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Reliability and validity of ten consumer activity trackers depend on walking speed

Purpose: To examine the test–retest reliability and validity of ten activity trackers for step counting at three different walking speeds. Methods:Thirty-one healthy participants walked twice on a treadmill for 30 min while wearing 10 activity trackers (Polar Loop, GarminVivosmart, Fitbit Charge HR, Apple Watch Sport, Pebble Smartwatch, Samsung Gear S, Misfit Flash, Jawbone Up Move, Flyfit, andMoves). Participants walked three walking speeds for 10 min each; slow (3.2 kmIhj1), average (4.8 kmIhj1), and vigorous (6.4 kmIhj1).To measure test–retest reliability, intraclass correlations (ICC) were determined between the first and second treadmill test. Validity wasdetermined by comparing the trackers with the gold standard (hand counting), using mean differences, mean absolute percentage errors,and ICC. Statistical differences were calculated by paired-sample t tests, Wilcoxon signed-rank tests, and by constructing Bland–Altmanplots. Results: Test–retest reliability varied with ICC ranging from j0.02 to 0.97. Validity varied between trackers and different walkingspeeds with mean differences between the gold standard and activity trackers ranging from 0.0 to 26.4%. Most trackers showed relativelylow ICC and broad limits of agreement of the Bland–Altman plots at the different speeds. For the slow walking speed, the GarminVivosmart and Fitbit Charge HR showed the most accurate results. The Garmin Vivosmart and Apple Watch Sport demonstrated the bestaccuracy at an average walking speed. For vigorous walking, the Apple Watch Sport, Pebble Smartwatch, and Samsung Gear S exhibitedthe most accurate results. Conclusion: Test–retest reliability and validity of activity trackers depends on walking speed. In general,consumer activity trackers perform better at an average and vigorous walking speed than at a slower walking speed.

Who uses running apps and sports watches?

Individual and unorganized sports with a health-related focus, such as recreational running, have grown extensively in the last decade. Consistent with this development, there has been an exponential increase in the availability and use of electronic monitoring devices such as smartphone applications (apps) and sports watches. These electronic devices could provide support and monitoring for unorganized runners, who have no access to professional trainers and coaches. The purpose of this paper is to gain insight into the characteristics of event runners who use running-related apps and sports watches. This knowledge is useful from research, design, and marketing perspectives to adequately address unorganized runners’ needs, and to support them in healthy and sustainable running through personalized technology. Data used in this study are drawn from the standardized online Eindhoven Running Survey 2014 (ERS14). In total, 2,172 participants in the Half Marathon Eindhoven 2014 completed the questionnaire (a response rate of 40.0%). Binary logistic regressions were used to analyze the impact of socio-demographic variables, running-related variables, and psychographic characteristics on the use of running-related apps and sports watches. Next, consumer profiles were identified. The results indicate that the use of monitoring devices is affected by socio-demographics as well as sports-related and psychographic variables, and this relationship depends on the type of monitoring device. Therefore, distinctive consumer profiles have been developed to provide a tool for designers and manufacturers of electronic running-related devices to better target (unorganized) runners’ needs through personalized and differentiated approaches. Apps are more likely to be used by younger, less experienced and involved runners. Hence, apps have the potential to target this group of novice, less trained, and unorganized runners. In contrast, sports watches are more likely to be used by a different group of runners, older and more experienced runners with higher involvement. Although apps and sports watches may potentially promote and stimulate sports participation, these electronic devices do require a more differentiated approach to target specific needs of runners. Considerable efforts in terms of personalization and tailoring have to be made to develop the full potential of these electronic devices as drivers for healthy and sustainable sports participation.

Feeding forward and feeding back

The bilingual education standard agreed by the Network for Dutch Bilingual schools requires subject teachers to be able to provide feedback on their learners’ language use. But this is not as easy as it seems. What type of language do learners produce in subject lessons? What language aspects could the feedback focus on, and how is this best done?In this workshop, we will look at various types of feedforward and feedback that teachers can use to support and develop their learners’ subject-specific language. By watching videos of pre-vocational learners performing tasks, we will focus on finding ways to support learners’ spoken language. At the end of the workshop, teachers will have gained insight into the principles of feedback and feedforward, will have designed feedback/forward activities and will have applied both to videotaped performances.

Greenpeace : Apple's iCloud dienst is vervuild

Bekwaam als leraar

Deze app biedt leraren en coaches in het po, vo en mbo een handvat bij de ontwikkeling van hun bekwaamheid als leraar.Het beschrijft bekwaamheden van leraren op verschillende niveau's als handvat voor reflectie en biedt een aantal reeflectieve vragen als startpunt voor een professionele dialoog tussen (startende) leraar en coach of binnen lerarenteams.De app komt voort uit het project Junior Leraar en is uitgebreid met een mbo variant.

Digitale sporen in smartphones. Een kennismaking met <i>pattern-of-life forensics</i>

Mensen laten in het dagelijks leven steeds meer digitale sporen achter in computers, tablets en smartphones die waardevol kunnen zijn voor de opsporing en voor de bewijsvoering in strafzaken. Naast sporen die we bewust achterlaten, zoals berichten, foto’s en video’s, laten onze handelingen ook allerlei sporen na waarvan we ons veelal niet bewust zijn. Tezamen bieden al deze sporen een kijkje in het dagelijks activiteitenpatroon van een gebruiker. Het digitaal forensisch onderzoek dat zich hierop richt, wordt aangeduid als pattern-of-life forensics. Dit onderzoek is een waardevolle aanvulling voor het toetsen en opstellen van scenario’s, maar tegelijkertijd is het ook een complex onderzoeksgebied vanwege de snelle ontwikkelingen in hardware en software.In dit artikel geven we een aantal voorbeelden van pattern-of-life forensics aan de hand van rechterlijke uitspraken, experimenten die zijn uitgevoerd met smartphones en onderzoek dat is gepubliceerd in de literatuur. In het bijzonder hebben we ons daarbij gericht op het aantonen of een handeling met voorbedachte raad is verricht, op het aantonen van eigenaarschap van een smartphone en op de betrouwbaarheid van digitale sporen als die worden betwist. Juist het dagelijkse patroon van activiteiten dat door middel van pattern-of-life forensics wordt vastgesteld, kan bij uitstek vergeleken worden met observaties uit andere bronnen zoals beveiligingscamera’s, andere inbeslaggenomen bewijsstukken en/of getuigenverklaringen.