Smartphones are very common devices in daily life that have a built-in tri-axial accelerometer. Similar to previously developed accelerometers, smartphones can be used to assess gait patterns. However, few gait analyses have been performed using smartphones, and their reliability and validity have not been evaluated yet. The purpose of this study was to evaluate the reliability and validity of a smartphone accelerometer. Thirty healthy young adults participated in this study. They walked 20 m at their preferred speeds, and their trunk accelerations were measured using a smartphone and a tri-axial accelerometer that was secured over the L3 spinous process. We developed a gait analysis application and installed it in the smartphone to measure the acceleration. After signal processing, we calculated the gait parameters of each measurement terminal: peak frequency (PF), root mean square (RMS), autocorrelation peak (AC), and coefficient of variance (CV) of the acceleration peak intervals. Remarkable consistency was observed in the test-retest reliability of all the gait parameter results obtained by the smartphone (p<0.001). All the gait parameter results obtained by the smartphone showed statistically significant and considerable correlations with the same parameter results obtained by the tri-axial accelerometer (PF r=0.99, RMS r=0.89, AC r=0.85, CV r=0.82; p<0.01). Our study indicates that the smartphone with gait analysis application used in this study has the capacity to quantify gait parameters with a degree of accuracy that is comparable to that of the tri-axial accelerometer.

A disturbance in gait pattern is a serious problem in patients with rheumatoid arthritis (RA). The aim of the present study was to examine the utility of the smartphone gait analysis application in patients with RA. The smartphone gait analysis application was used to assess 39 patients with RA (age 65.9 ± 10.0 years, disease duration 11.9 ± 9.4 years) and age-matched control individuals (mean age, 69.1 ± 5.8 years). For all RA patients, the following data were obtained: disease activity score (DAS) 28, modified health assessment questionnaire (mHAQ), and assessment of walking ability. Patients walked 20 m at their preferred speed, and trunk acceleration was measured using a Smartphone. After signal processing, we calculated the following gait parameters for each measurement terminal: peak frequency (PF), autocorrelation peak (AC), and coefficient of variance (CV) of the acceleration peak intervals. The gait parameters of RA and control groups were compared to examine the comparability of the 2 groups. Criterion-related validity was determined by evaluating the correlation between gait parameters and clinical parameters using Spearman's correlation coefficient. The RA group showed significantly lower scores for the walking speed, AC, and CV than the control group. There were no significant differences in PF. PF (gait cycle) was mildly associated with gait speed (P < 0.05). AC (gait balance) was moderately associated with the DAS, mHAQ, gait ability, and gait speed (P < 0.05). CV (gait variability) was moderately associated with the DAS, gait ability, and gait speed (P < 0.05). This is the first study to examine the use of a smartphone device for gait pattern measurement. The results suggest that some gait parameters recorded using the smartphone represent an acceptable assessment tool for gait in patients with RA.

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Heat conduction possesses (thermal) modes in analogy with acoustics even without oscillation. Here, we establish thermal mode spectroscopy to measure the thermal diffusivity of small specimens. Local heating with a light pulse excites such modes that show antinodes at the heating point, and photothermal detection at another antinode spot allows measuring relaxation behavior of the desired mode selectively: The relaxation time yields thermal diffusivity. The Ritz method is proposed for arbitrary geometry specimens. This method is applicable even to a diamond crystal with ∼1  mm dimensions.