This paper is in the following e-collection/theme issue:

Wearables and MHealth Viewpoints (75) mHealth for Data Collection and Research (917) mHealth for Data Collection and Research (917) Mobile Health (mhealth) (2777) Web-based and Mobile Health Interventions (3128) Mobile Health (mhealth) (2777) Web-based and Mobile Health Interventions (3128)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48803, first published .
Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones

Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones

Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones

Authors of this article:

Wonki Hong1 Author Orcid Image
Article Authors Cited by Tweetations (4) Metrics

    Department of Digital Healthcare, Daejeon University, Daejeon, Republic of Korea

    Corresponding Author:

    Wonki Hong, PhD


    Mobile health (mHealth) with continuous real-time monitoring is leading the era of digital medical convergence. Wearable devices and smartphones optimized as personalized health management platforms enable disease prediction, prevention, diagnosis, and even treatment. Ubiquitous and accessible medical services offered through mHealth strengthen universal health coverage to facilitate service use without discrimination. This viewpoint investigates the latest trends in mHealth technology, which are comprehensive in terms of form factors and detection targets according to body attachment location and type. Insights and breakthroughs from the perspective of mHealth sensing through a new form factor and sensor-integrated display overcome the problems of existing mHealth by proposing a solution of smartphonization of wearable devices and the wearable deviceization of smartphones. This approach maximizes the infinite potential of stagnant mHealth technology and will present a new milestone leading to the popularization of mHealth. In the postpandemic era, innovative mHealth solutions through the smartphonization of wearable devices and the wearable deviceization of smartphones could become the standard for a new paradigm in the field of digital medicine.

    JMIR Mhealth Uhealth 2024;12:e48803

    doi:10.2196/48803

    Keywords

    mobile health; mHealth; smartphonization; wearable deviceization; new form factor; sensor-integrated display 


    In the postpandemic era, the significance of mobile health (mHealth) has been highlighted, and explosive growth in this area is expected to continue [1,2]. Cutting-edge technologies are converging with health care, and mHealth, based on hyperconnected intelligence, is leading the paradigm shift in medical care [3,4]. Many countries have already entered a superaged society, and the proportion of gross domestic product expenditures for medical care is increasing due to an upsurge in the number of people with chronic diseases. In addition, the excessive demand compared to the available supply, the lack of health care infrastructure, and the unbalanced distribution of medical staff are also problems. Therefore, prediction, prevention, and management through artificial intelligence (AI)–based medical big data analysis are required, and for this purpose, ubiquitous and accessible medical services using personalized devices must be provided [5]. mHealth is a strong candidate to make this possible, and the ultimate goal is to dramatically improve the standard and satisfaction of living by providing quality services at affordable prices [6,7].

    Wearable electronics and smartphones are representative types of mobile systems optimized for personalized health care sensing. As shown in Figure 1 [8-19], wearable devices that cover the human body and smartphones, a necessity for modern people, enable comprehensive health management in real time.

    ‎
    Figure 1. Application and placement schematic illustration of wearable devices by body part and smartphone for mHealth management. The images were reprinted from Shin et al [8,19], Kim et al [9,19], Escobedo et al [10,19], Hwang et al [11], Nakamura et al [12,19], Hua et al [13,19], Moon et al [14,19], Zhao et al [15,19], Kim et al [16,19], Liu et al [17,19], and Chan et al [18,19]. e-Skin: electronic skin; mHealth: mobile health.

    However, the pace of the development and popularization of mHealth technology is progressing more slowly than expected. From the perspective of a paradigm shift from the smartphonization of wearable devices and the wearable deviceization of smartphones, this viewpoint aimed to propose ways to unleash the potentiality of mHealth in the postpandemic era. The smartphonization of wearable devices and the wearable deviceization of smartphones do not simply mean that current smartphones become wearable devices and that current wearable devices maintain the functions of current smartphones. The smartphonization of wearable devices is to completely replace the smartphone function with a wearable device, while upgrading health care performance by embedding the current smartphone’s computational power and sensor-integrated display, including large-area panels and user interaction, in the wearable system. In addition, the wearable deviceization of smartphones refers to a change in the form factor so that health care sensing can be performed by switching from the current rigid form to a form that can be attached to a curved skin surface. The new form factor, which features both wearable computer and smartphone functions, will improve detection performance through large-area sensing and increase the penetration rate.

    This viewpoint investigated recent trends in health care sensing methods using wearable devices and smartphones, which are the central axis of mHealth. In the case of wearable devices, the form factor for each detailed location on the body and the corresponding detection target technology was described. In the case of smartphones, it covered the detection target and principles of health care according to the application of internal and external sensors, materials, and software. This viewpoint also analyzed the prospects of and current challenges in existing mHealth systems and considered new health care solutions using flexible displays for the convergence form factor of smartphones and wearables. The differentiating point was to consider the direction of mHealth from the perspective of a sensor-integrated and new form factor display. Ultimately, from a display perspective, solutions for the smartphonization of wearable devices and the wearable deviceization of smartphones will provide insight into the health care paradigm shift.


    The primary classification of wearable electronics based on the attachment position can be divided into the face, upper body, limbs, and whole body. Wearable clothes all over the body can also be classified separately.

    Face

    Head

    The face, which is closest to the brain, is significant from a sensory point of view because it is where the 5 senses are concentrated. Face-wearable devices with various form factors, such as bands, caps, headsets, lenses, glasses, tattoos, mouthguards, and masks, may be distributed at each part of the head, eyes, nose, mouth, and ears to sense critical biosignals. In the case of the head, a wearable system that can analyze brain waves and psychological states can be applied [8,20,21]. Figure 2A [8] shows a wireless wearable electroencephalogram (EEG) measurement device based on a tattoo. AI can enhance decision-making by deep learning classification of received EEG data. Namely, it advances the decision performance of AI by feedback through brain waves. Additionally, it would be possible to grasp the degree of brain activation and mental condition of the frontal lobe and temporal lobe through the measurement of biosignals, such as brain waves.

    ‎
    Figure 2. Wearable devices attached to the face for mHealth. (A) Wearable EEG analysis platform with tattoo electrodes for EEG measurement and earbuds for wireless interaction. The images were reprinted from Shin et al [8,19]. (B) Stretchable corneal lenses for ocular electrodiagnosis. The images were reprinted from Kim et al [19,22]. (C) Intraoral electronics for sodium intake analysis through wireless remote control. The images were reprinted from Lee et al [23]. (D) Sensing platform for gaseous CO2 real-time determination inside filtering face piece 2 (FFP2) facemasks. The images were reprinted from Escobedo et al [10,19]. EEG: electroencephalogram; mHealth: mobile health.
    Eyes and Nose

    System form factors worn on the eye may be divided mainly into lenses and glasses. In the case of lenses, eye health factors, such as glucose, intraocular pressure, and electroretinographic measurements, can be determined using noninvasive methods [9,22,24-26]. For example, a corneal sensor embedded in a disposable soft contact lens can be deployed for electroretinography based on electrochemical anchoring, as shown in Figure 2B [22]. These corneal lenses are functional sensors tailored for ophthalmic electroretinographic testing in human eyes via a user-friendly interface and a design that can be deployed noninvasively. Glasses for health care are prescribed by doctors as an auxiliary tool for surgery and can also analyze the electrolyte and metabolite content of sweat flowing from the head [27-29]. In addition, a wearable system placed on the nose in the form of a nose pad on the glasses can sense the pulse wave, respiratory rate, and electrooculographic measurements [30,31].

    Mouth and Ears

    Wearable electronics related to the mouth take the form of mouth guards, tooth sensors, and masts and can analyze saliva and nutrients and monitor air quality [10,23,32-35]. For example, a small stretchable circuit and sensor that can be inserted into the human oral cavity may be integrated into a breathable, flexible microporous membrane for a tissue-friendly design, as shown in Figure 2C [23]. Such a device may be used in research to study the prevention of hypertension by facilitating continuous quantification analysis of sodium intake. Figure 2D [10] shows a sensing platform for detecting gaseous CO2 inside a face mask via stable inorganic phosphors whose luminescence is controlled by a pH indicator. A mask combining a battery-free printed near-field communication (NFC) tag and a photochemical sensor for noninvasive CO2 measurement was used to achieve detection performance with a resolution of 103 ppm. Practicality in physical activity has been increased through the compensation of the temperature noise and characterized analytical specifications of measurement systems. Moreover, health care wearable systems attached to the ears use earbuds to perform heart rate and sleep monitoring functions [36,37].

    Upper Body

    In addition to the face, wearable systems can be applied to the neck, chest, abdomen, internal organs, back, and waist to extract significant health values.

    Neck

    In the case of the neck, wearable devices with a necklace and patch form factor can record an electrocardiogram (ECG) and voice pressure and monitor the diet through an electroglottogram (EGG) using a neckband [38-40]. For example, a neck-attached wearable device incorporating a cross-linked polymer film and hole-patterned diaphragm structure detects and quantifies voice with an excellent sensitivity of 5.5 V Pa−1 over the voice frequency range, as shown in Figure 3A [39]. This device can be used for voice health management and security authentication by eliminating vibration distortions on the curved skin surface through excellent skin compatibility via using ultrathin profiles of ≥5 µm.

