Demographic and Indication-Specific Characteristics Have Limited Association With Social Network Engagement: Evidence From 24,954 Members of Four Health Care Support Groups

Nudges and Prompts Increase Engagement in Self-Guided Digital Health Treatment for Depression and Anxiety: Results From a 3-Arm Randomized Controlled Trial

BACKGROUND

Accessible and effective approaches to mental health treatment are important because of common barriers such as cost, stigma, and provider shortage. The effectiveness of self-guided treatment is well established, and its use has intensified because of the COVID-19 pandemic. Engagement remains important as dose-response relationships have been observed. Platforms such as Facebook (Meta Platform, Inc), LinkedIn (Microsoft Corp), and X Corp (formerly known as Twitter, Inc) use principles of behavioral economics to increase engagement. We hypothesized that similar concepts would increase engagement in self-guided digital health.

OBJECTIVE

This 3-arm randomized controlled trial aimed to test whether members of 2 digital self-health courses for anxiety and depression would engage with behavioral nudges and prompts. Our primary hypothesis was that members would click on 2 features: tips and a to-do checklist. Our secondary hypothesis was that members would prefer to engage with directive tips in arm 2 versus social proof and present bias tips in arm 3. Our tertiary hypothesis was that rotating tips and a to-do checklist would increase completion rates. The results of this study will form a baseline for future artificial intelligence-directed research.

METHODS

Overall, 13,224 new members registered between November 2021 and May 2022 for Evolution Health's self-guided treatment courses for anxiety and depression. The control arm featured a member home page without nudges or prompts. Arm 2 featured a home page with a tip-of-the-day section. Arm 3 featured a home page with a tip-of-the-day section and a to-do checklist. The research protocol for this study was published in JMIR Research Protocols on August 15, 2022.

RESULTS

Arm 3 had significantly younger members (F2,4564=40.97; P<.001) and significantly more female members (χ24=92.2; P<.001) than the other 2 arms. Control arm members (1788/13,224, 13.52%) completed an average of 1.5 course components. Arm 2 members (865/13,224, 6.54%) clicked on 5% of tips and completed an average of 1.8 course components. Arm 3 members (1914/13,224, 14.47%) clicked on 5% of tips, completed 2.7 of 8 to-do checklist items, and completed an average of 2.11 course components. Completion rates in arm 2 were greater than those in arm 1 (z score=3.37; P<.001), and completion rates in arm 3 were greater than those in arm 1 (z score=12.23; P<.001). Engagement in all 8 components in arm 3 was higher than that in arm 2 (z score=1.31; P<.001).

CONCLUSIONS

Members engaged with behavioral nudges and prompts. The results of this study may be important because efficacy is related to increased engagement. Due to its novel approach, the outcomes of this study should be interpreted with caution and used as a guideline for future research in this nascent field.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/37231.

Testing Behavioral Nudges and Prompts in Digital Courses for the Self-guided Treatment of Depression and Anxiety: Protocol for a 3-Arm Randomized Controlled Trial

BACKGROUND

Despite showing strong evidence of positive outcomes, a common problem in the field of digital health is poor engagement and adherence. Non-health care, for-profit digital ventures, such as Facebook, LinkedIn, and Twitter, conduct behavioral experiments to increase user engagement. To our knowledge, digital health organizations have not published similar types of experiments in ad libitum environments, and there are limited published data indicating whether nudges and prompts can be leveraged to increase engagement with digital health interventions.

OBJECTIVE

The main objective of our 3-arm randomized controlled trial is to test whether registered members in two well-established digital health courses for anxiety and depression will engage with four different types of nudges and prompts, and whether engaging with nudges and prompts increases engagement within the courses.

METHODS

New members who register for the self-guided anxiety and depression courses on the Evolution Health platform will be randomized into 1 of 3 arms. The first control arm will feature a member home page without any behavioral nudges or prompts. The second arm will feature a member home page with a Tip-of-the-Day section containing directive content. Arm 3 will feature a member home page with a Tip-of-the-Day section containing social proof and present bias content. The third arm will also feature a to-do item checklist.

