Evaluation of a recommender app for apps for the treatment of depression and anxiety: an analysis of longitudinal user engagement

Implementing a mental health app library in primary care: A feasibility study

Confronted with a wide range of digital health tools (DHT), professionals and patients need guidance to use these tools correctly and optimize health management. In the fall of 2020, a DHT library developed by Quebec-based company TherAppX was implemented in 22 institutions. The library was designed to enable healthcare professionals to use DHT in clinical care. The purpose of the current study was to assess the feasibility of implementing the library, including user experience, changes in DHT recommendation habits, and factors that helped or hindered the implementation process. A multi-methods design focusing on secondary use of quantitative data collected by TherAppX and semi-structured interviews with users was employed. While the quantitative analyses indicated infrequent use of the library, qualitative analyses highlighted several factors that hindered its implementation, including certain library and user characteristics and the unprecedented context of the COVID-19 pandemic. Nevertheless, the quantitative analyses confirmed interest in DHT and their usefulness during follow-ups. The results revealed a marginally significant pre-post changes in the frequency with which DHT were recommended. This study helped identify areas for improvements and indicates that further evaluation is needed. Future implementations would benefit from ensuring optimal conditions for a successful implementation.

Combining Behavioral Economics-Based Incentives With the Anchoring Strategy: Protocol for a Randomized Controlled Trial

BACKGROUND

Chronic (ie, long-term) elevated stress is associated with a number of mental and physical health conditions. Mindfulness meditation mobile apps are a promising tool for stress self-management that can overcome several barriers associated with in-person interventions; however, to date, poor app-based intervention adherence has limited the efficacy of these mobile health tools. Anchoring, or pairing, a new behavior with an existing routine has been shown to effectively establish habits that are maintained over time, but this strategy typically only works for those with high initial motivation and has yet to be tested for maintaining meditation with a mobile app.

OBJECTIVE

This study will test novel combinations of behavioral economics-based incentives with the anchoring strategy for establishing and maintaining adherence to an effective dose of meditation with a mobile app.

METHODS

This 16-week study will use a 5-arm, parallel, partially blinded (participants only), randomized controlled design. We will implement a fractional factorial study design that varies the use of self-monitoring messages and financial incentives to support participants' use of their personalized anchoring strategy for maintaining adherence to a ≥10 minute-per-day meditation prescription during an 8-week intervention period, followed by an 8-week postintervention observation period. Specifically, we will vary the use of self-monitoring messages of either the target behavior (ie, meditation tracking) or the outcome associated with the target behavior (ie, mood symptom tracking). We will also vary the use of financial incentives conditional on either meditation at any time of day or meditation performed at approximately the same time of day as participants' personalized anchors.

RESULTS

Continuous meditation app use data will be used to measure weekly meditation adherence over the 16-week study period as a binary variable equal to 1 if participants complete ≥10 minutes of meditation for ≥4 days per week and 0 otherwise. We will measure weekly anchoring plan adherence as a binary variable equal to 1 if participants complete ≥10 minutes of meditation within +1 or -1 hour of the timing of their chosen anchor on ≥4 days per week and 0 otherwise. In addition to these primary measures of meditation and anchoring plan adherence, we will also assess the secondary measures of stress, anxiety, posttraumatic stress disorder, sleep disturbance, and meditation app habit strength at baseline, week 8, and week 16.

CONCLUSIONS

This study will fill an important gap in the mobile health literature by testing novel intervention approaches for establishing and maintaining adherence to app-based mindfulness meditation. If successful, this study will identify an accessible and scalable stress self-management intervention that can help combat stress in the United States.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05217602; https://clinicaltrials.gov/ct2/show/NCT05217602.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/39930.

Monotone response surface of multi-factor condition: estimation and Bayes classifiers

We formulate the estimation of monotone response surface of multiple factors as the inverse of an iteration of partially ordered classifier ensembles. Each ensemble (called PIPE-classifiers) is a projection of Bayes classifiers on the constrained space. We prove the inverse of PIPE-classifiers (iPIPE) exists, and propose algorithms to efficiently compute iPIPE by reducing the space over which optimisation is conducted. The methods are applied in analysis and simulation settings where the surface dimension is higher than what the isotonic regression literature typically considers. Simulation shows iPIPE-based credible intervals achieve nominal coverage probability and are more precise compared to unconstrained estimation.

Mobile health-based home rehabilitation education improving early outcomes after anterior cruciate ligament reconstruction: A randomized controlled clinical trial

Objective

This study aimed to assess changes in joint range of motion (ROM) and knee joint function between patients who received the mobile health-based intervention and those who received regular care at 2 and 6 weeks after anterior cruciate ligament (ACL) reconstruction to provide better interventions in the future.

Methods

Patients who underwent ACL reconstruction were randomized into the experimental [Mobile health-based intervention (MHI); n = 62] and control (CON) groups (n = 63). The CON group underwent home-based rehabilitation exercise following the paper rehabilitation schedule, while the intervention group received additional mobile health-based education at weeks 1-6 after surgery. ROM, thigh circumference difference, and flexion pain were the primary outcomes. The secondary outcomes were the international knee documentation committee knee evaluation form (IKDC) scores and rehabilitation compliance scores. All the outcomes were measured 1 day before surgery as references and at 2 and 6 weeks after surgery.

