Rethinking Data Collection Methods During the Pandemic: Development and Implementation of CATI for the All of Us Research Program

The All of Us Research Program is a longitudinal cohort study aiming to build a diverse database to advance precision medicine. The COVID-19 pandemic hindered the ability of participants to receive in-person assistance at enrollment sites to complete digital surveys. Therefore, the program implemented Computer-Assisted Telephone Interviewing (CATI) to facilitate survey completion remotely to combat the disrupted data collection procedures. In January 2021, All of Us implemented a 1-year CATI Pilot supporting 9399 participants and resulting in 16 337 submitted surveys. The pilot showed that CATI was successful in increasing survey completion and retention activities for the All of Us Research Program, given the additional remote support offered to participants. Given the success of the CATI Pilot, multimodal survey administration will continue.

Design and Implementation of the All of Us Research Program COVID-19 Participant Experience (COPE) Survey

In response to the rapidly evolving coronavirus disease 2019 (COVID-19) pandemic, the All of Us Research Program longitudinal cohort study developed the COVID-19 Participant Experience (COPE) survey to better understand the pandemic experiences and health impacts of COVID-19 on diverse populations within the United States. Six survey versions were deployed between May 2020 and March 2021, covering mental health, loneliness, activity, substance use, and discrimination, as well as COVID-19 symptoms, testing, treatment, and vaccination. A total of 104,910 All of Us Research Program participants, of whom over 73% were from communities traditionally underrepresented in biomedical research, completed 275,201 surveys; 9,693 completed all 6 surveys. Response rates varied widely among demographic groups and were lower among participants from certain racial and ethnic minority populations, participants with low income or educational attainment, and participants with a Spanish language preference. Survey modifications improved participant response rates between the first and last surveys (13.9% to 16.1%, P < 0.001). This paper describes a data set with longitudinal COVID-19 survey data in a large, diverse population that will enable researchers to address important questions related to the pandemic, a data set that is of additional scientific value when combined with the program's other data sources.

The All of Us Research Program: Data quality, utility, and diversity

The All of Us Research Program seeks to engage at least one million diverse participants to advance precision medicine and improve human health. We describe here the cloud-based Researcher Workbench that uses a data passport model to democratize access to analytical tools and participant information including survey, physical measurement, and electronic health record (EHR) data. We also present validation study findings for several common complex diseases to demonstrate use of this novel platform in 315,000 participants, 78% of whom are from groups historically underrepresented in biomedical research, including 49% self-reporting non-White races. Replication findings include medication usage pattern differences by race in depression and type 2 diabetes, validation of known cancer associations with smoking, and calculation of cardiovascular risk scores by reported race effects. The cloud-based Researcher Workbench represents an important advance in enabling secure access for a broad range of researchers to this large resource and analytical tools.

•

The All of Us Research Program has released data for over 315,000 participants

•

Demonstration projects support the utility and validity of the All of Us dataset

•

The cloud-based Researcher Workbench provides secure, low-cost compute power

The engagement of participants in the research process and broad availability of data to diverse researchers are essential elements in building precision medicine equitably available for all. The NIH has established the ambitious All of Us Research Program to build one of the most diverse health databases in history with tools to support research to improve human health. Here, we present the initial launch of the Researcher Workbench with data types including surveys, physical measurements, and electronic health record data with validation studies to support researcher use of this novel platform. Broad access for researchers to data like these is a critical step in returning value to participants seeking to support the advancement of precision medicine and improved health for all.

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

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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

Trial Registration

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

International Registered Report Identifier (IRRID)

DERR1-10.2196/30710

A Smartphone-Based Self-management Intervention for Individuals With Bipolar Disorder (LiveWell): Protocol Development for an Expert System to Provide Adaptive User Feedback

BACKGROUND

Bipolar disorder is a severe mental illness that results in significant morbidity and mortality. While pharmacotherapy is the primary treatment, adjunctive psychotherapy can improve outcomes. However, access to therapy is limited. Smartphones and other technologies can increase access to therapeutic strategies that enhance self-management while simultaneously augmenting care by providing adaptive delivery of content to users as well as alerts to providers to facilitate clinical care communication. Unfortunately, while adaptive interventions are being developed and tested to improve care, information describing the components of adaptive interventions is often not published in sufficient detail to facilitate replication and improvement of these interventions.

OBJECTIVE

To contribute to and support the improvement and dissemination of technology-based mental health interventions, we provide a detailed description of the expert system for adaptively delivering content and facilitating clinical care communication for LiveWell, a smartphone-based self-management intervention for individuals with bipolar disorder.

METHODS

Information from empirically supported psychotherapies for bipolar disorder, health psychology behavior change theories, and chronic disease self-management models was combined with user-centered design data and psychiatrist feedback to guide the development of the expert system.