    ‎
    Figure 3. Wearable electronics mounted onto the upper body. (A) Vibration-responsive patch for sensing voice pressure. The images were reprinted from Lee et al [19,39]. (B) Epidermal cardiopulmonary patch based on laser fabrication. The images were reprinted from Rachim et al [41]. (C) Air-silicon composite transducer (ASiT) for breathing pattern monitoring. The images were reprinted from Cotur et al [19,42]. (D) Spine tracker sensor system. The image was reprinted from Stollenwerk et al [19,43]. (E) A belt for waistline measurement. The images were reprinted from Nakamura et al [12,19]. EPE: electrophysiological electrode; MES: mechano-acoustic sensor; PCB: printed circuit board.
    Thorax

    Thorax-related wearable electronics, such as patches, chest belts, and brassieres, enable ECG recording, temperature measurement, sleep monitoring, posture analysis, and galvanic skin response (GSR) assessment [11,41,44-48]. Figure 3B [41] shows a sensor designed for continuous monitoring of the cardiopulmonary biosignal via a CO2 laser–based manufacturing process. The epidermal patch consisting of a mechanoacoustic sensor and electrophysiological electrodes provides advanced functionality through a gas-permeable and biocompatible layer.

    Abdomen

    Abdomen-attached mHealth systems can sense glucose and breathing patterns through patches and straps [42,49]. For instance, an air-silicon composite transducer monitors respiratory activity by continuously measuring the force applied to the air channel embedded in the silicon-based elastomer, as shown in Figure 3C [42]. The system, which uses a pressure sensor and mixed-signal radio electronics, follows the principle of sensing the air pressure change inside the channel when breathing force is applied to the transducer surface. In particular, tactile sensing, including pressure sensing, is critical in health care. This is because tactile sensors attached to the skin detect physical stimuli, such as breathing patterns, heart rate, pulse, muscle activity, and body temperature, linked to biological signals. Skin, the most widely distributed organ among the five sense organs in the human body, is a tactile sensor with receptors that detect pressure, delicate movements, and temperature and is also an actuating organ that emits the same physical stimulation. Flexibility is a crucial element for the tactile sensor to be conformally attached to the skin to detect minute physical changes in detail and increase user convenience [50-52].

    Furthermore, digestible pills check medication compliance. Management of medication adherence can prevent patients with severe mental illness from experiencing relapses and hospitalizations [53]. In addition, capsule endoscopy can monitor the colon health or bladder pressure state [54,55].

    Back

    A wearable system attached to the back can be used to analyze changes in the spine’s shape during training. A spine tracker device shown in Figure 3D consists of 5 sensors, with each sensor attached to the lumbar spine, and can correct posture by providing real-time feedback [43].

    Waist

    In addition, a waist belt can be useful for obesity management [12,56]. The belt automatically measures waist circumference with high accuracy, with an F1-score of 0.95, and monitors the daily lifestyle using a magnetometer, an accelerometer, and a gyroscope, as shown in Figure 3E [12].

    Limbs

    In the case of the limbs, the main categories include the hands, arms, legs, and feet by attachment location.

    Hands

    The measurable health factors in a hand-related wearable device, such as a patch, ring, or glove, include rehabilitation evaluation analysis, ECG characteristics, oxygen saturation, dietary monitoring, pulse wave, and temperature [13,57-61]. For instance, a multisensory electronic skin integrated into a polyimide network simultaneously detects physical properties, such as temperature, strain, humidity, light, magnetic field, pressure, and proximity, in real time, as shown in Figure 4A [13]. It can also be used for rehabilitation evaluation using personalized intelligent prostheses.

    ‎
    Figure 4. Wearable devices attached to the hands and arms. (A) Stretchable and conformable electronic skin for multifunctional sensing. The images were reprinted from Hua et al [13,19]. (B) Power generation textile for wearable health care. The images were reprinted from Zhao et al [15,19]. (C) Stand-alone patch for health monitoring based on a stretchable organic optoelectronic system. The images were reprinted from Lee et al [62]. (D) Thermal patch for self-care treatment through temperature distribution sensing and thermotherapy based on wireless graphene. The images were reprinted from Kang et al [63]. (E) Sensor conformably attached to skin decoding epicentral human motions. The images were reprinted from Kim et al [19,64]. (F) A single wearable biosensor platform that simultaneously monitors sweat and interstitial fluid (ISF). The images were reprinted from Kim et al [16,19]. MEG: magnetoelastic generator; OLED: organic light-emitting diode; PDMS: polydimethylsiloxane; PI: polyimide; PPG: photoplethysmogram; PVA: polyvinyl alcohol; Temp.: temperature.
    Arms

    mHealth systems of various form factors related to the arm can also be useful for health management. Among them, wristwatches, bands, and bracelet devices can detect health factors, such as the heart rate, oxygen saturation, number of steps, blood pressure, ECG characteristics, glucose, blood sugar, and sweat metabolites [14,15,65-75]. Figure 4B [15] shows a magnetoelastic generator that provides the power to drive a wearable biosensor system. This generator can help measure cardiovascular parameters underwater without encapsulation for telemedicine and has excellent water vapor transmission characteristics.

    A patch sensor attached to the arm can measure the pH, sweat rate, lactate, heart rate, temperature, electromyogram (EMG) and ECG characteristics, blood pressure, and water content and can also be applied for wound treatment and rehabilitation evaluation [62-64,76-85]. For instance, a stand-alone organic skin patch for health care with an organic light-emitting display with sufficient pixels reports the heart rate via a stretchable photoplethysmogram (PPG) sensor, as shown in Figure 4C [62]. An ultrathin patch of 15 μm is configured on a soft elastomer substrate and can operate stably at 30% strain using a combination of a stress relief layer and deformable microcracks. Figure 4D [63] shows a wireless graphene patch that simultaneously provides thermal sensing and thermotherapy capabilities. This thermal patch consists of a graphene-based capacitive sensor, a graphene thermal pad, and a flexible wireless communication module to continuously monitor temperature changes with high resolution and sensitivity and perform thermal treatment through a graphene-based heater. Beyond the existing complex multisensor structure, skin patches alone may decode movements of 5-finger gestures by detecting microdeformation using the laser-induced crack structure, as shown in Figure 4E [64]. Based on the same principle, it can be attached to various body parts to track physical movements.

    Furthermore, ECG, EMG, temperature, sweat, and interstitial fluid analyses can be performed following health care monitoring through arm tattoos [16,86]. For instance, a noninvasive epidermal biosensing system includes physically separated electrochemical biosensors for the extraction of interstitial fluid at the cathode and sweat stimulus extraction at the anode, as shown in Figure 4F [16]. Namely, this biomarker monitoring system is a single wearable epidermal platform that simultaneously samples and analyses different biofluids.

    Legs and Feet

    Figure 5 describes a wearable health care device that may be applied to the legs, feet, or whole body. The mobile form factors applicable to the legs include patches, wearable robots, and straps, which perform moisture analysis at the wound area, gait analysis, ECG measurement, and rehabilitation evaluation [17,87-92]. For instance, appropriate dressing changes for exudative wounds are essential. Using a moisture sensor mounted on the bandage, as shown in Figure 5A [89], the change in the amount of dressing on the wound can be detected and the replacement time determined, increasing patient convenience. A motion capture device can accurately measure the movement of limbs during daily activities, strenuous exercise, and long-term exercise, as shown in Figure 5B [17]. Existing drift and instability problems are solved by integrating microtriaxis inertial and microtriaxis flow sensors. Additionally, it is possible to evaluate gait performance on irregular and uneven surfaces using a wearable sensor in the form of a strap with 6 inertial measurement units (IMUs) and an analysis algorithm, as shown in Figure 5C [92]. It is possible to implement edema measurement, gait analysis, and ulcer detection via plantar pressure analysis using wearable sensors attached to the shoes, socks, or soles of the feet [93-97].

    ‎
    Figure 5. mHealth apps for the legs and the whole body. (A) Moisture sensor for exudative wounds. The image was reprinted from Henricson et al [19,89]. (B) A motion capture device capable of detecting limb movements with high accuracy. The images were reprinted from Liu et al [17,19]. (C) Wearable strap sensor for gait analysis. The image was reprinted from Luo et al [19,92]. (D) An electronic textile conformable suit for distributed sensing wirelessly. The images were reprinted from Wicaksono et al [19,98]. mHealth: mobile health.

    Whole Body

    Furthermore, clothes worn on the whole body are also a type of wearable device. Figure 5D [98] shows a personalized and conformable suit of an electronics-based textile for multimodal health care sensing. The platform’s elasticity ensures intimate contact between the electronic device and the skin, and it can detect the skin temperature, heart rate, and respiration with high accuracy and precision. The suit with electronic textiles can measure the body temperature, respiratory rate, heart rate, oxygen saturation, and EMG and ECG characteristics and can also perform phototherapy [98-103]. As described before, form factor and detection targets by body part on wearable devices are summarized in Tables 1-4.

