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How mHealth programmes can treat depression: A randomised controlled trial

Authors: Litvin, Silja & Markus, Maier;

How mHealth programmes can treat depression: A randomised controlled trial

Abstract

An anti-depression app named PsycApps based on intervention methods such as psychoeducation, selfassessment, journalling, self-management, and goal-setting was scripted, developed and published on the iTunes app store in 2018. We proposed that using the app over a four-week timeframe would significantly lower depression levels. A randomised control trial with 276 participants (137 in the test group, 139 in the control group) was conducted, and both groups were tested using the Beck Depression Inventory (BDI), Life Satisfaction Scale, and a one-item Likert Scale for anxiety at the pre- and postevaluation phases within a four-week period. The test group used the app, while the control group filled out a digital survey with no further intervention. Results of repeated measures of ANOVAs showed statistically significant increases when using the app over a timeframe of four weeks. The app significantly lowered the test group’s depression levels, measured by the BDI as .57. While not significant, anxiety levels were lowered by .19 and, notably, the Life Satisfaction score was also lowered by .27. Smartphones are a valuable and under-utilised platform to offer mental health interventions. Mood disorders such as depression and anxiety can be effectively treated and there are promising indications for other mental illnesses such as eating disorders and mild forms of schizophrenia, as well as prevention and remittance programmes. Future research would benefit from the implementation of gamification and a general increase of attractive intervention offers.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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