Social Networking Smartphone Applications and Sexual Health Outcomes among Men Who Have Sex with Men

Several smartphone applications (apps) designed to help men who have sex with men (MSM) find casual sexual partners have appeared on the market recently. Apps of this nature have the potential to impact sexual health and behavior by providing constant access to a large supply of available partners. In this study, the sexual health history, behavior, and personality of MSM who use these apps was compared to MSM who meet partners in other ways. A sample of 110 adult MSM was recruited online to complete a cross-sectional survey. All participants were either single or involved in a non-exclusive romantic relationship. There were no statistically significant differences between app users and non-users in frequency of insertive or receptive anal sex without a condom. However, app users reported significantly more sexual partners and had a higher prevalence of ever being diagnosed with an STI than did non-users. App users did not differ from non-users on any demographic or personality variables (including erotophilia, sensation seeking, and self-control); however, when adjusting lifetime total sex partners for those met specifically through apps, app users still had significantly more partners. This pattern of results suggests that app users may be more sexually active in general. More work is needed to fully understand the association between this emerging technology and potential sexual health risks.     

Analysis of Trigger Factors in Episodic Migraineurs Using a Smartphone Headache Diary Applications

Background

Various stimuli can trigger migraines in susceptible individuals. We examined migraine trigger factors by using a smartphone headache diary application.

Method

Episodic migraineurs who agreed to participate in our study downloaded smartphone headache diary application, which was designed to capture the details regarding headache trigger factors and characteristics for 3 months. The participants were asked to access the smartphone headache diary application daily and to confirm the presence of a headache and input the types of trigger factors.

Results

Sixty-two participants kept diary entries until the end of the study. The diary data for 4,579 days were analyzed. In this data set, 1,099 headache days (336 migraines, 763 non-migraine headaches) were recorded; of these, 772 headache events had with trigger factors, and 327 events did not have trigger factors. The common trigger factors that were present on headache days included stress, fatigue, sleep deprivation, hormonal changes, and weather changes. The likelihood of a headache trigger was 57.7% for stress, 55.1% for sleep deprivation, 48.5% for fatigue, and 46.5% for any trigger. The headaches with trigger factors were associated with greater pain intensity (p<0.001), headache-related disability (p<0.001), abortive medication use (p = 0.02), and the proportion of migraine (p < 0.001), relative to those without trigger factors. Traveling (odd ratios [OR]: 6.4), hormonal changes (OR: 3.5), noise (OR: 2.8), alcohol (OR: 2.5), overeating (OR: 2.4), and stress (OR:1.8) were significantly associated with migraines compared to non-migraine headaches. The headaches that were associated with hormonal changes or noise were more often migraines, regardless of the preventive medication. The headaches due to stress, overeating, alcohol, and traveling were more often migraines without preventive medication, but it was not evident with preventive medication.

Conclusion

Smartphone headache diary application is an effective tool to assess migraine trigger factors. The headaches with trigger factors had greater severity or migraine features. The type of triggers and the presence of preventive medication influenced the headache characteristics; hence, an investigation of trigger factors would be helpful in understanding migraine occurrences.     

Crowdsourcing the Corpasome

The suffix -ome conveys “comprehensiveness” in some way. The idea of the Corpasome started half-jokingly, acknowledging the efforts to sequence five members of my family. After the unexpected response from many scientists from around the world, it has become clear how useful this approach could be for understanding the genomic information contained in our personal genomics tests.     

Verification in Referral-Based Crowdsourcing

Online social networks offer unprecedented potential for rallying a large number of people to accomplish a given task. Here we focus on information gathering tasks where rare information is sought through “referral-based crowdsourcing”: the information request is propagated recursively through invitations among members of a social network. Whereas previous work analyzed incentives for the referral process in a setting with only correct reports, misreporting is known to be both pervasive in crowdsourcing applications, and difficult/costly to filter out. A motivating example for our work is the DARPA Red Balloon Challenge where the level of misreporting was very high. In order to undertake a formal study of verification, we introduce a model where agents can exert costly effort to perform verification and false reports can be penalized. This is the first model of verification and it provides many directions for future research, which we point out. Our main theoretical result is the compensation scheme that minimizes the cost of retrieving the correct answer. Notably, this optimal compensation scheme coincides with the winning strategy of the Red Balloon Challenge.     

