Effect of a Mobile Monitoring System vs Usual Care on Depression Symptoms and Psychological Health: A Randomized Clinical Trial

The effects of treatment via telemedicine interventions for patients with depression on depressive symptoms and quality of life: a systematic review and meta-ranalysis

Aim: The aim of this systematic review and meta-analysis was to identify, evaluate, and synthesize the evidence from studies that have investigated the treatment effect via telemedicine interventions on depressive symptoms, quality of life, and work and social functioning in patients with depression.Methods: Six electronic databases (MEDLINE [1916-2021], PubMED [1950-2021], PsycINFO [1971-2021], Scopus [2004-2021], Embase [1972-2021], and CINAHL [1937-2021]) were systematically searched in March 2021. Reference lists of identified articles were hand searched. Randomized controlled trials were included if they investigated the treatment effects via telemedicine interventions in patients who had a depression diagnosis. Quality assessment was evaluated using the critical appraisal checklists developed by the Joanna Briggs Institute.Results: Seventeen (17) trials (n = 2,394) met eligibility criteria and were included in the analysis. Eleven (11) randomized controlled trials shared common outcome measures, allowing meta-analysis. The results provided evidence that treatment via telemedicine interventions were beneficial for depressive symptoms (standardized mean difference= -0.44; 95% CI= -0.64 to -0.25; p < .001) and quality of life (standardized mean difference= 0.25, 95% CI -0.01 to 0.49, p = .04) in patients of depression. There were insufficient data for meta-analysis of work and social functioning.Conclusion: This study showed the positive effects of treatment via telemedicine interventions on depressive symptoms and quality of life in patients with depression and supported the idea for clinical practice to establish a well-organized telepsychiatry system.KEY MESSAGESTelemedicine is effective at reducing symptoms of depression.Telemedicine can improve quality of life in persons with depression.

mHealth Intervention to Promote Physical Activity Among Employees Using a Deep Learning Model for Passive Monitoring of Depression and Anxiety: Single-Arm Feasibility Trial

BACKGROUND

Physical activity effectively prevents depression and anxiety. Although mobile health (mHealth) technologies offer promising results in promoting physical activity and improving mental health, conflicting evidence exists on their effectiveness, and employees face barriers to using mHealth services. To address these problems, we recently developed a smartphone app named ASHARE to prevent depression and anxiety in the working population; it uses a deep learning model for passive monitoring of depression and anxiety from information about physical activity.

OBJECTIVE

This study aimed to preliminarily investigate (1) the effectiveness of the developed app in improving physical activity and reducing depression and anxiety and (2) the app's implementation outcomes (ie, its acceptability, appropriateness, feasibility, satisfaction, and potential harm).

METHODS

We conducted a single-arm interventional study. From March to April 2023, employees aged ≥18 years who were not absent were recruited. The participants were asked to install and use the app for 1 month. The ideal usage of the app was for the participants to take about 5 minutes every day to open the app, check the physical activity patterns and results of an estimated score of psychological distress, and increase their physical activity. Self-reported physical activity (using the Global Physical Activity Questionnaire, version 2) and psychological distress (using the 6-item Kessler Psychological Distress Scale) were measured at baseline and after 1 month. The duration of physical activity was also recorded digitally. Paired t tests (two-tailed) and chi-square tests were performed to evaluate changes in these variables. Implementation Outcome Scales for Digital Mental Health were also measured for acceptability, appropriateness, feasibility, satisfaction, and harm. These average scores were assessed by comparing them with those reported in previous studies.

RESULTS

This study included 24 employees. On average, the app was used for 12.54 days (44.8% of this study's period). After using the app, no significant change was observed in physical activity (-12.59 metabolic equivalent hours per week, P=.31) or psychological distress (-0.43 metabolic equivalent hours per week, P=.93). However, the number of participants with severe psychological distress decreased significantly (P=.01). The digitally recorded duration of physical activity increased during the intervention period (+0.60 minutes per day, P=.08). The scores for acceptability, appropriateness, and satisfaction were lower than those in previous mHealth studies, whereas those for feasibility and harm were better.

CONCLUSIONS

The ASHARE app was insufficient in promoting physical activity or improving psychological distress. At this stage, the app has many issues that are to be addressed in terms of both implementation and effectiveness. The main reason for this low effectiveness might be the poor evaluation of the implementation outcomes by app users. Improving acceptability, appropriateness, and satisfaction are identified as key issues to be addressed in future implementation.

TRIAL REGISTRATION

University Hospital Medical Information Network Clinical Trials Registry UMIN000050430; https://tinyurl.com/mrx5ntcmrecptno=R000057438.

