How are guide profession and routine care setting related to adherence and symptom change in iCBT for depression? - an explorative log-data analysis

Application of a Sociotechnical Framework to Uncover Factors That Influence Effective User Engagement With Digital Mental Health Tools in Clinical Care Contexts: Scoping Review

BACKGROUND

Digital health tools such as mobile apps and patient portals continue to be embedded in clinical care pathways to enhance mental health care delivery and achieve the quintuple aim of improving patient experience, population health, care team well-being, health care costs, and equity. However, a key issue that has greatly hindered the value of these tools is the suboptimal user engagement by patients and families. With only a small fraction of users staying engaged over time, there is a great need to better understand the factors that influence user engagement with digital mental health tools in clinical care settings.

OBJECTIVE

This review aims to identify the factors relevant to user engagement with digital mental health tools in clinical care settings using a sociotechnical approach.

METHODS

A scoping review methodology was used to identify the relevant factors from the literature. Five academic databases (MEDLINE, Embase, CINAHL, Web of Science, and PsycINFO) were searched to identify pertinent articles using key terms related to user engagement, mental health, and digital health tools. The abstracts were screened independently by 2 reviewers, and data were extracted using a standardized data extraction form. Articles were included if the digital mental health tool had at least 1 patient-facing component and 1 clinician-facing component, and at least one of the objectives of the article was to examine user engagement with the tool. An established sociotechnical framework developed by Sittig and Singh was used to inform the mapping and analysis of the factors.

RESULTS

The database search identified 136 articles for inclusion in the analysis. Of these 136 articles, 84 (61.8%) were published in the last 5 years, 47 (34.6%) were from the United States, and 23 (16.9%) were from the United Kingdom. With regard to examining user engagement, the majority of the articles (95/136, 69.9%) used a qualitative approach to understand engagement. From these articles, 26 factors were identified across 7 categories of the established sociotechnical framework. These ranged from technology-focused factors (eg, the modality of the tool) and the clinical environment (eg, alignment with clinical workflows) to system-level issues (eg, reimbursement for physician use of the digital tool with patients).

CONCLUSIONS

On the basis of the factors identified in this review, we have uncovered how the tool, individuals, the clinical environment, and the health system may influence user engagement with digital mental health tools for clinical care. Future work should focus on validating and identifying a core set of essential factors for user engagement with digital mental health tools in clinical care environments. Moreover, exploring strategies for improving user engagement through these factors would be useful for health care leaders and clinicians interested in using digital health tools in care.

Use of Random Forest to Predict Adherence in an Online Intervention for Depression Using Baseline and Early Usage Data: Model Development and Validation on Retrospective Routine Care Log Data

BACKGROUND

Online interventions, such as the iFightDepression (iFD) tool, are increasingly recognized as effective alternatives to traditional face-to-face psychotherapy or pharmacotherapy for treating depression. However, particularly when used outside of study settings, low adherence rates and the resulting diminished benefits of the intervention can limit their effectiveness. Understanding the factors that predict adherence would allow for early, tailored interventions for individuals at risk of nonadherence, thereby enhancing user engagement and optimizing therapeutic outcomes.

OBJECTIVE

This study aims to develop and evaluate a random forest model that predicts adherence to the iFD tool to identify users at risk of noncompletion. The model was based on characteristics collected during baseline and the first week of the intervention in patients with depression.

METHODS

Log data from 4187 adult patients who registered for the iFD tool between October 1, 2016, and May 5, 2022, and provided informed consent were statistically analyzed. The resulting data set was divided into training (2932/4187, 70%) and test (1255/4187, 30%) sets using a randomly stratified split. The training data set was utilized to train a random forest model aimed at predicting each user's adherence at baseline, based on the hypothesized predictors: age, self-reported gender, expectations of the intervention, current or previous depression treatments, confirmed diagnosis of depression, baseline 9-item Patient Health Questionnaire (PHQ-9) score, accompanying guide profession, and usage behavior within the first week. After training, the random forest model was evaluated on the test data set to assess its predictive performance. The importance of each variable in predicting adherence was analyzed using mean decrease accuracy, mean decrease Gini, and Shapley Additive Explanations values.