RESULTS

Decision points determining the timing of intervention option adaptation were selected to occur daily and weekly based on self-report data for medication adherence, sleep duration, routine, and wellness levels. These data were selected for use as the tailoring variables determining which intervention options to deliver when and to whom. Decision rules linking delivery of options and tailoring variable thresholds were developed based on existing literature regarding bipolar disorder clinical status and psychiatrist feedback. To address the need for treatment adaptation with varying clinical statuses, decision rules for a clinical status state machine were developed using self-reported wellness rating data. Clinical status from this state machine was incorporated into hierarchal decision tables that select content for delivery to users and alerts to providers. The majority of the adaptive content addresses sleep duration, medication adherence, managing signs and symptoms, building and utilizing support, and keeping a regular routine, as well as determinants underlying engagement in these target behaviors as follows: attitudes and perceptions, knowledge, support, evaluation, and planning. However, when problems with early warning signs, symptoms, and transitions to more acute clinical states are detected, the decision rules shift the adaptive content to focus on managing signs and symptoms, and engaging with psychiatric providers.

CONCLUSIONS

Adaptive mental health technologies have the potential to enhance the self-management of mental health disorders. The need for individuals with bipolar disorder to engage in the management of multiple target behaviors and to address changes in clinical status highlights the importance of detailed reporting of adaptive intervention components to allow replication and improvement of adaptive mental health technologies for complex mental health problems.

Effect of a Digital Intervention on Depressive Symptoms in Patients With Comorbid Hypertension or Diabetes in Brazil and Peru: Two Randomized Clinical Trials

Importance

Depression is a leading contributor to disease burden globally. Digital mental health interventions can address the treatment gap in low- and middle-income countries, but the effectiveness in these countries is unknown.

Objective

To investigate the effectiveness of a digital intervention in reducing depressive symptoms among people with diabetes and/or hypertension.

Design, Setting, and Participants

Participants with clinically significant depressive symptoms (Patient Health Questionnaire-9 [PHQ-9] score ≥10) who were being treated for hypertension and/or diabetes were enrolled in a cluster randomized clinical trial (RCT) at 20 sites in São Paulo, Brazil (N=880; from September 2016 to September 2017; final follow-up, April 2018), and in an individual-level RCT at 7 sites in Lima, Peru (N=432; from January 2017 to September 2017; final follow-up, March 2018).

Interventions

An 18-session, low-intensity, digital intervention was delivered over 6 weeks via a provided smartphone, based on behavioral activation principles, and supported by nurse assistants (n = 440 participants in 10 clusters in São Paulo; n = 217 participants in Lima) vs enhanced usual care (n = 440 participants in 10 clusters in São Paulo; n = 215 participants in Lima).

Main Outcomes and Measures

The primary outcome was a reduction of at least 50% from baseline in PHQ-9 scores (range, 0-27; higher score indicates more severe depression) at 3 months. Secondary outcomes included a reduction of at least 50% from baseline PHQ-9 scores at 6 months.

Results

Among 880 patients cluster randomized in Brazil (mean age, 56.0 years; 761 [86.5%] women) and 432 patients individually randomized in Peru (mean age, 59.7 years; 352 [81.5%] women), 807 (91.7%) in Brazil and 426 (98.6%) in Peru completed at least 1 follow-up assessment. The proportion of participants in São Paulo with a reduction in PHQ-9 score of at least 50% at 3-month follow-up was 40.7% (159/391 participants) in the digital intervention group vs 28.6% (114/399 participants) in the enhanced usual care group (difference, 12.1 percentage points [95% CI, 5.5 to 18.7]; adjusted odds ratio [OR], 1.6 [95% CI, 1.2 to 2.2]; P = .001). In Lima, the proportion of participants with a reduction in PHQ-9 score of at least 50% at 3-month follow-up was 52.7% (108/205 participants) in the digital intervention group vs 34.1% (70/205 participants) in the enhanced usual care group (difference, 18.6 percentage points [95% CI, 9.1 to 28.0]; adjusted OR, 2.1 [95% CI, 1.4 to 3.2]; P < .001). At 6-month follow-up, differences across groups were no longer statistically significant.

Conclusions and Relevance

In 2 RCTs of patients with hypertension or diabetes and depressive symptoms in Brazil and Peru, a digital intervention delivered over a 6-week period significantly improved depressive symptoms at 3 months when compared with enhanced usual care. However, the magnitude of the effect was small in the trial from Brazil and the effects were not sustained at 6 months.

Trial Registration

ClinicalTrials.gov: NCT02846662 (São Paulo) and NCT03026426 (Lima).

A Smartphone-Based Self-management Intervention for Bipolar Disorder (LiveWell): User-Centered Development Approach

BACKGROUND

Bipolar disorder is a serious mental illness that results in significant morbidity and mortality. Pharmacotherapy is the primary treatment for bipolar disorder; however, adjunctive psychotherapy can help individuals use self-management strategies to improve outcomes. Yet access to this therapy is limited. Smartphones and other technologies have the potential to increase access to therapeutic strategies that enhance self-management while simultaneously providing real-time user feedback and provider alerts to augment care.

OBJECTIVE

This paper describes the user-centered development of LiveWell, a smartphone-based self-management intervention for bipolar disorder, to contribute to and support the ongoing improvement and dissemination of technology-based mental health interventions.

