The ongoing revolution in information technology is reshaping human life. In the realm of health behavior, wearable technology emerges as a leading digital solution, capturing physical behaviors (i.e., physical activity, sedentary habits, sleep patterns) within the 24-h cycle of daily life. Wearables are applied in research, clinical practice, and as lifestyle devices; most obvious, they promise to be a key element for increasing human physical activity, one of the biggest health challenges nowadays. However, despite the high expectations associated with wearable technology, fundamental aspects remain surprisingly neglected. Here, the lack of methodologically sound validation studies for wearables entering the market appears particularly critical. In our recent and comprehensive review of 967 validation studies, 1, 2, 3, 4 the overall low study quality was evident and alarming. In essence, standard regulatory processes are missing, although the scientific community is strongly advocating for improvements in the validation and trustworthiness of digital health products. Hence, we call for validity of wearables to be systematically tested to pave the way towards expedient digital health solutions and, in terms of reproducibility, to provide transparent information about the devices used (i.e., all data processing steps, analytical approaches, updates of algorithms). Toward this end, our viewpoint compiles challenges and suggests key elements for enhancing the quality of validation protocols (i.e., wearing position, criterion measurement, validated outcomes, sample size, statistical analyses) as well as issues pertinent to improving the validation process (i.e., replication of studies, access to raw data, the release of a new version). Moreover, to catalyze this comprehensive validation process, we are launching and introducing the project Wearable Landscape (www.wearable-landscape.info); the open science initiative not only compiles validation protocols but also facilitates collaboration efforts for sharing resources, equipment, set-up of multi-location studies, as well as joint data analysis and pre-processing
