JMIR Publications

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  Infodemiology and Infoveillance

  Nov 19, 2018

  Hookah-Related Posts to Twitter From 2017 to 2018: Thematic Analysis

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  Authors List:

  • Jon-Patrick Allem  
  • Likhit Dharmapuri  
  • Adam M Leventhal  
  • Jennifer B Unger  
  • Tess Boley Cruz  

  Abstract:

  Background: Hookah (or tobacco waterpipe) use has recently become prevalent in the United States. The contexts and experiences associated with hookah use are unclear, yet such information is abundant via publicly available hookah users’ social media postings. Objective: In this study, we utilized Twitter data to characterize Twitter users’ recent experiences with hookah. Methods: Twitter posts containing the term “hookah” were obtained from April 1, 2017 to 29 March, 2018. Text classifiers were used to identify clusters of topics that tended to co-occur in posts (n=176,706). Results: The most prevalent topic cluster was Person Tagging (use of @username to tag another Twitter account in a post) at 21.58% (38,137/176,706) followed by Promotional or Social Events (eg, mentions of ladies’ nights, parties, etc) at 20.20% (35,701/176,706) and Appeal or Abuse Liability (eg, craving, enjoying hookah) at 18.12% (32,013/176,706). Additional topics included Hookah Use Behavior (eg, mentions of taking a “hit” of hookah) at 11.67% (20,603/176,706), Polysubstance Use (eg, hookah use along with other substances) at 10.95% (19,353/176,706), Buying or Selling (eg, buy, order, purchase, sell) at 9.37% (16,552/176,706), and Flavors (eg, mint, cinnamon, watermelon) at 1.66% (2927/176,706). The topic Dislike of Hookah (eg, hate, quit, dislike) was rare at 0.59% (1043/176,706). Conclusions: Social events, appeal or abuse liability, flavors, and polysubstance use were the common contexts and experiences associated with Twitter discussions about hookah in 2017-2018. Considered in concert with traditional data sources about hookah, these results suggest that social events, appeal or abuse liability, flavors, and polysubstance use warrant consideration as targets in future surveillance, policy making, and interventions addressing hookah.

  • J Med Internet Res 2018;20(11):e11669

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  Personal Health Records,...

  Nov 19, 2018

  New Integrated Model Approach to Understand the Factors That Drive Electronic Health Record Portal Adoption: Cross-Sectional National Survey

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  Authors List:

  • Jorge Tavares  
  • Tiago Oliveira  

  Abstract:

  Background: The future of health care delivery is becoming more patient-focused, and electronic health record (EHR) portals are gaining more attention from worldwide governments that consider this technology as a valuable asset for the future sustainability of the national health care systems. Overall, this makes the adoption of EHR portals an important field to study. Objective: The aim of this study is to understand the factors that drive individuals to adopt EHR portals. Methods: We applied a new adoption model that combines 3 different theories, namely, extended unified theory of acceptance and use of technology, health belief model, and the diffusion of innovation; all the 3 theories provided relevant contributions for the understanding of EHR portals. To test the research model, we used the partial least squares causal modeling approach. We executed a national survey based on randomly generated mobile phone numbers. We collected 139 questionnaires. Results: Performance expectancy (beta=.203; t=2.699), compatibility (beta=.530; t=6.189), and habit (beta=.251; t=2.660) have a statistically significant impact on behavior intention (R2=76.0%). Habit (beta=.378; t=3.821), self-perception (beta=.233; t=2.971), and behavior intention (beta=.263; t=2.379) have a statistically significant impact on use behavior (R2=61.8%). In addition, behavior intention (beta=.747; t=10.737) has a statistically significant impact on intention to recommend (R2=69.0%), results demonstrability (beta=.403; t=2.888) and compatibility (beta=.337; t=2.243) have a statistically significant impact on effort expectancy (R2=48.3%), and compatibility (beta=.594; t=6.141) has a statistically significant impact on performance expectancy (R2=42.7%). Conclusions: Our research model yields very good results, with relevant R2 in the most important dependent variables that help explain the adoption of EHR portals, behavior intention, and use behavior.

