Database Open Access

# Multilevel Monitoring of Activity and Sleep in Healthy People

Published: June 19, 2020. Version: 1.0.0

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.

Rossi, A., Da Pozzo, E., Menicagli, D., Tremolanti, C., Priami, C., Sirbu, A., Clifton, D., Martini, C., & Morelli, D. (2020). A Public Dataset of 24-h Multi-Levels Psycho-Physiological Responses in Young Healthy Adults. Data, 5(4), 91. https://doi.org/10.3390/data5040091.

Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

## Abstract

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.

## Background

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.

## Methods

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):

• user_info.csv - anthropocentric characteristics of the participant:
• gender: M and F refer to Male and Female, respectively.
• height is expressed in centimetre (cm).
• weight is expressed in kilograms (kg).
• age is expressed in years.
• sleep.csv - information about sleep duration and sleep quality of the participant:
• In Bed Date: 1 and 2 refer to the first and second day of data recording, respectively.
• In Bed Time: time of the day (hours:minutes) when the user went to the bed.
• Out Bed Date: 1 and 2 refer to the first and second day of data recording, respectively.
• Out Bed Time: time of the day (hours:minutes) when the user went out of the bed.
• Onset Date: 1 and 2 refer to the first and second day of data recording, respectively.
• Onset Time: time of the day (hours:minutes) when the user falls asleep.
• Latency Efficiency: percentage of sleep time on total sleep in bed.
• Total Minutes in Bed: minutes spent in the bed per night.
• Total Sleep Time (TST): length of the sleep per night expressed in minutes.
• Wake After Sleep Onset (WASO): time spent awake after falling asleep the first time.
• Number of Awakenings during the night
• Average Awakening Length: time in seconds spent awakening during the night.
• Movement Index: number of minutes without movement expressed as a percentage of the movement phase (i.e., number of period with arm movement).
• Fragmentation Index: number of minutes with movement expressed as a percentage of the immobile phase (i.e., the number of period without arm movement).
• Sleep Fragmentation Index: ratio between the Movement and Fragmentation indices.
• RR.csv - beat-to-beat interval data:
• ibi_s: time in seconds between two consecutive beats.
• day: 1 and 2 refer to the first and second day of data recording, respectively.
• time: day time when the heartbeat happened (hours:minutes:seconds)
• questionnaire.csv - scores for all the questionnaires:
• MEQ: Morningness-Eveningness Questionnaire value. The chronotype score is ranging from 16 to 86: scores of 41 and below indicate Evening types, scores of 59 and above indicate Morning types, scores between 42-58 indicate intermediate types [1].
• STAI1: State Anxiety value obtained from State-Trait Anxiety Inventory. The results are range from 20 to 80. Scores less than 31 may indicate low or no anxiety, scores between 31 and 49 an average level of anxiety or borderline levels, and scores higher than 50 a high level of anxiety or positive test results [2].
• STAI2: Trait Anxiety value obtained from the State-Trait Anxiety Inventory. The results are range from 20 to 80. Scores less than 31 may indicate low or no anxiety, scores between 31 and 49 an average level of anxiety or borderline levels, and scores higher than 50 a high level of anxiety or positive test results [2].
• PSQI: Pittsburgh Sleep Quality Questionnaire Index. It gives a score rating from 0 to 21, with values lower than 6 indicating good sleep quality [3].
• BIS/BAS: Behavioural avoidance/inhibition index [4]. BIS/BAS scales are a typical measure of reinforcement sensitivity theory that establish biological roots in personality characteristics, derived from neuropsychological differences. The BIS/BAS scales comprise a selfreport measure of avoidance and approach tendencies that contains four sub-factors (A high score in one of the subscale describes the degree of that temperamental characteristic for the individual, according to the original sample):
• Bis facet reflects subject sensitivity toward aversive events that promote avoidance behaviours.
• Drive describes individual persistence and motivational intensity.
• Reward corresponds to Reward Responsiveness that indicates a propensity to show a higher degree of positive emotion for goal attainment.
• Fun corresponds to Fun-Seeking that is related to impulsivity and immediate reward due to sensory stimuli or risky situations.
• Daily_stress: Daily Stress Inventory value (DSI) is a 58 items self-reported measures which allows a person to indicate the events they experienced in the last 24 hours. After indicating which event occurred, they indicate the stressfulness of the invent on a Likert scale from 1 (occurred but was not stressful) to 7 (Cause me to panic). It gives a score between 0 and 406. The higher is this values, the higher is the frequency and degree of the events and the perceived daily stress [5].
• PANAS: Positive and Negative Affect Schedule. It gives a score rating between 5 and 50 for both positive and negative emotions [6]. The higher is the PANAS value, the higher is the perceived emotion. Columns name with 10, 14, 22 and 9+1 refer to the time of the day when the questionnaire is filled in. 9+1 indicates the 9 AM of the second recording day.
• Activity.csv - list of the activity categories throughout the day. The categories are (the activities listed below correspond to the numeric ID of each activity in the csv file):
• 1. sleeping.
• 2. laying down.
• 3. sitting, e.g. studying, eating and driving.
• 4. light movement, e.g. slow/medium walk, chores and work.
• 5. medium, e.g. fast walk and bike.
• 6. heavy, e.g. gym, running.
• 7. eating.
• 8. small screen usage, e.g. smartphone and computer.
• 9. large screen usage, e.g. TV and cinema.
• 10. caffeinated drink consumption, e.g. coffee or coke.
• 11. smoking.
• 12 alcohol assumption. 'Start' and 'end' columns refer to the time of the day (hours:minutes) when the event happened, while 'day' columns refers to the day when it happened (1 and 2 refer to the first and second day of data recording, respectively).
• Actigraph.csv - accelerometer and inclinometer data recorded throughout the day:
• Axis1: Raw Acceleration data of the X-axis expressed in Newton-meter.
• Axis2: Raw Acceleration data of the Y-axis expressed in Newton-meter.
• Axis3: Raw Acceleration data of the Z-axis expressed in Newton-meter.
• Steps: number of steps per second.
• HR: beats per minutes (bpm).
• Inclinometer Off: values equal to 1 refer to no activation of the inclinometer. The values are reported per second.
• Inclinometer Standing: values equal to 1 refer to the standing position of the user, while 0 refers to other user positions. Values are reported per second.
• Inclinometer Sitting: values equal to 1 refer to the sitting position of the user, while 0 refers to other user positions. Values are reported per second.
• Inclinometer Lying: values equal to 1 refer to the lying position of the user, while 0 refers to other user positions. Values are reported per second.
• Vector Magnitude: vector movement derived from raw acceleration data expressed in Newton-meter.
• day: 1 and 2 refer to the first and second day of data recording, respectively.
• time: day time when the heartbeat happened (hours:minutes:seconds)
• saliva.csv - clock genes and hormones concentrations in the saliva before going to bed and after waking up. Two samples per participant are included, one before sleep and one after waking up, as indicated by the "Sample" data column. Melatonin levels are reported in μg of melatonin per μg of protein, while cortisol levels are in μg of cortisol per 100 μg of protein. No clock genes and hormones concentrations data was provided for User_21 due to problem in the salivary samples that do not permit to analyse it.

