N-of-1 trials to improve sleep 2024

Overview
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Traditionally, treatment guidelines and health intervention recommendations are developed based on results of large cohort studies or randomized controlled trials (RCTs). However, the analysis of such studies only yields estimates of average effects. Hence, these results do not allow meaningful predictions whether an intervention will help a given single individual. In the advent of digital solutions, personalized approaches have been on the rise. N-of-1 trials and other modern study designs allow to derive individual treatment effects, but also to use the data to obtain and improve the precision of population-level effect estimates of health interventions.

From week 2-4, every student will plan and then later perform an N-of-1 trial and investigate the impact of self-determined interventions on sleep quality. The trial will be designed, implemented, and then run using the StudyU platform (studyu.health). This data will be anonymized, used throughout the course to illustrate the statistical methods, and potentially used in a joint publication afterwards.

Feasibility of N-of-1 trials to improve sleep using the StudyU application (https://studyu.health). This study was conducted during the course "Digital N-of-1 trials and their application" at Hasso Plattner Institute inthe summer term 2024.

Sponsor: No sponsor defined

SEEK ID: https://ldh.hpi.imise.uni-leipzig.de/projects/5

Public web page: https://studyu.health

NFDI4Health PIs: No PIs for this Trial Project

Trial Project created: 14th Nov 2024

Extended Metadata (Nfdi4Health MDS 3.3)

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Resource classification(*)

Type of the resource(*) : Study

Title(s)/name(s) of the Project

Title/name(*) : N-of-1 trials to improve sleep 2024
Language of the title/name(*) : English

Acronym(s) of the Project

Acronym(*) : NIS-2024
Language of the acronym(*) : English

Description(s) of the Project

Description(*) : Traditionally, treatment guidelines and health intervention recommendations are developed based on results of large cohort studies or randomized controlled trials (RCTs). However, the analysis of such studies only yields estimates of average effects. Hence, these results do not allow meaningful predictions whether an intervention will help a given single individual. In the advent of digital solutions, personalized approaches have been on the rise. N-of-1 trials and other modern study designs allow to derive individual treatment effects, but also to use the data to obtain and improve the precision of population-level effect estimates of health interventions. From week 2-4, every student will plan and then later perform an N-of-1 trial and investigate the impact of self-determined interventions on sleep quality. The trial will be designed, implemented, and then run using the StudyU platform (studyu.health). This data will be anonymized, used throughout the course to illustrate the statistical methods, and potentially used in a joint publication afterwards. Feasibility of N-of-1 trials to improve sleep using the StudyU application (https://studyu.health). This study was conducted during the course "Digital N-of-1 trials and their application" at Hasso Plattner Institute inthe summer term 2024.
Language of the description(*) : English

Keyword(s) describing the Project

Keyword(*) : sleep

Language(s) of the project : English

Web page of the project : https://hpi.de/studium/im-studium/lehrveranstaltungen/it-systems-engineering-ma/lehrveranstaltung/sose-24-3977-digital-n_of_1-trials-and-their-application.html

Contributor(s) of the Project

Is it a personal or organisational contribution?(*) : Personal
Details about the contributing organisation(s)/institution(s)/group(s)
Contributor type(*) : Contact
Funding identifier(s) : Not specified
Name of the organisation/institution/group(*) : Hasso Plattner Institute
Details about the contributing person(s)
Contributor type(*) : Principal investigator
Given name(*) : Stefan
Family name(s)(*) : Konigorski
Digital identifier(s)
Identifier(*) : 0000-0002-9966-6819
Scheme(*) : ORCID
Email address : stefan.konigorski@hpi.de
Phone number : +49 331 5509-4873
Organisation(s) associated with the contributor
Name(*) : Hasso Plattner Institute
Address : Not specified
Web page : https://www.hpi.de
Digital identifier(s)
Identifier(*) : 058rn5r42
Scheme(*) : ROR

