Title:Reliability and Validity of ten consumer activity trackers

Transcription

1 Author's response to reviews Title:Reliability and Validity of ten consumer activity trackers Authors: Thea JM Kooiman Manon L Dontje (m.l.dontje@alumnus.rug.nl) Siska R Sprenger (s.r.sprenger@pl.hanze.nl) Wim P Krijnen (w.p.krijnen@pl.hanze.nl) Martijn de Groot (ma.degroot@pl.hanze.nl) Cees P van der Schans (c.p.van.der.schans@pl.hanze.nl) Version:3Date:10 July 2015 Author's response to reviews: see over

2 Mr Ervin Cenzon, Dr Jonathan Williams BMC Sports science, Medicine and Rehabilitation July 10 th, 2015 Dear Mr Ervin Cenzon, dr Jonathan Williams, Thank you for considering our paper. We are pleased to resubmit our paper entitled Reliability and Validity of ten consumer activity trackers. We would like to thank the reviewers for their feedback and constructive comments that helped us to improve this manuscript. One comment of both reviewers was on the use of a single testing day within our study, and the question whether we have measured free-living activity or occupational activity. In addition, the reviewers asked some questions about the lower number of participants who have worn the Nike+ Fuelband and Moves app during the testing day. A third general comment concerned that not all limitations were discussed in a limitation section. We have clarified the choices we made, and adjusted the manuscript accordingly. Detailed responses to the specific comments of the reviewers are described below. Comments made by the reviewers are in bold, our answers are in normal font and changes to the manuscript are in italic and indented. In the manuscript, changes are in bold. For a few important deletions, track changes was used. Reviewer 1 Testing under free-living condition: Please provide a rational for the measurement of a single testing day, when typically most studies that look at the validity of physical activity monitors include a minimum of 3-day measurement, and suggest a minimum of hours of wear time (Matthews CE, et al. Am J Epidemiol. 2008;167(7): and Troiano RP, et al. Med Sci Sports Exerc. 2008;40(1):181 8). Based on the time period measured, this "activity" might be considered "occupational" activity and not necessarily "free-living" as the authors suggest. We thank the reviewer for these comments. We have chosen for a single testing day, because previous research from Dontje et al (Journal of Cardiovascular Nursing. 2014;29(3):218-26) found that the ICC between two measurements of steps per day was Therefore we expected that a single testing day would reflect a sufficient pattern of one s physical activity, especially because we used a working day for our measurements. Our motivation for testing the activity trackers during one working day (9:00 am -4:30 pm) was that we wanted to reduce confounding factors and biases due to travelling to work, for example. It is likely that a wearable worn deeply in the pants pockets, along with the gold

3 standard (the ActivPAL worn at the thigh) will measure some activity during cycling, while wrist-worn trackers will not. Therefore, the results for the wrist worn trackers could have been biased when cycling was allowed, since they will seem to underestimate the number of steps when a participant has cycled a lot. In the Netherlands, a large number of people cycle to work, therefore we decided to measure only in a period that the participants did not have to use their bikes: during a working day between 9:00 and 16:30. Occupational activity is a term we could also have used, but we feel this is more related to the type of activities a specific profession might entail. We were interested how the activity trackers work in a situation in which the wearer was able to move freely around; and therefore defined the term free-living as the activity the participant engaged in between 9:00 16:30. We clarified this for the readers at the method section and we highlighted this point at the limitation section; Methods: 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. Added to limitations: Second, for examining free-living activity, we used a time span of 9:00-16:30, in which mostly occupational activity was measured. The advantage of this method was that we were able to make a realistic comparison between the different trackers with different wearing positions because cycling was excluded. Cycling could have biased the results between centrally worn and wrist-worn trackers. However, the trackers might perform differently during a larger variety of activities, such as more intensive exercise activities. These activities were not measured in this study. Study Sample: Please elaborate on the participant's inclusion criteria of "owning a smartphone" if researchers were interested only in one specific device app. Could this inclusion criteria have affected the results of the study in the free-living condition, as participant would not have been as active as otherwise expected? Activity Trackers: Based on the design described by the authors, it would be difficult to make any type of inference on the accuracy of the Moves application considering two different phones systems (IOS and Android) were utilized in this study. What is more, authors do not provide any evidence of the accuracy of each participant's phone during the treadmill condition that could be carried out to the "free-living" condition. In essence, the 4S provided by the researchers for the treadmill condition was validated, but not each participant's device. Without knowing how accurate the participant's personal device was compared to the treadmill gold standard (difference phones would have difference firm ware that might affect the validity/accuracy of each device), it is hard to make any kind of

