Dataset Information

Psychiatry Outpatients' Willingness to Share Social Media Posts and Smartphone Data for Research and Clinical Purposes: Survey Study.

ABSTRACT: BACKGROUND:Psychiatry research has begun to leverage data collected from patients' social media and smartphone use. However, information regarding the feasibility of utilizing such data in an outpatient setting and the acceptability of such data in research and practice is limited. OBJECTIVE:This study aimed at understanding the outpatients' willingness to have information from their social media posts and their smartphones used for clinical or research purposes. METHODS:In this survey study, we surveyed patients (N=238) in an outpatient clinic waiting room. Willingness to share social media and passive smartphone data was summarized for the sample as a whole and broken down by sex, age, and race. RESULTS:Most patients who had a social media account and who were receiving talk therapy treatment (74.4%, 99/133) indicated that they would be willing to share their social media posts with their therapists. The percentage of patients willing to share passive smartphone data with researchers varied from 40.8% (82/201) to 60.7% (122/201) depending on the parameter, with sleep duration being the parameter with the highest percentage of patients willing to share. A total of 30.4% of patients indicated that media stories of social media privacy breaches made them more hesitant about sharing passive smartphone data with researchers. Sex and race were associated with willingness to share smartphone data, with men and whites being the most willing to share. CONCLUSIONS:Our results indicate that most patients in a psychiatric outpatient setting would share social media and passive smartphone data and that further research elucidating patterns of willingness to share passive data is needed.

SUBMITTER: Rieger A

PROVIDER: S-EPMC6754680 | biostudies-other | 2019 Aug

REPOSITORIES: biostudies-other

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Publications

Psychiatry Outpatients' Willingness to Share Social Media Posts and Smartphone Data for Research and Clinical Purposes: Survey Study.

Rieger Agnes A Gaines Averi A Barnett Ian I Baldassano Claudia Frances CF Connolly Gibbons Mary Beth MB Crits-Christoph Paul P

JMIR formative research 20190829 3

<h4>Background</h4>Psychiatry research has begun to leverage data collected from patients' social media and smartphone use. However, information regarding the feasibility of utilizing such data in an outpatient setting and the acceptability of such data in research and practice is limited.<h4>Objective</h4>This study aimed at understanding the outpatients' willingness to have information from their social media posts and their smartphones used for clinical or research purposes.<h4>Methods</h4>In ...[more]

PMID: 31493326

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Project description:BackgroundAs social media are increasingly used worldwide, more and more scientists are relying on them for their health-related projects. However, social media features, methodologies, and ethical issues are unclear so far because, to our knowledge, there has been no overview of this relatively young field of research.ObjectiveThis scoping review aimed to provide an evidence map of the different uses of social media for health research purposes, their fields of application, and their analysis methods.MethodsWe followed the scoping review methodologies developed by Arksey and O'Malley and the Joanna Briggs Institute. After developing search strategies based on keywords (eg, social media, health research), comprehensive searches were conducted in the PubMed/MEDLINE and Web of Science databases. We limited the search strategies to documents written in English and published between January 1, 2005, and April 9, 2020. After removing duplicates, articles were screened at the title and abstract level and at the full text level by two independent reviewers. One reviewer extracted data, which were descriptively analyzed to map the available evidence.ResultsAfter screening 1237 titles and abstracts and 407 full texts, 268 unique papers were included, dating from 2009 to 2020 with an average annual growth rate of 32.71% for the 2009-2019 period. Studies mainly came from the Americas (173/268, 64.6%, including 151 from the United States). Articles used machine learning or data mining techniques (60/268) to analyze the data, discussed opportunities and limitations of the use of social media for research (59/268), assessed the feasibility of recruitment strategies (45/268), or discussed ethical issues (16/268). Communicable (eg, influenza, 40/268) and then chronic (eg, cancer, 24/268) diseases were the two main areas of interest.ConclusionsSince their early days, social media have been recognized as resources with high potential for health research purposes, yet the field is still suffering from strong heterogeneity in the methodologies used, which prevents the research from being compared and generalized. For the field to be fully recognized as a valid, complementary approach to more traditional health research study designs, there is now a need for more guidance by types of applications of social media for health research, both from a methodological and an ethical perspective.International registered report identifier (irrid)RR2-10.1136/bmjopen-2020-040671.

Project description:BackgroundPatient-based analysis of social media is a growing research field with the aim of delivering precision medicine but it requires accurate classification of posts relating to patients' experiences. We motivate the need for this type of classification as a pre-processing step for further analysis of social media data in the context of related work in this area. In this paper we present experiments for a three-way document classification by patient voice, professional voice or other. We present results for a convolutional neural network classifier trained on English data from two different data sources (Reddit and Twitter) and two domains (cardiovascular and skin diseases).ResultsWe found that document classification by patient voice, professional voice or other can be done consistently manually (0.92 accuracy). Annotators agreed roughly equally for each domain (cardiovascular and skin) but they agreed more when annotating Reddit posts compared to Twitter posts. Best classification performance was obtained when training two separate classifiers for each data source, one for Reddit and one for Twitter posts, when evaluating on in-source test data for both test sets combined with an overall accuracy of 0.95 (and macro-average F1 of 0.92) and an F1-score of 0.95 for patient voice only.ConclusionThe main conclusion resulting from this work is that combining social media data from platforms with different characteristics for training a patient and professional voice classifier does not result in best possible performance. We showed that it is best to train separate models per data source (Reddit and Twitter) instead of a model using the combined training data from both sources. We also found that it is preferable to train separate models per domain (cardiovascular and skin) while showing that the difference to the combined model is only minor (0.01 accuracy). Our highest overall F1-score (0.95) obtained for classifying posts as patient voice is a very good starting point for further analysis of social media data reflecting the experience of patients.