    Table 1. Summary of form factor and detection targets on wearable devices for the face.
    Body position and form factorTarget(s) of detectionReference(s)
    Head
     TattooEEGa[8]
     Band, cap, headsetMental stress through EEG[20]
     Band, cap, headsetEEG[21]
    Eyes
     LensesGlucose[24,25]
     LensesIntraocular pressure[9,26]
     LensesElectroretinogram[22]
     GlassesAuxiliary surgical tool[27,28]
     GlassesSweat electrolytes, metabolites[29]
    Nose
     Nose padPulse wave, respiration rate[30]
     Nose padElectrooculogram[31]
    Mouth
     MouthguardSaliva monitoring[32-34]
     MouthguardNutrition analysis[23]
     Tooth sensorNutrition analysis[35]
     MaskAir quality monitoring[10]
    Ears
     EarbudsHeart rate[36]
     EarbudsSleep monitoring using EEG[37]

    aEEG: electroencephalogram.

    Table 2. Summary of form factor and detection targets on wearable devices for the upper body.
    Body position and form factorTarget(s) of detectionReference(s)
    Neck
     NecklaceECGa[38]
     PatchVoice pressure[39]
     BandEGGb[40]
    Thorax
     PatchECG[11,41,44]
     PatchECG, temperature[45]
     PatchSleep monitoring[46]
     Chest beltTrunk posture[47]
     BrassiereGalvanic skin response[48]
    Abdomen
     PatchGlucose[49]
     StrapRespiratory patterns[42,50-52]
    Internal organs
     Ingestible pill/capsuleMedication compliance[53]
     Ingestible pill/capsuleIntravesical pressure and colon monitoring[54,55]
    Back
     StrapSpine monitoring[43]
    Waist
     BeltObesity management[12,56]

    aECG: electrocardiogram.

    bEGG: electroglottogram.

    Table 3. Summary of form factor and detection targets on wearable devices for the limbs.
    Body position and form factorTarget(s) of detectionReference(s)
    Hands
     PatchRehabilitation[13]
     RingECGa[57]
     RingSpO2b[58]
     RingDietary management[59]
     RingPulse wave, temperature[60]
     GloveRehabilitation[61]
    Wrist
     Watch/band/braceletHeart rate, step number[65]
     Watch/band/braceletSpO2[66]
     Watch/band/braceletSpO2, heart rate, energy expenditure[67,68]
     Watch/band/braceletBlood pressure[14,69]
     Watch/band/braceletPulse management[15]
     Watch/band/braceletECG[70,71]
     Watch/band/braceletDiagnosis of Parkinson disease[72]
     Watch/band/braceletGlucose[73,74]
     Watch/band/braceletSweat metabolites (glucose, lactate)[75]
     PatchSweat rate, pH, lactate, glucose, chloride[76]
     PatchHeart rate[62]
     PatchWound management[77,78]
     PatchTemperature, thermotherapy[63]
     PatchECG, EMGc[79-81]
     PatchEMG[82,83]
     PatchBlood pressure, skin hydration, temperature[84]
     PatchBiometrics[85]
     PatchRehabilitation[64]
     TattooECG, EMG, temperature[86]
     TattooSweat and Interstitial fluid analysis[16]
    Legs
     PatchECG[87]
     PatchMoisture analysis at the wound area[88,89]
     Wearable robotRehabilitation[17,90,91]
     StrapGait analysis[92]
    Feet
     PatchEdema[93]
     ShoesGait analysis[94-96]
     SocksFoot pressure ulcer[97]

    aECG: electrocardiogram.

    bSpO2:oxygen saturation.

    cEMG: electromyogram.

    Table 4. Summary of form factor and detection targets on wearable devices for the whole body (clothes using electronic textiles).
    Target(s) of detectionReference(s)
    Temperature, respiration, heart rate[98]
    SpO2a, heart rate, temperature[99]
    Phototherapy, temperature, heart rate[100,101]
    EMGb[102]
    ECGc[103]

    aSpO2:oxygen saturation.

    bEMG: electromyogram.

    cECG: electrocardiogram.


    In addition to wearable devices, health care delivery is also possible using smartphones through built-in sensors, smartphone-interlocked gadgets, display-related materials, and apps.

    CMOS Only

    Smartphones have built-in 20-30 sensors; in particular, complementary metal-oxide-semiconductor (CMOS) image sensors may be used to monitor heart, eye, and skin-related diseases [18,104-109]. As shown in Figure 6A [18], the atrial fibrillation screening ability using PPG pulse analysis based on a smartphone camera and a commercialized app showed a similar performance level to that of patches used for single-lead ECG monitoring. It has been proven that prodromal stroke symptoms can be detected using only a smartphone in a primary care setting. In addition, the fingertip motion signal and color intensity signal, both heterogeneous signals, are acquired and analyzed using a camera to remove finger movement and optical noise, as shown in Figure 6B [104]. In this way, a clean heart rhythm signal with high accuracy can be extracted via smartphone monitoring, while minimizing noise artifacts.

    ‎
    Figure 6. Health care apps using built-in smartphone sensors. (A) Smartphone built-in camera and app-based atrial fibrillation diagnosis. The images were reprinted from Chan et al [18,19]. (B) Heart rhythm analysis using CMOS image sensor. The images were reprinted from Tabei et al [19,104]. (C) Smartphone-based blood pressure measurement through the oscillometric finger-pressing method. The images were reprinted from Chandrasekhar et al [19,110]. (D) Set and acquisition graph of smartphone and 3D-printed mouthpiece adapter for spirometry. The images were reprinted from Thap et al [19,111]. CMOS: complementary metal-oxide-semiconductor; PPG: photoplethysmogram.

    Hybrid Including CMOS

    New functions, such as blood pressure measurement and temperature and dietary monitoring, can be established by combining pressure sensors, temperature sensors, and the phone microphone instead of CMOS alone [110,112-115]. For instance, as shown in Figure 6C [110], absolute blood pressure is measured via a blood flow oscillometric signal through finger pressure using a strain gauge on the front of the smartphone, in addition to CMOS. A light-emitting display may also be added to this, so it is possible to measure blood pressure ultimately with pure smartphone components.

    IMU/Microphone/Ultrasonic Sensor

    In addition, sleep position monitoring and treatment can be performed by detecting body movements through an IMU of the smartphone, and the gait of patients with Parkinson disease can also be analyzed [116-118]. The smartphone’s built-in microphone sensor can also assess lung capacity and breathing sounds and monitor sleep [111,119-121]. Figure 6D [111] reports lung capacity and function parameter measurements following smartphone microphone–based, high-resolution time-frequency spectral analysis. A moisture-resistant ultrasonic sensor using polyvinylidene fluoride can be used for biometric authentication through fingerprinting [122].

    Touch Sensor/Digitizer

    Moreover, general user interfaces, such as a touch sensor and digitizer, can also be used for health care purposes. For example, the heart rate can be checked by assessing capacitance changes according to the heartbeat with a capacitive touch sensor. The touch sensor is also helpful in diagnosing Parkinson disease through touch accuracy analysis [123,124]. In addition, a digitizer for writing can be applied to biometric authentication through handwritten signature recognition [125,126].

    Interlocked Gadgets

    There is a case of combining various mHealth sensing techniques, such as pesticide analysis, otitis media diagnosis, malaria infection detection, and ECG measurement, by adding a separate gadget rather than using just the smartphone itself [127-133]. The platform shown in Figure 7A [128] performs a visual, quantitative analysis of pesticides using an optical system that combines a dark cavity and an ultraviolet lamp with a smartphone. In other words, integrating a smartphone and a gadget-based paper strip enables real-time and on-site food evaluation. Additionally, it was confirmed that the diagnosis of acute otitis media is possible with the same level of accuracy as that attained with existing otoscopes through the combination of a commercialized optical system and a camera in a smartphone, as shown in Figure 7B [130]. Figure 7C [132] shows a smartphone-based immunodiagnostic platform that performs a chemiluminescence-based enzyme-linked immunosorbent assay using a lyophilized chemiluminescence reagent. This hand-held point-of-care-testing analyzer can detect active malaria infections with a sensitivity of 8 ng/mL.

    ‎
    Figure 7. Health care apps using gadgets mounted on smartphones. (A) Smartphone platform for pesticide evaluation of food, integrated with an ultraviolet lamp and a dark cavity by 3D printing. The images were reprinted from Chu et al [128]. (B) Smartphone otoscope for diagnosis of acute otitis media. The images were reprinted from Mousseau et al [130]. (C) Smartphone-based immunodiagnosis using microfluidic assays. The images were reprinted from Ghosh et al [19,132]. (D) Antibacterial touchscreen for preventing contamination. The images were reprinted from Ippili et al [134]. (E) Digital biomarkers that reflect users’ moods, behaviors, and cognitions using text logs, browser history, human-computer interactions, and various sensors. The images were reprinted from Chen et al [19,135].

    Display Materials

    Health care delivery can also be achieved through materials used in manufacturing smartphones, such as window coatings for antireflection and display processes. An ecofriendly antibacterial coating with Zn-doped silicon oxide thin films can prevent infectious diseases caused by microbial contamination of touch events, as shown in Figure 7D [134]. In addition, it is possible to reduce the deformation of retinal cells by decreasing the blue light of the display through the material development of organic light-emitting or color filters [136].

