Oldest old's travel mode choice and new mobility technology acceptance: case in America and China.

Introduction: The oldest olds (aged 85 and over) are the fastest-growing age segment. However, our understanding of their mobility is limited. To address this gap, we invited 19 U.S. and 30 Chinese "oldest old" to take part in focus groups and complete a mobility questionnaire. We focus on travel mode choice, which includes changes in travel modes, frequency of usage, and perceptions of comfort. Methods: Older adults' familiarity and acceptance of new mobility technologies (e.g., ridesharing, carsharing, and autonomous vehicles) were measured by questionnaire and focus group. Word clouds were also used to illustrate people's reasons for choosing their primary mode of transportation. Results and discussion: The results show that both panels of older adults similarly feel some extent of travel limitations. But the responses among the two groups differ: 18 American participants chose "drive myself" as their primary option a decade ago, while 11 chose it now; no Chinese participants selected it either a decade ago or now. Both currently and 10 years ago, there was a significant difference in mode choice between participants in China and the United States. However, this gap has narrowed over the past decade. Participants in China have significantly changed their transportation preferences compared to 10 years ago, while participants in the US have remained nearly unchanged. American respondents consider "ease" as an important factor, while Chinese respondents pay more attention to "safety" and "no other option to get around" when making travel mode choices. Compared to Chinese participants, American participants were more comfortable with driving an autonomous vehicle. These differences may result from the various developmental stages and transportation policies of the two countries. This study supports the development of new mobility technologies for the oldest old to improve their quality of life.

Monipar: movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones.

Introduction: Parkinson's disease (PD) is a neurodegenerative disorder commonly characterized by motor impairments. The development of mobile health (m-health) technologies, such as wearable and smart devices, presents an opportunity for the implementation of clinical tools that can support tasks such as early diagnosis and objective quantification of symptoms. Objective: This study evaluates a framework to monitor motor symptoms of PD patients based on the performance of standardized exercises such as those performed during clinic evaluation. To implement this framework, an m-health tool named Monipar was developed that uses off-the-shelf smart devices. Methods: An experimental protocol was conducted with the participation of 21 early-stage PD patients and 7 healthy controls who used Monipar installed in off-the-shelf smartwatches and smartphones. Movement data collected using the built-in acceleration sensors were used to extract relevant digital indicators (features). These indicators were then compared with clinical evaluations performed using the MDS-UPDRS scale. Results: The results showed moderate to strong (significant) correlations between the clinical evaluations (MDS-UPDRS scale) and features extracted from the movement data used to assess resting tremor (i.e., the standard deviation of the time series: r = 0.772, p < 0.001) and data from the pronation and supination movements (i.e., power in the band of 1-4 Hz: r = -0.662, p < 0.001). Conclusion: These results suggest that the proposed framework could be used as a complementary tool for the evaluation of motor symptoms in early-stage PD patients, providing a feasible and cost-effective solution for remote and ambulatory monitoring of specific motor symptoms such as resting tremor or bradykinesia.

Sharing is caring: The potential of the sharing economy to support aging in place.

This article explores innovative applications of sharing economy services that have the potential to support a population aging in place, especially the "oldest old," aged 85 and older, and their caregivers. A mixed-methods study conducted by the MIT AgeLab examined perceptions of and experiences with sharing economy services, ultimately finding opportunities and barriers to use. Thus, although sharing economy services have potential to support aging in place, to do so successfully will require reconstructing how older adults, family caregivers, aging service professionals, gerontology educators, and gerontology students conceptualize and deliver care to an aging population. We suggest examples for gerontology educators to integrate into their classrooms to further cultivate an appreciation among students of multiple approaches to intervention, including those that leverage sharing economy and technology-enabled platforms to support older adults and their caregivers.

The Influence of Feelings While Driving Regular Cars on the Perception and Acceptance of Self-Driving Cars.

Self-driving vehicles will affect the future of transportation, but factors that underlie perception and acceptance of self-driving cars are yet unclear. Research on feelings as information and the affect heuristic has suggested that feelings are an important source of information, especially in situations of complexity and uncertainty. In this study (N = 1,484), we investigated how feelings related to traditional driving affect risk perception, benefit perception, and trust related to self-driving cars as well as people's acceptance of the technology. Due to limited experiences with and knowledge of self-driving cars, we expected that feelings related to a similar experience, namely, driving regular cars, would influence judgments of self-driving cars. Our results support this assumption. While positive feelings of enjoyment predicted higher benefit perception and trust, negative affect predicted higher risk and higher benefit perception of self-driving cars. Feelings of control were inversely related to risk and benefit perception, which is in line with research on the affect heuristic. Furthermore, negative affect was an important source of information for judgments of use and acceptance. Interest in using a self-driving car was also predicted by lower risk perception, higher benefit perception, and higher levels of trust in the technology. Although people's individual experiences with advanced vehicle technologies and knowledge were associated with perceptions and acceptance, many simply have never been exposed to the technology and know little about it. In the absence of this experience or knowledge, all that is left is the knowledge, experience, and feelings they have related to regular driving.

Development of a job rotation scheduling algorithm for minimizing accumulated work load per body parts.

BACKGROUND: For the successful implementation of job rotation, jobs should be scheduled systematically so that physical workload is evenly distributed with the use of various body parts. However, while the potential benefits are widely recognized by research and industry, there is still a need for a more effective and efficient algorithm that considers multiple work-related factors in job rotation scheduling. OBJECTIVE: This study suggests a type of job rotation algorithm that aims to minimize musculoskeletal disorders with the approach of decreasing the overall workload. METHODS: Multiple work characteristics are evaluated as inputs to the proposed algorithm. Important factors, such as physical workload on specific body parts, working height, involvement of heavy lifting, and worker characteristics such as physical disorders, are included in the algorithm. For evaluation of the overall workload in a given workplace, an objective function was defined to aggregate the scores from the individual factors. A case study, where the algorithm was applied at a workplace, is presented with an examination on its applicability and effectiveness. RESULTS: With the application of the suggested algorithm in case study, the value of the final objective function, which is the weighted sum of the workload in various body parts, decreased by 71.7% when compared to a typical sequential assignment and by 84.9% when compared to a single job assignment, which is doing one job all day. CONCLUSIONS: An algorithm was developed using the data from the ergonomic evaluation tool used in the plant and from the known factors related to workload. The algorithm was developed so that it can be efficiently applied with a small amount of required inputs, while covering a wide range of work-related factors. A case study showed that the algorithm was beneficial in determining a job rotation schedule aimed at minimizing workload across body parts.