  • J Med Internet Res 2018;20(11):e11032

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  Web-based and Mobile Health...

  Nov 19, 2018

  Evaluation of a Web-Based Intervention for Multiple Health Behavior Changes in Patients With Coronary Heart Disease in Home-Based Rehabilitation: Pilot...

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  Authors List:

  • Yan Ping Duan  
  • Wei Liang  
  • Lan Guo  
  • Julian Wienert  
  • Gang Yan Si  
  • Sonia Lippke  

  Abstract:

  Background: Web-based and theory-based interventions for multiple health behaviors appears to be a promising approach with respect to the adoption and maintenance of a healthy lifestyle in cardiac patients who have been discharged from the hospital. Until now, no randomized controlled trials have tested this assumption among Chinese rehabilitation patients with coronary heart disease using a Web-based intervention. Objective: The study aim was to evaluate the effect of an 8-week Web-based intervention in terms of physical activity (PA), fruit and vegetable consumption (FVC), lifestyle changes, social-cognitive outcomes, and health outcomes compared with a waiting control group in Chinese cardiac patients. The intervention content was theory-based on the health action process approach. Self-reported data were evaluated, including PA, FVC, healthy lifestyle (the synthesis of PA and FVC), internal resources (combination of intention, self-efficacy, and planning), and an external resource (social support) of PA and FVC behaviors, as well as perceived health outcomes (body mass index, quality of life, and depression). Methods: In a randomized controlled trial, 136 outpatients with coronary heart disease from the cardiac rehabilitation center of a hospital in China were recruited. After randomization and exclusion of unsuitable participants, 114 patients were assigned to 1 of the 2 groups: (1) the intervention group: first 4 weeks on PA and subsequent 4 weeks on FVC and (2) the waiting control group. A total of 2 Web-based assessments were conducted, including 1 at the beginning of the intervention (T1, N=114), and 1 at the end of the 8-week intervention (T2, N=83). The enrollment and follow-up took place from December 2015 to May 2016. Results: The Web-based intervention outperformed the control condition for PA, FVC, internal resources of PA and FVC, and an external resource of FVC, with an eta-squared effect size ranging from 0.06 to 0.43. Furthermore, the intervention effect was seen in the improvement of quality of life (F1,79=16.36, P<.001, η2=.17). When predicting a healthy lifestyle at follow-up, baseline lifestyle (odds ratio, OR 145.60, 95% CI 11.24-1886; P<.001) and the intervention (OR 21.32, 95% CI 2.40-189.20; P=.006) were found to be significant predictors. Internal resources for FVC mediated the effect of the intervention on the adoption of a healthy lifestyle (R2adj=.29; P=.001), indicating that if the intervention increased the internal resource of behavior, the adoption of a healthy lifestyle was more likely. Conclusions: Patients’ psychological resources such as motivation, self-efficacy, planning, and social support as well as lifestyle can be improved by a Web-based intervention that focuses on both PA and FVC. Such an intervention enriches extended rehabilitation approaches for cardiac patients to be active and remain healthy in daily life after hospital discharge. Trial Registration: ClinicalTrials.gov NCT01909349; https://clinicaltrials.gov/ct2/show/NCT01909349 (Archived by WebCite at http://www.webcitation.org/6pHV1A0G1)

  • J Med Internet Res 2018;20(11):e12052

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  New Methods

  Nov 16, 2018

  Measuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies

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  Authors List:

  • Camille E Short  
  • Ann DeSmet  
  • Catherine Woods  
  • Susan L Williams  
  • Carol Maher  
  • Anouk Middelweerd  
  • Andre Matthias Müller  
  • Petra A Wark  
  • Corneel Vandelanotte  
  • Louise Poppe  
  • Melanie D Hingle  
  • Rik Crutzen  

  Abstract:

