David Alexander Ellis

The Rise of Consumer Health Wearables: Promises and Barriers

Lukasz Piwek and colleagues consider whether wearable technology can become a valuable asset for health care.

Beyond self report:tools to compare estimated and real-world smartphone use

Psychologists typically rely on self-report data when quantifying mobile phone usage, despite little evidence of its validity. In this paper we explore the accuracy of using self-reported estimates when compared with actual smartphone use. We also include source code to process and visualise these data. We compared 23 participants’ actual smartphone use over a two-week period with self-reported estimates and the Mobile Phone Problem Use Scale. Our results indicate that estimated time spent using a smartphone may be an adequate measure of use, unless a greater resolution of data are required. Estimates concerning the number of times an individual used their phone across a typical day did not correlate with actual smartphone use. Neither estimated duration nor number of uses correlated with the Mobile Phone Problem Use Scale. We conclude that estimated smartphone use should be interpreted with caution in psychological research.

Weekday Affects Attendance Rate for Medical Appointments: Large-Scale Data Analysis and Implications

The financial cost of missed appointments is so great that even a small percentage reduction in Did Not Attend (DNA) rate could save significant sums of money. Previous studies have identified many factors that predict DNA rate, including patient age, gender, and transport options. However, it is not obvious how healthcare providers can use this information to improve attendance, as such factors are not under their control. One factor that is under administrative control is appointment scheduling. Here we asked whether DNA rate could be reduced by altering scheduling policy. In Study 1, we examined attendance records for 4,538,294 outpatient hospital appointments across Scotland between January 1st 2008 and December 31st 2010. DNA rate was highest for Mondays (11%), lowest for Fridays (9.7%), and decreased monotonically over the week (Monday-Friday comparison [χ2(1, N  = 1,585,545)  = 722.33, p<0.0001]; Relative Risk Reduction 11.8%). This weekly decline was present for male and female patient groups of all ages, but was steeper for younger age groups. In Study 2, we examined attendance records for 10,895 appointments at a single GP clinic in Glasgow. Here again, DNA rate was highest for Mondays (6.2%), lowest for Fridays (4.2%), and decreased monotonically over the week (Monday-Friday comparison [χ2(1, N  = 4767)  = 9.20, p<0.01]; Relative Risk Reduction 32.3%). In two very different settings, appointments at the beginning of the week were more likely to be missed than appointments at the end of the week. We suggest that DNA rate could be significantly reduced by preferentially loading appointments onto high-attendance days.

Stress Detection Using Wearable Physiological Sensors

As the population increases in the world, the ratio of health carers is rapidly decreasing. Therefore, there is an urgent need to create new technologies to monitor the physical and mental health of people during their daily life. In particular, negative mental states like depression and anxiety are big problems in modern societies, usually due to stressful situations during everyday activities including work. This paper presents a machine learning approach for stress detection on people using wearable physiological sensors with the final aim of improving their quality of life. The presented technique can monitor the state of the subject continuously and classify it into ”stressful” or ”non-stressful” situations. Our classification results show that this method is a good starting point towards real-time stress detection.

Stress detection using wearable physiological and sociometric sensors

Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and [Formula: see text]-nearest neighbor. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real-time stress detection. Finally, we present an study of the most discriminative features for stress detection.

N,N′,N″-1,4,7-Triazacyclononane with pendant alkyne arms: Crystal structures of [CuL2′][PF6]2, [NiL2′][ClO4]2 and CuL″Cl2 (L′=N-(4-but-2-yne)-1,4,7-triazacyclononane, L″=N-(5-pent-2-yne)-1,4,7-triazacyclononane)

Abstract We report the synthesis of a series of macrocyclic ligands based on N,N′,N″-1,4,7-triazacyclononane with pendant alkyne arms. N,N′,N″-tris-(3-prop-1-yne)-1,4,7-triazacyclononane (L) has three pendant alkyne arms while N-(4-but-2-yne)1,4,7-triazacyclononane (L′) and N-(5-pent-2-yne)-1,4,7-triazacyclononane (L″) each have a single pendant arm. The ligands form coordination complexes with Cu(II), Ni(II), Co(II) and Mo(0). The crystal structures of [CuL2′][PF6]2, [NiL2′][ClO4]2 and CuL″Cl2 are presented and discussed.

X-ray crystal structures of Ru3(μ-H)(μ-C,N-C5H4N)(CO)10-n(P i Pr3) n ,n=0,1: A A case of the hydride following the Phosphine?

