The use of self-tracking technology for health
Kooiman, Theresia Johanna Maria
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Kooiman, T. J. M. (2018). The use of self-tracking technology for health: Validity, adoption, and effectiveness. Rijksuniversiteit Groningen.
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The use of self-tracking
technology for health
Validity, adoption, and effectiveness
Printing this thesis was financially supported by:
- Research group Healthy Ageing, Allied Health Care and Nursing of the Hanze University of Applied Sciences
- University Medical Center Groningen (UMCG) - University of Groningen
- Graduate School for Health Services Research (SHARE)
- Vereniging van Oefentherapeuten Cesar en Mensendieck (VvOCM)
Cover design: Nelson Wagenaar
Lay-out: Thea Kooiman, Anne Zijlstra and GVO drukkers & vormgevers B.V. Printed by: GVO drukkers & vormgevers B.V.
ISBN: 978-94-034-1023-4 (printed version) ISBN: 978-94-034-1022-7 (electronic version)
©2018, Thea Kooiman, Groningen, the Netherlands
All rights reserved. No parts of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the copyright owner.
The use of self-tracking technology
for health
Validity, adoption, and effectiveness
Proefschrift
ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen
op gezag van de
rector magnificus prof. dr. E. Sterken en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op woensdag 7 november 2018 om 14.30 uur
door
Theresia Johanna Maria Kooiman
geboren op 26 februari 1988te Breukelen - Nederlandse Obesitas Kliniek
Printing this thesis was financially supported by:
- Research group Healthy Ageing, Allied Health Care and Nursing of the Hanze University of Applied Sciences
- University Medical Center Groningen (UMCG) - University of Groningen
- Graduate School for Health Services Research (SHARE)
- Vereniging van Oefentherapeuten Cesar en Mensendieck (VvOCM)
Cover design: Nelson Wagenaar
Lay-out: Thea Kooiman, Anne Zijlstra and GVO drukkers & vormgevers B.V. Printed by: GVO drukkers & vormgevers B.V.
ISBN: 978-94-034-1023-4 (printed version) ISBN: 978-94-034-1022-7 (electronic version)
©2018, Thea Kooiman, Groningen, the Netherlands
All rights reserved. No parts of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the copyright owner.
The use of self-tracking technology
for health
Validity, adoption, and effectiveness
Proefschrift
ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen
op gezag van de
rector magnificus prof. dr. E. Sterken en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op woensdag 7 november 2018 om 14.30 uur
door
Theresia Johanna Maria Kooiman
geboren op 26 februari 1988 te Breukelen
Copromotores Dr. M. de Groot Dr. A. Kooy
Beoordelingscommissie Prof. dr. R. Sanderman Prof. dr. R.O.B. Gans
Prof. dr. J.E.W.C. van Gemert-Pijnen
Copromotores Dr. M. de Groot Dr. A. Kooy
Beoordelingscommissie Prof. dr. R. Sanderman Prof. dr. R.O.B. Gans
Prof. dr. J.E.W.C. van Gemert-Pijnen
Chapter 1 General Introduction 9
Chapter 2 Reliability and Validity of ten consumer activity trackers 17
Thea J.M. Kooiman, Manon L. Dontje, Siska R. Sprenger, Wim P. Krijnen, Cees, P. van der Schans, Martijn de Groot
BMC Sports Science, Medicine and Rehabilitation (2015) 7:2
Chapter 3 Reliability and validity of consumer activity trackers depend 38
on walking speed
Tryntsje Fokkema, Thea J.M. Kooiman, Wim P. Krijnen, Cees P. van der Schans, Martijn de Groot
Medicine and Science in Sports and Exercise (2017) 49(4):793-800
Chapter 4 Behavioral Determinants for the Adoption of Self-tracking 55
Devices by Adults – a Longitudinal Study
Thea J.M. Kooiman, A. Dijkstra, J. Timmer, Wim P. Krijnen, Adriaan Kooy, Cees P. van der Schans, Martijn de Groot Submitted
Chapter 5 Do activity monitors increase physical activity in adults with 77
overweight or obesity? A systematic review and meta- analysis
Herman J. de Vries, Thea J.M. Kooiman, Miriam W. van Ittersum, Marco van Brussel, Martijn de Groot.
Obesity (2016) 24, 2078–2091
Chapter 6 Self-tracking of physical activity in people with type 2 100
diabetes - a randomized controlled trial
Thea J.M. Kooiman, Martijn de Groot, Klaas Hoogenberg, Wim P. Krijnen, Cees P. van der Schans, Adriaan Kooy Computers, Informatics, Nursing (2018) 36(7): 340-349
Chapter 7 The role of self-regulation in the effect of self-tracking of 121
physical activity and weight on BMI
Thea J.M. Kooiman, Arie Dijkstra, Adriaan Kooy, Cees P. van der Schans, Martijn de Groot
Submitted
Chapter 8 General Discussion 137
Samenvatting 154
Dankwoord 160
Over de auteur
ן
about the author 161Research Institute SHARE 163
Chapter 1 General Introduction 9
Chapter 2 Reliability and Validity of ten consumer activity trackers 17
Thea J.M. Kooiman, Manon L. Dontje, Siska R. Sprenger, Wim P. Krijnen, Cees, P. van der Schans, Martijn de Groot
BMC Sports Science, Medicine and Rehabilitation (2015) 7:2
Chapter 3 Reliability and validity of consumer activity trackers depend 39
on walking speed
Tryntsje Fokkema, Thea J.M. Kooiman, Wim P. Krijnen, Cees P. van der Schans, Martijn de Groot
Medicine and Science in Sports and Exercise (2017) 49(4):793-800
Chapter 4 Behavioral Determinants for the Adoption of Self-tracking 57
Devices by Adults – a Longitudinal Study
Thea J.M. Kooiman, A. Dijkstra, J. Timmer, Wim P. Krijnen, Adriaan Kooy, Cees P. van der Schans, Martijn de Groot Submitted
Chapter 5 Do activity monitors increase physical activity in adults with 79
overweight or obesity? A systematic review and meta- analysis
Herman J. de Vries, Thea J.M. Kooiman, Miriam W. van Ittersum, Marco van Brussel, Martijn de Groot.
