Anika Steinert

Evaluating an Online Cognitive Training Platform for Older Adults: User Experience and Implementation Requirements.

Decline of cognitive function is a part of aging. However, intensive cognitive training can improve important cognitive functions, such as attention and working memory. Because existing systems are not older adult-friendly and are usually not based on scientific evidence, an online platform was developed for cognitive training with information and communication features and evaluated in an 8-week field test. In a randomized clinical trial with 80 older adults, findings from log data analysis and questionnaires revealed a good use of the online platform. Communication or assistive features were not used often. Good usability ratings were given to the cognitive exercises. Subjective improvements of cognitive functions due to the training were reported. The current article presents concrete requirements and recommendations for deploying cognitive training software in older adult residential homes.

Gamification designs in Wearable Enhanced Learning for healthy ageing

Supporting healthy ageing through sustained physical fitness requires interventions that promote healthy levels of physical activity as part of daily life. Wearable devices, such as activity trackers are body-worn and may be seamlessly integrated into daily activities to promote fitness. Although wearables have been used by younger adopters to optimise physical fitness, little is known so far how these emerging technologies may be leveraged to enhance learning and improve well-being and fitness of seniors. In this paper we present a novel approach to designing Wearable Enhanced Learning (WELL) for healthy ageing as part of the R&D project “Fitness MOOC - interaction of seniors with wearable fitness trackers in the MOOC (fMOOC)”. The fMOOC project is a cooperation between Beuth University of Applied Sciences and the Geriatrics Research Group at Charité Berlin and aims at enhancing healthy ageing through an embodied learning experience in a Massive Open Online Course (MOOC) with support of wearable activity trackers. This paper focuses on gamification designs in wearable enhanced learning for senior users and addresses the issue of user engagement.

Activity-tracking devices for older adults: comparison and preferences

Research demonstrates that physical activity beneficially influences brain function, quality of life and reduces cardiovascular diseases. Therefore, many projects and technical developments aim to promote physical activity. As a result of the “quantified self”-movement, a great number of wearable technologies, which are mainly developed for a younger target group, emerged on the market. However, there is a lack of scientific studies to establish which systems are suitable for older adults and there is still little known about seniors’ needs and preferences regarding fitness trackers. In this crossover-designed usability study, 20 older adults aged over 60 took part. In the study, all participants were requested to rate the usability of five of the most widely used fitness trackers (Nike FuelBand, Jawbone Up, Fitbit Flex, Garmin vivofit, Sony SmartBand) and were asked about their preferences. Participants had to rate its ergonomic features such as handling, material and design. Overall, the fitness trackers differ a lot in tracking features, design, usability and acceptance of the seniors. We present an aggregated list of requirements for the “ideal” activity tracker for older adults. The results seem to be of high interest for developers, but also for scientists to choose appropriate sensors for technology-based interventions that may promote physical activity.

Automatic Detection of Visual Search for the Elderly using Eye and Head Tracking Data

With increasing age we often find ourselves in situations where we search for certain items, such as keys or wallets, but cannot remember where we left them before. Since finding these objects usually results in a lengthy and frustrating process, we propose an approach for the automatic detection of visual search for older adults to identify the point in time when the users need assistance. In order to collect the necessary sensor data for the recognition of visual search, we develop a completely mobile eye and head tracking device specifically tailored to the requirements of older adults. Using this device, we conduct a user study with 30 participants aged between 65 and 80 years (

Analyse des Umgangs älterer Internetnutzer mit unerwarteten Situationen

avg = 71.7,

Self-Monitoring of Health-Related Goals in Older Adults with Use of a Smartphone Application

avg=71.7, 50% female) to collect training and test data. During the study, each participant is asked to perform several activities including the visual search for objects in a real-world setting. We use the recorded data to train a support vector machine (SVM) classifier and achieve a recognition rate of 97.55% with the leave-one-user-out evaluation method. The results indicate the feasibility of an approach towards the automatic detection of visual search in the wild.

Touch interaction with Google Glass – Is it suitable for older adults?

Purpose – Cognitive changes occur with age and cognitive limitations can negatively influence computer use. Human-interaction studies show that especially older adults benefit considerably from using web platforms. The purpose of this paper is to measure the possible impact of cognitive impairment in web usability and to analyse the differences between older adults with and without cognitive impairment. Design/methodology/approach – In the presented pilot study, 50 older adults tested a web-based interface on a PC and tablet computer that was designed based on a styleguide for this specific user group. In two sessions participants had to conduct six tasks. In a third session older adults were left unsupervised in the laboratory where they were confronted with unexpected events triggered by a principal investigator. Findings – The performance results differed significantly between the two groups. Older adults with mild cognitive impairment (MCI) needed more time and were more likely to make mistakes when u...

Effects of a long-term smartphone-based self-monitoring intervention in patients with lipid metabolism disorders

BACKGROUND Although internet usage among older adults is steadily increasing, there is still a digital divide between generations. Younger internet users seem to be more open towards new media. Recent studies showed the negative influence of computer anxiety on internet usage. It is not known how older adults deal with computer and internet issues in their home environment and which problem-solving strategies they apply. AIM The behavior of elderly people in unexpected situations when using an internet portal was analyzed to establish whether older users can solve the problems without assistance and what individual reactions (e.g. facial expressions and gesticulations) they show during the interaction. MATERIAL AND METHODS In a clinical trial with 50 older adults aged 60 years and older various typical problems which may occur while using web platforms were simulated and user behavior was analyzed using logging data, videography and with questionnaires to measure the subjective opinion of the study participants. RESULTS The study participants had severe problems in solving the tasks on their own and many of them could not find a suitable solution at all. Overall, the videography data indicated an increased concentration of the participants during the whole session, which is in contrast to the low levels of perceived mental workload reported by the participants. Regarding task completion, no differences were found between seniors with and without cognitive impairment. CONCLUSION The results showed the serious difficulties of older adults when dealing with unexpected events while using a web platform. For developers of internet platforms for inexperienced seniors, it seems to be crucial to incorporate a simple integration of all available features within the platform, without including features requiring high multi-tasking skills.