  Engagement in electronic health (eHealth) and mobile health (mHealth) behavior change interventions is thought to be important for intervention effectiveness, though what constitutes engagement and how it enhances efficacy has been somewhat unclear in the literature. Recently published detailed definitions and conceptual models of engagement have helped to build consensus around a definition of engagement and improve our understanding of how engagement may influence effectiveness. This work has helped to establish a clearer research agenda. However, to test the hypotheses generated by the conceptual modules, we need to know how to measure engagement in a valid and reliable way. The aim of this viewpoint is to provide an overview of engagement measurement options that can be employed in eHealth and mHealth behavior change intervention evaluations, discuss methodological considerations, and provide direction for future research. To identify measures, we used snowball sampling, starting from systematic reviews of engagement research as well as those utilized in studies known to the authors. A wide range of methods to measure engagement were identified, including qualitative measures, self-report questionnaires, ecological momentary assessments, system usage data, sensor data, social media data, and psychophysiological measures. Each measurement method is appraised and examples are provided to illustrate possible use in eHealth and mHealth behavior change research. Recommendations for future research are provided, based on the limitations of current methods and the heavy reliance on system usage data as the sole assessment of engagement. The validation and adoption of a wider range of engagement measurements and their thoughtful application to the study of engagement are encouraged.

  • J Med Internet Res 2018;20(11):e292

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  Mobile Health (mhealth)

  Nov 15, 2018

  Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods

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  Authors List:

  • Quazi Abidur Rahman  
  • Tahir Janmohamed  
  • Meysam Pirbaglou  
  • Hance Clarke  
  • Paul Ritvo  
  • Jane M Heffernan  
  • Joel Katz  

  Abstract:

  Background: Measuring and predicting pain volatility (fluctuation or variability in pain scores over time) can help improve pain management. Perceptions of pain and its consequent disabling effects are often heightened under the conditions of greater uncertainty and unpredictability associated with pain volatility. Objective: This study aimed to use data mining and machine learning methods to (1) define a new measure of pain volatility and (2) predict future pain volatility levels from users of the pain management app, Manage My Pain, based on demographic, clinical, and app use features. Methods: Pain volatility was defined as the mean of absolute changes between 2 consecutive self-reported pain severity scores within the observation periods. The k-means clustering algorithm was applied to users’ pain volatility scores at the first and sixth month of app use to establish a threshold discriminating low from high volatility classes. Subsequently, we extracted 130 demographic, clinical, and app usage features from the first month of app use to predict these 2 volatility classes at the sixth month of app use. Prediction models were developed using 4 methods: (1) logistic regression with ridge estimators; (2) logistic regression with Least Absolute Shrinkage and Selection Operator; (3) Random Forests; and (4) Support Vector Machines. Overall prediction accuracy and accuracy for both classes were calculated to compare the performance of the prediction models. Training and testing were conducted using 5-fold cross validation. A class imbalance issue was addressed using a random subsampling of the training dataset. Users with at least five pain records in both the predictor and outcome periods (N=782 users) are included in the analysis. Results: k-means clustering algorithm was applied to pain volatility scores to establish a threshold of 1.6 to differentiate between low and high volatility classes. After validating the threshold using random subsamples, 2 classes were created: low volatility (n=611) and high volatility (n=171). In this class-imbalanced dataset, all 4 prediction models achieved 78.1% (611/782) to 79.0% (618/782) in overall accuracy. However, all models have a prediction accuracy of less than 18.7% (32/171) for the high volatility class. After addressing the class imbalance issue using random subsampling, results improved across all models for the high volatility class to greater than 59.6% (102/171). The prediction model based on Random Forests performs the best as it consistently achieves approximately 70% accuracy for both classes across 3 random subsamples. Conclusions: We propose a novel method for measuring pain volatility. Cluster analysis was applied to divide users into subsets of low and high volatility classes. These classes were then predicted at the sixth month of app use with an acceptable degree of accuracy using machine learning methods based on the features extracted from demographic, clinical, and app use information from the first month.

