The Effects of a Personalized Sleep Improvement Smartphone Application From SleepScore Labs: A Single-blinded Randomized Waitlist Controlled Trial
Phase: NA
Conditions: Health Behavior
Sponsor: ResMed

 
 
 
SleepScore Labs _ ResMed Project Protocol 052722DSDT 
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The effects of a personalized sleep improvement App from SleepScore Labs: a 
single-blinded randomized waitlist controlled trial 
 
 
Title 
The effects of a personalized sleep improvement App 
from SleepScore Labs: a single-blinded randomized 
waitlist controlled trial 
 
Protocol version identifier 
Apr 28, 2023 (replaces previous version from Jan 26, 
2023) 
Product 
SleepScore Labs App commercially distributed for 
measuring sleep and providing sleep advice based on 
a dynamic and personalized advice engine. 
Sponsor 
ResMed Germany Inc., Fraunhoferstr. 16, 82152 
Martinsried, Germany 
Research question and 
objectives 
Primary objective:  
- 
To assess changes in subjective sleep quality 
measured using validated questionnaires (SF-B) 
between active sleep-improvement condition 
through the Dein Schlaf. Dein Tag. App and waitlist 
condition after 6 weeks and 12 weeks  
Secondary objectives: 
- 
To assess changes in further metrics between app 
condition and waitlist condition comparing baseline 
with 6 weeks and 12 weeks follow-up including 
feeling of being refreshed in the morning, overall 
sleep complaints, and health measures using 
validated questionnaires (PSQI, PSS, SF-12, SF-
B). Within the app condition, six objective sleep 
parameters (sleep onset latency, wake after sleep 
onset, total sleep time, sleep efficiency, sleep 
staging, nighttime awakenings) will be assessed by 
comparing baseline with 6 weeks and 12 weeks 
follow-up. 
Country of study 
Germany 
Author 
 Christoph Schöbel (Principal Investigator) 
 Elie Gottlieb (Primary SSL) 
 Daniela Ehrsam-Tosi (Primary ResMed) 
 Susanne Fischer (Secondary ResMed) 
 
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Marketing authorisation holder(s) 
Marketing authorisation 
holder(s) 
Legal representative in Europe: ResMed Germany Inc., 
Fraunhoferstr. 16, 82152 Martinsried, Germany 
Phone: +49 (0) 89 99 01-001 
Fax: +49 (0) 89 99 01-10 08 
 
 
 
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1. Table of contents 
 
1. Table of contents .............................................................................................................. 3 
2. List of abbreviations ......................................................................................................... 5 
2.1 Declaration of Helsinki and Signatures ............................................................... 6 
3. Abstract .............................................................................................................................. 7 
4. Amendments and updates .............................................................................................. 9 
5. Milestones ....................................................................................................................... 10 
6. Rationale and background ............................................................................................ 11 
7. Research question and objectives .............................................................................. 12 
8. Intensity of Use ............................................................................................................... 12 
9. Research methods ......................................................................................................... 12 
9.1 Study design .......................................................................................................... 12 
9.2 Randomization and allocation concealment ..................................................... 13 
9.3 Blinding ................................................................................................................... 13 
9.4 Type/Design of study............................................................................................ 13 
9.4.1 
CONSORT Study Flow Diagram ..................................................................... 14 
9.4.2 
Primary Endpoint ........................................................................................... 14 
9.4.3 
Secondary Endpoints .................................................................................... 15 
9.4.4 
Study population ............................................................................................ 15 
9.4.5 
Study duration ................................................................................................ 16 
9.4.6 
Withdrawal from the trial ............................................................................... 16 
9.4.7 
Compensation ................................................................................................ 16 
9.4.8 
Sleep Measurement ...................................................................................... 16 
9.4.9 
Intervention and Control ............................................................................... 16 
9.4.10 
Inclusion of users and baseline parameters .............................................. 17 
9.4.11 
Assessments Table ....................................................................................... 18 
9.5 Data sources.......................................................................................................... 18 
9.5.1 
Data obtained from users ............................................................................. 18 
9.5.2 
Data imported from the Dein Schlaf. Dein Tag. App ................................ 18 
9.6 Study size ............................................................................................................... 18 
9.7 Data management ................................................................................................ 19 
9.7.1 
Personal data and data protection .............................................................. 19 
9.8 Data analysis ......................................................................................................... 20 
9.9 Quality control........................................................................................................ 20 
9.9.1 
Access to Source Data/Document .............................................................. 20 
9.9.2 
Quality assurance: ......................................................................................... 21 
9.10 Study monitoring ................................................................................................... 21 
9.10.1 
Audit ................................................................................................................. 21 
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9.11 Limitations of the research methods .................................................................. 21 
9.12 Other aspects ........................................................................................................ 21 
10. Protection of human subjects ....................................................................................... 21 
11. Management and reporting of Adverse Events/Adverse Reactions ...................... 22 
12. Plans for disseminating and communicating study results....................................... 22 
Annex 1. Questionnaires ....................................................................................................... 22 
Annex 2. References .............................................................................................................. 22 
 
 
 
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2. List of abbreviations 
Abbreviation 
Definition 
GCP 
Good Clinical Practice 
GDPR 
The General Data Protection Regulation 
ICH-GCP 
International Conference on Harmonisation on Good Clinical Practice 
IEC 
Independent Ethics Committee 
IRB 
Institutional Review Board 
 
 
PSQI 
Pittsburgh Sleep Quality Index 
PSS 
Perceived Stress Scale 
SF-12 
Short Form 12 Health Survey Questionnaire 
SF-B 
Schlaffragebogen B 
SOP 
Standard Operating Procedure 
 
 
 
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2.1 Declaration of Helsinki and Signatures 
The undersigned have read this protocol and agreed to conduct this study in accordance 
with all stipulations of the protocol and in accordance with the Declaration of Helsinki. 
 
