Clinical research in patient social networks

Two interesting new studies appeared in the last couple of weeks that incorporated patient social networks in novel and insightful ways.

First, this study appeared on 24 April in Nature Biotechnology:

Wicks P, Vaughan TE, Massagli MP, Heywood J. Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm. Nat Biotechnol [Internet]. 2011 Apr 24. Cited in: PubMed; PMID: 21516084.

This study focused on an online community of patients with a rare disease — amyotrophic lateral sclerosis (ALS, a/k/a “Lou Gehrig’s Disease”) — which had formed in a social network called PatientsLikeMe.com.  After a very small study in 2008 showed that lithium might slow the progression of ALS, a significant number of patients in this social network agreed that if their doctors prescribed lithium (off-label), they would each post their own data regarding disease progression.  The authors of the study sought consent from the 149 treated patients to gather the data they had posted and match it against multiple controls (447 controls total).  After 12 months, they ultimately concluded that the data showed lithium had no effect on disease progression.  It was something of a breakthrough as an investigational technique, though:

Although observational studies using unblinded data are not a substitute for double-blind randomized control trials, this study reached the same conclusion as subsequent randomized trials, suggesting that data reported by patients over the internet may be useful for accelerating clinical discovery and evaluating the effectiveness of drugs already in use.

Other coverage and commentary on this study:

The second study appeared recently in PLoS One:

Weitzman ER, Adida B, Kelemen S, Mandl KD. Sharing Data for Public Health Research by Members of an International Online Diabetes Social Network. PLoS One [Internet]. 2011 Apr 27. Cited in: PubMed; PMID: 21556358.

The authors explain that they set out to “test a low-cost and scalable model of citizen science for diabetes research and surveillance by launching and promoting a data-sharing software application into an established online international community of people with diabetes.” The established online community in this case is a social network called TuDiabetes.org, which launched in 2007 and had nearly 15,000 members by the time this study started (it now has nearly 20,000 members).

The researchers deployed a software “app” called TuAnalyze that users could elect to add to their profile in the social network.  Once participants consent to participation in the study, they can contribute certain key data — their glycosylated hemoglobin (A1c) level — to the “data donation drive”.    But more than just harvesting or mining data, this study engages with and gives back to the community:

An important feature of TuAnalyze is biosurveillance-derived display of live, aggregate, geo-referenced data back to the community for benchmarking at country, province and/or state level. Geographic areas within the map (e.g., a US state or Canadian province) illuminate with descriptive displays once a sufficient sample of participants from that area engages and shares data. Illumination of regions of the map is tied to TuAnalyze participation: a critical mass of TuDiabetes members is required to illuminate a region to protect individual identity, incent ongoing engagement, and provide a graphical and tabular data context against which engaged users can compare themselves. There is growing evidence that patients and consumers are interested in making such comparisons.

More coverage and commentary on this study:

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