    Apps

    Furthermore, health care sensing is possible through apps incorporating digital phenotypes and digital therapeutics [135,137-140]. A digital phenotype refers to a disease or health condition that is unintentionally reflected in patterns of use of digital devices. Mobile apps can collect human-smartphone interaction data to monitor smartphone usage and construct long-term patterns and trend changes. As shown in Figure 7E [135], analyzing a digital biomarker that reflects human effects, moods, behaviors, and cognition can predict psychiatric conditions, such as depression and smartphone addiction. In addition, digital therapeutics delivered through games, education, coaching, and counselling are based on cognitive behavioral therapy and can treat insomnia, alcohol addiction, drug addiction, panic disorder, and attention deficit hyperactivity disorder. Additionally, it effectively improves physical diseases, such as obesity and high blood glucose. Table 5 summarizes the sensing methods and targets using smartphones.

    Table 5. Summary of sensing methods and targets using smartphones.
    Type and sensing methodsTarget(s) of detectionReference(s)
    Built-in sensors
     CMOSaAtrial fibrillation[18]
     CMOSHeart rate[104,105]
     CMOSDiabetic retinopathy[106]
     CMOSSkin cancer[107-109]
     CMOS + microphoneHeart rate, SpO2b, blood pressure[112]
     CMOS + microphone + speakerDiet management[113]
     CMOS + strain gauge + displayBlood pressure[110,114]
     CMOS + temperature sensorTemperature, heart rate[115]
     IMUcSleep monitoring[116,117]
     IMUGait analysis[118]
     MicrophoneSpirometry[111,119]
     MicrophoneBreathing sound analysis[120]
     MicrophoneSleep monitoring[121]
     Ultrasonic sensorBiometric using fingerprint[122]
     Touch sensorHeart rate[123]
     Touch sensorParkinson disease[124]
     DigitizerBiometrics using signature[125,126]
    Gadgets interlocked with smartphones
     Optical platformPesticide evaluation in food[127-129]
     Smartphone CMOS + lensOtoscopy[130,131]
     Microfluidic platformMalaria infection[132]
     Patch electrodeECGd[133]
    Materials
     Window coatingAntibacterial[134]
     Light emittingBlocking of blue light[136]
    Apps
     Digital phenotypingAddiction, attention deficit hyperactivity disorder[135,137,138]
     Digital therapeuticsMental health[139,140]

    aCMOS: complementary metal-oxide-semiconductor.

    bSpO2:oxygen saturation.

    cIMU: inertial measurement unit.

    dECG: electrocardiogram.


    The industry of mHealth is expected to grow explosively in the future. In particular, the third generation of medicine and therapies that rely on novel solutions are emerging beyond the existing state of mHealth. Among them, bioelectronic medicine is a nonpharmacological treatment category that stimulates nerve functions with energy, such as electricity, light, and ultrasonic waves. This approach uses an electronic device that controls metabolic function to maintain homeostasis by regulating hormones [141]. To date, electroceuticals have been used for obesity, asthma, sleep apnea, brain tumors, epilepsy, and Parkinson disease and have shown substantial and significant therapeutic effects [142-144]. It is also one of the most innovative fields in medicine because it has significant advantages when considering the development time and cost of existing drugs.

    Using digital therapeutics, also referred to as “software as a medical device,” it is possible to manage and treat not only physical diseases but also psychiatric conditions, such as posttraumatic stress disorder and schizophrenia [145,146]. It is of great significance in terms of patient convenience that personal and sensitive mental health conditions can be diagnosed in real life, not in hospitals, through digital phenotypic analysis, such as smartphone usage patterns and uploaded social networking service (SNS) content.

    From the point of view of the wearable form factor, since much of health care sensing is possible on the wrist, the smartwatch is currently playing a pivotal role in health care. The finger (as well as the wrist) is a body part to focus on as it can be used to assess health factors, such as the heart rate, oxygen saturation, ECG characteristics, blood pressure, blood sugar, biometric authentication, body temperature, and dietary monitoring. Therefore, it is expected that in the future, the ring type of device for health care will pair with the smartwatch as the 2 main pillars.


    This viewpoint investigated comprehensive health care sensing technology using wearable electronics and smartphones. However, mHealth is less widely used than expected, unfortunately. Wearable devices are relatively more optimized for continuous and real-time health care sensing compared to smartphones [147,148]. However, the penetration rate compared to smartphones worldwide is sluggish [149-151]. A smartwatch, a representative wearable device, needs to be connected to a smartphone to operate, so users do not recognize the wearable device as an independent entity. Independent use is required to be fully positioned as a separate device. These devices lack effectiveness due to reduced user convenience because of their small screens, poor battery performance, low usage rate, clunky design, and high price. Wearable devices are recognized as a kind of subdevice rather than an essential and leading product because they do not have as much impact as smartphones. Therefore, in the case of wearable devices, innovative solutions are required to make them universal necessities for human beings, such as smartphones.

    However, in the case of smartphones, the penetration rate is high worldwide, including low- and middle-income countries [152]. In the case of current smartphones, the fundamental value in terms of user experience as well as utility is high. However, it is not such a great solution from the perspective of health care. It is challenging to conduct biosignal sensing using a smartphone while being in close contact with human skin all day long, so it is challenging to implement continuous real-time big data–based predictive and preventive medical care using smartphones from the health care perspective. Smartphones desperately require a breakthrough that can allow them to monitor health in real time continuously, 24 hours a day, through a form more closely adherent to the skin, while maintaining the current phone function.


    The display is a crucial component of a health care system. In other words, smartphones and wearable devices, as central axes of the mHealth system, are inseparable from their displays. In addition, displays and sensors in mobile devices are closely related. To improve the convenience of user interaction, the proportion of the active area of mobile displays is increasing. However, the increase in the active area has a limitation that reduces the sensing performance, including sensitivity. To overcome this, the upper part of the sensor covers the display by lowering the resolution of the display to prevent the deterioration of the sensing transmittance. A typical example is under-panel camera (UPC) technology that covers the camera with the display by reducing the display resolution on the top of the CMOS image sensor to increase light transmittance.

    Sensor-Integrated Display Solution

    However, the ultimate and ideal method is a sensor-integrated display solution. A sensor-integrated display has many advantages from a health care sensing point of view. This is because (1) many mHealth sensors use an optical approach, (2) it is relatively easy to manufacture large-area sensors, and (3) the application of a new form factor display can lead to an increase in the body contact area.

    First, the majority of mHealth sensing approaches are optical methods. Various health care parameters, such as the heart rate, oxygen saturation, blood pressure, blood sugar, body temperature, environmental monitoring, and ECH characteristics, can be measured optically. A display is an optical system that already has the means to transmit light. Therefore, a sensor-integrated display could be an optimized health care solution. To implement health care devices using optical systems, in addition to optical transmitters, receiver systems must also be equipped. For advanced performance, the light-emitting wavelength band needs to be expanded and supplemented, including infrared as well as visible light, through the development of materials for the light-emitting layer.

    Second, since the sensing area and detection performance are proportional, health care ability can be improved through a sensor embedded in a wide display area. It enables health care sensing in a large area over the entire display area when the built-in optical system is applied, considering design rules. In addition, it is more advantageous for wearability because of a reduction in volume due to the implementation of microlevel thickness because of the sensor-integrated display. Additionally, compared to the number of photomasks needed to manufacture a conventional display, the number of additional photomasks required to implement a display health care system with built-in sensors is far less. It can contribute to popularization due to the low manufacturing price according to the integral type. Ultrathin, low-cost health care devices with relatively simple processes have significant benefits over conventional, bulky, and expensive wearable computers.

    Finally, the new form factor device, such as a stretchable sensor-integrated display, increases the area of contact with the body and improves detection capability through health care sensing in close contact with the skin. Flexible panels with user convenience could be applied to the human skin, considering ergonomic factors [153-159]. The flexibility of not only the active matrix backplane and core of the panel but also the touch sensor, fingerprint sensor, and pressure sensor must be ensured, as shown in Figure 8A [153]. In a complete sensor-integrated display, the flexibility of the backplane allows the sensor part to gain flexibility naturally.

    ‎
    Figure 8. New form factor display and principle. (A) Wearable display with flexible and ultrathin active matrix backplane, touch screen panel, and fingerprint sensor components. The image was reprinted from Park et al [19,153]. (B) A flexible active matrix organic light-emitting diode (AMOLED) with large-area MoS2-based backplane for human skin display. The images were reprinted from Choi et al [154]. (C) A graph of compressive and tensile strength as the thickness increases in a single-layer structure (solid blue line) and laminated structure (dotted red line).

    Furthermore, Figure 8B [154] shows a wearable full-color organic light-emitting diode (OLED) display using a 2D material–based backplane transistor suitable for complex skin shapes. The 18×18 thin-film transistor array was fabricated on ultrathin MoS2 film and then transferred to Al2O3 (30 nm)/polyethene terephthalate (6 μm), providing mechanical flexibility beyond conventional OLED technology.