Date 
Name and Signature 
 
 
 
 
___________________ 
______________________________  
 
Elie Gottlieb (Applied Sleep Scientist, SleepScore Labs) 
 
 
 
 
 
___________________ 
______________________________ 
 
Daniela Ehrsam-Tosi (Director Medical Affairs CRS, 
 
ResMed) 
 
 
 
___________________ 
______________________________ 
Susanne Fischer (Senior Manager Medical Affairs CRS, 
ResMed) 
 
 
 
 
___________________ 
______________________________ 
Christoph Schöbel (Univ.-Prof. Dr. med., Principal 
Investigator) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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3. Abstract 
Title 
The effects of a personalized sleep improvement 
App from SleepScore Labs: a single-blinded 
randomized waitlist controlled trial 
 
Rationale and 
Background 
The Dein Schlaf. Dein Tag. sleep improvement App 
powered by SleepScore has not yet been 
empirically validated in the long-term assessment 
and management of people in the general 
population suffering from sleep complaints but did 
not yet fulfill the clinical criteria of an insomnia 
disorder. 
Research 
objectives 
Primary objective: 
Assessed at baseline, 6 weeks and 12 weeks, 
improvements in self-reported sleep quality while 
using a dynamic and personalized sleep advice 
engine app will be compared to a waitlist control 
group. 
Secondary objectives: 
Assessed at baseline, 6 weeks and 12 weeks, 
improvements in mental health, sleep problems, and 
stress will be compared to a waitlist control group. In 
the intervention (app) group, changes in objectively 
measured sleep parameters (sleep onset latency, 
wake after sleep onset, total sleep time, sleep 
staging, nighttime awakenings) over 6 and 12 weeks 
will be studied . 
 
Study design 
Single-blinded randomized waitlist controlled trial 
Population 
Inclusion criteria: 
 iOS user, or user of one of the following: Samsung 
Galaxy S7 Edge and S7,  S8, S8+, S9, S9+, S20, 
S20 FE, S20+, S21 Ultra Samsung Galaxy Note 8 
and 9 
 Regensburg Insomnia Scale (subclinical insomnia: 
Score 13-24) 
 ≥ 18 years old. 
 Naive to the SleepScore Labs App  
 Able to fully understand information on data 
protection and provide written informed consent 
 Not included in other treatments targeting sleep 
Exclusion criteria: 
 Clinically significant sleep disorders (e.g. Restless 
legs disorders, sleep-related breathing disorders, 
parasomnias) 
 Bedtime less than 6 hours 
 Any of the following medical problems: 
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o Current mental disorder affecting sleep 
(e.g., depression, anxiety disorders, bipolar 
disorder, schizophrenia)  
o Current severe medical conditions (e.g. 
chronic pain, cancer) 
 
 Any of the following medications/substance use: 
o Prescription of sleep medication or over-
the-counter 
sleep 
medication 
(e.g., 
melatonin, “Hoggar night”) 
o Medication for other conditions (e.g., 
anxiety, ADHD) that affect sleep, e.g., 
antidepressants, antipsychotics. 
o Consumption of 3+ units of alcohol on 4 or 
more nights per week 
o Recreational drug use 
 Pregnant or nursing mothers  
 Infant or child under age 1 at home 
 Shift work 
 Current use of other Sleep Tracking App’s 
 
Variables 
Questionnaire data will be collected at baseline, after 
6 and 12 weeks. 
Objective sleep data will be collected using the 
SleepScore App, and stored within the Microsoft 
Azure cloud system, an ISO 27001 certified and SOC 
1 and SOC 2 compliant cloud service provider.  Self-
reported questionnaire data will be captured using an 
EU data center of Forsta/Decipher which is GDPR 
compliant, ISO 27001 certified, with SOC2 accredited 
data centers. 
 
Data sources 
Data obtained from users:  
Baseline, 6-weeks, and 12-weeks measurements 
derived from validated questionnaires  
Data imported from the Dein Schlaf. Dein Tag. App:  
Objective sleep data, daily logging, and usage 
monitoring data recorded by the Dein Schlaf. Dein 
Tag. App 
Study size 
Overall, 600 users (300 app condition, 300 waitlist 
control group) will be included in Germany.  
 
Data analysis 
The questionnaire date will be analyzed by an 
ANOVA, with one factor (app condition vs. waiting list 
control group) and time (Baseline, 6-weeks, 12-
weeks). To handle missing values, a mixed-model 
approach with the same factors will be applied. The 
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critical statistical test would be the interaction term 
between app condition and the waiting-list control 
group. 
Objectively measured data will be analyzed using 
multi-level modeling (MLM) in a nested way (nights 
within participants),  allowing for analyzing changes 
within the intervention (app) group. To evaluate 
changes in primary and secondary outcome 
measures assessed by validated scales at baseline, 
6-weeks, and 12-weeks, we will compare within-group 
differences for each time-point using a t-test if 
normally distributed, or the Mann Whitney-U test for 
non-normally distributed continuous data. In addition, 
effect sizes for the changes within both groups 
(questionnaire data) and app group (objective sleep 
parameters) will be computed. 
 
