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While wearables primarily are buckled to consumers' wrists at this point, they'll soon find a new home: the ear, says Craig Stires, associate vice president for big data, software and analytics at IDC Asia Pacific. And they might even get a new moniker: hearables.
Mesure des pas effectués en une journée, suivi du sommeil, maîtrise du poids ou analyse des performances physiques : en 2017, un utilisateur de smartphone sur deux aura installé une application santé ou bien-être, soit 3,4 milliards de personnes dans le monde*. Comment accompagner le développement de ce marché, estimé à 26 milliards de dollars en 2017, tout en préservant la vie privée des utilisateurs ? s’interroge la Commission nationale de l’informatique et des libertés (CNIL) dans son rapport d’activité annuel.
No one has arms long enough to wear all of the activity-tracking wristbands currently on sale or awaiting release. These devices count your steps, measure your sleep and some even monitor your heart rate.
But do you know how this information immediately applies to your lifestyle, or what you should do with it?
The services behind these trackers need to invest in immediacy by providing useful information, ideally in real time, so we can optimize our wealth of data into action.
Everyone wants to be better, but nobody has a baseline for understanding themselves.
what use is the data without knowing in real time what you, individually, can do to change it?
I’d like to know whether I need to slow down. Am I pushing myself too hard?
If I got a dollar for each time I said this to someone in the last year, I would have got a million plus by now :) ... I am happy that others see this as a deal breaker for wearables too.
The mediXcel PHR is solving this very problem by trying to build a personalized analysis engine on top of the wearable databank it has which connects to 40 odd wearables at the moment.
A good example of how data is cool. But, in order to make it meaningful, it needs to be analysis!!
The concepts of “self-tracking” and “the quantified self” have recently begun to emerge in discussions of how best to optimize one’s life. These concepts refer to the practice of gathering data about oneself on a regular basis and then recording and analyzing the data to produce statistics and other data (such as images) relating to one’s bodily functions and everyday habits. Some self-trackers collect data on only one or two dimensions of their lives, and only for a short time. Others may do so for hundreds of phenomena and for long periods.
The tracking and analysis of aspects of one’s self and one’s body are not new practices. People have been recording their habits and health-related metrics for centuries as part of attempts at self-reflection and self-improvement.
What is indisputably new is the term “the quantified self” and its associated movement, which includes a dedicated website with that title, and regular meetings and conferences, as well as the novel ways of self-tracking using digital technologies that have developed in recent years.
A growing range of digital devices with associated apps are now available for self-tracking [1]. Many of these devices can be worn on or close to the body to measure elements of the user’s everyday life and activities and produce data that can be recorded and monitored by the user. They include not only digital cameras, smartphones, tablet computers, watches, wireless weight scales, and blood pressure monitors, but also wearable bands or patches, clip-on devices and jewelry with embedded sensors able to measure bodily functions or movement and upload data wirelessly.
In many of these devices global positioning devices, gyroscopes, altimeters, and accelerometers provide spatial location and quantify movement. These technologies allow self-trackers to collect data on their moods, diet, dreams, social encounters, posture, sexual activity, blood chemistry, heart rate, body temperature, exercise patterns, brain function, alcohol, coffee and tobacco consumption, and many other variables.
Read more at the original source: http://ieeexplore.ieee.org/stamp/stamp.jsp?reload=true&tp=&arnumber=6679313
Twitter is a widely used social medium. However, its application in promoting health behaviors is understudied.
In order to provide insights into designing health marketing interventions to promote physical activity on Twitter, this exploratory infodemiology study applied both social cognitive theory and the path model of online word of mouth to examine the distribution of different electronic word of mouth (eWOM) characteristics among personal tweets about physical activity in the United States.
This study used 113 keywords to retrieve 1 million public tweets about physical activity in the United States posted between January 1 and March 31, 2011. A total of 30,000 tweets were randomly selected and sorted based on numbers generated by a random number generator. Two coders scanned the first 16,100 tweets and yielded 4672 (29.02%) tweets that they both agreed to be about physical activity and were from personal accounts. Finally, 1500 tweets were randomly selected from the 4672 tweets (32.11%) for further coding. After intercoder reliability scores reached satisfactory levels in the pilot coding (100 tweets separate from the final 1500 tweets), 2 coders coded 750 tweets each. Descriptive analyses, Mann-Whitney U tests, and Fisher exact tests were performed.
Results: Tweets about physical activity were dominated by neutral sentiments (1270/1500, 84.67%). Providing opinions or information regarding physical activity (1464/1500, 97.60%) and chatting about physical activity (1354/1500, 90.27%) were found to be popular on Twitter.
Approximately 60% (905/1500, 60.33%) of the tweets demonstrated users’ past or current participation in physical activity or intentions to participate in physical activity. However, social support about physical activity was provided in less than 10% of the tweets (135/1500, 9.00%). Users with fewer people following their tweets (followers) (P=.02) and with fewer accounts that they followed (followings) (P=.04) were more likely to talk positively about physical activity on Twitter.
People with more followers were more likely to post neutral tweets about physical activity (P=.04). People with more followings were more likely to forward tweets (P=.04). People with larger differences between number of followers and followings were more likely to mention companionship support for physical activity on Twitter (P=.04).
Conclusions: Future health marketing interventions promoting physical activity should segment Twitter users based on their number of followers, followings, and gaps between the number of followers and followings.
The innovative application of both marketing and public health theory to examine tweets about physical activity could be extended to other infodemiology or infoveillance studies on other health behaviors (eg, vaccinations).
Pharma Manager & Geek ... #hcsmeu #hcsmeufr #health20fr
http://www.scoop.it/t/pharma-geek
http://www.tikimee.com/lionel-reichardt