METHODS

Individuals with bipolar disorder first participated in a field trial of a simple smartphone app for self-monitoring of behavioral targets. To develop a complete technology-based intervention for bipolar disorder, this field trial was followed by design sessions, usability testing, and a pilot study of a smartphone-based self-management intervention for bipolar disorder. Throughout all phases of development, intervention revisions were made based on user feedback.

RESULTS

The core of the LiveWell intervention consists of a daily self-monitoring tool, the Daily Check-in. This self-monitoring tool underwent multiple revisions during the user-centered development process. Daily Check-in mood and thought rating scales were collapsed into a single wellness rating scale to accommodate user development of personalized scale anchors. These anchors are meant to assist users in identifying early warning signs and symptoms of impending episodes to take action based on personalized plans. When users identified personal anchors for the wellness scale, the anchors most commonly reflected behavioral signs and symptoms (40%), followed by cognitive (25%), mood (15%), physical (10%), and motivational (7%) signs and symptoms. Changes to the Daily Check-in were also made to help users distinguish between getting adequate sleep and keeping a regular routine. At the end of the pilot study, users reported that the Daily Check-in made them more aware of early warning signs and symptoms and how much they were sleeping. Users also reported that they liked personalizing their anchors and plans and felt this process was useful. Users experienced some difficulties with developing, tracking, and achieving target goals. Users also did not consistently follow up with app recommendations to contact providers when Daily Check-in data suggested they needed additional assistance. As a result, the human support roles for the technology were expanded beyond app use support to include support for self-management and clinical care communication. The development of these human support roles was aided by feedback on the technology's usability from the users and the coaches who provided the human support.

CONCLUSIONS

User input guided the development of intervention content, technology, and coaching support for LiveWell. Users valued the provision of monitoring tools and the ability to personalize plans for staying well, supporting the role of monitoring and personalization as important features of digital mental health technologies. Users also valued human support of the technology in the form of a coach, and user difficulties with aspects of self-management and care-provider communication led to an expansion of the coach's support roles. Obtaining feedback from both users and coaches played an important role in the development of both the LiveWell technology and human support. Attention to all stakeholders involved in the use of mental health technologies is essential for optimizing intervention development.

RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices

Background

With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field.

Objective

Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy.

Methods

RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided.

Results

General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts.

Conclusions

RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

Development of the Initial Surveys for the All of Us Research Program

BACKGROUND

The All of Us Research Program is building a national longitudinal cohort and collecting data from multiple information sources (e.g., biospecimens, electronic health records, and mobile/wearable technologies) to advance precision medicine. Participant-provided information, collected via surveys, will complement and augment these information sources. We report the process used to develop and refine the initial three surveys for this program.

METHODS

The All of Us survey development process included: (1) prioritization of domains for scientific needs, (2) examination of existing validated instruments, (3) content creation, (4) evaluation and refinement via cognitive interviews and online testing, (5) content review by key stakeholders, and (6) launch in the All of Us electronic participant portal. All content was translated into Spanish.

RESULTS

We conducted cognitive interviews in English and Spanish with 169 participants, and 573 individuals completed online testing. Feedback led to over 40 item content changes. Lessons learned included: (1) validated survey instruments performed well in diverse populations reflective of All of Us; (2) parallel evaluation of multiple languages can ensure optimal survey deployment; (3) recruitment challenges in diverse populations required multiple strategies; and (4) key stakeholders improved integration of surveys into larger Program context.

CONCLUSIONS

This efficient, iterative process led to successful testing, refinement, and launch of three All of Us surveys. Reuse of All of Us surveys, available at http://researchallofus.org, may facilitate large consortia targeting diverse populations in English and Spanish to capture participant-provided information to supplement other data, such as genetic, physical measurements, or data from electronic health records.

Technology-Assisted Behavioral Intervention to Extend Sleep Duration: Development and Design of the Sleep Bunny Mobile App

BACKGROUND

Despite the high prevalence of short sleep duration (29.2% of adults sleep <6 hours on weekdays), there are no existing theory-based behavioral interventions to extend sleep duration. The popularity of wearable sleep trackers provides an opportunity to engage users in interventions.

OBJECTIVE

The objective of this study was to outline the theoretical foundation and iterative process of designing the "Sleep Bunny," a technology-assisted sleep extension intervention including a mobile phone app, wearable sleep tracker, and brief telephone coaching. We conducted a two-step process in the development of this intervention, which was as follows: (1) user testing of the app and (2) a field trial that was completed by 2 participants with short sleep duration and a cardiovascular disease risk factor linked to short sleep duration (body mass index [BMI] >25).

METHODS

All participants had habitual sleep duration <6.5 hours verified by 7 days of actigraphy. A total of 6 individuals completed initial user testing in the development phase, and 2 participants completed field testing. Participants in the user testing and field testing responded to open-ended surveys about the design and utility of the app. Participants in the field testing completed the Epworth Sleepiness Scale and also wore an actigraph for a 1-week baseline period and during the 4-week intervention period.

