Quantifying Step Count and Oxygen Consumption with Portable Technology during the 2-Min Walk Test in People with Lower Limb Amputation

Validation of Proprietary and Novel Step-counting Algorithms for Individuals Ambulating With a Lower Limb Prosthesis

OBJECTIVE

To compare the accuracy and reliability of 10 different accelerometer-based step-counting algorithms for individuals with lower limb loss, accounting for different clinical characteristics and real-world activities.

DESIGN

Cross-sectional study.

SETTING

General community setting (ie, institutional research laboratory and community free-living).

PARTICIPANTS

Forty-eight individuals with a lower limb amputation (N=48) wore an ActiGraph (AG) wGT3x-BT accelerometer proximal to the foot of their prosthetic limb during labeled indoor/outdoor activities and community free-living.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Intraclass correlation coefficient (ICC), absolute and root mean square error (RMSE), and Bland Altman plots were used to compare true (manual) step counts to estimated step counts from the proprietary AG Default algorithm and low frequency extension filter, as well as from 8 novel algorithms based on continuous wavelet transforms, fast Fourier transforms (FFTs), and peak detection.

RESULTS

All algorithms had excellent agreement with manual step counts (ICC>0.9). The AG Default and FFT algorithms had the highest overall error (RMSE=17.81 and 19.91 steps, respectively), widest limits of agreement, and highest error during outdoor and ramp ambulation. The AG Default algorithm also had among the highest error during indoor ambulation and stairs, while a FFT algorithm had the highest error during stationary tasks. Peak detection algorithms, especially those using pre-set parameters with a trial-specific component, had among the lowest error across all activities (RMSE=4.07-8.99 steps).

CONCLUSIONS

Because of its simplicity and accuracy across activities and clinical characteristics, we recommend the peak detection algorithm with set parameters to count steps using a prosthetic-worn AG among individuals with lower limb loss for clinical and research applications.

Measurement properties of device-based physical activity instruments in ambulatory adults with physical disabilities and/or chronic diseases: a scoping review

BACKGROUND

People with physical disabilities and/or chronic diseases tend to have an inactive lifestyle. Monitoring physical activity levels is important to provide insight on how much and what types of activities people with physical disabilities and/or chronic diseases engage in. This information can be used as input for interventions to promote a physically active lifestyle. Therefore, valid and reliable physical activity measurement instruments are needed. This scoping review aims 1) to provide a critical mapping of the existing literature and 2) directions for future research on measurement properties of device-based instruments assessing physical activity behavior in ambulant adults with physical disabilities and/or chronic diseases.

METHODS

Four databases (MEDLINE, CINAHL, Web of Science, Embase) were systematically searched from 2015 to April 16th 2023 for articles investigating measurement properties of device-based instruments assessing physical activity in ambulatory adults with physical disabilities and/or chronic diseases. For the majority, screening and selection of eligible studies were done in duplicate. Extracted data were publication data, study data, study population, device, studied measurement properties and study outcome. Data were synthesized per device.

RESULTS

One hundred three of 21566 Studies were included. 55 Consumer-grade and 23 research-grade devices were studied on measurement properties, using 14 different physical activity outcomes, in 23 different physical disabilities and/or chronic diseases. ActiGraph (n = 28) and Fitbit (n = 39) devices were most frequently studied. Steps (n = 68) was the most common used physical activity outcome. 97 studies determined validity, 11 studies reliability and 6 studies responsiveness.

CONCLUSION

This scoping review shows a large variability in research on measurement properties of device-based instruments in ambulatory adults with physical disabilities and/or chronic diseases. The variability highlights a need for standardization of and consensus on research in this field. The review provides directions for future research.

Cross-Cultural Adaptation and Validation of The Nepali Version of The Prosthetic Limb Users Survey of Mobility Short-Form (Plus-M™/Nepali-12Sf) In Lower Limb Prosthesis Users

BACKGROUND

Objective mobility measurement of Nepali prosthesis users is lacking.

OBJECTIVE

The objective of this study was to cross-culturally adapt, translate and evaluate construct validity of the Prosthetic Limb Users Survey of Mobility (PLUS-M™/Nepali-12 Short Form (SF)) instrument in lower limb prosthesis users residing in Nepal.

METHODOLOGY

Two forward translations, review and reconciliation, back translation, expert review, developer review to create the PLUS-M™/Nepali-12SF. Psychometric testing for internal consistency, test-retest reliability and construct validity against the Two-Minute Walk Test (2MWT) and Amputee Mobility Predictor with Prosthesis (AMPPRO) were performed on sixty-six lower limb prosthesis users.