Alternative identifiers

Type of the registry(*) : Not specified
Identifier(*) : Not specified

Collects nutritional data? : true

Characteristics of the Project

Is it an interventional or non-interventional Project? : Interventional
Specification of the type of the Project
Interventional Project type : Crossover
Non-interventional Project type : []
Primary health condition(s) or disease(s) considered in the Project
Primary health condition or disease name(*) : Sleep quality
Terminology/classification(*) : ICD-10
Code of the health condition or disease : F51.0
Groups of diseases or conditions(*)
Which groups of diseases or conditions were the data collected on? : Other
On which diseases or conditions were the data collected? : Lower level ICD-10 (https://icd.who.int/browse10/2019/en), with autocomplete
Collects mortality data? : No
Administrative information about the Project
Status of the ethics committee approval : Request for approval submitted, approval granted
Overall Project status : Completed: Recruitment, data collection, and data quality management completed normally
Participants enrolled by invitation? : No
Start date : Not specified
End date : Not specified
Mono- or multicentric? : Monocentric
Number of centers : 1
One or more data providers? : One data provider
Number of data providers : 1
Primary subject(*) : Person
Eligibility criteria for Project participants
Eligibility criteria: Minimum age
Minimum eligible age(*) : 20
Unit of age(*) : Years
Eligibility criteria: Maximum age
Maximum eligible age(*) : 35
Unit of age(*) : Years
Eligible gender : Male, Female, Diverse
Inclusion criteria : Healthy, between 20 and 35 years old, enrolled at HPI as a master student participating in the seminar "Digital N-of-1 trials and their application" in summer semester 2023, proficient in English (at least C1 level)
Exclusion criteria : Comorbidities that could potentially be destabilised when sleeping less well under a given intervention, acute psychological illness, acute major infection, or acute major metabolic, kidney, or any other serious condition, allergies/intolerances/sensitivities to animal-based protein or plant-based protein supplements, herbal tea, milk, or cranberry juice
Population of the Project(*)
Coverage : Regional
Country(ies) : Germany
Region(s) and/or city(ies) : Potsdam
Interventions of the Project
Name of the intervention(*) : Cold milk vs Cold water
Type of the intervention : Dietary supplement (e.g., vitamins, minerals)
Additional information about the intervention : 200ml, at 11pm
Name(s) of the arm(s) associated with the given intervention : Not specified
Name of the intervention(*) : 2x white noise vs. regular environment
Type of the intervention : Behavioral (e.g., psychotherapy, lifestyle counseling)
Additional information about the intervention : Not specified
Name(s) of the arm(s) associated with the given intervention : Not specified
Name of the intervention(*) : 1x allergy pills vs no allergy pills.
Type of the intervention : Drug (including placebo)
Additional information about the intervention : Not specified
Name(s) of the arm(s) associated with the given intervention : Not specified
Name of the intervention(*) : Drinking caffeine vs. no caffeine
Type of the intervention : Dietary supplement (e.g., vitamins, minerals)
Additional information about the intervention : Not specified
Name(s) of the arm(s) associated with the given intervention : Not specified
Name of the intervention(*) : Dinner vs. no dinner
Type of the intervention : Dietary supplement (e.g., vitamins, minerals)
Additional information about the intervention : calory intake
Name(s) of the arm(s) associated with the given intervention : Not specified
Exposures of the Project
Name of the exposure : Not specified
Type of the exposure : Not specified
Additional information about the exposure : Not specified
Name(s) of the group(s) associated with the given exposure : Not specified
Outcome measures in the Project
Name of the outcome measure(*) : How well did you sleep last night?
Description of the outcome measure : 1 is worst sleep ever, 10 is best sleep ever
Type of the outcome measure : Primary
Time point(s) of assessment : Daily
Name of the outcome measure(*) : How many hours did you sleep last night?
Description of the outcome measure : Measured in hours, ranging from 0 to 10 hours.
Type of the outcome measure : Secondary
Time point(s) of assessment : Daily
Name of the outcome measure(*) : How long did it take you to fall asleep (in minutes)?
Description of the outcome measure : Measured on a scale from 0 to 6, representing time ranges from less than 15 minutes to over 120 minutes.
Type of the outcome measure : Secondary
Time point(s) of assessment : Daily
Name of the outcome measure(*) : How deep did you sleep last night?
Description of the outcome measure : Measured by choice selection with the options: Not fast, Average fast, Fast, I don't know
Type of the outcome measure : Secondary
Time point(s) of assessment : Daily
Name of the outcome measure(*) : How rested do you feel after last night's sleep
Description of the outcome measure : Measured on a scale from 1 to 5, where 1 is the worst and 5 is the best.
Type of the outcome measure : Secondary
Time point(s) of assessment : Daily
Name of the outcome measure(*) : How many times did you wake up last night?
Description of the outcome measure : Measured on a scale from 1 to 3. Additionally, >3 and I don't know are available on the scale.
Type of the outcome measure : Secondary
Time point(s) of assessment : Daily
Name of the outcome measure(*) : How well did you sleep last night?
Description of the outcome measure : Measured by choice selection with the options: Bad, Not Bad
Type of the outcome measure : Secondary
Time point(s) of assessment : Daily
Name of the outcome measure(*) : How well do you expect to sleep tonight?
Description of the outcome measure : 1 is worst sleep ever, 10 is best sleep ever. Scale from 1 to 10
Type of the outcome measure : Other
Time point(s) of assessment : Daily
Additional information about the Project : Not specified
Additional assessments/measurements in the Project : Diet/Nutrition
Data sharing strategy of the Project(*)
Is it planned to share the data?(*) : Yes, there is a plan to make data available
Supporting documents available in addition to the data : Study protocol
When and for how long will the data be available? : Anytime
Criteria for data access : Anyone
Additional information about data sharing : Available in the StudyU Registry of the StudyU Designer at https://designer.studyu.health
DataSHIELD/Opal infrastructure available? : No
Link to data request application : https://designer.studyu.health
Web page with additional information about data sharing : https://designer.studyu.health
Record linkage possible? : false
Non-interventional aspects of the Project
Temporal design : Prospective
Target follow-up duration of the Project
Target follow-up duration(*) : 4
Unit of time(*) : Weeks
Number of follow-ups conducted : 28
Biosamples retained in a biorepository : None retained
Specific types of retained biosamples : Not specified
Interventional aspects of the Project
Numerical phase : Not applicable
Masking of intervention(s) assignment
Masking implemented? : false
Who is masked? : []
Additional information about masking : Not specified
Type of allocation of participants to an arm : Randomized
Off-label use of a drug product : No

Annotated Properties

Topic annotations

Preclinical and clinical studies

N-of-1 trials to improve sleep 2024

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