RESULTS

Of the 4187 patients evaluated, 1019 (24.34%) were classified as adherent based on our predefined definition. An initial random forest model that relied solely on sociodemographic and clinical predictors collected at baseline did not yield a statistically significant adherence prediction. However, after incorporating each patient's usage behavior during the first week, we achieved a significant prediction of adherence (P<.001). Within this prediction, the model achieved an accuracy of 0.82 (95% CI 0.79-0.84), an F1-score of 0.53, an area under the curve of 0.83, and a specificity of 0.94 for predicting nonadherent users. The key predictors of adherence included logs, word count on the first workshop's worksheet, and time spent on the tool, all measured during the first week.

CONCLUSIONS

Our results highlight that early engagement, particularly usage behavior during the first week of the online intervention, is a far greater predictor of adherence than any sociodemographic or clinical factors. Therefore, analyzing usage behavior within the first week and identifying nonadherers through the algorithm could be beneficial for tailoring interventions aimed at improving user adherence. This could include follow-up calls or face-to-face discussions, optimizing resource utilization in the process.

Health Literacy Promotion and Digital Interventions for Depressive Disorders

BACKGROUND

The lack of therapeutic treatment options for depressive disorders increases the relevance of digital interventions for patients and general practitioners (GPs), particularly to bridge waiting times. However, patients may require adequate (digital) health literacy (HL) to partake in such interventions.

OBJECTIVE

This study contrasts expected benefits and risks of digital mental health interventions, specifically digital therapeutics on prescription (DTx), among GPs and patients with depression according to HL dimensions (access, understanding, appraisal, and application) and patients' personal determinants.

METHODS

We conducted semi-structured interviews with purposively sampled GPs (n = 17) and patients with mild or moderate depression (n = 17) in Germany. The interviews were audio-recorded, transcribed verbatim, and coded. Data were analyzed using qualitative content analysis.

KEY RESULTS

DTx include benefits to access health information and promote HL as DTx can improve understanding of health content and foster self-management skills. But DTx also implies risks for routine care distribution and creates new media barriers. During waiting times patients appraise DTx for themselves as more positive than GPs expect. However, digital- and disease-specific challenges such as non-commitment, listlessness or lethargy appear to hinder the use of DTx in practical situations. While GPs were not convinced about the digital skills of their patients, patients raised concerns that depressive symptoms may inhibit them from actual use.

CONCLUSION

In the context of DTx for depressive disorders, patients' mental health needs are seldom noticed and rarely promoted. To foster (digital) HL, DTx should be comprehensively embedded in supply situations. [HLRP: Health Literacy Research and Practice. 2024;8(4):e236-e245.].

Empowerment of Italian general practitioners in depression and suicide prevention: the iFightDepression tool, a game changer of the EAAD-Best project

General Practitioners (GPs) play a key role in the early detection and management of depression and in preventing suicide risk. They are often the first healthcare professionals that people in crisis contact. However, their effectiveness can be limited by several barriers, including the lack of specific training and appropriate tools.The EAAD-Best project aims to fill these gaps through its iFightDepression tool, an online tool designed to support patients, psychologists, psychiatrists, and GPs in managing depression and preventing suicide. This article examines the implementation of the iFightDepression platform in Italy, assessing its impact on the empowerment of GPs in the fight against depression. Through a qualitative and quantitative analysis of the data collected by the project, the 'unmet need' of GPs' in Italy regarding their specific training in mental health is highlighted.The response of 2,068 Italian GPs in just 7 months after the start of the iFD project is an expression of GPs' engagement to work against depression and for suicide risk prevention.

A qualitative interview study of patients' attitudes towards and intention to use digital interventions for depressive disorders on prescription

Background

Depressive disorders are an emerging public health topic. Due to their increasing prevalence, patients with depressive disorders suffer from the lack of therapeutic treatment. Digital health interventions may offer an opportunity to bridge waiting times, supplement, or even substitute in-person treatment. Among others, the Unified Theory of Acceptance and Use of Technology (UTAUT) explains that actual technology use is affected by users' behavioural intention. However, patients' perspectives on digital interventions are rarely discussed within the specific context of primary care provided by general practitioners (GP) and need further exploration.