Obesity (2016) 24, 2078–2091
Chapter 6 Self-tracking of physical activity in people with type 2 103
diabetes - a randomized controlled trial
Thea J.M. Kooiman, Martijn de Groot, Klaas Hoogenberg, Wim P. Krijnen, Cees P. van der Schans, Adriaan Kooy Computers, Informatics, Nursing (2018) 36(7): 340-349
Chapter 7 The role of self-regulation in the effect of self-tracking of 125
physical activity and weight on BMI
Thea J.M. Kooiman, Arie Dijkstra, Adriaan Kooy, Cees P. van der Schans, Martijn de Groot
Submitted
Chapter 8 General Discussion 141
Samenvatting 159
Dankwoord 166
Over de auteur
ן
about the author 167Research Institute SHARE 169
Chapter 1 |
General introduction
Chapter 1 |
General introduction
The current health care system has begun to change. Rising costs, an ageing population, and an increase in the number of people with lifestyle related diseases have exposed the need for a transformation from a centralized health care model towards one that is user-centered and preventive.1,2 In the centralized model, health care is delivered from centralized places such as health care institutions and hospitals. Patients have a relatively inactive role in their disease management in this model. Evaluations and patient-related measurements are primarily conducted within the walls of the health care facility. In contrast, in the user-centered model, self-management is an important concept. Self-management is defined as the tasks that a person must do to monitor their own health and to make adjustments towards a satisfying health.3 The patient is more informed, has more responsibility, and is even a producer of individual knowledge. This focus on self-management is also reflected in a renewed conceptualization of health: ‘the ability to adapt and self-manage in the face of social, physical, and emotional challenges’.4 There is a general consensus that shifting the focus from ‘care’ to ‘self-management’ is crucial for many patient groups to be able to achieve sufficient disease management and subsequently a satisfying quality of life.1,2,5,6
An important reason for the need of a renewed health care model is the large group of people with overweight/obesity and the related chronic diseases. This group has been growing rapidly over the last few decades, with now one in every two adults in Europe having overweight (BMI 25-30) and one out of six having obesity (BMI>30).7–9
Overweight/obesity increases the risk of diabetes, cardiovascular diseases, cancer, and (neuro-)degenerative deterioration.10 It is well known that lifestyle factors such as physical inactivity strongly contribute to the onset of overweight/obesity and type 2 diabetes9,10 whereas engagement in sufficient physical activity such as 7500-10.000 steps per day is associated with major health benefits such as a healthy weight and glucose metabolism, a better functional performance, a lower risk for various chronic diseases, and a better quality of life.11,12
However, despite of the widespread evidence of the benefits of physical activity, most people do not comply with physical activity recommendations. In addition, attempts to increase physical activity within intervention programs, e.g., by education or counseling, are often disappointing. For instance, adherence to exercise recommendations of health care professionals has been found to be low in people with type 2 diabetes, but also in other patient groups.13,14 This low adherence is caused by different reasons, e.g., lack of
motivation, lack of an adequate exercise plan, and an inadequate building up which causes injuries.14 Also, intrapersonal factors (perceived health and beliefs towards physical activity), social factors (lack of support from friends or family), organizational factors (lack of
accessible exercise facilities and costs), and environmental factors (friendly physical activity environment) have been determined as being relevant barriers for people with mobility problems to engage in physical activity.13 Barriers for exercise in a general middle aged and elderly population are partly overlapping: lack of time, tiredness, lack of knowledge how to be active, inconvenience of being active, lack of an exercise companion, interference with
work or social activities, and lack of exercise facilities.15 Therefore, more knowledge is needed on how to overcome these barriers to physical activity and how to increase physical activity in the general population, individuals with overweight/obesity, and those with type 2 diabetes.
A possible, relatively new approach for stimulation of physical activity is the deployment of eHealth technology 1,16 which refers to the use of internet and communication technologies to improve health, well-being, and healthcare.16 The
manifestation of eHealth technology is very varied and can include digital health platforms, difference types of self-monitoring devices, smartphone applications, and patient monitoring systems. eHealth might have many benefits for health care delivery. For example, health data that is electronically uploaded by patients can add valuable information for the health care provider for both diagnosis and treatment purposes. In addition, the design and manifestation of eHealth technology can be adapted for a specific goal or target group, and eHealth can increase access to care (e.g., it is flexible with regard to time and place, or individuals who have a rare disease or live in rural areas might receive better access to care).1,16,17 Digital self-monitoring devices are a form of eHealth. Self-monitoring devices are often wearable devices that enable the user to monitor, for example, physical activity, diet, sleep, respiration, heart rate, blood pressure, or blood glucose.18 These self-monitoring devices are also known as ‘self-tracking devices’, ‘health self-quantification devices’, or simply ‘wearables’. Self-tracking devices are increasingly acknowledged as possible facilitators for self-management abilities.18–21 This is because of their ability to empower people with insight into their own health data and associated possibilities to stimulate the adoption of healthier behavior based on this data. For example, an individual who wants to increase his or her exercise activities is now able to gain insight into their current physical activity pattern through the use of an activity tracker. Whit this data visualized on an internet account or mobile application the user can see the course of their own physical activity pattern over time and how this is related to health recommendations such as taking a certain number of steps per day. This prevents overestimation of individual physical activity behavior and might stimulate making behavioral adjustments in physical activity habits, for example, by means of goals, reminders, prompts, and rewards.
From a theoretical perspective on behavioral change, self-monitoring of behavior is known as one of the self-regulation skills that are crucial to motivate and guide the desired behavior.22 Another important self-regulation skill is goal-setting. Interventions targeting lifestyle behaviors have been shown to be more effective when these self-regulation components were included.23,24 Therefore, goal-setting and self-monitoring of behavior are used as important Behavioral Change Techniques (BCTs).25,26 Other important BCTs are providing information (tips and suggestions on how to increase physical activity), prompting review of behavior, providing feedback on behavior, and rewards. All of these BCTs are being increasingly incorporated within modern consumer activity trackers.27 What is conceptually new about these devices is that they provide the user with objective knowledge about
1
The current health care system has begun to change. Rising costs, an ageing population, and an increase in the number of people with lifestyle related diseases have exposed the need for a transformation from a centralized health care model towards one that is user-centered and preventive.1,2 In the centralized model, health care is delivered from centralized places such as health care institutions and hospitals. Patients have a relatively inactive role in their disease management in this model. Evaluations and patient-related measurements are primarily conducted within the walls of the health care facility. In contrast, in the user-centered model, self-management is an important concept. Self-management is defined as the tasks that a person must do to monitor their own health and to make adjustments towards a satisfying health.3 The patient is more informed, has more responsibility, and is even a producer of individual knowledge. This focus on self-management is also reflected in a renewed conceptualization of health: ‘the ability to adapt and self-manage in the face of social, physical, and emotional challenges’.4 There is a general consensus that shifting the focus from ‘care’ to ‘self-management’ is crucial for many patient groups to be able to achieve sufficient disease management and subsequently a satisfying quality of life.1,2,5,6
An important reason for the need of a renewed health care model is the large group of people with overweight/obesity and the related chronic diseases. This group has been growing rapidly over the last few decades, with now one in every two adults in Europe having overweight (BMI 25-30) and one out of six having obesity (BMI>30).7–9
Overweight/obesity increases the risk of diabetes, cardiovascular diseases, cancer, and (neuro-)degenerative deterioration.10 It is well known that lifestyle factors such as physical inactivity strongly contribute to the onset of overweight/obesity and type 2 diabetes9,10 whereas engagement in sufficient physical activity such as 7500-10.000 steps per day is associated with major health benefits such as a healthy weight and glucose metabolism, a better functional performance, a lower risk for various chronic diseases, and a better quality of life.11,12
However, despite of the widespread evidence of the benefits of physical activity, most people do not comply with physical activity recommendations. In addition, attempts to increase physical activity within intervention programs, e.g., by education or counseling, are often disappointing. For instance, adherence to exercise recommendations of health care professionals has been found to be low in people with type 2 diabetes, but also in other patient groups.13,14 This low adherence is caused by different reasons, e.g., lack of
motivation, lack of an adequate exercise plan, and an inadequate building up which causes injuries.14 Also, intrapersonal factors (perceived health and beliefs towards physical activity), social factors (lack of support from friends or family), organizational factors (lack of
accessible exercise facilities and costs), and environmental factors (friendly physical activity environment) have been determined as being relevant barriers for people with mobility problems to engage in physical activity.13 Barriers for exercise in a general middle aged and elderly population are partly overlapping: lack of time, tiredness, lack of knowledge how to be active, inconvenience of being active, lack of an exercise companion, interference with
work or social activities, and lack of exercise facilities.15 Therefore, more knowledge is needed on how to overcome these barriers to physical activity and how to increase physical activity in the general population, individuals with overweight/obesity, and those with type 2 diabetes.
