Validity of the iphone 5S M7 motion co-processor. as a pedometer for able-bodied persons. Micah Alford 9/7/2014

Transcription

1 Validity of the iphone 5S M7 motion co-processor as a pedometer for able-bodied persons Micah Alford 9/7/2014

2 Abstract Objectives: The specific aim of this study is to assess the concurrent validity of the iphone 5S s M7 motion co-processor as a pedometer for able-bodied persons. Method: Participants of this trial will walk a 30 meter course at three self-selected speeds: self-selected comfortable, comfortably slow, and comfortably fast with the iphone 5S in the right hip pocket and a StepWatch 3 Activity monitor (Orthocare Innovations, Mountlake Terrace, WA) attached just proximally to their right lateral malleolus. Statistical analysis will be conducted to determine if the iphone 5S is a valid instrument as a pedometer in an able-bodied population. Results: Results show that the iphone compared to the manual count is only strongly correlated at fast speeds (ρ=.83, p<.001) Table 1. When the iphone was compared to the StepWatch, it was found to be moderately correlated at normal speeds (ρ=.406, p=.075) Table 1 and strongly correlated at fast speeds (ρ=.773, p<.001) Table 1. Speed was moderately and inversely correlated with error (ρ=-0.60, p<.001) Figure 4. Conclusions: This study shows that the iphone is not yet a valid instrument across all speeds, but the potential is there. Further research is needed to determine if it is valid is real world situations.

3 Introduction In 2007, the American College of Sports Medicine (ACSM) and the American Heart Association (AHA) released an updated recommendation for physical activity in adults ages 18 to 65 of moderate-intensity physical activity for 30 minutes 5 times a week. Moderate-intensity physical activity includes activities such as walking at speeds of 3-4 mph 1. The benefits of physical activity have been shown to reduce the number of coronary heart disease events 2, diabetes mellitus 3, as well as many other health conditions; however more and more Americans are not able to meet a basic exercise program as defined by the ACSM and the AHA 4. A useful and accessible method for addressing the barriers facing adults seeking to meet their recommended physical activity requirements is through the use of pedometers to monitor step count 5,6. Tracking step count provides evidence of satisfying the AHA-recommended amount of physical activity, and also estimation of distance traveled, speed, and intensity of physical activity 5,7,8, which can be used to help clinicians assess the level of function that their patient has. Low-cost pedometers, such as the Yamax pedometer (YAMAX Health & Sports, Inc., San Antonio, TX) or the StepWatch 3 (Orthocare Innovations, Mountlake Terrace, WA), have been shown to be clinically useful instruments to monitor and assess activity levels in adults 9. By tracking number of steps throughout a bout of physical activity, a physician can start to get ideas of the level of physical activities at home, which can be useful when diagnosing and prescribing treatment options. However, given the large assortment of commercially available pedometers, a clinician must be careful to select a device that has been validated (the degree to which a measurement accurately represents the real world) for the specific patient group of interest. This is clinically important so that healthcare providers are able to rely on these devices to measure steps while they are not with the patient.

4 Given the high percentage of individuals in the US who carry smartphones 10, smartphone manufacturers have begun integrating accelerometer-based pedometers into their devices. The clinical advantage of having a pedometer integrated into a smartphone is that many patients will already own these devices and clinicians can monitor patient physical activity without avoid incurring the additional expense of purchasing stand-alone pedometers. Importantly, the validity of smartphone-based pedometers depends on the accelerometer hardware and application software algorithm that identifies individual steps based on acceleration signals. Consequently, this creates a challenging task for researchers when validating smartphone-based pedometer systems due to the myriad application algorithms available. Another confounding factor is that new smartphones are constantly being released, making it difficult for researchers to keep up with the latest phones on the market due to constantly changing hardware and software. These challenges have resulted in a limited number of smartphone-based pedometer systems that have been assessed. Several studies have assessed the validity 4, reliability 11, or both 12 of various iphone (Apple Inc., Cupertino, CA) models and applications, to act as pedometers, and these investigations have produced mixed results due to differences in hardware and software combinations. In a study conducted by Boyce et. al. in 2012, the validity and reliability of three different iphone applications were assessed for able-bodied individuals at varying speeds (3, 6, 9 km/h) and sensitivities (i.e. the amount of force required to count a step; low, medium, high, self-calibrated). The single subject walked indoors on a treadmill and outdoors on a 1 km route with the iphone 4 attached to the midline posterior waist with a belt clip. The results of this study suggested poor validity (error of 20 steps in a 100 step trial) of the assessed applications for measuring steps at various speeds 12. Adjusting the sensitivities of the application could help improve the validity, but no one application was deemed valid and reliable for all speeds. The

