Ladder use in older people: Type, frequency, tasks and predictors of risk behaviours

Ladder fall and injury risk increases with age. People who present to a hospital after an injurious ladder fall have been surveyed, but little is known about ladder use in the community. The purpose of this study was to: (1) document salient factors related to ladder safety, and (2) determine physical, executive function, psychological and frequency-of-use factors associated with unsafe ladder use in older people. One hundred and two older people (aged 65+ years) were recruited. Participants completed questionnaires on demographics, health, and ladder use (type, frequency, task, behaviours) and underwent assessments of physical and executive function ability. Results showed both older men and women commonly use step ladders (61% monthly, 96% yearly), mostly inside the home for tasks such as changing a lightbulb (70%) and decorating (43%). Older men also commonly use straight ladders (27% monthly, 75% yearly), mostly outside the home for tasks such as clearing gutters (74%) and pruning trees (40%). Unsafe ladder use was more common in males and individuals with greater ladder use frequency, greater quadriceps strength, better upper limb dexterity, better balance, better stepping ability, greater self-reported everyday risk-taking, a lower fear of falling, and fewer health problems compared to their counterparts (all p < 0.05). These findings document ladder use by older people and provide insight into unsafe ladder behaviours that may be amenable to interventions to reduce ladder falls and associated injuries

Simplification and Shift in Cognition of Political Difference: Applying the Geometric Modeling to the Analysis of Semantic Similarity Judgment

Perceiving differences by means of spatial analogies is intrinsic to human cognition. Multi-dimensional scaling (MDS) analysis based on Minkowski geometry has been used primarily on data on sensory similarity judgments, leaving judgments on abstractive differences unanalyzed. Indeed, analysts have failed to find appropriate experimental or real-life data in this regard. Our MDS analysis used survey data on political scientists' judgments of the similarities and differences between political positions expressed in terms of distance. Both distance smoothing and majorization techniques were applied to a three-way dataset of similarity judgments provided by at least seven experts on at least five parties' positions on at least seven policies (i.e., originally yielding 245 dimensions) to substantially reduce the risk of local minima. The analysis found two dimensions, which were sufficient for mapping differences, and fit the city-block dimensions better than the Euclidean metric in all datasets obtained from 13 countries. Most city-block dimensions were highly correlated with the simplified criterion (i.e., the left–right ideology) for differences that are actually used in real politics. The isometry of the city-block and dominance metrics in two-dimensional space carries further implications. More specifically, individuals may pay attention to two dimensions (if represented in the city-block metric) or focus on a single dimension (if represented in the dominance metric) when judging differences between the same objects. Switching between metrics may be expected to occur during cognitive processing as frequently as the apparent discontinuities and shifts in human attention that may underlie changing judgments in real situations occur. Consequently, the result has extended strong support for the validity of the geometric models to represent an important social cognition, i.e., the one of political differences, which is deeply rooted in human nature

Individual factors that influence task performance on a straight ladder in older people

Older adults have the highest incidence of domestic ladder falls, but little investigation has been given to this important injury issue. There is therefore a need to understand the influence of individual factors like physical and cognitive ability and psychological status on safe and effective ladder use in this population. This study investigated associations between vision, lower and upper limb sensation, upper limb control, strength, balance, cognitive function and psychological status with task completion time and number of ladder moves taken in a simulated roof gutter clearing task on a straight ladder in 97 older adults. Several measures from upper limb control, strength, balance, processing speed, executive function and psychological domains were significantly associated with the two ladder task performance measures. Upper limb bimanual coordination, knee extension strength, coordinated leaning balance, and self-reported risk-taking were identified as independent and significant predictors of task completion time in a multiple regression model, predicting 56% of the variability in ladder task completion time. Upper limb bimanual coordination and proprioception, simple reaction time and coordinated leaning balance were independent and significant predictors of the number of ladder moves in a separate multiple regression model, predicting 38% of the variability in ladder moves taken. These findings help identify individuals at greater ladder fall risk and can guide ladder fall interventions, such as strength and balance training, ladder design and targeted safety instructions

Big data vs accurate data in health research: Large-scale physical activity monitoring, smartphones, wearable devices and risk of unconscious bias

Fundamental to the advancement of scientific knowledge is unbiased, accurate and validated measurement techniques. Recent United Nations and landmark Nature publications highlight the global uptake of mobile technology and the staggering potential for big data to encourage people to be physically active and to influence health policy. However, concerns exist about inconsistencies in smartphone health apps. Big data has many benefits, but noisy data may lead to wrong conclusions. In reaction to the increasing availability of low quality data; we call for a rigorous debate into the validity of substituting big data for accurate data in health research. We evaluated the step counting accuracy of a smartphone app previously used by 717,527 people from 111 countries. Our new data (from 48 participants; aged 21–59 years; body mass index 17.7–33.5 kg/m2) revealed significant (15–66%) undercounting by Apple phones. In contrast to the generally positive performances of wearable devices for stereotypical treadmill like walking, we observed extraordinarily large (0–200% of steps taken) error ranges for both Android and Apple phones. Unconscious bias (developers’ perceptions of usual behaviour) may be embedded into many unvalidated smartphone apps. Consumer-grade wearable devices appear unsuitable to detect steps in people with slow, short or non-stereotypical gait patterns. Specifically, there is a risk of systematically undercounting the steps by obese people, females or people from different ethnic groups resulting in biases when reporting associations between physical inactivity and obesity. More research is required to develop smartphone apps suitable for all people of the heterogeneous global population