Maximal Walking Distance in Persons with a Lower Limb Amputation

Abstract

The distance one can walk at a time could be considered an important functional outcome in people with a lower limb amputation. In clinical practice, walking distance in daily life is based on self-report (SIGAM mobility grade (Special Interest Group in Amputee Medicine)), which is known to overestimate physical activity. The aim of this study was to assess the number of consecutive steps and walking bouts in persons with a lower limb amputation, using an accelerometer sensor. The number of consecutive steps was related to their SIGAM mobility grade and to the consecutive steps of age-matched controls in daily life. Twenty subjects with a lower limb amputation and ten age-matched controls participated in the experiment for two consecutive days, in their own environment. Maximal number of consecutive steps and walking bouts were obtained by two accelerometers in the left and right trouser pocket, and one accelerometer on the sternum. In addition, the SIGAM mobility grade was determined and the 10 m walking test (10 MWT) was performed. The maximal number of consecutive steps and walking bouts were significantly smaller in persons with a lower limb amputation, compared to the control group (p < 0.001). Only 4 of the 20 persons with a lower limb amputation had a maximal number of consecutive steps in the range of the control group. Although the maximal covered distance was moderately correlated with the SIGAM mobility grade in participants with an amputation (r = 0.61), for 6 of them, the SIGAM mobility grade did not match with the maximal covered distance. The current study indicated that mobility was highly affected in most persons with an amputation and that the SIGAM mobility grade did not reflect what persons with a lower limb amputation actually do in daily life. Therefore, objective assessment of the maximal number of consecutive steps of maximal covered distance is recommended for clinical treatment.

Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

“…The presented sensor-based method may provide a useful approach for early detecting and tailoring the treatment of circadian disruptions in rehabilitation populations. Hofstad et al (2020) [15] measured the number of consecutive steps and walking bouts in persons with a lower limb amputation using three accelerometers: one in each trouser pocket and one on the sternum. Measurements were performed for two consecutive days in 20 persons with a lower limb amputation and 10 age-matched controls.…”

“…Hofstad et al (2020) [ 15 ] measured the number of consecutive steps and walking bouts in persons with a lower limb amputation using three accelerometers: one in each trouser pocket and one on the sternum. Measurements were performed for two consecutive days in 20 persons with a lower limb amputation and 10 age-matched controls.…”

Reported Outcome Measures in Studies of Real-World Ambulation in People with a Lower Limb Amputation: A Scoping Review

“…Six studies added a Global Positioning System (GPS) device in addition to the activity monitors [ 54 , 103 , 105 , 110 , 114 ]. Other wearable technologies used in the included studies were TEMEC activity monitor (Temec Technologies BV, Heerlen, The Netherlands) [ 63 , 67 ], Dynaport ADL (McRoberts BV, The Hague, The Netherlands) [ 62 , 119 ], Power Walker (Yamax Health & Sports Inc., Shropshire, UK) [ 85 ], EmpowerGO (Hanger Inc., Austin, TX, USA) [ 24 ], Up move (Jawbone, San Francisco, CA, USA) [ 35 ], Activ 8 (VitaMove, Veldhoven, The Netherlands) [ 38 ], Uptimer (National Ageing Research Institute, Victoria, Australia) [ 66 ], AMP 331 (Dynastream Innovations, Cochrane, AB, Canada) [ 97 ], and the MiniMods Dynaport (McRoberts BV, The Hague, The Netherlands) [ 33 ]. Two studies used an accelerometer, but did not specify the manufacturer [ 55 , 93 ].…”

“…The articles were structured into four categories, based on the primary reason for using wearable technology: observational studies ( n = 46), interventional studies ( n = 34), algorithm/method development studies ( n = 12), and validity/feasibility studies ( n = 6). Of the 46 included observational studies, 13 studies aimed to describe characteristics of real-world behavior [ 21 , 26 , 33 , 40 , 60 , 63 , 66 ], real-world measurements in a specific population [ 44 , 50 , 62 ], or at a specific moment in time, such as post-rehabilitation [ 21 , 45 , 50 ], or during a mountaineering expedition [ 59 ]. Four studies observed changes in real-world behavior over time, including changes between before and during the COVID-19 pandemic [ 24 , 27 ], during in-patient and leave periods [ 36 ], and before and after prosthetic disuse [ 25 ].…”

“…Step count was also reported in combination with other variables of walking activity, such as number of steps per intensity activity [49,90], per walking technique [59], per walking bout [24,105], per activity classification [102,112], or the maximal number of consecutive steps taken [33]. Some articles reported on step count related to location, such as number of steps taken at home and away from home [70,105,110], per community category [54,57], or the difference in steps between a day inpatient and a day out-patient [36].…”