5 authors suggest that this error is possibly due to either hardware or software limitations. Furthermore, the iphone used in this study is now 3 generations behind the currently available iphone 5S and does not represent the state-of-the-art technology. An investigation on the validity of the iphone 3GS in combination with a different iphone application to those assessed by Boyce et al., suggested a significant underestimation of steps (max mean error of steps not counted by the iphone over 2 minutes of walking) and weak correlations when the iphone was attached to various locations on the body (waist: r= -0.08, pocket: r= -0.05). One moderate correlation (r=.46) was found with the iphone attached to the arm 4. The authors suggest that further research is needed determine if there is a software application that is accurate or if there is a fundamental design flaw with the accelerometer in the iphone. Only one single-subject study suggested adequate validity (96% confidence interval, 4% error) of the iphone 3G using a custom application not available to the public 11, however this article has serious limitations. First, the iphone was attached to the ankle, which is not a location that is commonly used to store a smartphone. Second, the statistically power of this investigation is poor given the small sample size (n=1). To address this issue, Apple recently standardized the accelerometer hardware and software platform (M7 motion co-processor and associated application programming interfaces (APIs)) in the iphone 5S model and ios 7 operating system (released September of 2013). The M7 motion co-processor is a stand alone CPU inside the iphone 5S designed to estimate and record user activity related data, including but not limited to number of steps 13. Consequently, the number of steps is estimated by Apple s internal system (i.e., integrated accelerometer and algorithm) rather than custom-built applications, thereby limiting the sources of step count error. To date, validity (i.e., accuracy) of the M7 motion co-processor system has not been assessed. Therefore, the

6 aim of this study is to assess the concurrent validity of the iphone 5S s M7 motion coprocessor as a pedometer for able-bodied persons. To date, no research has been conducted to assess the validity of the current iphone generation technology that has been implemented to addresses previous shortcomings of this system to be used as a pedometer. The purpose of the proposed study is to assess the concurrent validity of the iphone 5S s M7 motion co-processor as a pedometer such that this system may serve as an instrument to accurately monitor step count for clinical use. The step count estimations of iphone 5S were compared to a manual count and the StepWatch 3 Activity Monitor, considered the clinical criterion ( gold ) standard for wearable activity monitors. We chose to validate the iphone to a clinical gold standard as well as a manual count to see if the results would suggest that the iphone could be a valid replacement of the StepWatch. Methods A convenience sample of able-bodied participants was recruited from the Northwestern University and Chicago-land area. Inclusion criteria required that participants be able to walk continuously for 30 meters without the use of an aid and possess no self-reported musculoskeletal and/or neurological disorders that might affect gait or balance. The StepWatch 3 has been previously validated as a pedometer in numerous populations, including able-bodied adults 14, lower limb amputations 8, stroke 15, and multiple sclerosis 16. The StepWatch is connected to a computer via a USB docking system. Using the StepWatch program version 3.2, step counts can be downloaded and saved onto the computer in the form of strides 17. Multiplying this number by two will give you the number of steps that the StepWatch counted.