The reaction ofRu3(μ-H)(μ-C,N-C5H4N)(CO)10 (1) with Pt(PiPr3)(nb)2 {nb = bicyclo-[2.2.1]hept-2-ene} does not afford any Ru-Pt mixed metal clusters, but gives instead the mono-substituted phosphine derivative Ru3(μ-H) (μ-C,N-C5H4N)(CO)9(PiPr3) (2) as the sole isolable product. Single crystal X-ray studies have been carriedout on 1 and 2. Crystal data for 1: monoclinic, space group P21/c,a = 16.9637(10) A,b = 7.6632(5) Á,c = 17.4058(11) Á, β = 117.214(5)°,V = 2009.0(2) Á3,R(Rw) = 0.022 (0.034) for 3090 independent absorption corrected data. Crystal data for 2: triclinic, space group PĪ,a = 9.3389(5) Á,b = 11.4376(6) Á,c = 15.1781(8) Á,α = 76.454(4),β = 79.900(5),γ = 67.428(5)°,V = 1448.8(2) Á3R(Rw) = 0.024 (0.034) for 4564 independent absorption corrected data. In cluster 1 the Ru-Ru bonds are in the range 2.8462(4)-2.8986(4) Á. The hydride andσ-pyridyl ligand bridge the same Ru-Ru vector, and the Ru(μ-H) bridge is symmetric, with Ru-H = 1.78(4) and 1.77(4) Á. In cluster 2 the Ru-Ru distances show a greater ranger 2.7267(3)-3.0604(3) Á. The phosphine ligand is bonded to the Ru atom which is not involved in theσ-pyridyl bridge. In contrast to 1, the hydride andσ-pyridyl ligands in 2 bridge different Ru-Ru vectors and the resultant Ru(μ-H)Ru bridge is asymmetric, with Ru-H = 1.70(4) and 1.89(4) Á.

Predicting Smartphone Operating System from Personality and Individual Differences

Android and iPhone devices account for over 90 percent of all smartphones sold worldwide. Despite being very similar in functionality, current discourse and marketing campaigns suggest that key individual differences exist between users of these two devices; however, this has never been investigated empirically. This is surprising, as smartphones continue to gain momentum across a variety of research disciplines. In this article, we consider if individual differences exist between these two distinct groups. In comparison to Android users, we found that iPhone owners are more likely to be female, younger, and increasingly concerned about their smartphone being viewed as a status object. Key differences in personality were also observed with iPhone users displaying lower levels of Honesty-Humility and higher levels of emotionality. Following this analysis, we were also able to build and test a model that predicted smartphone ownership at above chance level based on these individual differences. In line with extended self-theory, the type of smartphone owned provides some valuable information about its owner. These findings have implications for the increasing use of smartphones within research particularly for those working within Computational Social Science and PsychoInformatics, where data are typically collected from devices and applications running a single smartphone operating system.

Can Programming Frameworks Bring Smartphones into the Mainstream of Psychological Science

Smartphones continue to provide huge potential for psychological science and the advent of novel research frameworks brings new opportunities for researchers who have previously struggled to develop smartphone applications. However, despite this renewed promise, smartphones have failed to become a standard item within psychological research. Here we consider the key issues that continue to limit smartphone adoption within psychological science and how these barriers might be diminishing in light of ResearchKit and other recent methodological developments. We conclude that while these programming frameworks are certainly a step in the right direction it remains challenging to create usable research-orientated applications with current frameworks. Smartphones may only become an asset for psychology and social science as a whole when development software that is both easy to use and secure becomes freely available.

Understanding repeated non-attendance in health services: a pilot analysis of administrative data and full study protocol for a national retrospective cohort

Introduction Understanding the causes of low engagement in healthcare is a pre-requisite for improving health services’ contribution to tackling health inequalities. Low engagement includes missing healthcare appointments. Serially (having a pattern of) missing general practice (GP) appointments may provide a risk marker for vulnerability and poorer health outcomes. Methods and analysis A proof of concept pilot using GP appointment data and a focus group with GPs informed the development of missed appointment categories: patients can be classified based on the number of appointments missed each year. The full study, using a retrospective cohort design, will link routine health service and education data to determine the relationship between GP appointment attendance, health outcomes, healthcare usage, preventive health activity and social circumstances taking a life course approach and using data from the whole journey in the National Health Service (NHS) healthcare. 172 practices will be recruited (∼900 000 patients) across Scotland. The statistical analysis will focus on 2 key areas: factors that predict patients who serially miss appointments, and serial missed appointments as a predictor of future patient outcomes. Regression models will help understand how missed appointment patterns are associated with patient and practice characteristics. We shall identify key factors associated with serial missed appointments and potential interactions that might predict them. Ethics and dissemination The results of the project will inform debates concerning how best to reduce non-attendance and increase patient engagement within healthcare systems. Significant non-academic beneficiaries include governments, policymakers and medical practitioners. Results will be disseminated via a combination of academic outputs (papers, conferences), social media and through collaborative public health/policy fora.