal study examines (a) which employees would continually engage with a cognitive behavioural therapy-informed mHealth application ('Intellect'); and (b) if daily engagement of 'Intellect' would relate to better occupational outcomes on the following day. A total of 515 working adults in Singapore and Hong Kong (Mage = 32.4, SDage = 8.17) completed daily in-app items on mood and resilience components (i.e. sleep hours, sleep quality, physical activity, and stress levels). Our results revealed that employees with lower baseline resilience (β = -0.048, odds ratio (OR) = 0.953, p < 0.01), specifically poorer sleep quality (β = -0.212, OR = 0.809, p = 0.001) and/or higher stress levels (β = -0.255, OR = 0.775, p = 0.05), were more likely to resume engagement on the application. Among the 150 active users (i.e. ≥3 consecutive days of engagement) (Mage = 32.2, SDage = 8.17), daily engagement predicted higher resilience (β = 0.122; 95% confidence interval (CI) 0.039-0.206), specifically lower stress levels (β = 0.018; 95% CI 0.004-0.032), higher physical activity (β = 0.079; 95% CI 0.032-0.126), and mood levels (β = 0.020; 95% CI 0.012-0.029) on the following day even after controlling for same-day outcomes. Our preliminary findings suggest that engaging with a mHealth application was associated with higher dynamic resilience and emotional well-being in employees.

Crowdsourcing smartphone data for biomedical research: Ethical and legal questions

The use of smartphones has greatly increased in the last decade and has revolutionized the way that health data are being collected and shared. Mobile applications leverage the ubiquity and technological sophistication of modern smartphones to record and process a variety of metrics relevant to human health, including behavioral measures, clinical data, and disease symptoms. Information processed by mobile applications may have significant utility for increasing biomedical knowledge, both through conventional research and emerging discovery paradigms such as citizen science. However, the ways in which smartphone-collected data may be used in nontraditional modes of biomedical discovery are not well understood, such as using data to train artificially intelligent algorithms and for product development purposes. This paper argues that the use of mobile health data for algorithm training and product development is (a) likely to become a prominent fixture in medicine, (b) likely to raise significant ethical and legal challenges, and (c) warrants immediate scrutiny by policymakers and scholars. We introduce the concept of "smartphone-crowdsourced medical data," or SCMD, and set out a broad research agenda for addressing concerns associated with this new and potentially momentous practice. We conclude that SCMD for algorithm training raises a number of ethical and legal issues which require further scholarly attention to ensure that individual interests are protected and that emerging health information sources can be used in ways that maximally, and safely, promote medical innovation.

Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behavioural indicators through a machine-learning approach

Schizophrenia (SCZ) and depression (MDD) are two chronic mental disorders that seriously affect the quality of life of millions of people worldwide. We aim to develop machine-learning methods with objective linguistic, speech, facial, and motor behavioral cues to reliably predict the severity of psychopathology or cognitive function, and distinguish diagnosis groups. We collected and analyzed the speech, facial expressions, and body movement recordings of 228 participants (103 SCZ, 50 MDD, and 75 healthy controls) from two separate studies. We created an ensemble machine-learning pipeline and achieved a balanced accuracy of 75.3% for classifying the total score of negative symptoms, 75.6% for the composite score of cognitive deficits, and 73.6% for the total score of general psychiatric symptoms in the mixed sample containing all three diagnostic groups. The proposed system is also able to differentiate between MDD and SCZ with a balanced accuracy of 84.7% and differentiate patients with SCZ or MDD from healthy controls with a balanced accuracy of 82.3%. These results suggest that machine-learning models leveraging audio-visual characteristics can help diagnose, assess, and monitor patients with schizophrenia and depression.

Acceptability of Health Information Exchange and Patient Portal Use in Depression Care Among Underrepresented Patients

BACKGROUND

Depression is often untreated or undertreated, particularly among underrepresented groups, such as racial and ethnic minorities, and individuals of lower socioeconomic status. Electronic health information exchange (HIE) is a recommended practice to improve care coordination and encourage patient engagement in services, but it remains underutilized in depression care. Understanding factors affecting acceptance and adoption of this technology among underrepresented patient populations is needed to increase dissemination of HIE within mental health treatment.

OBJECTIVE

The present study aims to identify patient barriers and facilitators towards the acceptance of HIE within the context of depression treatment and to examine how HIE impacts depression-related care coordination and patient activation.

DESIGN

Semi-structured qualitative interviews were conducted with 27 patients.

PARTICIPANTS

Respondents were English-speaking adults (> 18) receiving depression treatment within a large, safety-net primary care clinic.

APPROACH

A grounded theory approach was used to code and analyze data for emergent themes. Thematic analysis was guided by the Unified Theory of Acceptance and Use of Technology, a leading informatics theory used to predict end-user adoption of technology.

KEY RESULTS

Respondents reported that HIE made depression care more convenient, transparent, and trustworthy. Though respondents desired greater access to their health records, stigma surrounding depression inhibited acceptance of electronic communication and information sharing. Confusing electronic interface also diminished perceived benefits of HIE.

CONCLUSION(S)

Respondents desire greater transparency in their depression care. While HIE was perceived to improve the overall quality of depression care, stigma associated with mental illness undermined more robust adoption of this technology among underserved populations.

Compassionate Technology: A Systematic Scoping Review of Compassion as Foundation for Blended and Digital Mental Health Interventions (Preprint)

BACKGROUND

An essential value in mental health care is compassion: awareness of suffering, tolerating difficult feelings in the face of suffering, and acting or being motivated to alleviate suffering. Currently, technologies for mental health care are on the rise and could offer several advantages, such as more options for self-management by clients and more accessible and economically viable care. However, digital mental health interventions (DMHIs) have not been widely implemented in daily practice. Developing and evaluating DMHIs around important mental health care values, such as compassion, could be key for a better integration of technology in the mental health care context.

OBJECTIVE

This systematic scoping review explored the literature for previous instances where technology for mental health care has been linked to compassion or empathy to investigate how DMHIs can support compassion in mental health care.

METHODS

Searches were conducted in the PsycINFO, PubMed, Scopus, and Web of Science databases, and screening by 2 reviewers resulted in 33 included articles. From these articles, we extracted the following data: technology types, goals, target groups, and roles of the technologies in the intervention; study designs; outcome measures; and the extent to which the technologies met a 5-step proposed definition of compassion.

RESULTS

We found 3 main ways in which technology can contribute to compassion in mental health care: by showing compassion to people, by enhancing self-compassion in people, or by facilitating compassion between people. However, none of the included technologies met all 5 elements of compassion nor were they evaluated in terms of compassion.