7 The iphone 5s (M7 motion co-processor, ios 8.1.1, Model: NE296LL/A) was used for this study. A custom application was developed for this study which only served to collect step count in real-time (updated with each registered step) as estimated by the M7 motion co-processor during each walking trial and did not perform any postprocessing of the acceleration data. This application was initiated and terminated by pressing a radio button on the iphone screen. Figure 1a & 1b: Images of the custom created application (1a) (1b) Data was collected during a single visit from each participant, as they wore their own comfortable clothing with hip pockets. Prior to testing, participants signed the University-approved consent form and then their age, height, and weight were recorded. As there is no manufacturer recommendation for location of the iphone on the body for collection of step count, the iphone 5S was stored in the right hip pocket of

8 each participant during testing. The hip pocket was chosen because it is a common location of smartphones when stored on the person 11. Following the standard guidelines of the manufacturer, the StepWatch was programmed with each participant s height and weight and securely attached proximally to the lateral malleolus of the right ankle. Participants first stood quietly as the StepWatch and custom iphone application were activated for data collection, and then requested to walk to the end of a defined 30m straight-line path in a level carpeted hallway and stand quietly until the devices were deactivated and estimated steps were recorded. This task was performed at three self-selected speeds: comfortable, comfortably slow, as if you were window shopping without stopping, and comfortably fast without breaking into a jog. For each trial, video was collected to record the number of steps and elapsed time to walk the 30m path. Data Analysis To assess concurrent validity of the iphone system, step count as estimated by the iphone was compared to the manual count from video recordings and the StepWatch device. Average self-selected walking speed for each walking condition was determined by dividing the predetermined distance by the time required to complete the trial. For each speed condition, Spearman s correlation analyses were conducted to assess the strength of correlation between measurements. A correlation coefficient (ρ) of was considered a weak correlation, was considered a moderate correlation, and was considered a strong correlation 18. Bland-Altman graphs were also constructed for each speed condition to assess the presence of fixed and/or proportional bias of the iphone measurements and determine the 95% confidence limits. A one sample t-test was used to assess if the mean error was significantly different than zero (i.e., fixed bias) and Spearman s correlation analysis was used to estimate the strength of correlation between measure error and average (i.e., proportional bias).

9 Finally, to determine the relationship between walking speed and iphone accuracy, a Spearman s correlation analysis and best fit curve (with weighted error to account for outliers) was applied to the absolute manual count and iphone measurement error and walking speed across all participants and conditions. The critical α was set at 0.05 and given the multiple speed conditions per assessment, a Bonferroni correction was used to account for the family-wise Type I error rate, lowering the critical α to Statistical analyses were conducted using SPSS (IBM, Armonk, NY) and curve fitting was conducted using the curve fitting toolbox in Matlab (Mathworks, Natick, MA). Results Data were collected on twenty participants (10 male, 10 female, 28±5 years of age, 1.71±.14 meters tall, 74.8±21.2 kg). The average normal, slow, and fast walking speed was 1.33±1SD m/s 0.95±1SD m/s, and 1.77±1SD m/s. The Bland-Altman graphs for the comparison of iphone to manual count for the selected speeds suggest that no fixed or proportional bias was present for normal and fast speeds, but a fixed bias of 8 steps (p=0.015) and a strong correlation between mean and error (ρ=0.761, p<.001) was present for the slow speed Figure 2. The Bland-Altman graphs for the comparison of the StepWatch to manual count for the selected speeds suggest that no fixed or proportional bias was present for fast speed, but a fixed bias of steps (p=.023), a proportional bias for slow speed, and a strong correlation between mean and error for slow (ρ=0.796, p<.001) and fast (ρ=0.911, p<.001) speeds Figure 3. The Bland-Altman graphs for the comparison of the iphone to the StepWatch for the selected speeds suggest that no fixed or proportional bias for normal and fast speeds, but a fixed bias of 8.4 steps (p=.029), a proportional bias of (p=.003) for slow speed, and a strong correlation between mean and error for fast speeds (ρ=0.733, p<.001) Figure 4.

