Denominator Issues for Personally Generated Data in Population Health Monitoring

Associations Between Patient-Reported Outcome Measures of Physical and Psychological Functioning and Willingness to Share Social Media Data for Research Among Adolescents With a Chronic Rheumatic Disease: Cross-Sectional Survey

Background

Social media data may augment understanding of the disease and treatment experiences and quality of life of youth with chronic medical conditions. Little is known about the willingness to share social media data for health research among youth with chronic medical conditions and the differences in health status between sharing and nonsharing youth with chronic medical conditions.

Objective

We aimed to evaluate the associations between patient-reported measures of disease symptoms and functioning and the willingness to share social media data.

Methods

Between February 2018 and August 2019, during routine clinic visits, survey data about social media use and the willingness to share social media data (dependent variable) were collected from adolescents in a national rheumatic disease registry. Survey data were analyzed with patient-reported measures of disease symptoms and functioning and a clinical measure of disease activity, which were collected through a parent study. We used descriptive statistics and multivariate logistic regression to compare patient-reported outcomes between youth with chronic medical conditions who opted to share social media data and those who did not opt to share such data.

Results

Among 112 youths, (age: mean 16.1, SD 1.6 y; female: n=72, 64.3%), 83 (74.1%) agreed to share social media data. Female participants were more likely to share (P=.04). In all, 49 (43.8%) and 28 (25%) participants viewed and posted about rheumatic disease, respectively. Compared to nonsharers, sharers reported lower mobility (T-score: mean 49.0, SD 9.4 vs mean 53.9, SD 8.9; P=.02) and more pain interference (T-score: mean 45.7, SD 8.8 vs mean 40.4, SD 8.0; P=.005), fatigue (T-score: mean 49.1, SD 11.0 vs mean 39.7, SD 9.7; P<.001), depression (T-score: mean 48.1, SD 8.9 vs mean 42.2, SD 8.4; P=.003), and anxiety (T-score: mean 45.2, SD 9.3 vs mean 38.5, SD 7.0; P<.001). In regression analyses adjusted for age, sex, study site, and Physician Global Assessment score, each 1-unit increase in symptoms was associated with greater odds of willingness to share social media data, for measures of pain interference (Adjusted Odds Ratio [AOR] 1.07, 95% CI 1.001-1.14), fatigue (AOR 1.08, 95% CI 1.03-1.13), depression (AOR 1.07, 95% CI 1.01-1.13), and anxiety (AOR 1.10, 95% CI 1.03-1.18).

Conclusions

High percentages of youth with rheumatic diseases used and were willing to share their social media data for research. Sharers reported worse symptoms and functioning compared to those of nonsharers. Social media may offer a potent information source and engagement pathway for youth with rheumatic diseases, but differences between sharing and nonsharing youth merit consideration when designing studies and evaluating social media-derived findings.

Building Public Health Surveillance 3.0: Emerging Timely Measures of Physical, Economic, and Social Environmental Conditions Affecting Health

In response to rapidly changing societal conditions stemming from the COVID-19 pandemic, we summarize data sources with potential to produce timely and spatially granular measures of physical, economic, and social conditions relevant to public health surveillance, and we briefly describe emerging analytic methods to improve small-area estimation. To inform this article, we reviewed published systematic review articles set in the United States from 2015 to 2020 and conducted unstructured interviews with senior content experts in public heath practice, academia, and industry. We identified a modest number of data sources with high potential for generating timely and spatially granular measures of physical, economic, and social determinants of health. We also summarized modeling and machine-learning techniques useful to support development of time-sensitive surveillance measures that may be critical for responding to future major events such as the COVID-19 pandemic. (Am J Public Health. 2022;112(10):1436-1445. https://doi.org/10.2105/AJPH.2022.306917).

Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube

Background

Common methods for extracting content in health communication research typically involve using a set of well-established queries, often names of medical procedures or diseases, that are often technical or rarely used in the public discussion of health topics. Although these methods produce high recall (ie, retrieve highly relevant content), they tend to overlook health messages that feature colloquial language and layperson vocabularies on social media. Given how such messages could contain misinformation or obscure content that circumvents official medical concepts, correctly identifying (and analyzing) them is crucial to the study of user-generated health content on social media platforms.

