Would I Lie to You - Would You Notice?

The quantified self-paradigm is well established. Its main purpose is to use numbers from sensors to derive self-knowledge. The massive availability of persuasive technology to monitor physiological parameters of humans made the paradigm available to a tremendous number of people. A multitude of different hard- and software platforms are available at the market. They all have different properties at different levels of quality. All in common is their promise to provide accurate and precise data about the humans’ physiological condition and performed activities. Basically, they all provide a tool to make people aware of formerly hidden, non-observable, body signals. The gained awareness can then be used by people to e.g. improve their health or fitness level. In this work, we emphasize the perception of the gathered sensory data by the people. We focus on the question of how the trustworthiness of the recorded and presented data is perceived by people. As a fact, non-credible data can be understood by the user as being trustworthy and can have a negative impact on users’ behavior. This can be especially critical for human’s health in the fitness and medical application domain. It is of high importance to understand how people perceive and correlate their intrinsic body feelings with the data collected and presented by a mobile smart device like a smart watch or a fitness tracker.

[1]  Thomas Zimmermann,et al.  Persuasive technology in the real world: a study of long-term use of activity sensing devices for fitness , 2014, CHI.

[2]  Scott E Crouter,et al.  Spring-levered versus piezo-electric pedometer accuracy in overweight and obese adults. , 2005, Medicine and science in sports and exercise.

[3]  Mark W. Newman,et al.  When fitness trackers don't 'fit': end-user difficulties in the assessment of personal tracking device accuracy , 2015, UbiComp.

[4]  J. Prochaska,et al.  Transtheoretical therapy: Toward a more integrative model of change. , 1982 .

[5]  Mohamed Ismail Nounou,et al.  Are Currently Available Wearable Devices for Activity Tracking and Heart Rate Monitoring Accurate, Precise, and Medically Beneficial? , 2015, Healthcare informatics research.

[6]  Warren W. Tryon,et al.  Activity Measurement in Psychology and Medicine , 2013 .

[7]  R. Furberg,et al.  Systematic review of the validity and reliability of consumer-wearable activity trackers , 2015, International Journal of Behavioral Nutrition and Physical Activity.

[8]  Harri Oinas-Kukkonen,et al.  Understanding persuasive software functionality in practice: a field trial of polar FT60 , 2009, Persuasive '09.

[9]  Thomas Franke,et al.  Trust in activity tracker measurement and its link to user acceptance , 2018, Mensch & Computer.

[10]  Scott E. Crouter,et al.  Step Counting: A Review of Measurement Considerations and Health-Related Applications , 2016, Sports Medicine.

[11]  Thomas Franke,et al.  I track, therefore I walk - Exploring the motivational costs of wearing activity trackers in actual users , 2019, Int. J. Hum. Comput. Stud..

[12]  Feng Qian,et al.  Characterizing Smartwatch Usage in the Wild , 2017, MobiSys.

[13]  I. Mcalister,et al.  Wrist-Worn Physical Activity Trackers Tend To Underestimate Steps During Walking , 2017 .

[14]  Gerry McGovern,et al.  Reestablishing the value of content , 2002, UBIQ.

[15]  J. Prochaska,et al.  Toward a Comprehensive Model of Change , 1986 .

[16]  Apichai Wattanapisit,et al.  Evidence Behind 10,000 Steps Walking , 2017 .

[17]  Richard P Troiano,et al.  Evolution of accelerometer methods for physical activity research , 2014, British Journal of Sports Medicine.

[18]  T. Hastie,et al.  Accuracy in Wrist-Worn, Sensor-Based Measurements of Heart Rate and Energy Expenditure in a Diverse Cohort , 2016, bioRxiv.

[19]  Sean A. Munson,et al.  Exploring the design space of glanceable feedback for physical activity trackers , 2016, UbiComp.

[20]  Jodi Forlizzi,et al.  A stage-based model of personal informatics systems , 2010, CHI.

[21]  S. Edney,et al.  Users’ experiences of wearable activity trackers: a cross-sectional study , 2017, BMC Public Health.

[22]  B. J. Fogg,et al.  Persuasive technology: using computers to change what we think and do , 2002, UBIQ.

[23]  Alois Ferscha,et al.  Size does matter - positioning on the wrist a comparative study: SmartWatch vs. SmartPhone , 2017, 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[24]  Michael A. Rupp,et al.  The Impact of Technological Trust and Self-Determined Motivation on Intentions to use Wearable Fitness Technology , 2016 .

[25]  Virginia Pensabene,et al.  Assessment of the Fitbit Charge 2 for monitoring heart rate , 2018, PloS one.

[26]  John Hansen,et al.  Evaluation of Commercial Self-Monitoring Devices for Clinical Purposes: Results from the Future Patient Trial, Phase I , 2017, Sensors.

[27]  Stavros Asimakopoulos,et al.  Motivation and User Engagement in Fitness Tracking: Heuristics for Mobile Healthcare Wearables , 2017, Informatics.

[28]  Vincent Onywera,et al.  The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): design and methods , 2013, BMC Public Health.

[29]  I. Olkin,et al.  Using pedometers to increase physical activity and improve health: a systematic review. , 2007, JAMA.

[30]  Matthias Kranz,et al.  Biofeedback in the Wild - A SmartWatch Approach , 2018, 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[31]  Lora Giangregorio,et al.  Behavior Change Techniques Present in Wearable Activity Trackers: A Critical Analysis , 2016, JMIR mHealth and uHealth.