Results

There was no statistical difference in the patients' ROM, thigh circumference difference, and VAS scores at the 2-week follow-up. At the 6-week follow-up, the ROM of the affected leg was (118.1 ± 20.5)° in the CON group and (126.6 ± 20.5)° in the MHI group, and the difference was statistically significant (P = 0.011). The difference in thigh circumference was 3.0 (2.0, 3.5) cm in the CON group and 2.5 (1.0, 3.0) cm in the MHI group. The difference was statistically significant (P < 0.001). The VAS score in the CON group was 3.0 (2.0, 4.0), and the MHI group was 2.5 (1.0, 3.0). The difference was statistically significant (P < 0.05). At the 6-week follow-up, the compliance score of patients in the MHI group was significantly higher than that in the CON group (P = 0.047, β = 2.243, 95%CI: 0.026-4.459). There is no statistically significant difference in IKDC scores.

Conclusion

Mobile health-based intervention positively affected patients undergoing ACL reconstruction surgery, particularly in improving the clinical outcome indicators of the knee joint.

Recommender System Based on Collaborative Filtering for Personalized Dietary Advice: A Cross-Sectional Analysis of the ELSA-Brasil Study

This study aimed to predict dietary recommendations and compare the performance of algorithms based on collaborative filtering for making predictions of personalized dietary recommendations. We analyzed the baseline cross-sectional data (2008-2010) of 12,667 participants of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). The participants were public employees of teaching and research institutions, aged 35-74 years, and 59% female. A semiquantitative Food Frequency Questionnaire (FFQ) was used for dietary assessment. The predictions of dietary recommendations were based on two machine learning (ML) algorithms-user-based collaborative filtering (UBCF) and item-based collaborative filtering (IBCF). The ML algorithms had similar precision (88-91%). The error metrics were lower for UBCF than for IBCF: with a root mean square error (RMSE) of 1.49 vs. 1.67 and a mean square error (MSE) of 2.21 vs. 2.78. Although all food groups were used as input in the system, the items eligible as recommendations included whole cereals, tubers and roots, beans and other legumes, oilseeds, fruits, vegetables, white meats and fish, and low-fat dairy products and milk. The algorithms' performances were similar in making predictions for dietary recommendations. The models presented can provide support for health professionals in interventions that promote healthier habits and improve adherence to this personalized dietary advice.

Identifying essential factors that influence user engagement with digital mental health tools in clinical care settings: Protocol for a Delphi study

Introduction

Improving effective user engagement with digital mental health tools has become a priority in enabling the value of digital health. With increased interest from the mental health community in embedding digital health tools as part of care delivery, there is a need to examine and identify the essential factors in influencing user engagement with digital mental health tools in clinical care. The current study will use a Delphi approach to gain consensus from individuals with relevant experience and expertise (e.g. patients, clinicians and healthcare administrators) on factors that influence user engagement (i.e. an essential factor).

Methods

Participants will be invited to complete up to four rounds of online surveys. The first round of the Delphi study comprises of reviewing existing factors identified in literature and commenting on whether any factors they believe are important are missing from the list. Subsequent rounds will involve asking participants to rate the perceived impact of each factor in influencing user engagement with digital mental health tools in clinical care contexts. This work is expected to consolidate the perspectives from relevant stakeholders and the academic literature to identify a core set of factors considered essential in influencing user engagement with digital mental health tools in clinical care contexts.

Exploring and Characterizing Patient Multibehavior Engagement Trails and Patient Behavior Preference Patterns in Pathway-Based mHealth Hypertension Self-Management: Analysis of Use Data

Background

Hypertension is a long-term medical condition. Mobile health (mHealth) services can help out-of-hospital patients to self-manage. However, not all management is effective, possibly because the behavior mechanism and behavior preferences of patients with various characteristics in hypertension management were unclear.

Objective

The purpose of this study was to (1) explore patient multibehavior engagement trails in the pathway-based hypertension self-management, (2) discover patient behavior preference patterns, and (3) identify the characteristics of patients with different behavior preferences.

Methods

This study included 863 hypertensive patients who generated 295,855 use records in the mHealth app from December 28, 2016, to July 2, 2020. Markov chain was used to infer the patient multibehavior engagement trails, which contained the type, quantity, time spent, sequence, and transition probability value (TP value) of patient behavior. K-means algorithm was used to group patients by the normalized behavior preference features: the number of behavioral states that a patient performed in each trail. The pages in the app represented the behavior states. Chi-square tests, Z-test, analyses of variance, and Bonferroni multiple comparisons were conducted to characterize the patient behavior preference patterns.

Results

Markov chain analysis revealed 3 types of behavior transition (1-way transition, cycle transition, and self-transition) and 4 trails of patient multibehavior engagement. In perform task trail (PT-T), patients preferred to start self-management from the states of task blood pressure (BP), task drug, and task weight (TP value 0.29, 0.18, and 0.20, respectively), and spent more time on the task food state (35.87 s). Some patients entered the states of task BP and task drug (TP value 0.20, 0.25) from the reminder item state. In the result-oriented trail (RO-T), patients spent more energy on the ranking state (19.66 s) compared to the health report state (13.25 s). In the knowledge learning trail (KL-T), there was a high probability of cycle transition (TP value 0.47, 0.31) between the states of knowledge list and knowledge content. In the support acquisition trail (SA-T), there was a high probability of self-transition in the questionnaire (TP value 0.29) state. Cluster analysis discovered 3 patient behavior preference patterns: PT-T cluster, PT-T and KL-T cluster, and PT-T and SA-T cluster. There were statistically significant associations between the behavior preference pattern and gender, education level, and BP.

