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Examples of the international precision health and wellness profiling type of studies, similar to Finnish Digital Health Revolution pilot study, are presented shortly below. Characteristic to these studies is the collection of longitudinal health related information from healthy participants utilizing various data sources and collections methods.

Sophisticated analysis and inter-relations of the combined data has provided tools to recognize early transitions from health to disease. These studies have also empowered participants to take active role in the maintenance of their own health by providing feedback of actionable risk factors, as well as professional coaching to support profitable life style changes.

The Hundred Person Wellness Project

The Hundred Person Wellness Project (HPWP) was a 10-month pilot study initiated in early 2014 by Leroy Hood and his team Institute for Systems Biology in Seattle USA, where 108 healthy individuals were intensively monitored by collecting data from whole genome sequencing, clinical laboratory tests, metabolomes, proteomes and microbiomes and daily activity tracking. Researches created personal, dense and dynamic data clouds for each participant to increase understanding of health and disease and enable identification of disease mechanisms, new biomarkers and early transitions to disease states. During the study, participants received regular feedback and behavioural health coaching, based on actionable recommendation to enhance individual health. Pilot study revealed actionable health concerns from almost all participants, for example 95 had low vitamin D levels, 81 had high mercury levels and 52 were prediabetic.

However, since the opportunities for observing health transitions in a pilot study setting were limited in respect of small number of participants and short follow-up time, Hood´s team ambitious goal is to scale the project up to 100,000 individuals by 2020 to continue to achieve the three primary goals set for the project:

  1. To test the P4 paradigm in practice to demonstrate its effectiveness,
  2. To gather dynamic, longitudinal data to create and validate proposed wellness metrics and demonstrate that their actionability creates an opportunity for the participant to optimize their wellness and for the health system to focus on identifying early disease transitions and reversing or at least mitigating them;
  3. To develop a framework for scaling these findings and to demonstrate cost effectiveness, methodologies for application in the real world and participant-centric management of individual health.

Hood et al., 2015

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Hood L, Lovejoy JC, Price ND. Integrating big data and actionable health coaching to optimize wellness. BMC Med, 13:4, 2015.

Price ND, Magis AT, Earls JC, Glusman G, Levy R, Lausted C, McDonald DT, Kusebauch U, Moss CL, Zhou Y, Qin S, Moritz RL, Brogaard K, Omenn GS, Lovejoy JC, Hood L. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat Biotechnol, 35(8):747-756, 2017.

Digital health study utilizing wearable biosensors

Standford university study by Michael Snyder and his team demonstrates, how continuous longitudinal data, including physiological metrics from various biosensor devices combined with the periodic clinical laboratory analysis, can be used to identify factors affecting health. During the study participants wore from one to seven biosensor devices collecting their physiological parameters (heart rate, SpO2, skin temperature), activity related parameters (e.g. sleep, steps, walking, calories), weight and even radiation exposure. According to Snyder, an important component of the study was to establish a range of normal values for the individuals attending the study. Biosensor data combined with frequently taken medical measurements can then be used to identify deviations from normal patterns, link those with early signs of disease and reveal the impact of environmental conditions, such as airplane flights, on health. The pioneering work showed the value of different wearable biosensors in detecting early signs of inflammatory diseases, recognizing insulin resistance and the association of decreased blood oxygen levels and fatigue.

References: Li X, Dunn J, Salins D, Zhou G, Zhou W, Schüssler-Fiorenza Rose SM, Perelman D, Colbert E, Runge R, Rego S, Sonecha R, Datta S, McLaughlin T, Snyder MP. Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information. PLoS Biol, 15(1):e2001402, 2017.

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SCAPIS – SciLifeLab Wellness Profiling study

SCAPIS – SciLifeLab Wellness Profiling study, a follow-up effort of the Swedish CArdioPulmonary bioImage Study (SCAPIS), aims to develop a system that, through active monitoring of key biological parameters, allows early detection of cardiovascular and lung disease. Molecular markers from blood, urine and stool samples, along with physical measurements, sleep and activity will be analyzed from a subset of individuals recruited to the previous, larger effort, first at baseline and subsequently during the follow-up period. Utilizing samples collected to a specific SCAPIS Wellness Profiling biobank, the effort is an example of an integrative multi-omic profiling study that will provide means for assessing individuals’ wellness and sustain health.

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