RESULTS

The feedback suggests that users enjoyed the wearable sleep tracker and found the app visually pleasing, but they suggested improvements to the notification and reminder features of the app. The 2 participants who completed the field test demonstrated significant improvements in sleep duration and daytime sleepiness.

CONCLUSIONS

Further testing is needed to determine effects of this intervention in populations at risk for the mental and physical consequences of sleep loss.

Strategies for mHealth research: lessons from 3 mobile intervention studies

The capacity of Mobile Health (mHealth) technologies to propel healthcare forward is directly linked to the quality of mobile interventions developed through careful mHealth research. mHealth research entails several unique characteristics, including collaboration with technologists at all phases of a project, reliance on regional telecommunication infrastructure and commercial mobile service providers, and deployment and evaluation of interventions "in the wild", with participants using mobile tools in uncontrolled environments. In the current paper, we summarize the lessons our multi-institutional/multi-disciplinary team has learned conducting a range of mHealth projects using mobile phones with diverse clinical populations. First, we describe three ongoing projects that we draw from to illustrate throughout the paper. We then provide an example for multidisciplinary teamwork and conceptual mHealth intervention development that we found to be particularly useful. Finally, we discuss mHealth research challenges (i.e. evolving technology, mobile phone selection, user characteristics, the deployment environment, and mHealth system "bugs and glitches"), and provide recommendations for identifying and resolving barriers, or preventing their occurrence altogether.

Design and evaluation of a peer network to support adherence to a web-based intervention for adolescents

BACKGROUND

Depression during adolescence is common but can be prevented. Behavioral intervention technologies (BITs) designed to prevent depression in adolescence, especially standalone web-based interventions, have shown mixed outcomes, likely due to poor intervention adherence. BIT research involving adults has shown that the presence of coaches or peers promotes intervention use. Developmentally, adolescence is a time when peer-based social relationships take precedence. This study examines whether peer-networked support may promote adherence to BITs in this age group.

OBJECTIVE

Adopting the framework of the Supportive Accountability model, which defines the types of human support and interactions required to maintain engagement and persistence with BITs, this paper presents a feasibility study of a peer-networked online intervention for depression prevention among adolescents. We described the development of the peer network, the evaluation of participant use of the peer networking features, and qualitative user feedback to inform continued BIT development.

METHOD

Two groups of adolescents (N = 13) participated in 10-week programs of the peer networked based online intervention. Adolescents had access to didactic lessons, CBT based mood management tools, and peer networking features. The peer networking features are integrated into the site by making use expectations explicit, allow network members to monitor the activities of others, and to supportively hold each other accountable for meeting use expectations. The study collected qualitative feedback from participants as well as usage of site features and tools.

RESULTS

Participants logged in an average of 12.8 sessions over an average of 10.4 unique days during the 10-week program. On average, 66% of all use sessions occurred within the first 3 weeks of use. The number of "exchange comments", that is, comments posted that were part of an exchange between two or more participants, was significantly positively correlated with mean time spent on site (r = 0.62, p = 0.032), use of the Activity Tracker (r = 0.70, p = 0.012) and Didactic Lesson (r = 0.73, p = 0.007). Qualitative interviews revealed that adolescents generally liked and were motivated by the peer networking features during the first weeks of the intervention when general site use by group members was high. However, the decrease of site use by group members during the subsequent weeks negatively affected participants' desire to log on or engage with group members.

CONCLUSIONS

This pilot study highlights the potential that a BIT designed to harness the connection among a peer network, thereby promoting supportive accountability, may improve adolescent adherence to BITs for depression prevention.

Patterns and Predictors of Compliance in a Prospective Diary Study of Substance Use and Sexual Behavior in a Sample of Young Men Who Have Sex With Men

Behavioral diaries are used for observing health-related behaviors prospectively. Little is known about patterns and predictors of diary compliance to better understand differential attrition. An analytic sample of 241 young men who have sex with men (YMSM) from a 2-month diary study of substance use and sexual behavior were randomized to complete daily or weekly timeline followback diaries. Latent class growth analyses were used to analyze data. Weekly and daily diary groups produced similar compliance patterns: high, low, and declining compliance groups. Black YMSM were more likely to be in the declining compared with the high compliance group. YMSM who were randomly assigned to receive automated feedback about risk behaviors did not differ in compliance rate compared with those who did not. Risk behavior engagement did not predict compliance in the daily condition, but some substances predicted compliance in the weekly condition. Implications for observational and behavior change methods are discussed.

MedLink: A mobile intervention to improve medication adherence and processes of care for treatment of depression in general medicine

Background

Major depressive disorder is a common psychological problem affecting up to 20% of adults in their lifetime. The majority of people treated for depression receive antidepressant medication through their primary care physician. This commonly results in low rates of recovery. Failure points in the process of care contributing to poor outcomes include patient non-adherence to medications, failure of physicians to optimize dose and absence of communication between patients and physicians.

Objective

This pilot study evaluated the feasibility of a systemic digital intervention (MedLink) designed to address failure points and improve treatment of depression in primary care among patients during the first eight weeks of initiating a new course of antidepressant therapy.