RESULTS

The experiment was designed in August 2021 and was launched in November 2021. Initially, we will measure engagement with the tips and the to-do checklist by calculating the frequency of use by age and gender. If members do engage, we will then, according to age and gender, examine whether nudges and prompts result in higher course completion rates and whether specific types of prompts and nudges are more popular than others.

CONCLUSIONS

Our 3-arm randomized controlled trial will be the first to compare four distinct types of behavioral prompts and nudges in two self-guided digital health courses that were designed to treat mental health issues. We expect the results to generate insights into which types of behavioral prompts and nudges work best in the population. If they are shown to increase engagement, the insights will then be used to apply prompts and nudges to the platform's addiction-focused courses. Based on the results of the experiment, the insights will be applied to using artificial intelligence to train the platform to recognize different usage patterns and provide specific engagement recommendations to stratified users.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/37231.

Web-Based Interventions to Help Australian Adults Address Depression, Anxiety, Suicidal Ideation, and General Mental Well-being: Scoping Review

BACKGROUND

A large number of Australians experience mental health challenges at some point in their lives. However, in many parts of Australia, the wait times to see general practitioners and mental health professionals can be lengthy. With increasing internet use across Australia, web-based interventions may help increase access to timely mental health care. As a result, this is an area of increasing research interest, and the number of publicly available web-based interventions is growing. However, it can be confusing for clinicians and consumers to know the resources that are evidence-based and best meet their needs.

OBJECTIVE

This study aims to scope out the range of web-based mental health interventions that address depression, anxiety, suicidal ideation, or general mental well-being and are freely available to Australian adults, along with their impact, acceptability, therapeutic approach, and key features.

METHODS

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews (PRISMA-ScR [PRISMA extension for Scoping Reviews]) guided the review process. Keywords for the search were depression, anxiety, suicide, and well-being. The search was conducted using Google as well as the key intervention databases Beacon, Head to Health, and e-Mental Health in Practice. Interventions were deemed eligible if they targeted depression, anxiety, suicidal ideation, or general mental well-being (eg, resilience) in adults; and were web-based, written in English, interactive, free, and publicly available. They also had to be guided by an evidence-based therapeutic approach.

RESULTS

Overall, 52 eligible programs were identified, of which 9 (17%) addressed depression, 15 (29%) addressed anxiety, 13 (25%) addressed general mental well-being, and 13 (25%) addressed multiple issues. Only 4% (2/52) addressed distress in the form of suicidal ideation. The most common therapeutic approach was cognitive behavioral therapy. Half of the programs guided users through exercises in a set sequence, and most programs enabled users to log in and complete the activities on their own without professional support. Just over half of the programs had been evaluated for their effectiveness in reducing symptoms, and 11% (6/52) were being evaluated at the time of writing. Program evaluation scores ranged from 44% to 100%, with a total average score of 85%.

CONCLUSIONS

There are numerous web-based programs for depression, anxiety, suicidal ideation, and general well-being, which are freely and publicly available in Australia. However, identified gaps include a lack of available web-based interventions for culturally and linguistically diverse populations and programs that use newer therapeutic approaches such as acceptance and commitment therapy and dialectical behavior therapy. Despite most programs included in this review being of good quality, clinicians and consumers should pay careful attention when selecting which program to recommend and use, as variations in the levels of acceptability and impact of publicly available programs do exist.

Diabetes Self-Management in the Age of Social Media: Large-Scale Analysis of Peer Interactions Using Semiautomated Methods

BACKGROUND

Online communities have been gaining popularity as support venues for chronic disease management. User engagement, information exposure, and social influence mechanisms can play a significant role in the utility of these platforms.

OBJECTIVE

In this paper, we characterize peer interactions in an online community for chronic disease management. Our objective is to identify key communications and study their prevalence in online social interactions.