  • J Med Internet Res 2018;20(11):e12001

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  New Methods

  Nov 15, 2018

  Feasibility, Acceptability, and Adoption of Digital Fingerprinting During Contact Investigation for Tuberculosis in Kampala, Uganda: A Parallel-Convergent...

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  Authors List:

  • Elizabeth B White  
  • Amanda J Meyer  
  • Joseph M Ggita  
  • Diana Babirye  
  • David Mark  
  • Irene Ayakaka  
  • Jessica E Haberer  
  • Achilles Katamba  
  • Mari Armstrong-Hough  
  • John Lucian Davis  

  Abstract:

  Background: In resource-constrained settings, challenges with unique patient identification may limit continuity of care, monitoring and evaluation, and data integrity. Biometrics offers an appealing but understudied potential solution. Objective: The objective of this mixed-methods study was to understand the feasibility, acceptability, and adoption of digital fingerprinting for patient identification in a study of household tuberculosis contact investigation in Kampala, Uganda. Methods: Digital fingerprinting was performed using multispectral fingerprint scanners. We tested associations between demographic, clinical, and temporal characteristics and failure to capture a digital fingerprint. We used generalized estimating equations and a robust covariance estimator to account for clustering. In addition, we evaluated the clustering of outcomes by household and community health workers (CHWs) by calculating intraclass correlation coefficients (ICCs). To understand the determinants of intended and actual use of fingerprinting technology, we conducted 15 in-depth interviews with CHWs and applied a widely used conceptual framework, the Technology Acceptance Model 2 (TAM2). Results: Digital fingerprints were captured for 75.5% (694/919) of participants, with extensive clustering by household (ICC=.99) arising from software (108/179, 60.3%) and hardware (65/179, 36.3%) failures. Clinical and demographic characteristics were not markedly associated with fingerprint capture. CHWs successfully fingerprinted all contacts in 70.1% (213/304) of households, with modest clustering of outcomes by CHWs (ICC=.18). The proportion of households in which all members were successfully fingerprinted declined over time (ρ=.30, P<.001). In interviews, CHWs reported that fingerprinting failures lowered their perceptions of the quality of the technology, threatened their social image as competent health workers, and made the technology more difficult to use. Conclusions: We found that digital fingerprinting was feasible and acceptable for individual identification, but problems implementing the hardware and software lead to a high failure rate. Although CHWs found fingerprinting to be acceptable in principle, their intention to use the technology was tempered by perceptions that it was inconsistent and of questionable value. TAM2 provided a valuable framework for understanding the motivations behind CHWs’ intentions to use the technology. We emphasize the need for routine process evaluation of biometrics and other digital technologies in resource-constrained settings to assess implementation effectiveness and guide improvement of delivery.

  • J Med Internet Res 2018;20(11):e11541

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  Clinical Information and Decision Making

  Nov 14, 2018

  An Interpretable and Expandable Deep Learning Diagnostic System for Multiple Ocular Diseases: Qualitative Study

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  Authors List:

  • Kai Zhang  
  • Xiyang Liu  
  • Fan Liu  
  • Lin He  
  • Lei Zhang  
  • Yahan Yang  
  • Wangting Li  
  • Shuai Wang  
  • Lin Liu  
  • Zhenzhen Liu  
  • Xiaohang Wu  
  • Haotian Lin  

  Abstract:

