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Robert W. Cox, Rodrigo E. Martinez, Russell T. Baker and Lindsay Warren

goniometry, is performed in a similar manner to goniometry, but the inclinometer is aligned with the distal limb to measure motion. 2 , 4 , 5 The ease and availability of digital inclinometers, specifically those created as smartphone applications, have anecdotally increased the use of inclinometry for ROM

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Reabias de A. Pereira, José Luiz de B. Alves, João Henrique da C. Silva, Matheus da S. Costa and Alexandre S. Silva

Objective: To evaluate the accuracy of the smartphone application (app) HRV Expert (CardioMood) and a chest strap (H10 Polar) for recording R-R intervals compared with electrocardiogram (ECG). Methods: A total of 31 male recreational runners (age 36.1 [6.3] y) volunteered for this study. R-R intervals were recorded simultaneously by the smartphone app and ECG for 5 minutes to analyze heart-rate variability in both the supine and sitting positions. Time-domain indexes (heart rate, mean R-R, SD of RR intervals, count of successive normal R-R intervals differing by more than 50 ms, percentage of successive normal R-R intervals differing by more than 50 ms, and root mean square of successive differences between normal R-R intervals), frequency-domain indexes (low frequency, normalized low frequency, high frequency, normalized high frequency, low-frequency to high-frequency ratio, and very low frequency), and nonlinear indexes (SD of instantaneous beat-to-beat variability and long-term SD of continuous R-R intervals) were compared by unpaired t test, Pearson correlation, simple linear regression, and Bland–Altman plot to evaluate the agreement between the devices. Results: High similarity with P value varying between .97 and 1.0 in both positions was found. The correlation coefficient of the heart-rate-variability indexes was perfect (r = 1.0; P = .00) for all variables. The constant error, standard error of estimation, and limits of agreement between ECG and the smartphone app were considered small. Conclusion: The smartphone app and chest strap provide excellent ECG compliance for all variables in the time domain, frequency domain, and nonlinear indexes, regardless of the assessed position. Therefore, the smartphone app replaces ECG for any heart-rate-variability analysis in runners.

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Merrill D. Funk, Cindy L. Salazar, Miriam Martinez, Jesus Gonzalez, Perla Leyva, David Bassett Jr. and Murat Karabulut

levels. They can also intervene and motivate users to increase physical activity without the need for additional devices beyond the phone which most users carry regularly throughout the day. An increasing number of smartphone applications are being developed and downloaded by millions of users to monitor

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Alejandro Pérez-Castilla, Antonio Piepoli, Gabriel Garrido-Blanca, Gabriel Delgado-García, Carlos Balsalobre-Fernández and Amador García-Ramos

-based resistance training approach. 2 However, the interest in wearable wireless technology (camera-based optoelectronic device, inertial measurement units, or smartphone applications) is growing. 3 – 5 However, conflicting results exist regarding the validity of wearable wireless devices to measure movement

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Kurusart Konharn, Wichai Eungpinichpong, Kluaymai Promdee, Paramaporn Sangpara, Settapong Nongharnpitak, Waradanai Malila and Jirachai Karawa


The suitability of smartphone applications (apps) currently used to track walking/running may differ depending on a person’s weight condition. This study aimed to examine the validity and reliability of apps for both normal-weight and overweight/obese young adults.


Thirty normal-weight (aged 21.7 ± 1.0 years, BMI 21.3 ± 1.9 kg/m2) and 30 overweight/ obese young adults (aged 21.0 ± 1.4 years, BMI 28.6 ± 3.7 kg/m2) wore a smartphone and pedometer on their right hip while walking/running at 3 different intensities on treadmills. Apps was randomly assigned to each individual for measuring average velocity, step count, distance, and energy expenditure (EE), and these measurements were then analyzed.


The apps were not accurate in counting most of the measured variables and data fell significantly lower in the parameters than those measured with standard-reference instruments in both light and moderate intensity activity among the normal-weight group. Among the overweight and obese group, the apps were not accurate in detecting velocity, distance, or EE during either light or vigorous intensities. The percentages of mean difference were 30.1% to 48.9%.


Apps may not have sufficient accuracy to monitor important physical parameters of human body movement. Apps need to be developed that can, in particular, respond differently based on a person’s weight status.

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Scott W. Cheatham, Morey J. Kolber and Michael P. Ernst


Pulse rate is commonly measured manually or with commercial wrist or belt monitors. More recently, pulse-rate monitoring has become convenient with the use of mobile technology that allows monitoring through a smartphone camera. This optical technology offers many benefits, although the clinimetric properties have not been extensively studied.


