Predictors of Pain Management Strategies in Adults with Low-Back Pain: A Secondary Analysis of Amazon Mechanical Turk Survey Data.

Objective: To evaluate the associations between baseline demographics, health conditions, pain management strategies, and health-related quality-of-life (HRQoL) measures with pain management strategies at 3-month follow-up in respondents reporting current low-back pain (LBP). Study design: Cohort study of survey data collected from adults with LBP sampled from Amazon Mechanical Turk crowdsourcing panel. Methods: Demographics, health conditions, and the Patient-Reported Outcomes Measurement Information System (PROMIS)-10 were included in the baseline survey. Respondents reporting LBP completed a more comprehensive survey inquiring about pain management strategies and several HRQoL measures. Bivariate then multivariate logistic regression estimated odds ratios (ORs) with 95% confidence intervals (CIs) for the association between baseline characteristics and pain management utilization at 3-month follow-up. Model fit statistics were evaluated to assess the predictive value. Results: The final cohort included 717 respondents with completed surveys. The most prevalent pain management strategy at follow-up was other care (n = 474), followed by no care (n = 94), conservative care only (n = 76), medical care only (n = 51), and medical and conservative care combined (n = 22). The conservative care only group had higher (better) mental and physical health PROMIS-10 scores as opposed to the medical care only and combination care groups, which had lower (worse) physical health scores. In multivariate models, estimated ORs (95% CIs) for the association between baseline and follow-up pain management ranged from 4.6 (2.7-7.8) for conservative care only to 16.8 (6.9-40.7) for medical care only. Additional significant baseline predictors included age, income, education, workman's compensation claim, Oswestry Disability Index score, and Global Chronic Pain Scale grade. Conclusions: This study provides important information regarding the association between patient characteristics, HRQoL measures, and LBP-related pain management utilization.

Crowdsourcing Electronic Health Record Improvements at Scale across an Integrated Health Care Delivery System.

BACKGROUND AND OBJECTIVE: Despite widespread adoption of electronic health records (EHRs), these systems have significant room for improved efficiency and efficacy. While the idea of crowdsourcing EHR improvement ideas has been reported, little is known about how this might work across an integrated health care delivery system in practice. METHODS: Our program solicited EHR improvement submissions during two timeframes across 10 hospitals and 60 clinics in an upper-Midwest integrated health care delivery system. Submissions were primarily collected via an EHR help feature. RESULTS: A total of 262 and 294 submissions were received in 2019 and 2022, with a majority initiated from physicians (73.5 and 46.9%, 2019 and 2022) specializing in family medicine (52.0 and 59.3%). In 2022, the program reached a larger variety of personnel than 2019, with 53.0% of submissions from advanced practice providers, nurses, administrative staff, and other roles (p < 0.0001). Many ideas (36.4 and 50.0% in 2019 and 2022) reflected a lack of user understanding of EHR features and were addressed through training/education. Significant (27.1 and 25.9%) or simple (24.0 and 14.7%) EHR optimizations were required to address most remaining suggestions, with a number part of planned EHR improvement projects already (16.3 and 17.6%). CONCLUSION: Our experience using a crowdsourcing approach for EHR improvement ideas provided clinicians and staff the opportunity to address frustrations with the EHR and offered concrete feedback and solutions. While previous studies have suggested EHR technology improvements as paramount, we observed large numbers of users having a misunderstanding of EHR features, highlighting the need for improved EHR user competency and training.

Patients' Experiences With Therapeutic Approaches for Post-COVID Syndrome: Results of a Crowdsourced Research Survey.

Some patients develop multiple protracted sequelae after infection with SARS-CoV-2, collectively known as post-COVID syndrome or long COVID. To date, there is no evidence showing benefit of specific therapies for this condition, and patients likely resort to self-initiated therapies. We aimed to obtain information about therapies used by and needs of this population via inductive crowdsourcing research. Patients completed an online questionnaire about their symptoms and experiences with therapeutic approaches. Responses of 499 participants suggested few approaches (eg, mind-body medicine, respiratory therapy) had positive effects and showed a great need for patient-centered communication (eg, more recognition of this syndrome). Our findings can help design clinical studies and underscore the importance of the holistic approach to care provided by family medicine.

