Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study.
FHIR
apps
digital health
digital phenotyping
health data
mHealth
mental health, mobile phone
mobile apps
mobile health
patient-generated health data
smartphones
wearables
Journal
JMIR mHealth and uHealth
ISSN: 2291-5222
Titre abrégé: JMIR Mhealth Uhealth
Pays: Canada
ID NLM: 101624439
Informations de publication
Date de publication:
07 01 2022
07 01 2022
Historique:
received:
19
05
2021
accepted:
11
11
2021
revised:
18
08
2021
entrez:
7
1
2022
pubmed:
8
1
2022
medline:
2
2
2022
Statut:
epublish
Résumé
There is a growing need for the integration of patient-generated health data (PGHD) into research and clinical care to enable personalized, preventive, and interactive care, but technical and organizational challenges, such as the lack of standards and easy-to-use tools, preclude the effective use of PGHD generated from consumer devices, such as smartphones and wearables. This study outlines how we used mobile apps and semantic web standards such as HTTP 2.0, Representational State Transfer, JSON (JavaScript Object Notation), JSON Schema, Transport Layer Security (version 1.3), Advanced Encryption Standard-256, OpenAPI, HTML5, and Vega, in conjunction with patient and provider feedback to completely update a previous version of mindLAMP. The Learn, Assess, Manage, and Prevent (LAMP) platform addresses the abovementioned challenges in enhancing clinical insight by supporting research, data analysis, and implementation efforts around PGHD as an open-source solution with freely accessible and shared code. With a simplified programming interface and novel data representation that captures additional metadata, the LAMP platform enables interoperability with existing Fast Healthcare Interoperability Resources-based health care systems as well as consumer wearables and services such as Apple HealthKit and Google Fit. The companion Cortex data analysis and machine learning toolkit offer robust support for artificial intelligence, behavioral feature extraction, interactive visualizations, and high-performance data processing through parallelization and vectorization techniques. The LAMP platform incorporates feedback from patients and clinicians alongside a standards-based approach to address these needs and functions across a wide range of use cases through its customizable and flexible components. These range from simple survey-based research to international consortiums capturing multimodal data to simple delivery of mindfulness exercises through personalized, just-in-time adaptive interventions.
Sections du résumé
BACKGROUND
There is a growing need for the integration of patient-generated health data (PGHD) into research and clinical care to enable personalized, preventive, and interactive care, but technical and organizational challenges, such as the lack of standards and easy-to-use tools, preclude the effective use of PGHD generated from consumer devices, such as smartphones and wearables.
OBJECTIVE
This study outlines how we used mobile apps and semantic web standards such as HTTP 2.0, Representational State Transfer, JSON (JavaScript Object Notation), JSON Schema, Transport Layer Security (version 1.3), Advanced Encryption Standard-256, OpenAPI, HTML5, and Vega, in conjunction with patient and provider feedback to completely update a previous version of mindLAMP.
METHODS
The Learn, Assess, Manage, and Prevent (LAMP) platform addresses the abovementioned challenges in enhancing clinical insight by supporting research, data analysis, and implementation efforts around PGHD as an open-source solution with freely accessible and shared code.
RESULTS
With a simplified programming interface and novel data representation that captures additional metadata, the LAMP platform enables interoperability with existing Fast Healthcare Interoperability Resources-based health care systems as well as consumer wearables and services such as Apple HealthKit and Google Fit. The companion Cortex data analysis and machine learning toolkit offer robust support for artificial intelligence, behavioral feature extraction, interactive visualizations, and high-performance data processing through parallelization and vectorization techniques.
CONCLUSIONS
The LAMP platform incorporates feedback from patients and clinicians alongside a standards-based approach to address these needs and functions across a wide range of use cases through its customizable and flexible components. These range from simple survey-based research to international consortiums capturing multimodal data to simple delivery of mindfulness exercises through personalized, just-in-time adaptive interventions.
Identifiants
pubmed: 34994710
pii: v10i1e30557
doi: 10.2196/30557
pmc: PMC8783287
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e30557Informations de copyright
©Aditya Vaidyam, John Halamka, John Torous. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 07.01.2022.
Références
Int Rev Psychiatry. 2021 Jun;33(4):394-403
pubmed: 33792463
Epidemiol Psychiatr Sci. 2020 Jan 31;29:e100
pubmed: 32000876
JMIR Mhealth Uhealth. 2020 Feb 3;8(2):e16741
pubmed: 32012102
Healthcare (Basel). 2014 May 06;2(2):220-33
pubmed: 27429272
NPJ Digit Med. 2020 Jan 23;3:9
pubmed: 31993507
Digit Biomark. 2020 Nov 26;4(Suppl 1):119-135
pubmed: 33442585
JMIR Ment Health. 2020 Mar 26;7(3):e18848
pubmed: 32213476
Asian J Psychiatr. 2021 Apr;58:102587
pubmed: 33618070
Database (Oxford). 2016 Feb 11;2016:
pubmed: 26868052
J Am Coll Health. 2021 Mar 26;:1-13
pubmed: 33769927
JMIR Ment Health. 2016 May 05;3(2):e16
pubmed: 27150677
Nurs Outlook. 2019 Jul - Aug;67(4):311-330
pubmed: 31277895
J Am Med Inform Assoc. 2016 Sep;23(5):899-908
pubmed: 26911829
Gen Hosp Psychiatry. 2020 Sep - Oct;66:59-66
pubmed: 32688094
J Psychiatr Pract. 2020 Mar;26(2):80-88
pubmed: 32134881
BJPsych Open. 2021 Jan 07;7(1):e29
pubmed: 33407986