Maximizing New Technologies to Treat Depression

Digital Health Interventions for Delivery of Mental Health Care: Systematic and Comprehensive Meta-Review

BACKGROUND

The COVID-19 pandemic has shifted mental health care delivery to digital platforms, videoconferencing, and other mobile communications. However, existing reviews of digital health interventions are narrow in scope and focus on a limited number of mental health conditions.

OBJECTIVE

To address this gap, we conducted a comprehensive systematic meta-review of the literature to assess the state of digital health interventions for the treatment of mental health conditions.

METHODS

We searched MEDLINE for secondary literature published between 2010 and 2021 on the use, efficacy, and appropriateness of digital health interventions for the delivery of mental health care.

RESULTS

Of the 3022 records identified, 466 proceeded to full-text review and 304 met the criteria for inclusion in this study. A majority (52%) of research involved the treatment of substance use disorders, 29% focused on mood, anxiety, and traumatic stress disorders, and >5% for each remaining mental health conditions. Synchronous and asynchronous communication, computerized therapy, and cognitive training appear to be effective but require further examination in understudied mental health conditions. Similarly, virtual reality, mobile apps, social media platforms, and web-based forums are novel technologies that have the potential to improve mental health but require higher quality evidence.

CONCLUSIONS

Digital health interventions offer promise in the treatment of mental health conditions. In the context of the COVID-19 pandemic, digital health interventions provide a safer alternative to face-to-face treatment. However, further research on the applications of digital interventions in understudied mental health conditions is needed. Additionally, evidence is needed on the effectiveness and appropriateness of digital health tools for patients who are marginalized and may lack access to digital health interventions.

Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults

Artificial intelligence (AI) and machine learning are changing our world through their impact on sectors including health care, education, employment, finance, and law. AI systems are developed using data that reflect the implicit and explicit biases of society, and there are significant concerns about how the predictive models in AI systems amplify inequity, privilege, and power in society. The widespread applications of AI have led to mainstream discourse about how AI systems are perpetuating racism, sexism, and classism; yet, concerns about ageism have been largely absent in the AI bias literature. Given the globally aging population and proliferation of AI, there is a need to critically examine the presence of age-related bias in AI systems. This forum article discusses ageism in AI systems and introduces a conceptual model that outlines intersecting pathways of technology development that can produce and reinforce digital ageism in AI systems. We also describe the broader ethical and legal implications and considerations for future directions in digital ageism research to advance knowledge in the field and deepen our understanding of how ageism in AI is fostered by broader cycles of injustice.

Why Do Users of Online Mental Health Communities Get Likes and Reposts: A Combination of Text Mining and Empirical Analysis

An online community is one of the important ways for people with mental disorders to receive assistance and obtain support. This study aims to help users with mental disorders to obtain more support and communication through online communities, and to provide community managers with the possible influence mechanisms based on the information adoption model. We obtained a total of 49,047 posts of an online mental health communities in China, over a 40-day period. Then we used a combination of text mining and empirical analysis. Topic and sentiment analysis were used to derive the key variables—the topic of posts that the users care about most, and the emotion scores contained in posts. We then constructed a theoretical model based on the information adoption model. As core independent variables of information quality, on online mental health communities, the topic of social experience in posts (0.368 ***), the topic of emotional expression (0.353 ***), and the sentiment contained in the text (0.002 *) all had significant positive relationships with the number of likes and reposts. This study found that the users of online mental health communities are more attentive to the topics of social experience and emotional expressions, while they also care about the non-linguistic information. This study highlights the importance of helping community users to post on community-related topics, and gives administrators possible ways to help users gain the communication and support they need.

Depression: Screening, Assessment, and Interventions in Oncology Nursing

BACKGROUND

A high prevalence of depression exists among individuals with cancer, which negatively affects their health outcomes, quality of life, and adherence to cancer treatment.

OBJECTIVES

This article provides an overview and synthesis of depression screening, assessment, and nonpharmacologic treatments to help oncology nurses in their practices.

METHODS

Key insights are presented from a synthesized literature review regarding patients with cancer with depression. Resources for patient treatment and nurse training are also provided.

FINDINGS

To improve patient outcomes, nurses need to understand and follow a process that addresses the screening, assessment, and nonpharmacologic treatment of depression in patients with cancer.