CONCLUSIONS

We discuss the potential of compassionate technology, its challenges, and the need to evaluate technology for mental health care on compassion. Our findings could contribute to the development of compassionate technology, in which elements of compassion are explicitly embedded in its design, use, and evaluation.

A Group-Facilitated, Internet-Based Intervention to Promote Mental Health and Well-Being in a Vulnerable Population of University Students: Randomized Controlled Trial of the Be Well Plan Program

BACKGROUND

A growing literature supports the use of internet-based interventions to improve mental health outcomes. However, most programs target specific symptoms or participant groups and are not tailored to facilitate improvements in mental health and well-being or do not allow for needs and preferences of individual participants. The Be Well Plan, a 5-week group-facilitated, internet-based mental health and well-being group intervention addresses these gaps, allowing participants to select a range of activities that they can tailor to their specific characteristics, needs, and preferences.

OBJECTIVE

This study aims to test whether the Be Well Plan program was effective in improving primary outcomes of mental well-being, resilience, anxiety, and depression compared to a waitlist control group during the COVID-19 pandemic; secondary outcomes included self-efficacy, a sense of control, and cognitive flexibility. The study further seeks to examine participants' engagement and satisfaction with the program.

METHODS

A randomized controlled trial (RCT) was conducted with 2 parallel arms, an intervention and a waitlist control group. The intervention involved 5 weekly 2-hour sessions, which were facilitated in group format using Zoom videoconferencing software. University students were recruited via social media posts, lectures, emails, flyers, and posters.

RESULTS

Using an intentional randomization 2:1 allocation strategy, we recruited 215 participants to the trial (n=126, 58.6%, intervention group; n=89, 41.4%, waitlist control group). Of the 126 participants assigned to the intervention group, 75 (59.5%) commenced the program and were included in modified intention-to-treat (mITT) analyses. mITT intervention participants attended, on average, 3.41 sessions (SD 1.56, median 4); 55 (73.3%) attended at least 4 sessions, and 25 (33.3%) attended all 5 sessions. Of the 49 intervention group participants who completed the postintervention assessment, 47 (95.9%) were either very satisfied (n=31, 66%) or satisfied (n=16, 34%). The mITT analysis for well-being (F_1,162=9.65, P=.002, Cohen d=0.48) and resilience (F_1,162=7.85, P=.006, Cohen d=0.44) showed significant time × group interaction effects, suggesting that both groups improved over time, but the Be Well Plan (intervention) group showed significantly greater improvement compared to the waitlist control group. A similar pattern of results was observed for depression and anxiety (Cohen d=0.32 and 0.37, respectively), as well as the secondary outcomes (self-efficacy, Cohen d=0.50; sense of control, Cohen d=0.42; cognitive flexibility, Cohen d=0.65). Larger effect sizes were observed in the completer analyses. Reliable change analysis showed that the majority of mITT participants (58/75, 77.3%) demonstrated a significant reliable improvement in at least 1 of the primary outcomes.

CONCLUSIONS

The Be Well Plan program was effective in improving mental health and well-being, including mental well-being, resilience, depression, and anxiety. Participant satisfaction scores and attendance indicated a high degree of engagement and satisfaction with the program.

TRIAL REGISTRATION

Australian New Zealand Clinical Trial Registry ACTRN12621000180819; https://tinyurl.com/2p8da5sk.

Ecological momentary intervention to enhance emotion regulation in healthcare workers via smartphone: a randomized controlled trial protocol

BACKGROUND

CUIDA-TE is an APP that offers transdiagnostic cognitive behavioral therapy focused on enhancing emotion regulation. As a novelty, it incorporates ecological momentary interventions (EMI), which can provide psychological support in real time, when suffering arises. The main goal of the study is to evaluate the efficacy of CUIDA-TE to improve emotion regulation in healthcare workers, a population that has been particularly emotionally impacted by the COVID-19 pandemic.

METHODS

In this three-arm, randomized controlled trial (RCT) the study sample will be composed of a minimum of 174 healthcare workers. They will be randomly assigned to a 2-month EMI group (CUIDA-TE APP, n ≥ 58), a 2-month ecological momentary assessment (EMA) only group (MONITOR EMOCIONAL APP, n ≥ 58), or a wait-list control group (no daily monitoring nor intervention, n ≥ 58). CUIDA-TE will provide EMI if EMA reveals emotional problems, poor sleep quality/quantity, burnout, stress, or low perceived self-efficacy when regulating emotions. Depression will be the primary outcome. Secondary outcomes will include emotion regulation, quality of life, and resilience. Treatment acceptance and usability will also be measured. Primary and secondary outcomes will be obtained at pre- and post-intervention measurements, and at the 3-month follow-up for all groups.

DISCUSSION

To our knowledge, this is the first RCT that evaluates the efficacy of an APP-based EMI to improve emotion regulation skills in healthcare workers. This type of intervention might ultimately help disseminate treatments and reach a larger number of individuals than traditional face-to-face individual therapies.

TRIAL REGISTRATION

ClinicalTrial.gov : NCT04958941 Registered 7 Jun 2021.

STUDY STATUS

Participant recruitment has not started.

Dose-Response Effects of Patient Engagement on Health Outcomes in an mHealth Intervention: Secondary Analysis of a Randomized Controlled Trial

Background

The dose–response relationship between patient engagement and long-term intervention effects in mobile health (mHealth) interventions are understudied. Studies exploring long-term and potentially changing relationships between patient engagement and health outcomes in mHealth interventions are needed.

Objective

This study aims to examine dose–response relationships between patient engagement and 3 psychosocial outcomes in an mHealth intervention, Run4Love, using repeated measurements of outcomes at baseline and 3, 6, and 9 months.

Methods

This study is a secondary analysis using longitudinal data from the Run4Love trial, a randomized controlled trial with 300 people living with HIV and elevated depressive symptoms to examine the effects of a 3-month mHealth intervention on reducing depressive symptoms and improving quality of life (QOL). We examined the relationships between patient engagement and depressive symptoms, QOL, and perceived stress in the intervention group (N=150) using 4–time-point outcome measurements. Patient engagement was assessed using the completion rate of course assignments and frequency of items completed. Cluster analysis was used to categorize patients into high- and low-engagement groups. Generalized linear mixed effects models were conducted to investigate the dose–response relationships between patient engagement and outcomes.