  Background: Although artificial intelligence performs promisingly in medicine, few automatic disease diagnosis platforms can clearly explain why a specific medical decision is made. Objective: We aimed to devise and develop an interpretable and expandable diagnosis framework for automatically diagnosing multiple ocular diseases and providing treatment recommendations for the particular illness of a specific patient. Methods: As the diagnosis of ocular diseases highly depends on observing medical images, we chose ophthalmic images as research material. All medical images were labeled to 4 types of diseases or normal (total 5 classes); each image was decomposed into different parts according to the anatomical knowledge and then annotated. This process yields the positions and primary information on different anatomical parts and foci observed in medical images, thereby bridging the gap between medical image and diagnostic process. Next, we applied images and the information produced during the annotation process to implement an interpretable and expandable automatic diagnostic framework with deep learning. Results: This diagnosis framework comprises 4 stages. The first stage identifies the type of disease (identification accuracy, 93%). The second stage localizes the anatomical parts and foci of the eye (localization accuracy: images under natural light without fluorescein sodium eye drops, 82%; images under cobalt blue light or natural light with fluorescein sodium eye drops, 90%). The third stage carefully classifies the specific condition of each anatomical part or focus with the result from the second stage (average accuracy for multiple classification problems, 79%-98%). The last stage provides treatment advice according to medical experience and artificial intelligence, which is merely involved with pterygium (accuracy, >95%). Based on this, we developed a telemedical system that can show detailed reasons for a particular diagnosis to doctors and patients to help doctors with medical decision making. This system can carefully analyze medical images and provide treatment advices according to the analysis results and consultation between a doctor and a patient. Conclusions: The interpretable and expandable medical artificial intelligence platform was successfully built; this system can identify the disease, distinguish different anatomical parts and foci, discern the diagnostic information relevant to the diagnosis of diseases, and provide treatment suggestions. During this process, the whole diagnostic flow becomes clear and understandable to both doctors and their patients. Moreover, other diseases can be seamlessly integrated into this system without any influence on existing modules or diseases. Furthermore, this framework can assist in the clinical training of junior doctors. Owing to the rare high-grade medical resource, it is impossible that everyone receives high-quality professional diagnosis and treatment service. This framework can not only be applied in hospitals with insufficient medical resources to decrease the pressure on experienced doctors but also deployed in remote areas to help doctors diagnose common ocular diseases.

  • J Med Internet Res 2018;20(11):e11144

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  Web-based and Mobile Health...

  Nov 14, 2018

  Internet-Delivered Early Interventions for Individuals Exposed to Traumatic Events: Systematic Review

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  Authors List:

  • Naomi Ennis  
  • Iris Sijercic  
  • Candice M Monson  

  Abstract:

  Background: Over 75% of individuals are exposed to a traumatic event, and a substantial minority goes on to experience mental health problems that can be chronic and pernicious in their lifetime. Early interventions show promise for preventing trauma following psychopathology; however, a face-to-face intervention can be costly, and there are many barriers to accessing this format of care. Objective: The aim of this study was to systematically review studies of internet-delivered early interventions for trauma-exposed individuals. Methods: A literature search was conducted in PsycINFO and PubMed for papers published between 1991 and 2017. Papers were included if the following criteria were met: (1) an internet-based intervention was described and applied to individuals exposed to a traumatic event; (2) the authors stated that the intervention was intended to be applied early following trauma exposure or as a preventive intervention; and (3) data on mental health symptoms at pre-and postintervention were described (regardless of whether these were primary outcomes). Methodological quality of included studies was assessed using the Downs and Black checklist. Results: The interventions in the 7 studies identified were categorized as selected (ie, delivered to an entire sample after trauma regardless of psychopathology symptoms) or indicated (ie, delivered to those endorsing some level of posttraumatic distress). Selected interventions did not produce significant symptom improvement compared with treatment-as-usual or no intervention control groups. However, indicated interventions yielded significant improvements over other active control conditions on mental health outcomes. Conclusions: Consistent with the notion that many experience natural recovery following trauma, results imply that indicated early internet-delivered interventions hold the most promise in future prevention efforts. More studies that use rigorous methods and clearly defined outcomes are needed to evaluate the efficacy of early internet-delivered interventions. Moreover, basic research on risk and resilience factors following trauma exposure is necessary to inform indicated internet-delivered interventions.

  • J Med Internet Res 2018;20(11):e280

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  Physician and Health Services Rating...