Project description:BACKGROUND:Mental illness affects a significant portion of the worldwide population. Online mental health forums can provide a supportive environment for those afflicted and also generate a large amount of data that can be mined to predict mental health states using machine learning methods. OBJECTIVE:This study aimed to benchmark multiple methods of text feature representation for social media posts and compare their downstream use with automated machine learning (AutoML) tools. We tested on datasets that contain posts labeled for perceived suicide risk or moderator attention in the context of self-harm. Specifically, we assessed the ability of the methods to prioritize posts that a moderator would identify for immediate response. METHODS:We used 1588 labeled posts from the Computational Linguistics and Clinical Psychology (CLPsych) 2017 shared task collected from the Reachout.com forum. Posts were represented using lexicon-based tools, including Valence Aware Dictionary and sEntiment Reasoner, Empath, and Linguistic Inquiry and Word Count, and also using pretrained artificial neural network models, including DeepMoji, Universal Sentence Encoder, and Generative Pretrained Transformer-1 (GPT-1). We used Tree-based Optimization Tool and Auto-Sklearn as AutoML tools to generate classifiers to triage the posts. RESULTS:The top-performing system used features derived from the GPT-1 model, which was fine-tuned on over 150,000 unlabeled posts from Reachout.com. Our top system had a macroaveraged F1 score of 0.572, providing a new state-of-the-art result on the CLPsych 2017 task. This was achieved without additional information from metadata or preceding posts. Error analyses revealed that this top system often misses expressions of hopelessness. In addition, we have presented visualizations that aid in the understanding of the learned classifiers. CONCLUSIONS:In this study, we found that transfer learning is an effective strategy for predicting risk with relatively little labeled data and noted that fine-tuning of pretrained language models provides further gains when large amounts of unlabeled text are available.

Project description:Importance:Using social media to recruit participants is a common and cost-effective practice. Willingness to participate (WTP) in biomedical research is a function of trust in the scientific team, which is closely tied to the source of funding and institutional connections. Objective:To determine whether WTP and willingness to share social media data are associated with the type of research team and online recruitment platform. Design, Setting, and Participants:This mixed-methods longitudinal survey and qualitative study was conducted over 2 points (T1 and T2) using Amazon's Mechanical Turk (MTurk) platform. Participants were US adults aged 18 years or older who use at least 1 social media platform. Recruitment was stratified to match race/ethnicity proportions of the 2010 US Census. The volunteer sample consisted of 914 participants at T1, and 655 participants completed the follow-up survey 5 months later (T2). Main Outcomes and Measures:Outcomes were (1) past experience with online research and sharing social media data for research; (2) WTP in research advertised online; (3) WTP in a study sponsored by a pharmaceutical company, a university, or a federal agency; and (4) willingness to share social media data. Opinions were solicited regarding the European Union's General Data Protection Regulation statute, which came into effect between T1 and T2. Results:Of 914 participants completing the first survey (T1), 604 (66.1%) were aged 18 to 39 years and 494 (54.0%) were female. Of these, 655 participants (71.7%) responded at T2. While 680 participants (74.4%) indicated WTP in biomedical research, only 454 (49.3%) were willing to share their social media data. Participants were significantly less likely to participate in federally sponsored (odds ratio [OR], 0.58; 95% CI, 0.51-0.64; P?<?.001) or pharmaceutical company (OR, 0.59; 95% CI, 0.53-0.66; P?<?.001) research than university-led studies. They were also less likely to share their social medial data for federal (OR, 0.65; 95% CI, 0.58-0.72; P?<?.001) or pharmaceutical company (OR, 0.50; 95% CI, 0.44-0.56; P?<?.001) research compared with academic studies. Willingness to participate in pharmaceutical company-led research decreased 11.89% from T1 to T2 (OR for T2, 0.62; 95% CI, 0.54-0.77; P?<?.001). Reasons for WTP were interest in furthering science, financial incentives, trust in the organization, and data security. While 63.0% of respondents reported seeing new privacy policy emails related to the General Data Protection Regulation law, only 27.1% indicated this positively influenced their WTP. Thematic analysis of responses indicated that WTP may improve with stronger data security measures. Conclusions and Relevance:This study suggests that researchers may see reduced online research participation and data sharing, particularly for research conducted outside academia.

Dataset Information

Psychiatry Outpatients' Willingness to Share Social Media Posts and Smartphone Data for Research and Clinical Purposes: Survey Study.

Publications

Psychiatry Outpatients' Willingness to Share Social Media Posts and Smartphone Data for Research and Clinical Purposes: Survey Study.

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