Inferring Mathematical Equations Using Crowdsourcing

Crowdsourcing, understood as outsourcing work to a large network of people in the form of an open call, has been utilized successfully many times, including a very interesting concept involving the implementation of computer games with the objective of solving a scientific problem by employing users to play a game—so-called crowdsourced serious games. Our main objective was to verify whether such an approach could be successfully applied to the discovery of mathematical equations that explain experimental data gathered during the observation of a given dynamic system. Moreover, we wanted to compare it with an approach based on artificial intelligence that uses symbolic regression to find such formulae automatically. To achieve this, we designed and implemented an Internet game in which players attempt to design a spaceship representing an equation that models the observed system. The game was designed while considering that it should be easy to use for people without strong mathematical backgrounds. Moreover, we tried to make use of the collective intelligence observed in crowdsourced systems by enabling many players to collaborate on a single solution. The idea was tested on several hundred players playing almost 10,000 games and conducting a user opinion survey. The results prove that the proposed solution has very high potential. The function generated during weeklong tests was almost as precise as the analytical solution of the model of the system and, up to a certain complexity level of the formulae, it explained data better than the solution generated automatically by Eureqa, the leading software application for the implementation of symbolic regression. Moreover, we observed benefits of using crowdsourcing; the chain of consecutive solutions that led to the best solution was obtained by the continuous collaboration of several players.     

Crowdsourcing Dialect Characterization through Twitter

We perform a large-scale analysis of language diatopic variation using geotagged microblogging datasets. By collecting all Twitter messages written in Spanish over more than two years, we build a corpus from which a carefully selected list of concepts allows us to characterize Spanish varieties on a global scale. A cluster analysis proves the existence of well defined macroregions sharing common lexical properties. Remarkably enough, we find that Spanish language is split into two superdialects, namely, an urban speech used across major American and Spanish citites and a diverse form that encompasses rural areas and small towns. The latter can be further clustered into smaller varieties with a stronger regional character.     

The Impacts of Using Smartphone Dating Applications on Sexual Risk Behaviours in College Students in Hong Kong

Dating applications (apps) on smartphones have become increasingly popular. The aim of this study was to explore the association between the use of dating apps and risky sexual behaviours. Data were collected in four university campuses in Hong Kong. Subjects completed a structured questionnaire asking about the use of dating apps, sexual behaviours, and sociodemographics. Multiple linear and logistics regressions were used to explore factors associated with sexual risk behaviours. Six hundred sixty-six subjects were included in the data analysis. Factors associated with having unprotected sexual intercourse with more lifetime sexual partners included use of dating apps (β = 0.93, p<0.01), having one’s first sexual intercourse before 16 years of age (β = 1.74, p<0.01), being older (β = 0.4, p<0.01), currently being in a relationship (= 0.69, p<0.05), having a monthly income at least HKD$5,000 (β = 1.34, p<0.01), being a current smoker (β = 1.52, p<0.01), and being a current drinker (β = 0.7, p<0.01). The results of a multiple logistic regression analysis found that users of dating apps (adjust odds ratio: 0.52, p<0.05) and current drinkers (adjust odds ratio: 0.40, p<0.01) were less likely to have consistent condom use. Users of dating apps (adjust odds ratio: 1.93, p<0.05), bisexual/homosexual subjects (adjust odds ratio: 2.57, p<0.01) and female subjects (adjust odds ratio: 2.00, p<0.05) were more likely not to have used condoms the last time they had sexual intercourse. The present study found a robust association between using dating apps and sexual risk behaviours, suggesting that app users had greater sexual risks. Interventions that can target app users so that they can stay safe when seeking sexual partners through dating apps should be developed.     

Lexical Variation and Change in British Sign Language

This paper presents results from a corpus-based study investigating lexical variation in BSL. An earlier study investigating variation in BSL numeral signs found that younger signers were using a decreasing variety of regionally distinct variants, suggesting that levelling may be taking place. Here, we report findings from a larger investigation looking at regional lexical variants for colours, countries, numbers and UK placenames elicited as part of the BSL Corpus Project. Age, school location and language background were significant predictors of lexical variation, with younger signers using a more levelled variety. This change appears to be happening faster in particular sub-groups of the deaf community (e.g., signers from hearing families). Also, we find that for the names of some UK cities, signers from outside the region use a different sign than those who live in the region.     