METHODS

The American Diabetes Association Online community is an online social network for diabetes self-management. We analyzed 80,481 randomly selected deidentified peer-to-peer messages from 1212 members, posted between June 1, 2012, and May 30, 2019. Our mixed methods approach comprised qualitative coding and automated text analysis to identify, visualize, and analyze content-specific communication patterns underlying diabetes self-management.

RESULTS

Qualitative analysis revealed that "social support" was the most prevalent theme (84.9%), followed by "readiness to change" (18.8%), "teachable moments" (14.7%), "pharmacotherapy" (13.7%), and "progress" (13.3%). The support vector machine classifier resulted in reasonable accuracy with a recall of 0.76 and precision 0.78 and allowed us to extend our thematic codes to the entire data set.

CONCLUSIONS

Modeling health-related communication through high throughput methods can enable the identification of specific content related to sustainable chronic disease management, which facilitates targeted health promotion.

Who says what? Content and participation characteristics in an online depression community

BACKGROUND

An increasingly important source of informal help for people with depression are online depression communities. This study investigates the prevailing topics in an online depression community and how they are related to participation styles.

METHODS

A topic model with 26 topics of N = 16,291 posts and N = 71,543 comments of N = 20,037 users in a depression forum on Reddit was created using Latent Dirichlet allocation (LDA). The topics' proportions in the corpus were correlated with five participation measures, i.e. sum of scores, number of comments, posts to comments ratio, posting frequency, and word count.

RESULTS

The most common topics were Feelings, Motivation, The Community on Reddit, and Time. There were many significant, small to moderate correlations between topic proportions and participation style measures. The topics Feelings, Offering Support, and Small Talk generated a bigger response in the form of scores and comments. Talking about the past and relationships was more common in longer posts, whereas small talk, offering emotional support, and employing cognitive strategies was more readily found in short comments. Lower posting frequency was related to talking about feelings and romantic relationships.

LIMITATIONS

No information on users' demographics or mental health status was available. Topic modeling cannot capture elements of style and tone of text.

CONCLUSIONS

A wide spectrum of topics was uncovered in the topic modeling. Patterns in the correlations point to users with different participation styles preferring different topics. Results of this study can aid the development of online interventions for depression.

The Association Between Increased Levels of Patient Engagement With an Internet Support Group and Improved Mental Health Outcomes at 6-Month Follow-Up: Post-Hoc Analyses From a Randomized Controlled Trial

BACKGROUND

We recently reported that depressed and anxious primary care patients randomized to a moderated internet support group (ISG) plus computerized cognitive behavioral therapy (cCBT) did not experience improvements in depression and anxiety over cCBT alone at 6-month follow-up.

OBJECTIVE

The 1% rule posits that 1% of participants in online communities generate approximately 90% of new user-created content. The aims of this study were to apply the 1% rule to categorize patient engagement with the ISG and identify whether any patient subgroups benefitted from ISG use.

METHODS

We categorized the 302 patients randomized to the ISG as: superusers (3/302, 1.0%), top contributors (30/302, 9.9%), contributors (108/302, 35.8%), observers (87/302, 28.8%) and those who never logged in (74/302, 24.5%). We then applied linear mixed models to examine associations between engagement and 6-month changes in health-related quality of life (HRQoL; Short Form Health Survey Mental Health Component, SF-12 MCS) and depression and anxiety symptoms (Patient-Reported Outcomes Measurement Information System, PROMIS).

RESULTS

At baseline, participant mean age was 42.6 years, 81.1% (245/302) were female, and mean Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder scale (GAD-7), and SF-12 MCS scores were 13.4, 12.6, and 31.7, respectively. Of the 75.5% (228/302) who logged in, 61.8 % (141/228) created ≥1 post (median 1, interquartile range, IQR 0-5); superusers created 42.3 % (630/1488) of posts (median 246, IQR 78-306), top contributors created 34.6% (515/1488; median 11, IQR 10-18), and contributors created 23.1 % (343/1488; median 3, IQR 1-5). Compared to participants who never logged in, the combined superuser + top contributor subgroup (n=33) reported 6-month improvements in anxiety (PROMIS: -11.6 vs -7.8; P=.04) and HRQoL (SF-12 MCS: 16.1 vs 10.1; P=.01) but not in depression. No other subgroup reported significant symptom improvements.