Conclusions

This study identified the dynamic, longitudinal, and multidimensional characteristics of patient behavior. Patients preferred to focus on BP, medications, and weight conditions and paid attention to BP and medications using reminders. The diet management and questionnaires were complicated and difficult to implement and record. Competitive methods such as ranking were more likely to attract patients to pay attention to their own self-management states. Female patients with lower education level and poorly controlled BP were more likely to be highly involved in hypertension health education.

Technology-supported strategies for promoting user engagement with digital mental health interventions: A systematic review

Background

Although digital mental health interventions (DMHIs) offer a potential solution for increasing access to mental health treatment, their integration into real-world settings has been slow. A key reason for this is poor user engagement. A growing number of studies evaluating strategies for promoting engagement with DMHIs means that a review of the literature is now warranted. This systematic review is the first to synthesise evidence on technology-supported strategies for promoting engagement with DMHIs.

Methods

MEDLINE, EmbASE, PsycINFO and PubMed databases were searched from 1 January 1995 to 1 October 2021. Experimental or quasi-experimental studies examining the effect of technology-supported engagement strategies deployed alongside DMHIs were included, as were secondary analyses of such studies. Title and abstract screening, full-text coding and quality assessment were performed independently by two authors. Narrative synthesis was used to summarise findings from the included studies.

Results

24 studies (10,266 participants) were included. Engagement strategies ranged from reminders, coaching, personalised information and peer support. Most strategies were disseminated once a week, usually via email or telephone. There was some empirical support for the efficacy of technology-based strategies towards promoting engagement. However, findings were mixed regardless of strategy type or study aim.

Conclusions

Technology-supported strategies appear to increase engagement with DMHIs; however, their efficacy varies widely by strategy type. Future research should involve end-users in the development and evaluation of these strategies to develop a more cohesive set of strategies that are acceptable and effective for target audiences, and explore the mechanism(s) through which such strategies promote engagement.

What Works Best to Engage Participants in Mobile App Interventions and e-Health: A Scoping Review

Background:

Despite the growing popularity of mobile app interventions, specific engagement components of mobile apps have not been well studied.

Methods:

The objectives of this scoping review are to determine which components of mobile health intervention apps encouraged or hindered engagement, and examine how studies measured engagement.

Results:

A PubMed search on March 5, 2020 yielded 239 articles that featured the terms engagement, mobile app/mobile health, and adult. After applying exclusion criteria, only 54 studies were included in the final analysis.

Discussion:

Common app components associated with increased engagement included: personalized content/feedback, data visualization, reminders/push notifications, educational information/material, logging/self-monitoring functions, and goal-setting features. On the other hand, social media integration, social forums, poor app navigation, and technical difficulties appeared to contribute to lower engagement rates or decreased usage. Notably, the review revealed a great variability in how engagement with mobile health apps is measured due to lack of established processes.

Conclusion:

There is a critical need for controlled studies to provide guidelines and standards to help facilitate engagement and its measurement in research and clinical trial work using mobile health intervention apps.

Health Recommender Systems: Systematic Review

Background

Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior.

Objective

We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations.

Methods

We conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review.

Results

Recommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used.

Conclusions

There is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.

Understanding the Relationship between Mood Symptoms and Mobile App Engagement Among Breast Cancer Patients: A Machine Learning Process (Preprint)

Background

Health interventions delivered via smart devices are increasingly being used to address mental health challenges associated with cancer treatment. Engagement with mobile interventions has been associated with treatment success; however, the relationship between mood and engagement among patients with cancer remains poorly understood. A reason for this is the lack of a data-driven process for analyzing mood and app engagement data for patients with cancer.

Objective

This study aimed to provide a step-by-step process for using app engagement metrics to predict continuously assessed mood outcomes in patients with breast cancer.

Methods

We described the steps involved in data preprocessing, feature extraction, and data modeling and prediction. We applied this process as a case study to data collected from patients with breast cancer who engaged with a mobile mental health app intervention (IntelliCare) over 7 weeks. We compared engagement patterns over time (eg, frequency and days of use) between participants with high and low anxiety and between participants with high and low depression. We then used a linear mixed model to identify significant effects and evaluate the performance of the random forest and XGBoost classifiers in predicting weekly mood from baseline affect and engagement features.

Results

We observed differences in engagement patterns between the participants with high and low levels of anxiety and depression. The linear mixed model results varied by the feature set; these results revealed weak effects for several features of engagement, including duration-based metrics and frequency. The accuracy of predicting depressed mood varied according to the feature set and classifier. The feature set containing survey features and overall app engagement features achieved the best performance (accuracy: 84.6%; precision: 82.5%; recall: 64.4%; F1 score: 67.8%) when used with a random forest classifier.

Conclusions

The results from the case study support the feasibility and potential of our analytic process for understanding the relationship between app engagement and mood outcomes in patients with breast cancer. The ability to leverage both self-report and engagement features to analyze and predict mood during an intervention could be used to enhance decision-making for researchers and clinicians and assist in developing more personalized interventions for patients with breast cancer.

