에 의해 게시 University of Illinois
1. In this ResearchKit study, we would like to understand the relationship between mood and neurocognitive functioning in bipolar disorder using keystroke dynamics (such as typing speed and typing errors) and other passive sensor information (such as the accelerometer).
2. For this reason, we want to investigate if keyboard dynamics and sensor data from iPhone serve as more objective biomarkers or 'virtual mental-health footprints' of bipolar disorder and mood in general.
3. Currently, diagnosis and treatment of bipolar disorder rely on careful history taking and examination by a clinician, at times aided by self-report or caretaker-informed questionnaires.
4. You don't have to be a person with bipolar disorder to help us test this app—we also need healthy participants to use the app and serve as a control group.
5. In general, these reports have to be interpreted by providers in order to extract patterns that could indicate an imminent change in mood.
6. On the other hand, the pervasive use of mobile wireless devices has significantly shaped interpersonal communications in modern life.
7. Indeed, as personal smartphone technology advances, people are increasingly interacting with one another via typed (rather than oral) communications.
8. Moreover, they do not necessarily represent objective psycho-physiological markers.
9. Thank you for trying BiAffect.
또는 아래 가이드를 따라 PC에서 사용하십시오. :
PC 버전 선택:
소프트웨어 설치 요구 사항:
직접 다운로드 가능합니다. 아래 다운로드 :
설치 한 에뮬레이터 애플리케이션을 열고 검색 창을 찾으십시오. 일단 찾았 으면 BiAffect 검색 막대에서 검색을 누릅니다. 클릭 BiAffect응용 프로그램 아이콘. 의 창 BiAffect Play 스토어 또는 앱 스토어의 스토어가 열리면 에뮬레이터 애플리케이션에 스토어가 표시됩니다. Install 버튼을 누르면 iPhone 또는 Android 기기 에서처럼 애플리케이션이 다운로드되기 시작합니다. 이제 우리는 모두 끝났습니다.
"모든 앱 "아이콘이 표시됩니다.
클릭하면 설치된 모든 응용 프로그램이 포함 된 페이지로 이동합니다.
당신은 아이콘을 클릭하십시오. 그것을 클릭하고 응용 프로그램 사용을 시작하십시오.
다운로드 BiAffect Mac OS의 경우 (Apple)
다운로드 | 개발자 | 리뷰 | 평점 |
---|---|---|---|
Free Mac OS의 경우 | University of Illinois | 11 | 4.64 |
PC를 설정하고 Windows 11에서 BiAffect 앱을 다운로드하는 단계:
Thank you for trying BiAffect. In this ResearchKit study, we would like to understand the relationship between mood and neurocognitive functioning in bipolar disorder using keystroke dynamics (such as typing speed and typing errors) and other passive sensor information (such as the accelerometer). You don't have to be a person with bipolar disorder to help us test this app—we also need healthy participants to use the app and serve as a control group. Currently, diagnosis and treatment of bipolar disorder rely on careful history taking and examination by a clinician, at times aided by self-report or caretaker-informed questionnaires. In general, these reports have to be interpreted by providers in order to extract patterns that could indicate an imminent change in mood. Moreover, they do not necessarily represent objective psycho-physiological markers. On the other hand, the pervasive use of mobile wireless devices has significantly shaped interpersonal communications in modern life. Indeed, as personal smartphone technology advances, people are increasingly interacting with one another via typed (rather than oral) communications. For this reason, we want to investigate if keyboard dynamics and sensor data from iPhone serve as more objective biomarkers or 'virtual mental-health footprints' of bipolar disorder and mood in general.
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