## 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.

## Acknowledgements

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.

## References

1. Horne JA, Ostberg O (1976). "A Self Assessment Questionnaire to DetermineMorningness Eveningness in Human Circadian Rhythms". Int J Chronobiology. 4: 97–110. PMID 1027738.
2. CD Spielberger, Gorsuch RL, Lushene RE, Vagg PR, Jacobs GA (1983). "Manual for the State-Trait Anxiety Inventory. (Form Y)". Palo Alto, California: Consulting Psychologists Press.
3. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ (1989). "The Pittsburgh Sleep Quality Index: A New Instrument for Psychiatric Practice and Research". Psychiatry Res. 28: 193-213. DOI 10.1016/0165-1781(89)90047-4.
4. Carver CS, White TL (1994). "Behavioural inhibition, behavioural activation, and affective responses to impending reward and punishment: The BIS/BAS Scales". J Pers Soc Psychol. 67: 319–333. DOI 10.1037/0022-3514.67.2.319.
5. Brantley PL, Waggoner C, Jones GN, Rappaport N (1989). "A Daily Stress Inventory: Development, reliability, and validity". J Behav Med. 10: 61-73. DOI 10.1007/BF00845128.
6. Crawford JR (2004). "The Positive and Negative Affect Schedule (PANAS): Construct validity, measurement properties and normative data in a large non-clinical sample". Br J Clin Psychol. 43: 245–265. DOI 10.1348/0144665031752934.

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