Self-Management of Subclinical Common Mental Health Disorders (Anxiety, Depression and Sleep Disorders) Using Wearable Devices
Abstract
:1. Introduction
Health Technology
2. Background
2.1. Defining Common Mental Health Disorders
2.2. The Promise of Telehealth and the Integration of Health Self-Service
3. Review Aims and Objectives
- Analyse the dominant wearable biometrics currently used in wearable devices as described within the current literature.
- Analyse the central purpose of wearable devices as described within the current literature.
- Understand the major machine learning algorithms deployed within these devices.
- Analyse the potential cost-benefit wearable devices have.
4. Methodology
4.1. Information Sources
4.2. Selection Process
- Included results within the date range 2018–2022. A five-year search window was chosen due to the exponential growth in wearable technology, the speed of technological development, and to capture the influence of COVID-19 in the results.
- Included results were common mental disorders (anxiety or depressive disorder, sleeping disorder) as they were the primary focus.
- Results describe or evaluate e-mental health or wearable technology.
- Returned results outside of 2018–2022.
- Exclude articles with a low number of participants .
- Exclude articles focused on professional practice and the well-being of healthcare workers within the clinical setting or directly related to occupational stress.
- Exclude articles focused on professional performance enhancement within sport.
- Language exclusions: only include English language results.
- Excluded results did not focus on common mental health conditions (anxiety or depression) as the primary focal point i.e., excluded those that considered mental health a secondary factor to patient care.
- Excluded studies focused on the clinical outcomes of sensing as part of treatment.
- Excluded results related to the improvement of psychiatric education practice.
4.3. Data Collection and Analysis
5. Results
5.1. Study Characteristics
Methodological Quality
Study | Demographic | Mental Health Disorder | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Reference | Sample Size | Participants (Male %) | Participants (Female %) | Participants Age Range | Participant’s Age (Mean) | Target Group | Targeted Disorder | Intended Purpose | Passive Sensing | Intervention |
[27] | 55 | 0.69 | 0.31 | 18–25 | 23.2 | Young Adults | Anxiety | Well-being (Biofeedback) | N | Y |
[28] | 55 | 0.88 | 0.12 | 33–59 | 46.5 | Adults | Depression | Detection | Y | N |
[29] | 183 | 33–59 | Adults | Stress | Self-Monitoring | Y | N | |||
[30] | 23 | 0.7 | 0.3 | 22–56 | 30.35 | Employees | Stress | Validation | Y | N |
[31] | 20 | 33–59 | University Students | Depression | Self-Monitoring | Y | N | |||
[32] | 201 | 0.55 | 0.45 | 18–25 | University Students | Stress, Depression, Anxiety | Self-Monitoring | Y | N | |
[33] | 82 | 0.35 | 0.65 | 17–38 | University Students | Stress | Self-Monitoring | Y | N | |
[34] | 169 | 0.45 | 0.55 | 33–59 | 33 | Employees | Stress | Well-being (Biofeedback) | Y | Y |
[35] | 328 | 0.57 | 0.43 | 33–59 | 38.9 | Employees | Stress | Self-Monitoring | Y | N |
[36] | 5895 | Depression | Self-Monitoring | Y | N | |||||
[37] | 80 | 0.50 | 0.50 | 50–70 | Older Adults | Depression | Self-Monitoring | N | N | |
[38] | 196 | 0.33 | 0.77 | 28.8–48.4 | Employees | Anxiety | Self-Monitoring | Y | N |
5.2. Wearable Devices and Device Types
5.3. Wearable Device Data Type
5.4. Economic Analysis of Technologies
5.4.1. Domains
5.4.2. Assumptions
5.4.3. Public Value Benefit
6. Discussion
6.1. Opportunities
6.1.1. Wearables as a Data Collection Platform
6.1.2. Widening Access to Treatments through Wearables
6.1.3. Empowering the Therapeutic Process
6.2. Limitations
6.2.1. The True Economic Case for Wearables
6.2.2. Lack of Evidence Base
