The effect of mobile phone task and age on gait: A systematic review and meta-analysis

Validity across four common street-crossing distraction indicators to predict pedestrian safety

BACKGROUND

Multiple distraction indicators have been applied to measure street-crossing distraction but their validities in predicting pedestrian safety are poorly understood.

METHODS

Based on a video-based observational study, we compared the validity of four commonly used distraction indicators (total duration of distraction while crossing a street, proportion of distracted time over total street-crossing time, duration of the longest distraction time, and total number of distractions) in predicting three pedestrian safety outcomes (near-crash incidence, frequency of looking left and right, and speed crossing the street) across three types of distraction (mobile phone use, talking to other pedestrians, eating/drinking/smoking). Change in Harrell's C statistic was calculated to assess the validity of each distraction indicator based on multivariable regression models including only covariates and including both covariates and the distraction indicator.

RESULTS

Heterogeneous capacities in predicting the three safety outcomes across the four distraction indicators were observed: 1) duration of the longest distraction time was most predictive for the occurrence of near-crashes and looks left and right among pedestrians with all three types of distraction combined and talking with other pedestrians (Harrell's C statistic changes ranged from 0.0310 to 0.0335, P < 0.05), and for the occurrence of near-crashes for pedestrians involving mobile phone use (Harrell's C statistic change: 0.0053); 2) total duration of distraction was most predictive for speed crossing the street among pedestrians with the combination and each of the three types of distraction (Harrell's C statistic changes ranged from 0.0037 to 0.0111, P < 0.05), frequency of looking left and right among pedestrians distracted by mobile phone use (Harrell's C statistic change: 0.0115), and the occurrence of near-crash among pedestrians eating, drinking, or smoking (Harrell's C statistic change: 0.0119); and 3) the total number of distractions was the most predictive indicator of frequency of looking left and right among pedestrians eating, drinking, or smoking (Harrell's C statistic change: 0.0013). Sensitivity analyses showed the results were robust to change in grouping criteria of the four distraction indicators.

CONCLUSIONS

Future research should consider the pedestrian safety outcomes and type of distractions to select the best distraction indicator.

Effects of smartphone use while walking on external knee abduction moment peak: A crossover randomized trial on an instrumented treadmill

Introduction

In addition to its effects on cognitive awareness, smartphone use while walking may reduce the speed, regularity, and symmetry of walking. Although its effects on spatiotemporal gait parameters, such as walking speed and step width, have already been studied, little is currently known about the impact of smartphone dual tasking on lower limb kinetics.

Research question

Does smartphone use during walking alter gait patterns (i.e., walking speed and step width) and consequently external knee moments?

Methods

In a four-period crossover trial, external knee moment peaks, walking speed, and step width were assessed in 27 healthy adults during matched-speed walking, self-paced walking, self-paced walking with spoken calculation tasks, and self-paced walking with smartphone-entered calculation tasks. Differences between the smartphone use condition and all other conditions were determined using repeated measures ANOVA with predefined contrasts.

Results

Decreased walking speed and increased step width were observed during smartphone use. The mean external knee abduction moment peak (Nm/kg) differed significantly (p < 0.01) across the intervention condition, namely walking with smartphone-entered calculations (0.15; 95 % CI: 0.12, 0.18), and the control conditions, namely matched-speed walking (0.11; 95 % CI: 0.08, 0.13), self-paced walking (0.11; 95 % CI: 0.09, 0.14), and walking with spoken calculations (0.14; 95 % CI: 0.12, 0.16). After confounder adjustment for walking speed, step width, gender, and age, this primary outcome was significantly different between using the smartphone and self-paced walking (p < 0.01, r = 0.51). This effect size was reduced when comparing smartphone use with spoken calculations (p = 0.04, r = 0.32).

Conclusion

When using a smartphone while walking, walking speed is slowed down, step width is increased, and knee moments are adversely altered compared to walking without dual tasking. These altered knee moments are partially, but not entirely, attributable to the cognitive calculation task. These effects are age-independent, but women are more affected than men. Nevertheless, it remains unclear whether sustained walking while using a smartphone adversely affects the development of knee joint pathologies.