Assessing the usability and user engagement of Thought Spot - A digital mental health help-seeking solution for transition-aged youth

OBJECTIVE

To evaluate the perceived usability of and user engagement with a digital platform (Thought Spot) designed to enhance mental health and wellness help-seeking among transition-aged youth (TAY; 17-29-years old).

MATERIALS AND METHODS

Survey responses and usage patterns were collected as part of a randomized controlled trial evaluating the efficacy of Thought Spot. Participants given Thought Spot completed an adapted Usefulness, Satisfaction, and Ease of Use (USE) Questionnaire to measure perceived usability of the platform. User engagement patterns on Thought Spot were examined using analytics data collected throughout the study (March 2018-June 2019).

RESULTS

A total of 131 transition-aged participants completed the USE questionnaire and logged on to Thought Spot at least once. Ease of learning scored higher than ease of use, usefulness and satisfaction. Participants identified numerous strengths and challenges related to usability, visual appeal, functionality and usefulness of the content. In terms of user engagement, most participants stopped using the platform after 3 weeks. Participants searched and were interested in a variety of resources, including mental health, counselling and social services.

DISCUSSION

Participants reported mixed experiences while using Thought Spot and exhibited low levels of long-term user engagement. User satisfaction, the willingness to recommend Thought Spot to others, and the willingness for future use appeared to be influenced by content relevance, ease of learning, available features, and other contextual factors. Analysis of the types of resources viewed and searches conducted by TAY end-users provided insight into their behaviour and needs.

CONCLUSION

Users had mixed perceptions about the usability of Thought Spot, which may have contributed to the high attrition rate. User satisfaction and engagement appears to be influenced by content relevance, ease of learning, and the types of features available. Further investigation to understand the contextual factors that affect TAYs' adoption and engagement with digital mental health tools is required.

Influence on Forgiveness, Character Strengths and Satisfaction with Life of a Short Mindfulness Intervention via a Spanish Smartphone Application

Mindfulness-based interventions (MBI) are a recognized effective psychological practice characterized by attention control, awareness, acceptance, non-reactivity, and non-judgmental thinking obtained through the practice of meditation. They have been shown to be useful in reducing stress and enhancing well-being in different contexts. In this research, the effectiveness of an MBI was evaluated on variables that can promote successful job performance such as mindfulness trait, positive and negative affect, forgiveness, personality strengths and satisfaction with life. The intervention was carried out through a smartphone application called "Aire Fresco" (Fresh Air) during 14 days in the middle of the quarantine produced by the Covid-19 pandemic. The study sample was composed of 164 Spanish people who were distributed in two groups: control group and experimental group, which were evaluated before and after the intervention. The MANCOVA performed showed an overall positive effect of the intervention on the variables evaluated. The different ANCOVAs carried out showed that the intervention was beneficial in increasing mindfulness trait, reducing negative affect or increasing life satisfaction, among others. Our study is, as far as we know, the first to demonstrate the effectiveness of a brief intervention in mindfulness conducted using a smartphone application in Spanish.

Leveraging Implementation Science to Understand Factors Influencing Sustained Use of Mental Health Apps: a Narrative Review

Mental health (MH) smartphone applications (apps), which can aid in self-management of conditions such as depression and anxiety, have demonstrated dramatic growth over the past decade. However, their effectiveness and potential for sustained use remain uncertain. This narrative review leverages implementation science theory to explore factors influencing MH app uptake. The review is guided by the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework and discusses the role of the innovation, its recipients, context, and facilitation in influencing successful implementation of MH apps. The review highlights critical literature published between 2015 and 2020 with a focus on depression and anxiety apps. Sources were identified via PubMed, Google Scholar, and Twitter using a range of keywords pertaining to MH apps. Findings suggest that for apps to be successful, they must be advantageous over alternative tools, relatively easy to navigate, and aligned with users’ needs, skills, and resources. Significantly more attention must be paid to the complex contexts in which MH app implementation is occurring in order to refine facilitation strategies. The evidence base is still uncertain regarding the effectiveness and usability of MH apps, and much can be learned from the apps we use daily; namely, simpler is better and plans to integrate full behavioral treatments into smartphone form may be misguided. Non-traditional funding mechanisms that are nimble, responsive, and encouraging of industry partnerships will be necessary to move the course of MH app development in the right direction.

Personalized Policy Learning using Longitudinal Mobile Health Data

Personalized policy represents a paradigm shift from one-decision-rule-for-all users to an individualized decision rule for each user. Developing personalized policy in mobile health applications imposes challenges. First, for lack of adherence, data from each user are limited. Second, unmeasured contextual factors can potentially impact on decision making. Aiming to optimize immediate rewards, we propose using a generalized linear mixed modeling framework where population features and individual features are modeled as fixed and random effects, respectively, and synthesized to form the personalized policy. The group lasso type penalty is imposed to avoid overfitting of individual deviations from the population model. We examine the conditions under which the proposed method work in the presence of time-varying endogenous covariates, and provide conditional optimality and marginal consistency results of the expected immediate outcome under the estimated policies. We apply our method to develop personalized push ("prompt") schedules in 294 app users, with the goal to maximize the prompt response rate given past app usage and other contextual factors. The proposed method compares favorably to existing estimation methods including using the R function "glmer" in a simulation study.