    New Form Factor Display

    The left picture of Figure 8C simulates a multilayered display, and when this display is bent, tensile strength is applied at the top and compressive strength is applied at the bottom. Assuming that it is formed with only a single layer of the same thickness rather than a laminated structure, extreme tensile and compressive forces occur on the upper and lower surfaces, resulting in cracks in the display, as shown by the solid blue line in the right graph. However, in the stacked structure, a pressure-sensitive adhesive (PSA) between the display layers continues to create new neutral planes, as shown by the dotted red line. In response, the magnitude of the tension and compression force at the top and bottom surfaces does not increase, even if the thickness of the display increases. In other words, using the PSA, it is possible to implement a flexible display without cracks.

    No part of the human body is flat. When the health care system and the skin conformally adhere, sensing performance improves. Display technology based on PSA with the harmony of creep and recovery characteristics induces form changes in wearable devices and smartphones. A new form factor with flexibility based on PSA technology that creates a new neutral plane will facilitate a critical conversion of the mHealth system.


    A new form factor display for health care with flexibility and display convergence technology using an optical method attaches a large-area health care system to the human skin conformally and continuously detects health care factors in real time, thereby providing a framework for collecting big data. As a result, the existing smartphone becomes a wearable device attached to the body, and the existing wearable device is equipped with smartphone functions suitable for user convenience. Namely, convergence health care technology with the sensor-integrated and new form factor display is an indispensable element that enables the smartphonization of a wearable device and the wearable deviceization of the smartphone. Of course, health care systems with new form factors and sensor-integrated displays do not solve all mHealth problems. In other words, advances in big data AI software analysis and medical security should go hand in hand with the smartphonization of wearable devices and the wearable deviceization of smartphones. Furthermore, it will be necessary to supplement the medical system policy so that these benefits do not become the exclusive property of the upper class of the economy and so that people from lower social classes can also benefit. Advanced and popularized mHealth system technology could ensure universal health coverage so that everyone can use essential, high-quality medical services without discrimination. In other words, the authentic democratization of health care could become a reality, and a standard for a future health care paradigm in the post-pandemic era could arise.


    Personalized platforms, such as wearable devices and smartphones, can be applied to AI-based disease prediction, prevention, and treatment. This viewpoint researched the latest technology trends in mHealth regarding form factors and detection targets according to body attachment location and type. In particular, the sensor convergence technology of the new form factor display provides a framework to analyze health factors in real time by conformally adhering a large-area system to the skin. Innovation in form factors in sensor-integrated displays and convergence health care solutions enable the smartphonization of wearable devices and the wearable deviceization of smartphones. In addition, the strategy for the smartphonization of wearable devices and the wearable deviceization of smartphones can accelerate the development of mHealth, realizing the democratization of medical care so that anyone can use essential services of high quality. Furthermore, it is expected to create a new milestone for the medical paradigm shift in the postpandemic era.

    Acknowledgments

    This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT RS-2022-00165631). This research was also supported by Daejeon University Research Grants (2022).

    Authors' Contributions

    WH conceived the idea and concept of the viewpoint, created the figure sets, summarized the tables, and wrote the manuscript.

    Conflicts of Interest

    None declared.