Results

The cluster analysis identified 2 clusters that were distinctively different from each other. The first cluster comprised 72 participants with good compliance to the intervention, completing an average of 74% (53/72) of intervention items (IQR 0.22). The second cluster comprised 78 participants with low compliance to the intervention, completing an average of 15% (11/72) of intervention items (IQR 0.23). Results of the generalized linear mixed effects models showed that, compared with the low-engagement group, the high-engagement group had a significant reduction in more depressive symptoms (β=−1.93; P=.008) and perceived stress (β=−1.72; P<.001) and an improved QOL (β=2.41; P=.01) over 9 months. From baseline to 3, 6, and 9 months, the differences in depressive symptoms between the 2 engagement groups were 0.8, 1.6, 2.3, and 3.7 points, respectively, indicating widening between-group differences over time. Similarly, between-group differences in QOL and perceived stress increased over time (group differences in QOL: 0.9, 1.9, 4.7, and 5.1 points, respectively; group differences in the Perceived Stress Scale: 0.9, 1.4, 2.3, and 3.0 points, respectively).

Conclusions

This study revealed a positive long-term dose–response relationship between patient engagement and 3 psychosocial outcomes among people living with HIV and elevated depressive symptoms in an mHealth intervention over 9 months using 4 time-point repeat measurement data. The high- and low-engagement groups showed significant and widening differences in depressive symptoms, QOL, and perceived stress at the 3-, 6-, and 9-month follow-ups. Future mHealth interventions should improve patient engagement to achieve long-term and sustained intervention effects.

Trial Registration

Chinese Clinical Trial Registry ChiCTR-IPR-17012606; https://www.chictr.org.cn/showproj.aspx?proj=21019

Development of medical device software for the screening and assessment of depression severity using data collected from a wristband-type wearable device: SWIFT study protocol

INTRODUCTION

Few biomarkers can be used clinically to diagnose and assess the severity of depression. However, a decrease in activity and sleep efficiency can be observed in depressed patients, and recent technological developments have made it possible to measure these changes. In addition, physiological changes, such as heart rate variability, can be used to distinguish depressed patients from normal persons; these parameters can be used to improve diagnostic accuracy. The proposed research will explore and construct machine learning models capable of detecting depressive episodes and assessing their severity using data collected from wristband-type wearable devices.

METHODS AND ANALYSIS

Patients with depressive symptoms and healthy subjects will wear a wristband-type wearable device for 7 days; data on triaxial acceleration, pulse rate, skin temperature, and ultraviolet light will be collected. On the seventh day of wearing, the severity of depressive episodes will be assessed using Structured Clinical Interview for DSM-5 (SCID-5), Hamilton Depression Rating Scale (HAMD), and other scales. Data for up to five 7-day periods of device wearing will be collected from each subject. Using wearable device data associated with clinical symptoms as supervisory data, we will explore and build a machine learning model capable of identifying the presence or absence of depressive episodes and predicting the HAMD scores for an unknown data set.

DISCUSSION

Our machine learning model could improve the clinical diagnosis and management of depression through the use of a wearable medical device.

CLINICAL TRIAL REGISTRATION

[https://jrct.niph.go.jp/latest-detail/jRCT1031210478], identifier [jRCT1031210478].

Mobile Health Platform to Augment Behavioral Health in Primary Care: A Feasibility Study (Preprint)

Background

The collaborative care model is a well-established system of behavioral health care within primary care settings. There is potential for mobile health (mHealth) technology to augment collaborative behavioral health care in primary care settings, thereby improving scalability, efficiency, and clinical outcomes.

Objective

We aimed to assess the feasibility of engaging with and the preliminary clinical outcomes of an mHealth platform that was used to augment an existing collaborative care program in primary care settings.

Methods

We performed a longitudinal, single-arm feasibility study of an mHealth platform that was used to augment collaborative care. A total of 3 behavioral health care managers, who were responsible for coordinating disease management in 6 primary care practices, encouraged participants to use a mobile app to augment the collaborative model of behavioral health care. The mHealth platform’s functions included asynchronous chats with the behavioral health care managers, depression self-report assessments, and psychoeducational content. The primary outcome was the feasibility of engagement, which was based on the number and type of participant-generated actions that were completed in the app. The primary clinical end point was a comparison of the baseline and final assessments of the Patient Health Questionnaire-9.

Results

Of the 245 individuals who were referred by their primary care provider for behavioral health services, 89 (36.3%) consented to app-augmented behavioral health care. Only 12% (11/89) never engaged with the app during the study period. Across all participants, we observed a median engagement of 7 (IQR 12; mean 10.4; range 0-130) actions in the app (participants: n=78). The chat function was the most popular, followed by psychoeducational content and assessments. The subgroup analysis revealed no significant differences in app usage by age (P=.42) or sex (P=.84). The clinical improvement rate in our sample was 73% (32/44), although follow-up assessments were only available for 49% (44/89) of participants.

Conclusions

Our preliminary findings indicate the moderate feasibility of using mHealth technology to augment behavioral health care in primary care settings. The results of this study are applicable to improving the design and implementation of mobile apps in collaborative care.

Using Co-design in Mobile Health System Development: A Qualitative Study With Experts in Co-design and Mobile Health System Development

BACKGROUND

The proliferation of mobile devices has enabled new ways of delivering health services through mobile health systems. Researchers and practitioners emphasize that the design of such systems is a complex endeavor with various pitfalls, including limited stakeholder involvement in design processes and the lack of integration into existing system landscapes. Co-design is an approach used to address these pitfalls. By recognizing users as experts of their own experience, co-design directly involves users in the design process and provides them an active role in knowledge development, idea generation, and concept development.

OBJECTIVE

Despite the existence of a rich body of literature on co-design methodologies, limited research exists to guide the co-design of mobile health (mHealth) systems. This study aims to contextualize an existing co-design framework for mHealth applications and construct guidelines to address common challenges of co-designing mHealth systems.

METHODS

Tapping into the knowledge and experience of experts in co-design and mHealth systems development, we conducted an exploratory qualitative study consisting of 16 semistructured interviews. Thereby, a constructivist ontological position was adopted while acknowledging the socially constructed nature of reality in mHealth system development. Purposive sampling across web-based platforms (eg, Google Scholar and ResearchGate) and publications by authors with co-design experience in mHealth were used to recruit co-design method experts (n=8) and mHealth system developers (n=8). Data were analyzed using thematic analysis along with our objectives of contextualizing the co-design framework and constructing guidelines for applying co-design to mHealth systems development.

