Wearable digital health technology [Neurosurgery Wiki]

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====== Wearable digital health technology ======

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[[Wearable]] [[digital health technology]] refers to [[electronic]] [[device]]s that can be worn on the body to [[monitor]], track, and sometimes even manage health-related [[data]] in [[real-time]]. These devices are designed to provide continuous or periodic [[monitoring]] of various physiological parameters, which can help individuals and [[healthcare provider]]s make [[informed decision]]s about health and wellness.

### Key Components of Wearable Digital Health Technology

1. **Sensors**:
   - Wearable devices typically include a variety of sensors to measure physiological metrics. Common sensors include:
     - **Heart rate sensors**: Monitor heart rate and variability.
     - **Accelerometers and gyroscopes**: Track movement, steps, and activity levels.
     - **Optical sensors**: Measure blood oxygen levels, heart rate, and sometimes even glucose levels.
     - **Electrocardiogram (ECG) sensors**: Record the electrical activity of the heart.
     - **Temperature sensors**: Monitor body or skin temperature.
     - **Galvanic skin response sensors**: Measure stress levels through skin conductivity.

2. **Data Processing and Storage**:
   - Wearable devices typically have built-in processors to analyze sensor data in real time. Data can be stored on the device or synced to smartphones, computers, or cloud platforms for further analysis and long-term storage.

3. **Connectivity**:
   - Most wearable health devices are equipped with wireless communication technologies like Bluetooth, Wi-Fi, or even cellular connections. This allows them to sync with other devices or directly upload data to health apps or platforms.

4. **User Interface**:
   - Wearables often include a simple user interface, such as a touchscreen or LED indicators. However, many devices rely on smartphone apps or web dashboards for detailed data visualization and user interaction.

### Types of Wearable Health Technologies

1. **Fitness Trackers**:
   - Devices like Fitbit, Garmin, and Xiaomi bands primarily track physical activity, heart rate, sleep patterns, and sometimes stress levels.

2. **Smartwatches**:
   - Examples include the Apple Watch, Samsung Galaxy Watch, and other brands. These combine the features of fitness trackers with additional functionalities like ECG monitoring, fall detection, and integration with other health apps.

3. **Wearable ECG Monitors**:
   - Devices like the KardiaMobile and certain smartwatches can perform ECGs, which can be particularly useful for individuals with heart conditions.

4. **Wearable Blood Pressure Monitors**:
   - Some smartwatches and dedicated devices can monitor blood pressure, offering a convenient alternative to traditional cuff-based measurements.

5. **Wearable Glucose Monitors**:
   - Continuous glucose monitors (CGMs) like the Dexcom G6 and Abbott’s FreeStyle Libre are used by diabetics to monitor blood glucose levels continuously, providing real-time feedback on their condition.

6. **Sleep Trackers**:
   - Devices specifically designed to monitor sleep patterns, such as Oura Ring and WHOOP, help users understand and improve their sleep quality.

7. **Wearable Pain Management Devices**:
   - Some wearables use electrical nerve stimulation to manage pain, offering a drug-free alternative for chronic pain sufferers.

### Applications of Wearable Health Technology

- **Chronic Disease Management**: Wearables help monitor conditions like diabetes, hypertension, and heart disease, providing continuous data that can be shared with healthcare providers.
  
- **Preventive Health**: By tracking metrics like activity levels, heart rate variability, and sleep, wearables can help individuals maintain a healthy lifestyle and prevent the onset of chronic conditions.
  
- **Remote Patient Monitoring**: Healthcare providers can use data from wearables to monitor patients remotely, reducing the need for frequent in-person visits.
  
- **Personalized Health**: Wearables provide data that can be used to tailor health interventions and lifestyle changes to the individual’s specific needs.

- **Research**: Large-scale data collection from wearables can contribute to medical research, helping to identify trends and improve healthcare outcomes.

### Challenges and Considerations

- **Data Privacy and Security**: Wearables collect sensitive health data, making it crucial to ensure that this data is protected from unauthorized access.

- **Accuracy**: The accuracy of wearable devices can vary, which is a critical consideration for their use in medical decision-making.

- **User Compliance**: The effectiveness of wearable health technology depends on consistent use, which may be a challenge for some individuals.

- **Cost**: Some advanced wearable health technologies can be expensive, limiting accessibility for some populations.