Optimizing Smartphone-Delivered Cognitive Behavioral Therapy for Body Dysmorphic Disorder Using Passive Smartphone Data: Initial Insights From an Open Pilot Trial

Background

Smartphone-delivered cognitive behavioral therapy (CBT) is becoming more common, but research on the topic remains in its infancy. Little is known about how people typically engage with smartphone CBT or which engagement and mobility patterns may optimize treatment. Passive smartphone data offer a unique opportunity to gain insight into these knowledge gaps.

Objective

This study aimed to examine passive smartphone data across a pilot course of smartphone CBT for body dysmorphic disorder (BDD), a psychiatric illness characterized by a preoccupation with a perceived defect in physical appearance, to inform hypothesis generation and the design of subsequent, larger trials.

Methods

A total of 10 adults with primary diagnoses of BDD were recruited nationally and completed telehealth clinician assessments with a reliable evaluator. In a 12-week open pilot trial of smartphone CBT, we initially characterized natural patterns of engagement with the treatment and tested how engagement and mobility patterns across treatment corresponded with treatment response.

Results

Most participants interacted briefly and frequently with smartphone-delivered treatment. More frequent app usage (r=–0.57), as opposed to greater usage duration (r=–0.084), correlated strongly with response. GPS-detected time at home, a potential digital marker of avoidance, decreased across treatment and correlated moderately with BDD severity (r=0.49).

Conclusions

The sample was small in this pilot study; thus, results should be used to inform the hypotheses and design of subsequent trials. The results provide initial evidence that frequent (even if brief) practice of CBT skills may optimize response to smartphone CBT and that mobility patterns may serve as useful passive markers of symptom severity. This is one of the first studies to examine the value that passively collected sensor data may contribute to understanding and optimizing users’ response to smartphone CBT. With further validation, the results can inform how to enhance smartphone CBT design.

A generalization of functional clustering for discrete multivariate longitudinal data

This paper presents a new model-based generalized functional clustering method for discrete longitudinal data, such as multivariate binomial and Poisson distributed data. For this purpose, we propose a multivariate functional principal component analysis (MFPCA)-based clustering procedure for a latent multivariate Gaussian process instead of the original functional data directly. The main contribution of this study is two-fold: modeling of discrete longitudinal data with the latent multivariate Gaussian process and developing of a clustering algorithm based on MFPCA coupled with the latent multivariate Gaussian process. Numerical experiments, including real data analysis and a simulation study, demonstrate the promising empirical properties of the proposed approach.

Understanding Participant Needs for Engagement and Attitudes towards Passive Sensing in Remote Digital Health Studies

Digital psychiatry is a rapidly growing area of research. Mobile assessment, including passive sensing, could improve research into human behavior and may afford opportunities for rapid treatment delivery. However, retention is poor in remote studies of depressed populations in which frequent assessment and passive monitoring are required. To improve engagement and understanding participant needs overall, we conducted semi-structured interviews with 20 people representative of a depressed population in a major metropolitan area. These interviews elicited feedback on strategies for long-term remote research engagement and attitudes towards passive data collection. Our results found participants were uncomfortable sharing vocal samples, need researchers to take a more active role in supporting their understanding of passive data collection, and wanted more transparency on how data were to be used in research. Despite these findings, participants trusted researchers with the collection of passive data. They further indicated that long term study retention could be improved with feedback and return of information based on the collected data. We suggest that researchers consider a more educational consent process, giving participants a choice about the types of data they share in the design of digital health apps, and consider supporting feedback in the design to improve engagement.

Mobile health applications for improving the sexual health outcomes among adults with chronic diseases: A systematic review

Aims

Chronic diseases may affect sexual health as an important factor for well-being. Mobile health (m-health) interventions have the potential to improve sexual health in patients with chronic conditions. The aim of this systematic review was to summarise the published evidence on mobile interventions for sexual health in adults with chronic diseases.

Methods

Five electronic databases were searched for English language peer-reviewed literature from 1 January 2009 to 31 December 2019. Appropriate keywords were identified based on the study's aim. Study selection was based on the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. The full texts of potential studies were reviewed, and final studies were selected. The m-health evidence reporting and assessment (mERA) checklist was used to assess the quality of the selected studies. After data extraction from the studies, data analysis was conducted.

Results

Nine studies met the inclusion criteria. All interventions were delivered through websites, and a positive effect on sexual problems was reported. Prostate and breast cancer were considered in most studies. Interventions were delivered for therapy, self-help and consultation purposes. Quality assessment of studies revealed an acceptable quality of reporting and methodological criteria in the selected studies. Replicability, security, cost assessment and conceptual adaptability were the criteria that had not been considered in any of the reviewed studies.

Conclusions

Reviewed studies showed a positive effect of mobile interventions on sexual health outcomes in chronic patients. For more effective interventions, researchers should design web-based interventions based on users' needs and consider the m-health essential criteria provided by mERA. Additionally, mobile interventions can be more effective in combination with smartphone apps.

