Samantha J Yim1, Leanna M W Lui1, Yena Lee1, Joshua D Rosenblat2, Renee-Marie Ragguett1, Caroline Park3, Mehala Subramaniapillai1, Bing Cao4, Aileen Zhou1, Carola Rong5, Kangguang Lin6, Roger C Ho7, Alexandria S Coles1, Amna Majeed1, Elizabeth R Wong1, Lee Phan3, Flora Nasri1, Roger S McIntyre8. 1. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada. 2. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Department of Pharmacology, University of Toronto, Toronto, Canada. 3. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada. 4. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; School of Public Health, Peking University, Beijing 100191, P. R. China. 5. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Department of Psychiatry, University of Texas Health Science Center, Houston, Texas, USA. 6. Department of Affective Disorders, The Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Academician workstation of Mood and Brain Sciences, Guangzhou Medical University, China; GMH Institute of CNS Regeneration, Jinan University, Guangzhou, China; Laboratory of Neuropsychology and Laboratory of Social Cognitive Affective, Neuroscience, Department of Psychology, University of Hong Kong, Hong Kong. 7. Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore; Biomedical Global Institute of Healthcare Research & Technology (BIGHEART), National University of Singapore, Singapore 117599, Singapore; Centre of Excellence in Behavioral Medicine, Nguyen Tat Thanh University (NTTU), Ho Chi Minh City, 70000, Vietnam; Faculty of Education, Huaibei Normal University, 100 Dongshan Road, Huaibei, Anhui 235000, China. 8. Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Department of Pharmacology, University of Toronto, Toronto, Canada; Brain and Cognition Discovery Foundation, Toronto, Canada. Electronic address: roger.mcintyre@uhn.ca.
Abstract
BACKGROUND: Major Depressive Disorder (MDD) is a common and debilitating mood disorder. Individuals with MDD are often misdiagnosed or diagnosed in an untimely manner, exacerbating existing functional impairments. Ecological momentary assessment (EMA) involves the repeated sampling of an individual's symptoms within their natural environment and has been demonstrated to assist in illness assessment and characterization. Capturing data in this way would set the stage for improved treatment outcomes and serve as a complementary resource in the management and treatment of depressive symptoms. METHODS: Online databases PubMed/MedLine and PsycINFO were searched using PRISMA guidelines and combinations of the following keywords: EMA, depression, smartphone app, diagnosing, symptoms, phone, app, ecological momentary assessment, momentary assessment, data mining, unobtrusive, passive data, GPS, sensor. RESULTS: A total of nineteen original articles were identified using our search parameters and ten articles met the inclusion criteria for full-text review. Among the ten relevant studies, three studies evaluated feasibility, seven evaluated detection, and three evaluated treatment of MDD. LIMITATIONS: Limitations include that the design of all of the studies included in this review are non-randomized. It should be noted that most of the studies included were pilot studies and/or exploratory trials lacking a control group. CONCLUSIONS: Available evidence suggests that the use of passive smartphone-based applications may lead to improved management of depressive symptoms. This review aids the creation of new EMA applications, highlights the potential of EMA usage in clinical settings and drug development, emphasizes the importance for regulation of applications in the mental health field, and provides insight into future directions.
BACKGROUND: Major Depressive Disorder (MDD) is a common and debilitating mood disorder. Individuals with MDD are often misdiagnosed or diagnosed in an untimely manner, exacerbating existing functional impairments. Ecological momentary assessment (EMA) involves the repeated sampling of an individual's symptoms within their natural environment and has been demonstrated to assist in illness assessment and characterization. Capturing data in this way would set the stage for improved treatment outcomes and serve as a complementary resource in the management and treatment of depressive symptoms. METHODS: Online databases PubMed/MedLine and PsycINFO were searched using PRISMA guidelines and combinations of the following keywords: EMA, depression, smartphone app, diagnosing, symptoms, phone, app, ecological momentary assessment, momentary assessment, data mining, unobtrusive, passive data, GPS, sensor. RESULTS: A total of nineteen original articles were identified using our search parameters and ten articles met the inclusion criteria for full-text review. Among the ten relevant studies, three studies evaluated feasibility, seven evaluated detection, and three evaluated treatment of MDD. LIMITATIONS: Limitations include that the design of all of the studies included in this review are non-randomized. It should be noted that most of the studies included were pilot studies and/or exploratory trials lacking a control group. CONCLUSIONS: Available evidence suggests that the use of passive smartphone-based applications may lead to improved management of depressive symptoms. This review aids the creation of new EMA applications, highlights the potential of EMA usage in clinical settings and drug development, emphasizes the importance for regulation of applications in the mental health field, and provides insight into future directions.
Authors: John Torous; Sandra Bucci; Imogen H Bell; Lars V Kessing; Maria Faurholt-Jepsen; Pauline Whelan; Andre F Carvalho; Matcheri Keshavan; Jake Linardon; Joseph Firth Journal: World Psychiatry Date: 2021-10 Impact factor: 49.548
Authors: Laura Nykänen; Daniele Manfredini; Frank Lobbezoo; Antti Kämppi; Anna Colonna; Alessandra Zani; André Mariz Almeida; Alona Emodi-Perlman; Aslak Savolainen; Alessandro Bracci; Jari Ahlberg Journal: J Clin Med Date: 2022-06-15 Impact factor: 4.964
Authors: Maikel Luis Kolling; Leonardo B Furstenau; Michele Kremer Sott; Bruna Rabaioli; Pedro Henrique Ulmi; Nicola Luigi Bragazzi; Leonel Pablo Carvalho Tedesco Journal: Int J Environ Res Public Health Date: 2021-03-17 Impact factor: 3.390