Wearable digital health technology is rapidly evolving, offering new possibilities for personalized health management, preventive care, and even clinical applications. As these technologies become more integrated into everyday life, they hold the potential to significantly impact healthcare delivery and outcomes.
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Wearable [[technology]], wearables, fashion technology, smart wear, tech togs, skin electronics, or fashion electronics are smart electronic devices ([[electronic]] [[device]]s with micro-controllers) that are worn close to and/or on the surface of the skin, where they detect, analyze, and transmit information concerning e.g. body signals such as vital signs, and/or ambient data and which allow in some cases immediate biofeedback to the wearer.
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Pressure sensors with high sensitivity, a wide linear range, and a quick response time are critical for building an intelligent disease diagnosis system that directly detects and recognizes pulse signals for medical and health applications. However, conventional pressure sensors have limited sensitivity and nonideal response ranges. We proposed a multichannel flexible pulse perception array based on polyimide/multiwalled carbon nanotube-polydimethylsiloxane nanocomposite/polyimide (PI/MPN/PI) sandwich-structure pressure sensor that can be applied for remote disease diagnosis. Furthermore, we established a mechanical model at the molecular level and guided the preparation of MPN. At the structural level, we achieved high sensitivity (35.02 kPa-1) and a broad response range (0-18 kPa) based on a pyramid-like bilayer microstructure with different upper and lower surfaces. A 27-channel (3 × 9) high-density sensor array was integrated at the device level, which can extract the spatial and temporal distribution information on a pulse. Furthermore, two intelligent algorithms were developed for extracting six-dimensional pulse information and automatic pulse recognition (the recognition rate reaches 97.8%). The results indicate that intelligent disease diagnosis systems have great potential applications in wearable healthcare devices
((Liu T, Gou GY, Gao F, Yao P, Wu H, Guo Y, Yin M, Yang J, Wen T, Zhao M, Li T, Chen G, Sun J, Ma T, Cheng J, Qi Z, Chen J, Wang J, Han M, Fang Z, Gao Y, Liu C, Xue N. Multichannel Flexible Pulse Perception Array for Intelligent Disease Diagnosis System. ACS Nano. 2023 Jan 30. doi: 10.1021/acsnano.2c11897. Epub ahead of print. PMID: 36716225.))

===== Research Studies =====

The objective of Goodday et al. is to describe how participant [[engagement]] techniques and different study designs affect participant adherence, retention, and overall engagement in research involving personal DHTs.

Methods: Quantitative and qualitative analysis of engagement factors are reported across 6 unique personal DHT research studies that adopted aspects of a participant-centric design. Study populations included (1) frontline health care workers; (2) a conception, pregnant, and postpartum population; (3) individuals with Crohn disease; (4) individuals with pancreatic cancer; (5) individuals with central nervous system tumors; and (6) families with a Li-Fraumeni syndrome affected member. All included studies involved the use of a study smartphone app that collected both daily and intermittent passive and active tasks, as well as using multiple wearable devices including smartwatches, smart rings, and smart scales. All studies included a variety of participant-centric engagement strategies centered on working with participants as co-designers and regular check-in phone calls to provide support over study participation. Overall retention, probability of staying in the study, and median adherence to study activities are reported.

Results: The median proportion of participants retained in the study across the 6 studies was 77.2% (IQR 72.6%-88%). The probability of staying in the study stayed above 80% for all studies during the first month of study participation and stayed above 50% for the entire active study period across all studies. Median adherence to study activities varied by study population. Severely ill cancer populations and postpartum mothers showed the lowest adherence to personal DHT research tasks, largely the result of physical, mental, and situational barriers. Except for the cancer and postpartum populations, median adherences for the Oura smart ring, Garmin, and Apple smartwatches were over 80% and 90%, respectively. Median adherence to the scheduled check-in calls was high across all but one cohort (50%, IQR 20%-75%: low-engagement cohort). Median adherence to study-related activities in this low-engagement cohort was lower than in all other included studies.

Conclusions: Participant-centric engagement strategies aid in participant retention and maintain good adherence in some populations. Primary barriers to engagement were participant burden (task fatigue and inconvenience), physical, mental, and situational barriers (unable to complete tasks), and low perceived benefit (lack of understanding of the value of personal DHTs). More population-specific tailoring of personal DHT designs is needed so that these new tools can be perceived as personally valuable to the end user
((Goodday SM, Karlin E, Brooks A, Chapman C, Harry C, Lugo N, Peabody S, Rangwala S, Swanson E, Tempero J, Yang R, Karlin DR, Rabinowicz R, Malkin D, Travis S, Walsh A, Hirten RP, Sands BE, Bettegowda C, Holdhoff M, Wollett J, Szajna K, Dirmeyer K, Dodd A, Hutchinson S, Ramotar S, Grant RC, Boch A, Wildman M, Friend SH. Value of Engagement in Digital Health Technology Research: Evidence Across 6 Unique Cohort Studies. J Med Internet Res. 2024 Sep 3;26:e57827. doi: 10.2196/57827. PMID: 39226552.)).