Maria Ferré-Bergadà1, Aida Valls2, Laia Raigal-Aran3, Jael Lorca-Cabrera3, Núria Albacar-Riobóo3, Teresa Lluch-Canut4, Carme Ferré-Grau3. 1. Dept. Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Avda. Països Catalans, 26, 43007, Tarragona, Catalonia, Spain. 2. Dept. Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Avda. Països Catalans, 26, 43007, Tarragona, Catalonia, Spain. aida.valls@urv.cat. 3. Nursing Department, Universitat Rovira i Virgili, Avinguda Catalunya 35, 43002, Tarragona, Catalonia, Spain. 4. Nursing Department, Universitat de Barcelona, Carrer de La Feixa Llarga S/N, 08907, L'Hospitalet de Llobregat, Barcelona, Spain.
Abstract
BACKGROUND: Taking care of chronic or long-term patients at home is an arduous task. Non-professional caregivers suffer the consequences of doing so, especially in terms of their mental health. Performing some simple activities through a mobile phone app may improve their mindset and consequently increase their positivity. However, each caregiver may need support in different aspects of positive mental health. In this paper, a method is defined to calculate the utility of a set of activities for a particular caregiver in order to personalize the intervention plan proposed in the app. METHODS: Based on the caregivers' answers to a questionnaire, a modular averaging method is used to calculate the personal level of competence in each positive mental health factor. A reward-penalty scoring procedure then assigns an overall impact value to each activity. Finally, the app ranks the activities using this impact value. RESULTS: The results of this new personalization method are provided based on a pilot test conducted on 111 caregivers. The results indicate that a conjunctive average is appropriate at the first stage and that reward should be greater than penalty in the second stage. CONCLUSIONS: The method presented is able to personalize the intervention plan by determining the best order of carrying out the activities for each caregiver, with the aim of avoiding a high level of deterioration in any factor.
BACKGROUND: Taking care of chronic or long-term patients at home is an arduous task. Non-professional caregivers suffer the consequences of doing so, especially in terms of their mental health. Performing some simple activities through a mobile phone app may improve their mindset and consequently increase their positivity. However, each caregiver may need support in different aspects of positive mental health. In this paper, a method is defined to calculate the utility of a set of activities for a particular caregiver in order to personalize the intervention plan proposed in the app. METHODS: Based on the caregivers' answers to a questionnaire, a modular averaging method is used to calculate the personal level of competence in each positive mental health factor. A reward-penalty scoring procedure then assigns an overall impact value to each activity. Finally, the app ranks the activities using this impact value. RESULTS: The results of this new personalization method are provided based on a pilot test conducted on 111 caregivers. The results indicate that a conjunctive average is appropriate at the first stage and that reward should be greater than penalty in the second stage. CONCLUSIONS: The method presented is able to personalize the intervention plan by determining the best order of carrying out the activities for each caregiver, with the aim of avoiding a high level of deterioration in any factor.
Entities:
Keywords:
Caregivers; Mobile health; Personalization; Positive mental health; Utility measurement
Authors: J Roldán-Merino; M T Lluch-Canut; I Casas; M Sanromà-Ortíz; C Ferré-Grau; C Sequeira; A Falcó-Pegueroles; D Soares; M Puig-Llobet Journal: J Psychiatr Ment Health Nurs Date: 2017-02-01 Impact factor: 2.952
Authors: Victoria Cristancho-Lacroix; Jérémy Wrobel; Inge Cantegreil-Kallen; Timothée Dub; Alexandra Rouquette; Anne-Sophie Rigaud Journal: J Med Internet Res Date: 2015-05-12 Impact factor: 5.428
Authors: Carme Ferré-Grau; Laia Raigal-Aran; Jael Lorca-Cabrera; Maria Ferré-Bergadá; Mar Lleixà-Fortuño; Maria Teresa Lluch-Canut; Montserrat Puig-Llobet; Núria Albacar-Riobóo Journal: BMC Public Health Date: 2019-07-05 Impact factor: 3.295
Authors: Matheus Costa Stutzel; Michel Pedro Filippo; Alexandre Sztajnberg; Rosa Maria E M da Costa; André da Silva Brites; Luciana Branco da Motta; Célia Pereira Caldas Journal: BMC Med Inform Decis Mak Date: 2019-07-22 Impact factor: 2.796