A method to determine a personalized set of online exercises for improving the positive mental health of a caregiver of a chronically ill patient

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.

Keywords: Caregivers; Mobile health; Personalization; Positive mental health; Utility measurement.

Fig. 1

App interface examples: a first, every day, the app asks the caregiver how she/he feels b next, the application presents the corresponding activity and, finally, c the application shows the evolution of the avatar and the points that the caregiver won

Fig. 2

Flowchart of the of the caregiver’s actions revisited

Fig. 3

Values of the ordered weighted average, OWA, weights for different $\alpha$

Fig. 4

Histogram of the PMH level obtained for different values of $\alpha$. F1, Personal Satisfaction; F2, Prosocial Attitude; F3, Self-control; F4, Autonomy; F5, Problem-solving and Self-actualization and F6, Interpersonal Relationship Skills

Fig. 5

Distribution of the user’s PMH values calculated with conjunctive aggregation policies, in comparison with the neutral one