4 assessment as to their accuracy in the free-living environment. We thank the reviewer for these justified comments on the results with the smartphone app. Though mentioned separately by the reviewer, they point out to one and the same problem that appeared during the execution of the research. We started our research with the free-living condition study and then halfway this study initiated the lab study. During the free living study we discovered that the inclusion criterion for owing a smartphone was not sufficient to guarantee the inclusion of the Moves app. Smartphone owners were not always willing to download the app because of battery restrains or prepayment issue (as for iphone users). We didn t foresee this. This explains the lower N for the Moves app during the free living study and also why only android phones were used during free living. To overcome this problem for the lab study we decided to purchase one smartphone (an Iphone 4S). As a result we obtained data from a single smartphone type during the lab study and less smartphone data during free living (whereby all data was based on Android devices). As a result the lab and free living results are not fully comparable with each other. In the study of Case et al (2015), the Moves app was tested on both type of phones: Android and IOS. Hereby, the IOS device overestimated the number of steps, and Android device underestimated the number of steps. This finding was in line with our findings for the different type of phones utilized. For better understanding we discarded the inclusion criterion of owning a smartphone from the method section and elaborated on these limitations and findings of Case et al in the discussion. For the Moves app, only preliminary conclusions can be drawn on the validity in freeliving conditions. This is due to the low N, consequently a lower power of 39%, and because the Moves app was tested on different types of phones compared to the laboratory study (Android vs. IOS devices). Therefore, results of the free-living condition cannot be compared with the lab condition, because the different types of firmware may have influenced the results. However, our results for the Moves app on the different types of phones are comparable with the study of Case et al [16]. They showed that Android devices are associated with a modest underestimation and IOS devices show a modest overestimation of step counting, which is in line with our results. Statistical Analysis: Please elaborate on the use of 10% and 15% difference for the treadmill and free-living condition respectively. These numbers seem to be arbitrary as presented by the authors. Please support with previous evidence or make it clear to the reader as to why these are appropriate. These numbers indeed seem arbitrary. When we determined the sample size, we reasoned that the difference we allowed for in the free-living condition should be larger compared to

5 the treadmill condition because of the larger possible variation in both type of activity (with disturbing factors possible like carrying a cup during walking) and more variation possible between te participants (some participants will be more active during their day than other participants). When using 10% and 15% difference, our sample size analysis showed a need for 24 participants in the laboratory (with an estimated mean of 3000, SD 500), and 58 in the free-living condition (with an estimated mean of 5000, SD 2000). Comparing this outcome to comparable validation studies, a N of approximately 30 for laboratory conditions, and a N of 58 for free-living conditions seemed appropriate. For example Takacs et al (2013) used an N of 30 for their treadmill study, and Ryan et al (2006) used an N of 20 for the validation of the ActivPAL, both on a treadmill as for walking outside. Lee et al (2014) used an sample size of 60 participants in semi-free-living conditions and Tully et al (2014) used a sample size of 50 participants for validation of the Fitbit Zip in free-living conditions. Therefore, we decided that our reasoned difference of 10% and 15% seemed appropriate for calculating the sample size. We clarified these arguments in the method section: As previous data on relevant differences for sample size calculation does not exist, we reasoned that a difference of 10% for the laboratory condition, and 15% for the freeliving condition seemed appropriate. These numbers of participants are comparable to other validation studies [12, 14, 15]. This reassured our reasoned choice for using 10% and 15 % as cut off points for the mean difference. Results: It is not clear why the Nike+ Fuelband and Moves app were tested with significantly less participants. Please provide an explanation for this. Could this have affected the device's results considering the calculated sample size of 58 (Nike+ N = 20; Moves app N = 11)? Please elaborate on this last point further. The number of participants for the Nike+ and Moves app is indeed low, and reflects a limitation in our study. Unfortunately, we experienced a major delay in the delivery of the Nike+ Fuelband. Nevertheless we decided to add this wearable to the study, because we were still able to obtain 20 measurements. The reason for the low N for the Moves app was explained in response to the second comment ( owing a smartphone was not enough to guarantee inclusion of the Moves app). We calculated 95% Confidence Intervals for the mean number of steps measured by the two devices. For the Moves app the CI was: 1586; For the Nike+Fuelband 2232; 4310 (figure 1). In addition, we calculated the power of the paired samples t-test of these two devices with the ActivPAL (gold standard). For the Nike Fuelband (N=20, mean ActivPAL for these 20 cases = 4099, SD of the difference scores is 1155 steps, used delta = 615 (15% of 4099)), we calculated a power of 62%.

