Every iPhone needs a few good biaffect3 apps. We benchmark 167+ of them on App Store rating, freshness, and pricing, then surface the standouts so you don't have to scroll the App Store yourself.
This app helps you track your mood and cognitive function using your iPhone's keyboard and sensor data. It analyzes typing patterns to provide insights into mental health and assists researchers in developing better diagnostic tools for mood disorders.
This ResearchKit study explores the link between mood and cognitive function in bipolar disorder using typing patterns and passive sensor data. Both individuals with bipolar disorder and healthy participants are needed to help identify objective biomarkers.
This app helps college students navigate campus life by connecting them with essential resources and people. It offers features for scheduling appointments, managing to-dos and class schedules, finding campus locations, forming study groups, and accessing important academic documents.
BiAffect3 tops our biaffect3 apps ranking as of July 2026, with a 5.0-star average from 6 App Store ratings, and it's free to download.
Are there free biaffect3 apps for iPhone?
Yes — 20 of the top biaffect3 apps we rank are free to download, including BiAffect3 and BiAffect.
Does BiAffect3 have ads or in-app purchases?
BiAffect3 has no ads and has no in-app purchases, according to its App Store listing as of July 2026.
How many biaffect3 apps are on the App Store?
We track 167+ biaffect3 apps on the iOS App Store as of July 2026, ranked by rating, recency, and analysis of real user reviews.
What is BiAffect3?
BiAffect3 is a research-backed application designed to help users track their mood and neurocognitive functioning. It utilizes a custom virtual keyboard to collect typing metadata and sensor data, analyzing patterns to offer insights into mental health.
How does BiAffect3 track mood?
BiAffect3 employs a unique approach by analyzing keystroke dynamics, such as typing speed, errors, and pauses, collected through its virtual keyboard. This data, combined with sensor information, is processed by machine learning algorithms to identify patterns associated with different mood states.