A Novel Mobile Phone App Intervention With Phone Coaching to Reduce Symptoms of Depression in Survivors of Women's Cancer: Pre-Post Pilot Study

BACKGROUND

Psychological distress is a major issue among survivors of women's cancer who face numerous barriers to accessing in-person mental health treatments. Mobile phone app-based interventions are scalable and have the potential to increase access to mental health care among survivors of women's cancer worldwide.

OBJECTIVE

This study aimed to evaluate the acceptability and preliminary efficacy of a novel app-based intervention with phone coaching in a sample of survivors of women's cancer.

METHODS

In a single-group, pre-post, 6-week pilot study in the United States, 28 survivors of women's cancer used iCanThrive, a novel app intervention that teaches skills for coping with stress and enhancing well-being, with added phone coaching. The primary outcome was self-reported symptoms of depression (Center for Epidemiologic Studies Depression Scale). Emotional self-efficacy and sleep disruption were also assessed at baseline, 6-week postintervention, and 4 weeks after the intervention period. Feedback obtained at the end of the study focused on user experience of the intervention.

RESULTS

There were significant decreases in symptoms of depression and sleep disruption from baseline to postintervention. Sleep disruption remained significantly lower at 4-week postintervention compared with baseline. The iCanThrive app was launched a median of 20.5 times over the intervention period. The median length of use was 2.1 min. Of the individuals who initiated the intervention, 87% (20/23) completed the 6-week intervention.

CONCLUSIONS

This pilot study provides support for the acceptability and preliminary efficacy of the iCanThrive intervention. Future work should validate the intervention in a larger randomized controlled study. It is important to develop scalable interventions that meet the psychosocial needs of different cancer populations. The modular structure of the iCanThrive app and phone coaching could impact a large population of survivors of women's cancer.

Clinically Meaningful Use of Mental Health Apps and its Effects on Depression: Mixed Methods Study

Background

User engagement is key to the effectiveness of digital mental health interventions. Considerable research has examined the clinical outcomes of overall engagement with mental health apps (eg, frequency and duration of app use). However, few studies have examined how specific app use behaviors can drive change in outcomes. Understanding the clinical outcomes of more nuanced app use could inform the design of mental health apps that are more clinically effective to users.

Objective

This study aimed to classify user behaviors in a suite of mental health apps and examine how different types of app use are related to depression and anxiety outcomes. We also compare the clinical outcomes of specific types of app use with those of generic app use (ie, intensity and duration of app use) to understand what aspects of app use may drive symptom improvement.

Methods

We conducted a secondary analysis of system use data from an 8-week randomized trial of a suite of 13 mental health apps. We categorized app use behaviors through a mixed methods analysis combining qualitative content analysis and principal component analysis. Regression analyses were used to assess the association between app use and levels of depression and anxiety at the end of treatment.

Results

A total of 3 distinct clusters of app use behaviors were identified: learning, goal setting, and self-tracking. Each specific behavior had varied effects on outcomes. Participants who engaged in self-tracking experienced reduced depression symptoms, and those who engaged with learning and goal setting at a moderate level (ie, not too much or not too little) also had an improvement in depression. Notably, the combination of these 3 types of behaviors, what we termed “clinically meaningful use,” accounted for roughly the same amount of variance as explained by the overall intensity of app use (ie, total number of app use sessions). This suggests that our categorization of app use behaviors succeeded in capturing app use associated with better outcomes. However, anxiety outcomes were neither associated with specific behaviors nor generic app use.

Conclusions

This study presents the first granular examination of user interactions with mental health apps and their effects on mental health outcomes. It has important implications for the design of mobile health interventions that aim to achieve greater user engagement and improved clinical efficacy.

A review of popular smartphone apps for depression and anxiety: Assessing the inclusion of evidence-based content

Smartphone applications for the treatment of depression and anxiety have acquired millions of users, yet little is known about whether they include evidence-based therapeutic content. We examined the extent to which popular mental health applications (MH apps) for depression and anxiety contain treatment elements found in empirically supported psychotherapy protocols (i.e., "common elements"). Of the 27 MH apps reviewed, 23 included at least one common element, with a median of three elements. Psychoeducation (in 52% of apps), relaxation (44%), meditation (41%), mindfulness (37%), and assessment (37%) were the most frequent elements, whereas several elements (e.g., problem solving) were not found in any apps. We also identified gaps between app content and empirically supported treatments. Cognitive restructuring was more common in depression protocols than in depression apps (75% of protocols vs. 31% of apps), as was problem solving (34% vs. 0%). For anxiety, exposure (85%, 12%), cognitive restructuring (60%, 12%), and problem solving (25%, 0%) were more common in protocols than apps. Overall, our findings highlight empirically supported treatment elements that are poorly represented in current MH apps. The absence of several core treatment elements underscores the need for future research, including randomized trials testing the effectiveness of popular MH apps.

Use of Web Conferencing Technology for Conducting Online Focus Groups Among Young People With Lived Experience of Suicidal Thoughts: Mixed Methods Research

BACKGROUND

There is an increasing interest in engaging people with lived experience in suicide prevention research. However, young people with suicidal thoughts have been described as a "hard-to-include" population due to time, distance, stigma, and social barriers.

OBJECTIVE

This study aims to investigate whether conducting synchronous Web conferencing technology-based online focus groups (W-OFGs) is a feasible method to engage young people with lived experience of suicidal thoughts in suicide prevention research.