Methods

Participants were provided with the MedLink mobile app that provided dose reminders, information and surveys of symptoms and side effects. A cellularly enabled pillbox monitored antidepressant medication adherence. Reports were provided to physicians and participants to prompt changes in medication regimen. Study outcomes were assessed via self-report and interview measures at baseline, week 4 and week 8.

Results

Medication adherence detected by the MedLink system was 82%. Participants demonstrated significant decreases in depressive symptoms on the patient health questionnaire-9 (PHQ-9) (p = 0.0005) and the Quick Inventory of Depressive Symptomatology (p = 0.0008) over the eight-week trial. Usability was generally rated favorably.

Conclusions

The MedLink system demonstrated promise as an intervention to address failure points in the primary care treatment of major depressive disorder. Current findings support the further development of MedLink through a randomized controlled trial to evaluate the efficacy of improving processes of care, patient adherence and symptoms of depression.

mHealth for Schizophrenia: Patient Engagement With a Mobile Phone Intervention Following Hospital Discharge

BACKGROUND

mHealth interventions that use mobile phones as instruments for illness management are gaining popularity. Research examining mobile phone‒based mHealth programs for people with psychosis has shown that these approaches are feasible, acceptable, and clinically promising. However, most mHealth initiatives involving people with schizophrenia have spanned periods ranging from a few days to several weeks and have typically involved participants who were clinically stable.

OBJECTIVE

Our aim was to evaluate the viability of extended mHealth interventions for people with schizophrenia-spectrum disorders following hospital discharge. Specifically, we set out to examine the following: (1) Can individuals be engaged with a mobile phone intervention program during this high-risk period?, (2) Are age, gender, racial background, or hospitalization history associated with their engagement or persistence in using a mobile phone intervention over time?, and (3) Does engagement differ by characteristics of the mHealth intervention itself (ie, pre-programmed vs on-demand functions)?

METHODS

We examined mHealth intervention use and demographic and clinical predictors of engagement in 342 individuals with schizophrenia-spectrum disorders who were given the FOCUS mobile phone intervention as part of a technology-assisted relapse prevention program during the 6-month high-risk period following hospitalization.

RESULTS

On average, participants engaged with FOCUS for 82% of the weeks they had the mobile phone. People who used FOCUS more often continued using it over longer periods: 44% used the intervention over 5-6 months, on average 4.3 days a week. Gender, race, age, and number of past psychiatric hospitalizations were associated with engagement. Females used FOCUS on average 0.4 more days a week than males. White participants engaged on average 0.7 days more a week than African-Americans and responded to prompts on 0.7 days more a week than Hispanic participants. Younger participants (age 18-29) had 0.4 fewer days of on-demand use a week than individuals who were 30-45 years old and 0.5 fewer days a week than older participants (age 46-60). Participants with fewer past hospitalizations (1-6) engaged on average 0.2 more days a week than those with seven or more. mHealth program functions were associated with engagement. Participants responded to prompts more often than they self-initiated on-demand tools, but both FOCUS functions were used regularly. Both types of intervention use declined over time (on-demand use had a steeper decline). Although mHealth use declined, the majority of individuals used both on-demand and system-prompted functions regularly throughout their participation. Therefore, neither function is extraneous.

CONCLUSIONS

The findings demonstrated that individuals with schizophrenia-spectrum disorders can actively engage with a clinically supported mobile phone intervention for up to 6 months following hospital discharge. mHealth may be useful in reaching a clinical population that is typically difficult to engage during high-risk periods.

Uptake and Usage of IntelliCare: A Publicly Available Suite of Mental Health and Well-Being Apps

BACKGROUND

Treatments for depression and anxiety have several behavioral and psychological targets and rely on varied strategies. Digital mental health treatments often employ feature-rich approaches addressing several targets and strategies. These treatments, often optimized for desktop computer use, are at odds with the ways people use smartphone applications. Smartphone use tends to focus on singular functions with easy navigation to desired tools. The IntelliCare suite of apps was developed to address the discrepancy between need for diverse behavioral strategies and constraints imposed by typical app use. Each app focuses on one strategy for a limited subset of clinical aims all pertinent to depression and anxiety. This study presents the uptake and usage of apps from the IntelliCare suite following an open deployment on a large app marketplace.

METHODS

Thirteen lightweight apps, including 12 interactive apps and one Hub app that coordinates use across those interactive apps, were developed and made free to download on the Google Play store. De-identified app usage data from the first year of IntelliCare suite deployment were analyzed for this study.

RESULTS

In the first year of public availability, 5,210 individuals downloaded one or more of the IntelliCare apps, for a total of 10,131 downloads. Nearly a third of these individuals (31.8%) downloaded more than one of these apps. The modal number of launches for each of the apps was 1, however the mean number of app launches per app ranged from 3.10 to 16.98, reflecting considerable variability in the use of each app.

CONCLUSIONS

The use rate of the IntelliCare suite of apps is higher than public deployments of other comparable digital resources. Our findings suggest that people will use multiple apps and provides support for the concept of app suites as a useful strategy for providing diverse behavioral strategies.