CONCLUSIONS

Patient engagement with the ISG was more broadly distributed than predicted by the 1% rule. The 11% of participants with the highest engagement levels reported significant improvements in anxiety and HRQoL.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01482806; https://clinicaltrials.gov/ct2/show/NCT01482806 (Archived by WebCite at http://www.webcitation.org/708Bjlge9).

How Online Communities of People With Long-Term Conditions Function and Evolve: Network Analysis of the Structure and Dynamics of the Asthma UK and British Lung Foundation Online Communities

Background

Self-management support can improve health and reduce health care utilization by people with long-term conditions. Online communities for people with long-term conditions have the potential to influence health, usage of health care resources, and facilitate illness self-management. Only recently, however, has evidence been reported on how such communities function and evolve, and how they support self-management of long-term conditions in practice.

Objective

The aim of this study is to gain a better understanding of the mechanisms underlying online self-management support systems by analyzing the structure and dynamics of the networks connecting users who write posts over time.

Methods

We conducted a longitudinal network analysis of anonymized data from 2 patients’ online communities from the United Kingdom: the Asthma UK and the British Lung Foundation (BLF) communities in 2006-2016 and 2012-2016, respectively.

Results

The number of users and activity grew steadily over time, reaching 3345 users and 32,780 posts in the Asthma UK community, and 19,837 users and 875,151 posts in the BLF community. People who wrote posts in the Asthma UK forum tended to write at an interval of 1-20 days and six months, while those in the BLF community wrote at an interval of two days. In both communities, most pairs of users could reach one another either directly or indirectly through other users. Those who wrote a disproportionally large number of posts (the superusers) represented 1% of the overall population of both Asthma UK and BLF communities and accounted for 32% and 49% of the posts, respectively. Sensitivity analysis showed that the removal of superusers would cause the communities to collapse. Thus, interactions were held together by very few superusers, who posted frequently and regularly, 65% of them at least every 1.7 days in the BLF community and 70% every 3.1 days in the Asthma UK community. Their posting activity indirectly facilitated tie formation between other users. Superusers were a constantly available resource, with a mean of 80 and 20 superusers active at any one time in the BLF and Asthma UK communities, respectively. Over time, the more active users became, the more likely they were to reply to other users’ posts rather than to write new ones, shifting from a help-seeking to a help-giving role. This might suggest that superusers were more likely to provide than to seek advice.

Conclusions

In this study, we uncover key structural properties related to the way users interact and sustain online health communities. Superusers’ engagement plays a fundamental sustaining role and deserves research attention. Further studies are needed to explore network determinants of the effectiveness of online engagement concerning health-related outcomes. In resource-constrained health care systems, scaling up online communities may offer a potentially accessible, wide-reaching and cost-effective intervention facilitating greater levels of self-management.

Mining social media data: How are research sponsors and researchers addressing the ethical challenges?

Background:

Data representing people’s behaviour, attitudes, feelings and relationships are increasingly being harvested from social media platforms and re-used for research purposes. This can be ethically problematic, even where such data exist in the public domain. We set out to explore how the academic community is addressing these challenges by analysing a national corpus of research ethics guidelines and published studies in one interdisciplinary research area.

Methods:

Ethics guidelines published by Research Councils UK (RCUK), its seven-member councils and guidelines cited within these were reviewed. Guidelines referring to social media were classified according to published typologies of social media research uses and ethical considerations for social media mining. Using health research as an exemplar, PubMed was searched to identify studies using social media data, which were assessed according to their coverage of ethical considerations and guidelines.