 
4. Amendments and updates 
Amendment No. 1, Jan 26 2023 
Amendment No. 2, Apr 28 2023 
Section  
Amendment or update detail 
Reason 
5 Milestones 
Dates changed 
Adapted according to 
preparation of study 
9.4 Design 
Deleted “written”, added “A 
participant information document 
with consent form will be provided 
with page-turning function. 
Participant has to agree that 
his/her data will be used in this 
study by clicking “yes, I agree” or 
“no I don`t agree and do not want 
to participate”.” 
Electronic consent ensures a 
that participants can follow a 
defined workflow and pathway 
and will be guided smoothly 
through the recruitment portal. 
This avoids that too many 
potential participants are being 
lost during the course of 
recruitment. 
9.4.1 
Adapted flowchart: Informed 
Consent – then baseline 
assessment – then randomization 
Now follows the routine in study 
practice. Before the 
randomization should have 
occurred before the baseline 
assessment. Since 
randomization means access to 
the app for the “app group”, the 
timepoint of filling out the 
baseline questionnaire could 
have been after first app usage. 
This will be avoided when doing 
baseline before randomization 
and app download.  
9.5.1 Data  
Deleted Munich Chronotype 
Questionnaire 
Conversion into electronic 
format problematic; would have 
compromised the outcomes 
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9.5.1 Data 
Deleted Landecker Inventar zur 
Erfassung von Schlafstörungen 
(LISST) 
Study flow and assessment of 
participants for eligibility in the 
e-portal required a combination 
of in-/exclusion criteria with the 
LISST to ensure all screening 
criteria could be met. LISST is 
not required to be applied as a 
separate questionnaire. 
9.7.1 Data 
management 
Deleted […]„forwarded to 
SleepScore, there combined with 
objectively measured sleep data, 
and then securely shared with 
ResMed where it will be stored for 
10 years.” Added: “Objective sleep 
data from the app as collected and 
controlled by SleepScore Labs and 
self-reported questionnaire data as 
collected by a vendor on behalf of 
ResMed Objective sleep data from 
the Dein Schlaf. Dein Tag. App as 
specified in this protocol, and 
without personal identifiers will be 
send to vendor, combined with 
self-reported questionnaire data by 
vendor and equipped with new 
identifiers to result in a de-
identified set of study data. 
[…]shared with Sleepscore Labs 
for joint analysis.” Added: “De-
identified study data will be stored 
at ResMed for 10 years.” 
Changes were necessary to be 
compliant with EU-GDPR. 
Processes had to be 
restructured to prevent ResMed 
from potentially getting access 
to personal data of study 
participants.   
Annex 1 
Deleted LISST 
Not required as separate 
questionnaire. Accurate 
recruitment of participants 
through automated electronic 
assessment made a fusion of 
eligibility criteria necessary. 
Annex 1 
Deleted Munich Chrontype 
Questionnaire 
Conversion into electronic 
format problematic; would have 
compromised the outcomes 
Inclusion criteria Added “Samsung Galaxy S7 Edge 
and S7,  S8, S8+, S9, S9+, S20, 
S20 FE, S20+, S21 Ultra Samsung 
Galaxy Note 8 and 9” 
Increasing the number of active 
users of the application by also 
allowing other devices which 
have been validated to deliver a 
similar performance in running 
the app. 
 
 
5. Milestones 
 
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Milestone 
Planned date 
Start of enrolment 
January 2023 (actual) 
End of enrolment 
May 2023 (anticipated) 
Final results  
July/August 2023 (anticipated) 
 
6. Rationale and background 
 
Sleep disturbances have a documented impact on general mental and physical well-being 
across many domains (e.g., Cappuccio et al., 2010; Dinges et al., 1997; Knutson et al., 
2007). It has been estimated that approximately 20-35% of individuals report moderate 
symptoms of poor sleep (Hillman and Lack, 2013). Sleep-wake dysfunction contributes to 
the pathogenesis and evolution of neuropsychiatric conditions including depression, 
anxiety, and chronic pain, and are associated with an increased risk of chronic 
neurological health conditions including cerebrovascular disease, cardiovascular disease 
and neurodegenerative disorders. Those with poor sleep may experience general 
dissatisfaction with sleep quality or quantity which may be associated with minor to 
moderate difficulty initiating or maintaining adequate sleep (Benca et al., 2005).  
 
The cause of sleep-wake dysfunction may be attributed to a myriad of environmental (e.g., 
excessive bright light before sleep), cognitive (e.g., pre-sleep worrying and anxiety), 
behavioral (e.g., caffeine or alcohol consumption prior to sleep), and physiological factors 
(e.g., pre-sleep arousal). A variety of sleep improvement options exist for poor sleep 
including pharmacological interventions, over-the-counter sleep aids, and cognitive and 
behavioral interventions. While the preferred treatment for those with clinical threshold 
poor sleep is cognitive behavioral therapy for insomnia (CBT-i) delivered in person, this 
effective approach is both time-consuming and costly, with implementation limited by an 
unmet need for trained providers (Espie et al., 2001). Furthermore, studies suggest that 
many individuals with sleep difficulties do not seek professional help and report a 
preference for non-pharmacological and personalized, self-help strategies (Vincent and 
Lionber, 2001, Morin et al., 2006). Despite this, few evidenced-based sleep improvement 
digital programs exist founded on the principles of behavioral change psychology and 
sleep hygiene targeting non-clinical populations for primary prevention.  
 
Due to poor sleep’s multitude of causes (e.g., environmental, cognitive, behavioral, or 
physiological) and symptoms (e.g., long sleep onset latency, short sleep duration, elevated 
wake after sleep onset and nighttime awakenings, poor sleep quality), a personalized and 
dynamic sleep improvement program tailored to a user’s lifestyle and specific needs may 
be required to help nudge users towards sleep-promoting behaviors and attitudes. Digital 
and mobile health platforms are also highly scalable and cost-effective, thus allowing for 
widespread implementation across larger populations. 
 