A Model of Normality Inspired Deep Learning Framework for Depression Relapse Prediction Using Audiovisual Data

BACKGROUND

Depression (Major Depressive Disorder) is one of the most common mental illnesses. According to the World Health Organization, more than 300 million people in the world are affected. A first depressive episode can be solved by a spontaneous remission within 6 to 12 months. It has been shown that depression affects speech production and facial expressions. Although numerous studies are proposed in the literature for depression recognition using audiovisual cues, depression relapse using audiovisual cues has not been studied in the literature.

METHOD

In this paper, we propose a deep learning-based approach for depression recognition and depression relapse prediction using audiovisual data. For more versatility and reusability, the proposed approach is based on a Model of Normality inspired framework where we define depression relapse by the closeness of the audiovisual patterns of a subject after a symptom-free period to the audiovisual patterns of depressed subjects. A model of Normality is an anomaly detection distance-based approach that computes a distance of normality between the deep audiovisual encoding of a test sample and a learned representation from audiovisual encodings of anomaly-free data.

RESULTS

The proposed approach shows a very promising results with an accuracy of 87.4% and a F1-score of 82.3% for relapse/depression prediction using a Leave-One-Subject-Out training strategy on the DAIC-Woz dataset.

CONCLUSION

The proposed model of normality-based framework is accurate in detecting depression and in predicting depression relapse. A prospective monitoring system is proposed for assisting depressed patients. The proposed framework is easily extensible and others modalities will be integrated in future works.

Digital Mental Health Interventions for Depression: Scoping Review of User Engagement

BACKGROUND

While many digital mental health interventions (DMHIs) have been found to be efficacious, patient engagement with DMHIs has increasingly emerged as a concern for implementation in real-world clinical settings. To address engagement, we must first understand what standard engagement levels are in the context of randomized controlled trials (RCTs) and how these compare with other treatments.

OBJECTIVE

This scoping review aims to examine the state of reporting on intervention engagement in RCTs of mobile app-based interventions intended to treat symptoms of depression. We sought to identify what engagement metrics are and are not routinely reported as well as what the metrics that are reported reflect about standard engagement levels.

METHODS

We conducted a systematic search of 7 databases to identify studies meeting our eligibility criteria, namely, RCTs that evaluated use of a mobile app-based intervention in adults, for which depressive symptoms were a primary outcome of interest. We then extracted 2 kinds of information from each article: intervention details and indices of DMHI engagement. A 5-element framework of minimum necessary DMHI engagement reporting was derived by our team and guided our data extraction. This framework included (1) recommended app use as communicated to participants at enrollment and, when reported, app adherence criteria; (2) rate of intervention uptake among those assigned to the intervention; (3) level of app use metrics reported, specifically number of uses and time spent using the app; (4) duration of app use metrics (ie, weekly use patterns); and (5) number of intervention completers.

RESULTS

Database searching yielded 2083 unique records. Of these, 22 studies were eligible for inclusion. Only 64% (14/22) of studies included in this review specified rate of intervention uptake. Level of use metrics was only reported in 59% (13/22) of the studies reviewed. Approximately one-quarter of the studies (5/22, 23%) reported duration of use metrics. Only half (11/22, 50%) of the studies reported the number of participants who completed the app-based components of the intervention as intended or other metrics related to completion. Findings in those studies reporting metrics related to intervention completion indicated that between 14.4% and 93.0% of participants randomized to a DMHI condition completed the intervention as intended or according to a specified adherence criteria.

CONCLUSIONS

Findings suggest that engagement was underreported and widely varied. It was not uncommon to see completion rates at or below 50% (11/22) of those participants randomized to a treatment condition or to simply see completion rates not reported at all. This variability in reporting suggests a failure to establish sufficient reporting standards and limits the conclusions that can be drawn about level of engagement with DMHIs. Based on these findings, the 5-element framework applied in this review may be useful as a minimum necessary standard for DMHI engagement reporting.

Apps and gaps in bipolar disorder: A systematic review on electronic monitoring for episode prediction

BACKGROUND

Long-term clinical monitoring in bipolar disorder (BD) is an important therapeutic tool. The availability of smartphones and wearables has sparked the development of automated applications to remotely monitor patients. This systematic review focus on the current state of electronic (e-) monitoring for episode prediction in BD.

METHODS

We systematically reviewed the literature on e-monitoring for episode prediction in adult BD patients. The systematic review was done according to the guidelines for reporting of systematic reviews and meta-analyses (PRISMA) and was registered in PROSPERO on April 29, 2020 (CRD42020155795). We conducted a search of Web of Science, MEDLINE, EMBASE, and PsycINFO (all 2000-2020) databases. We identified and extracted data from 17 published reports on 15 relevant studies.