A possible, relatively new approach for stimulation of physical activity is the deployment of eHealth technology 1,16 which refers to the use of internet and communication technologies to improve health, well-being, and healthcare.16 The
manifestation of eHealth technology is very varied and can include digital health platforms, difference types of self-monitoring devices, smartphone applications, and patient monitoring systems. eHealth might have many benefits for health care delivery. For example, health data that is electronically uploaded by patients can add valuable information for the health care provider for both diagnosis and treatment purposes. In addition, the design and manifestation of eHealth technology can be adapted for a specific goal or target group, and eHealth can increase access to care (e.g., it is flexible with regard to time and place, or individuals who have a rare disease or live in rural areas might receive better access to care).1,16,17 Digital self-monitoring devices are a form of eHealth. Self-monitoring devices are often wearable devices that enable the user to monitor, for example, physical activity, diet, sleep, respiration, heart rate, blood pressure, or blood glucose.18 These self-monitoring devices are also known as ‘self-tracking devices’, ‘health self-quantification devices’, or simply ‘wearables’. Self-tracking devices are increasingly acknowledged as possible facilitators for self-management abilities.18–21 This is because of their ability to empower people with insight into their own health data and associated possibilities to stimulate the adoption of healthier behavior based on this data. For example, an individual who wants to increase his or her exercise activities is now able to gain insight into their current physical activity pattern through the use of an activity tracker. Whit this data visualized on an internet account or mobile application the user can see the course of their own physical activity pattern over time and how this is related to health recommendations such as taking a certain number of steps per day. This prevents overestimation of individual physical activity behavior and might stimulate making behavioral adjustments in physical activity habits, for example, by means of goals, reminders, prompts, and rewards.
From a theoretical perspective on behavioral change, self-monitoring of behavior is known as one of the self-regulation skills that are crucial to motivate and guide the desired behavior.22 Another important self-regulation skill is goal-setting. Interventions targeting lifestyle behaviors have been shown to be more effective when these self-regulation components were included.23,24 Therefore, goal-setting and self-monitoring of behavior are used as important Behavioral Change Techniques (BCTs).25,26 Other important BCTs are providing information (tips and suggestions on how to increase physical activity), prompting review of behavior, providing feedback on behavior, and rewards. All of these BCTs are being increasingly incorporated within modern consumer activity trackers.27 What is conceptually new about these devices is that they provide the user with objective knowledge about
individual daily routines and provide different forms of feedback which stimulates learning.28 This may enhance sustained behavior change.29,30
Modern consumer level technology may thus have potential for broad applications for both general public health purposes and within health care for specific patient groups. However, before activity trackers can be deployed within health care, they must comply with certain conditions such as a satisfying reliability and validity. Not much is known yet about the reliability and validity of the large number of activity trackers that are currently on the market. This information is very important for users, health care providers, and researchers in order to be able to rely on information from these devices.
Another important point of consideration when using self-tracking technology, is knowledge about the adoption of these devices. Before self-tracking technology can impact behavior, they must be adopted by the user, and there has to be an certain engagement with the device.31 Although the development of self-monitoring technology has led to an increased number of people who actively engage in self-measurements,18 the sustained use of wearable devices by consumers is not yet that high. Several studies on the adoption of consumer level self-tracking devices have found that the percentages of people who stopped using their device within relative short-term follow up periods may vary between 33 and 75%.32–35 Factors that influence the adoption of self-monitoring technology are not yet completely clear. Factors determined thus far include different types, including personal factors, devices factors, and behavioral factors.32–34,36 Not much is known yet about theory based behavioral factors that explain adoption of technology. Knowledge about these factors is important in order to be able to tailor interventions based on this information, direct future developments, or possibly distinguish between people who may or may not be suitable for treatment with use of modern consumer level technology.
When activity trackers are employed as (part of) an intervention to increase physical activity, an important condition is knowledge about the potential effects this technology may have. At the moment, eHealth including consumer activity trackers is not yet widely used in healthcare.16,37,38 In accordance with this, not much is known yet about the impact of these devices on lifestyle behaviors and health outcomes both in the general population as well as for people who have overweight/obesity, or type 2 diabetes. As described earlier, the use of consumer-level self-monitoring devices that can measure physical activity might be an effective approach for the incremental increase of physical activity, including people with overweight/obesity or individuals who have type 2 diabetes. However, thus far, mostly simple pedometers without additional BCTs have been deployed in interventions targeting this group, and the evidence for physical activity and health outcome measures such as weight/BMI and glycemic control is not yet conclusive.39,40 Therefore, more knowledge is needed about the effectiveness of eHealth technology, including the use of activity trackers.
Aims and outline of this dissertation
This dissertation aims to increase knowledge about the use and effectiveness of eHealth and self-monitoring techniques, especially activity trackers, in the current healthcare system. The focus will be on the general population as well on people with overweight/obesity and those with type 2 diabetes. Three domains will be distinguished.
The first domain is the reliability and validity of new consumer self-tracking devices. Before new technology can be integrated into health care, it must be known whether these devices are reliable and valid. Therefore, the purpose of Chapter 2 and 3 is to examine the reliability and validity of 20 activity trackers, apps, and smartwatches.
The second domain focuses on the adoption of self-monitoring devices in the general population. For this, the purpose in Chapter 4 is to examine the adoption and factors associated with the adoption of self-tracking devices that quantify physical activity, sleep, and weight.
The third domain is about the effect of using consumer level activity-tracking devices and eHealth applications in healthy people, people with overweight/obesity, and those with type 2 diabetes. First, the aim of Chapter 5 is to systematically review all studies done so far to the impact of self-monitoring of physical activity on activity levels in people with overweight and obesity. In Chapter 6 a randomized controlled trial will be conducted to the effect of a modern consumer activity tracker connected to an online lifestyle program on physical activity, glycemic control, and other health outcome measures in people with type 2 diabetes. The purpose of Chapter 7 is to investigate short-term and long-term effects of self-tracking of physical activity and weight on BMI change in the general population and to what extent a change in self-regulation capabilities can explain weight loss.
1
individual daily routines and provide different forms of feedback which stimulates learning.28 This may enhance sustained behavior change.29,30
Modern consumer level technology may thus have potential for broad applications for both general public health purposes and within health care for specific patient groups. However, before activity trackers can be deployed within health care, they must comply with certain conditions such as a satisfying reliability and validity. Not much is known yet about the reliability and validity of the large number of activity trackers that are currently on the market. This information is very important for users, health care providers, and researchers in order to be able to rely on information from these devices.