Objective

Health communication scholars would benefit from a retrieval process that goes beyond the use of standard terminologies as search queries. Motivated by this, this study aims to put forward a search term identification method to improve the retrieval of user-generated health content on social media. We focused on cancer screening tests as a subject and YouTube as a platform case study.

Methods

We retrieved YouTube videos using cancer screening procedures (colonoscopy, fecal occult blood test, mammogram, and pap test) as seed queries. We then trained word embedding models using text features from these videos to identify the nearest neighbor terms that are semantically similar to cancer screening tests in colloquial language. Retrieving more YouTube videos from the top neighbor terms, we coded a sample of 150 random videos from each term for relevance. We then used text mining to examine the new content retrieved from these videos and network analysis to inspect the relations between the newly retrieved videos and videos from the seed queries.

Results

The top terms with semantic similarities to cancer screening tests were identified via word embedding models. Text mining analysis showed that the 5 nearest neighbor terms retrieved content that was novel and contextually diverse, beyond the content retrieved from cancer screening concepts alone. Results from network analysis showed that the newly retrieved videos had at least one total degree of connection (sum of indegree and outdegree) with seed videos according to YouTube relatedness measures.

Conclusions

We demonstrated a retrieval technique to improve recall and minimize precision loss, which can be extended to various health topics on YouTube, a popular video-sharing social media platform. We discussed how health communication scholars can apply the technique to inspect the performance of the retrieval strategy before investing human coding resources and outlined suggestions on how such a technique can be extended to other health contexts.

Estimating the global and regional burden of meningitis in children caused by Haemophilus influenzae type b: A systematic review and meta-analysis

Background

Haemophilus influenzae Type B (Hib) meningitis caused significant public health concern for children. Recent assessment in 2015 suggests vaccination has virtually eliminated invasive Hib diseases. However, many countries launched their programs after 2010, and few are yet to establish routine Hib immunisations. We therefore aimed to update the most recent global burden of Hib meningitis before the impact of COVID-19 pandemic, from 2010 to 2020, in order to aid future public health policies on disease management and prevention.

Methods

Epidemiological data regarding Hib meningitis in children <5 years old were systematically searched and evaluated from PubMed and Scopus in August, 2020. We included studies published between 2010 and 2019 that reported incidence, prevalence, mortality, or case-fatality-ratio (CFR), and confirmation of meningitis by cerebrospinal fluid culture, with a minimum one year study period and ten cases. Each data was stratified by one study-year. Median study-year was used if information was not available. Quality of all studies were assessed using our adapted assessment criteria from Grading of Recommendations Assessment, Development and Evaluation (GRADE) and Study Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies from National Heart, Lung and Blood Institute (NHLBI). We constructed and visually inspected a funnel plot of standard error by the incidence rate and performed an Egger’s regression test to statistically assess publication bias. To ascertain incidence and CFR, we performed generalised linear mixed models on crude individual study estimates. Heterogeneity was assessed using I-squared statistics whilst further exploring heterogeneity by performing subgroup analysis.

Results

33 studies were identified. Pooled incidence of global Hib meningitis in children was 1.13 per 100 000-child-years (95% confidence interval (CI) = 0.80-1.59). Southeast Asian Region (SEAR) of World Health Organisation (WHO) region reported the highest incidence, and European Region (EUR) the lowest. Considering regions with three or more data, Western Pacific Region (WPR) had the highest incidence rate of 5.22 (95% CI = 3.12-8.72). Post-vaccination incidence (0.67 cases per 100 000-child-years, 95% CI = 0.48-0.94) was dramatically lower than Pre-vaccination incidence (4.84 cases per 100 000-child-years, 95% CI = 2.95-7.96). Pooled CFR in our meta-analysis was 11.21% (95% CI = 7.01-17.45). Eastern Mediterranean Region (EMR) had the highest CFR (26.92, 95% CI = 13.41-46.71) while EUR had the lowest (4.13, 95% CI = 1.73-9.54). However, considering regions with three or more data, African Region (AFR) had the highest CFR at 21.79% (95% CI = 13.65-32.92). Before the coronavirus disease 2019 (COVID-19) impact, the estimation for global Hib meningitis cases in 2020 is 7645 and 857 deaths.

Conclusions

Global burden of Hib meningitis has markedly decreased, and most regions have implemented vaccination programs. Extrapolating population-at-risk from studies has possibly led to an underestimation. Continuous surveillance is necessary to monitor vaccination impact, resurgence, vaccine failures, strain variance, COVID-19 impact, and to track improvement of regional and global Hib meningitis mortality.