RESULTS

The contextualized framework captures important considerations of the mHealth context, including dedicated prototyping and implementation phases, and an emphasis on immersion in real-world contexts. In addition, 7 guidelines were constructed that directly pertain to mHealth: understanding stakeholder vulnerabilities and diversity, health behavior change, co-design facilitators, immersion in the mHealth ecosystem, postdesign advocates, health-specific evaluation criteria, and usage data and contextual research to understand impact.

CONCLUSIONS

System designers encounter unique challenges when engaging in mHealth systems development. The contextualized co-design framework and constructed guidelines have the potential to serve as a shared frame of reference to guide the co-design of mHealth systems and facilitate interdisciplinary collaboration at the nexus of information technology and health research.

A Protocol Study to Establish Psychological Outcomes From the Use of Wearables for Health and Fitness Monitoring

Background: The last few decades people have increasingly started to use technological tools for health and activity monitoring, such as tracking apps and wearables. The main assumption is that these tools are effective in reinforcing self-empowerment because they support better-informed lifestyle decision-making. However, experimental research assessing the effectiveness of the technological tools on such psychological outcomes is limited.

Methods and Design: Three studies will be conducted. First, we will perform a systematic review to examine the experimental evidence on the effects of self-tracking apps on psychological outcome measurements. Second, we will conduct a longitudinal field experiment with a between subject design. Participants (N = 150) begin a 50-day exercise program, either with or without the aid of the self-tracking app Strava. Among those who use Strava, we vary between those who use all features and those who use a limited set of features. Participants complete questionnaires at baseline, at 10, 25, and 50 days, and provide details on what information has been tracked via the platform. Third, a subset of participants is interviewed to acquire additional qualitative data. The study will provide a rich set of data, enabling triangulation, and contextualization of the findings.

Discussion: People increasingly engage in self-tracking whereby they use technological tools for health and activity monitoring, although the effects are still unknown. Considering the mixed results of the existing evidence, it is difficult to draw firm conclusions, showing more research is needed to develop a comprehensive understanding.

Trial registration: Netherlands Trial registration: NL9402, received on 20 April 2021; https://www.trialregister.nl/trial/9402.

Technology can`t replace people, but it is a helpful tool: A qualitative study on patients’ experiences of using a smartphone-application post-cardiac rehabilitation (Preprint)

Background

Exercise-based cardiac rehabilitation (CR) is a crucial part of the treatment of patients with cardiac diseases, and adherence to healthy behavior is a prerequisite to improve long-term prognosis. Unfortunately, adherence to healthy behavior adapted in CR is challenging for many cardiac patients in the long term. Recently, we demonstrated that follow-up conducted via an app for 1 year significantly improved adherence to healthy behavior after CR. To increase the knowledge and understanding of mobile Health (mHealth) interventions that can promote acceptance and adherence, qualitative research investigating patients’ experiences with these interventions is warranted.

Objective

The aim was to investigate patient experiences with individualized long-term follow-up conducted via an app for 1 year and their thoughts about what features promoted adherence to healthy behavior after CR. The purpose was to increase the understanding of significant findings previously reported and to guide future development of similar interventions in the field of adherence.

Methods

A qualitative study with individual interviews was conducted from November 2018 to May 2019. A thematic interview guide was used when conducting the semistructured in-depth interviews. The interviews were audio recorded and transcribed successively during the period in which the interviews were conducted. Texts were managed and systematized by NVivo. Interviews were analyzed by qualitative content analysis. Codes and themes were inductively developed.

Results

Ten patients who had participated in a randomized controlled trial evaluating the effect of follow-up conducted via an app on adherence to healthy behavior after CR were included. The median patient age was 65 years (range 46-72 years), and both genders were represented. The analysis resulted in the following 4 themes describing the patients’ experiences: (1) The person behind the app is crucial for motivation and adherence; (2) The app as a commitment; (3) The app as a path to independence; and (4) Suggestions for improvements. Features experienced as beneficial to promote adherence were individualized feedback and the use of goal setting. The significance of the person behind the app (the supervisor) who provided individualized feedback was a consistent finding. This person seemed to promote motivation in general and to enable other known behavioral change techniques.

Conclusions

The person behind the app (the supervisor) seems to be one of the most significant success factors in promoting adherence to healthy behavior after CR. This indicates that a health care provider must actively participate in a patient’s process of adherence to healthy behavior, even when using interventions, including an app. Future development of interventions in the field of adherence should strive to create tools that enable an ongoing collaborative relationship between the patient and the health care provider. The follow-up should be based on the patient’s own goals, and individualized feedback should be provided.

[Symptom Monitoring and Detection of Early Warning Signs in Bipolar Episodes Via App - Views of Patients and Relatives on e-Health Need]

BACKGROUND

The onset and early warning signs of episodes of bipolar disorder are often realized late by those affected. The earlier an incipient episode is treated, the more prognostically favorable the course will be. Symptom monitoring via smartphone application (app) could be an innovative way to recognize and react to early warning signs more swiftly. The aim of this study was to find out whether patients and their relatives consider technical support through an app to be useful and practical in the early warning sign detection and treatment.

METHODS

In the present study, 51 patients with bipolar disorder and 28 relatives were interviewed. We gathered information on whether participants were able to perceive early warning signs in form of behavioral changes sufficiently and in a timely fashion and also whether they would use an app as treatment support tool.

RESULTS

Although 94.1% of the surveyed patients and 78.6% of their relatives felt that they were well informed about the disease, 13.7% and 35.7%, respectively were not fully satisfied with the current treatment options. Early warning signs of every depressive development were noticed by 25.5% of the patients (relatives 10.7%). Every (hypo)manic development was only noticed by 11.8% of the patients (relatives 7.1%); 88.2% of the patients and 85.7% of the relatives noticed the same symptoms recurrently at the beginning of a depression and 70.6% and 67.9%, respectively, at the beginning of a (hypo)manic episode (in particular changes in physical activity, communication behavior and the sleep-wake rhythm). 84.3% of the patients and 89.3% of the relatives stated that they considered technical support that draws attention to mood and activity changes as useful and that they would use such an app for the treatment.

DISCUSSION

The current options for perceiving early warning signs of a depressive or (hypo)manic episode in bipolar disorder are clinically inadequate. Those affected and their relatives desire innovative, technical support. Early detection of symptoms, which often manifest themselves in changes in behavior or activity patterns, is essentiell for managing the course of bipolar disorder. In the future, smartphone apps could be used for clinical treatment and research through objective, continuous and.

