Energy expenditure does not solely explain step length-width choices during walking.

Healthy young adults have a most preferred walking speed, step length and step width that are close to energetically optimal. However, people can choose to walk with a multitude of different step lengths and widths, which can vary in both energy expenditure and preference. Here, we further investigated step length-width preferences and their relationship to energy expenditure. In line with a growing body of research, we hypothesized that people's preferred stepping patterns would not be fully explained by metabolic energy expenditure. To test this hypothesis, we used a two-alternative forced-choice paradigm. Fifteen participants walked on an oversized treadmill. Each trial, participants performed two prescribed stepping patterns and then chose the pattern they preferred. Over time, we adapted the choices such that there was 50% chance of choosing one pattern over another (equally preferred). If people's preferences are based solely on metabolic energy expenditure, then these equally preferred stepping patterns should have equal energy expenditure. In contrast, we found that energy expenditure differed across equally preferred step length-width patterns (P<0.001). On average, longer steps with higher energy expenditure were preferred over shorter and wider steps with lower energy expenditure (P<0.001). We also asked participants to rank a set of shorter, wider and longer steps from most preferred to least preferred, and from most energy expended to least energy expended. Only 7/15 participants had the same rankings for their preferences and perceived energy expenditure. Our results suggest that energy expenditure is not the only factor influencing a person's conscious gait choices.

Smartwatches Can Detect Walker and Cane Use in Older Adults.

BACKGROUND AND OBJECTIVES: Clinicians commonly prescribe assistive devices such as walkers or canes to reduce older adults' fall risk. However, older adults may not consistently use their assistive device, and measuring adherence can be challenging due to self-report bias or cognitive deficits. Because walking patterns can change while using an assistive device, we hypothesized that smartphones and smartwatches, combined with machine-learning algorithms, could detect whether an older adult was walking with an assistive device. RESEARCH DESIGN AND METHODS: Older adults at an Adult Day Center (n = 14) wore an Android smartphone and Actigraph smartwatch while completing the six-minute walk, 10-meter walk, and Timed Up and Go tests with and without their assistive device on five separate days. We used accelerometer data from the devices to build machine-learning algorithms to detect whether the participant was walking with or without their assistive device. We tested our algorithms using cross-validation. RESULTS: Smartwatch classifiers could accurately detect assistive device use, but smartphone classifiers performed poorly. Customized smartwatch classifiers, which were created specifically for one participant, had greater than 95% classification accuracy for all participants. Noncustomized smartwatch classifiers (ie, an "off-the-shelf" system) had greater than 90% accuracy for 10 of the 14 participants. A noncustomized system performed better for walker users than cane users. DISCUSSION AND IMPLICATIONS: Our approach can leverage data from existing commercial devices to provide a deeper understanding of walker or cane use. This work can inform scalable public health monitoring tools to quantify assistive device adherence and enable proactive fall interventions.

Hand, belt, pocket or bag: Practical activity tracking with mobile phones.

For rehabilitation and diagnoses, an understanding of patient activities and movements is important. Modern smartphones have built in accelerometers which promise to enable quantifying minute-by-minute what patients do (e.g. walk or sit). Such a capability could inform recommendations of physical activities and improve medical diagnostics. However, a major problem is that during everyday life, we carry our phone in different ways, e.g. on our belt, in our pocket, in our hand, or in a bag. The recorded accelerations are not only affected by our activities but also by the phone's location. Here we develop a method to solve this kind of problem, based on the intuition that activities change rarely, and phone locations change even less often. A hidden Markov model (HMM) tracks changes across both activities and locations, enabled by a static support vector machine (SVM) classifier that probabilistically identifies activity-location pairs. We find that this approach improves tracking accuracy on healthy subjects as compared to a static classifier alone. The obtained method can be readily applied to patient populations. Our research enables the use of phones as activity tracking devices, without the need of previous approaches to instruct subjects to always carry the phone in the same location.

Development of a scale for estimating procedural distress in the newborn intensive care unit: the Procedural Load Index.

BACKGROUND: Infants in the newborn intensive care unit (NICU) are exposed to routine procedures that often cause distress and carry a negative burden or load on the infant's neurodevelopment. AIM: A ratio level index is introduced to estimate procedural load so as to begin to develop a system to monitor the intensity of distress associated with common NICU procedures. STUDY DESIGN: Two psychophysical methods, magnitude estimation (ME) and the general labeled magnitude scale (gLMS) were used to survey 86 clinicians via the internet to estimate the distress associated with 55 common NICU procedures. RESULTS: gLMS and ME estimations correlated highly across all procedures (r = 0.97). gLMS values were used to derive the procedural load index (PLI) as a ratio level estimation of procedural distress. CONCLUSION: The PLI ranks and differentiates distress among common NICU procedures more precisely than current tools. This methodology, if correlated with infant physiological indices and health outcomes, may be operationalized at the bedside to measure procedural distress, and help to guide the ideal timing to perform procedures and minimize their negative consequence.

Joint impedance decreases during movement initiation.

The mechanical properties of the joint influence how we interact with our environment and hence are important in the control of both posture and movement. Many studies have investigated how the mechanical properties-specifically the impedance-of different joints vary with different postural tasks. However, studies on how joint impedance varies with movement remain limited. The few studies that have investigated how impedance varies with movement have found that impedance is lower during movement than during posture. In this study we investigated how impedance changed as people transitioned from a postural task to a movement task. We found that subjects' joint impedances decreased at the initiation of movement, prior to increasing at the cessation of movement. This decrease in impedance occurred even though the subjects' torque and EMG levels increased. These findings suggest that during movement the central nervous system may control joint impedance independently of muscle activation.