Observational study of reliability.


University kinesiology laboratory.


30 healthy, recreationally active adults.


Concurrent measurement of pulse rate using 2 smartphone applications (fingertip, face-scan,) with the Polar H7 belt and pulse oximeter.

Main Outcome Measure:

Average resting pulse rate for 5 min in 3 positions (supine, sitting, and prone).


Concurrent validity in supine and standing was good between the 2 applications and the Polar H7 (intraclass correlation coefficient [ICC] .80–.98) and pulse oximeter (ICC .82–98). For sitting, the validity was good between the fingertip application, Polar H7 (ICC .97), and pulse oximeter (ICC .97). The face-scan application had moderate validity with the Polar H7 (ICC .74) and pulse oximeter (ICC .69). The minimal detectable change (MDC90) between the fingertip application and Polar H7 ranged from 1.38 to 4.36 beats/min (BPM) and from 0.69 to 2.97 BPM for the pulse oximeter with both positions. The MDC90 between the face-scan application and Polar H7 ranged from 11.88 to 12.83 BPM and from 0.59 to 17.72 BPM for the pulse oximeter. The 95% limits of agreement suggest that the fingertip application may vary between 2.40 and 3.59 BPM with the Polar H7 and between 3.40 and 3.42 BPM with the pulse oximeter. The face-scan application may vary between 3.46 and 3.52 BPM with the Polar H7 and between 2.54 and 3.46 BPM with the pulse oximeter.


Pulse-rate measurements may be effective using a fingertip application, belt monitor, and pulse oximeter. The fingertip scanner showed superior results compared with the face scanner, which only demonstrated modest validity compared with the Polar H7 and pulse oximeter.

Open access

Mohammadreza Pourahmadi, Hamid Hesarikia, Ali Ghanjal and Alireza Shamsoddini

WG . Research designs: choosing and fine-tuning a design for your study . Sportscience . 2008 ; 12 ( 1 ): 1 – 3 . 23. Bruyneel AV . Smartphone applications for range of motion measurement in clinical practice: a literature review of inclinometer and goniometric tools . Ann Phys Rehabil Med

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Catherine R. Marinac, Mirja Quante, Sara Mariani, Jia Weng, Susan Redline, Elizabeth M. Cespedes Feliciano, J. Aaron Hipp, Daniel Wang, Emily R. Kaplan, Peter James and Jonathan A. Mitchell

within an individual. We, therefore, tested if the timing of meals, light exposure, physical activity, and sleep were associated with body mass index (BMI) in a sample of healthy adults who recorded the timing of behaviors over multiple days using a novel smartphone application and actigraphy. We first

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Christanie Monreal, Lindsay Luinstra, Lindsay Larkins and James May

or chronic neck pain–related conditions. 1 These instruments can sometimes be cumbersome and expensive. Interestingly, clinicians are now using smartphone applications, colloquially known as “apps,” as measurement tools in the clinical setting. 2 Smartphones are often equipped with an accelerometer

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Katie Weatherson, Lira Yun, Kelly Wunderlich, Eli Puterman and Guy Faulkner

Background: Ecological momentary assessment (EMA) is a method of collecting behavioral data in real time. The purpose of this study was to examine EMA compliance, identify factors predicting compliance, assess criterion validity of, and reactivity to, using EMA in a workplace intervention study. Methods: Forty-five adults (91.1% female, 39.7 [9.6] y) were recruited for a workplace standing desk intervention. Participants received 5 surveys each day for 5 workdays via smartphone application. EMA items assessed current position (sitting/standing/stepping). EMA responses were time matched to objectively measured time in each position before and after each prompt. Multilevel logistic regression models estimated factors influencing EMA response. Cohen kappa measured interrater agreement between EMA-reported and device-measured position. Reactivity was assessed by comparing objectively measured sitting/standing/stepping in the 15 minutes before and after each EMA prompt using multilevel repeated-measures models. Results: Participants answered 81.4% of EMA prompts. Differences in compliance differed by position. There was substantial agreement between EMA-reported and device-measured position (κ = .713; P < .001). Following the EMA prompt, participants sat 0.87 minutes more than before the prompt (P < .01). Conclusion: The use of EMA is a valid assessment of position when used in an intervention to reduce occupational sitting and did not appear to disrupt sitting in favor of the targeted outcome.