Exploring Social Media Recruitment Strategies and Preliminary Acceptability of an mHealth Tool for Teens with Eating Disorders

(1) Background: The current study leveraged social media to connect with teens with EDs to identify population specific characteristics and to gather feedback on an mHealth intervention. (2) Methods: We recruited teens with EDs from social media in two phases: (1) Discovery Group, (2) Testing Group. The Discovery Group (n = 14) participants were recruited from Facebook/Instagram and were asked to review the app for up to one week and provide qualitative feedback. After incorporating feedback from the Discovery Group, we refined our social media outreach methods to connect with 30 teens with EDs to pilot this mobile app. Recruitment from a variety of platforms on social media was successful, with the majority of enrolled participants in the Testing Group coming from Snapchat (60%) and a large percentage of participants belonging to gender and sexual minority groups (63%). (3) Results: Participants from both groups experienced extremely high rates of depression (100% Discovery, 90% Testing) and/or anxiety symptoms (100% Discovery, 93% Testing) in addition to ED symptoms, and noted this as a possible barrier to app engagement. (4) Conclusion: Use of social media for recruitment of teens with EDs is feasible and may connect with groups who may be more difficult to reach using traditional recruitment methods. Among the Discovery Group there was high acceptability of and interest in an app to support ED recovery, and characteristics of both groups demonstrated need for support in other mental health domains. Future studies should evaluate the preliminary efficacy of such tools among teens to determine the effects of such interventions on ED symptoms and other mental health outcomes.

A New Characterization of Mental Health Disorders Using Digital Behavioral Data: Evidence from Major Depressive Disorder

Mental health disorders are ambiguously defined and diagnosed. The established diagnosis technique, which is based on structured interviews, questionnaires and data subjectively reported by the patients themselves, leaves the mental health field behind other medical areas. We support these statements with examples from major depressive disorder (MDD). The National Institute of Mental Health (NIMH) launched the Research Domain Criteria (RDoC) project in 2009 as a new framework to investigate psychiatric pathologies from a multidisciplinary point of view. This is a good step in the right direction. Contemporary psychiatry considers mental illnesses as diseases that manifest in the mind and arise from the brain, expressed as a behavioral condition; therefore, we claim that these syndromes should be characterized primarily using behavioral characteristics. We suggest the use of smartphones and wearable devices to passively collect quantified behavioral data from patients by utilizing digital biomarkers of mental disorder symptoms. Various digital biomarkers of MDD symptoms have already been detected, and apps for collecting this longitudinal behavioral data have already been developed. This quantified data can be used to determine a patient’s diagnosis and personalized treatment, and thereby minimize the diagnosis rate of comorbidities. As there is a wide spectrum of human behavior, such a fluidic and personalized approach is essential.

Assessing College Students' Perceptions of and Intentions to Use a Mobile App for Mental Health

Background: College students face high rates of depression, anxiety, and suicide. Therefore, the focus of this study was to examine college students' intention to use a mental health app provided by the university, called MySSP, through use of the unified theory of acceptance and use of technology (UTAUT). Materials and Methods: An online survey was developed based on responses to focus groups previously conducted by the authors regarding MySSP. A multiple linear regression was conducted to test the associations between the UTAUT variables and behavioral intention. In addition, moderation analyses were conducted to explore the effects of depression, anxiety, stigma, and quality of life as moderators. Results: Results suggest that performance expectancy (PE) (t = 3.088, p = 0.003) and social influence (SI) (t = -2.163, p = 0.03) were the only significant predictors of behavioral intention. The only significant moderator was stigma in the relationship between effort expectancy and behavioral intention (b = -1.6081, standard error (SE) = 0.7454, confidence interval (CI): -3.0691 to -0.1472) as well as facilitating conditions (FCs) and behavioral intention (b = -2.2292, SE = 0.8580, CI: -3.9110 to -0.5475). Conclusions: This study provides an initial exploration of college students' intentions to use MySSP. The results suggest that successful interventions include features related to PE (usefulness) and SI (norms), which should be focused on when developing and testing apps for mental health. Additionally, the stigma of mental health has significant negative impacts on the adoption of mental health apps and should be considered in implementation of mental health interventions geared toward college students.

Depression and executive functioning bidirectionally impair one another across 9 years: Evidence from within-person latent change and cross-lagged models

BACKGROUND

Scar and vulnerability models assert that increased psychopathology may predict subsequent executive functioning (EF) deficits (and vice versa) over protracted timescales, yet most prior work on this topic has been cross-sectional. Thus, we tested the within- and between-person relations between EF, depression, and anxiety.

METHODS

Older adult participants (n = 856) were assessed across four waves, approximately 2 years apart. Performance-based EF and caregiver-rated symptom measures were administered. Bivariate latent change score and random-intercept cross-lagged panel models were conducted.

RESULTS

Within persons, random-intercept cross-lagged panel models revealed that prior greater depression forecasted lower subsequent EF, and vice versa (d = -0.292 vs. -0.292). Bivariate dual latent change score models showed that within-person rise in depression predicted EF decreases, and vice versa (d = -0.245 vs. -0.245). No within-person, cross-lagged, EF-anxiety relations emerged. Further, significant negative between-person EF-symptom relations were observed (d = -0.264 to -0.395).

CONCLUSION

Prospective, within-person findings offer some evidence for developmental scar and vulnerability models.

Ecological momentary assessment as a measurement tool in depression trials

We used ecological momentary assessment (EMA) to track symptoms during a clinical trial. Thirty-six participants with major depressive disorder (MDD) and MADRS scores ≥20 were enrolled in a nonrandomized 6-week open-label trial of commercially available antidepressants. Twice daily, a mobile device prompted participants to self-report the 6 items of the HamD₆ sub-scale derived from the Hamilton rating scale for depression (HamD₁₇). Morning EMA reports asked "how do you feel now" whereas evening reports gathered a full-day impression. Clinicians who were blinded to the EMA data rated the MADRS, HamD₁₇ and HamD₆ at screen, baseline and weeks 2,4, and 6. Hierarchical linear modeling (HLM) examined the course of the EMA assessments and convergence between EMA scores and clinician ratings. HLM analyses revealed strong correlations between AM and PM EMA derived HamD₆ scores and revealed significant improvements over time. EMA improvements were significantly correlated with the clinician rated HamD₆ scores at endpoint and predicted clinician rated HamD₆ score changes from baseline to endpoint (p < .001). There was a large correlation between EMA and clinician derived HamD₆ scores at each in-person assessment after baseline. Treatment response defined by EMA matched the clinician rated HamD₆ treatment responses in 33 of 36 cases (91.7%). EMA derived symptom scores appear to be efficient and valid measures to track daily symptomatic change in clinical trials and may provide more accurate measures of symptom severity than the episodic "snapshots" that are currently used as clinical outcomes. These findings support further investigation of EMA for assessment in clinical trials.