6.2.3. Empowering the Already Empowered
6.2.4. Limitations of This Review
7. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Term | Google Scholar | Web of Science | Scopus | PubMed | Cochrane Library | PsycINFO | Total |
---|---|---|---|---|---|---|---|
Review AND (Anxiety OR depression) | 3,110,000 | 105,939 | 156,452 | 95,590 | 6716 | 52,223 | 3,526,920 |
(“sleep disorders” OR (Common mental health disorders)) | 3,150,000 | 60,990 | 104,776 | 50,117 | 10,706 | 46,173 | 3,422,762 |
(“self-management” OR “self-care”) | 17,900 | 52,266 | 89,471 | 64,278 | 0 | 30,781 | 254,696 |
(“subclinical” OR “home management”) | 18,100 | 4661 | 5402 | 3858 | 599 | 1095 | 33,715 |
(Wearables OR “wearable devices” OR “Smart Devices” OR “measurement device” OR “monitoring device” OR “smart wearables”) | 157,000 | 24,508 | 115,854 | 25,605 | 3469 | 1225 | 327,661 |
(Remote OR sensor OR “sensing device”) | 7,160,000 | 1,509,175 | 1,877,800 | 301,094 | 12,259 | 19,868 | 10,880,196 |
Abbreviation | Measurement Scale Full Name | Record Count |
---|---|---|
HAM-D | Hamilton depression rating scale | 2 |
BDI (BDI-II) | Beck Depression Inventory (I, II) | 1 |
BSI | Brief Symptom Inventory | 1 |
STAI | State-Trait Anxiety Inventory | 3 |
PSS | Perceived Stress Scale—PSS | 4 |
POMS | Profile of mood states | 1 |
PHQ-9 | Patient Health Questionnaire-9 | 1 |
EMA | Ecological Momentary Assessment | 1 |
MASQ | Mood and Anxiety Symptoms Questionnaire | 1 |
MADRS | Montgomery–Åsberg Depression Rating Scale | 1 |
SRI | Stress Response Inventory | 1 |
CDC HRQOL-14 | Centre for Disease Control’s Healthy Days Core and Symptoms Modules | 1 |
Wearable Device | Algorithm | |||||||
---|---|---|---|---|---|---|---|---|
Manufacturer | Wearable Model | Device Type | ML Model(s) | Accuracy | Biometric Signals | Self-Reporting Scales | Evaluation Scales | Reference |
InteraXon Inc & SmithOptics Inc. | Muse™, Lowdown Focus | Headband, Glasses | 0.77 | EEG, HRV | PSS, POMS, STAI | BDI, BSI | [27] | |
Eee Holter Technology Co. | Mindo-4S Jellyfish | Headband | SVM RBF | 0.81 | EEG | HAM-D | [28] | |
Empatica | E4 | Wristband | Binary logistic regression model | 0.85 | GSR | [29] | ||
Apple | Watch 6 | SmartWatch | HR | [30] | ||||
Samsung | Gear S3 Frontier | SmartWatch | SVM RF | 0.96 | Physical Activity, HR | EMA, PHQ-9 | BDI-II, STAI | [31] |
Affectiva | Q-sensor | SmartWatch | SVM RBF | 0.87 | Physical Activity, SC, S-Temp, Ambient light | PSS | [32] | |
Microsoft | Smartband 2 | SmartWatch | Neural Network (NN) | 0.78 | SC, Sleep, Calorie intake, S-Temp, HR, HRV, PPG, RR | STAI | PSS, SRI | [33] |
Spire Health | Spire Stone | Necklace | - | - | Respiratory Rate | MASQ | CDC HRQOL-14 | [34] |
Intelligent Galaxy | Chillband | SmartWatch | Statistical mixed design model | 0.75 | Physical Activity, ECG, SC, S-Temp, HR, Circadian Harmonic | PSS | [35] | |
Cambridge Neurotechnology Ltd. | Actiwatch | SmartWatch | Feature extraction and RF | 0.89 | Physical Activity | MADRS | [36] | |
Acculi Labs Pvt. Ltd. | LENS | Bracelet | - | 0.93 | Physical Activity, S-Temp, menstrual cycle, Sp02, Sleep monitoring, PPG | HAM-D | [37] | |
FitBit, OMsignal | OMsignal smart-shirt, Fitbit Charge 2 | SmartWatch, Smart Shirt | SVM RBF | 0.92 | Physical Activity, HR, HRV, Sleep, ECG, PPG, RR, GSR | daily survey | [38] |
Biometric | Collection | Count | Location | Description | Reference |
---|---|---|---|---|---|
Physical activity | Auto/Self-reported | 6 | On person | Physical activity is a commonly used metric default in many consumer wearable devices, which includes acceleration and step counts. | [2,14,43] |
Electrodermal activity (EDA/GSR/SC/Skin Temperature) | Auto | 9 | Wrist | Electrodermal activity (EDA), galvanic skin response (GSR) and skin temperature are measures of skin conductance indicative of sweat gland activity and, therefore, emotional arousal. | [39,40,41,42] |