Mindfulness and music interventions in the workplace: assessment of sustained attention and working memory using a crowdsourcing approach.

BACKGROUND: Occupational stress has huge financial as well as human costs. Application of crowdsourcing might be a way to strengthen the investigation of occupational mental health. Therefore, the aim of the study was to assess Danish employees' stress and cognition by relying on a crowdsourcing approach, as well as investigating the effect of a 30-day mindfulness and music intervention. METHODS: We translated well-validated neuropsychological laboratory- and task-based paradigms into an app-based platform using cognitive games measuring sustained attention and working memory and measuring stress via. Cohen's Perceived Stress Scale. A total of 623 healthy volunteers from Danish companies participated in the study and were randomized into three groups, which consisted of a 30-day intervention of either mindfulness or music, or a non-intervention control group. RESULTS: Participants in the mindfulness group showed a significant improvement in the coefficient of sustained attention, working memory capacity and perceived stress (p < .001). The music group showed a 38% decrease of self-perceived stress. The control group showed no difference from pre to post in the survey or cognitive outcome measures. Furthermore, there was a significant correlation between usage of the mindfulness and music app and elevated score on both the cognitive games and the perceived stress scale. CONCLUSION: The study supports the nascent field of crowdsourcing by being able to replicate data collected in previous well-controlled laboratory studies from a range of experimental cognitive tasks, making it an effective alternative. It also supports mindfulness as an effective intervention in improving mental health in the workplace.

Can crowdsourced data help to optimize atopic dermatitis treatment? Comparing web search data and environmental data in Germany.

BACKGROUND: Atopic dermatitis (AD) is one of the most common chronic skin diseases worldwide, showing various manifestations and having a severe impact on quality of life. As previous studies demonstrated, internet search analysis can help identify public interest in diseases and possible influencing factors on search behavior. OBJECTIVE: To identify AD-related topics of interest in Germany using internet search volume. METHODS: Google Ads Keyword Planner was used to identify AD-related search terms including their search volume in Germany on a national level as well as in 16 selected cities from January 2016 to December 2019. Identified keywords were qualitatively analyzed, and temporal trends as well as the influence of seasonal and environmental factors on search volume were assessed. RESULTS: Overall, 1222 AD-related search terms with a search volume of 8 842 360 searches were identified. An increase from 2016 to 2019 and seasonal peaks from January to April of each year were observed. Nationwide, the search volume correlated with mean monthly temperature and sun duration. With increasing temperature and sun duration, a significant decrease in search queries was observed. The most populated cities showed the lowest number of searches per 100 000 inhabitants (Berlin, Hamburg, and Munich). In the eight categories formed (comorbidities, general, influential factors, localization, stage of life, symptoms/severity, therapy/information, and questions on AD), the highest proportion of search queries were assigned to the category "therapy/information" in most cities. In this category, a focus on the topics of "alternative medicine" and "home remedies" could be observed. CONCLUSION: The overall high and increasing search volume indicates a high interest in AD-related topics, especially regarding treatment and disease education. Information provided by internet search volume analyses can optimize AD therapy and patient-centered care by providing insight into patient needs and predicting potential climatic trigger factors.

An Exploratory Approach to Deriving Nutrition Information of Restaurant Food from Crowdsourced Food Images: Case of Hartford.