Feasibility, acceptability, and preliminary efficacy of a smartphone intervention for schizophrenia

The FOCUS smartphone intervention was developed to provide automated real-time/real-place illness management support to individuals with schizophrenia. The system was specifically designed to be usable by people with psychotic disorders who may have cognitive impairment, psychotic symptoms, negative symptoms, and/or low reading levels. FOCUS offers users both prescheduled and on-demand resources to facilitate symptom management, mood regulation, medication adherence, social functioning, and improved sleep. In this study, 33 individuals with schizophrenia or schizoaffective disorder used FOCUS over a 1-month period in their own environments. Participants were able to learn how to use the intervention independently, and all but one participant completed the trial successfully and returned the smartphones intact. Completers used the system on 86.5% of days they had the device, an average of 5.2 times a day. Approximately 62% of use of the FOCUS intervention was initiated by the participants, and 38% of use was in response to automated prompts. Baseline levels of cognitive functioning, negative symptoms, persecutory ideation, and reading level were not related to participants' use of the intervention. Approximately 90% of participants rated the intervention as highly acceptable and usable. Paired samples t tests found significant reductions in psychotic symptoms, depression, and general psychopathology, after 1 month of FOCUS use. This study demonstrated the feasibility, acceptability, and preliminary efficacy of the FOCUS intervention for schizophrenia and introduces a new treatment model which has promise for extending the reach of evidence-based care beyond the confines of a physical clinic using widely available technologies.

Purple: a modular system for developing and deploying behavioral intervention technologies

The creation, deployment, and evaluation of Web-based and mobile-based applications for health, mental health, and wellness within research settings has tended to be siloed, with each research group developing their own systems and features. This has led to technological features and products that are not sharable across research teams, thereby limiting collaboration, reducing the speed of dissemination, and raising the bar for entry into this area of research. This paper provides an overview of Purple, an extensible, modular, and repurposable system created for the development of Web-based and mobile-based applications for health behavior change. Purple contains features required to construct applications and to manage and evaluate research trials using these applications. Core functionality of Purple includes elements that support user management, content authorship, content delivery, and data management. We discuss the history and development of the Purple system guided by the rationale of producing a system that allows greater collaboration and understanding across research teams interested in investigating similar questions and using similar methods. Purple provides a useful tool to meet the needs of stakeholders involved in the creation, provision, and usage of eHealth and mHealth applications. Housed in a non-profit, academic institution, Purple also offers the potential to facilitate the diffusion of knowledge across the research community and improve our capacity to deliver useful and usable applications that support the behavior change of end users.

Skin self-examination education for early detection of melanoma: a randomized controlled trial of Internet, workbook, and in-person interventions

Background

Early detection of melanoma improves survival. Since many melanoma patients and their spouses seek the care of a physician after discovering their melanoma, an ongoing study will determine the efficacy of teaching at-risk melanoma patients and their skin check partner how to conduct skin self-examinations (SSEs). Internet-based health behavior interventions have proven efficacious in creating behavior change in patients to better prevent, detect, or cope with their health issues. The efficacy of electronic interactive SSE educational intervention provided on a tablet device has not previously been determined.

Objective

The electronic interactive educational intervention was created to develop a scalable, effective intervention to enhance performance and accuracy of SSE among those at-risk to develop melanoma. The intervention in the office was conducted using one of the following three methods: (1) in-person through a facilitator, (2) with a paper workbook, or (3) with a tablet device used in the clinical office. Differences related to method of delivery were elucidated by having the melanoma patient and their skin check partner provide a self-report of their confidence in performing SSE and take a knowledge-based test immediately after receiving the intervention.

Methods

The three interventions used 9 of the 26 behavioral change techniques defined by Abraham and Michie to promote planning of monthly SSE, encourage performing SSE, and reinforce self-efficacy by praising correct responses to knowledge-based decision making and offering helpful suggestions to improve performance. In creating the electronic interactive SSE educational intervention, the educational content was taken directly from both the scripted in-person presentation delivered with Microsoft PowerPoint by a trained facilitator and the paper workbook training arms of the study. Enrollment totaled 500 pairs (melanoma patient and their SSE partner) with randomization of 165 pairs to the in-person, 165 pairs to the workbook, and 70 pairs to electronic interactive SSE educational intervention.

Results

The demographic survey data showed no significant mean differences between groups in age, education, or income. The tablet usability survey given to the first 30 tablet pairs found that, overall, participants found the electronic interactive intervention easy to use and that the video of the doctor-patient-partner dialogue accompanying the dermatologist’s examination was particularly helpful in understanding what they were asked to do for the study. The interactive group proved to be just as good as the workbook group in self-confidence of scoring moles, and just as good as both the workbook and the in-person intervention groups in self-confidence of monitoring their moles. While the in-person intervention performed significantly better on a skill-based quiz, the electronic interactive group performed significantly better than the workbook group. The electronic interactive and in-person interventions were more efficient (30 minutes), while the workbook took longer (45 minutes).