Crowdsourcing a Collective Sense of Place

Place can be generally defined as a location that has been assigned meaning through human experience, and as such it is of multidisciplinary scientific interest. Up to this point place has been studied primarily within the context of social sciences as a theoretical construct. The availability of large amounts of user-generated content, e.g. in the form of social media feeds or Wikipedia contributions, allows us for the first time to computationally analyze and quantify the shared meaning of place. By aggregating references to human activities within urban spaces we can observe the emergence of unique themes that characterize different locations, thus identifying places through their discernible sociocultural signatures. In this paper we present results from a novel quantitative approach to derive such sociocultural signatures from Twitter contributions and also from corresponding Wikipedia entries. By contrasting the two we show how particular thematic characteristics of places (referred to herein as platial themes) are emerging from such crowd-contributed content, allowing us to observe the meaning that the general public, either individually or collectively, is assigning to specific locations. Our approach leverages probabilistic topic modelling, semantic association, and spatial clustering to find locations are conveying a collective sense of place. Deriving and quantifying such meaning allows us to observe how people transform a location to a place and shape its characteristics.     

Smartphone Plasmonics for Doxycycline Detection with Silver-Lignin Bio-spacer at Attomolar Sensitivity

Plasmonics - We demonstrate the achievement of 100-fold fluorescence enhancement and attomolar doxycycline (DC) detection with the use of Ag-lignin nanocomposite in spacer architecture in surface...     

Direct Measurements of Smartphone Screen-Time: Relationships with Demographics and Sleep

BackgroundSmartphones are increasingly integrated into everyday life, but frequency of use has not yet been objectively measured and compared to demographics, health information, and in particular, sleep quality.AimsThe aim of this study was to characterize smartphone use by measuring screen-time directly, determine factors that are associated with increased screen-time, and to test the hypothesis that increased screen-time is associated with poor sleep.MethodsWe performed a cross-sectional analysis in a subset of 653 participants enrolled in the Health eHeart Study, an internet-based longitudinal cohort study open to any interested adult (≥ 18 years). Smartphone screen-time (the number of minutes in each hour the screen was on) was measured continuously via smartphone application. For each participant, total and average screen-time were computed over 30-day windows. Average screen-time specifically during self-reported bedtime hours and sleeping period was also computed. Demographics, medical information, and sleep habits (Pittsburgh Sleep Quality Index–PSQI) were obtained by survey. Linear regression was used to obtain effect estimates.ResultsTotal screen-time over 30 days was a median 38.4 hours (IQR 21.4 to 61.3) and average screen-time over 30 days was a median 3.7 minutes per hour (IQR 2.2 to 5.5). Younger age, self-reported race/ethnicity of Black and "Other" were associated with longer average screen-time after adjustment for potential confounders. Longer average screen-time was associated with shorter sleep duration and worse sleep-efficiency. Longer average screen-times during bedtime and the sleeping period were associated with poor sleep quality, decreased sleep efficiency, and longer sleep onset latency.ConclusionsThese findings on actual smartphone screen-time build upon prior work based on self-report and confirm that adults spend a substantial amount of time using their smartphones. Screen-time differs across age and race, but is similar across socio-economic strata suggesting that cultural factors may drive smartphone use. Screen-time is associated with poor sleep. These findings cannot support conclusions on causation. Effect-cause remains a possibility: poor sleep may lead to increased screen-time. However, exposure to smartphone screens, particularly around bedtime, may negatively impact sleep.     

Lessons Learned from Crowdsourcing Complex Engineering Tasks

Crowdsourcing

Crowdsourcing is the practice of obtaining needed ideas, services, or content by requesting contributions from a large group of people. Amazon Mechanical Turk is a web marketplace for crowdsourcing microtasks, such as answering surveys and image tagging. We explored the limits of crowdsourcing by using Mechanical Turk for a more complicated task: analysis and creation of wind simulations.

Harnessing Crowdworkers for Engineering

Our investigation examined the feasibility of using crowdsourcing for complex, highly technical tasks. This was done to determine if the benefits of crowdsourcing could be harnessed to accurately and effectively contribute to solving complex real world engineering problems. Of course, untrained crowds cannot be used as a mere substitute for trained expertise. Rather, we sought to understand how crowd workers can be used as a large pool of labor for a preliminary analysis of complex data.

Virtual Wind Tunnel

We compared the skill of the anonymous crowd workers from Amazon Mechanical Turk with that of civil engineering graduate students, making a first pass at analyzing wind simulation data. For the first phase, we posted analysis questions to Amazon crowd workers and to two groups of civil engineering graduate students. A second phase of our experiment instructed crowd workers and students to create simulations on our Virtual Wind Tunnel website to solve a more complex task.

