A randomized preference trial to inform personalization of a parent training program implemented in community mental health clinics

Transl Behav Med. 2016 Mar;6(1):73-80. doi: 10.1007/s13142-015-0366-4.

Abstract

Incorporating participant preferences into intervention decision-making may optimize health outcomes by improving participant engagement. We describe the rationale for a preference-based approach to the personalization of community-based interventions. Compensating for the limitations of traditional randomized controlled trials (RCTs) and partially randomized preference trials (PRPTs), we employed a doubly randomized preference trial in the present study. Families (N = 129) presenting to community mental health clinics for child conduct problems were randomized to choice or no-choice conditions. Within each condition, parents were again randomized, or offered choices between home- and clinic-based, individual and group versions of a parent training program or services-as-usual. Participants were assessed at baseline, and treatment retention data were gathered. Families assigned to the choice condition were significantly less likely to drop out of treatment than those in the no-choice condition. In the choice condition, in-home treatment was the preferred modality, and across conditions, families were less likely to be retained in group and clinic modalities. Research on preferences may boost participant engagement and inform shared decision-making.

Keywords: Parent training; Personalization; Preference; Treatment retention.

Publication types

  • Multicenter Study
  • Randomized Controlled Trial
  • Research Support, N.I.H., Extramural

MeSH terms

  • Adult
  • Child
  • Community Health Centers* / statistics & numerical data
  • Female
  • Humans
  • Male
  • Mental Disorders / therapy*
  • Parents / education*
  • Parents / psychology*
  • Patient Dropouts / psychology
  • Patient Dropouts / statistics & numerical data
  • Patient Preference / psychology*
  • Patient Preference / statistics & numerical data
  • Pilot Projects