The purpose of the present study is to conduct a randomized controlled trial to evaluate a 
digital smartphone application, the Dein Schlaf. Dein Tag. application powered by 
SleepScore, designed to improve sleep and sleep-permissive behaviors. The Dein Schlaf. 
Dein Tag smartphone application utilizes a dynamic and personalized sleep advice engine 
founded in the principles of behavior change psychology and sleep hygiene that offers a 
novel, non-invasive and non-pharmacological solution designed to improve sleep 
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outcomes in those with common sleeping difficulties. While the sleep measurement 
technology behind Dein Schlaf. Dein Tag. has already been previously validated against 
gold-standard polysomnography (Zaffaroni et al., 2019), its sleep improvement features 
have not yet been empirically validated in the long-term assessment and management of 
people in the general population with a broader range of sleep problems. 
7. Research question and objectives 
 
The primary objective of the present study is to examine improvement regarding self-
reported sleep quality when using the Dein Schlaf. Dein Tag.  sleep improvement engine 
App when compared to a waitlist condition. Validated questionnaires will be administered to 
assess self-reported sleep quality, health perception, psychosocial factors and sleep-
permissive behaviors (i.e., preventative health). 
 
 
Due to the dynamic and personalized nature of the intervention and its potential impact on 
various sleep-wake parameters depending on the user’s objectively measured sleep (only 
app condition) and self-assessed goals, we have chosen multiple secondary endpoints: 
sleep onset latency, wake after sleep onset and total sleep time will be used to determine 
sleep changes.  
 
 
Participants will be required to complete the following tasks during the study period: 
1. Start the sleep tracking device via a smartphone app before lying down in bed and 
turning off the lights to go to sleep (only for intervention (app) group). 
2. In the morning, turn off the sleep tracking device as soon as they wake up and decide 
to leave the bed (only for intervention (app) group).  
3. Log day and regularly use the different modules addressing different aspects of sleep, 
including advice for change (only for intervention (app) group). 
4. At the three time points shown in 8.4.1 flow diagram, complete a longer questionnaire 
using an E-Link. These questionnaires will cover demographic information, subjective 
mental and physical health, subjective sleep, and stress level. (valid for app condition 
and waitlist control group) 
8. Intensity of Use 
 
Although app condition participants will be instructed to track sleep and engage with the app 
every night, the minimal internal compliance threshold for intensity of use is set at 3 nights 
of tracking per week. If participants track less than 3 nights per week, they will receive up to 
3 reminder follow-up emails. The primary analysis will include all randomized participants. 
Secondary analyses will be conducted using an adherent to treatment approach, excluding 
participants who did not sufficiently engage with app throughout the study (<3 tracked nights 
of sleep per week). 
 
9. Research methods 
9.1 Study design 
 
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The study is a randomized waitlist controlled trial of a sleep improvement app. The present 
study design is summarized in Figure 1. We decided to use a between-group design as we 
want to determine the effect of the sleep improvement app vs. not using anything, as the 
study app is aiming to improve subjective sleep quality in order to prevent progression to 
clinical relevant sleep problems (primary prevention). Participants will be randomly 
assigned to the Sleep Improvement App intervention or the waitlist condition. Online 
assessments will take place at Baseline (0 weeks), 6 weeks (during the intervention), and 
12 weeks (after intervention).  
9.2 Randomization and allocation concealment 
 
This study will use simple randomization with an allocation ratio of 1:1, as recommended 
for large randomized controlled trials. Randomization will be conducted by an automated 
online system; hence the research team will be unable to influence randomization and 
have no access to future allocations. 
9.3 Blinding 
Assessments will be self-reported and carried out online, and the research team will be 
blinded to outcomes during the trial. Participants will be informed of their randomization 
outcome by an automatic email, and hence they will not be blinded to treatment allocation. 
It is notable that the study sponsors will not have any contact with research participants 
and will therefore be unable to bias the allocation or assessments. If participants do 
contact the team and reveal the allocation, the absence of assessments carried out by an 
investigator prevents any biases. 
9.4 Type/Design of study 
Naïve participants will be consecutively enrolled into the trial and randomized into either 
the wait-list condition or Sleep Improvement App-condition, provided that all inclusion and 
exclusion criteria are met and consent is given to use their data according to data privacy 
regulations. A participant information document with consent form will be provided with 
page-turning function. Participant has to agree that his/her data will be used in this study 
by clicking “yes, I agree” or “no I don`t agree and do not want to participate”.   
Eligible participants will receive written information about all relevant aspects of the study 
and that their participation in the study is voluntary and they have the right to refuse or 
withdraw their consent at any time without negative consequences. The study has been 
registered with ClinicalTrials.gov (210915DSDT). This paper contains modifications, which 
will be submitted to the Ethics Committee for approval prior to implementation. The current 
and further amendments will be documented in detail in the ClinicalTrials.gov registry. On 
the consent form, participants will be asked if they agree to the use of their data in case of 
withdrawing from the trial. Participants will also be asked for permission for the research 
team to share relevant data with people from SleepScoreLabs and ResMed taking part in 
the research or from regulatory authorities, where relevant. This trial does not involve 
collecting biological specimens for storage. 
 
