Bäcker A, Forsström D, Hommerberg L, Johansson M, Hensler I, Lindner P. A novel self-rating instrument designed for long-term, app-based monitoring of ADHD symptoms: A mixed-methods development and validation study.
Digit Health 2024;
10:20552076241280037. [PMID:
39323431 PMCID:
PMC11423372 DOI:
10.1177/20552076241280037]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 08/08/2024] [Indexed: 09/27/2024] Open
Abstract
Background
Regular outcome monitoring is essential for effective attention deficit hyperactivity disorder (ADHD) treatment, yet routine care often limits long-term contacts to annual visits. Smartphone apps can complement current practice by offering low-threshold, long-term sustainable monitoring capabilities. However, special considerations apply for such measurement which should be anchored in stakeholder preferences.
Methods
This mixed-methods study engaged 13 experienced clinicians from Region Stockholm in iterative qualitative interviews to inform development of an instrument for app-based ADHD monitoring: the mHealth scale for Continuous ADHD Symptom Self-monitoring (mCASS). A subsequent survey, including the mCASS and addressing app-based monitoring preferences, was administered to 397 individuals with self-reported ADHD. Psychometric properties of the mCASS were explored through exploratory factor analysis and examinations of internal consistency. Concurrent validity was calculated between the mCASS and the Adult ADHD Self-Report Scale-V1.1 (ASRS-V1.1). Additional quantitative analyses included summary statistics and repeated-measures ANOVAs.
Results
Clinicians identified properties influencing willingness to use and adherence including content validity, clinical relevance, respondent burden, tone, wording and preferences for in-app results presentation. The final 12-item mCASS version demonstrated four factors covering everyday tasks, productivity, rest and recovery and interactions with others, explaining 47.4% of variance. Preliminary psychometric assessment indicated satisfactory concurrent validity (r = .595) and internal consistency (α = .826).
Conclusions
The mCASS, informed by clinician and patient experiences, appears to be valid for app-based assessment of ADHD symptoms. Furthermore, insights are presented regarding important considerations when developing mobile health (mHealth) instruments for ADHD individuals. These can be of value for future, similar endeavours.
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