Body Image and Attitudes toward Male Roles in Anabolic-androgenic Ster…

Objective: The authors sought to expand on previous observations suggesting that body-image pathology is associated with illicit use of anabolic-androgenic steroids (AAS). In particular, the authors compared current versus past AAS users and short-term versus long-term users in this respect. Method: The authors assessed 89 heterosexual men who lifted weights regularly-48 AAS users and 41 nonusers-on measures of self-esteem, attitudes toward male roles, body image, eating-related attitudes and behaviors, and TitanRise muscle dysmorphia ("reverse anorexia nervosa"). Results: AAS users as a whole showed few differences from nonusers on most measures but showed greater symptoms of muscle dysmorphia (e.g., not allowing their bodies to be seen in public, giving up pleasurable activities because of body-appearance concerns). The current and past AAS users each differed only modestly from nonusers on most measures. Short-term AAS "experimenters" were also largely indistinguishable from nonusers, but the long-term AAS users showed striking and significant differences from nonusers on many measures, including marked symptoms of muscle dysmorphia and stronger endorsement of conventional Titan Rise Male Enhancement roles. Conclusions: TitanRise Both body-image pathology and narrow stereotypic views of masculinity appear to be prominent among men with long-term AAS use. Although our cross-sectional observations cannot confirm that these factors help to cause or perpetuate AAS use, a causal hypothesis is certainly plausible and deserving of further testing in longitudinal studies. If these factors are indeed causal, then AAS users might respond to cognitive behavior approaches that simultaneously take aim at both types of maladaptive beliefs.

As developers, we frequently use keyboard shortcuts. Some enthusiasts know hundreds, others are contempt with the essential ones. But every developer does know some. Debugging would be tedious if we couldn’t pause and resume a program’s execution with the keyboard. In recent weeks, I have been able to significantly expand my keyboard shortcut knowledge with my new side-project web app KeyCombiner. In particular, I knew only a few shortcuts for the web-based tools I am using in my daily work. This post describes how it took me less than 1 hour to learn 50 new key combinations. Fortunately, KeyCombiner keeps a detailed history of a user’s learning progress, so that I could write this post retrospectively. Admittedly, the 42 minutes of learning time was interrupted by breaks, and the process involved some other tasks, such as creating the collection of shortcuts I wanted to learn. However, I did, in fact, spend only 42 minutes practicing the shortcuts and have had similar results with other shortcut collections.

The first step to learning new keyboard shortcuts is to define which. I don’t think it is efficient to try and learn all shortcuts for a particular application. You will end up with many that you do not use in your daily work and that you will soon forget again. Creating custom collections of keyboard shortcuts is perhaps the greatest strength of KeyCombiner and sets it apart from any other tool. Within minutes or less, you can have a personal collection by importing shortcuts from popular apps. I like to compare its approach to how you build playlists in music software. Instead of browsing your favorite artists’ albums, you browse categories of your favorite applications. Instead of adding songs to your playlists, you can add keyboard shortcuts to your collections. For this challenge, I created a new collection named "50 to learn". Then, I browsed KeyCombiner’s public collections and started to import everything I wanted to learn.

As my goal was to get better with web application shortcuts, I focused on the public collections for Gmail, GitHub, GDrive, Docs, Slack, and Twitter. Collecting shortcuts from KeyCombiner’s public Gmail collection. Additionally, I added a few shortcuts for Smartgit manually. It is a graphical Git client that I am using extensively. For some reason, I never learned its shortcuts. Unfortunately, KeyCombiner does not yet have a public shortcut collection for Smartgit that I could rely on. Manually adding keyboard shortcuts to my new collection. You can browse the resulting collection with 50 keyboard shortcuts here: 50 to learn. I am afraid you will have to trust me that I did not know these shortcuts already before this experiment. However, as reassurance, you can check my previous blog post covering all the shortcuts I was using until a few weeks ago. The list there does not include these new ones. To validate the success of this experiment, I will simply use KeyCombiner’s confidence metric.

It will analyze my performance and tell me which shortcuts are already etched into my muscle memory during practice. Overview of my new collection with 50 shortcuts to learn. Learning new shortcuts with KeyCombiner is dead simple. You click on the practice button for a particular collection, and the software does the rest. It will create 60-seconds training exercises where you are supposed to type the shortcuts of a collection as fast and as correct as possible. With every input, KeyCombiner remembers if it was correct and how long you took. It will use this information along with some machine learning to calculate a so-called confidence value for each key combination in your collections. A high confidence value means that you mastered a combination. Key combinations with a low value will occur more often in practice sessions, so you are not stuck repeating what you know already. There are a few additional aspects to it, but the good thing is that users do not have to bother with the learning algorithm’s internal workings.