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Alterations Of Gait Mechanics Observed Using Wearable Gait Sensors In Patients With Diabetic Neuropathy
Zoe K. Haffner, BS¹, Krishna K. Sharma, BA², Firras S. Garada, DPM¹, John D. Miller, DPM³, Christopher E. Attinger, MD¹, John S. Steinberg, DPM, FACFAS¹, Jayson N. Atves, DPM, AACFAS¹, Karen K. Evans, MD¹.
¹MedStar Georgetown University Hospital, Washington, DC, USA, ²Georgetown University School of Medicine, Washington, DC, USA, ³Foot and Ankle Specialists of the Mid-Atlantic, Colonial Heights, VA, USA.

PURPOSE: Few studies assess the mobility of patients with chronic disease. Studies have shown diabetes increases the risk of falls, which is associated with poor ambulatory performance. Our study aimed to assess the ambulatory function of patients at our tertiary wound care center and determine whether diabetic sequelae were correlated with altered gait mechanics.
METHODS: Adult patients who could safely ambulate unassisted were approached for study participation. Gait was evaluated using wearable Opal sensors (APDM, Portland Oregon). Each gait evaluation included 60-second walk and 30-second balance tests. Chart review was performed to collect pertinent medical and surgical data.
RESULTS: 39 patients participated in the study. Diabetic neuropathy was predictive of reduced gait speed (β=-0.166, p=0.021), increased height of foot swing (β=0.731, p=0.019), and increased postural sway (β=0.073, p=0.025). Decreased speed was also correlated with age and other comorbidities (Table 1). Major lower extremity surgery was correlated with increased postural sway (p=0.045).
CONCLUSION: Our results demonstrate that patients with diabetes have altered gait mechanics and decreased ambulatory function compared to nondiabetic patients. Further studies are needed to correlate these findings with surgical outcomes and assess whether gait measurements are a reliable predictor of fall risk. Assessing gait in patients receiving lower extremity wound care is a promising, objective measure that may determine a patient's risk of poor outcomes and identify the need for appropriately timed interventions.

Table 1: Gait Parameters Compared to Patient Demographics, Comorbidities, and Surgical History

	Overall	Gait Speed (m/s)	p-value	Elevation of Foot Mid-Swing (cm)*	p-value	Step Duration (s)*	p-value	Postural Sway RMS (m/s2)*	p-value
Study Participants	39	0.88 ± 0.22	-	1.33 (0.76, 1.88)	-	0.60 (0.56, 0.67)	-	0.13 (0.08, 0.18)	-
Age (years)	61.4 ± 13.0	**β = -0.007	0.013	β = -0.008	0.450	β = 0.001	0.557	β = 0.004	0.112
Charlson Comorbidity Index (CCI)	4 (2, 5)*	**β = -0.041	0.031	β = 0.113	0.135	β = 0.003	0.700	β = 0.005	0.780
Diabetes	28 (71.8%)	0.83 ± 0.20	0.023	1.46 (1.04, 2.32)	0.070	0.61 (0.56, 0.67)	0.111	0.13 (0.08, 0.22)	0.134
Neuropathy	23 (59.0%)	0.81 ± 0.20	0.021	1.52 (1.13, 2.45)	0.019	0.62 (0.56, 0.68)	0.104	0.15 (0.10, 0.35)	0.025
Chronic Kidney Disease (CKD)	9 (23.1%)	0.73 ± 0.17	0.022	1.40 (1.18, 2.67)	0.317	0.64 (0.59, 0.70)	0.106	0.16 (0.12, 0.42)	0.124
Charcot arthropathy	2 (5.1%)	0.55 ± 0.15	0.031	1.38 (0.34, 2.43)	0.702	0.76 (0.76, 0.77)	0.036	0.49 (0.10, 0.87)	0.396
Major Lower Extremity Surgery or Amputation	16 (41.0%)	0.81 ± 0.22	0.080	1.81 (0.85, 2.93)	0.189	0.63 (0.57, 0.69)	0.104	0.16 (0.10, 0.38)	0.049
*continuous variables that did not follow a normal distribution are denoted as: median (Q1, Q3). **beta coefficient (β) denotes the degree of change in the outcome variable for one unit increase in the predictor variable, negative β values indicate an average decrease in the outcome variable. RMS - root mean squared

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