Industrial and Organizational Psychology: Big Data Analysis (original) (raw)
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Journal of Medical Internet Research
Background: New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects. Objective: The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research. Methods: We searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands. Results: We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands. Conclusions: The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated.
radistysabila, 2021
Definition According James Manyika (2011), "The amount of data in our world has been exploding, and analyzing large data sets so-called big data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus" According to Steve Lohr (2013), "Work force science, in short, is what happens when big data meets H.R. In the past, studies of worker behavior were typically based on observing a few hundred people at most. Today, studies can include thousands or hundreds of thousands of workers, an exponential leap ahead" According to Jose M. Cortina (2016), "We're seeing a revolution in measurement, and it will revolutionize organizational economics and personnel economics" Base on that statement, I can conclude that, "Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data processing application software" Big Data Characteristics According to Cortina M (2016), Big data can be understood with regard to three primary characteristics : (1) volume-large number of data points, (2) velocity-both the throughput of the data (amount being added constantly) and the latency in using this information, and (3) variety-multiple sources of data being integrated. What Are the Emerging Opportunities for Science and Practice? What could it mean for the study and practice of organizational psychology if we had access to varied and dynamic data? How can we apply new analytic strategies to understand workplace dynamics in more nuanced ways? What could we learn and how could we enhance organizational effectiveness and employee wellness? According to Cortina M (2016), This is the world of big data, which represents an opportunity to build our science and expand the impact of our discipline. Here we hope to ignite interest in this topic by brainstorming about the major areas of scholarship and practice in organizational psychology that could be explored, expanded, and impacted though big data.-Sociometric sensors. Sociometric sensors are wearable technology that can collect a wide range of information automatically from users and individuals around them. These devices exploit the fact that many people are already comfortable with wearable electronics, such as cell phones, digital watches, pedometers, and the emerging device category around personal biometrics such as Fitbit and Google Glass.-Social media data, text analysis, and sentiment analyses. An obvious area of focus in the world of big data is social media. Social media include websites and applications that enable users to create and share content or participate in social networking. The content of this electronic communication is a treasure trove of psychologically relevant information about people, their relationships, and their behavior. Analyses might involve simply tracking patterns of viewing or clicking, time spent in different virtual spaces, or social network patterns such as who is interacting with whom.-Microexpression analyses. Another exciting tool with a range of potential applications involves microexpression analyses. Microexpressions can be understood as representations of brief and unconscious reactions to stimuli that cannot be masked but can be detected through careful observation.-Neuro/psychophysiological tools. A wide set of tools has been developed to detect subtle changes in physiological reactions to stimuli such as brain activation, heart rate, and hormonal variation. This might include EEGs, blood pressure or heart rate monitors, automatic hormone testers, and FMRI imaging