  Nov 12, 2018

  Automatic Classification of Online Doctor Reviews: Evaluation of Text Classifier Algorithms

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  Authors List:

  • Ryan Rivas  
  • Niloofar Montazeri  
  • Nhat XT Le  
  • Vagelis Hristidis  

  Abstract:

  Background: An increasing number of doctor reviews are being generated by patients on the internet. These reviews address a diverse set of topics (features), including wait time, office staff, doctor’s skills, and bedside manners. Most previous work on automatic analysis of Web-based customer reviews assumes that (1) product features are described unambiguously by a small number of keywords, for example, battery for phones and (2) the opinion for each feature has a positive or negative sentiment. However, in the domain of doctor reviews, this setting is too restrictive: a feature such as visit duration for doctor reviews may be expressed in many ways and does not necessarily have a positive or negative sentiment. Objective: This study aimed to adapt existing and propose novel text classification methods on the domain of doctor reviews. These methods are evaluated on their accuracy to classify a diverse set of doctor review features. Methods: We first manually examined a large number of reviews to extract a set of features that are frequently mentioned in the reviews. Then we proposed a new algorithm that goes beyond bag-of-words or deep learning classification techniques by leveraging natural language processing (NLP) tools. Specifically, our algorithm automatically extracts dependency tree patterns and uses them to classify review sentences. Results: We evaluated several state-of-the-art text classification algorithms as well as our dependency tree–based classifier algorithm on a real-world doctor review dataset. We showed that methods using deep learning or NLP techniques tend to outperform traditional bag-of-words methods. In our experiments, the 2 best methods used NLP techniques; on average, our proposed classifier performed 2.19% better than an existing NLP-based method, but many of its predictions of specific opinions were incorrect. Conclusions: We conclude that it is feasible to classify doctor reviews. Automatically classifying these reviews would allow patients to easily search for doctors based on their personal preference criteria.

  • J Med Internet Res 2018;20(11):e11141

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  Review

  Nov 9, 2018

  The Generalizability of Randomized Controlled Trials of Self-Guided Internet-Based Cognitive Behavioral Therapy for Depressive Symptoms: Systematic Review...

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  Authors List:

  • Lorenzo Lorenzo-Luaces  
  • Emily Johns  
  • John R Keefe  

  Abstract:

  Background: Self-guided internet-based cognitive behavioral therapies (iCBTs) for depressive symptoms may substantially increase accessibility to mental health treatment. Despite this, questions remain as to the generalizability of the research on self-guided iCBT. Objective: We sought to describe the clinical entry criteria used in studies of self-guided iCBT, explore the criteria’s effects on study outcomes, and compare the frequency of use of these criteria with their use in studies of face-to-face psychotherapy and antidepressant medications. We hypothesized that self-guided iCBT studies would use more stringent criteria that would bias the sample toward those with a less complex clinical profile, thus inflating treatment outcomes. Methods: We updated a recently published meta-analysis by conducting a systematic literature search in PubMed, MEDLINE, PsycINFO, and EMBASE. We conducted a meta-regression analysis to test the effect of the different commonly used psychiatric entry criteria on the treatment-control differences. We also compared the frequency with which exclusion criteria were used in the self-guided iCBT studies versus studies of face-to-face psychotherapy and antidepressants from a recently published review. Results: Our search yielded 5 additional studies, which we added to the 16 studies identified by Karyotaki and colleagues in 2017. Few self-guided iCBT studies excluded patients with severe depressive symptoms (6/21, 29%), but self-guided iCBT studies were more likely than antidepressant (14/170, 8.2%) studies to use this criterion. However, self-guided iCBT studies did not use this criterion more frequently than face-to-face psychotherapy studies (6/16, 38%). Beyond this, we found no evidence that self-guided iCBTs used more stringent entry criteria. Strong evidence suggested that they were actually less likely to use most entry criteria, especially exclusions on the basis of substance use or personality pathology. None of the entry criteria used had an effect on outcomes. Conclusions: A conservative interpretation of our findings is that the patient population sampled in the literature on self-guided iCBT is relatively comparable with that of studies of antidepressants or face-to-face psychotherapy. Alternatively, studies of unguided cognitive behavioral therapy may sample from a more heterogeneous and representative patient population. Until evidence emerges to suggest otherwise, the patient population sampled in self-guided iCBT studies cannot be considered as less complex than the patient population from face-to-face psychotherapy or antidepressant studies.