Another important point of consideration when using self-tracking technology, is knowledge about the adoption of these devices. Before self-tracking technology can impact behavior, they must be adopted by the user, and there has to be an certain engagement with the device.31 Although the development of self-monitoring technology has led to an increased number of people who actively engage in self-measurements,18 the sustained use of wearable devices by consumers is not yet that high. Several studies on the adoption of consumer level self-tracking devices have found that the percentages of people who stopped using their device within relative short-term follow up periods may vary between 33 and 75%.32–35 Factors that influence the adoption of self-monitoring technology are not yet completely clear. Factors determined thus far include different types, including personal factors, devices factors, and behavioral factors.32–34,36 Not much is known yet about theory based behavioral factors that explain adoption of technology. Knowledge about these factors is important in order to be able to tailor interventions based on this information, direct future developments, or possibly distinguish between people who may or may not be suitable for treatment with use of modern consumer level technology.
When activity trackers are employed as (part of) an intervention to increase physical activity, an important condition is knowledge about the potential effects this technology may have. At the moment, eHealth including consumer activity trackers is not yet widely used in healthcare.16,37,38 In accordance with this, not much is known yet about the impact of these devices on lifestyle behaviors and health outcomes both in the general population as well as for people who have overweight/obesity, or type 2 diabetes. As described earlier, the use of consumer-level self-monitoring devices that can measure physical activity might be an effective approach for the incremental increase of physical activity, including people with overweight/obesity or individuals who have type 2 diabetes. However, thus far, mostly simple pedometers without additional BCTs have been deployed in interventions targeting this group, and the evidence for physical activity and health outcome measures such as weight/BMI and glycemic control is not yet conclusive.39,40 Therefore, more knowledge is needed about the effectiveness of eHealth technology, including the use of activity trackers.
Aims and outline of this dissertation
This dissertation aims to increase knowledge about the use and effectiveness of eHealth and self-monitoring techniques, especially activity trackers, in the current healthcare system. The focus will be on the general population as well on people with overweight/obesity and those with type 2 diabetes. Three domains will be distinguished.
The first domain is the reliability and validity of new consumer self-tracking devices. Before new technology can be integrated into health care, it must be known whether these devices are reliable and valid. Therefore, the purpose of Chapter 2 and 3 is to examine the reliability and validity of 20 activity trackers, apps, and smartwatches.
The second domain focuses on the adoption of self-monitoring devices in the general population. For this, the purpose in Chapter 4 is to examine the adoption and factors associated with the adoption of self-tracking devices that quantify physical activity, sleep, and weight.
The third domain is about the effect of using consumer level activity-tracking devices and eHealth applications in healthy people, people with overweight/obesity, and those with type 2 diabetes. First, the aim of Chapter 5 is to systematically review all studies done so far to the impact of self-monitoring of physical activity on activity levels in people with overweight and obesity. In Chapter 6 a randomized controlled trial will be conducted to the effect of a modern consumer activity tracker connected to an online lifestyle program on physical activity, glycemic control, and other health outcome measures in people with type 2 diabetes. The purpose of Chapter 7 is to investigate short-term and long-term effects of self-tracking of physical activity and weight on BMI change in the general population and to what extent a change in self-regulation capabilities can explain weight loss.
References
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3. El-Gayar O, Timsina P, Nawar N, Eid W. A systematic review of IT for diabetes self-management: Are we there yet? Int J Med Inform. 2013;82(8):637-652. doi:10.1016/j.ijmedinf.2013.05.006.
4. Huber M, André Knottnerus J, Green L, et al. How should we define health? BMJ. 2011;343(7817). doi:10.1136/bmj.d4163.
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12. Tudor-Locke C, Craig CL, Brown WJ, et al. How many steps/day are enough? for adults. Int J Behav Nutr Phys Act. 2011;8(1):79. doi:10.1186/1479-5868-8-79.
13. Vasudevan V, Rimmer JH, Kviz F. Development of the Barriers to Physical Activity Questionnaire for People with Mobility Impairments. Disabil Health J. 2015;8(4):547-556.
doi:10.1016/j.dhjo.2015.04.007.
14. García-Pérez, L. E., Álvarez, M., Dilla, T., Gil-Guillén, V., & Orozco-Beltrán D. Adherence to Therapies in Patients with Type 2 Diabetes. Diabetes Ther. 2013;4(2):175-194. doi:10.1007/s13300-013-0034-y. 15. Justine M, Azian A, Hassan V, Manaf H. Barriers to participation in physical activity and exercise among
middle-aged and elderly individuals. Singapore Med J. 2013;54(10):581-586.
16. Van Gemert-Pijnen, J.E.W.C, Peters,O., & Ossebaard HC. Improving eHealth. Eleven International Publishing; 2013. http://lib.myilibrary.com?ID=673579.
17. Steinhubl SR, Muse ED, Topol EJ. Can Mobile Health Technologies Transform Health Care? Jama. 2013;310(22):2395. doi:10.1001/jama.2013.281078.
18. Fawcett T. Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data Science. Big Data. 2015;3(4):249-266. internal-pdf://228.60.152.96/Fawcett.
19. Almalki M, Gray K, Martin-Sanchez F. Activity Theory as a Theoretical Framework for Health Self-Quantification: A Systematic Review of Empirical Studies. J Med Internet Res. 2016;18(5):e131. 20. Grnvall E, Verdezoto N. Beyond self-monitoring: understanding non-functional aspects of home-based
healthcare technology. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM; 2013:587-596.
21. Almalki M, Gray K, Martin-Sanchez F. Refining the Concepts of Self-quantification Needed for Health Self-management: A Thematic Literature Review. Computer (Long Beach Calif). 2015;79:1-5. 22. Bandura A. Health promotion from the perspective of social cognitive theory. Psychol Heal.
1998;13(4):623-649.
23. Greaves CJ, Sheppard KE, Abraham C, et al. Systematic review of reviews of intervention components
associated with increased effectiveness in dietary and physical activity interventions. BMC Public Health. 2011;11(1):1.
24. Teixeira PJ, Carraa E V, Marques MM, et al. Successful behavior change in obesity interventions in adults: a systematic review of self-regulation mediators. BMC Med. 2015;13(1):1.
25. Michie S, Ashford S, Sniehotta FF, Dombrowski SU, Bishop A, French DP. A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: The CALO-RE taxonomy. Psychol Health. 2011;26(11):1479-1498.
doi:10.1080/08870446.2010.540664.
26. Michie S, Richardson M, Johnston M, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013;46(1):81-95.
27. Lyons EJ, Lewis ZH, Mayrsohn BG, Rowland JL. Behavior change techniques implemented in electronic lifestyle activity monitors: A systematic content analysis. J Med Internet Res. 2014;16(8).
doi:10.2196/jmir.3469.
28. Menninga K. Learning abstinence theory - PhD thesis under supervision of A. Dijkstra. Univ Groningen. 2012.
29. Carver CS, Scheier MF. Control theory: A useful conceptual framework for personality-social, clinical, and health psychology. Psychol Bull. 1982;92(1):111-135. doi:10.1037/0033-2909.92.1.111. 30. Kluger AN, DeNisi A. The effects of feedback interventions on performance: A historical review, a
meta-analysis, and a preliminary feedback intervention theory. Psychol Bull. 1996;119(2):254-284. doi:10.1037/0033-2909.119.2.254.
31. Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. 2016:1-14.
32. Fritz T, Huang EM, Murphy GC, Zimmermann T. Persuasive technology in the real world: a study of long-term use of activity sensing devices for fitness. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM; 2014:487-496.
33. Gouveia R, Karapanos E, Hassenzahl M. How do we engage with activity trackers?: a longitudinal study of Habito. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM; 2015:1305-1316.
34. Lazar A, Koehler C, Tanenbaum J, Nguyen DH. Why we use and abandon smart devices. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM; 2015:635-646.
35. Clawson J, Pater JA, Miller AD, Mynatt ED, Mamykina L. No longer wearing: investigating the abandonment of personal health-tracking technologies on craigslist. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM; 2015:647-658. 36. Epstein D, Caraway M, Johnston C, Ping A, Fogarty J, Munson S. Beyond Abandonment to Next Steps.
In: CHI ’16. ACM; :1109-1113. doi:10.1145/2858036.2858045.
37. Griebel L, Kolominsky-Rabas P, Schaller S, et al. Acceptance by laypersons and medical professionals of the personalized eHealth platform, eHealthMonitor. Informatics Heal Soc Care. 2016:1-18.
38. Paton C, Hansen M, Fernandez-Luque L, Lau a YS. Self-Tracking, Social Media and Personal Health Records for Patient Empowered Self-Care. Contribution of the IMIA Social Media Working Group. Yearb Med Inform. 2012;7(1):16-24. doi:me12010016 [pii].
39. Rollo M, Aguiar E, Williams R. eHealth technologies to support nutrition and physical activity behaviors in diabetes self-management. Obes Targets …. 2016;9:381.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5104301/.
40. McMillan KA, Kirk A, Hewitt A, MacRury S. A systematic and integrated review of mobile-based technology to promote active lifestyles in people with type 2 diabetes. J Diabetes Sci Technol. 2017;11(2):299-307.
1
References
1. Arnrich B, Mayora O, Bardram J, Trster G. Pervasive healthcare. Methods Inf Med. 2010;49(1):67-73. 2. Swan M. Emerging patient-driven health care models: an examination of health social networks,
consumer personalized medicine and quantified self-tracking. Int J Environ Res Public Health. 2009;6(2):492-525.
3. El-Gayar O, Timsina P, Nawar N, Eid W. A systematic review of IT for diabetes self-management: Are we there yet? Int J Med Inform. 2013;82(8):637-652. doi:10.1016/j.ijmedinf.2013.05.006.
4. Huber M, André Knottnerus J, Green L, et al. How should we define health? BMJ. 2011;343(7817). doi:10.1136/bmj.d4163.
5. Sharon T. Self-Tracking for Health and the Quantified Self: Re-Articulating Autonomy, Solidarity, and Authenticity in an Age of Personalized Healthcare. Philos Technol. 2016:1-29.
6. Panagioti M, Richardson G, Small N, et al. Self-management support interventions to reduce health care utilisation without compromising outcomes: a systematic review and meta-analysis. BMC Health Serv Res. 2014;14(1):356. doi:10.1186/1472-6963-14-356.
7. Tamayo T, Rosenbauer J, Wild SH, et al. Diabetes in Europe: An update. Diabetes Res Clin Pract. 2014;103(2):206-217. doi:10.1016/j.diabres.2013.11.007.
8. Eurostat. European Health Interview Survey: Almost 1 adult in 6 in the EU is considered obese. Prem Off News. 2016;2014(October):7-11. http://ec.europa.eu/eurostat/documents/2995521/7700898/3-20102016-BP-EN.pdf/c26b037b-d5f3-4c05-89c1-00bf0b98d646.
9. Lee IM, Shiroma EJ, Lobelo F, et al. Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy. Lancet. 2012;380(9838):219-229. doi:10.1016/S0140-6736(12)61031-9.
10. Musto AA. The effects of an incremental pedometer program on metabolic syndrome components in sedentary overweight women. Diss Abstr Int Sect B Sci Eng. 2008;69(3-B):1598.
http://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,shib&db=psyh&AN=2008-99180-343&site=ehost-live&custid=s4121186.
11. Haskell WL, Lee IM, Pate RR, et al. Physical activity and public health: Updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc. 2007;39(8):1423-1434. doi:10.1249/mss.0b013e3180616b27.
12. Tudor-Locke C, Craig CL, Brown WJ, et al. How many steps/day are enough? for adults. Int J Behav Nutr Phys Act. 2011;8(1):79. doi:10.1186/1479-5868-8-79.
13. Vasudevan V, Rimmer JH, Kviz F. Development of the Barriers to Physical Activity Questionnaire for People with Mobility Impairments. Disabil Health J. 2015;8(4):547-556.
doi:10.1016/j.dhjo.2015.04.007.
14. García-Pérez, L. E., Álvarez, M., Dilla, T., Gil-Guillén, V., & Orozco-Beltrán D. Adherence to Therapies in Patients with Type 2 Diabetes. Diabetes Ther. 2013;4(2):175-194. doi:10.1007/s13300-013-0034-y. 15. Justine M, Azian A, Hassan V, Manaf H. Barriers to participation in physical activity and exercise among
middle-aged and elderly individuals. Singapore Med J. 2013;54(10):581-586.
16. Van Gemert-Pijnen, J.E.W.C, Peters,O., & Ossebaard HC. Improving eHealth. Eleven International Publishing; 2013. http://lib.myilibrary.com?ID=673579.
17. Steinhubl SR, Muse ED, Topol EJ. Can Mobile Health Technologies Transform Health Care? Jama. 2013;310(22):2395. doi:10.1001/jama.2013.281078.
18. Fawcett T. Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data Science. Big Data. 2015;3(4):249-266. internal-pdf://228.60.152.96/Fawcett.
19. Almalki M, Gray K, Martin-Sanchez F. Activity Theory as a Theoretical Framework for Health Self-Quantification: A Systematic Review of Empirical Studies. J Med Internet Res. 2016;18(5):e131. 20. Grnvall E, Verdezoto N. Beyond self-monitoring: understanding non-functional aspects of home-based
healthcare technology. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM; 2013:587-596.
21. Almalki M, Gray K, Martin-Sanchez F. Refining the Concepts of Self-quantification Needed for Health Self-management: A Thematic Literature Review. Computer (Long Beach Calif). 2015;79:1-5. 22. Bandura A. Health promotion from the perspective of social cognitive theory. Psychol Heal.
1998;13(4):623-649.
23. Greaves CJ, Sheppard KE, Abraham C, et al. Systematic review of reviews of intervention components
associated with increased effectiveness in dietary and physical activity interventions. BMC Public Health. 2011;11(1):1.
24. Teixeira PJ, Carraa E V, Marques MM, et al. Successful behavior change in obesity interventions in adults: a systematic review of self-regulation mediators. BMC Med. 2015;13(1):1.