Effects of Patient-Generated Health Data: Comparison of Two Versions of Long-Term Mobile Personal Health Record Usage Logs

Patient-generated health data (PGHD) can be managed easily by a mobile personal health record (mPHR) and can increase patient engagement. This study investigated the effect of PGHD functions on mPHR usage. We collected usage log data from an mPHR app, My Chart in My Hand (MCMH), for seven years. We analyzed the number of accesses and trends for each menu by age and sex according to the version-up. Generalized estimating equation (GEE) analysis was used to determine the likelihood of continuous app usage according to the menus and version-up. The total number of users of each version were 15,357 and 51,553, respectively. Adult females under 50 years were the most prevalent user group (30.0%). The “My Chart” menu was the most accessed menu, and the total access count increased by ~10 times after the version-up. The “Health Management” menu designed for PGHD showed the largest degree of increase in its likelihood of continuous usage after the version-up (1.245; p < 0.0001) across menus (range: 0.925–1.050). Notably, improvement of PGHD management in adult females over 50 years is needed.

Psychoeducational Messaging to Reduce Alcohol Use for College Students With Type 1 Diabetes: Internet-Delivered Pilot Trial

Background

College environments promote high-volume or binge alcohol consumption among youth, which may be especially harmful to those with type 1 diabetes (T1D). Little is known about the acceptability and effectiveness of interventions targeting reduced alcohol use by college students with T1D, and it is unclear whether intervention framing (specifically, the narrator of intervention messages) matters with respect to affecting behavior change. Interventions promoted by peer educators may be highly relatable and socially persuasive, whereas those delivered by clinical providers may be highly credible and motivating.

Objective

The aim of this study is to determine the acceptability and impacts of an alcohol use psychoeducational intervention delivered asynchronously through web-based channels to college students with T1D. The secondary aim is to compare the impacts of two competing versions of the intervention that differed by narrator (peer vs clinician).

Methods

We recruited 138 college students (aged 17-25 years) with T1D through web-based channels and delivered a brief intervention to participants randomly assigned to 1 of 2 versions that differed only with respect to the audiovisually recorded narrator. We assessed the impacts of the exposure to the intervention overall and by group, comparing the levels of alcohol- and diabetes-related knowledge, perceptions, and use among baseline, immediately after the intervention, and 2 weeks after intervention delivery.

Results

Of the 138 enrolled participants, 122 (88.4%) completed all follow-up assessments; the participants were predominantly women (98/122, 80.3%), were White non-Hispanic (102/122, 83.6%), and had consumed alcohol in the past year (101/122, 82.8%). Both arms saw significant postintervention gains in the knowledge of alcohol’s impacts on diabetes-related factors, health-protecting attitudes toward drinking, and concerns about drinking. All participants reported significant decreases in binge drinking 2 weeks after the intervention (21.3%; odds ratio 0.48, 95% CI 0.31-0.75) compared with the 2 weeks before the intervention (43/122, 35.2%). Changes in binge drinking after the intervention were affected by changes in concerns about alcohol use and T1D. Those who viewed the provider narrator were significantly more likely to rate their narrator as knowledgeable and trustworthy; there were no other significant differences in intervention effects by the narrator.

Conclusions

The intervention model was highly acceptable and effective at reducing self-reported binge drinking at follow-up, offering the potential for broad dissemination and reach given the web-based format and contactless, on-demand content. Both intervention narrators increased knowledge, improved health-protecting attitudes, and increased concerns regarding alcohol use. The participants’ perceptions of expertise and credibility differed by narrator.

Trial Registration

ClinicalTrials.gov NCT02883829; https://clinicaltrials.gov/ct2/show/NCT02883829

International Registered Report Identifier (IRRID)

RR2-10.1177/1932296819839503

Rapid deployment of a community engagement study and educational trial via social media: implementation of the UC-COVID study

Background

In response to the COVID-19 pandemic and associated adoption of scarce resource allocation (SRA) policies, we sought to rapidly deploy a novel survey to ascertain community values and preferences for SRA and to test the utility of a brief intervention to improve knowledge of and values alignment with a new SRA policy. Given social distancing and precipitous evolution of the pandemic, Internet-enabled recruitment was deemed the best method to engage a community-based sample. We quantify the efficiency and acceptability of this Internet-based recruitment for engaging a trial cohort and describe the approach used for implementing a health-related trial entirely online using off-the-shelf tools.