6 For the Moves app (N=11, mean ActivPAL for these 11 cases = 4894, SD of difference scores = 1311, used delta = 734), we calculated a power of 39%. Based on these power calculations, we still think the outcomes of the Nike+ Fuelband are worthwhile presenting. The Nike+ Fuelband in the free-living condition are consistent with the results in the laboratory condition; in both situations there was a clear underestimation of the number of steps. Also, the results are in accordance with other studies which investigated the Nike + Fuelband, in which Ferguson used a similar sample size for free-living conditions (N=21). (Ferguson et al, 2015, Lee et al, 2014, Case et al 2015) For the Moves app, our results might be too limited to draw conclusions about the validity in free-living conditions, because of the low N and low power. Therefore, we decided to adjust our conclusions about the Moves, and mentioned that the results should be interpreted cautiously in the limitation section. Also, we mentioned and clarified this point in the result section: Results: As shown in figure 1, the Nike+Fuelband and Moves app give a relatively large confidence interval for the mean number of steps, which is partly due to a lower number of measurements of these devices. Therefore, additional power analyses were executed, which are shown below. In the free-living condition, there was a significant difference in the number of steps between the ActivPAL (gold standard) and the Fitbit Flex, Nike+ Fuelband, Fitbit Zip, Withings Pulse, Digiwalker, and the Moves app (Table 2). Again, the MAPE values of the trackers were small (less than 10%), except for the Nike+ Fuelband and the Moves app (24% and 37.6% respectively). The smallest MAPE values were between the ActivPAL and the Omron (0.4%) and Lumoback (0.4%). The power for the calculation of the Nike+ Fuelband and Moves app was 62% and 39% respectively. The power for the remaining devices was high; either 99% or 100%. Discussion: The third limitation was that in the free-living condition the Nike+ Fuelband and Moves app were tested at a lower number of participants. Because of a reasonable power (62%), consistent results with the laboratory condition, and consistent results with other studies [12, 16, 17], the results of the Nike+ Fuelband are considered as reliable. For the Moves app, only preliminary conclusions can be drawn at the validity in free-living conditions. This is due to the low N, consequently a lower power of 39%, and.

7 Validity: It would be informative for the reader to learn about the "adherence between the two gold standards" before reading any of the differences, correlations and level of agreement between the devices for the two conditions. I would suggest the section "Agreement between the gold standards" is inserted immediately following the descriptive statistics section. As suggested by the reviewer, we moved the agreement between the two gold standards section to the beginning of the results, after the descriptive statistics section. Please provide additional evidence of why the use of a 3% and 8% MAPE was used in this study. The reason for using 3% and 8 % was because most trackers showed values below these percentages and to show some differences between the trackers in amount of error. For consistency, we have changed this to the same values as we used in the discussion thus 1% for the laboratory and 10% for the free-living condition (based on Tudor-Locke et al, 2004). Discussion: Paragraph 2: Please provide rational for leading statement considering Lee et al. Med Sci Sport Exerc (2014) and Ferguson et al. Int J Behav Nutr Phys Act (2015) provided similar insight. Are the authors referring to a specific measurement variable (i.e. steps)? We agree with the reviewer that this statement is somewhat vague. This statement was based on test-retest reliability for measuring steps; to our knowledge we are the first study that has examined test-retest reliability. Lee et al (2014) and Ferguson et al (2015) investigated validity of some of the same trackers, but not test-retest reliability. Because we could not compare our results with the literature, we now decided to show this paragraph only at the result section, and delete in from the discussion section. Paragraph 3: Please address the "practicality" of these findings considering the cost associated with these devices compared with others. Does a 0.2% (Digiwalker) and 1.5% (Omron) difference be "worth" the extra cost associated with the more technological advance device? Studies by Feito et al. Med Sci Sport Exerc (2012 and 2015) discuss only reporting values greater than 5% considering the practical relevance of smaller 'statistically significant' values. We agree with the reviewer that the differences of the Digiwalker and Omron are on average very small. We mentioned this in the discussion section: The Digiwalker and Omron had an error deviation slightly higher than the 1% threshold; 1.2% and 2.5%, respectively, which still represents a very low MAPE. We have added a basic sentence about considerations for cost. As known from sales numbers from large companies like Fitbit and Jawbone, consumers are willing to pay some extra money for a more advanced activity tracker. Therefore we think it is important to