Evaluation of the Impact of Virtual Reality-Enhanced Cardiac Rehabilitation on Depressive and Anxiety Symptoms in Patients with Coronary Artery Disease: A Randomised Controlled Trial

The aim of this study was to assess the efficacy of virtual reality (VR)-enhanced cardiac rehabilitation (CR) in reducing the intensity of depression and anxiety symptoms in patients undergoing phase II of CR in ambulatory conditions. One hundred participants (mean age 65.7 years) were divided randomly into two groups. Both groups took part in eight sessions of standard CR (three times per week). The experimental group was additionally supported by eight sessions of VR therapy using the VR TierOne device and the control group by eight sessions of Schultz Autogenic Training. The Hospital Anxiety and Depression Scale (HADS) was used as the primary outcome measure. The Perception of Stress Questionnaire was used as the secondary outcome measure. The data from 77 participants were subject to analysis. Post-intervention, in the experimental group, the overall HADS score was statistically significantly reduced by 13.5%, HADS-Depression by 20.8%, and the general stress level by 12.8% (p < 0.05). In the control group, the scores of the HADS, HADS-Anxiety and the general stress level were statistically significantly higher, by 4.8%, 6.5%, and 4.9%, respectively. VR-enhanced CR for individuals with cardiovascular disease reduced the level of anxiety and depression symptoms compared to standard CR.

Review of Randomized Controlled Trials Using e-Health Interventions for Patients With Eating Disorders

BACKGROUND

In a world of technological advancements, electronic devices and services seem to be a promising way to increase patients' engagement in treatment and to help manage their symptoms. Here, we identified and analyzed the current evidence of RCTs to evaluate the effectiveness and acceptability of e-health interventions in the eating disorder (ED) field.

METHODS

We screened an initial cluster of 581 papers. In the end, 12 RCTs in clinical ED cohorts were included.

RESULTS

Some studies were conceived as stand-alone interventions, while others were presented as add-ons to ED-specific treatments. Studies varied in the type of EDs under investigation and in the e-health intervention applied (with vs. without therapist support vs. blended interventions; smartphone- vs. web-based). Only four studies reported explicit acceptability measures. Out of those, two reported high acceptability, one reported low acceptability, and one reported no significant difference in acceptability between groups. Four studies reported higher effectiveness of the e-health intervention compared to the control condition, e.g., reduction in maladaptive eating behaviors. Regarding control groups, three used a wait list design and nine had another kind of intervention (e.g., face-to-face CBT, or treatment as usual) as control.

DISCUSSION

So far, the evidence for acceptability and effectiveness of e-health interventions in EDs is very limited. There is also a lack of studies in older patients, adolescents, men, sexual and ethnic minorities. Shame/stigma is discussed in the context of e-health interventions for EDs. It remains unclear how severity of EDs affects the effectiveness of e-health interventions, how patients can channel the knowledge they acquire from e-health interventions into their actual behaviors, and how such interventions can better fit the needs of the individual patient to increase acceptability and effectiveness.

mHealth Apps Assessment among Postpartum Women with Obesity and Depression

BACKGROUND

Pregnancy has become the main constituent for women to become overweight or obese during the postpartum phase. This could lead women to suffer from postpartum depression as well. Information technology (IT) has become more prevalent in the healthcare industry. It offers patients the opportunity to manage their health conditions via the use of several applications, one being the mHealth applications.

OBJECTIVE

The main purpose of this study is to experiment and understand the effects the mHealth applications (i.e., fitness and nutrition applications) have on the body mass index (BMI) and depression levels amongst postpartum women.

METHODS

Online questionnaires were sent to postpartum women within one year after their pregnancy, of which 819 completed questionnaires were returned. The frequency of the mHealth applications usage was categorized into daily, weekly, rarely and never streams. Therefore, the frequency of use of the mHealth applications for BMI and depression levels was analyzed based on the available statistical data. Descriptive statistics, ANOVA, and Dunnet tests were applied to analyze the experimental data.

RESULTS

Out of 819 respondents, 37.9% and 42.1% of them were overweight and obese, respectively. Almost 32.9% of the respondents were likely depressed, and 45.6% were at an increased risk. This study reports that only 23.4% and 28.6% of respondents never used the fitness and nutrition applications. The impact of the frequency of using the fitness applications on BMI and depression levels was obvious. This means that with the increased use of the fitness applications, there was also a significant effect in maintaining and decreasing the BMI and depression levels amongst Malaysians postpartum women. However, from the data of weekly and daily use of fitness applications, we found that the contribution toward the BMI and depression levels was high (p = 0.000). However, nutrition applications amongst the users were not significant within the main variables (p > 0.05). From the Dunnet test, the significance of using the fitness applications within the depression levels started from daily usage, whereas for BMI, it started from weekly usage.

CONCLUSION

The efficiency of the fitness applications toward the BMI and depression levels has been proven in this research work. While nutrition applications did not affect the BMI and depression levels, some of the respondents were still categorized as weekly and daily users. Thus, the improvements in BMI and depression levels are associated with the types of mHealth app that had been used.