Conclusions

This study suggests that an electronic interactive intervention can deliver skills training comparable to other training methods, and the experience can be accommodated during the customary outpatient office visit with the physician. Further testing of the electronic interactive intervention’s role in the anxiety of the pair and pair-discovered melanomas upon self-screening will elucidate the impact of these tools on outcomes in at-risk patient populations.

Trial Registration

ClinicalTrials.gov NCT01013844; http://clinicaltrials.gov/show/NCT01013844 (Archived by WebCite at http://www.webcitation.org/6LvGGSTKK).

A single vs. multi-sensor approach to enhanced detection of smartphone placement

In this paper, the authors evaluate the ability to detect on-body device placement of smartphones. A feasibility study is undertaken with N=5 participants to identify nine key locations, including in the hand, thigh and backpack, using a multitude of commonly available smartphone sensors. Sensors examined include the accelerometer, magnetometer, gyroscope, pressure and light sensors. Each sensor is examined independently, to identify the potential contributions it can offer, before a fused approach, using all sensors is adopted. A total of 139 features are generated from these sensors, and used to train five machine learning algorithms, i.e. C4.5, CART, Naïve Bayes, Multilayer Perceptrons, and Support Vector Machines. Ten-fold cross validation is used to validate these models, achieving classification results as high as 99%.

Development and Usability Testing of FOCUS: A Smartphone System for Self-Management of Schizophrenia

OBJECTIVE

Mobile Health (mHealth) approaches can support the rehabilitation of individuals with psychiatric conditions. In the current article, we describe the development of a smartphone illness self-management system for people with schizophrenia.

METHODS

The research was conducted with consumers and practitioners at a community-based rehabilitation agency. Stage 1: 904 individuals with schizophrenia or schizoaffective disorder completed a survey reporting on their current use of mobile devices and interest in mHealth services. Eight practitioners completed a survey examining their attitudes and expectations from an mHealth intervention, and identified needs and potential obstacles. Stage 2: A multidisciplinary team incorporated consumer and practitioner input and employed design principles for the development of e-resources for people with schizophrenia to produce an mHealth intervention. Stage 3: 12 consumers participated in laboratory usability sessions. They performed tasks involved in operating the new system, and provided "think aloud" commentary and post-session usability ratings.

RESULTS

570 (63%) of survey respondents reported owning a mobile device and many expressed interest in receiving mHealth services. Most practitioners believed that consumers could learn to use and would benefit from an mHealth intervention. In response, we developed a smartphone system that targets medication adherence, mood regulation, sleep, social functioning, and coping with symptoms. Usability testing revealed several design vulnerabilities, and the system was adapted to address consumer needs and preferences accordingly.

CONCLUSIONS AND IMPLICATIONS FOR PRACTICE

Through a comprehensive development process, we produced an mHealth illness self-management intervention that is likely to be used successfully, and is ready for deployment and systemic evaluation in real-world conditions.

A randomized controlled trial evaluating a manualized TeleCoaching protocol for improving adherence to a web-based intervention for the treatment of depression

BACKGROUND

Web-based interventions for depression that are supported by coaching have generally produced larger effect-sizes, relative to standalone web-based interventions. This is likely due to the effect of coaching on adherence. We evaluated the efficacy of a manualized telephone coaching intervention (TeleCoach) aimed at improving adherence to a web-based intervention (moodManager), as well as the relationship between adherence and depressive symptom outcomes.

METHODS

101 patients with MDD, recruited from primary care, were randomized to 12 weeks moodManager+TeleCoach, 12 weeks of self-directed moodManager, or 6 weeks of a waitlist control (WLC). Depressive symptom severity was measured using the PHQ-9.

RESULTS

TeleCoach+moodManager, compared to self-directed moodManager, resulted in significantly greater numbers of login days (p = 0.01), greater time until last use (p = 0.007), greater use of lessons (p = 0.03), greater variety of interactive tools used (p = 0.02), but total instances of tool use did not reach statistical significance. (p = 0.07). TeleCoach+moodManager produced significantly lower PHQ-9 scores relative to WLC at week 6 (p = 0.04), but there were no other significant differences in PHQ-9 scores at weeks 6 or 12 (ps>0.20) across treatment arms. Baseline PHQ-9 scores were no significantly related to adherence to moodManager.

CONCLUSIONS

TeleCoach produced significantly greater adherence to moodManager, relative to self-directed moodManager. TeleCoached moodManager produced greater reductions in depressive symptoms relative to WLC, however, there were no statistically significant differences relative to self-directed moodManager. While greater use was associated with better outcomes, most users in both TeleCoach and self-directed moodManager had dropped out of treatment by week 12. Even with telephone coaching, adherence to web-based interventions for depression remains a challenge. Methods of improving coaching models are discussed.

TRIAL REGISTRATION

Clinicaltrials.gov NCT00719979.

Project onward: an innovative e-health intervention for cancer survivors

OBJECTIVE

This study examined the feasibility and acceptability of an Individual Internet Intervention (III) embedded and integrated into an Internet Support Group (ISG) with the ultimate goal of enhancing adherence and learning, compared with an individual internet invention alone.