  • J Med Internet Res 2018;20(11):e10113

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  Medicine 2.0: Social Media, Open,...

  Nov 8, 2018

  Correlations Between Hospitals’ Social Media Presence and Reputation Score and Ranking: Cross-Sectional Analysis

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  Authors List:

  • Justin D Triemstra  
  • Rachel Stork Poeppelman  
  • Vineet M Arora  

  Abstract:

  Background: The US News and World Report reputation score correlates strongly with overall rank in adult and pediatric hospital rankings. Social media affects how information is disseminated to physicians and is used by hospitals as a marketing tool to recruit patients. It is unclear whether the reputation score for adult and children’s hospitals relates to social media presence. Objective: The objective of our study was to analyze the association between a hospital’s social media metrics and the US News 2017-2018 Best Hospital Rankings for adult and children’s hospitals. Methods: We conducted a cross-sectional analysis of the reputation score, total score, and social media metrics (Twitter, Facebook, and Instagram) of hospitals who received at least one subspecialty ranking in the 2017-2018 US News publicly available annual rankings. Regression analysis was employed to analyze the partial correlation coefficients between social media metrics and a hospital’s total points (ie, rank) and reputation score for both adult and children’s hospitals while controlling for the bed size and time on Twitter. Results: We observed significant correlations for children’s hospitals’ reputation score and total points with the number of Twitter followers (total points: r=.465, P<.001; reputation: r=.524, P<.001) and Facebook followers (total points: r=.392, P=.002; reputation: r=.518, P<.001). Significant correlations for the adult hospitals reputation score were found with the number of Twitter followers (r=.848, P<.001), number of tweets (r=.535, P<.001), Klout score (r=.242, P=.02), and Facebook followers (r=.743, P<.001). In addition, significant correlations for adult hospitals total points were found with Twitter followers (r=.548, P<.001), number of tweets (r=.358, P<.001), Klout score (r=.203, P=.05), Facebook followers (r=.500, P<.001), and Instagram followers (r=.692, P<.001). Conclusions: A statistically significant correlation exists between multiple social media metrics and both a hospital’s reputation score and total points (ie, overall rank). This association may indicate that a hospital’s reputation may be influenced by its social media presence or that the reputation or rank of a hospital drives social media followers.

  • J Med Internet Res 2018;20(11):e289

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  Tutorial

  Nov 8, 2018

  Using Facebook for Large-Scale Online Randomized Clinical Trial Recruitment: Effective Advertising Strategies

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  Authors List:

  • Laura Akers  
  • Judith S Gordon  

  Abstract:

  Targeted Facebook advertising can be an effective strategy to recruit participants for a large-scale online study. Facebook advertising is useful for reaching people in a wide geographic area, matching a specific demographic profile. It can also target people who would be unlikely to search for the information and would thus not be accessible via Google AdWords. It is especially useful when it is desirable not to raise awareness of the study in a demographic group that would be ineligible for the study. This paper describes the use of Facebook advertising to recruit and enroll 1145 women over a 15-month period for a randomized clinical trial to teach support skills to female partners of male smokeless tobacco users. This tutorial shares our study team’s experiences, lessons learned, and recommendations to help researchers design Facebook advertising campaigns. Topics covered include designing the study infrastructure to optimize recruitment and enrollment tracking, creating a Facebook presence via a fan page, designing ads that attract potential participants while meeting Facebook’s strict requirements, and planning and managing an advertising campaign that accommodates the rapid rate of diminishing returns for each ad.

  • J Med Internet Res 2018;20(11):e290

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