    1. Weinstein RS, Lopez AM, Joseph BA, et al. Telemedicine, telehealth, and mobile health applications that work: opportunities and barriers. Am J Med. Mar 2014;127(3):183-187. [CrossRef] [Medline]
    2. Figueroa CA, Luo T, Aguilera A, Lyles CR. The need for feminist intersectionality in digital health. Lancet Digit Health. Aug 2021;3(8):e526-e533. [CrossRef] [Medline]
    3. Gunasekeran DV, Tham Y-C, Ting DSW, Tan GSW, Wong TY. Digital health during COVID-19: lessons from operationalising new models of care in ophthalmology. Lancet Digit Health. Feb 2021;3(2):e124-e134. [CrossRef] [Medline]
    4. Kimberly J, Cronk I. Making value a priority: how this paradigm shift is changing the landscape in health care. Ann N Y Acad Sci. Oct 2016;1381(1):162-167. [CrossRef] [Medline]
    5. Bartold PM, Ivanovski S. P4 medicine as a model for precision periodontal care. Clin Oral Investig. Sep 2022;26(9):5517-5533. [CrossRef] [Medline]
    6. Muzny M, Henriksen A, Giordanengo A, et al. Wearable sensors with possibilities for data exchange: analyzing status and needs of different actors in mobile health monitoring systems. Int J Med Inform. Jan 2020;133:104017. [CrossRef] [Medline]
    7. Mitratza M, Goodale BM, Shagadatova A, et al. The performance of wearable sensors in the detection of SARS-Cov-2 infection: a systematic review. Lancet Digit Health. May 2022;4(5):e370-e383. [CrossRef] [Medline]
    8. Shin JH, Kwon J, Kim JU, et al. Wearable EEG electronics for a brain–AI closed-loop system to enhance autonomous machine decision-making. npj Flex Electron. May 30, 2022;6(1):32. [CrossRef]
    9. Kim J, Kim M, Lee M-S, et al. Wearable smart sensor systems integrated on soft contact lenses for wireless ocular diagnostics. Nat Commun. Apr 27, 2017;8(1):14997. [CrossRef]
    10. Escobedo P, Fernández-Ramos MD, López-Ruiz N, et al. Smart facemask for wireless CO monitoring. Nat Commun. Jan 10, 2022;13(1):72. [CrossRef]
    11. Hwang W, Kim J, Park S, et al. A Breathable and stretchable metastructure for a versatile hybrid electronic skin patch with long‐term skin comfort. Adv Mater Technol. Jul 28, 2022;8(1):2200477. [CrossRef]
    12. Nakamura Y, Matsuda Y, Arakawa Y, Yasumoto K. Waistonbelt X: a belt-type Wearable device with sensing and intervention toward health behavior change. Sensors (Basel). Oct 22, 2019;19(20):4600. [CrossRef] [Medline]
    13. Hua Q, Sun J, Liu H, et al. Skin-inspired highly stretchable and conformable matrix networks for multifunctional sensing. Nat Commun. Jan 16, 2018;9(1):244. [CrossRef] [Medline]
    14. Moon JH, Kang M-K, Choi C-E, Min J, Lee H-Y, Lim S. Validation of a wearable cuff-less wristwatch-type blood pressure monitoring device. Sci Rep. Nov 4, 2020;10(1):19015. [CrossRef]
    15. Zhao X, Zhou Y, Xu J, et al. Soft fibers with magnetoelasticity for wearable electronics. Nat Commun. Nov 19, 2021;12(1):6755. [CrossRef]
    16. Kim J, Sempionatto JR, Imani S, et al. Simultaneous monitoring of sweat and interstitial fluid using a single wearable biosensor platform. Advanced Science. Oct 2018;5(10):1800880. URL: https://onlinelibrary.wiley.com/toc/21983844/5/10 [CrossRef]
    17. Liu S, Zhang J, Zhang Y, Zhu R. A wearable motion capture device able to detect dynamic motion of human limbs. Nat Commun. Nov 5, 2020;11(1):5615. [CrossRef]
    18. Chan P-H, Wong C-K, Poh YC, et al. Diagnostic performance of a smartphone‐based photoplethysmographic application for atrial fibrillation screening in a primary care setting. J Am Heart Assoc. Jul 21, 2016;5(7):e003428. [CrossRef] [Medline]
    19. Attribution 4.0 International (CC BY 4.0). Creative Commons. URL: https://creativecommons.org/licenses/by/4.0/ [Accessed 2024-01-11]
    20. Kamińska D, Smółka K, Zwoliński G. Detection of mental stress through EEG signal in virtual reality environment. Electronics. Nov 18, 2021;10(22):2840. [CrossRef]
    21. McDowell K, et al. Biosensor technologies for augmented brain-computer interfaces in the next decades. Proc IEEE. May 2012;100(Special Centennial Issue):1553-1566. [CrossRef]
    22. Kim K, Kim HJ, Zhang H, et al. All-printed stretchable corneal sensor on soft contact lenses for noninvasive and painless ocular electrodiagnosis. Nat Commun. Mar 9, 2021;12(1):1544. [CrossRef]
    23. Lee Y, Howe C, Mishra S, et al. Wireless, intraoral hybrid electronics for real-time quantification of sodium intake toward hypertension management. Proc Natl Acad Sci USA. May 22, 2018;115(21):5377-5382. [CrossRef]
    24. Falk M, Andoralov V, Silow M, Toscano MD, Shleev S. Miniature biofuel cell as a potential power source for glucose-sensing contact lenses. Anal Chem. Jul 2, 2013;85(13):6342-6348. [CrossRef] [Medline]
    25. Yao H, Liao Y, Lingley AR, et al. A contact lens with integrated telecommunication circuit and sensors for wireless and continuous tear glucose monitoring. J Micromech Microeng. Jul 1, 2012;22(7):075007. [CrossRef]
    26. Kim J, Park J, Park Y-G, et al. A soft and transparent contact lens for the wireless quantitative monitoring of intraocular pressure. Nat Biomed Eng. Jul 3, 2021;5(7):772-782. [CrossRef]
    27. Kulak O, Drobysheva A, Wick N, et al. Smart glasses as a surgical pathology grossing tool. Arch Pathol Lab Med. Apr 1, 2021;145(4):457-460. [CrossRef] [Medline]
    28. Hiranaka T, Nakanishi Y, Fujishiro T, et al. The use of smart glasses for surgical video streaming. Surg Innov. Apr 2017;24(2):151-154. [CrossRef] [Medline]
    29. Sempionatto JR, Nakagawa T, Pavinatto A, et al. Eyeglasses based wireless electrolyte and metabolite sensor platform. Lab Chip. May 16, 2017;17(10):1834-1842. [CrossRef] [Medline]
    30. Nguyen T-V, Ichiki M. MEMS-based sensor for simultaneous measurement of pulse wave and respiration rate. Sensors (Basel). Nov 13, 2019;19(22):4942. [CrossRef] [Medline]
    31. Rostaminia S, Lamson A, Maji S, Rahman T, Ganesan D. W!NCE: unobtrusive sensing of upper facial action units with EOG-based eyewear. Proc ACM Interact Mob Wearable Ubiquitous Technol. Mar 29, 2019;3(1):1-26. [CrossRef]
    32. Kim J, Imani S, de Araujo WR, et al. Wearable salivary uric acid mouthguard biosensor with integrated wireless electronics. Biosens Bioelectron. Dec 15, 2015;74:1061-1068. [CrossRef] [Medline]
    33. Arakawa T, Kuroki Y, Nitta H, et al. Mouthguard biosensor with telemetry system for monitoring of saliva glucose: a novel Cavitas sensor. Biosens Bioelectron. Oct 15, 2016;84:106-111. [CrossRef] [Medline]
    34. Moonla C, Lee DH, Rokaya D, et al. Review—lab-in-a-mouth and advanced point-of-care sensing systems: detecting bioinformation from the oral cavity and saliva. ECS Sens Plus. Jun 1, 2022;1(2):021603. [CrossRef]
    35. Tseng P, Napier B, Garbarini L, Kaplan DL, Omenetto FG. Functional, RF-Trilayer sensors for tooth-mounted, wireless monitoring of the oral cavity and food consumption. Adv Mater. May 2018;30(18):e1703257. [CrossRef] [Medline]
    36. Boukhayma A, Barison A, Haddad S, Caizzone A. Earbud-embedded micro-power mm-sized optical sensor for accurate heart beat monitoring. IEEE Sensors J. Sep 15, 2021;21(18):19967-19977. [CrossRef]
    37. Nakamura T, Goverdovsky V, Morrell MJ, Mandic DP. Automatic sleep monitoring using ear-EEG. IEEE J Transl Eng Health Med. 2017;5:2800108. [CrossRef] [Medline]
    38. Santala OE, Lipponen JA, Jäntti H, et al. Necklace-embedded electrocardiogram for the detection and diagnosis of atrial fibrillation. Clin Cardiol. May 2021;44(5):620-626. [CrossRef] [Medline]
    39. Lee S, Kim J, Yun I, et al. An Ultrathin conformable vibration-responsive electronic skin for quantitative vocal recognition. Nat Commun. Jun 18, 2019;10(1):2468. [CrossRef]
    40. Farooq M, Fontana JM, Sazonov E. A novel approach for food intake detection using electroglottography. Physiol Meas. May 2014;35(5):739-751. [CrossRef] [Medline]
    41. Rachim VP, Lee J, Kim Y, Oh J, Jeong U, Park S. A scalable laser‐centric fabrication of an epidermal cardiopulmonary patch. Adv Materials Technologies. Nov 2022;7(11):2200242. URL: https://onlinelibrary.wiley.com/toc/2365709x/7/11 [CrossRef]
    42. Cotur Y, Olenik S, Asfour T, et al. Bioinspired stretchable transducer for wearable continuous monitoring of respiratory patterns in humans and animals. Adv Mater. Aug 2022;34(33):e2203310. [CrossRef] [Medline]
    43. Stollenwerk K, Müller J, Hinkenjann A, Krüger B. Analyzing spinal shape changes during posture training using a wearable device. Sensors (Basel). Aug 20, 2019;19(16):3625. [CrossRef] [Medline]
    44. Vuorinen T, Noponen K, Vehkaoja A, et al. Validation of printed, skin‐mounted multilead electrode for ECG measurements. Adv Materials Technologies. Sep 2019;4(9):1900246. URL: https://onlinelibrary.wiley.com/toc/2365709x/4/9 [CrossRef]
    45. Yamamoto Y, Yamamoto D, Takada M, et al. Efficient skin temperature sensor and stable gel‐less sticky ECG sensor for a wearable flexible healthcare patch. Adv Healthc Mater. Sep 2017;6(17):1700495. [CrossRef] [Medline]
    46. Hsu Y-S, Chen T-Y, Wu D, Lin C-M, Juang J-N, Liu W-T. Screening of obstructive sleep apnea in patients who snore using a patch-type device with electrocardiogram and 3-axis accelerometer. J Clin Sleep Med. Jul 15, 2020;16(7):1149-1160. [CrossRef] [Medline]
    47. Lee W, Seto E, Lin K-Y, Migliaccio GC. An evaluation of wearable sensors and their placements for analyzing construction worker's trunk posture in laboratory conditions. Applied Ergonomics. Nov 2017;65:424-436. [CrossRef]