 
 
 
 
 
 
 
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9.4.1 CONSORT Study Flow Diagram 
 
 
   
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9.4.2 Primary Endpoint 
 
Statistically significant improvement in subjective sleep quality measured using a 
validated questionnaire between active sleep-improvement condition through the Dein 
Schlaf. Dein Tag. App and the waitlist condition comparing baseline with 6 weeks and 12 
weeks measures.  
o Self-report sleep parameters: Sleep Quality score (SQ) of the SF-B (9 
Items) 
Assessed for eligibility (n=  ) 
Excluded (n=   ) 
   Not meeting inclusion criteria (n=  ) 
   Declined to participate (n=  ) 
   Other reasons (n=  ) 
Allocated to Sleep Improvement App  
(n= 300) 
Online assessments at: 
6 weeks assessment  
12 weeks assessment 
Online assessments at: 
6 weeks assessment 
12 weeks assessment 
Allocated to Waiting List 
 (n=300) 
Intention to treat analysis 
Intention to treat analysis 
Informed Consent 
Randomization 
0 weeks baseline assessment: 
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9.4.3 Secondary Endpoints 
Improvements in sleep metrics between app condition and waitlist condition comparing 
baseline with 6 weeks and 12 weeks measures 
Note, given the dynamic and personalized nature of the intervention and its potential 
impact on various self-assessed goals, we have chosen multiple secondary endpoints 
that we expect to see statistically significant sleep improvements in depending on 
baseline self-reported sleep parameters, including the following: 
o Health measures including overall sleep problems, feeling of being refreshed 
in the morning, mental health stress (PSQI, PSS, SF-12, SF-B) 
o Objective sleep parameters (only app condition): Total sleep time, sleep 
onset latency, wake after sleep onset, sleep efficiency. 
o Self-reported app-based items in app condition group. 
9.4.4 Study population 
This research aims to target a non-clinical population with sublinical  sleep disturbances that 
is interested in improving their sleep. To ensure we recruit a non-clinical sample, we will 
exclude users who have indicated they have a diagnosed sleep disorder as well as those 
identified as being at high risk of a sleep disorder based on validated screening tools. This 
research has no specific recruitment goals with respect to race or ethnicity and will not base 
participant eligibility on such criteria. However, this information may be recorded as part of 
the data collected.  
Inclusion criteria: 
 iOS user, or user of one of the following: Samsung Galaxy S7 Edge and S7,  S8, S8+, 
S9, S9+, S20, S20 FE, S20+, S21 Ultra Samsung Galaxy Note 8 and 9 
 Regensburg Insomnia Scale (poor sleep indicated by a score of 13-24, excluding those 
without sleep problems and those with probable insomnia disorder) 
 ≥ 18 years old. 
 Naive to the SleepScore Labs App 
 Not included in other treatments around sleep   
 Able to fully understand information on data protection and provide written informed 
consent 
Exclusion criteria: 
 Bedtime less than 6 hours   
 Any of the following medical problems: 
o Untreated psychological disorder affecting sleep (e.g., depression , anxiety 
disorders, bipolar disorder, schizophrenia)  
o Current severe medical conditions (e.g. chronic pain, cancer) 
 Any of the following medications/substance use: 
o Prescription sleep medication or regular use of over-the-counter sleep 
medication  
o Medication for other conditions (e.g., anxiety, ADHD) that affect sleep, e.g. 
antidepressants, antipsychotics 
 Consumption of 3+ units of alcohol on 4 or more nights per week 
 Recreational or nightly drug use 
 Pregnant or nursing mothers  
 Shift work 
 Travel across 2 or more time zones during study period 
 Sleeping more than 7 nights not at home during study period 
 Users who newly received a diagnosis of a sleep disorders  
 Users who start sleep or other psychoactive medication during the study period  
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 Use of other Sleep Tracking App’s during the study period 
9.4.5 Study duration 
 
Recruitment for the study will be rolling, Baseline Assessment is at  week 0. A follow up is 
scheduled after 6 weeks and 12 weeks of both the app and waitlist group. The end of data 
collection is planned after 12 weeks from baseline of the last user included. Final intention 
to treat analysis of study data will subsequently be performed. 
9.4.6 Withdrawal from the trial 
 
Participants are free to withdraw from the trial at any time without giving any reasons. They 
will not have any negative consequences in the relationship to SleepScore Labs or 
ResMed. In the event that a user withdraws consent, the date and reason for termination – 
if provided – will be documented. For participants who drop out of the study, assessments 
that are part of the study design will be conducted only if the participants agree. 
9.4.7 Compensation 
 
All enrolled participants will receive 20 euros for completing each follow-up assessment 
including baseline, 6-weeks, and 12-weeks, paid at the end of the study. 
Regular use of the sleep app will be compensated with 50 Euros.  
9.4.8 Sleep Measurement 
 
The Dein Schlaf. Dein Tag. application powered by SleepScore includes a 
polysomnography-validated non-contact sleep monitoring technology employing inaudible 
sonar to monitor the movements and respiration of a participant in bed. A validation study 
of the sonar technology against gold-standard polysomnography showed high sensitivity 
(88%) and specificity (65%), exceeding agreement that is typically reported for actigraphy 
based devices (Zaffaroni et al., 2019). 
 
Sleep measurement is achieved via a smartphone app, which have become ubiquitous in 
the last decade, eliminating the initial barrier to adoption represented by the cost and effort 
associated with purchasing a custom piece of hardware. The average emitted acoustic 
power is less than 75dB, well within the most stringent guidelines on sound exposure and 
the frequencies employed are above 18 KHz, above the audibility range of the vast 
majority of the adult population. The app utilizes a patented sensing technology and 
custom algorithms. Sleep monitoring can be achieved by placing a smartphone onto the 
bedside locker and starting the app. The app employs sophisticated algorithms to identify 
sleep stages: Wake (W), Light Sleep (N1, N2 sleep), Deep Sleep (N3 sleep), REM or 
Absence. The algorithms for the app technology were developed by using several 
hundred-night recordings from multiple subjects in their home consisting of concurrent 
measurements performed via a smartphone equipped with the Drive app and a ResMed 
S+ device respectively.  
 
