When using this resource, please cite: (show more options)
Rossi, A., Da Pozzo, E., Menicagli, D., Tremolanti, C., Priami, C., Sirbu, A., Clifton, D., Martini, C., & Morelli, D. (2020). Multilevel Monitoring of Activity and Sleep in Healthy People (version 1.0.0). PhysioNet. https://doi.org/10.13026/cerq-fc86.


Multilevel Monitoring of Activity and Sleep in Healthy people (MMASH) dataset provides 24 hours of continuous beat-to-beat heart data, triaxial accelerometer data, sleep quality, physical activity and psychological characteristics (i.e., anxiety status, stress events and emotions) for 22 healthy participants. Moreover, saliva bio-markers (i.e.cortisol and melatonin) and activity log were also provided in this dataset. The MMASH dataset will enable researchers to test the correlations between physical activity, sleep quality, and psychological characteristics.


Wearable activity trackers that collect data 24 hours a day, 7 days a week, have become more and more popular to monitor physical activity, Heart Rate (HR) and sleep quality. The combination of this kind of data enables the development of tools that can predict the users’ well being. We believe that these data can be greatly beneficial to the scientific community because they can contribute to research in several fields, enabling the assessment of the relations between physical, psychological and physiological characteristics.


The data were collected and provided by BioBeats (biobeats.com) in collaboration with researchers from the University of Pisa. BioBeats operates in the health science industry that produces IoT wearable devices aiming to detect people’s psychophysiological stress. The data were recorded by sport and health scientists, psychologists and chemists with the objective of assessing psychophysiological response to stress stimuli and sleep.

22 healthy young adult males were recruited. Before starting, the participants signed an informed consent to take part in this study. This provided information about the research protocol, possible risks and data usage, in accordance with the General Data Protection Regulation: Regulation - EU 2016/679 of the European Parliament and of the Council 27/04/2016 - on the protection of private persons with regard to the processing of personal data and on the free movement of such data. In accordance with the Helsinki Declaration as revised in 2013, the study was approved by the Ethical Committee of the University of Pisa (#0077455/2018).

At the start of the data recording, anthropomorphic characteristics (i.e. age, height and weight) of the participants were recorded. At the same time, participants filled in a set of initial questionnaires that provide information about participants psychological status: Morningness-Eveningness Questionnaire (MEQ), State-Trait Anxiety Inventory (STAI-Y), Pittsburgh Sleep Quality Questionnaire Index (PSQI) and Behavioural avoidance/inhibition (BIS/BAS). During the test, participants wore two devices continuously for 24 hours: a heart rate monitor (Polar H7 heart rate monitor - Polar Electro Inc., Bethpage, NY, USA) to record heartbeats and beat-to-beat interval, and an actigraph (ActiGraph wGT3X-BT - ActiGraph LLC, Pensacola, FL, USA) to record actigraphy information such as accelerometer data, sleep quality and physical activity. Moreover, the perceived mood (Positive and Negative Affect Schedule - PANAS) were recorded at different times of the day (i.e. 10, 14, 18, 22 and 9 of the next day). Additionally, participants filled in Daily Stress Inventory (DSI) before going to sleep, to summarize the stressful events of the day.

Twice a day (i.e., before going to bed and when they woke up) the subjects collected saliva samples at home in appropriate vials. Saliva samples were used to extract RNA and measure the induction of specific clock genes, and to assess specific hormones. A washout period from drugs of at least a week was required from the participants in the study.

Data Description

MMASH consists of seven files for each participant (the description of each column provided in the csv file were provided below):

Usage Notes

To the best of our knowledge, MMASH is the first dataset providing several aspects of people’s everyday life such as cardiovascular responses, psychological perceptions (e.g., stress, anxiety, and emotions), sleep quality, movement information (e.g., wrist accelerometer data and steps) and hourly activity descriptions. Due to the complexity of this data, experts from several research fields could use this dataset to investigate the relationship between several aspects of psychophysiological responses having a complete overview of the users’ daily life. For example, it is possible to investigate the relationship between perceived (PSQI) and observed sleep quality (e.g., melatonin, cortisol, sleep fragmentation index and sleep length) by individual characteristics such as daily stress, anxiety status, emotion perceived throughout the previous day and daily activities. Moreover, machine learning algorithms could be developed to detect daily activities, moods, emotions, individual predisposition to react toward aversive or positive events and stress following cardiovascular responses (e.g., heart rate and heart rate variability) and/or actigraphy data. These algorithms could be used to predict people’s routine by using accelerometers data and cardiovascular responses that are nowadays continuously recorded by wrist-worn devices that have become more and more popular thanks to the technological advent of the last two decades. These are only a few examples of all the possible research topics that could be rise by using this dataset. The main reason to release MMASH is the difficulty to record this kind of data for a long period. This dataset would give researchers and companies the chance to have a ground truth of several psychophysiological responses to develop predictive models and thus passively assess people’s everyday life following wrist-worn devices estimating their well-being.  


This work is partially supported by the European Community’s H2020 Program under the funding scheme INFRAIA-1-2014-2015: Research Infrastructures grant agreement 654024, www.sobigdata.eu, SoBigData. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study.

Conflicts of Interest

There are no conflicts of interest relevant to this dataset.


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