METHODS

Young people aged between 16 and 25 years and living in Sydney, Australia, were recruited through flyers, emails, and social media advertisements. The W-OFGs were established using a Web conferencing technology called GoToMeeting. Participants' response rate, attendance, and feedback of the W-OFGs were analyzed to determine whether the W-OFG system is feasible for suicide prevention research. Researchers' reflections about how to effectively implement the W-OFGs were also reported as part of the results.

RESULTS

In the pre-W-OFG survey, 39 (97.5%) young people (n=40) chose to attend the online focus group. Among the 22 participants who responded to the W-OFG invitations, 15 confirmed that they would attend the W-OFGs, of which 11 participants attended the W-OFGs. Feedback collected from the participants in the W-OFG and the post-W-OFG survey suggested that online focus groups are acceptable to young people in suicide prevention research. Considerations for selecting the Web conferencing platform, conducting the mock W-OFGs, implementing the risk management procedure, inviting participants to the W-OFGs, and hosting and moderating the W-OFGs as well as a few potential ethical and pragmatic challenges in using this method are discussed in this study.

CONCLUSIONS

The Web conferencing technology provides a feasible replacement for conventional methods, particularly for qualitative research involving vulnerable populations and stigmatized topics including suicide prevention. Our results indicate that this modality is an optimal alternative to engage young people in the focus group discussion. Future studies should compare the data collected from the Web conferencing technology and conventional face-to-face methods in suicide prevention research to determine if these two methods are equivalent in data quality from a quantitative approach.

Objective User Engagement With Mental Health Apps: Systematic Search and Panel-Based Usage Analysis

Background

Understanding patterns of real-world usage of mental health apps is key to maximizing their potential to increase public self-management of care. Although developer-led studies have published results on the use of mental health apps in real-world settings, no study yet has systematically examined usage patterns of a large sample of mental health apps relying on independently collected data.

Objective

Our aim is to present real-world objective data on user engagement with popular mental health apps.

Methods

A systematic engine search was conducted using Google Play to identify Android apps with 10,000 installs or more targeting anxiety, depression, or emotional well-being. Coding of apps included primary incorporated techniques and mental health focus. Behavioral data on real-world usage were obtained from a panel that provides aggregated nonpersonal information on user engagement with mobile apps.

Results

In total, 93 apps met the inclusion criteria (installs: median 100,000, IQR 90,000). The median percentage of daily active users (open rate) was 4.0% (IQR 4.7%) with a difference between trackers (median 6.3%, IQR 10.2%) and peer-support apps (median 17.0%) versus breathing exercise apps (median 1.6%, IQR 1.6%; all z≥3.42, all P<.001). Among active users, daily minutes of use were significantly higher for mindfulness/meditation (median 21.47, IQR 15.00) and peer support (median 35.08, n=2) apps than for apps incorporating other techniques (tracker, breathing exercise, psychoeducation: medians range 3.53-8.32; all z≥2.11, all P<.05). The medians of app 15-day and 30-day retention rates were 3.9% (IQR 10.3%) and 3.3% (IQR 6.2%), respectively. On day 30, peer support (median 8.9%, n=2), mindfulness/meditation (median 4.7%, IQR 6.2%), and tracker apps (median 6.1%, IQR 20.4%) had significantly higher retention rates than breathing exercise apps (median 0.0%, IQR 0.0%; all z≥2.18, all P≤.04). The pattern of daily use presented a descriptive peak toward the evening for apps incorporating most techniques (tracker, psychoeducation, and peer support) except mindfulness/meditation, which exhibited two peaks (morning and night).

Conclusions

Although the number of app installs and daily active minutes of use may seem high, only a small portion of users actually used the apps for a long period of time. More studies using different datasets are needed to understand this phenomenon and the ways in which users self-manage their condition in real-world settings.

Comparison of the Effects of Coaching and Receipt of App Recommendations on Depression, Anxiety, and Engagement in the IntelliCare Platform: Factorial Randomized Controlled Trial

BACKGROUND

IntelliCare is a modular platform that includes 12 simple apps targeting specific psychological strategies for common mental health problems.

OBJECTIVE

This study aimed to examine the effect of 2 methods of maintaining engagement with the IntelliCare platform, coaching, and receipt of weekly recommendations to try different apps on depression, anxiety, and app use.

METHODS

A total of 301 participants with depression or anxiety were randomized to 1 of 4 treatments lasting 8 weeks and were followed for 6 months posttreatment. The trial used a 2X2 factorial design (coached vs self-guided treatment and weekly app recommendations vs no recommendations) to compare engagement metrics.

RESULTS

The median time to last use of any app during treatment was 56 days (interquartile range 54-57), with 253 participants (84.0%, 253/301) continuing to use the apps over a median of 92 days posttreatment. Receipt of weekly recommendations resulted in a significantly higher number of app use sessions during treatment (overall median=216; P=.04) but only marginal effects for time to last use (P=.06) and number of app downloads (P=.08). Coaching resulted in significantly more app downloads (P<.001), but there were no significant effects for time to last download or number of app sessions (P=.36) or time to last download (P=.08). Participants showed significant reductions in the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) across all treatment arms (P s<.001). Coached treatment led to larger GAD-7 reductions than those observed for self-guided treatment (P=.03), but the effects for the PHQ-9 did not reach significance (P=.06). Significant interaction was observed between receiving recommendations and time for the PHQ-9 (P=.04), but there were no significant effects for GAD-7 (P=.58).