Blood oxygen saturation (Sp02) | Auto | 1 | Wrist | Blood oxygen saturation (SP02) is a common biometric within clinical practice and is typically collected via pulse oximetry, reflecting the percentage of oxygen in the blood. | [44,45,46,47] |
Heart Rate (HR) | Auto | 6 | Wrist | Heart Rate is the number of heart beats per minute | [41,47] |
Heart Rate Variability (HRV) | Auto | 3 | Wrist | Heart rate variability (HRV) is a variation of the interval between heartbeats. | [41,47] |
Sleep | Auto/Self-reported | 3 | Wrist | Changes in sleep patterns are commonly associated with signs of mental health deterioration. Therefore, sleep patterns are regular indicators of mental health status. | [43,48,49] |
Ambient light and audio | Auto | 1 | Wrist | Ambient light or audio is a commonly used metric in conjunction with physical activity and SC to determine sleep activity and quality. | [31,43] |
Menstrual cycle | Self-reported | 1 | Off person | Psychological stress has a detrimental effect on menstrual cycle regularity. | [50] |
Photoplethysmography (PPG) | Auto | 3 | Wrist | Commonly used metric to determine the amount of light absorbed by blood vessels in living tissue. PPG can be used as a proxy for blood pressure due to the correlation between arterial blood volume and distention with blood pressure. | [47,51,52] |
Electrocardiogram (ECG) | Auto | 2 | Wrist | A plot of the heart’s electrical activity is traditionally used to calculate HR and HRV | [46] |
Electroencephalography (EEG) | Auto | 2 | Head | Electroencephalography (EEG) is a measure of the brains electrical activity over time and is one of the most effective physiological signals for identification of psychological stress. | [41,46] |
Respiratory Rate | Auto | 2 | Chest | Respiratory patterns, i.e., inspiration/expiration ratio, respiratory pauses, irregularity etc. are influenced by various mental stressors and therefore is a common indicator of mental state. | [42] |
Calorie intake | Self-reported | 1 | Off person | Calorie intake indicators are typically self-reported and are of interest because food intake is shown to be correlated with depressive symptoms | [31,53] |
Outcome | Benefit | Recipient |
---|---|---|
Improved well-being of individuals | Increased confidence/self-esteem | Individual |
Reduced isolation | Individual | |
Positive functioning (autonomy, control, aspirations) | Individual | |
Emotional well-being | Individual | |
Improved family well-being | Improved family relationships | Family |
Positive functioning (autonomy, control, aspirations) | Family | |
Emotional well-being | Family | |
Improved community well-being | Sense of trust and belonging | Community |
Positive functioning (autonomy, control, aspirations) | Community | |
Improved relationships | Community | |
Mental health | Reduced health cost of interventions | NHS/Individuals |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Robinson, T.; Condell, J.; Ramsey, E.; Leavey, G. Self-Management of Subclinical Common Mental Health Disorders (Anxiety, Depression and Sleep Disorders) Using Wearable Devices. Int. J. Environ. Res. Public Health 2023, 20, 2636. https://doi.org/10.3390/ijerph20032636
Robinson T, Condell J, Ramsey E, Leavey G. Self-Management of Subclinical Common Mental Health Disorders (Anxiety, Depression and Sleep Disorders) Using Wearable Devices. International Journal of Environmental Research and Public Health. 2023; 20(3):2636. https://doi.org/10.3390/ijerph20032636
Chicago/Turabian StyleRobinson, Tony, Joan Condell, Elaine Ramsey, and Gerard Leavey. 2023. "Self-Management of Subclinical Common Mental Health Disorders (Anxiety, Depression and Sleep Disorders) Using Wearable Devices" International Journal of Environmental Research and Public Health 20, no. 3: 2636. https://doi.org/10.3390/ijerph20032636