Deep learning models can recognize the food item in an image and derive their nutrition information, including calories, macronutrients (carbohydrates, fats, and proteins), and micronutrients (vitamins and minerals). This technology has yet to be implemented for the nutrition assessment of restaurant food. In this paper, we crowdsource 15,908 food images of 470 restaurants in the Greater Hartford region on Tripadvisor and Google Place. These food images are loaded into a proprietary deep learning model (Calorie Mama) for nutrition assessment. We employ manual coding to validate the model accuracy based on the Food and Nutrient Database for Dietary Studies. The derived nutrition information is visualized at both the restaurant level and the census tract level. The deep learning model achieves 75.1% accuracy when compared with manual coding. It has more accurate labels for ethnic foods but cannot identify portion sizes, certain food items (e.g., specialty burgers and salads), and multiple food items in an image. The restaurant nutrition (RN) index is further proposed based on the derived nutrition information. By identifying the nutrition information of restaurant food through crowdsourced food images and a deep learning model, the study provides a pilot approach for large-scale nutrition assessment of the community food environment.

Crowdsourced mapping of unexplored target space of kinase inhibitors.

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

Crowdfunding Campaigns and COVID-19 Misinformation.

Objectives. To understand whether and how crowdfunding campaigns are a source of COVID-19-related misinformation.Methods. We searched the GoFundMe crowdfunding platform using 172 terms associated with medical misinformation about COVID-19 prophylaxes and treatments. We screened resulting campaigns for those making statements about the ability of these searched-for or related terms to prevent or treat COVID-19.Results. There were 208 campaigns worldwide that requested $21 475 568, raised $324 305 from 4367 donors, and were shared 24 158 times. The most discussed interventions were dietary supplements and purported immune system boosters (n = 231), followed by other forms of complementary and alternative medicine (n = 24), and unproven medical interventions (n = 15). Most (82.2%) of the campaigns made definitive efficacy claims.Conclusions. Campaigners focused their efforts on dietary supplements and immune system boosters. Campaigns for purported COVID-19 treatments are particularly concerning, but purported prophylaxes could also distract from known effective preventative approaches. GoFundMe should join other online and social media platforms to actively restrict campaigns that spread misinformation about COVID-19 or seek to better inform campaigners about evidence-based prophylaxes and treatments.

Comparing the Recruitment of Research Participants With Chronic Low Back Pain Using Amazon Mechanical Turk With the Recruitment of Patients From Chiropractic Clinics: A Quasi-Experimental Study.

OBJECTIVE: The purpose of this study was to compare the crowdsourcing platform Amazon Mechanical Turk (MTurk) with in-person recruitment and web-based surveys as a method to (1) recruit study participants and (2) obtain low-cost data quickly from chiropractic patients with chronic low back pain in the United States. METHODS: In this 2-arm quasi-experimental study, we used in-person clinical sampling and web-based surveys from a separate study (RAND sample, n = 1677, data collected October 2016 to January 2017) compared with MTurk (n = 310, data collected November 2016) as a sampling and data collection tool. We gathered patient-reported health outcomes and other characteristics of adults with chronic low back pain receiving chiropractic care. Parametric and nonparametric tests were run. We assessed statistical and practical differences based on P values and effect sizes, respectively. RESULTS: Compared with the RAND sample, the MTurk sample was statistically significantly younger (mean age 35.4 years, SD 9.7 vs 48.9, SD 14.8), made less money (24% vs 17% reported less than $30,000 annual income), and reported worst mental health than the RAND sample. Other differences were that the MTurk sample had more men (37% vs 29%), fewer White patients (87% vs 92%), more Hispanic patients (9% vs 5%), fewer people with a college degree (59% vs 68%), and patients were more likely to be working full time (62% vs 58%). The MTurk sample was more likely to have chronic low back pain (78% vs 66%) that differed in pain frequency and duration. The MTurk sample had less disability and better global health scores. In terms of efficiency, the surveys cost $2.50 per participant in incentives for the MTurk sample. Survey development took 2 weeks and data collection took 1 month. CONCLUSION: Our results suggest that there may be differences between crowdsourcing and a clinic-based sample. These differences range from small to medium on demographics and self-reported health. The low incentive costs and rapid data collection of MTurk makes it an economically viable method of collecting data from chiropractic patients with low back pain. Further research is needed to explore the utility of MTurk for recruiting clinical samples, such as comparisons to nationally representative samples.