Methods

We recruited 1971 adult participants (≥ 18 years) via engagement with community partners and organizations and outreach through direct and social media messaging. We quantified response rate and participant characteristics of our sample, examine sample representativeness, and evaluate potential non-response bias.

Results

Recruitment was similarly derived from direct referral from partner organizations and broader social media based outreach, with extremely low study entry from organic (non-invited) search activity. Of social media platforms, Facebook was the highest yield recruitment source. Bot activity was present but minimal and identifiable through meta-data and engagement behavior. Recruited participants differed from broader populations in terms of sex, ethnicity, and education, but had similar prevalence of chronic conditions. Retention was satisfactory, with entrance into the first follow-up survey for 61% of those invited.

Conclusions

We demonstrate that rapid recruitment into a longitudinal intervention trial via social media is feasible, efficient, and acceptable. Recruitment in conjunction with community partners representing target populations, and with outreach across multiple platforms, is recommended to optimize sample size and diversity. Trial implementation, engagement tracking, and retention are feasible with off-the-shelf tools using preexisting platforms.

Trial registration

ClinicalTrials.gov NCT04373135. Registered on May 4, 2020

Rapid Deployment of A Community Engagement Study And Educational Trial Via Social Media: Implementation of The UC-COVID Study

Background:

In response to the COVID-19 pandemic and associated adoption of scarce resource allocation (SRA) policies, we sought to rapidly deploy a novel survey to ascertain community values and preferences for SRA, and to test the utility of a brief intervention to improve knowledge of and values alignment with a new SRA policy. Given social distancing and precipitous evolution of the pandemic, Internet enabled recruitment was deemed the best method to engage a community-based sample. We quantify the efficiency and acceptability of this Internet-based recruitment for engaging a trial cohort and describe the approach used for implementing a health-related trial entirely online using off-the-shelf tools.

Methods:

We recruited 1,971 adult participants (≥18 years) via engagement with community partners and organizations and outreach through direct and social media messaging. We quantified response rate and participant characteristics of our sample, examine sample representativeness, and evaluate potential non-response bias.

Results:

Recruitment was similarly derived from direct referral from partner organizations and broader social media based outreach, with extremely low study entry from organic (non-invited) search activity. Of social media platforms, Facebook was the highest yield recruitment source. Bot activity was present but minimal and identifiable through meta-data and engagement behavior. Recruited participants differed from broader populations in terms of sex, ethnicity, and education, but had similar prevalence of chronic conditions. Retention was satisfactory, with entrance into the first follow-up survey for 61% of those invited.

Conclusions:

We demonstrate that rapid recruitment into a longitudinal intervention trial via social media is feasible, efficient, and acceptable. Recruitment in conjunction with community partners representing target populations, and with outreach across multiple platforms, is recommended to optimize sample size and diversity. Trial implementation, engagement tracking, engagement and retention are feasible with off-the-shelf tools using preexisting platforms.

Trial Registration:

ClinicalTrials.gov registration NCT04373135.

Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study

Background

Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study.

Objective

This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset.

Methods

This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users’ tweets.

Results

Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19).

Conclusions

To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.

Online Searching and Social Media to Detect Alcohol Use Risk at Population Scale

INTRODUCTION

Harnessing engagement in online searching and social media may provide complementary information for monitoring alcohol use, informing prevention and policy evaluation, and extending knowledge available from national surveys.

METHODS

Relative search volumes for 7 alcohol-related keywords were estimated from Google Trends (data, 2014-2017), and the proportion of alcohol use-related Twitter posts (data, 2014-2015) was estimated using natural language processing. Searching/posting measures were created for all 50 U.S. states plus Washington, D.C. Survey reports of alcohol use and summaries of state alcohol policies were obtained from the Behavioral Risk Factor Surveillance System (data, 2014-2016) and the Alcohol Policy Scale. In 2018-2019, associations among searching/posting measures and same state/year Behavioral Risk Factor Surveillance System reports of recent (past-30-day) alcohol use and maximum number of drinks consumed on an occasion were estimated using logistic and linear regression, adjusting for sociodemographics and Internet use, with moderation tested in regressions that included interactions of select searching/posting measures and the Alcohol Policy Scale.