8 provide insight in the quality of these devices, but it s difficult for us to speculate in terms of cost vs. validity. Eventually, consumers decide whether they are willing to pay the extra cost for a more advanced, and/or a more valid device. We cannot objectively identify what considerations consumers have when the decide to buy a certain activity tracker. Therefore, we investigated a range of different activity trackers in different price classes, so consumers can make their own choice. This sentence was added to the discussion: Furthermore, a consumer can make a choice between a more advanced -and mostly more expensive- or a more valid device, and a more simple and cheaper device. This study demonstrated that cheaper devices do not necessarily have to be less valid. Paragraph 4: Please elaborate on the sentence is not enough to create issues in clinical interpretation What kind of issues do the authors refer to here? With the issues in clinical interpretation, we meant a clinically relevant over- or underestimation of one s physical activity level. However, we decided to delete this sentence from the manuscript, because interpretation of Bland-Altman plots might be assumed as common knowledge. In addition, we had to shorten the text where possible. The reference to the original article of Bland and Altman (1986) is still in the text. Paragraph 4: When describing the limits of agreement, it is not clear if the values provided by the authors are +/- from the mean or simply absolute values of agreement. Please provide further clarification for the reader. This was indeed not clearly stated. The values provided are ranges between the lower and upper limit of agreement, so +1.96*SD and *SD from the mean difference. We clarified these values in the manuscript: The difference between the lower and upper limit of agreement (Mean difference ± 1.96SD of difference scores )ranged from Paragraph 6: The authors should acknowledge that the ICC reported for the Nike+ Fuelband and Moves app (0.83 and 0.80) although lower than the rest, they are still within acceptable ranges. Additionally, discuss the role that a reduced number of participants might have played with these devices. As stated by the authors, these two devices had significantly lower number of participants using them compared to the others. We have adjusted the text at this point, and explained that the confidence intervals of the ICC s of the Nike+ Fuelband and Moves app were broad. The possible influence of the lower N are mentioned at the results section and limitation section (as described above). The Nike+ Fuelband and the Moves app showed ICCs which were a bit lower and had broad confidence intervals (.83 [CI.37;.94] and.80 [CI ] respectively).

9 Paragraph 10: Authors allude to wearing position as a factor when using activity trackers and compare their findings to those of Atallah et al.; however, the authors never really mention the position of their devices throughout the manuscript other than when describing the actual devices. Please elaborate on how the different activity trackers examined by the authors and the placement might have affected the accuracy of each device. We clarified the different wearing positions at this statement: Looking at the wearing position of the trackers (wrist-worn for the Fitbit Flex, Jawbone UP and Nike+Fuelband and centrally worn, e.g. close to the pelvis or trunk, for the remaining devices), our results indicate that activity trackers worn close to the body exhibit a better validity than the wrist-worn activity trackers, especially during free-living conditions. For wrist-worn activity trackers, more measurement error can occur due to more variation in the way the arms are used in free-living. This finding is supported by the research of Atallah et al [29]. It is imperative for the authors to include a limitations section in this manuscript. As they conclude the make claims as to how this research can assist consumers, researchers, and health care providers however, it is important to highlight how their findings may only be applicable to a very selected group of participants and might not be generalizable for the entire population. The age of the participants was never described in detail (> 18 years) where these all college students? Wear time was only limited to one day and between a very specific time period this merely results in a free-living environment. The author s used a specific phone for one condition (treadmill) and never validated the phone used in the free-living condition. Thus, it is difficult to truly evaluate the validity and accuracy of the phone app authors also never gave any indication as to what system was perhaps more accurate (IOS vs. Android). There are definitely limitations to this study and those must be address by the authors. As suggested we ve added a limitation section to the discussion of the manuscript. With regard to the generalizability of the study, we do think that the results of this study are representative to a larger group of users since our study sample were mainly employees who varied in age, BMI and type of activity performed. We clarified the variance within our study population in the manuscript: Results: Participants were mainly employees, with an office job. Activities which were performed by participants during the test day were sitting (e.g. at the computer), standing (e.g. teaching activities) and walking. Some participants were highly active (e.g. made a long lunch walk) whereas others were mainly sedentary during the test day.

