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Mobile Enhancement of Motivation in Schizophrenia: A Pilot Trial of a Personalized Text-Message Intervention for Motivation Deficits

deficits remain an unmet treatment need in schizophrenia. Recent preclinical research
has identified novel mechanisms underlying motivation deficits, namely impaired
effort-cost computations and reduced future reward-value representation
maintenance, that may serve as more effective treatment targets to improve
motivation. The main aim of this study was to test the
feasibility and preliminary effectiveness of a translational mechanism-based
intervention, MEMS (Mobile Enhancement of Motivation in Schizophrenia),
which leverages mobile technology to target these mechanisms with
text-messages. Fifty-six participants with a
schizophrenia-spectrum disorder were randomized to MEMS (<i>n</i> = 27) or a control condition (<i>n</i>
= 29). All participants set recovery goals to complete over eight-weeks. The
MEMS group also received personalized, interactive text-messages each weekday
to support motivation. Retention and engagement
in MEMS was high: 92.6% completed 8 weeks of MEMS, with an 86.1% text-message
response rate, and 100% reported that they were satisfied with the
text-messages. Compared to the control condition, the MEMS group had
significantly greater improvements in interviewer-rated motivation and
anticipatory pleasure and obtained significantly more recovery-oriented goals
at the end of the 8-week period. There were no significant group differences in
performance-based effort-cost computations and future reward-value
representations, self-reported motivation, quality of life, functioning, or
additional secondary outcomes of positive symptoms, mood symptoms, or
neurocognition. Results suggest that MEMS is feasible as a relatively brief,
low-intensity mobile intervention that could effectively improve interviewer-rated
motivation, anticipatory pleasure, and recovery goal attainment in those with
schizophrenia-spectrum disorders. </p>

  1. 10.25394/pgs.8101658.v1
Date16 October 2019
CreatorsLauren Luther (6685082)
Source SetsPurdue University
Detected LanguageEnglish
TypeText, Thesis
RightsCC BY 4.0

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