CONCLUSIONS

IntelliCare produced strong engagement with apps across all treatment arms. Coaching was associated with stronger anxiety outcomes, and receipt of recommendations enhanced depression outcomes.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02801877; https://clinicaltrials.gov/ct2/show/NCT02801877.

Exploring the Use of Multiple Mental Health Apps Within a Platform: Secondary Analysis of the IntelliCare Field Trial

BACKGROUND

IntelliCare is a mental health app platform with 14 apps that are elemental, simple and brief to use, and eclectic. Although a variety of apps may improve engagement, leading to better outcomes, they may require navigation aids such as recommender systems that can quickly direct a person to a useful app.

OBJECTIVE

As the first step toward developing navigation and recommender tools, this study explored app-use patterns across the IntelliCare platform and their relationship with depression and anxiety outcomes.

METHODS

This is a secondary analysis of the IntelliCare Field Trial, which recruited people with depression or anxiety. Participants of the trial received 8 weeks of coaching, primarily by text, and weekly random recommendations for apps. App-use metrics included frequency and lifetime use. Depression and anxiety, measured using the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7, respectively, were assessed at baseline and end of treatment. Cluster analysis was utilized to determine patterns of app use; ordinal logistic regression models and log-rank tests were used to determine if these use metrics alone, or in combination, predicted improvement or remission in depression or anxiety.

RESULTS

The analysis included 96 people who generally followed recommendations to download and try new apps each week. Apps were clustered into 5 groups: Thinking (apps that targeted or relied on thinking), Calming (relaxation and insomnia), Checklists (apps that used checklists), Activity (behavioral activation and activity), and Other. Both overall frequency of use and lifetime use predicted response for depression and anxiety. The Thinking, Calming, and Checklist clusters were associated with improvement in depression and anxiety, and the Activity cluster was associated with improvement in Anxiety only. However, the use of clusters was less strongly associated with improvement than individual app use.

CONCLUSIONS

Participants in the field trial remained engaged with a suite of apps for the full 8 weeks of the trial. App-use patterns did fall into clusters, suggesting that some knowledge about the use of one app may be useful in selecting another app that the person is more likely to use and may help suggest apps based on baseline symptomology and personal preference.

A Practical Do-It-Yourself Recruitment Framework for Concurrent eHealth Clinical Trials: Identification of Efficient and Cost-Effective Methods for Decision Making (Part 2)

Background

The ability to successfully recruit participants for electronic health (eHealth) clinical trials is largely dependent on the use of efficient and effective recruitment strategies. Determining which types of recruitment strategies to use presents a challenge for many researchers.

Objective

The aim of this study was to present an analysis of the time-efficiency and cost-effectiveness of recruitment strategies for eHealth clinical trials, and it describes a framework for cost-effective trial recruitment.

Methods

Participants were recruited for one of 5 eHealth trials of interventions for common mental health conditions. A multipronged recruitment approach was used, including digital (eg, social media and Craigslist), research registry-based, print (eg, flyers and posters on public transportation), clinic-based (eg, a general internal medicine clinic within an academic medical center and a large nonprofit health care organization), a market research recruitment firm, and traditional media strategies (eg, newspaper and television coverage in response to press releases). The time costs and fees for each recruitment method were calculated, and the participant yield on recruitment costs was calculated by dividing the number of enrolled participants by the total cost for each method.

Results

A total of 777 participants were enrolled across all trials. Digital recruitment strategies yielded the largest number of participants across the 5 clinical trials and represented 34.0% (264/777) of the total enrolled participants. Registry-based recruitment strategies were in second place by enrolling 28.0% (217/777) of the total enrolled participants across trials. Research registry-based recruitment had a relatively high conversion rate from potential participants who contacted our center for being screened to be enrolled, and it was also the most cost-effective for enrolling participants in this set of clinical trials with a total cost per person enrolled at US $8.99.

Conclusions

On the basis of these results, a framework is proposed for participant recruitment. To make decisions on initiating and maintaining different types of recruitment strategies, the resources available and requirements of the research study (or studies) need to be carefully examined.

Quantile Coarsening Analysis of High-Volume Wearable Activity Data in a Longitudinal Observational Study

Owing to advances in sensor technologies on wearable devices, it is feasible to measure physical activity of an individual continuously over a long period. These devices afford opportunities to understand individual behaviors, which may then provide a basis for tailored behavior interventions. The large volume of data however poses challenges in data management and analysis. We propose a novel quantile coarsening analysis (QCA) of daily physical activity data, with a goal to reduce the volume of data while preserving key information. We applied QCA to a longitudinal study of 79 healthy participants whose step counts were monitored for up to 1 year by a Fitbit device, performed cluster analysis of daily activity, and identified individual activity signature or pattern in terms of the clusters identified. Using 21,393 time series of daily physical activity, we identified eight clusters. Employment and partner status were each associated with 5 of the 8 clusters. Using less than 2% of the original data, QCA provides accurate approximation of the mean physical activity, forms meaningful activity patterns associated with individual characteristics, and is a versatile tool for dimension reduction of densely sampled data.