Evaluating a Strengths-Based mHealth Tool (MyStrengths): Explorative Feasibility Trial
Bibliographic record
Abstract
BACKGROUND: As the number of people living with chronic illnesses increases, providing wide-reaching and easy-to-use support tools is becoming increasingly important. Supporting people in this group to recognize and use more of their personal strengths has the potential to improve their quality of life. With this in mind, we have developed the MyStrengths app prototype, a gamefully designed app aimed at aiding users in both identifying their strengths and using these strengths more actively in their daily life. OBJECTIVE: The goal of this study was to evaluate the user-reported feasibility and usefulness of the MyStrengths app. The study additionally aimed to explore whether the use of MyStrengths could be associated with selected psychosocial outcomes. METHODS: A 31-day explorative feasibility trial with a pretest-posttest design and an optional end of study interview was conducted. Data collection included system-use log data, demographic information, pre- and post-psychosocial measures (ie, strengths use, self-efficacy, health-related quality of life, depression), user experience measures (ie, usability, engagement, flow), and interview data. RESULTS: In total, 34 people with at least 1 chronic condition were enrolled in the study, with 26 participants (mean age 48 years, range 29-62 years; 1 male) completing the trial. Among these individuals, 18 were also interviewed posttrial. Participants used the MyStrengths app an average of 6 days during the trial period, with 54% (14/26) using the app over a period of at least 19 days. In total, 8738 unique app actions were registered. Of the psychosocial outcome measures, only 1 subscale, general health in the RAND 36-Item Health Survey, yielded significant pre- and posttest changes. Posttrial interviews showed that the number of participants who considered the MyStrengths app to be useful, somewhat useful, or not useful was evenly distributed across 3 groups. However, every participant did voice support for the strengths approach. All participants were able to identify a multitude of personal strengths using the MyStrengths app. Most participants that reported it to be useful had little or no previous experience with the personal strengths approach. A multitude of users welcomed the gameful design choices, particularly the rolling die feature, suggesting strengths exercises, activities that use a specific strength, were well received. CONCLUSIONS: Although the reported usefulness and feedback from use varied, most participants were favorable to the strengths-focused approach to care and support. Consequently, low-threshold and wide-reaching mobile health tools that use a strengths-focused approach, such as MyStrengths, hold the potential to support people living with chronic illness in performing self-management and achieving mastery of their life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".