Method

A qualitative study design with semi-structured interviews was used to explore DTx-acceptance of patients with mild or moderate depression (n = 17). The audio-recorded interviews were transcribed verbatim, coded, and thematically analysed by qualitative content analysis.

Results

Patients' performance expectancies reveal that DTx are not perceived as a substitute for face-to-face treatment. Effort expectancies include potential advantages and efforts concerning technical, motivational, and skill-based aspects. Moreover, we identified health status and experience with depressive disorders as other determinants and potential barriers to patients' DTx acceptance: Difficult stages of depression or long-time experience are perceived hurdles for DTx use. GPs' recommendations were just partly relevant for patients and varied according to patients' consultancy preferences. But still, GPs have a crucial role for access due to prescription. GPs' influence on patients' DTx acceptance varies between three situations: (1) pre-use for consultation, (2) pre-use for access and (3) during DTx-use. Further, GPs' guidance could be especially relevant for patients during DTx-use in routine care.

Discussion

The UTAUT-based exploration suggests that acceptance determinants should be considered independently and embedded in personal and situational aspects. DTx require a healthcare professional to prescribe or diagnose the disease, unlike other digital offerings. We identified prescription- and depression-related determinants, exceeding existing theoretical constructs. GPs' guidance can compensate for some barriers to DTx use e.g., by increasing commitment and motivational support to strengthen patients' acceptance.

Conclusion

We argue for a multidimensional integration of acceptance determinants for further development of health technology acceptance research. Future research should specify how DTx can be integrated into routine care to strengthen user acceptance.

Covering digital health applications in the public insurance system: how to foster innovation in patient care while mitigating financial risks-evidence from Germany

Context

Digital health applications that support patients in managing their condition can have a positive impact on patients' health and improve the overall care process. In late 2019, as the first country worldwide, Germany included digital health applications in the benefit basket of the statutory health insurance (SHI) system to enable fast, broad-scale patient access and encourage innovation in the digital health industry. While the policy is widely recognized as a pioneering step toward improving patient care through digital technologies, there are concerns regarding the mechanics of the policy and the resulting financial risks for the SHI system.

Goals

The primary objective of this article is to provide a comprehensive and balanced overview of the German policy by evaluating its success in achieving its goals and by reviewing challenges that have emerged. The secondary objective is to delineate prospective policy options and areas warranting future research.

Approach

The article analyzes publicly available data of the Federal Institute for Drugs and Medical Devices collected between February 1st and July 17th, 2023, and complements it with empirical findings published by academic institutions and sickness funds. It discusses policy options and related areas of future research to overcome the identified challenges without jeopardizing the purpose of the legislation to encourage innovation in the digital health industry to improve patient care.

Conclusion

In line with the goals of the reimbursement policy, the inclusion of digital health applications in the SHI benefit basked has entailed new digital treatment options for patients across multiple disease areas. However, from a health policy perspective, the policy has several shortcomings, including low prescription rates, the temporary reimbursement of digital health applications that lack proven benefit, and a pricing framework that does not take into account the efficacy and efficiency of a treatment and may lead to a suboptimal allocation of public resources. Rather than the public system covering digital health applications without proven benefit, the authors suggest giving SHI organizations more budget authority to directly incentivize research and development activities and to introduce value-based pricing. More research is needed to determine the details of these mechanisms.

Assessing Patient Adherence to and Engagement With Digital Interventions for Depression in Clinical Trials: Systematic Literature Review

BACKGROUND

New approaches to the treatment of depression are necessary for patients who do not respond to current treatments or lack access to them because of barriers such as cost, stigma, and provider shortage. Digital interventions for depression are promising; however, low patient engagement could limit their effectiveness.

OBJECTIVE

This systematic literature review (SLR) assessed how participant adherence to and engagement with digital interventions for depression have been measured in the published literature, what levels of adherence and engagement have been reported, and whether higher adherence and increased engagement are linked to increased efficacy.