25. Michie S, Ashford S, Sniehotta FF, Dombrowski SU, Bishop A, French DP. A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: The CALO-RE taxonomy. Psychol Health. 2011;26(11):1479-1498.
doi:10.1080/08870446.2010.540664.
26. Michie S, Richardson M, Johnston M, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013;46(1):81-95.
27. Lyons EJ, Lewis ZH, Mayrsohn BG, Rowland JL. Behavior change techniques implemented in electronic lifestyle activity monitors: A systematic content analysis. J Med Internet Res. 2014;16(8).
doi:10.2196/jmir.3469.
28. Menninga K. Learning abstinence theory - PhD thesis under supervision of A. Dijkstra. Univ Groningen. 2012.
29. Carver CS, Scheier MF. Control theory: A useful conceptual framework for personality-social, clinical, and health psychology. Psychol Bull. 1982;92(1):111-135. doi:10.1037/0033-2909.92.1.111. 30. Kluger AN, DeNisi A. The effects of feedback interventions on performance: A historical review, a
meta-analysis, and a preliminary feedback intervention theory. Psychol Bull. 1996;119(2):254-284. doi:10.1037/0033-2909.119.2.254.
31. Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. 2016:1-14.
32. Fritz T, Huang EM, Murphy GC, Zimmermann T. Persuasive technology in the real world: a study of long-term use of activity sensing devices for fitness. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM; 2014:487-496.
33. Gouveia R, Karapanos E, Hassenzahl M. How do we engage with activity trackers?: a longitudinal study of Habito. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM; 2015:1305-1316.
34. Lazar A, Koehler C, Tanenbaum J, Nguyen DH. Why we use and abandon smart devices. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM; 2015:635-646.
35. Clawson J, Pater JA, Miller AD, Mynatt ED, Mamykina L. No longer wearing: investigating the abandonment of personal health-tracking technologies on craigslist. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM; 2015:647-658. 36. Epstein D, Caraway M, Johnston C, Ping A, Fogarty J, Munson S. Beyond Abandonment to Next Steps.
In: CHI ’16. ACM; :1109-1113. doi:10.1145/2858036.2858045.
37. Griebel L, Kolominsky-Rabas P, Schaller S, et al. Acceptance by laypersons and medical professionals of the personalized eHealth platform, eHealthMonitor. Informatics Heal Soc Care. 2016:1-18.
38. Paton C, Hansen M, Fernandez-Luque L, Lau a YS. Self-Tracking, Social Media and Personal Health Records for Patient Empowered Self-Care. Contribution of the IMIA Social Media Working Group. Yearb Med Inform. 2012;7(1):16-24. doi:me12010016 [pii].
39. Rollo M, Aguiar E, Williams R. eHealth technologies to support nutrition and physical activity behaviors in diabetes self-management. Obes Targets …. 2016;9:381.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5104301/.
40. McMillan KA, Kirk A, Hewitt A, MacRury S. A systematic and integrated review of mobile-based technology to promote active lifestyles in people with type 2 diabetes. J Diabetes Sci Technol. 2017;11(2):299-307.
Chapter 2 |
Reliability and validity of ten
consumer activity trackers
Thea J.M. Kooiman Manon L. Dontje Siska R. Sprenger Wim P. Krijnen Cees P. van der Schans Martijn de Groot
BMC Sports Science, Medicine and Rehabilitation (2015) 7:24
Chapter 2 |
Reliability and validity of ten
consumer activity trackers
Thea J.M. Kooiman Manon L. Dontje Siska R. Sprenger Wim P. Krijnen Cees P. van der Schans Martijn de Groot
BMC Sports Science, Medicine and Rehabilitation (2015) 7:24
Abstract
Background
Activity trackers can potentially stimulate users to increase their physical activity behavior. The aim of this study was to examine the reliability and validity of ten consumer activity trackers for measuring step count in both laboratory and free-living conditions.
Method
Healthy adult volunteers (n=33) walked twice on a treadmill (4.8 km/h) for 30 minutes while wearing ten different activity trackers (i.e. Lumoback, Fitbit Flex, Jawbone Up, Nike+ Fuelband SE, Misfit Shine, Withings Pulse, Fitbit Zip, Omron HJ-203, Yamax Digiwalker SW-200 and Moves mobile application). In free-living conditions, 56 volunteers wore the same activity trackers for one working day. Test-retest reliability was analyzed with the Intraclass Correlation Coefficient (ICC). Validity was evaluated by comparing each tracker with the gold standard (Optogait system for laboratory and ActivPAL for free-living conditions), using paired samples t-tests, mean absolute percentage errors, correlations and Bland-Altman plots.
Results
Test-retest analysis revealed high reliability for most trackers except for the Omron (ICC .14), Moves app (ICC .37) and Nike+ Fuelband (ICC .53). The mean absolute percentage errors of the trackers in laboratory and free-living conditions respectively, were: Lumoback (-0.2, -0.4), Fibit Flex (-5.7, 3.7), Jawbone Up (-1.0, 1.4), Nike+ Fuelband (-18, -24), Misfit Shine (0.2, 1.1), Withings Pulse (-0.5, -7.9), Fitbit Zip (-0.3, 1.2), Omron (2.5, -0.4), Digiwalker (-1.2, -5.9), and Moves app (9.6, -37.6). Bland-Altman plots demonstrated that the limits of agreement varied from 46 steps (Fitbit Zip) to 2422 steps (Nike+ Fuelband) in the laboratory condition, and 866 steps (Fitbit Zip) to 5150 steps (Moves app) in the free-living condition.
Conclusion
The reliability and validity of most trackers for measuring step count is good. The Fitbit Zip is the most valid whereas the reliability and validity of the Nike+ Fuelband is low.
Introduction
Activity trackers are developed to increase an individual’s awareness about physical activity behavior throughout the day. It is well known that regular physical activity decreases the risk of many chronic diseases and can improve quality of life.1-3 A commonly used physical activity guideline is the 10,000 steps/day norm: healthy adults are recommended to take 10,000 steps per day to maintain physical fitness and health.4 However, many people worldwide are not aware if they comply with this recommendation.1 In addition, previous research has indicated that most people tend to overestimate their level of physical activity [5, 6]. Activity trackers may potentially overcome this issue.
Over the past five to ten years, an increasing number and variety of activity trackers have become available on the consumer market. Activity trackers are small and user-friendly devices that measure the number of steps taken and/or the amount of time spent
performing physical activities at different intensities. Most activity trackers also convert the number of steps with algorithms into measures such as the distance covered and the number of calories burned. Associated (mobile) applications provide users with insight into their individual physical activity behavior over a certain period of time. This might work as a motivator to increase physical activity.7,8 Consumer activity trackers might also be beneficial for scientific research, due to their ease of usability and relatively low cost. Examples of popular devices are the Fitbit, Jawbone Up, and Withings Pulse.