10 Limitation section: Our study has some limitations. First, in the laboratory condition, only one type of activity was examined (walking), however, activity trackers can possibly perform differently during different activities or velocities (such as slow walking). The advantage of the 30-minute measurement was that reliable data for average walking speed was obtained. Second, for examining free-living activity, we used a time span of 9:00-16:30, in which mostly occupational activity was measured. The advantage of this method was that we were able to make a realistic comparison between the different trackers with different wearing positions because cycling was excluded. Cycling could have biased the results between centrally worn and wrist-worn trackers. However, the trackers might perform differently during a larger variety of activities, such as more intensive exercise activities. These activities were not measured in this study. The third limitation was that in the free-living condition the Nike+ Fuelband and Moves app were tested under a lower number of participants. Because of a reasonable power (62%), consistent results with the laboratory condition, and consistent results with other studies [12, 16, 17], the results of the Nike+ Fuelband are considered as reliable. For the Moves app, only preliminary conclusions can be drawn on the validity in free-living conditions. This is due to the low N, consequently a lower power of 39%, and because the Moves app was tested on different types of phones compared to the laboratory study (Android vs. IOS devices). Therefore, results of the free-living condition cannot be compared with the lab condition, because the different types of firmware may have influenced the results. However, our results for the Moves app on the different types of phones are comparable with the study of Case et al [16]. They showed that Android devices are associated with a modest underestimation and IOS devices show a modest overestimation of step counting, which is in line with our results. Tables 2-4 Please include the ActiPal on the list, even if the values state it is the reference. The ActiPal was also included in the analysis and should be presented on all the tables. We added the values of the ActivPal at the tables. Minor Essential Revisions The authors are encouraged to denote with (TM) or (R) whether each brand name is a trademark or registered brand. Authors do some in some instances, but not in others. We added this information at the description of the activity trackers. Introduction: Paragraph 2: switch "which" for "that" where it reads "...user friendly devices

11 which measure..." Changed as suggested. Results Descriptive statistics Second sentence: add the figure label after "laboratory" and "freeliving condition". Sentence should read "...in both the laboratory (A) and free-living condition (B)" - as this is how the authors have the figure labeled. Changed as suggested. Paragraph 5: Please insert a comma, after valid. Sentence should read most trackers are valid, with the Changed as suggested. Paragraph 6: When discussing differences for the Fitbit Zip and Jawbone please insert in that study when comparing to the study by Lee et al. Final sentence should read The differences are larger for the Fitbit Zip the different outcome measures that were used (energy expenditure vs. step counts) in that study. Changed as suggested. Tables 1 & 3 For consistency, please include zeros before all decimal values as done before throughout the manuscript. Changed as suggested. Figure 2 I would recommend all plots to have the same scaling for the x- and y-axis. Unfortunately, the same scaling for the x- and y-axis is not possible in this figure, since figure 2.a-c are about the laboratory condition, in which there was less variance in number of steps taken compared to the free-living condition ( figure 2.d-f) The range on the x-axis in the laboratory condition is between 2800 and 3600 steps and the range in the free-living condition is from 0 to steps. However, figure a-c and figure d-f do have the same scaling, so the top three trackers in the two separate conditions can be compared with each other. Discretionary Revisions: For consistency, I would recommend the author use indentations at the beginning of every paragraph. Changed as suggested. Reviewer 2 Free-living validity was assessed during one working day, which may not present sufficient challenges in measurement. There is little explained about what participants were doing