9.4.9 Intervention and Control 
 
The Dein Schlaf. Dein Tag. App powered by SleepScore is a personalized and dynamic 
digital sleep improvement program offered via smartphone. The app begins with a brief 
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onboarding and registration process that informs the personalized sleep advice, followed 
by sleep improvement personalization goals including the following potential options: sleep 
longer, wake up less, fall asleep easier, sharpen my mind, recharge my body, stay asleep 
until morning, or perfect my sleep (improve SleepScore). Users are then asked to select 
health-related measures that are most important for them, the regularity of their bedtime, 
how often they exercise, common sleep disturbances, and overall self-reported sleep 
quality.  
 
Based on the user’s onboarding data and nightly objective sleep tracking data, the Dein 
Schlaf. Dein Tag. application powered by SleepScore provides personalized evidenced-
based sleep advice and sleep education content founded in the principles of sleep hygiene 
and cognitive behavioural skills. Additional sleep improvement features embedded within 
the app include weekly sleep challenges, sleep sanctuary bedroom checks, sleep sounds, 
smart alarm, bedtime reminders, and sleep education blogs. 
 
To increase user engagement and adherence to tracking and sleep improvement features, 
the application integrates contextual reinforcement strategies through daily and weekly 
prompts to encourage positive sleep health-related behaviours. These prompts include 
smartphone push notifications, in-app overlays, and emails providing sleep education 
content, sleep tips, individual sleep feedback, tracking/daily log reminders.  
 
The app uses several behaviour change techniques to improve sleep-wake functioning, 
including the following: 
 
Goal setting and planning. Goal setting is a key component to app onboarding and the 
advice engine. Users can select any of the following goals: sleep longer, wake up less, fall 
asleep easier, sharpen my mind, recharge my body, stay asleep until morning, or perfect 
my sleep (improve SleepScore). Users are then asked to select health-related measures 
that are most important for them, the regularity of their bedtime, how often they exercise, 
common sleep disturbances, and overall self-reported sleep quality. The app then uses  
criteria to give feedback to users when they are nearing or meeting sleep improvement 
criteria based on these goals. 
Feedback and monitoring. The sleep history page shows retrospective SleepScore’s in 
order to reduce false beliefs about sleep by providing objective sleep data. In relation to 
goals and planning, feedback on behaviour is provided based on the measurement of 
objective sleep and daytime behaviours logged in the log-your-day feature. 
Shaping knowledge. We use advice and insights based on the sleep guide and log your 
day items with instructions on how to remove or reduce aversive stimuli (e.g., excessive 
caffeine intake, excessive pre-bed light, alcohol consumption, sedentary behavior) and 
improve stimulus control (e.g., only using bed for sleep and intimacy). 
Natural consequences. Information about health consequences and the salience of 
insufficient or poor quality sleep are provided to users in the sleep guide and research 
articles summarized as blog posts. 
9.4.10 Inclusion of users and baseline parameters 
The investigator will inform eligible users about the study and the use of data. If the user is 
willing to participate, the informed consent form for data use will be handed out for 
signature. After signing by user and investigator, study relevant data will be reported after 
the participants has completed relevant questionnaires (see Annex1)  
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9.4.11 Assessments Table 
Assessment 
Baseline 
6 weeks 
12 weeks 
Inclusion and exclusion criteria 
X 
 
 
Demographics 
X 
 
 
History 
X 
 
 
Social status 
X 
 
 
Self-report questionnaires (see Annex 1) 
X 
X 
X 
Assessment of objective sleep 
parameters via Dein Schlaf. Dein 
Tag App (each night) 
X 
X 
X 
 
 
9.5 Data sources 
9.5.1 Data obtained from users 
Baseline and 6-weeks and 12-weeks data regarding sleep, stress, and mental health 
questionnaires including:  
 
1 
Regensburg Insomnia Scale 
2 
Schlaffragebogen B 
3 
Short Form 12 Health Survey Questionnaire (SF-12) 
4 
Pittsburgh Sleepiness Quality Index (PSQI) 
5 
Perceived Stress Scale (PSS) 
9.5.2 Data imported from the Dein Schlaf. Dein Tag. App  
 
Digital data objectively measured using the Dein Schlaf. Dein Tag. App will contain the 
following parameters,  
- 
Sleep stage and sleep-wake data included by not limited to total sleep time, sleep 
onset latency, wake after sleep onset, sleep efficiency and usage patterns. 
9.6 Study size 
 
An a priori power analysis was performed using GPower version 3.1.9.7. Previous 
research indicates that effect sizes between 0.20 and 0.50 can be expected using a digital 
sleep application. A conservative approach (assuming an effect size of 0.20) will require 
150 participants in each group (N=150) to detect significant effects between app group and 
wait-list control group for both primary and secondary outcome measures (alpha: 0.05, 
power: 0.80, allocation ratio 1:1). As trials with solely online recruitment may have higher 
drop out rates, we increased the participants in each group to 300 (total: 600 participants).  
 
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9.7 Data management 
 
Applicable national and international legal requirements for data handling and data 
archiving will be met. Participant study materials including recruitment materials, 
participant informed consent documents (PICF), study withdrawal forms, and study 
progress will be collected using an EU data center of Forsta/Decipher which is GDPR 
compliant, ISO 27001 certified, with SOC2 accredited data centers. 
 