METHODS

We focused on a participant population of adults (aged ≥18 years) with depression or major depressive disorder as the primary diagnosis and included clinical trials, feasibility studies, and pilot studies of digital interventions for treating depression, such as digital therapeutics. We screened 756 unique records from Ovid MEDLINE, Embase, and Cochrane published between January 1, 2000, and April 15, 2022; extracted data from and appraised the 94 studies meeting the inclusion criteria; and performed a primarily descriptive analysis. Otsuka Pharmaceutical Development & Commercialization, Inc (Princeton, New Jersey, United States) funded this study.

RESULTS

This SLR encompassed results from 20,111 participants in studies using 47 unique web-based interventions (an additional 10 web-based interventions were not described by name), 15 mobile app interventions, 5 app-based interventions that are also accessible via the web, and 1 CD-ROM. Adherence was most often measured as the percentage of participants who completed all available modules. Less than half (44.2%) of the participants completed all the modules; however, the average dose received was 60.7% of the available modules. Although engagement with digital interventions was measured differently in different studies, it was most commonly measured as the number of modules completed, the mean of which was 6.4 (means ranged from 1.0 to 19.7) modules. The mean amount of time participants engaged with the interventions was 3.9 (means ranged from 0.7 to 8.4) hours. Most studies of web-based (34/45, 76%) and app-based (8/9, 89%) interventions found that the intervention group had substantially greater improvement for at least 1 outcome than the control group (eg, care as usual, waitlist, or active control). Of the 14 studies that investigated the relationship between engagement and efficacy, 9 (64%) found that increased engagement with digital interventions was significantly associated with improved participant outcomes. The limitations of this SLR include publication bias, which may overstate engagement and efficacy, and low participant diversity, which reduces the generalizability.

CONCLUSIONS

Patient adherence to and engagement with digital interventions for depression have been reported in the literature using various metrics. Arriving at more standardized ways of reporting adherence and engagement would enable more effective comparisons across different digital interventions, studies, and populations.

Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms

Introduction

Personalization is a much-discussed approach to improve adherence and outcomes for Digital Mental Health interventions (DMHIs). Yet, major questions remain open, such as (1) what personalization is, (2) how prevalent it is in practice, and (3) what benefits it truly has.

Methods

We address this gap by performing a systematic literature review identifying all empirical studies on DMHIs targeting depressive symptoms in adults from 2015 to September 2022. The search in Pubmed, SCOPUS and Psycinfo led to the inclusion of 138 articles, describing 94 distinct DMHIs provided to an overall sample of approximately 24,300 individuals.

Results

Our investigation results in the conceptualization of personalization as purposefully designed variation between individuals in an intervention's therapeutic elements or its structure. We propose to further differentiate personalization by what is personalized (i.e., intervention content, content order, level of guidance or communication) and the underlying mechanism [i.e., user choice, provider choice, decision rules, and machine-learning (ML) based approaches]. Applying this concept, we identified personalization in 66% of the interventions for depressive symptoms, with personalized intervention content (32% of interventions) and communication with the user (30%) being particularly popular. Personalization via decision rules (48%) and user choice (36%) were the most used mechanisms, while the utilization of ML was rare (3%). Two-thirds of personalized interventions only tailored one dimension of the intervention.

Discussion

We conclude that future interventions could provide even more personalized experiences and especially benefit from using ML models. Finally, empirical evidence for personalization was scarce and inconclusive, making further evidence for the benefits of personalization highly needed.

Systematic Review Registration

Identifier: CRD42022357408.

Effectiveness of cognitive behavioural therapy-based interventions for maternal perinatal depression: a systematic review and meta-analysis

BACKGROUND

Depression during the perinatal period (during pregnancy and the year after childbirth) is common and associated with a range of negative effects for mothers, infants, family members, and wider society. Although existing evidence suggests cognitive behavioral therapy (CBT) based interventions are effective for perinatal depression, less is known about the effect of CBT-based interventions on important secondary outcomes, and a number of potential clinical and methodological moderators have not been examined.

METHODS

A systematic review and meta-analysis primarily examined the effectiveness of CBT-based interventions for perinatal depression on symptoms of depression. Secondary aims examined the effectiveness of CBT-based interventions for perinatal depression on symptoms of anxiety, stress, parenting, perceived social support, and perceived parental competence; and explored clinical and methodological moderators potentially associated with effectiveness. A systematic search of electronic databases and other sources was performed up to November 2021. We included randomized controlled trials comparing CBT-based interventions for perinatal depression with control conditions allowing for the isolation of the effects of CBT.