    48. Kim J, Kwon S, Seo S, Park K. Highly wearable galvanic skin response sensor using flexible and conductive polymer foam. Presented at: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Aug 26-30, 2014; Chicago, IL. [CrossRef] [Medline]
    49. Castorino K, Polsky S, O’Malley G, et al. Performance of the dexcom G6 continuous glucose monitoring system in pregnant women with diabetes. Diabetes Technol Ther. Dec 2020;22(12):943-947. [CrossRef] [Medline]
    50. Hu J, Dun G, Geng X, Chen J, Wu X, Ren T-L. Recent progress in flexible micro-pressure sensors for wearable health monitoring. Nanoscale Adv. Jun 13, 2023;5(12):3131-3145. [CrossRef]
    51. Seesaard T, Wongchoosuk C. Flexible and stretchable pressure sensors: from basic principles to state-of-the-art applications. Micromachines (Basel). Aug 20, 2023;14(8):1638. [CrossRef] [Medline]
    52. Mishra S, Mohanty S, Ramadoss A. Functionality of flexible pressure sensors in cardiovascular health monitoring: a review. ACS Sens. Sep 23, 2022;7(9):2495-2520. [CrossRef] [Medline]
    53. Alipour A, Gabrielson S, Patel PB. Ingestible sensors and medication adherence: focus on use in serious mental illness. Pharmacy (Basel). Jun 16, 2020;8(2):103. [CrossRef] [Medline]
    54. Kalantar-Zadeh K, Ha N, Ou JZ, Berean KJ. Ingestible sensors. ACS Sens. Apr 28, 2017;2(4):468-483. [CrossRef] [Medline]
    55. Liao C-H, Cheng C-T, Chen C-C, et al. An Ingestible electronics for continuous and real-time intraabdominal pressure monitoring. J Pers Med. Dec 24, 2020;11(1):12. [CrossRef] [Medline]
    56. Lee M, Shin J. Change in waist circumference with continuous use of a smart belt: an observational study. JMIR Mhealth Uhealth. May 2, 2019;7(5):e10737. [CrossRef] [Medline]
    57. Kwon S, Hong J, Choi E-K, et al. Detection of atrial fibrillation using a ring-type wearable device (cardiotracker) and deep learning analysis of photoplethysmography signals: prospective observational proof-of-concept study. J Med Internet Res. May 21, 2020;22(5):e16443. [CrossRef] [Medline]
    58. Lochner CM, Khan Y, Pierre A, Arias AC. All-organic optoelectronic sensor for pulse oximetry. Nat Commun. Dec 10, 2014;5(1):5745. [CrossRef] [Medline]
    59. Hong W, Lee J, Lee WG. A finger-perimetric tactile sensor for analyzing the gripping force by chopsticks towards personalized dietary monitoring. Sensors and Actuators A: Physical. Jan 2022;333:113253. [CrossRef]
    60. Wu Y, Chen P-F, Hu Z-H, Chang C-H, Lee G-C, Yu W-C, editors. A mobile health monitoring system using RFID ring-type pulse sensor. Presented at: Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing; Dec 12-14, 2009; Chengdu, China. [CrossRef]
    61. Yang S-H, Koh C-L, Hsu C-H, et al. An instrumented glove-controlled portable hand-exoskeleton for bilateral hand rehabilitation. Biosensors (Basel). Dec 3, 2021;11(12):495. [CrossRef] [Medline]
    62. Lee Y, Chung JW, Lee GH, et al. Standalone real-time health monitoring patch based on a stretchable organic optoelectronic system. Sci Adv. Jun 4, 2021;7(23):eabg9180. [CrossRef]
    63. Kang M, Jeong H, Park S-W, et al. Wireless graphene-based thermal patch for obtaining temperature distribution and performing thermography. Sci Adv. Apr 15, 2022;8(15):eabm6693. [CrossRef]
    64. Kim KK, Ha I, Kim M, et al. A deep-learned skin sensor decoding the epicentral human motions. Nat Commun. May 1, 2020;11(1):2149. [CrossRef] [Medline]
    65. Bai Y, Hibbing P, Mantis C, Welk GJ. Comparative evaluation of heart rate-based monitors: Apple Watch vs Fitbit Charge HR. J Sports Sci. Aug 2018;36(15):1734-1741. [CrossRef] [Medline]
    66. Phillips C, Liaqat D, Gabel M, de Lara E. Wristo2: reliable peripheral oxygen saturation readings from wrist-worn pulse oximeters. Presented at: 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops); Mar 22-26, 2021; Kassel, Germany. [CrossRef]
    67. Spaccarotella C, Polimeni A, Mancuso C, Pelaia G, Esposito G, Indolfi C. Assessment of non-invasive measurements of oxygen saturation and heart rate with an apple Smartwatch: comparison with a standard pulse oximeter. J Clin Med. Mar 8, 2022;11(6):1467. [CrossRef] [Medline]
    68. Yamagami K, Nomura A, Kometani M, et al. Early detection of symptom exacerbation in patients with SARS-Cov-2 infection using the Fitbit Charge 3 (DEXTERITY): pilot evaluation. JMIR Form Res. Sep 16, 2021;5(9):e30819. [CrossRef] [Medline]
    69. Golbus JR, Dempsey W, Jackson EA, Nallamothu BK, Klasnja P. Microrandomized trial design for evaluating just-in-time adaptive interventions through mobile health technologies for cardiovascular disease. Circ Cardiovasc Qual Outcomes. Feb 2021;14(2):e006760. [CrossRef] [Medline]
    70. Strik M, Caillol T, Ramirez FD, et al. Validating QT-interval measurement using the apple watch ECG to enable remote monitoring during the COVID-19 pandemic. Circulation. Jul 28, 2020;142(4):416-418. [CrossRef] [Medline]
    71. Kobel M, Kalden P, Michaelis A, et al. Accuracy of the apple watch iECG in children with and without congenital heart disease. Pediatr Cardiol. Jan 2022;43(1):191-196. [CrossRef] [Medline]
    72. Wile DJ, Ranawaya R, Kiss ZHT. Smart watch accelerometry for analysis and diagnosis of tremor. J Neurosci Methods. Jun 15, 2014;230:1-4. [CrossRef] [Medline]
    73. Hadar E, Chen R, Toledano Y, Tenenbaum-Gavish K, Atzmon Y, Hod M. Noninvasive, continuous, real-time glucose measurements compared to reference laboratory venous plasma glucose values. J Matern Fetal Neonatal Med. Oct 2019;32(20):3393-3400. [CrossRef] [Medline]
    74. Alsunaidi B, Althobaiti M, Tamal M, Albaker W, Al-Naib I. A review of non-invasive optical systems for continuous blood glucose monitoring. Sensors (Basel). Oct 14, 2021;21(20):6820. [CrossRef] [Medline]
    75. Gao W, Emaminejad S, Nyein HYY, et al. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature. Jan 28, 2016;529(7587):509-514. [CrossRef] [Medline]
    76. Bandodkar AJ, Gutruf P, Choi J, et al. Battery-free, skin-interfaced microfluidic/electronic systems for simultaneous electrochemical, colorimetric, and volumetric analysis of sweat. Sci Adv. Jan 2019;5(1):eaav3294. [CrossRef] [Medline]
    77. Wang C, Sani ES, Gao W. Wearable bioelectronics for chronic wound management. Adv Funct Mater. Apr 25, 2022;32(17):2111022. [CrossRef] [Medline]
    78. Jeon Y, Choi H, Lim M, et al. A wearable photobiomodulation patch using a flexible red‐wavelength OLED and its in vitro differential cell proliferation effects. Adv Materials Technologies. May 2018;3(5):1700391. URL: https://onlinelibrary.wiley.com/toc/2365709x/3/5 [CrossRef]
    79. Shahandashti PF, Pourkheyrollah H, Jahanshahi A, Ghafoorifard H. Highly conformable stretchable dry electrodes based on inexpensive flex substrate for long-term Biopotential (EMG/ECG) monitoring. Sensors and Actuators A: Physical. Aug 2019;295:678-686. [CrossRef]
    80. Myers AC, Huang H, Zhu Y. Wearable silver nanowire dry electrodes for electrophysiological sensing. RSC Adv. 2015;5(15):11627-11632. [CrossRef]
    81. Kisannagar RR, Jha P, Navalkar A, Maji SK, Gupta D. Fabrication of silver nanowire/polydimethylsiloxane dry electrodes by a vacuum filtration method for electrophysiological signal monitoring. ACS Omega. May 12, 2020;5(18):10260-10265. [CrossRef] [Medline]
    82. Roland T, Wimberger K, Amsuess S, Russold MF, Baumgartner W. An insulated flexible sensor for stable electromyography detection: application to prosthesis control. Sensors (Basel). Feb 24, 2019;19(4):961. [CrossRef] [Medline]
    83. Ng CL, Reaz MBI, Chowdhury MEH. A low noise capacitive electromyography monitoring system for remote healthcare applications. IEEE Sens J. Mar 15, 2020;20(6):3333-3342. [CrossRef]
    84. Nassar JM, Mishra K, Lau K, Aguirre‐Pablo AA, Hussain MM. Recyclable nonfunctionalized paper‐based ultralow‐cost wearable health monitoring system. Adv Materials Technologies. Apr 2017;2(4):1600228. URL: https://onlinelibrary.wiley.com/toc/2365709x/2/4 [CrossRef]
    85. Yokota T, Nakamura T, Kato H, et al. A conformable Imager for biometric authentication and vital sign measurement. Nat Electron. Jan 20, 2020;3(2):113-121. [CrossRef]
    86. Kim D-H, Lu N, Ma R, et al. Epidermal electronics. Science. Aug 12, 2011;333(6044):838-843. [CrossRef] [Medline]
    87. Garabelli P, Stavrakis S, Albert M, et al. Comparison of QT interval readings in normal sinus rhythm between a smartphone heart monitor and a 12‐Lead ECG for healthy volunteers and inpatients receiving sotalol or dofetilide. J Cardiovasc Electrophysiol. Jul 2016;27(7):827-832. [CrossRef] [Medline]
    88. Mehmood N, Hariz A, Templeton S, Voelcker NH. A flexible and low power telemetric sensing and monitoring system for chronic wound diagnostics. Biomed Eng Online. Mar 1, 2015;14(1):17. [CrossRef] [Medline]
    89. Henricson J, Sandh J, Iredahl F. Moisture sensor for exudative wounds - a pilot study. Skin Res Technol. Sep 2021;27(5):918-924. [CrossRef] [Medline]
    90. Huo W, Mohammed S, Moreno JC, Amirat Y. Lower limb wearable robots for assistance and rehabilitation: a state of the art. IEEE Systems Journal. Sep 2016;10(3):1068-1081. [CrossRef]
    91. Birch N, Graham J, Priestley T, et al. Results of the first interim analysis of the RAPPER II trial in patients with spinal cord injury: ambulation and functional exercise programs in the REX powered walking aid. J Neuroeng Rehabil. Jun 19, 2017;14(1):60. [CrossRef] [Medline]
    92. Luo Y, Coppola SM, Dixon PC, Li S, Dennerlein JT, Hu B. A database of human gait performance on irregular and uneven surfaces collected by wearable sensors. Sci Data. Jul 8, 2020;7(1):219. [CrossRef] [Medline]
