ADHD: Is There an App for That? A Suitability Assessment of Apps for the Parents of Children and Young People With ADHD
Bibliographic record
Abstract
BACKGROUND: Attention-deficit hyperactivity disorder (ADHD) is a highly comorbid disorder that can impact significantly on the individual and their family. ADHD is managed via pharmacological and nonpharmacological interventions. Parents also gain support from parent support groups, which may include chat rooms, as well as face-to-face meetings. With the growth of technology use over recent years, parents have access to more resources that ever before. A number of mobile apps have been developed to help parents manage ADHD in their children and young people. Unfortunately many of these apps are not evidence-based, and little is known of their suitability for the parents or whether they are helpful in ADHD management. OBJECTIVE: The aim of this study was to explore the (1) parents' views of the suitability of the top ten listed apps for parents of children and young people with ADHD and (2) the views of clinicians that work with them on the suitability and value of the apps. METHODS: The top 10 listed apps specifically targeted toward the parents of children and young people with ADHD were identified via the Google Play (n=5) and iTunes store (n=5). Interviews were then undertaken with 7 parents of children or young people with ADHD and 6 clinicians who specialize in working with this population to explore their opinions of the 10 apps identified and what they believe the key components are for apps to be suitable and valuable for this population. RESULTS: Four themes emerged from clinician and parent interviews: (1) the importance of relating to the app, (2) apps that address ADHD-related difficulties, (3) how the apps can affect family relationships, and (4) apps as an educational tool. Two additional themes emerged from the clinician interviews alone: monitoring ADHD symptoms and that apps should be practical. Parents also identified an additional theme: the importance of the technology. Overall, the characteristics of the current top 10 listed apps did not appear to match well to the views of our sample. CONCLUSIONS: Findings suggest that these apps may not fully meet the complex needs of this parent population. Further research is required to explore the value of apps with this population and how they can be tailored to their very specific needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".