FINDINGS

The majority of populations were with transtibial amputation 45 (68%), with transfemoral amputation 15 (23%), with knee disarticulation 5 (7.5%) and with syme's amputation 1 (1.5%). The most common cause of amputation among the population was trauma and the least was tumor. Chronbach's alpha for the PLUS-M™/Nepali-12SF was 0.90, mean T-Score was 52.90, test-retest intraclass correlation coefficient (ICC) was 0.94 (95% confidence interval 0.90-0.96). Construct validity with the 2MWT was good (r = 0.62, p< 0.001) and moderately positive with the AMPPRO (r = 0.57, p< 0.001).

CONCLUSION

Our research evidenced that the PLUS-M™/Nepali-12SF had excellent reproducibility. The significance of this work is that it may allow for the measurement of mobility in austere locations of Nepal.

Gait Alteration in Individual with Limb Loss: The Role of Inertial Sensors

Amputation has a big impact on the functioning of patients, with negative effects on locomotion and dexterity. In this context, inertial measurement units represent a useful tool in clinical practice for motion analysis, and in the development of personalized aids to improve a patient's function. To date, there is still a gap of knowledge in the scientific literature on the application of inertial sensors in amputee patients. Thus, the aim of this narrative review was to collect the current knowledge on this topic and stimulate the publication of further research. Pubmed, Embase, Scopus, and Cochrane Library publications were screened until November 2022 to identify eligible studies. Out of 444 results, we selected 26 articles focused on movement analysis, risk of falls, energy expenditure, and the development of sensor-integrated prostheses. The results showed that the use of inertial sensors has the potential to improve the quality of life of patients with prostheses, increasing patient safety through the detection of gait alteration; enhancing the socio-occupational reintegration through the development of highly technologic and personalized prosthesis; and by monitoring the patients during daily life to plan a tailored rehabilitation program.

High-Level Mobility of Trans-Tibial Prosthesis Users Wearing Commercial and sPace Energy-Storing Prosthetic Feet

Outcomes of users provided with a commercial ESR Vari-Flex foot (Össur, Reykjavik, Iceland) and a locally designed sPace foot were investigated. Step activity with users' own prosthetic foot compared to the sPace foot was explored.

METHODS

Eleven individuals with unilateral trans-tibial amputation participated and were provided with an sPace and Vari-Flex foot. Ten- and twenty-meter walk tests (10/20MWT) at comfortable and fast walking speeds (CWS/FWS), the two-minute walk test (2-MWT) and Comprehensive High-Level Activity Mobility Predictor (CHAMP) were administered. A subgroup was provided a pedometer to record their steps over a 7-day period in their own foot and later the sPace.

RESULTS

The sPace foot performed well in a battery of high-level mobility outcome measures. On CHAMP, participants scored 16.94 ± 5.41 and 16.72 ± 6.09 with the sPace and Vari-Flex feet, respectively. Subgroup testing of step activity showed 4490 ± 3444 steps in users' own feet and 3115 ± 1967 in the sPace foot, p = 0.176.

CONCLUSIONS

Participants using the sPace foot were capable of performing walking, high-level mobility and activity outcome measures.

Accurate Step Count with Generalized and Personalized Deep Learning on Accelerometer Data

Physical activity (PA) is globally recognized as a pillar of general health. Step count, as one measure of PA, is a well known predictor of long-term morbidity and mortality. Despite its popularity in consumer devices, a lack of methodological standards and clinical validation remains a major impediment to step count being accepted as a valid clinical endpoint. Previous works have mainly focused on device-specific step-count algorithms and often employ sensor modalities that may not be widely available. This may limit step-count suitability in clinical scenarios. In this paper, we trained neural network models on publicly available data and tested on an independent cohort using two approaches: generalization and personalization. Specifically, we trained neural networks on accelerometer signals from one device and either directly applied them or adapted them individually to accelerometer data obtained from a separate subject cohort wearing multiple distinct devices. The best models exhibited highly accurate step-count estimates for both the generalization (96-99%) and personalization (98-99%) approaches. The results demonstrate that it is possible to develop device-agnostic, accelerometer-only algorithms that provide highly accurate step counts, positioning step count as a reliable mobility endpoint and a strong candidate for clinical validation.