RESULTS

Recent alcohol use was reported by 52.93% of 1,297,168 Behavioral Risk Factor Surveillance System respondents, which was associated with all state-level searching/posting measures in unadjusted and adjusted models (p<0.0001). Among drinkers, most searching/posting measures were associated with maximum number of drinks consumed (p<0.0001). Associations varied with exposure to high versus low levels of state policy controls on alcohol.

CONCLUSIONS

Strong associations were found among individual alcohol use and state-level alcohol-related searching/posting measures, which were moderated by the strength of state alcohol policies. Findings support using novel personally generated data to monitor alcohol use and possibly evaluate effects of alcohol control policies.

Clinical Trial Recruitment and Retention of College Students with Type 1 Diabetes via Social Media: An Implementation Case Study

BACKGROUND

We sought to quantify the efficiency and acceptability of Internet-based recruitment for engaging an especially hard-to-reach cohort (college-students with type 1 diabetes, T1D) and to describe the approach used for implementing a health-related trial entirely online using off-the-shelf tools inclusive of participant safety and validity concerns.

METHOD

We recruited youth (ages 17-25 years) with T1D via a variety of social media platforms and other outreach channels. We quantified response rate and participant characteristics across channels with engagement metrics tracked via Google Analytics and participant survey data. We developed decision rules to identify invalid (duplicative/false) records (N = 89) and compared them to valid cases (N = 138).

RESULTS

Facebook was the highest yield recruitment source; demographics differed by platform. Invalid records were prevalent; invalid records were more likely to be recruited from Twitter or Instagram and differed from valid cases across most demographics. Valid cases closely resembled characteristics obtained from Google Analytics and from prior data on platform user-base. Retention was high, with complete follow-up for 88.4%. There were no safety concerns and participants reported high acceptability for future recruitment via social media.

CONCLUSIONS

We demonstrate that recruitment of college students with T1D into a longitudinal intervention trial via social media is feasible, efficient, acceptable, and yields a sample representative of the user-base from which they were drawn. Given observed differences in characteristics across recruitment channels, recruiting across multiple platforms is recommended to optimize sample diversity. Trial implementation, engagement tracking, and retention are feasible with off-the-shelf tools using preexisting platforms.

Socio-spatial Self-organizing Maps: Using Social Media to Assess Relevant Geographies for Exposure to Social Processes

Social media offers a unique window into attitudes like racism and homophobia, exposure to which are important, hard to measure and understudied social determinants of health. However, individual geo-located observations from social media are noisy and geographically inconsistent. Existing areas by which exposures are measured, like Zip codes, average over irrelevant administratively-defined boundaries. Hence, in order to enable studies of online social environmental measures like attitudes on social media and their possible relationship to health outcomes, first there is a need for a method to define the collective, underlying degree of social media attitudes by region. To address this, we create the Socio-spatial-Self organizing map, "SS-SOM" pipeline to best identify regions by their latent social attitude from Twitter posts. SS-SOMs use neural embedding for text-classification, and augment traditional SOMs to generate a controlled number of nonoverlapping, topologically-constrained and topically-similar clusters. We find that not only are SS-SOMs robust to missing data, the exposure of a cohort of men who are susceptible to multiple racism and homophobia-linked health outcomes, changes by up to 42% using SS-SOM measures as compared to using Zip code-based measures.

Tracking health seeking behavior during an Ebola outbreak via mobile phones and SMS

The recent Ebola outbreak in West Africa was an exemplar for the need to rapidly measure population-level health-seeking behaviors, in order to understand healthcare utilization during emergency situations. Taking advantage of the high prevalence of mobile phones, we deployed a national SMS-poll and collected data about individual-level health and health-seeking behavior throughout the outbreak from 6694 individuals from March to June 2015 in Liberia. Using propensity score matching to generate balanced subsamples, we compared outcomes in our survey to those from a recent household survey (the 2013 Liberian Demographic Health Survey). We found that the matched subgroups had similar patterns of delivery location in aggregate, and utilizing data on the date of birth, we were able to show that facility-based deliveries were significantly decreased during, compared to after the outbreak (p < 0.05) consistent with findings from retrospective studies using healthcare-based data. Directly assessing behaviors from individuals via SMS also enabled the measurement of public and private sector facility utilization separately, which has been a challenge in other studies in countries including Liberia which rely mainly on government sources of data. In doing so, our data suggest that public facility-based deliveries returned to baseline values after the outbreak. Thus, we demonstrate that with the appropriate methodological approach to account for different population denominators, data sourced via mobile tools such as SMS polling could serve as an important low-cost complement to existing data collection strategies especially in situations where higher-frequency data than can be feasibly obtained through surveys is useful.