Objective sleep data will be collected using the Dein Schlaf. Dein Tag. application, and 
stored within an EU data center of the Microsoft Azure cloud system, an ISO 27001 
certified and SOC 1, SOC 2, and GDPR compliant cloud service provider. Self-reported 
questionnaire data will be captured using an EU data center of Forsta/Decipher which is 
GDPR compliant, ISO 27001 certified, with SOC2 accredited data centers. Objective sleep 
data is encrypted in transit and at rest and additionally, personally identifiable information 
is hashed before being stored in the database. Access to databases for data analysis 
which include person identifiable information is limited to approved administration staff and 
is locked to the corporate IP address in the EU.  
 
Personal information identifiers are separated from measurements so that employees of 
SleepScore Labs International LTD can work with accordance of least privilege to perform 
adequate work duties. Authentication controls are also implemented to protect customer 
data using passwords and usernames – external parties do not have any authentication 
services. Upon completion of data collection, de-identified objective data from Microsoft 
Azure cloud system and self-reported Forsta/Decipher data will be merged into a GDPR 
compliant data repository. Azure Data Factory will be used to combine the data, with 
processed data being stored in a Microsoft Azure SQL Server hosted in the EU. Access to 
this repository, which is required for analysis, will be limited to specific EU-based 
employees. 
9.7.1 Personal data and data protection 
 
Data collected in the course of this study are subject to strict data protection measures. 
Objective data in the system is encrypted and secure while in transit and at rest using the 
latest security protocols. The data protection notice informs end-users about their data 
protection rights and how to exercise them (i.e. make data subject rights requests). We will 
comply with the requirements of Articles 12 to 21 GDPR, as applicable. All study 
participant data that is recorded will be pseudonymized for storage and analysis: Objective 
sleep data from the app as collected and controlled by SleepScore Labs and self-reported 
questionnaire data as collected by a vendor on behalf of ResMed Objective sleep data 
from the Dein Schlaf. Dein Tag. App as specified in this protocol, and without personal 
identifiers will be send to vendor, combined with self-reported questionnaire data by 
vendor and equipped with new identifiers to result in a de-identified set of study data. At 
the end of the project, the data will be deleted from the vendor’s servers, transferred to 
ResMed and shared with Sleepscore Labs for joint analysis.  De-identified study data will 
be stored at ResMed for 10 years.. Data will be collected and processed to reach the goals 
of the study and furthermore possible evaluations for sleep research and publications. 
Participants will be informed about their rights in terms of data usage, data storage, 
correction of data and deletion of stored data through the data protection notice. This 
notice sets out the data protection rights of the user and how they can exercise their data 
protection rights under GDPR. 
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9.8 Data analysis 
 
Demographic and other key study variables will be compared between intervention group 
and control group at the baseline using t tests for continuous variables and chi squared for 
categorical variables to test whether the randomization has worked adequately.  
The primary analysis will include all randomized participants. Secondary analyses will be 
conducted using an adherent to treatment approach, excluding participants who did not 
sufficiently engage with app throughout the study (<3 tracked nights of sleep per week, 
respectively used the app modules regularly).  
 
The questionnaire data will be analyzed by an ANOVA, with one factor (app condition vs. 
waitlist control group) and time (Baseline, 6-weeks, 12-weeks). To handle missing values, 
a mixed-model approach with the same factors will be applied. The critical statistical test 
would be the interaction term between app condition and the waitlist control group. 
 
For objectively recorded sleep data, repeated measured data will be analyzed using multi-
level modeling (MLM) in a nested way (nights within participants), will be performed 
allowing for analyzing changes with the intervention (app) group. To evaluate changes in 
primary and secondary outcome measures assessed by validated scales at baseline, 6-
weeks, and 12-weeks, we will compare within-group differences for each time-point using 
a t-test if normally distributed, or the Mann Whitney-U test for non-normally distributed 
continuous data. In addition, effect sizes for the changes within both groups (questionnaire 
data) and app group (objective sleep parameters) will be computed. 
9.9 Quality control  
 
Quality control is defined as the operational techniques and activities, such as monitoring, 
undertaken within the quality assurance system to verify that the requirements for quality 
of the study related activities have been fulfilled. Quality control should be applied to each 
stage of data handling to ensure that all data are reliable and have been processed 
correctly. Quality assurance is defined as the planned and systematic actions that are 
established to ensure that the study is performed, and the data generated, documented 
(recorded) and reported, in compliance with Good Clinical Practice (GCP) and the 
applicable regulatory requirements. 
9.9.1 Access to Source Data/Document  
 
The investigator will permit, and participating subjects will consent for, potential study-
related monitoring, audits, Ethics Committee review and regulatory inspections, providing 
direct access to primary participant’s data (i.e. source data) that support data in the 
Microsoft Azure cloud. Direct access is defined as the permission to examine, analyze, 
verify and reproduce any records and reports that are important to evaluation of a study. 
Any party (e.g. domestic and foreign regulatory authorities, the sponsor and/or authorized 
representatives of the sponsor such as monitors and auditors) with direct access should 
take all reasonable precautions within the constraints of the applicable regulatory 
requirements to maintain the confidentiality of users identities and sponsor proprietary 
information. 
 
 
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9.9.2 Quality assurance: 
 
Quality assurance is defined as the planned and systematic actions that are established to 
ensure that the study is performed, and the data are generated, documented (recorded) 
and reported in compliance with GCP and the applicable regulatory requirements. 
 