For accurate measurement and interpretation of the data, these devices must be reliable and valid. A number of studies have examined consumer tracker accuracy,6,9-18 however, six studies were based upon earlier versions of Fitbit devices, and the methodology for assessing reliability and validity varied considerably. For example, different types of activity were used (walking on a treadmill at different speeds, lab cycling, walking stairs, daily activities), and different gold standards were utilized (energy expenditure [EE] measured by breath-to-breath analysis, self-reported physical activity translated to EE [in METs], and real step count). Five studies were performed in a laboratory condition,9-11, 14,16 and six studies examined the reliability or validity of activity trackers during
(semi-structured) free-living conditions.6,12,13,15,17,18 The validity of activity trackers may differ in free-living conditions compared to standardized lab conditions because of the increased variety in walking speeds, directions, intensities, etc. in free-living. To date, no studies have assessed reliability and validity of consumer trackers in both laboratory and free-living conditions. The aim of this study was to determine the reliability and validity of ten consumer activity trackers, in both a standardized laboratory condition and in free-living conditions.
2
Abstract
Background
Activity trackers can potentially stimulate users to increase their physical activity behavior. The aim of this study was to examine the reliability and validity of ten consumer activity trackers for measuring step count in both laboratory and free-living conditions.
Method
Healthy adult volunteers (n=33) walked twice on a treadmill (4.8 km/h) for 30 minutes while wearing ten different activity trackers (i.e. Lumoback, Fitbit Flex, Jawbone Up, Nike+ Fuelband SE, Misfit Shine, Withings Pulse, Fitbit Zip, Omron HJ-203, Yamax Digiwalker SW-200 and Moves mobile application). In free-living conditions, 56 volunteers wore the same activity trackers for one working day. Test-retest reliability was analyzed with the Intraclass Correlation Coefficient (ICC). Validity was evaluated by comparing each tracker with the gold standard (Optogait system for laboratory and ActivPAL for free-living conditions), using paired samples t-tests, mean absolute percentage errors, correlations and Bland-Altman plots.
Results
Test-retest analysis revealed high reliability for most trackers except for the Omron (ICC .14), Moves app (ICC .37) and Nike+ Fuelband (ICC .53). The mean absolute percentage errors of the trackers in laboratory and free-living conditions respectively, were: Lumoback (-0.2, -0.4), Fibit Flex (-5.7, 3.7), Jawbone Up (-1.0, 1.4), Nike+ Fuelband (-18, -24), Misfit Shine (0.2, 1.1), Withings Pulse (-0.5, -7.9), Fitbit Zip (-0.3, 1.2), Omron (2.5, -0.4), Digiwalker (-1.2, -5.9), and Moves app (9.6, -37.6). Bland-Altman plots demonstrated that the limits of agreement varied from 46 steps (Fitbit Zip) to 2422 steps (Nike+ Fuelband) in the laboratory condition, and 866 steps (Fitbit Zip) to 5150 steps (Moves app) in the free-living condition.
Conclusion
The reliability and validity of most trackers for measuring step count is good. The Fitbit Zip is the most valid whereas the reliability and validity of the Nike+ Fuelband is low.
Introduction
Activity trackers are developed to increase an individual’s awareness about physical activity behavior throughout the day. It is well known that regular physical activity decreases the risk of many chronic diseases and can improve quality of life.1-3 A commonly used physical activity guideline is the 10,000 steps/day norm: healthy adults are recommended to take 10,000 steps per day to maintain physical fitness and health.4 However, many people worldwide are not aware if they comply with this recommendation.1 In addition, previous research has indicated that most people tend to overestimate their level of physical activity [5, 6]. Activity trackers may potentially overcome this issue.
Over the past five to ten years, an increasing number and variety of activity trackers have become available on the consumer market. Activity trackers are small and user-friendly devices that measure the number of steps taken and/or the amount of time spent
performing physical activities at different intensities. Most activity trackers also convert the number of steps with algorithms into measures such as the distance covered and the number of calories burned. Associated (mobile) applications provide users with insight into their individual physical activity behavior over a certain period of time. This might work as a motivator to increase physical activity.7,8 Consumer activity trackers might also be beneficial for scientific research, due to their ease of usability and relatively low cost. Examples of popular devices are the Fitbit, Jawbone Up, and Withings Pulse.
For accurate measurement and interpretation of the data, these devices must be reliable and valid. A number of studies have examined consumer tracker accuracy,6,9-18 however, six studies were based upon earlier versions of Fitbit devices, and the methodology for assessing reliability and validity varied considerably. For example, different types of activity were used (walking on a treadmill at different speeds, lab cycling, walking stairs, daily activities), and different gold standards were utilized (energy expenditure [EE] measured by breath-to-breath analysis, self-reported physical activity translated to EE [in METs], and real step count). Five studies were performed in a laboratory condition,9-11, 14,16 and six studies examined the reliability or validity of activity trackers during
(semi-structured) free-living conditions.6,12,13,15,17,18 The validity of activity trackers may differ in free-living conditions compared to standardized lab conditions because of the increased variety in walking speeds, directions, intensities, etc. in free-living. To date, no studies have assessed reliability and validity of consumer trackers in both laboratory and free-living conditions. The aim of this study was to determine the reliability and validity of ten consumer activity trackers, in both a standardized laboratory condition and in free-living conditions.
Methods
Study design
The following ten activity trackers were examined: the Lumoback, Fitbit Flex, Nike+ Fuelband SE, Jawbone Up, Misfit Shine, Withings Pulse, Fitbit Zip, Omron HJ-203, Yamax Digiwalker SW-200 and the Moves mobile application. The Optogait system (OPTOGait, Microgate S.r.I,
Italy, 2010) was used as the gold standard on the treadmill in the laboratory condition. This
system consists of two beams attached to the sides of the treadmill. The system uses an LED lighting system to precisely measure the number of steps which is a reliable and valid method for measuring step count (cadence).19 The ActivPAL (PAL Technologies Ltd., Glasgow,
UK) was used as the gold standard in the free-living condition. The ActivPAL was worn on the
thigh underneath the clothing. Previous research has demonstrated that the ActivPAL is a reliable and valid tool for measuring the number of steps taken both on a treadmill and in free-living conditions.20-22
Study sample
Only healthy adult volunteers (age ≥18, <65 years) were included in the study. Participants were recruited through advertisements within the Hanze University and by using the individual networks of the researchers. Subscribers were excluded from participation if they experienced problems with standing or normal ambulation as well as if they performed daily activities which could possibly damage the activity trackers while being worn (when
participating in the free-living study). All components of the study are described below in more detail. The study was in accordance with the principles as outlined in the Declaration of Helsinki and an exemption was obtained by the Medical Ethical Committee of the University Medical Center of Groningen for a comprehensive application. All participants were informed about the study procedures and provided informed consent prior to the initiation of this study.
Testing under laboratory conditions
In order to examine the test-retest reliability and the validity of the ten trackers in a standardized situation, the participants walked for 30 minutes on a treadmill at a walking speed of 4.8 km/h. This walking velocity was similar to velocities used in previous treadmill studies and is based on an average walking speed.14,23 During the treadmill test, the participants wore all ten activity trackers and the ActivPAL. The Optogait system on the treadmill was used as the gold standard. The primary outcome measure was the total number of steps measured within the duration of the 30-minute treadmill test. All participants repeated this test one week later.