METHOD

Thirty-one posttreatment cancer survivors were randomized in groups of seven to nine to either the 8-week III + ISG intervention or the 8-week III condition. Seventeen participants met the Hospital Anxiety and Depression Scale (HADS) criteria for depressive symptoms (HADS ≥ 8).

RESULTS

Among all participants, the mean number of logins over 8 weeks was 20.8 ± 17.7 logins for the III + ISG compared with 12.5 ± 12.5 in III-only (p = 0.15). Two participants in the III + ISG dropped out, compared with five in III (p = 0.39). Among the 17 participants with depressive symptoms at baseline, both the Onward and the III-only condition showed large reductions in the depression scale of HADS (d = 1.27 and 0.89, respectively). Improvement over time and time x treatment effects only reached trend significance levels (ps = 0.07 & 0.12) as this pilot was not powered to detect these differences.

CONCLUSION

Both the III + ISG and III-only demonstrated pre-post reductions in depressive symptoms and high rates of utilization compared with other web-based treatments for depression. Although it is premature to make any determination as to the efficacy of the interventions tested in this feasibility study, these results indicate that pursuing the III + ISG model, as well as standard IIIs, may be fruitful areas of future research.

Harnessing context sensing to develop a mobile intervention for depression

Background

Mobile phone sensors can be used to develop context-aware systems that automatically detect when patients require assistance. Mobile phones can also provide ecological momentary interventions that deliver tailored assistance during problematic situations. However, such approaches have not yet been used to treat major depressive disorder.

Objective

The purpose of this study was to investigate the technical feasibility, functional reliability, and patient satisfaction with Mobilyze!, a mobile phone- and Internet-based intervention including ecological momentary intervention and context sensing.

Methods

We developed a mobile phone application and supporting architecture, in which machine learning models (ie, learners) predicted patients’ mood, emotions, cognitive/motivational states, activities, environmental context, and social context based on at least 38 concurrent phone sensor values (eg, global positioning system, ambient light, recent calls). The website included feedback graphs illustrating correlations between patients’ self-reported states, as well as didactics and tools teaching patients behavioral activation concepts. Brief telephone calls and emails with a clinician were used to promote adherence. We enrolled 8 adults with major depressive disorder in a single-arm pilot study to receive Mobilyze! and complete clinical assessments for 8 weeks.

Results

Promising accuracy rates (60% to 91%) were achieved by learners predicting categorical contextual states (eg, location). For states rated on scales (eg, mood), predictive capability was poor. Participants were satisfied with the phone application and improved significantly on self-reported depressive symptoms (beta_week = –.82, P < .001, per-protocol Cohen d = 3.43) and interview measures of depressive symptoms (beta_week = –.81, P < .001, per-protocol Cohen d = 3.55). Participants also became less likely to meet criteria for major depressive disorder diagnosis (b_week = –.65, P = .03, per-protocol remission rate = 85.71%). Comorbid anxiety symptoms also decreased (beta_week = –.71, P < .001, per-protocol Cohen d = 2.58).

Conclusions

Mobilyze! is a scalable, feasible intervention with preliminary evidence of efficacy. To our knowledge, it is the first ecological momentary intervention for unipolar depression, as well as one of the first attempts to use context sensing to identify mental health-related states. Several lessons learned regarding technical functionality, data mining, and software development process are discussed.

Trial Registration

Clinicaltrials.gov NCT01107041; http://clinicaltrials.gov/ct2/show/NCT01107041 (Archived by WebCite at http://www.webcitation.org/60CVjPH0n)

Multimodal e-mental health treatment for depression: a feasibility trial

BACKGROUND

Internet interventions for depression have shown less than optimal adherence. This study describes the feasibility trial of a multimodal e-mental health intervention designed to enhance adherence and outcomes for depression. The intervention required frequent brief log-ins for self-monitoring and feedback as well as email and brief telephone support guided by a theory-driven manualized protocol.

OBJECTIVE

The objective of this feasibility trial was to examine if our Internet intervention plus manualized telephone support program would result in increased adherence rates and improvement in depression outcomes.

METHODS

This was a single arm feasibility trial of a 7-week intervention.

RESULTS

Of the 21 patients enrolled, 2 (9.5%) dropped out of treatment. Patients logged in 23.2 ± 12.2 times over the 7 weeks. Significant reductions in depression were found on all measures, including the Patient Health Questionnaire depression scale (PHQ-8) (Cohen's d = 1.96, P < .001), the Hamilton Rating Scale for Depression (d = 1.34, P < .001), and diagnosis of major depressive episode (P < .001).

CONCLUSIONS

The attrition rate was far lower than seen either in Internet studies or trials of face-to-face interventions, and depression outcomes were substantial. These findings support the feasibility of providing a multimodal e-mental health treatment to patients with depression. Although it is premature to make any firm conclusions based on these data, they do support the initiation of a randomized controlled trial examining the independent and joint effects of Internet and telephone administered treatments for depression.