One of the challenges faced by behavioral scientists is the lack of modeling methodologies for accurately determining when a behavior becomes problematic. The authors propose “behavioral posology” as a novel modeling paradigm for quantifying the healthy limits of behaviors through the concept of behavioral dose. As an example of this paradigm, a pharmacokinetic/pharmacodynamic model of a hypothetical digital behavior is presented, based on opponent process theory. The generic model can be adapted to simulate Solomon and Corbit's model of affective dynamics from 1974, and the model predicts features of addiction such as hedonic allostasis, withdrawal, and apparent tolerance. A behavioral frequency response analysis (BFRA) of the model demonstrates how behavior repetition may result in a hormetic dose–response relationship that depends on the frequency of the behavior. The model can be experimentally validated using Ecological Momentary Assessment, allowing researchers to hypothesize, model, and test causal mechanisms for behavioral addictions. The potential for behavioral posology to be applied as a clinical support tool in psychological medicine is discussed, as this modeling framework may help to detect and limit behaviors being performed too frequently based on factors such as the person's moral beliefs.

We introduce ‘behavioral posology’ as a novel modeling paradigm to analyze digital addictions through the concept of behavioral dose. As an example of this paradigm, a pharmacokinetic/pharmacodynamic model of a hypothetical digital behavior is presented, based on opponent process theory. Our simulation results replicate Solomon & Corbit’s model of affective dynamics from 1974, and the model predicts features of addiction such as hedonic allostasis, withdrawal, and apparent tolerance. Using Frequency Response Analysis, we show how behavior repetition may result in a hormetic dose-response relationship that depends on the frequency of the behavior and is moderated by the subject’s moral beliefs. The model can be experimentally validated using Ecological Momentary Assessment, allowing researchers to hypothesize, model, and test causal mechanisms for behavioral addictions. We also discuss the potential for behavioral posology as a clinical support tool in psychiatry, as this modeling framework may help to detect and limit behaviors being performed too frequently based on the person’s moral beliefs.

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 mobile health solutions for problematic pornography use. Little is also known about the content, structure, and features of these apps. Objective This study aims to characterize the purpose, content, and popularity of mobile apps that claim to manage pornography addiction. Methods The phrase “pornography addiction” was entered as a search term in the app stores of the two major mobile phone platforms (Android and iOS). App features were categorized according to a coding scheme that contained 16 categories. Apps were included in the analysis if they were described as helpful for reducing pornography use, and data were extracted from the store descriptions of the apps. Metrics such as number of user ratings, mean rating score, and number of installations were analyzed on a per-feature basis. 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 and minimum possible number of installations, were the ability to track the time since last relapse (apps with feature=72/170, 42.4%; minimum possible number of installations=6,388,000), tutorials and coaching (apps with feature=63/170, 37.1%; minimum possible number of installations=9,286,505), access to accountability partners or communities (apps with feature=51/170, 30%; minimum possible number of installations=5,544,500), content blocking or content monitoring (apps with feature=46/170, 27.1%; minimum possible number of installations=17,883,000), and a reward system for progress (apps with feature=34/170, 20%; minimum possible number of installations=4,425,300). Of these features, content-blocking apps had the highest minimum possible number of installations. Content blocking was also the most detected feature combination in a combinatorial analysis (with 28 apps having only this feature), but it also had the lowest mean consumer satisfaction rating (4.04) and second-lowest median rating (4.00) out of 5 stars. None of the apps reviewed contained references to literature that provided direct evidence for the app’s efficacy or safety. Conclusions There are several apps with the potential to provide low- or zero-cost real-time interventions for people struggling to manage problematic pornography use. Popular app features include blockers of pornographic content, behavior monitoring, and tutorials that instruct users how to eliminate pornography use. However, there is currently no empirical 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.

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. CLINICALTRIAL pornography; pornography addiction; m-Health; problematic pornography use (PPU); mobile intervention; Just-In-Time Adaptive Intervention; smartphone-based therapy; addiction; psychology; Internet addiction