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.

The Future of Obsessive-Compulsive Spectrum Disorders: A Research Perspective

Obsessive-compulsive disorder (OCD) sits at the epicenter of a spectrum of related conditions (often referred to as obsessive-compulsive related disorders (OCRD) or obsessive-compulsive spectrum disorders (OCSD)) that can be as disabling as they are varied in presentation. Research in the field now encompasses diverse disciplines ranging from inflammatory mechanisms to computational psychiatry, to neurocognitive endophenotypes to functional imaging to pharmacogenomics to brain stimulation approaches. As these disorders become more clearly elucidated, there is a need to continually re-evaluate the implications of research findings and to incorporate these findings into new treatment approaches that benefit both patients and clinicians. Even the Diagnostic and Statistical Manual of Mental Disorders 5th Edition (DSM-5) is intended to be flexible and to incorporate validated and reliable biomarkers and neuroscience findings as they become available. This concluding chapter highlights just a few areas of study that promise to influence our understanding of the pathophysiology and clinical practice of OCRD. These include patient-centered outcomes research, the study of developmental brain trajectories in spectrum conditions, robot models of OCRDs, goal-directed versus habit-based behaviors, pharmacogenomics, problematic use of the Internet, and digital interventions. For example, digital medicine may become increasingly useful by identifying patients early on in the course of their illness; providing biomarkers to subtype patients; predicting treatment response; serving as a more proximal outcome measure of treatment response; or providing easily accessible and less costly forms of care. In order to address unmet clinical needs in OCRD, it is helpful to take an interdisciplinary perspective, and the work described in this collection of articles is likely to be invaluable in shaping the future of the field.

Using Digital Technologies to Facilitate Care Coordination Between Youth Mental Health Services: A Guide for Implementation

Enhanced care coordination is essential to improving access to and navigation between youth mental health services. By facilitating better communication and coordination within and between youth mental health services, the goal is to guide young people quickly to the level of care they need and reduce instances of those receiving inappropriate care (too much or too little), or no care at all. Yet, it is often unclear how this goal can be achieved in a scalable way in local regions. We recommend using technology-enabled care coordination to facilitate streamlined transitions for young people across primary, secondary, more specialised or hospital-based care. First, we describe how technology-enabled care coordination could be achieved through two fundamental shifts in current service provisions; a model of care which puts the person at the centre of their care; and a technology infrastructure that facilitates this model. Second, we detail how dynamic simulation modelling can be used to rapidly test the operational features of implementation and the likely impacts of technology-enabled care coordination in a local service environment. Combined with traditional implementation research, dynamic simulation modelling can facilitate the transformation of real-world services. This work demonstrates the benefits of creating a smart health service infrastructure with embedded dynamic simulation modelling to improve operational efficiency and clinical outcomes through participatory and data driven health service planning.

Monitoring Sleep Changes via a Smartphone App in Bipolar Disorder: Practical Issues and Validation of a Potential Diagnostic Tool

Background: Sleep disturbances are common early warning signs of an episode of bipolar disorder, and early recognition can favorably impact the illness course. Symptom monitoring via a smartphone app is an inexpensive and feasible method to detect an early indication of changes such as sleep. The study aims were (1) to assess the acceptance of apps and (2) to validate sleeping times measured by the smartphone app UP!. Methods:UP! was used by 22 individuals with bipolar disorder and 23 controls. Participants recorded their time of falling asleep and waking-up using UP! for 3 weeks. Results were compared to a validated accelerometer and the Pittsburgh Sleep Quality Index. Additionally, participants were interviewed regarding early warning signs and their feedback for apps as monitoring tools in bipolar disorder (NCT03275714). Results: With UP!, our study did not find strong reservations concerning data protection or continual smartphone usage. Correlation analysis demonstrates UP! to be a valid tool for measuring falling asleep and waking-up times. Discussion: Individuals with bipolar disorder assessed the measurement of sleep disturbances as an early warning sign with a smartphone as positive. The detection of early signs could change an individual's behavior and strengthen self-management. The study showed that UP! can be used to measure changes in sleep durations accurately. Further investigation of smartphone apps' impact to measure other early signs could significantly contribute to clinical treatment and research in the future through objective, continuous, and individual data collection.

Long-term follow-up with a smartphone application improves exercise capacity post cardiac rehabilitation: A randomized controlled trial

BACKGROUND

Mobile health interventions, especially smartphone applications (apps), have been proposed as promising interventions for supporting adherence to healthy behaviour in patients post cardiac rehabilitation (CR). The overall aim of the study was to examine the effect of individualized follow-up with an app for one year on peak oxygen uptake (VO2peak) in patients completing CR.

DESIGN

The study was designed as a single-blinded multicentre randomized controlled trial.

METHODS

The intervention group (IG) received individualized follow-up enabled with an app for one year, while the control group (CG) received usual care. The primary outcome was difference in VO2peak. Secondary outcomes included exercise performance (time to exhaustion, peak incline (%) and peak velocity (km/h)), bodyweight, resting blood pressure, lipid profile, triglycerides, exercise habits, health-related quality of life, health status and self-perceived goal achievement.

RESULTS

In total, 113 patients completing CR (73.4% with coronary artery disease, 16.8% after valve surgery and 9.8% with other heart diseases) were randomly allocated to the IG or CG. Intention to treat analyses showed a statistically significant difference in VO2peak between the groups at follow-up of 2.2 ml/kg/min, 95% confidence interval 0.9-3.5 (p < 0.001). Statistically significant differences were also observed in exercise performance, exercise habits and in self-perceived goal achievement.

CONCLUSIONS

Individualized follow-up for one year with an app significantly improved VO2peak, exercise performance and exercise habits, as well as self-perceived goal achievement, compared with a CG in patients post-CR. There were no statistically significant differences between the groups at follow-up in the other outcome measures evaluated.

