Mental Health Pathways in Internet Support Groups

Study Overview
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Brief Summary
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People facing serious health threats increasingly use Internet health support communities to obtain informational support, emotional support and other resources. This study introduces software algorithms similar to those used by social media sites to put people in touch with helpful information and social interactions. Participants from the American Cancer Society's Cancer Support Network will have access to this online support group using the default interface that orders content by broad content category and date or with a new interface that highlights communication content and people that match users' interests and needs.

Detailed Description
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Internet support groups (ISGs) are online communities where people come together to exchange information, emotional support and other resources. They are an important resource for patients grappling with serious medical conditions. Although participation in health-related ISGs has been associated with significant reductions in participant-reported depression, anxiety and other indicators of psychological distress, many ISG members leave too soon to benefit. In a parallel study, we are using state-of-the art machine learning and automated language analysis techniques to assess the types of interactions that keep people participating in these groups and that lead to improved psychosocial well-being and health quality of life and how these interactions develop. The clinical trial described here uses these technologies and insights from our empirical research to build, deploy, and evaluate interventions that improve the interactions in Internet health support groups. We will develop and pilot-test interventions to encourage effective communication processes identified in our empirical research. Participants from the American Cancer Society's Cancer Support Network will access this support group using either the default interface that orders content by disease diagnosis and date or with a new interface that sometimes highlights communication content and people who match their interests and needs. We will test whether mood, satisfaction with interactions and engagement in the group increase following interventions that (a) increase participants' receipt of individualized support from others; (b) provide participants with opportunities to offer support to others; (c) facilitate participants' expression of emotions; and (d) help participants form relationships with compatible peers. In a series of small, randomized experiments, we will examine how these interventions affect participants' communication behaviors as well as short-term engagement and satisfaction with their online interactions.

Official Title
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Internet Support Groups: Identifying and Improving Pathways for Mental Health

Conditions
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Depression, Stress, Anxiety

Intervention / Treatment
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* Behavioral: Order by time and topic
* Behavioral: Order by information relevance
* Behavioral: Order by social relationship
* Behavioral: Order by help giving
* Behavioral: Order by self-disclosure


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Is an existing or new registrant to the American Cancer Society's Cancer Support Network. Exclusion Criteria: -

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Supportive Care
Allocation: Randomized
Intervention Model: Single Group Assignment
Masking: Double


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Placebo Comparator: Order by time and topic<br>Volunteers from the American Cancer Society's Cancer Survivors' Network (CSN) will see some of their messages delivered using CSN's default ordering, which shows messages within a conversational thread ordered by time stamp. Conversational threads are nested within a broad topic-based forum, like breast cancer or colorectal cancer survivors. Note that this is a within-participant trial, so that all participants participate in all arms of the trial. Messages, not people, are randomly assigned to condition. | Behavioral: Order by time and topic<br> <br> |
| Active Comparator: Order by information relevance<br>In this condition some messages will be highlighted if they match the type of content the user has previously shown interest in, by previously contributing or reading semantically similar material. | Behavioral: Order by information relevance<br> <br> |
| Active Comparator: Order by social relationship<br>In this condition some messages will be highlighted because they come from people the user has previously shown interest in, by previously reading their posts or communicating with them. | Behavioral: Order by social relationship<br> <br> |
| Active Comparator: Order by help giving<br>In this condition some messages will be highlighted because they seek help and therefore provide an opportunity for participants to provide social support to others. | Behavioral: Order by help giving<br> <br> |
| Active Comparator: Order by self-disclosure<br>In this condition some messages will be highlighted because in them the writer is self-disclosing, and they provide provide an opportunity for participants to self-disclose in return. | Behavioral: Order by self-disclosure<br> <br> |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Read message (Does the user read the message they were exposed to?) |  | 1 day | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Interaction satisfaction (Self-report measure of satisfaction 3-item survey) | Self-report measure of satisfaction with a random sample of messages. This is a 3-item survey that will be delivered as a pop-up questionnaire following a random sample of the messages users were exposed to. | 1 day | 
| Reply to message (How long does it take the users to reply to a message they were exposed to, if they reply at all.) | How long does it take the users to reply to a message they were exposed to, if they reply at all. | 2 days | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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social support