| Literature DB >> 35295772 |
Tosca-Marie Heunis1, Stacey Bissell2, Anna W Byars3, Jamie K Capal4, Nola Chambers5, Sebastián Cukier6, Peter E Davis7, Liesbeth De Waele8,9, Jennifer Flinn10, Sugnet Gardner-Lubbe11, Tanjala Gipson12,13, J Christopher Kingswood14,15, Darcy A Krueger16,17, Aubrey J Kumm5, Mustafa Sahin7,18, Eva Schoeters19,20, Catherine Smith21, Shoba Srivastava22, Megumi Takei23, Stephanie Vanclooster1, Agnies M van Eeghen24,25, Robert Waltereit26, Anna C Jansen1,27, Petrus J de Vries5.
Abstract
Introduction: Tuberous Sclerosis Complex (TSC) is a multi-system genetic disorder with various TSC-Associated Neuropsychiatric Disorders (TAND) that significantly impact the mental health and wellbeing of individuals with TSC and their caregivers. TAND represents the number one concern to families worldwide, yet is highly under-identified and under-treated. The clinician-administered TAND-Checklist (Lifetime version, TAND-L) has improved identification of TAND in clinical settings. However, many individuals with TSC and their caregivers still have difficulty accessing suitable support for diagnosis and evidence-informed interventions. The TANDem study is a community-based participatory research project with a broad range of TSC stakeholders aimed at reducing the TAND identification and treatment gap.Entities:
Keywords: TSC-associated neuropsychiatric disorders (TAND); behavioural phenotypes; digital technology; health app; personalised medicine; rare diseases; tuberous sclerosis complex
Year: 2022 PMID: 35295772 PMCID: PMC8919327 DOI: 10.3389/fpsyt.2022.834628
Source DB: PubMed Journal: Front Psychiatry ISSN: 1664-0640 Impact factor: 4.157
TANDem project aims and objectives.
| Aim 1: To develop and validate a self-report, quantified TAND Checklist (TAND-SQ), and to build it into a smartphone application (app). | Objective 1.1 Generate a self-report TAND Checklist |
| Objective 1.2 Quantify the TAND Checklist | |
| Objective 1.3 Develop a smartphone app based on the TAND-SQ | |
| Objective 1.4 Validate self-completed TAND app data against expert clinical data | |
| Aim 2: To generate consensus clinical recommendations for the identification and treatment of TAND, and incorporate these into the TAND app. | Objective 2.1 Scoping review of existing literature on interventions for TAND |
| Objective 2.2 Generate consensus clinical recommendations for identification and treatment of TAND | |
| Objective 2.3 Generate a TAND toolkit based on literature and consensus recommendations | |
| Objective 2.4 Integration of the TAND toolkit into the TAND app | |
| Objective 2.5 Feasibility evaluation, including acceptability and appropriateness, of the final TAND app | |
| Aim 3: To build a scalable and sustainable global TAND consortium through networking, capacity-building and public engagement activities. | Objective 3.1 Conduct networking activities between all global collaborators |
| Objective 3.2 Capacity-building of emerging TAND researchers | |
| Objective 3.3 Public engagement activities to understand societal perspectives on TSC and TAND and to raise awareness of TAND and TSC | |
| Objective 3.4 Perform a multi-stakeholder review of the TAND app and integrated toolkit | |
| Objective 3.5 Plan and coordinate scale-up, scale-out and future TAND research |
Figure 1An overview of the TANDem project.
Figure 2TAND consortium.
Figure 3TAND consortium working groups. PI, principal investigator; Co-PI, Co-principal investigator.
Cluster groups and members.
| Autism spectrum disorder-like cluster | Nola Chambers (lead), Jamie Capal (co-lead), Eva Schoeters, Sebastián Cukier, Shoba Srivastava |
| Dysregulated behavior cluster | Tanjala Gipson (lead), Peter Davis (co-lead), Agnies van Eeghen |
| Eat/sleep cluster | Stacey Bissell (lead), Katie Smith (co-lead), Peter Davis |
| Mood/Anxiety cluster | Agnies van Eeghen (lead), Jamie Capal (co-lead), Megumi Takei, Robert Waltereit |
| Neuropsychological cluster | Anna Byars (lead), Jennifer Flinn (co-lead) |
| Overactive/impulsive cluster | Robert Waltereit (lead), Stacey Bissell (co-lead), Katie Smith, Megumi Takei |
| Psychosocial cluster | Stephanie Vanclooster (lead), Sebastián Cukier (co-lead), Chris Kingswood, Eva Schoeters, Katie Smith, |
| Scholastic cluster | Jennifer Flinn (lead), Peter Davis (co-lead), Shoba Srivastava |
Steps of data collection and analysis.
