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.
Medical hypotheses