9.10 Study monitoring 
 
The sponsor or authorized, qualified representatives of the sponsor will accomplish the 
monitoring during the study. Source documents will be reviewed for verification of 
consistency. The investigator agrees that representatives or the designees of the sponsor, 
and appropriate regulatory bodies will be given direct access to the regular files of the 
user. It is important that the investigator and their relevant personnel are available during 
the monitoring visits and possible audits and that an appropriate location and sufficient 
amount of time is devoted to the process. During the monitoring visit a PC with internet 
connection should be available to the monitor for direct connection to the internet database 
of the study and to all the data of the users if stored in the data system.  
9.10.1 Audit 
 
An audit is a systematic and independent review of study related activities and documents 
to determine whether the validated study related activities were conducted and the data 
were recorded, analyzed and accurately reported according to the protocol, designated 
Standard Operating Procedure (SOPs), GCP and the applicable regulatory requirements. 
9.11 Limitations of the research methods 
 
It is expected that variations in the use of the Dein Schlaf. Dein Tag will occur, i.e., some 
users will use the app very intensely, others might use it regularly but not so intense. Due 
to the expected of variations in procedures and in particular in usage times of the sleep 
advice engine, the magnitude of statistical significance (i.e., effect size) is expected to be 
smaller but still within the range of 0.20 to 0.50. 
9.12 Other aspects 
 
None 
 
 
 
10. Protection of human subjects 
 
All sleep advice engine support activities used are decided individually by the user and are 
not pre-defined by the study protocol. All sleep advice engine support activities are to be 
used in line with their marketing authorisation. Participation does not pose additional risks 
and adverse events are not expected given the behavioural nature of the intervention. All 
Sleep advice engine support activities are performed in accordance with all applicable 
ethical and regulatory standards. Before initiating the study in a country, approval of the 
IRB / IEC will be obtained. 
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11. Management and reporting of Adverse Events/Adverse Reactions 
Unanticipated problems or any unexpected adverse events will be promptly reported by 
the PI to an IRB representative from the Ethik-Kommission der Medizinischen Fakultät der 
Universität Duisburg-Essen by phone (+49 (0) 201 - 723 3448) or email 
(ethikkommission@uk-essen.de), as well as any other medium (e.g. faxed form) requested 
by the 3rd party IRB following email/phone notification. 
12. Plans for disseminating and communicating study results 
 
Study results will be pooled for the purpose of publication that will be jointly coordinated by 
SleepScore Labs and ResMed. Preparation of the comprehensive publication will occur at 
the completion of the study. Publications arising from this study will be jointly defined, 
executed, and published or alternately consented by both sides. 
 
Annex 1. 
Questionnaires 
 
Items Recruitment 
Baseline 
6 
Weeks 
12 
Weeks 
Purpose 
Regensburg 
Insomnia Scale 
10 
X 
 
 
 
Eligibility  
SF-B 
29 
 
X 
X 
X 
Effect on 
sleep 
SF-12 
12 
 
X 
X 
X 
Effect on 
preventative 
health 
Pittsburgh Sleep 
Quality Index 
(PSQI) 
19 
 
X 
X 
X 
Effect on 
sleep quality 
Perceived 
Stress Scale 
10 
 
X 
X 
X 
Effect on 
Stress 
 
 
 
 
 
Annex 2. 
References  
Dein Schlaf. Dein Tag. App: Zaffaroni A., Coffrey S., Dodd S., Kilron H., Lyon G., 
O’Rourke D., Lederer K., Fietze I., Penzel T. Sleep stage monitoring based on sonar 
smartphone technology. Annu Int Conf IEEE Eng Med Biol Soc. 2019; 2019:2230-2233. 
LISST: Landecker Inventar zur Erfassung von Schlafstörungen; Hans-Günter Weeß, 
Enzyklopädie der Schlafmedizin, 2020, 1-2; DOI: 10.1007/978-3-642-54672-3_261-1 
Munich Chronotype Questionnaire: Ghotbi, N., Pilz, L. K., Winnebeck, E. C., Vetter, C., 
Zerbini, G., Lenssen, D., Frighetto, G., Salamanca, M., Costa, R., Montagnese, S., & 
Roenneberg, T. (2019). The µMCTQ: An Ultra-Short Version of the Munich ChronoType 
Questionnaire. Journal of Biological Rhythms, 1, 98–110.  
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Perceived Stress Scale: Klein, E. M., Brähler, E., Dreier, M., Reinecke, L., Müller, K. W., 
Schmutzer, G., Wölfling, K., & Beutel, M. E. (2016). The German version of the Perceived 
Stress Scale – psychometric characteristics in a representative German community 
sample. BMC Psychiatry, 1.  
PSQI: Backhaus, J., Junghanns, K., Broocks, A., Riemann, D., & Hohagen, F. (2002). 
Test–retest reliability and validity of the Pittsburgh Sleep Quality Index in primary 
insomnia. Journal of Psychosomatic Research, 3, 737–740.  
Regensburg Insomnia Scale: Crönlein, T., Langguth, B., Popp, R., Lukesch, H., Pieh, C., 
Hajak, G., & Geisler, P. (2013). Regensburg Insomnia Scale (RIS): a new short rating 
scale for the assessment of psychological symptoms and sleep in insomnia; Study design: 
development and validation of a new short self-rating scale in a sample of 218 patients 
suffering from insomnia and 94 healthy controls. Health and Quality of Life Outcomes, 1, 
65.  
SF-12: Standardization of the SF-12 Version 2.0 Assessing Health-Related Quality of Life 
in a Representative German Sample; Wirtz, Markus Antonius; Morfeld, Matthias; 
Glaesmer, Heide; Brähler, Elmar; Diagnostica, 2018, Vol 64 (4), 215-226 
 
SF-B: Schredl, M., Schenck, W., Görtelmeyer, R. et al. Einflußfaktoren auf die 
Schlafqualität bei Gesunden. Somnologie 2, 99–103 (1998).  
 
 
 
 
 
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