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| Step 1 | TAND-SQ pre-pilot study | ± 20 participants | Each participant and consortium member will complete a paper TAND-SQ and a checklist feedback form. | Mixed-methods analysis of feasibility data. This will inform final TAND-SQ design. |
| Step 2 | App phase I pre-pilot study | ± 20 TAND consortium members | Each consortium member will conduct user acceptance testing of phase I of the app, and complete an app feedback form. | Mixed-methods analysis of app feedback data. This will inform final app design before validation (step 3). |
| Step 3 | TAND-SQ validation study | ± 100 participants from BCH | BCH and CCH participants will complete a TSC Story, the TAND-SQ Checklist, and an app feedback form. | BCH/CCH data will be used to evaluate the external and predictive validity of the TAND-SQ by comparing app data to detailed phenotypic data collected as part of a TSC research project ( |
| Step 4 | App phase II pre-pilot study | ± 20 TAND consortium members | Each consortium member will conduct user acceptance testing of phase II of the app, and complete a toolkit feedback form. | Mixed-methods analysis of toolkit feedback data. This will inform final app design before feasibility evaluation. |
| Step 5 | App feasibility evaluation study | ± 40 participants from BCH, CCH, TSC Alliance, UZB, UZL | Each participant will conduct user acceptance testing of the full app (phases I and II), complete an app feedback form, a toolkit feedback form, and participate in a focus group/semi-structured interview. | Mixed-methods analysis of app and toolkit feedback data and framework analysis of qualitative data. |
BCH, Boston Children's Hospital; CCH, Cincinnati Children's Hospital; UZB, Universitair Ziekenhuis Brussel; UZL, Universitaire Ziekenhuizen Leuven.
Considerations when developing a GDPR compliant app for research purposes.
| 1. | Conduct a data protection impact assessment to ensure that all risks are identified, assessed and mitigated |
| 2. | Determine which data protection regulations are applicable based on the locations of all data collection and data processing sites involved |
| 3. | Create a data management plan |
| 4. | Create a data sharing agreement |
| 5. | Create a data processing agreement |
| 6. | Search for app developers who have expertise in developing and hosting apps in compliance with the GDPR |
| 7. | Sign a non-disclosure agreement with the app developers and a contract that ensures that one retains ownership of the app, intellectual property and data |
| 8. | Develop the app on an open-source platform rather than a proprietary/exclusive platform; this will allow one to more easily transfer the app development/support to another service provider in future should it be required |
| 9. | Develop an electronic informed consent for the app; in the app the app user must be able to view and download/print this document |
| 10. | Develop a privacy policy for the app; in the app the app user must be able to view and download/print this document |
| 11. | Develop terms of use for the app; in the app the app user must be able to view and download/print this document |
| 12. | The first step in the app is to have prospective app users read and agree to the electronic informed consent, privacy policy, and terms of use. Only once app users have provided this consent can any app user registration and other data be captured |
| 13. | The app should be password protected |
| 14. | The app and data administration panel must implement the necessary encryption protocols and strategies for data protection and security |
| 15. | App users must be able to control, access and delete all of their own app data on the mobile device and the storage servers |
| 16. | Multifactor authentication must be implemented for all means of accessing captured/stored data via the app data administration panel or secure cloud storage solution |
| 17. | Ensure that the location of the app hosting servers and the applicable data protection legislation in that country/state/region meet the requirements for GDPR compliance |
| 18. | Retain separate staging (testing) and production environments of the app data administration panel, this way all ongoing iterative development and testing can be done in the staging environment without affecting the app and live data in the production environment |
| 19. | Ensure that only the data controllers/processors have access to the live (real person) data in the production environment. If support is required from an app developer, server manager, or other third party, ensure that a sufficient data processing agreement has been signed by all parties involved |
Figure 4TANDem project impact loop.