RESULTS

Studies were heterogeneous and most had substantial methodological and technical limitations. Models varied widely in their performance. Published metrics were too heterogeneous to lend themselves to a meta-analysis. Four studies reported sensitivity (range: 0.21 - 0.95); and two reported specificity for prediction of mood episodes (range: 0.36 - 0.99). Two studies reported accuracy (range: 0.64 - 0.88) and four reported area under the curve (AUC; range: 0.52-0.95). Overall, models were better in predicting manic or hypomanic episodes, but their performance depended on feature type.

LIMITATIONS

Our conclusions are tempered by the lack of appropriate information impeding our ability to synthesize the available evidence.

CONCLUSIONS

Given the clinical variability in BD, predicting mood episodes remains a challenging task. Emerging e-monitoring technology for episode prediction in BD requires more development before it can be adopted clinically.

Technological prescription: evaluation of the effectiveness of mobile applications to improve depression and anxiety. Systematic review

Several studies have shown that, due to their features, mobile applications have a great potential to address mental health in depression and anxiety. We carried out a systematic review of publications from the last 10 years: from 1 January 2010 until 31 March 2020. Systematic reviews and meta-analyses related to the research question were also selected to identify other potentially eligible studies. The literature search in selected databases returned a total of 3,011 records from which a total of 22 articles were finally selected. The main conclusion of the study is that most of the scientific evidence found supports the hypothesis that mobile applications significantly improve the symptoms associated with depression and anxiety. Therefore, their effectiveness as a digital tool in the treatment of such health problems is proven. However, further studies and further evaluations of mobile applications are required (also in other languages) to incorporate this resource into the healthcare context. In addition, since mobile applications allow reinforcing concepts such as patient empowerment, shared decision-making and health literacy, their use would be highly positive for depression and anxiety, where there is a strong element of self-managing the disease.

Digital Health Interventions in Prevention, Relapse, and Therapy of Mild and Moderate Depression: Scoping Review

BACKGROUND

Depression is a major cause for disability worldwide, and digital health interventions are expected to be an augmentative and effective treatment. According to the fast-growing field of information and communication technologies and its dissemination, there is a need for mapping the technological landscape and its benefits for users.

OBJECTIVE

The purpose of this scoping review was to give an overview of the digital health interventions used for depression. The main goal of this review was to provide a comprehensive review of the system landscape and its technological state and functions, as well as its evidence and benefits for users.

METHODS

A scoping review was conducted to provide a comprehensive overview of the field of digital health interventions for the treatment of depression. PubMed, PSYNDEX, and the Cochrane Library were searched by two independent researchers in October 2020 to identify relevant publications of the last 10 years, which were examined using the inclusion and exclusion criteria. To conduct the review, we used Rayyan, a freely available web tool.

RESULTS

In total, 65 studies were included in the qualitative synthesis. After categorizing the studies into the areas of prevention, early detection, therapy, and relapse prevention, we found dominant numbers of studies in the area of therapy (n=52). There was only one study for prevention, 5 studies for early detection, and 7 studies for relapse prevention. The most dominant therapy approaches were cognitive behavioral therapy, acceptance and commitment therapy, and problem-solving therapy. Most of the studies revealed significant effects of digital health interventions when cognitive behavioral therapy was applied. Cognitive behavioral therapy as the most dominant form was often provided through web-based systems. Combined interventions consisting of web-based and smartphone-based approaches are increasingly found.

CONCLUSIONS

Digital health interventions for treating depression are quite comprehensive. There are different interventions focusing on different fields of care. While most interventions can be beneficial to achieve a better depression treatment, it can be difficult to determine which approaches are suitable. Cognitive behavioral therapy through digital health interventions has shown good effects in the treatment of depression, but treatment for depression still stays very individualistic.

Digital solutions for shaping mood and behavior among individuals with mood disorders

Mood disorders present on-going challenges to the medical field, with difficulties ranging from establishing effective treatments to understanding complexities of one's mood. One solution is the use of mobile apps and wearables for measuring physiological symptoms and real-time mood in order to shape mood and behavior. Current digital research is focused on increasing engagement in monitoring mood, uncovering mood dynamics, predicting mood, and providing digital microinterventions. This review discusses the importance and risks of user engagement, as well as barriers to improving it. Research on mood dynamics highlights the possibility to reveal data-driven computational phenotypes that could guide treatment. Mobile apps are being used to track voice patterns, GPS, and phone usage for predicting mood and treatment response. Future directions include utilizing mobile apps to deliver and evaluate microinterventions. To continue these advances, standardized reporting and study designs should be considered to improve digital solutions for mood disorders.