10 Absolute manual count and iphone measurement error versus walking speed is displayed in Figure 5. Speed was strongly correlated with error (ρ=-0.60, p<.001), and this relationship was best fit with a power function. The equation for this best fit function is f(x) = 16.8*x ( (-2.718)), r 2 = 0.607, and adjusted r 2 = Table 1: Spearman's Correlation Analysis Measures for Correlation Speed Condition Spearman s ρ p-value Slow Manual-iPhone Normal Fast 0.83 <0.001 Slow <0.001 Manual-StepWatch Normal Fast <0.001 Slow StepWatch-iPhone Normal Fast <0.001

11 Figure 2: Manual count / iphone Bland-Altman plots for normal (2a), slow (2b), and fast (2c) walking speeds. (2a) (2b) (2c)

12 Figure 3: Manual count / StepWatch Bland-Altman plots for normal (3a), slow (3b), and fast (3c) walking speeds. (3a) (3b) (3c)

13 Figure 4: StepWatch count / iphone Bland-Altman plots for normal (4a), slow (4b), and fast (4c) walking speeds. (4a) (4b) (4c)

14 Figure 5: Walking speed versus error between manual step count and iphone step count.

15 Discussion In this study we show that the iphone as a step counter in an able body population is feasible, however there are still inherent issues with it that limits its use in a clinical population. This article agrees with similar articles in that the iphone is not yet a valid tool for count step at all speeds 4,12. As speed increases, the iphone gets more and more accurate as seen by the correlation between error and speed (ρ=-0.597). A negative correlation indicates that as the speed decreases, error increases. This is likely due to the decreasing forces through the iphone as the speed decreases. Compounded upon that, Bland-Altman graphs indicate a fixed and proportional bias at the slowest speeds Figure 3a. This could indicate an issue with the accuracy of the hardware inside the device or some error in the algorithm that calculates steps at slower speeds. Using the iphone as a step counter in a clinical population would be difficult as pathologies that slow walking speed would lead to a more inaccurate step count. However, clinicians may be able to use it as a convenient tool to get a rough idea as to the mobility of the patient on a daily basis. Increasing error with decreasing speed can also be partially explained by the location of the iphone on the body. We chose to place the iphone in the right hip pocket instead of attaching it to one of the ankles, as the StepWatch is attached. The front pocket is a much more common location for the iphone in the real world, however there are much larger accelerations going through the ankle joint than the hip joint. Attaching the iphone distally on the limb would likely increase the validity of the iphone across all speeds. However, this limits its clinical use feasibility because a person will not want or be comfortable with a phone attached to their ankle at all times. The findings of this article are also important in light of the recent announcement made by Apple to allow for researchers to use the iphone as a tool to anonymously collet research data from its users. As researchers start to possibly use this information

16 for clinical decisions, further validity and refinement of the hardware/software combination must be conducted to ensure that these decisions are based on accurate data. One finding that deviated from previous studies was the presence of a fixed bias for the StepWatch in the normal condition Figure 4a and a proportional bias in the slow condition Figure 4b. Previous literature has already validated the StepWatch without and finding of biases. These biases are more likely due to some methodological error rather than an inherent error in the StepWatch system. In review of the data, the StepWatch program may not have always been programmed with the weight and age on every trial. Some of the StepWatch logs indicate that it was save others do not. Another possible source of error would be the subject reported information. Weight was determined by asking the subject without actually measuring it. Study Limitations Limitations of this study include a moderately small sample size. Due to the availability of the subjects, generalizability to the public is limited. This research was also conducted persons with no walking impairment, inside on a level carpeted hallway. Further research must be conducted to see if the iphone as a step counter holds up in real world situations. Also, since the writing of this paper, a newer iphone was released with newer software. Conclusions This study has shown that the iphone is not yet a valid step counter across a range of speeds. The iphone has the potential to be as shown by its validity at faster speeds. Further research must be conducted to see if the iphone is a valid instrument in real world situations and in a pathological population.

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