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Currently submitted to: Journal of Medical Internet Research

Date Submitted: Oct 4, 2017
Open Peer Review Period: Oct 4, 2017 - Nov 29, 2017

NOTE: This is an unreviewed Preprint

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Assessing therapeutic alliance in the context of m-health interventions for mental health problems

  • Katherine Berry; 
  • Amy Salter; 
  • Rohan Morris; 
  • Susannah James; 
  • Sandra Bucci

ABSTRACT

Background:

There are a variety of digital health interventions (DHIs) in the form of smartphone apps, with a growing proportion of these aimed at improving mental health. This is an important development, enabling people access to support as and when needed without having to overcome the stigma many people experience in accessing routine mental health services. If we are to evaluate m-health apps and advance scientific understanding in this field, we also need tools to help us understand in what ways m-health interventions are or are not effective. The concept of therapeutic alliance (TA), a measure of the quality of the relationship between a healthcare provider and a service user, is a key factor in explaining the effects of face-to-face mental health interventions. To date, the concept of TA in relation to m-health interventions has received little attention.

Objective:

This study presents the first attempt to: i) explore service users’ views of the concept of ‘relationship’ within m-health mental health interventions; and ii) adapt a well validated face-to-face measure of TA, the Agnew Relationship Measure (ARM), for use with m-health interventions.

Methods:

Our methodology involved three stages. In stage one, we interviewed nine mental health service users about the concept of TA in the context of a DHI and derived key themes from interview transcripts using thematic analysis. In stage two, we used a combination of rating scales and open-ended questions and elicited views from fourteen service users and ten mental health staff about the content and face validity of a version of the ARM which replaced the word ‘therapist’ with the word ‘app’. In stage three, we used the findings from stages one and two to adapt the measure with the support of a decision-making algorithm about which items to drop, retain or adapt.

Results:

Findings suggest that service users do identify relationship concepts when thinking about m-health interventions, including forming a bond with an app and the ability to be open with an app. However, there were key differences between relationships with health professionals and relationships with apps, such as apps not being as tailored and responsive to each person’s unique needs and apps not being capable of portraying uniquely human-like qualities such as friendliness, collaboration and agreement.

Conclusions:

We present an m-health version of the ARM, the m-ARM, which has good face and content validity. We encourage researchers to include this easy to use administer tool in DHI studies to develop further data about its psychometric properties and advance our understanding of the efficacy of m-health interventions and the TA in the context of DHIs.