RESULTS

In total, 31 studies (5291 participants) were included in the systematic review and 26 studies (4658 participants) were included in the meta-analysis. The overall effect size was medium (hedges g = - 0.53 [95% CI - 0.65 to - 0.40]); with high heterogeneity. Significant effects were also found for anxiety, individual stress, and perceived social support, however few studies examined secondary outcomes. Subgroup analysis identified type of control, type of CBT, and type of health professional as significant moderators of the main effect (symptoms of depression). Some concerns of risk of bias were present in the majority of studies and one study had a high risk of bias.

CONCLUSIONS

CBT-based interventions for depression during the perinatal period appear effective, however results should be interpreted with caution given high levels of heterogeneity and low quality of included studies. There is a need to further investigate possibly important clinical moderators of effect, including the type of health professional delivering interventions. Further, results indicate a need to establish a minimum core data set to improve the consistency of secondary outcome collection across trials and to design and conduct trials with longer-term follow-up periods.

TRIAL REGISTRATION

CRD42020152254 .

Finding My Way from clinical trial to open access dissemination: comparison of uptake, adherence, and psychosocial outcomes of an online program for cancer-related distress

PURPOSE

Few digital psycho-oncology programs have been adopted into routine practice; how these programs are used after trial completion remains unexplored. To address this, the present study transitioned our evidence-based 6-module CBT-based program, Finding My Way, into open access (OA) after completion of the RCT, and compared uptake, usage, and psychosocial outcomes to the earlier RCT.

METHODS

Recruitment was passive, via promotion through (1) media and social media releases, (2) public lectures, (3) radio interviews and podcasts, and (4) clinician-initiated referral. Measures included number of enrolled users, number of modules completed, and pre- and optional post-measures of distress and quality of life (QOL).

RESULTS

Uptake was lower in OA (n = 120; 63% of RCT). Usage was markedly lower: 1.5 modules were completed on average (vs 3.7 in RCT), and only 13% completed a 'therapeutic dose' of 4 + modules (vs. 50% in RCT). Research attrition was high; n = 13 completed post-measures. OA users were more sociodemographically and clinically diverse than RCT users, had higher baseline distress (OA M_pre = 36.7, SD = 26.5; RCT M_pre = 26.5, SD = 21.7), and reported larger pre-post reductions than their RCT counterparts (OA M_post = 23.9, SD = 20.7; RCT M_post = 21.2, SD = 21.2). Moderate improvements in mental QOL occurred during OA (M_pre = 37.3, SD = 12.6; M_post = 44.5, SD = 12.1), broadly replicating RCT findings.

CONCLUSION

Findings that OA users were more medically and sociodemographically diverse and distressed at baseline than their RCT counterparts, and - despite having lower usage of the program - achieved larger changes from baseline to post-program, will help to shape future intervention design, tailoring, and dissemination.

The Importance of Project Description to Charitable Crowdfunding Success: The Mediating Role of Forwarding Times

The COVID-19 outbreak has been a public health crisis of international concern, causing huge impact on people's lives. As an important part of social public crisis management, how to quickly and effectively raise resources to participate in emergency relief in the era of self-media is a common challenge faced by global charitable organizations. This article attempts to use empirical evidence from Tencent charitable crowdfunding platform, the largest charitable crowdfunding platform in China, to answer this question. We consider 205 COVID-19 charitable projects and 11,177,249 donors to assess the process by which non-profit organizations raise funds through the information about project descriptions. Based on the effects of information and emotional framing, we explore the effects of the readability (i.e., complexity and understandability) and negative tone of the project description on fundraising amount. We then investigate the mediating role of forwarding times, as affective response to the text might explain forwarding times, which in turn affects money raised by increasing the visibility of the campaign. On this basis, the moderating role of recipient's crisis involvement is tested during this process. The empirical results indicate that the complexity of the description will reduce the fundraising amount, while understandability and negative tone help to improve it. Furthermore, we found that forwarding times played an important mediating role in this process. Then the buffer effect of crisis involvement on the negative effect of complexity was validated, and its amplification on the positive effects of understandability was also verified.