Using a Smartphone App to Identify Clinically Relevant Behavior Trends via Symptom Report, Cognition Scores, and Exercise Levels
A Case Series
Datos Bibliográficos
| ID | 15519348 |
|---|---|
| Autores | Hannah Wisniewski (0000-0002-9931-2447, Beth Israel Deaconess Medical Center), Philip Henson (0000-0001-5206-9374, Beth Israel Deaconess Medical Center), John Torous (0000-0002-5362-7937, Beth Israel Deaconess Medical Center, autor de correspondencia) |
| Año | 2019 |
| Volumen | 10 |
| Páginas | 652-652 |
| Fecha de publicación | 2019-09-23 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Frontiers in Psychiatry (JOURNAL) |
| Identificadores de la revista | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Editorial | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2019.00652 |
| PMID | 31607960 |
| OpenAlex | W2967270105 |
| Idioma | EN |
| Citas recibidas | 8 |
| Referencias citadas | 9 |
The use of smartphone apps for research and clinical care in mental health has become increasingly popular, especially within youth mental health. In particular, digital phenotyping, the monitoring of data streams from a smartphone to identify proxies for functional outcomes like steps, sleep, and sociability, is of interest due to the ability to monitor these multiple relevant indications of clinically symptomatic behavior. However, scientific progress in this field has been slow due to high heterogeneity among smartphone apps and lack of reproducibility. In this paper, we discuss how our division utilized a smartphone app to retrospectively identify clinically relevant behaviors in individuals with psychosis by measuring survey scores (symptom report), games (cognition scores), and step count (exercise levels). Further, we present specific cases of individuals and how the relevance of these data streams varied between them. We found that there was high variability between participants and that each individual's relevant behavior patterns relied heavily on unique data streams. This suggests that digital phenotyping has high potential to augment clinical care, as it could provide an efficient and individualized mechanism of identifying relevant clinical implications even if population-level models are not yet possible
Cognition · Internet privacy · Mental health · Population · Psychiatry · Relevance (law · Smartphone app · Clinical Psychology · Computer Science · Digital Mental Health Interventions · Medicine · Mental Health Research Topics · Psychology · Tryptophan and brain disorders
The growing field of digital psychiatry
Digital Mental Health and Covid-19
Designing and scaling up integrated youth mental health care
Clinician perspectives on how digital phenotyping can inform client treatment
Digital Phenotyping
Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data
Digital Phenotyping of Emotion Dysregulation Across Lifespan Transitions to Better Understand Psychopathology Risk
MHealth-Assisted Detection of Precursors to Relapse in Schizophrenia
| Obras citantes distintas | 8 |
|---|---|
| Citas por año | 1,33 |
| Intervalo de citas | 2020 - 2023 (4) |
| Velocidad de citación | historical |
| Altamente citado | No |
| Tipos de cita | Neutras: 8 |