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SmartEater

Financiado

Enhancing recovery from eating and weight disorders using mHealth and psychologi...

Smartphones are ubiquitous in all age groups and socioeconomic levels and digitalization of various life domains is in full progress. While there are several areas where skepticism is justified, the personal health domain still ho... ver más

30/06/2020

PLUS

150K€

Presupuesto del proyecto: 150K€

Líder del proyecto

PARISLODRONUNIVERSITAT SALZBURG No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 3 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-06-30

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2018-PoC: ERC Proof of Concept Grant

I+D

Cerrada hace 6 años

0% 100% 100%

3 Participantes

PLUS

61.25K€ | Lider

SEGURIDAD INDUSTRIAL SA

268.64K€ | Participante

FHS

88.75K€ | Participante

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Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 18 meses Fecha Inicio: 2018-12-19
Fecha Fin: 2020-06-30

Líder del proyecto

PARISLODRONUNIVERSITAT SALZBURG

TRL 4-5

Presupuesto del proyecto 150K€

Descripción del proyecto Smartphones are ubiquitous in all age groups and socioeconomic levels and digitalization of various life domains is in full progress. While there are several areas where skepticism is justified, the personal health domain still holds high promises, particularly when applied in specific settings. The proposed mHealth app SmartEater provides intelligent mobile logging of stress, and eating behavior as a basis for intervention and follow-up care in clinics treating eating disorders and obesity. Current apps require frequent and cumbersome entries, resulting in low user adherence and poor data quality. Evidence for their usefulness is often missing. Further, therapeutic content is not personalized. In SmartEater, users repeatedly enter data on experienced craving for foods and stress. SmartEater then ‘learns’ from the user through sophisticated machine learning algorithms: data from smartphone usage patterns (e.g. screen-on time, calls, messages, internet traffic) and sensor data (e.g. movement, background noise) are combined to substitute for manual user input, thereby progressively reducing user burden. Temporal pattern analysis of individual time-series allows prediction of stress and craving bouts into the near future. Such predictions allows the app to respond to upcoming eating 'crises’ e.g. overeating/binge eating and launch situation-appropriate tips that have been developed individually for the user during in-patient treatment. SmartEater will be routed in psychological models of eating behavior and rigorously tested in the described population to evaluate efficacy. Due to the sensitive nature of such data, SmartEater enforces strict privacy protection. Targeted markets include health insurances which profit from improved patient health and successful transfer into daily life after professional treatment as well as clinics with an eating/weight disorder focus in German speaking coutries.

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