No abstract

Substantial ongoing research now uses smartphones as a research platform for various studies and interventions. With the ageing population becoming a frequent focus of needed research, an increasing number of studies and projects attempt to develop technological interventions for the elderly population. Yet, it is not clear exactly how widespread is the adoption and use of smartphones amongst seniors. Many studies acknowledge that today's elders are not particularly keen on using smartphones, but in the near future we can expect this trend to change. In this paper we present an in-depth survey of statistics on smartphone adoption within the elder population, and describe both the popularity and type of use that smartphones enjoy amongst elders. We show that far from being ubiquitous, smartphones are still overshadowed by traditional feature phones today, and substantial geographical differences also do exist between countries. Furthermore, those seniors who do adopt smartphones tend to use them as feature phones, and do not adopt services that are popular amongst younger users. Our survey provides an assessment on the ubiquity of smartphones amongst seniors, that can be used to inform the assumptions of our research community.

No abstract

Researchers who analyse smartphone usage logs often make the assumption that users who lock and unlock their phone for brief periods of time (e.g., less than a minute) are continuing the same "session" of interaction. However, this assumption is not empirically validated, and in fact different studies apply different arbitrary thresholds in their analysis. To validate this assumption, we conducted a field study where we collected user-labelled activity data through ESM and sensor logging. Our results indicate that for the majority of instances where users return to their smartphone, i.e., unlock their device, they in fact begin a new session as opposed to continuing a previous one. Our findings suggest that the commonly used approach of ignoring brief standby periods is not reliable, but optimisation is possible. We therefore propose various metrics related to usage sessions and evaluate various machine learning approaches to classify gaps in usage.