Wearable technology to assess bradykinesia and immobility in patients with severe depression undergoing electroconvulsive therapy: A pilot study

The psychomotor retardation that may be seen in major depression represents an interesting parallel to bradykinesia, a core feature of Parkinson's disease. Psychomotor retardation has been correlated with the severity of depression and is a predictor of response to electroconvulsive therapy (ECT). Psychomotor retardation has typically been assessed by subjective clinical judgement including clinical rating scales. Gross activity levels have also been measured with actigraphy previously. The Parkinson's KinetiGraph (PKG) was developed to assess bradykinesia, dyskinesia and tremor in Parkinson's disease and allows for an objective assessment of motor symptoms over time. It has not been used previously to assess motor symptoms in depression. The aim of the current pilot study was to use the PKG to objectively measure both bradykinesia and immobility in depressed inpatients undergoing ECT before, during and at the end of therapy and review correlations with depressive symptomatology and treatment response. The majority of patients (9/12) had PKG defined bradykinesia at baseline and 7/9 of these improved with ECT. All patients with bradykinesia who remitted clinically demonstrated improvements in bradykinesia scores. PKG defined immobility was present at baseline in 11/12 total patients and improved in the majority of these patients (9/11) post ECT. Correlations between clinically assessed melancholia and PKG measures were significant (r = 0.701, p 0.011 at baseline to r_s = 0.655, p 0.021 at end). A strong association between bradykinesia and immobility scores and depression severity was not seen. The PKG is a potentially useful wearable technology to objectively assess motor symptoms in depression.

Comparative study of paper-and-pencil and electronic formats of the Snaith-Hamilton Pleasure Scale

The gold standard for measuring anhedonia is the Snaith-Hamilton Pleasure Scale (SHAPS). To date, there are no validated electronic versions of this questionnaire. We aim to study the equivalence between the traditional paper-and-pencil format and a digital version of the SHAPS. A group of 67 patients completed both SHAPS formats, and differences between formats were assessed. McNemar's test showed no significant differences between the two systems. The Kappa coefficient was over 40% for most items, and reliability was above 0.8, showing good to excellent levels of internal consistency. Thus, we have demonstrated a close equivalence between paper-and-pencil and electronic SHAPS.

Mitochondria under the spotlight: On the implications of mitochondrial dysfunction and its connectivity to neuropsychiatric disorders

Neuropsychiatric disorders (NPDs) such as bipolar disorder (BD), schizophrenia (SZ) and mood disorder (MD) are hard to manage due to overlapping symptoms and lack of biomarkers. Risk alleles of BD/SZ/MD are emerging, with evidence suggesting mitochondrial (mt) dysfunction as a critical factor for disease onset and progression. Mood stabilizing treatments for these disorders are scarce, revealing the need for biomarker discovery and artificial intelligence approaches to design synthetically accessible novel therapeutics. Here, we show mt involvement in NPDs by associating 245 mt proteins to BD/SZ/MD, with 7 common players in these disease categories. Analysis of over 650 publications suggests that 245 NPD-linked mt proteins are associated with 800 other mt proteins, with mt impairment likely to rewire these interactions. High dosage of mood stabilizers is known to alleviate manic episodes, but which compounds target mt pathways is another gap in the field that we address through mood stabilizer-gene interaction analysis of 37 prescriptions and over-the-counter psychotropic treatments, which we have refined to 15 mood-stabilizing agents. We show 26 of the 245 NPD-linked mt proteins are uniquely or commonly targeted by one or more of these mood stabilizers. Further, induced pluripotent stem cell-derived patient neurons and three-dimensional human brain organoids as reliable BD/SZ/MD models are outlined, along with multiomics methods and machine learning-based decision making tools for biomarker discovery, which remains a bottleneck for precision psychiatry medicine.

Issues in reporting of systematic review methods in health app-focused reviews: A scoping review

No guidelines exist for the conduct and reporting of manuscripts with systematic searches of app stores for, and then appraisal of, mobile health apps ('health app-focused reviews'). We undertook a scoping review including a systematic literature search for health app-focused reviews describing systematic app store searches and app appraisal, for apps designed for patients or clinicians. We created a data extraction template which adapted data elements from the PRISMA guidelines for systematic literature reviews to data elements operationalised for health app-focused reviews. We extracted the data from included health app-focused reviews to describe: (1) which elements of the adapted 'usual' methods of systematic review are used; (2) methods of app appraisal; and (3) reporting of clinical efficacy and recommendations for app use. From 2798 records, the 26 included health app-focused reviews showed incomplete or unclear reporting of review protocol registration; use of reporting guidelines; processes of screening apps; data extraction; and appraisal tools. Reporting of clinical efficacy of apps or recommendations for app use were infrequent. The reporting of methods in health app-focused reviews is variable and could be improved by developing a consensus reporting standard for health app-focused reviews.

Informing the development of an E-platform for monitoring wellbeing in schools: involving young people in a co-design process

BACKGROUND

The use of new technologies and methodologies in young people's mental health research is needed to allow more frequent and reliable sampling. Mobile applications and e-platforms create exciting potential for the collection of large-scale cohort data, however there are various feasibility and ethical issues to consider. Consultation with young people is needed to inform the research agenda, and ensure these technologies are engaging, useful and safe. This article describes the process of Public and Patient Involvement (PPI) with a sample of young people in London, with the aim of i) informing the development of a mood-monitoring e-platform, and ii) providing feedback and advice for researchers developing web-based technologies in the mental health field.

METHODS

A total of 26 young people were consulted across four advisory group co-design sessions. All young people were students enrolled at one of the participating London based sixth form colleges, and voluntarily attended a workshop session. Audio recordings of the sessions were analysed using a thematic analysis framework.

RESULTS

We found that young people were engaged in discussions around mobile health technologies and valued the opportunity to collaborate throughout the early stages of the development process The advisory groups identified key considerations for future web-development work to encourage engagement and prolonged use, including, the promotion of trust and transparency, consideration of accessibility, provision of support, production of engaging and functional design, and acknowledgment of specific contextual influences surrounding young people's wellbeing.

CONCLUSIONS

Involving young people in the development process of e-health technologies contributes to optimising the successful adoption and prolonged usage of new methodologies. The thematic map and informant examples can be used to guide researchers interested in developing web-based technologies in the mental health field and will be directly applicable to the development of a mood-monitoring e-platform.

Smartphone-based ecological momentary assessment (EMA) in psychiatric patients and student controls: A real-world feasibility study

BACKGROUND

Smartphone-based ecological momentary assessment (EMA) is a promising methodology for mental health research. The objective of this study is to determine the feasibility of smartphone-based active and passive EMA in psychiatric outpatients and student controls.

METHODS

Two smartphone applications -MEmind and eB²- were developed for behavioral active and passive monitoring. The applications were tested in psychiatric patients with a history of suicidal thoughts and/or behaviors (STB), psychiatric patients without a history of STB, and student controls. Main outcome was feasibility, measured as response to recruitment, retention, and EMA compliance. Secondary outcomes were patterns of smartphone usage.

