What inspired the MoodRing?
Psychologist John Weisz and colleagues have developed and validated a strategy in which youth and their caregivers separately list, rank, and describe the problems that concern them most. The top three problems identified by each are then used to help determine treatment and are assessed. As they note, this approach can inform program staff and focus the intervention and assessment efforts on those issues that youth (and caregivers) consider most important. The Top Problems measure that we adopted into MoodRing is meant to complement regular assessments in ways that can help to focus the intervention on the problems that youth consider most important. And, completing the top problems measure every seven days can also generate evidence on trajectories of change in those problems during treatment. Although future versions may also include parent-rated challenges, in the current version of MentorHub, we focused only on youth ratings. The goal was to point mentees toward the best apps and to enable mentees, mentors, and programs to track changes over time.
As the authors note: asking youth to report on their top problems “is ecologically valid as an efficient, respectful, clinically sensitive approach that builds directly on procedures already common in clinical practice. The repeated assessment approach is quite accessible to clinicians, given the brevity and simplicity of the TP measure. Finally, the rating system is simultaneously idiographic and systematic, thus having the potential to support clinical practice (e.g., monitoring treatment response and progress toward goals) and clinical research (e.g., measuring trajectories of change), as described in the introduction. If this kind of evidence has clinical appeal, perhaps it can contribute to making every-day clinical care more systematic, evidence-informed, and effective.”
MentorHub automatically graphs progress on the MoodRing so students, mentors, and programs can see how much they’re improving. To accomplish this, MentorHub has incorporated sophisticated data collection techniques (e.g., time sampling youth’s moods, period assessments, automatic scoring, and visualization of data) which, aided by machine learning, can simplify, expedite and improve the capacity of programs to monitor and evaluate their efforts. Increasing the frequency, accuracy, and efficiency of data collection and analysis has potentially far-reaching effects (e.g., enabling early detection of problems and more targeted support as well as reducing the need for costly program evaluations).
Weisz, J. R., Chorpita, B. F., Frye, A., Ng, M. Y., Lau, N., Bearman, S. K., … & Hoagwood, K. E. (2011). Youth Top Problems: using idiographic, consumer-guided assessment to identify treatment needs and to track change during psychotherapy. Journal of consulting and clinical psychology, 79(3), 369.
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