EVOTION v1.0

• Research type

  Research Study

• Full title

  EVOTION-EVidenced based management of hearing impairments: Public health pΟlicy making based on fusing big data analytics and simulaTION.

• IRAS ID

  222391

• Contact name

  Doris-Eva Bamiou

• Contact email

  d.bamiou@ucl.ac.uk

• Sponsor organisation

  University College London (UCL)

• Clinicaltrials.gov Identifier

  536693, UCL PROJECT NUMBER

• Duration of Study in the UK

  2 years, 0 months, 0 days

• Research summary

  Hearing Loss (HL) affects over 5% of the world’s population (WHO 2014) and is the 5th leading cause of Years Lived with Disability. HL is currently managed with Hearing Aids (HAs), i.e. programmable sound amplification devices that are worn by the hearing impaired subjects to address their hearing difficulties.
  HA use however is often problematic, costly and with poor overall benefits. The holistic management of HL requires appropriate public health policies for HL prevention, early diagnosis, long-term treatment and rehabilitation; detection and prevention of cognitive decline; and socioeconomic inclusion of HL patients. Currently the evidential basis for forming such policies is limited. The EVOTION project proposes to address this by collecting and analysing a big set of heterogeneous data, including HA usage, audiological, physiological, cognitive, clinical and medication, personal, behavioural, life style, occupational and environmental data.

  This will be done by:
  i. accessing big datasets of existing HA user data from the EVOTION clinical partners (UCL and GSST in the UK; OTICON in Denmark)
  ii. collection of prospective HA user data who will be recruited to the prospective EVOTION study and who will undergo some additional assessments
  iii. collection of real time dynamic data of the human participant HA users who will be given a smart phone with different apps (auditory tests; auditory training), sensors(recording of heart, respiratory rate etc) and smart HAs (recording environmental factors such as noise levels type of noise etc) so that real life contextual factors that affect HA usage and outcome can be identified.

  These data will be analysed with big data analysis/data mining techniques in order to identify relationships between these in order to use this information to derive and support public health decisions.

• REC name

  London - South East Research Ethics Committee

• REC reference

  17/LO/0789

• Date of REC Opinion

  24 May 2017

• REC opinion

  Favourable Opinion