What was lost, missing, sought and hoped for: Qualitatively exploring medical crowdfunding campaign narratives for Lyme disease.

Lyme disease remains a contested illness in Canada, thereby making the diagnostic and treatment journeys difficult for some people. One outcome of this is that increasing numbers of people are turning to medical crowdfunding to support access to alternative therapies, non-local health care providers and assist with managing the costs of everyday life. In this analysis, we qualitatively explore the narratives shared in Canadians' crowdfunding campaigns to support Lyme disease treatment or diagnosis to identify whether or not any common elements shared in these narratives exist, and if so, what they are. We identified 238 campaigns for inclusion from three prominent crowdfunding platforms. Thematic analysis of the campaign narratives shows four consistent themes shared in these campaigns: what is lost (e.g. bodily ability), what is missing (e.g. local care options), what is sought (e.g. funds to cover treatment abroad) and what is hoped for (e.g. return to wellbeing). These themes demonstrate the highly personal and emotional nature of medical crowdfunding, particularly in the context of a contested illness that may lead some to question the legitimacy of one's financial need. This analysis contributes valuable new insights to the nascent scholarship on medical crowdfunding, and particularly to our understanding of how people communicate about their health and bodily needs on this public platform. It also identifies important directions for future research, including the potential for crowdfunding narratives to be used for advocacy.

Crowdsourcing neuroscience: Inter-brain coupling during face-to-face interactions outside the laboratory.

When we feel connected or engaged during social behavior, are our brains in fact "in sync" in a formal, quantifiable sense? Most studies addressing this question use highly controlled tasks with homogenous subject pools. In an effort to take a more naturalistic approach, we collaborated with art institutions to crowdsource neuroscience data: Over the course of 5 years, we collected electroencephalogram (EEG) data from thousands of museum and festival visitors who volunteered to engage in a 10-min face-to-face interaction. Pairs of participants with various levels of familiarity sat inside the Mutual Wave Machine-an artistic neurofeedback installation that translates real-time correlations of each pair's EEG activity into light patterns. Because such inter-participant EEG correlations are prone to noise contamination, in subsequent offline analyses we computed inter-brain coupling using Imaginary Coherence and Projected Power Correlations, two synchrony metrics that are largely immune to instantaneous, noise-driven correlations. When applying these methods to two subsets of recorded data with the most consistent protocols, we found that pairs' trait empathy, social closeness, engagement, and social behavior (joint action and eye contact) consistently predicted the extent to which their brain activity became synchronized, most prominently in low alpha (~7-10 Hz) and beta (~20-22 Hz) oscillations. These findings support an account where shared engagement and joint action drive coupled neural activity and behavior during dynamic, naturalistic social interactions. To our knowledge, this work constitutes a first demonstration that an interdisciplinary, real-world, crowdsourcing neuroscience approach may provide a promising method to collect large, rich datasets pertaining to real-life face-to-face interactions. Additionally, it is a demonstration of how the general public can participate and engage in the scientific process outside of the laboratory. Institutions such as museums, galleries, or any other organization where the public actively engages out of self-motivation, can help facilitate this type of citizen science research, and support the collection of large datasets under scientifically controlled experimental conditions. To further enhance the public interest for the out-of-the-lab experimental approach, the data and results of this study are disseminated through a website tailored to the general public (wp.nyu.edu/mutualwavemachine).

Crowdfunding for complementary and alternative medicine: What are cancer patients seeking?