Managing Patient-Generated Health Data Through Mobile Personal Health Records: Analysis of Usage Data

Background

Personal health records (PHRs) and mHealth apps are considered essential tools for patient engagement. Mobile PHRs (mPHRs) can be a platform to integrate patient-generated health data (PGHD) and patients’ medical information. However, in previous studies, actual usage data and PGHD from mPHRs have not been able to adequately represent patient engagement.

Objective

By analyzing 5 years’ PGHD from an mPHR system developed by a tertiary hospital in South Korea, we aimed to evaluate how PGHD were managed and identify issues in PGHD management based on actual usage data. Additionally, we analyzed how to improve patient engagement with mPHRs by analyzing the actively used services and long-term usage patterns.

Methods

We gathered 5 years (December 2010 to December 2015) of log data from both hospital patients and general users of the app. We gathered data from users who entered PGHD on body weight, blood pressure (BP), blood glucose levels, 10-year cardiovascular disease (CVD) risk, metabolic syndrome risk, medication schedule, insulin, and allergy. We classified users according to whether they were patients or general users based on factors related to continuous use (≥28 days for weight, BP, and blood glucose, and ≥180 days for CVD and metabolic syndrome), and analyzed the patients’ characteristics. We compared PGHD entry counts and the proportion of continuous users for each PGHD by user type.

Results

The total number of mPHR users was 18,265 (patients: n=16,729, 91.59%) with 3620 users having entered weight, followed by BP (n=1625), blood glucose (n=1374), CVD (n=764), metabolic syndrome (n=685), medication (n=252), insulin (n=72), and allergy (n=61). Of those 18,256 users, 3812 users had at least one PGHD measurement, of whom 175 used the PGHD functions continuously (patients: n=142, 81.14%); less than 1% of the users had used it for more than 4 years. Except for weight, BP, blood glucose, CVD, and metabolic syndrome, the number of PGHD records declined. General users’ continuous use of PGHD was significantly higher than that of patients in the blood glucose (P<.001) and BP (P=.03) functions. Continuous use of PGHD in health management (BP, blood glucose, and weight) was significantly greater among older users (P<.001) and men (P<.001). In health management (BP, weight, and blood glucose), overall chronic disease and continuous use of PGHD were not statistically related (P=.08), but diabetes (P<.001) and cerebrovascular diseases (P=.03) were significant.

Conclusions

Although a small portion of users managed PGHD continuously, PGHD has the potential to be useful in monitoring patient health. To realize the potential, specific groups of continuous users must be identified, and the PGHD service must target them. Further evaluations for the clinical application of PGHD, feedback regarding user interfaces, and connections with wearable devices are needed.

High-resolution Temporal Representations of Alcohol and Tobacco Behaviors from Social Media Data

Understanding tobacco- and alcohol-related behavioral patterns is critical for uncovering risk factors and potentially designing targeted social computing intervention systems. Given that we make choices multiple times per day, hourly and daily patterns are critical for better understanding behaviors. Here, we combine natural language processing, machine learning and time series analyses to assess Twitter activity specifically related to alcohol and tobacco consumption and their sub-daily, daily and weekly cycles. Twitter self-reports of alcohol and tobacco use are compared to other data streams available at similar temporal resolution. We assess if discussion of drinking by inferred underage versus legal age people or discussion of use of different types of tobacco products can be differentiated using these temporal patterns. We find that time and frequency domain representations of behaviors on social media can provide meaningful and unique insights, and we discuss the types of behaviors for which the approach may be most useful.

Pharmaceutical Perspective: How Digital Biomarkers and Contextual Data Will Enable Therapeutic Environments

Digital biomarkers are helping to reshape the understanding of health and disease, which will have an impact in how an individual's relationship to the environment is assessed, how research is conducted, and how treatment effectiveness is determined. In particular, this article highlights key activities by the pharmaceutical industry as they explore the utility and relevance of digital biomarkers across the value chain. Lastly, this paper will discuss how digital biomarkers, in conjunction with digital environmental markers, will pave the way for the creation of healthy spaces that more directly improve patient outcomes.