Background: Wearable activity monitors such as ActiGraph monitoring devices are widely used, especially in research settings. Various research studies have assessed the criterion validity of ActiGraph devices for step counting and distance estimation in adults and older adults. Although several studies have used the ActiGraph devices as a reference system for activity monitoring, there is no summarized evidence of the psychometric properties. The main objective of this systematic review was to summarize evidence related to the criterion validity of ActiGraph monitoring devices for step counting and distance estimation in adults and/or older adults. Methods: Literature searches were conducted in six databases (Medline (OVID), Embase, IEEExplore, CINAHL, Engineering Village and Web of Science). Two reviewers independently conducted selection, a quality analysis of articles (using COSMIN and MacDermid’s grids) and data extraction. Results: This review included 21 studies involving 637 participants (age 30.3 ± 7.5 years (for adults) and 82.7 ± 3.3 years (for older adults)). Five ActiGraph devices (7164, GT1M, wGTX +, GT3X +/wGT3X + and wGT3X − BT) were used to collect data at the hip, wrist and ankle to assess various walking and running speeds (ranging from 0.2 m/s to 4.44 m/s) over durations of 2 min to 3 days (13 h 30 mins per day) for step counting and distance estimation. The ActiGraph GT3X +/wGT3X + and wGT3X − BT had better criterion validity than the ActiGraph 7164, wGTX + and GT1M according to walking and running speeds for step counting. Validity of ActiGraph wGT3X + was good for distance estimation. Conclusion: The ActiGraph wGT3X − BT and GT3X +/wGT3X + have good criterion validity for step counting, under certain conditions related to walking speeds, positioning and data processing.

With the growing interest in daily activity monitoring, several insole designs have been developed to identify postures, detect activities, and count steps. However, the validity of these devices is not clearly established. The aim of this systematic review was to synthesize the available information on the criterion validity of instrumented insoles in detecting postures activities and steps. The literature search through six databases led to 33 articles that met inclusion criteria. These studies evaluated 17 different insole models and involved 290 participants from 16 to 75 years old. Criterion validity was assessed using six statistical indicators. For posture and activity recognition, accuracy varied from 75.0% to 100%, precision from 65.8% to 100%, specificity from 98.1% to 100%, sensitivity from 73.0% to 100%, and identification rate from 66.2% to 100%. For step counting, accuracies were very high (94.8% to 100%). Across studies, different postures and activities were assessed using different criterion validity indicators, leading to heterogeneous results. Instrumented insoles appeared to be highly accurate for steps counting. However, measurement properties were variable for posture and activity recognition. These findings call for a standardized methodology to investigate the measurement properties of such devices.

Despite the accessibility of several step count measurement systems, count accuracy in real environments remains a major challenge. Microelectromechanical systems and pressure sensors seem to present a potential solution for step count accuracy. The purpose of this study was to equip an insole with pressure sensors and to test a novel and potentially more accurate method of detecting steps. Methods: Five force-sensitive resistors (FSR) were integrated under the heel, the first, third, and fifth metatarsal heads and the great toe. This system was tested with twelve healthy participants at self-selected and maximal walking speeds in indoor and outdoor settings. Step counts were computed based on previously reported calculation methods, individual and averaged FSR-signals, and a new method: cumulative sum of all FSR-signals. These data were compared to a direct visual step count for accuracy analysis. Results: This system accurately detected steps with success rates ranging from 95.5 ± 3.5% to 98.5 ± 2.1% (indoor) and from 96.5 ± 3.9% to 98.0 ± 2.3% (outdoor) for self-selected walking speeds and from 98.1 ± 2.7% to 99.0 ± 0.7% (indoor) and 97.0 ± 6.2% to 99.4 ± 0.7% (outdoor) for maximal walking speeds. Cumulative sum of pressure signals during the stance phase showed high step detection accuracy (99.5 ± 0.7%–99.6 ± 0.4%) and appeared to be a valid method of step counting. Conclusions: The accuracy of step counts varied according to the calculation methods, with cumulative sum-based method being highly accurate.

Although guidelines are established for performing the six-minute walking test (6MWT), it is not always possible to implement this test in any setting, due to physical and space limitations. Yet, variations in the conditions of the test could be responsible for heterogeneous outcomes. However, the impact of the condition of the 6MWT is not clearly established in literature. The objective is to determine the influence of different implementation conditions on the validity of the 6MWT. Seventeen articles were retained after a literature review, including 597 participants. Seven articles mention that performing the test on a predetermined short back-and-forth pathway led to lower performance than when the test was performed on pathways of greater distances. The walking distance covered on a rectangular path or on a 10-m eight-form path is greater than with the back and forth on a five- to ten-meter path. Seven articles suggest that the performance achieved on a treadmill is generally lower than that obtained while walking on the ground. Evidence shows that the conditions while performing the 6MWT significantly influence the score, hence the validity of the results. The use of a ground pathway, comprising the longest linear distance possible, seems critical to ensure good validity.

The ability to walk is essential in daily life, making walking outcomes key measures in clinical practice. This study aims to develop and validate two novel stride length model-based approaches for total distance estimation using a smart insole. Eight participants wore a pair of smart insoles. For a period of six minutes, each participant walked back and forth on a predefined 20 m pathway, and the numbers of round trips and strides taken were counted. Two stride length estimation approaches based on the director coefficients of acceleration data (Approach 1) and dynamic time warping (Approach 2) using smart insoles were used. The median accuracies of the total distance using Approach 1 are 98.92% [1.24%] (ICC = 0.992) and 98.69% [2.44%] (ICC = 0.994) for the right and left sides, respectively. For Approach 2, the average accuracies are 98.95% [0.18%] (ICC = 0.996) for the right side and 99.03% [0.14%] (ICC = 0.991) for the left side. The Mann–Whitney U test shows no statistically significant difference between the actual distance and smart insole for the total distance covered. Furthermore, there is no statistically significant difference between Approach 1 and Approach 2 for stride length. Although the sample size was small, the estimated total distance using the novel model-based algorithms appears to be accurate in comparison to the actual total distance.

Several studies have demonstrated that instrumented insoles enable the quantification of steps taken in people with or without walking limitations. However, the number and location of embedded pressure sensors in an insole vary considerably in the literature, resulting in variable step count accuracies. The objective of this study was to determine optimum locations to consider with a minimum pressure sensors (fewer than 5), without altering the accuracy of step detection. An insole was equipped with five pressure sensors (FSRs) located under the heel (FSRH), the first (FSRM1), the third (FSRM3) and the fifth (FSRM5) metatarsal heads, and the great toe (FSRT). Step detection was based on single or combined pressure signals using a step count algorithm, in twelve healthy people who walked during six-minutes at self-selected comfortable speeds. Results showed that there was a statistically significant difference (F=17.8, p<0.05) between the average step count accuracy of single and combinations from two-to-five pressure signals. The best accuracies per sensor combinations were as follows: FSRM1-FSRM5 (99.0±0.9%), FSRM3-FSRM5-FSRT (99.3±0.7%), FSRH-FSRM3-FSRM5-FSRT (99.5±0.4%) and all five FSRs (99.5±0.4%). For a single pressure signal, the best accuracy was 98.0±2.3% with FSRH. These results suggest that the combination of at least two pressure sensors in the insole can yield an accuracy of 99% for step counting in adult people