Testing under free-living conditions
In order to examine the validity of the ten trackers in free-living conditions during a working day, the activity behavior of the participants was measured during one working day between 9.00 am and 4:30 pm. The participants wore each ten different trackers and the ActivPAL simultaneously. During the specified day, participants performed their normal daily activities; however, they were requested to abstain from cycling or driving a vehicle during the test period. This was required in order to be able to make a realistic comparison between the trackers; because the different wearing positions of the trackers might
influence step measurements during these activities. The primary outcome measure was the total number of steps measured between 9 am to 4:30 pm.
Activity trackers
All devices utilized in this study are able to track step count.
Lumoback: The Lumoback™ (Lumo BodyTech, Inc. Palo Alto, California, USA) was worn around the lower back and was calibrated to the user by utilizing the associated application. Fitbit Flex: The Fitbit Flex™ (Fitbit, Inc., San Francisco, CA, USA) is a wrist-worn tri-axial accelerometer and was worn on the non-dominant arm.
Jawbone UP: The Jawbone UP™ (JAWBONE, San Francisco, CA, USA, is a wrist-worn three-dimensional activity tracker and was worn on the non-dominant arm.
Nike+ Fuelband: The Nike+ Fuelband SE ™ (Nike Inc., Beaverton, OR, USA) is a wrist-worn three-dimensional activity tracker and was worn on the non-dominant arm.
Misfit Shine: The Misfit Shine™ (Misfit Wearables, Burlingame, California, USA) is a small tri-axial accelerometer which was carried in the front pocket of the trousers.
Pulse: The Withings Pulse™ (Withings, Issy les Moulineaux, France) is a small tri-axial accelerometer which was carried in the front pocket of the trousers.
Fitbit Zip: The Fitbit Zip™ (Fitbit, Inc., San Francisco, CA, USA) is a small tri-axial accelerometer which was carried in the front pocket of the trousers.
Omron: The Omron Walking Style III™ (type HJ-203) (OMRON Healthcare Europe B.V.,
Hoofddorp, the Netherlands) is a pedometer with a two-dimensional sensor which was
carried in the front pocket of the trousers.
Digiwalker: The Yamax Digiwalker SW-200™ (YAMAX Health & Sports, Inc. San Antonio, USA) is a two-dimensional pedometer that was attached to the participant’s waistband.
Moves: The MovesR is a smartphone application. It uses acceleration sensors from a smartphone and GPS to measure the number of steps taken. The mobile phone used in the laboratory study was an Iphone 4S (Iphone 4S, Apple Inc., USA). During the free-living study the smartphone of the participant was used (IOS/Android) and carried in the front pocket of the trousers.
2
Methods
Study design
The following ten activity trackers were examined: the Lumoback, Fitbit Flex, Nike+ Fuelband SE, Jawbone Up, Misfit Shine, Withings Pulse, Fitbit Zip, Omron HJ-203, Yamax Digiwalker SW-200 and the Moves mobile application. The Optogait system (OPTOGait, Microgate S.r.I,
Italy, 2010) was used as the gold standard on the treadmill in the laboratory condition. This
system consists of two beams attached to the sides of the treadmill. The system uses an LED lighting system to precisely measure the number of steps which is a reliable and valid method for measuring step count (cadence).19 The ActivPAL (PAL Technologies Ltd., Glasgow,
UK) was used as the gold standard in the free-living condition. The ActivPAL was worn on the
thigh underneath the clothing. Previous research has demonstrated that the ActivPAL is a reliable and valid tool for measuring the number of steps taken both on a treadmill and in free-living conditions.20-22
Study sample
Only healthy adult volunteers (age ≥18, <65 years) were included in the study. Participants were recruited through advertisements within the Hanze University and by using the individual networks of the researchers. Subscribers were excluded from participation if they experienced problems with standing or normal ambulation as well as if they performed daily activities which could possibly damage the activity trackers while being worn (when
participating in the free-living study). All components of the study are described below in more detail. The study was in accordance with the principles as outlined in the Declaration of Helsinki and an exemption was obtained by the Medical Ethical Committee of the University Medical Center of Groningen for a comprehensive application. All participants were informed about the study procedures and provided informed consent prior to the initiation of this study.
Testing under laboratory conditions
In order to examine the test-retest reliability and the validity of the ten trackers in a standardized situation, the participants walked for 30 minutes on a treadmill at a walking speed of 4.8 km/h. This walking velocity was similar to velocities used in previous treadmill studies and is based on an average walking speed.14,23 During the treadmill test, the participants wore all ten activity trackers and the ActivPAL. The Optogait system on the treadmill was used as the gold standard. The primary outcome measure was the total number of steps measured within the duration of the 30-minute treadmill test. All participants repeated this test one week later.
Testing under free-living conditions
In order to examine the validity of the ten trackers in free-living conditions during a working day, the activity behavior of the participants was measured during one working day between 9.00 am and 4:30 pm. The participants wore each ten different trackers and the ActivPAL simultaneously. During the specified day, participants performed their normal daily activities; however, they were requested to abstain from cycling or driving a vehicle during the test period. This was required in order to be able to make a realistic comparison between the trackers; because the different wearing positions of the trackers might
influence step measurements during these activities. The primary outcome measure was the total number of steps measured between 9 am to 4:30 pm.
Activity trackers
All devices utilized in this study are able to track step count.
Lumoback: The Lumoback™ (Lumo BodyTech, Inc. Palo Alto, California, USA) was worn around the lower back and was calibrated to the user by utilizing the associated application. Fitbit Flex: The Fitbit Flex™ (Fitbit, Inc., San Francisco, CA, USA) is a wrist-worn tri-axial accelerometer and was worn on the non-dominant arm.
Jawbone UP: The Jawbone UP™ (JAWBONE, San Francisco, CA, USA, is a wrist-worn three-dimensional activity tracker and was worn on the non-dominant arm.
Nike+ Fuelband: The Nike+ Fuelband SE ™ (Nike Inc., Beaverton, OR, USA) is a wrist-worn three-dimensional activity tracker and was worn on the non-dominant arm.
Misfit Shine: The Misfit Shine™ (Misfit Wearables, Burlingame, California, USA) is a small tri-axial accelerometer which was carried in the front pocket of the trousers.
Pulse: The Withings Pulse™ (Withings, Issy les Moulineaux, France) is a small tri-axial accelerometer which was carried in the front pocket of the trousers.
Fitbit Zip: The Fitbit Zip™ (Fitbit, Inc., San Francisco, CA, USA) is a small tri-axial accelerometer which was carried in the front pocket of the trousers.
Omron: The Omron Walking Style III™ (type HJ-203) (OMRON Healthcare Europe B.V.,
Hoofddorp, the Netherlands) is a pedometer with a two-dimensional sensor which was
carried in the front pocket of the trousers.
Digiwalker: The Yamax Digiwalker SW-200™ (YAMAX Health & Sports, Inc. San Antonio, USA) is a two-dimensional pedometer that was attached to the participant’s waistband.
Moves: The MovesR is a smartphone application. It uses acceleration sensors from a smartphone and GPS to measure the number of steps taken. The mobile phone used in the laboratory study was an Iphone 4S (Iphone 4S, Apple Inc., USA). During the free-living study the smartphone of the participant was used (IOS/Android) and carried in the front pocket of the trousers.
