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Accepted for/Published in: JMIR Formative Research

Date Submitted: May 26, 2022
Date Accepted: Sep 14, 2022

The final, peer-reviewed published version of this preprint can be found here:

mHealth Technologies for Managing Problematic Pornography Use: Content Analysis

Henry N, Donkin L, Williams M, Pedersen M

mHealth Technologies for Managing Problematic Pornography Use: Content Analysis

JMIR Form Res 2022;6(10):e39869

DOI: 10.2196/39869

PMID: 36227634

PMCID: 9614626

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

m-Health Technologies for Managing Problematic Pornography Use: A Literature Review and Content Analysis of Current Apps

  • Nathan Henry; 
  • Liesje Donkin; 
  • Matt Williams; 
  • Mangor Pedersen

ABSTRACT

Background:

Several mobile apps are currently available that purportedly help with managing pornography addiction. However, the utility of these apps is unclear, given the lack of literature on the effectiveness of m-Health solutions for problematic pornography use (PPU).

Objective:

This study aimed to characterize the purpose and content of mobile apps that claim to manage pornography addiction.

Methods:

The phrase ‘pornography addiction’ was entered as a search term for the two major mobile phone platforms (Android and iOS). Apps were included in the analysis if they were described as reducing pornography use, and data were extracted from the store descriptions of the apps.

Results:

In total, 170 apps from both app stores met the inclusion criteria. The five most common and popular features, both in terms of number of apps with each feature (NAF) and minimum possible number of installations (MPNI) were the ability to track the time since last relapse (NAF=42%, 72/170; MPNI=6,388,000), tutorials and coaching (NAF=37%, 63/170; MPNI=9,286,505), access to accountability partners or communities (NAF=30%, 51/170; MPNI=5,544,500), content blocking or monitoring (NAF=27%, 46/170; MPNI=17,883,000), and badges (NAF=20%, 34/170; MPNI=4,425,300). Of these features, content blocking or monitoring had the highest MPNI. Content blocking was also the most detected feature combination in a combinations analysis (with 28 apps having only this feature), but also had the lowest mean consumer satisfaction rating (4.04) and second-lowest median rating (4.00) out of 5 stars.

Conclusions:

There are several apps with the potential to provide low- or zero-cost, real-time interventions for people struggling to manage PPU, particularly those who are unable or unwilling to access a therapist. However, there is currently little evidence to support the effectiveness and safety of these apps. Further research is required to be able to provide recommendations about which apps (and app features) are safe for public consumption. This research strategy may enable the creation of combination therapies that employ a ‘Defence in Depth’ approach to manage PPU. Clinical Trial: pornography; pornography addiction; m-Health; problematic pornography use (PPU); mobile intervention; Just-In-Time Adaptive Intervention; smartphone-based therapy; addiction; psychology; Internet addiction


 Citation

Please cite as:

Henry N, Donkin L, Williams M, Pedersen M

mHealth Technologies for Managing Problematic Pornography Use: Content Analysis

JMIR Form Res 2022;6(10):e39869

DOI: 10.2196/39869

PMID: 36227634

PMCID: 9614626

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