Conclusions

With a sufficiently comprehensive tutorial and compensation similar to typical crowd-sourcing wages, we were able to enlist crowd workers to effectively complete longer, more complex tasks with competence comparable to that of graduate students with more comprehensive, expert-level knowledge. Furthermore, more complex tasks require increased communication with the workers. As tasks become more complex, the employment relationship begins to become more akin to outsourcing than crowdsourcing. Through this investigation, we were able to stretch and explore the limits of crowdsourcing as a tool for solving complex problems.     

SPEAR Trial: Smartphone Pediatric ElectrocARdiogram Trial

Objectives

Smartphone-enabled ECG devices have the potential to improve patient care by enabling remote ECG assessment of patients with potential and diagnosed arrhythmias. This prospective study aimed to assess the usefulness of pediatric ECG tracings generated by the AliveCor device (Oklahoma City, OK) and to assess user satisfaction.

Study Design

Enrolled pediatric patients with documented paroxysmal arrhythmia used the AliveCor device over a yearlong study period. Pediatric electrophysiologists reviewed all transmitted ECG tracings. Patient completed surveys were analyzed to assess user satisfaction.

Results

35 patients were enrolled with the following diagnoses: supraventricular tachycardia (SVT, 57%), atrial fibrillation (AF, 11%), ectopic atrial tachycardia (EAT, 6%), atrial tachycardia (AT, 3%), and ventricular tachycardia (VT, 23%). A total of 238 tracings were received from 20 patients, 96% of which were of diagnostic quality for sinus rhythm, sinus tachycardia, SVT, and AF. 126 patient satisfaction surveys (64% from parents) were completed. 98% of the survey responses indicated that it was easy to obtain tracings, 93% found it easy to transmit the tracings, 98% showed added comfort in managing arrhythmia by having the device, and 93% showed interest in continued use of the device after the study period ended.

Conclusions

Smartphone-enabled ECG devices can generate tracings of diagnostic quality in children. User satisfaction was extremely positive. Use of the device to manage certain patients with AF and SVT showcases the future role of remote ECGs in the successful outpatient management of arrhythmias in children by potentially reducing Emergency Department visits and healthcare costs.     

Face Averages Enhance User Recognition for Smartphone Security

Our recognition of familiar faces is excellent, and generalises across viewing conditions. However, unfamiliar face recognition is much poorer. For this reason, automatic face recognition systems might benefit from incorporating the advantages of familiarity. Here we put this to the test using the face verification system available on a popular smartphone (the Samsung Galaxy). In two experiments we tested the recognition performance of the smartphone when it was encoded with an individual’s ‘face-average’ – a representation derived from theories of human face perception. This technique significantly improved performance for both unconstrained celebrity images (Experiment 1) and for real faces (Experiment 2): users could unlock their phones more reliably when the device stored an average of the user’s face than when they stored a single image. This advantage was consistent across a wide variety of everyday viewing conditions. Furthermore, the benefit did not reduce the rejection of imposter faces. This benefit is brought about solely by consideration of suitable representations for automatic face recognition, and we argue that this is just as important as development of matching algorithms themselves. We propose that this representation could significantly improve recognition rates in everyday settings.     

Zsyntax: A Formal Language for Molecular Biology with Projected Applications in Text Mining and Biological Prediction

We propose a formal language that allows for transposing biological information precisely and rigorously into machine-readable information. This language, which we call Zsyntax (where Z stands for the Greek word ζωή, life), is grounded on a particular type of non-classical logic, and it can be used to write algorithms and computer programs. We present it as a first step towards a comprehensive formal language for molecular biology in which any biological process can be written and analyzed as a sort of logical “deduction”. Moreover, we illustrate the potential value of this language, both in the field of text mining and in that of biological prediction.     

Change Rates and Prevalence of a Dichotomous Variable: Simulations and Applications

A common modelling approach in public health and epidemiology divides the population under study into compartments containing persons that share the same status. Here we consider a three-state model with the compartments: A, B and Dead. States A and B may be the states of any dichotomous variable, for example, Healthy and Ill, respectively. The transitions between the states are described by change rates, which depend on calendar time and on age. So far, a rigorous mathematical calculation of the prevalence of property B has been difficult, which has limited the use of the model in epidemiology and public health. We develop a partial differential equation (PDE) that simplifies the use of the three-state model. To demonstrate the validity of the PDE, it is applied to two simulation studies, one about a hypothetical chronic disease and one about dementia in Germany. In two further applications, the PDE may provide insights into smoking behaviour of males in Germany and the knowledge about the ovulatory cycle in Egyptian women.