Results:

Of the 13 guidelines published or recommended by RCUK, only those from the Economic and Social Research Council, the British Psychological Society, the International Association of Internet Researchers and the National Institute for Health Research explicitly mentioned the use of social media. Regarding data re-use, all four mentioned privacy issues but varied with respect to other ethical considerations. The PubMed search revealed 156 health-related studies involving social media data, only 50 of which mentioned ethical concepts, in most cases simply stating that they had obtained ethical approval or that no consent was required. Of the nine studies originating from UK institutions, only two referred to RCUK ethics guidelines or guidelines cited within these.

Conclusions:

Our findings point to a deficit in ethical guidance for research involving data extracted from social media. Given the growth of studies using these new forms of data, there is a pressing need to raise awareness of their ethical challenges and provide actionable recommendations for ethical research practice.

Engagement as a Driver of Growth of Online Health Forums: Observational Study

Background

The emerging research on nurturing the growth of online communities posits that it is in part attributed to network effects, wherein every increase in the volume of user-generated content increases the value of the community in the eyes of its potential new members. The recently introduced metric engagement capacity offers a means of quantitatively assessing the ability of online platform users to engage each other into generating content; meanwhile, the quantity engagement value is useful for quantifying communication-based platform use. If the claim that higher engagement leads to accelerated growth holds true for online health forums (OHFs), then engagement tracking should become an important tool in the arsenal of OHF managers. Indeed, it might allow for quantifying the ability of an OHF to exploit network effects, thus predicting the OHF’s future success.

Objective

This study aimed to empirically analyze the relationship between internal OHF use (quantified using engagement measurement), and external growth.

Methods

We collected data from 7 OHFs posted between the years 1999 and 2016. Longitudinal analyses were conducted by evaluating engagement in the OHFs over time. We analyzed 2-way causality effects between the engagement value and metrics evaluating OHF growth using Granger causality tests. User activity metrics per week were correlated with engagement metrics, followed by linear regression analyses.

Results

Observational data showed a 1-way causal relationship between the OHF engagement value and reach (P=.02). We detected a 2-way causal relationship between the engagement value and delurking, with further analysis indicating that the engagement value was more likely to cause delurking (P<.001 with lag 2; for the reverse hypothesis, P=.01 with lag 2). Users who engaged each other more were more likely (up to 14 times, depending on how much one user engaged another) to develop personal connections. Finally, we found that the more engaging an OHF user was in a given week, the more likely (up to 2 times, depending on their ability to engage others) they were to remain active in the OHF in the following week.

Conclusions

This study supports the claim that network effects play an important role in accelerating OHF growth, opening the door to exploiting these effects in calculated ways. In such efforts, engagement metrics can be used to monitor the “health” of an OHF and to identify the users most important to its success.

Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data

In electronic health (eHealth) research, limited insight has been obtained on process outcomes or how the use of technology has contributed to the users’ ability to have a healthier life, improved well-being, or activate new attitudes in their daily tasks. As a result, eHealth is often perceived as a black box. To open this black box of eHealth, methodologies must extend beyond the classic effect evaluations. The analyses of log data (anonymous records of real-time actions performed by each user) can provide continuous and objective insights into the actual usage of the technology. However, the possibilities of log data in eHealth research have not been exploited to their fullest extent. The aim of this paper is to describe how log data can be used to improve the evaluation and understand the use of eHealth technology with a broader approach than only descriptive statistics. This paper serves as a starting point for using log data analysis in eHealth research. Here, we describe what log data is and provide an overview of research questions to evaluate the system, the context, the users of a technology, as well as the underpinning theoretical constructs. We also explain the requirements for log data, the starting points for the data preparation, and methods for data collection. Finally, we describe methods for data analysis and draw a conclusion regarding the importance of the results for both scientific and practical applications. The analysis of log data can be of great value for opening the black box of eHealth. A deliberate log data analysis can give new insights into how the usage of the technology contributes to found effects and can thereby help to improve the persuasiveness and effectiveness of eHealth technology and the underpinning behavioral models.