Cohorts like ALSPAC typically collect data on their participants over several years, but since data collection is usually both expensive and burdensome these data collection events tend to take place every few years, measuring or recording information at a particular instance in time e.g. via questionnaires or clinic visits. Hence, these data contain a limited amount of information on phenotypic variability across the life-course, and restricts the research questions that can be asked using these data. There is much more scope to exploit existing and emerging technologies to collect data ‘continuously’ over the longer term in cost-effective and less burdensome ways.

Digital health devices have been successfully used to collect data on specific traits over a number of days (e.g. physical activity measured with accelerometers), but these devices tend to each focus on particular traits such that collecting data in this way is expensive (having to buy specific devices to collect specific phenotypes), and many types of phenotypes do not lend themselves to this type of data collection, in particular, those that can only (currently) be collected via self-report. Recent advances in artificial intelligence and voice recognition technologies means it is now feasible to use voice-based systems to collect self-reported data continuously over several days or weeks in a less burdensome way. However, to date, voice-based data collection has not been exploited for collecting health data.