12 during free-living test days. The average number of steps across deices indicate a fairly low rate of activity. It would be useful to assess device validity across different types of freeliving days to include days with greater variation in activity and exercise intensity. For example, previous research indicates that activity monitors may be more accurate at slower speeds. What would be expected under these conditions? We like to thank the reviewer for these comments. We have chosen for a single testing day, because previous research from Dontje et al (Journal of Cardiovascular Nursing. 2014;29(3):218-26) found that the ICC between two measurements of steps per day was Therefore we expected that a single testing day would reflect a sufficient pattern of one s physical activity, especially because we used a working day for our measurements. Our motivation for testing the activity trackers during one working day (9:00 am -4:30 pm) was that we wanted to reduce confounding factors and biases due to travelling to work, for example. It is likely that a wearable worn deeply in the pants pockets, along with the gold standard (the ActivPAL worn at the thigh) will measure some activity during cycling, while wrist-worn trackers will not. Therefore, the results for the wrist worn trackers could have been biased when cycling was allowed, since they will seem to underestimate the number of steps when a participant has cycled a lot. In the Netherlands, a large number of people cycle to work, therefore we decided to measure only in a period that the participants did not have to use their bikes: during a working day between 9:00 and 16:30. Another reason for measuring at one day was to prevent practical problems; it is difficult to engage in sports activities for example, (especially contact sports), while wearing 10 different activity trackers. For measuring these type of activities, we feel it might be better to include a lower number of trackers, so one s sports activities are not hindered by different type of trackers with different wearing positions and different attachment methods. As suggested, we clarified the type of activity participants were engaged in in the manuscript (result section). The participants varied in age, BMI and type of activity performed. Some participants were highly active during the test day, whereas others were mainly sedentary. Thus, we expect that the trackers were tested in sufficiently varied conditions for daily living circumstances. However, sports activity with a higher intensity, which were not measured in our study, would definitely be interesting as well. Also, the measurement of different velocities on the treadmill is interesting, which is lacking in our manuscript. We mentioned these points at the result and limitation section: Result section: Participants were mainly employees, with an office job. Activities which were performed by participants during the test day were sitting (e.g. at the computer), standing (e.g. teaching activities) and walking. Some participants were highly active (e.g. made a long lunch walk) whereas others were mainly sedentary during the test day.

13 Limitation paragraph: Limitation section: Our study has some limitations. First, in the laboratory condition, only one type of activity was examined (walking), however, activity trackers can possibly perform differently during different activities or velocities (such as slow walking). The advantage of the 30-minute measurement was that reliable data for average walking speed was obtained. Second, for examining free-living activity, we used a time span of 9:00-16:30, in which mostly occupational activity was measured. The advantage of this method was that we were able to make a realistic comparison between the different trackers with different wearing positions because cycling was excluded. Cycling could have biased the results between centrally worn and wrist-worn trackers. However, the trackers might perform differently during a larger variety of activities, such as more intensive exercise activities. These activities were not measured in this study. The third limitation was that in the free-living condition the Nike+ Fuelband and Moves app were tested under a lower number of participants. Because of a reasonable power (62%), consistent results with the laboratory condition, and consistent results with other studies [12, 16, 17], the results of the Nike+ Fuelband are considered as reliable. For the Moves app, only preliminary conclusions can be drawn on the validity in free-living conditions. This is due to the low N, consequently a lower power of 39%, and because the Moves app was tested on different types of phones compared to the laboratory study (Android vs. IOS devices). Therefore, results of the free-living condition cannot be compared with the lab condition, because the different types of firmware may have influenced the results. However, our results for the Moves app on the different types of phones are comparable with the study of Case et al [16]. They showed that Android devices are associated with a modest underestimation and IOS devices show a modest overestimation of step counting, which is in line with our results. Minor Essential Revisions No information about source(s) of participant recruitment. We added the sources of participants recruitment in the method section: Participants were recruited through advertisements within the Hanze university, and by using the own network of the researchers. No information characterizing participant baseline activity patterns other than healthy. In the result section we added some information about physical activity patterns of the participants during the test day. With the descriptive term healthy participants, we meant that the participants were free of any disease or mobility problems. We do not mean to say here that the participants had healthy physical activity patterns in daily life.

14 The discussion section is long and repeats many findings found in the results. This section should be shortened and highlight key findings and similarities/differences compared to previous studies. We agree with the reviewer that the discussion section is long. This is mainly due because we described two different studies in one article, and wanted to compare our study results systematically and clearly with findings from other research. We have tried to shorten the discussion section where possible, for example, by deleting some text about the algorithms the trackers use to define steps, and some repetitions from the result section. is no limitations section in discussion. The authors mention that the study was performed with healthy volunteers and these results may not generalize to other populations. Any other limitations should be noted. We added a limitation section, which we described above We hope we have answered the questions satisfactorily. We d like to end our response letter with a note that we have incorporated two newly published articles on validity of consumer trackers in our manuscript; the research letter of Case et al (2015), and the study of Ferguson et al (2015). With kind regards, also on behalf of all co-authors, Thea Kooiman, MSc Hanze University of applied sciences, research group Healthy ageing, Allied health care and Nursing. Eyssoniusplein 18 PO Box 3109, 9701 DC Groningen The Netherlands Phone (research group) Phone (mobile) t.j.m.kooiman@pl.hanze.nl