BACKGROUND: Complementary and alternative medicine (CAM) is increasingly being integrated into conventional medical care for cancer, used to counter the side effects of conventional cancer treatment, and offered as an alternative to conventional cancer care. Our aim is to gain a broader understanding of trends in CAM interventions for cancer and crowdfunding campaigns for these interventions. METHODS: GoFundMe campaigns fundraising for CAM were retrieved through a database of crowdfunding campaign data. Search terms were drawn from two National Institutes of Health lists of CAM cancer interventions and a previous study. Campaigns were excluded that did not match these or related search terms or were initiated outside of June 4th, 2018 to June 4th, 2019. RESULTS: 1,396 campaigns were identified from the US (n = 1,037, 73.9%), Canada (n = 165, 11.8%), and the UK (n = 107, 7.7%). Most common cancer types were breast (n = 344, 24.6%), colorectal (n = 131, 9.4%), and brain (n = 98, 7.0%). CAM interventions sought included supplements (n = 422, 30.2%), better nutrition (n = 293, 21.0%), high dose vitamin C (n = 276, 19.8%), naturopathy (n = 226, 16.2%), and cannabis products (n = 211, 15.1%). Mexico (n = 198, 41.9%), and the US (n = 169, 35.7%) were the most common treatment destinations. CONCLUSIONS: These findings confirm active and ongoing interest in using crowdfunding platforms to finance CAM cancer interventions. They confirm previous findings that CAM users with cancer tend to have late stage cancers, cancers with high mortality rates, and specific diseases such as breast cancer. These findings can inform targeted responses where facilities engage in misleading marketing practices and the efficacy of interventions is unproven.

New medical big data for P4 medicine on allergic conjunctivitis.

Allergic conjunctivitis affects approximately 15-20% of the global population and can permanently deteriorate one's quality of life (QoL) and work productivity, leading to societal work force costs. Although not fully understood, allergic conjunctivitis is a multifactorial disease with a complex network of environmental, lifestyle, and host contributory risk factors. To effectively enhance the quality of treatment for patients with allergic conjunctivitis, as well as other allergic diseases, the field must first comprehend the pathology underlying various individualized subjective symptoms and stratify the disease according to risk factors and presentations. Such competent stratification and societal reconstruction that targets the alleviation of the damage due to allergic diseases would greatly help ramify personalized treatments and prevent the projected increase in societal costs imposed by allergic diseases. Owing to the rapid advancements in the information and technology sector, medical big data are greatly accessible and useful to decipher the pathophysiology of many diseases. Such data collected through multi-omics and mobile health have been effective for research on chronic diseases including allergic and immune-mediated diseases. Novel big data containing vast and continuous information on individuals with allergic conjunctivitis and other allergic symptoms are being used to search for causative genes of diseases, gain insights into new biomarkers, prevent disease progression, and, ultimately, improve QoL. The individualized and holistic data accrued from new angles using technological innovations are helping the field realize the principles of P4 medicine: predictive, preventive, personalized, and participatory medicine.

Using crowd-sourced allergic rhinitis symptom data to improve grass pollen forecasts and predict individual symptoms.

Seasonal allergic rhinitis (AR), also known as hay fever, is a common respiratory condition brought on by a range of environmental triggers. Previous work has characterised the relationships between community-level AR symptoms collected using mobile apps in two Australian cities, Canberra and Melbourne, and various environmental covariates including pollen. Here, we build on these relationships by assessing the skill of models that provide a next-day forecast of an individual's risk of developing AR and that nowcast ambient grass pollen concentrations using crowd-sourced AR symptoms as a predictor. Categorical grass pollen forecasts (low/moderate/high) were made based on binning mean daily symptom scores by corresponding categories. Models for an individual's risk were constructed by forward variable selection, considering environmental, demographic, behaviour and health-related inputs, with non-linear responses permitted. Proportional-odds logistic regression was then applied with the variables selected, modelling the symptom scores on their original five-point scale. AR symptom-based estimates of today's average grass pollen concentration were more accurate than those provided by two benchmark forecasting methods using various metrics for assessing accuracy. Predictions of an individual's next-day AR symptoms rated on a five-point scale were correct in 36% of cases and within one point on this scale in 82% of cases. Both outcomes were significantly better than chance. This large-scale AR symptoms measurement program shows that crowd-sourced symptom scores can be used to predict the daily average grass pollen concentration, as well as provide a personalised AR forecast.