RESULTS

Response rate was 87.3% in patients with a history of STB, 85.1% in patients without a history of STB, and 75.0% in student controls. 457 participants installed the MEmind app (120 patients with a history of STB and 337 controls) and 1,708 installed the eB² app (139 patients with a history of STB, 1,224 patients with no history of STB and 346 controls). For the MEmind app, participants were followed-up for a median of 49.5, resulting in 22,622 person-days. For the eB² application, participants were followed-up for a median of 48.9 days, resulting in 83,521 person-days. EMA compliance rate was 65.00% in suicidal patients and 75.21% in student controls. At the end of the follow-up, over 60% of participants remained in the study.

LIMITATIONS

Cases and controls were not matched by age and sex. Cases were patients who were receiving adequate psychopharmacological treatment and attending their appointments, which may result in an overstatement of clinical compliance.

CONCLUSIONS

Smartphone-based active and passive monitoring are feasible methods in psychiatric patients in real-world settings. The development of applications with friendly interfaces and directly useful features can help increase engagement without using incentives. The MEmind and eB² applications are promising clinical tools that could contribute to the management of mental disorders. In the near future, these applications could serve as risk monitoring devices in the clinical practice.

Jamaican adolescents' receptiveness to digital mental health services: A cross-sectional survey from rural and urban communities

BACKGROUND

Improving access to mental health resources for young people is an urgent healthcare challenge. As the majority of youth live in low and middle-income countries (LMICs) mental ill health can exert substantial adverse impacts on societies that can least afford it. Digital mental health technologies might help close the treatment gap but we need to understand barriers to implementing these strategies, especially in resource constrained contexts such as LMICs.

METHODS

We surveyed adolescents (N = 107; aged 10-19 years) from Jamaican communities using questionnaires adopted from previous studies conducted in LMICs. The questions addressed mental health help-seeking preferences, expectations of help-seeking effectiveness, and practical and attitudinal barriers to using mobile-phone-based mental health resources. We present descriptive data alongside exploratory analyses of differences in attitudes and preferences expressed by subgroups of respondents.

RESULTS

Adolescents reported very few practical or infrastructure barriers to accessing digital mental health resources. >90% of the sample had access to a smartphone, 78% expected that digital solutions could benefit adolescents with symptoms of mental distress, and 56% were interested in using mental health apps to monitor their own mental health. Stigma, shame, and embarrassment were major barriers to help-seeking and formal professional help was only preferred for more severe conditions such as psychosis and substance abuse.

CONCLUSIONS

Practical barriers are unlikely to impede the uptake of digital mental health resources by Jamaican adolescents. Our data suggest that mental health literacy, stigma, and embarrassment pose more serious blocks to help-seeking.

A Mobile Health Approach for Improving Outcomes in Suicide Prevention (SafePlan)

Background

Suicide is a prominent cause of death worldwide, particularly among young people. It was the second leading cause of death among those aged 15-29 years globally in 2016. Treatment for patients with suicidal thoughts or behaviors often includes face-to-face psychological therapy with a mental health professional. These forms of interventions may involve maintaining and updating paper-based reports or worksheets in between sessions. Mobile technology can offer a way to support the implementation of evidence-based psychological techniques and the acquisition of protective coping skills.

Objective

This study aims to develop a mobile app to facilitate service users’ access to mental health support and safety planning. This process involved eliciting expert input from clinicians who are actively engaged in the provision of mental health care.

Methods

A survey was distributed to targeted health care professionals to determine what features should be prioritized in a new mobile app relating to suicide prevention. On the basis of the survey results, a clinical design group, comprising 6 members with experience in fields such as mobile health (mHealth), clinical psychology, and suicide prevention, was established. This group was supplemented with further input from additional clinicians who provided feedback over three focus group sessions. The sessions were centered on refining existing app components and evaluating new feature requests. This process was iterated through regular feedback until agreement was reached on the overall app design and functionality.

Results

A fully functional mobile app, known as the SafePlan app, was developed and tested with the input of clinicians through an iterative design process. The app’s core function is to provide an interactive safety plan to support users with suicidal thoughts or behaviors as an adjunct to face-to-face therapy. A diary component that facilitates the generalization of skills learned through dialectical behavior therapy was also implemented. Usability testing was carried out on the final prototype by students from a local secondary school, who are representative of the target user population in both age and technology experience. The students were asked to complete a system usability survey (SUS) at the end of this session. The mean overall SUS rating was 71.85 (SD 1.38).

Conclusions

The participatory process involving key stakeholders (clinicians, psychologists, and information technology specialists) has resulted in the creation of an mHealth intervention technology that has the potential to increase accessibility to this type of mental health service for the target population. The app has gone through the initial testing phase, and the relevant recommendations have been implemented, and it is now ready for trialing with both clinicians and their patients.

Technology for monitoring everyday prosthesis use: a systematic review

BACKGROUND

Understanding how prostheses are used in everyday life is central to the design, provision and evaluation of prosthetic devices and associated services. This paper reviews the scientific literature on methodologies and technologies that have been used to assess the daily use of both upper- and lower-limb prostheses. It discusses the types of studies that have been undertaken, the technologies used to monitor physical activity, the benefits of monitoring daily living and the barriers to long-term monitoring, with particular focus on low-resource settings.

METHODS

A systematic literature search was conducted in PubMed, Web of Science, Scopus, CINAHL and EMBASE of studies that monitored the activity of prosthesis users during daily-living.

RESULTS

Sixty lower-limb studies and 9 upper-limb studies were identified for inclusion in the review. The first studies in the lower-limb field date from the 1990s and the number has increased steadily since the early 2000s. In contrast, the studies in the upper-limb field have only begun to emerge over the past few years. The early lower-limb studies focused on the development or validation of actimeters, algorithms and/or scores for activity classification. However, most of the recent lower-limb studies used activity monitoring to compare prosthetic components. The lower-limb studies mainly used step-counts as their only measure of activity, focusing on the amount of activity, not the type and quality of movements. In comparison, the small number of upper-limb studies were fairly evenly spread between development of algorithms, comparison of everyday activity to clinical scores, and comparison of different prosthesis user populations. Most upper-limb papers reported the degree of symmetry in activity levels between the arm with the prosthesis and the intact arm.

CONCLUSIONS

Activity monitoring technology used in conjunction with clinical scores and user feedback, offers significant insights into how prostheses are used and whether they meet the user's requirements. However, the cost, limited battery-life and lack of availability in many countries mean that using sensors to understand the daily use of prostheses and the types of activity being performed has not yet become a feasible standard clinical practice. This review provides recommendations for the research and clinical communities to advance this area for the benefit of prosthesis users.