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Mobile Data Collection: Smart, but Not (Yet) Smart Enough

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      Challenges of Big Data Analysis.

      Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.
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        Patients' memories of painful medical treatments: real-time and retrospective evaluations of two minimally invasive procedures.

        Patients' memories of painful medical procedures may influence their decisions about future treatments, yet memories are imperfect and susceptible to bias. We recorded in real-time the intensity of pain experienced by patients undergoing colonoscopy (n = 154) and lithotripsy (n = 133). We subsequently examined patients' retrospective evaluations of the total pain of the procedure, and related these evaluations to the real-time recording obtained during the experience. We found that individuals varied substantially in the total amount of pain they remembered. Patients' judgments of total pain were strongly correlated with the peak intensity of pain (P < 0.005) and with the intensity of pain recorded during the last 3 min of the procedure (P < 0.005). Despite substantial variation in the duration of the experience, lengthy procedures were not remembered as particularly aversive. We suggest that patients' memories of painful medical procedures largely reflect the intensity of pain at the worst part and at the final part of the experience.
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          The Smartphone Psychology Manifesto.

          By 2025, when most of today's psychology undergraduates will be in their mid-30s, more than 5 billion people on our planet will be using ultra-broadband, sensor-rich smartphones far beyond the abilities of today's iPhones, Androids, and Blackberries. Although smartphones were not designed for psychological research, they can collect vast amounts of ecologically valid data, easily and quickly, from large global samples. If participants download the right "psych apps," smartphones can record where they are, what they are doing, and what they can see and hear and can run interactive surveys, tests, and experiments through touch screens and wireless connections to nearby screens, headsets, biosensors, and other peripherals. This article reviews previous behavioral research using mobile electronic devices, outlines what smartphones can do now and will be able to do in the near future, explains how a smartphone study could work practically given current technology (e.g., in studying ovulatory cycle effects on women's sexuality), discusses some limitations and challenges of smartphone research, and compares smartphones to other research methods. Smartphone research will require new skills in app development and data analysis and will raise tough new ethical issues, but smartphones could transform psychology even more profoundly than PCs and brain imaging did.

            Author and article information

            1University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich , Zurich, Switzerland
            2Department of Communication and Media Research, University of Zurich , Zurich, Switzerland
            3Department of Psychology, University of Zurich , Zurich, Switzerland
            Author notes

            Edited by: Myra Spiliopoulou, Otto-von-Guericke Universität Magdeburg, Germany

            Reviewed by: Henning Peters, Neuroimaging Center (TUM-NIC), Germany; Johannes Schobel, University of Ulm, Germany

            *Correspondence: Alexander Seifert alexander.seifert@

            This article was submitted to Neural Technology, a section of the journal Frontiers in Neuroscience

            Front Neurosci
            Front Neurosci
            Front. Neurosci.
            Frontiers in Neuroscience
            Frontiers Media S.A.
            18 December 2018
            : 12
            Copyright © 2018 Seifert, Hofer and Allemand.

            This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

            Figures: 0, Tables: 0, Equations: 0, References: 47, Pages: 4, Words: 3671
            Funded by: Universität Zürich 10.13039/501100006447


            smartphone, big data, real-life study, experience sampling, mobile data


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