Crowd-sourced allergic rhinitis symptom data: The influence of environmental and demographic factors.

Allergic Rhinitis (AR) affects over half a billion people worldwide with an estimated prevalence of 1 in 5 individuals in developed countries. Although ambient pollen exposure is a causal factor in AR, the symptom-exposure relationship is typically not studied in the broader community but in small, well-characterised cohorts drawn from clinical populations. To identify relationships between AR symptoms in the community and a range of environmental factors, we used a database containing over 96,000 symptom score reports collected over a 3-year period (2014-2016) through freely available smartphone apps released in two Australian cities, Melbourne and Canberra. Ambient pollen levels and symptom scores were strongly related, with grass pollen explaining most of the symptom variation. Other factors correlated with higher symptom scores included temperature (R > 0.73) and wind speed (R > 0.75). In general, worse symptom scores were reported by younger participants, women, and those who had taken medication for AR in the preceding 24 h. The strength of this relationship varied between the two cities. Smartphone-based symptom surveys offer a cost-effective means of studying real-world risk factors for AR in a broader 'extra-clinical' population.

Wake Forest Alcohol Imagery Set: Development and Validation of a Large Standardized Alcohol Imagery Dataset.

BACKGROUND: The study of alcohol use frequency utilizes alcohol-related cue imagery. Although a number of alcohol-image databases currently exist, they have several limitations: Many are not publicly available, some use stock images or clip art rather than real photographs, several eliminate any photographs displaying brand information, and predominantly they contain relatively few images. The aim of this project was to develop a large, open-access database of alcohol-related cue images, containing photographs with and without brand information, taken in real-world environments, with images in a variety of orientations and dimensions. METHODS: The study collected 1,650 images voluntarily from the larger community, to capture photographs with a wide range of content, environments, and relation to alcohol. All images were then rated on scales of valence, arousal, and relation to alcohol by 1,008 Amazon Mechanical Turk workers, using classical emotion validation methods based on the International Affective Picture System (IAPS). Survey respondents were screened with the Alcohol Use Disorders Identification Test (AUDIT), and Cronbach's alpha scores were calculated to determine the interrater reliability of scores across the whole sample, and within low-risk, moderate-risk, and high-risk drinkers for each rating domain. Univariate ANOVAs were run to determine differences in ratings across drinking groups. RESULTS: All Cronbach's alpha scores indicated high interrater reliability within the whole sample, and across drinking severity groups. Tukey's HSD post hoc results indicated greater arousal and affect in response to image viewing in moderate- and high-risk drinkers, and higher relation-to-alcohol ratings in low-risk drinkers. All images had categorization tags assigned by members of the study team. CONCLUSIONS: The established imagery set includes 1,650 alcohol-related images, rated on scales of valence, arousal, and relation to alcohol, and categorized by type of alcohol depicted. The imagery database will be available for open-access download and use through Google Photos.

What Can Be Learned From Crowdsourced Population Asparagus Urinary Odor Kinetics?

Asparagus consumption is associated with the production of malodorous urine. Interindividual variability was previously characterized by an American Society for Clinical Pharmacology and Therapeutics crowdsourced study. To further characterize urinary odor kinetics, we conducted a study with consenting participants from Takeda Pharmaceutical International Company. The participants were randomized to consume a specified number of asparagus spears and asked to record urine odor. A kinetic-pharmacodynamic model characterized the data from both the newly conducted Takeda study (N = 42) and the previously analyzed American Society for Clinical Pharmacology and Therapeutics studies (total N = 139). The updated model included the identification of an absorption process with a half-life of 25 minutes. We estimated the elimination half-life of the asparagus effect on malodorous urine to be 7.2 hours, which was 44% longer in our study. We built on previous experience using an improved R-Shiny app for conducting the crowdsourcing experiment, further demonstrating the utility of this population kinetics approach in organizational and educational settings.