Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Aug 30, 2018
Date Accepted: Jul 28, 2019
(closed for review but you can still tweet)
Establishing Mental Health App Integrity: The Construct Validation Of Psychologist In A Pocket
ABSTRACT
Background:
Mobile health (mHealth) is a fast-growing professional sector. As of 2016, there are more than 259,000 mHealth apps available internationally. However, while mHealth apps are growing in acceptance, relatively little attention and limited effort have been invested to establish their scientific integrity through statistical validation. This paper presents the external validation of Psychologist in a Pocket (PiaP), an Android-based mental mHealth app which supports traditional approaches in depression screening and monitoring through the analysis of electronic data.
Objective:
The study’s main objectives are: 1) to externally-validate the construct of the depression lexicon of Pia with standardized psychological paper-and-pencil tools and 2) to determine the comparability of PiaP, a new depression measure, to a psychological gold standard in identifying depression.
Methods:
College participants downloaded PiaP for a two-week administration. Afterwards, they were asked to complete four psychological depression instruments. PiaP’s 1-week and 2-week Total Scores (TS) were correlated with: a) Beck’s Depression Index (BDI) and Center for Epidemological Studies-Depression (CES-D) Scale for congruent construct validation, b) Affect Balance Scale (ABS) – Negative Affect for convergent construct validation and c) Satisfaction with Life Scale (SWLS) and ABS – Positive Affect for divergent construct validation.
Results:
Based on the Pearson-Product Moment Correlation, significant positive correlations exist between a) PiaP 1-week TS and CES-D; b) PiaP 2-week TS and BDI-II; and, c) PiaP 2-week TS and SWLS. Concordance analysis (Bland-Altman) suggests that PiaP’s approach to depression screening is comparable to the gold-standard (BDI-II).
Conclusions:
The evaluation of mental health has historically relied on subjective measurements. With the integration of novel approaches using mobile technology (and, by extension, mHealth apps) in mental healthcare, the validation process becomes more compelling to ensure their accuracy and credibility. This study suggests that PiaP’s approach to depression screening by analysing electronic data is comparable to traditional and well-established depression instruments and can be used to augment the process of measuring depression symptoms.
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