    93. Hong W, Lee J, Lee WG. A size-cuttable, skin-interactive wearable sensor for digital deciphering of epidermis wavy deformation. Biosensors (Basel). Jul 29, 2022;12(8):580. [CrossRef] [Medline]
    94. Lee SI, Park E, Huang A, et al. Objectively Quantifying walking ability in degenerative spinal disorder patients using sensor equipped smart shoes. Med Eng Phys. May 2016;38(5):442-449. [CrossRef] [Medline]
    95. Lin F, Wang A, Zhuang Y, Tomita MR, Xu W. Smart Insole: a wearable sensor device for unobtrusive gait monitoring in daily life. IEEE Trans Ind Inf. Dec 2016;12(6):2281-2291. [CrossRef]
    96. Lin Z, Wu Z, Zhang B, et al. A triboelectric nanogenerator‐based smart insole for multifunctional gait monitoring. Adv Mater Technol. Oct 31, 2018;4(2):1800360. [CrossRef]
    97. Waaijman R, de Haart M, Arts MLJ, et al. Risk factors for plantar foot ulcer recurrence in neuropathic diabetic patients. Diabetes Care. Jun 2014;37(6):1697-1705. [CrossRef] [Medline]
    98. Wicaksono I, Tucker CI, Sun T, et al. A tailored, electronic textile conformable suit for large-scale spatiotemporal physiological sensing in vivo. npj Flex Electron. Apr 23, 2020;4(1):5. [CrossRef]
    99. Islam MR, Afroj S, Beach C, et al. Fully printed and multifunctional graphene-based wearable E-textiles for personalized healthcare applications. iScience. Mar 18, 2022;25(3):103945. [CrossRef] [Medline]
    100. Libanori A, Chen G, Zhao X, Zhou Y, Chen J. Smart textiles for personalized healthcare. Nat Electron. Mar 28, 2022;5(3):142-156. [CrossRef]
    101. Lyu S, He Y, Yao Y, Zhang M, Wang Y. Photothermal clothing for thermally preserving pipeline transportation of crude oil. Adv Funct Materials. Jul 2019;29(27):1900703. URL: https://onlinelibrary.wiley.com/toc/16163028/29/27 [CrossRef]
    102. Shafti A, Ribas Manero RB, Borg AM, Althoefer K, Howard MJ. Embroidered electromyography: a systematic design guide. IEEE Trans Neural Syst Rehabil Eng. Sep 2017;25(9):1472-1480. [CrossRef]
    103. Cho G, Jeong K, Paik MJ, Kwun Y, Sung M. Performance evaluation of textile-based electrodes and motion sensors for smart clothing. IEEE Sensors J. Dec 2011;11(12):3183-3193. [CrossRef]
    104. Tabei F, Zaman R, Foysal KH, Kumar R, Kim Y, Chong JW. A novel diversity method for smartphone camera-based heart rhythm signals in the presence of motion and noise artifacts. PLoS One. 2019;14(6):e0218248. [CrossRef] [Medline]
    105. De Ridder B, Van Rompaey B, Kampen JK, Haine S, Dilles T. Smartphone apps using photoplethysmography for heart rate monitoring: meta-analysis. JMIR Cardio. Feb 27, 2018;2(1):e4. [CrossRef] [Medline]
    106. Sengupta S, Sindal MD, Baskaran P, Pan U, Venkatesh R. Sensitivity and specificity of smartphone-based retinal imaging for diabetic retinopathy: a comparative study. Ophthalmol Retina. Feb 2019;3(2):146-153. [CrossRef] [Medline]
    107. Freeman K, Dinnes J, Chuchu N, et al. Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ. Feb 10, 2020;368:m127. [CrossRef] [Medline]
    108. Rat C, Hild S, Rault Sérandour J, et al. Use of smartphones for early detection of melanoma: systematic review. J Med Internet Res. Apr 13, 2018;20(4):e135. [CrossRef] [Medline]
    109. de Carvalho TM, Noels E, Wakkee M, Udrea A, Nijsten T. Development of smartphone apps for skin cancer risk assessment: progress and promise. JMIR Dermatol. Jul 11, 2019;2(1):e13376. [CrossRef]
    110. Chandrasekhar A, Natarajan K, Yavarimanesh M, Mukkamala R. An iPhone application for blood pressure monitoring via the oscillometric finger pressing method. Sci Rep. Sep 3, 2018;8(1):13136. [CrossRef] [Medline]
    111. Thap T, Chung H, Jeong C, et al. High-resolution time-frequency spectrum-based lung function test from a smartphone microphone. Sensors (Basel). Aug 17, 2016;16(8):1305. [CrossRef] [Medline]
    112. Nemcova A, Jordanova I, Varecka M, et al. Monitoring of heart rate, blood oxygen saturation, and blood pressure using a smartphone. Biomedical Signal Processing and Control. May 2020;59:101928. [CrossRef]
    113. Gao J, Tan W, Ma L, Wang Y, Tang W. MUSEFood: multi-sensor-based food volume estimation on smartphones. arXiv. Preprint posted online in 2019. [CrossRef]
    114. Chandrasekhar A, Kim C-S, Naji M, Natarajan K, Hahn J-O, Mukkamala R. Smartphone-based blood pressure monitoring via the oscillometric finger-pressing method. Sci Transl Med. Mar 7, 2018;10(431):eaap8674. [CrossRef] [Medline]
    115. Rethnakumar R, Johar M, Alkawaz M, Helmi R, Tahir N. Smartphone based application for body temperature and heart rate measurements. Presented at: ICSGRC 2021: 12th IEEE Control and System Graduate Research Colloquium; Aug 7, 2021; Shah Alam, Malaysia. [CrossRef]
    116. Ferrer-Lluis I, Castillo-Escario Y, Montserrat JM, Jané R. Sleeppos app: an automated smartphone application for angle based high resolution sleep position monitoring and treatment. Sensors (Basel). Jul 1, 2021;21(13):4531. [CrossRef] [Medline]
    117. Fino E, Mazzetti M. Monitoring healthy and disturbed sleep through smartphone applications: a review of experimental evidence. Sleep Breath. Mar 2019;23(1):13-24. [CrossRef] [Medline]
    118. Pierce A, Ignasiak NK, Eiteman-Pang WK, Rakovski C, Berardi V. Mobile phone sensors can discern medication-related gait quality changes in Parkinson's patients in the home environment. Comput Methods Programs Biomed Update. 2021;1:100028. [CrossRef]
    119. Chung H, Jeong C, Luhach AK, Nam Y, Lee J. Remote pulmonary function test monitoring in cloud platform via smartphone built-in microphone. Evol Bioinform Online. 2019;15:1176934319888904. [CrossRef] [Medline]
    120. Faezipour M, Abuzneid A. Smartphone-based self-testing of COVID-19 using breathing sounds. Telemed J E Health. Oct 2020;26(10):1202-1205. [CrossRef] [Medline]
    121. Nakano H, Hirayama K, Sadamitsu Y, et al. Monitoring sound to quantify snoring and sleep apnea severity using a smartphone: proof of concept. J Clin Sleep Med. Jan 15, 2014;10(1):73-78. [CrossRef] [Medline]
    122. Peng C, Chen M, Wang H, Shen J, Jiang X. P(VDF-Trfe) thin-film-based transducer for under-display ultrasonic fingerprint sensing applications. IEEE Sensors J. Oct 1, 2020;20(19):11221-11228. [CrossRef]
    123. Kim J, Song W, Jung S, et al. Capacitive heart-rate sensing on touch screen panel with laterally interspaced electrodes. Sensors. Jul 17, 2020;20(14):3986. [CrossRef]
    124. Aghanavesi S, Nyholm D, Senek M, Bergquist F, Memedi M. A smartphone-based system to quantify dexterity in Parkinson's disease patients. Inform Med Unlocked. 2017;9:11-17. [CrossRef]
    125. Abazid M, Houmani N, Garcia-Salicetti S. Enhancing security on touch-screen sensors with augmented handwritten signatures. Sensors (Basel). Feb 10, 2020;20(3):933. [CrossRef] [Medline]
    126. Blanco-Gonzalo R, Miguel-Hurtado O, Sanchez-Reillo R, Gonzalez-Ramirez A. Usability analysis of a handwritten signature recognition system applied to mobile scenarios. Presented at: ICCST 2013: 47th International Carnahan Conference on Security Technology; Oct 8-11, 2013; Medellín, Colombia. [CrossRef]
    127. Coskun AF, Wong J, Khodadadi D, Nagi R, Tey A, Ozcan A. A personalized food allergen testing platform on a cellphone. Lab Chip. Feb 21, 2013;13(4):636-640. [CrossRef] [Medline]
    128. Chu S, Wang H, Ling X, Yu S, Yang L, Jiang C. A portable smartphone platform using a ratiometric fluorescent paper strip for visual quantitative sensing. ACS Appl Mater Interfaces. Mar 18, 2020;12(11):12962-12971. [CrossRef]
    129. Liang P-S, Park TS, Yoon J-Y. Rapid and reagentless detection of microbial contamination within meat utilizing a smartphone-based biosensor. Sci Rep. Aug 5, 2014;4(1):5953. [CrossRef] [Medline]
    130. Mousseau S, Lapointe A, Gravel J. Diagnosing acute otitis media using a smartphone otoscope; a randomized controlled trial. Am J Emerg Med. Oct 2018;36(10):1796-1801. [CrossRef] [Medline]
    131. Demant MN, Jensen RG, Bhutta MF, Laier GH, Lous J, Homøe P. Smartphone otoscopy by non-specialist health workers in rural Greenland: a cross-sectional study. Int J Pediatr Otorhinolaryngol. Nov 2019;126:109628. [CrossRef] [Medline]
    132. Ghosh S, Aggarwal K, U VT, Nguyen T, Han J, Ahn CH. A new microchannel capillary flow assay (MCFA) platform with lyophilized chemiluminescence reagents for a smartphone-based POCT detecting malaria. Microsyst Nanoeng. 2020;6(1):5. [CrossRef] [Medline]
    133. Peritz DC, Howard A, Ciocca M, Chung EH. Smartphone ECG AIDS real time diagnosis of palpitations in the competitive college athlete. J Electrocardiol. 2015;48(5):896-899. [CrossRef] [Medline]
    134. Ippili S, Kim B, Jella V, Jung J-S, Vuong V-H, Yoon S-G. Antireflective, transparent, water-resistant, and antibacterial Zn-doped silicon oxide thin films for touchscreen-based display applications. ACS Sustainable Chem Eng. Feb 14, 2022;10(6):2136-2147. [CrossRef]
    135. Chen I-M, Chen Y-Y, Liao S-C, Lin Y-H. Development of digital biomarkers of mental illness via mobile apps for personalized treatment and diagnosis. JPM. Jun 6, 2022;12(6):936. [CrossRef]
    136. Moon J, Yun J, Yoon YD, et al. Blue light effect on retinal pigment epithelial cells by display devices. Integr Biol (Camb). May 22, 2017;9(5):436-443. [CrossRef] [Medline]
    137. Kumar A, Sharma K, Sharma A. Hierarchical deep neural network for mental stress state detection using iot based biomarkers. Pattern Recogn Lett. May 2021;145:81-87. [CrossRef]
    138. Jacobson NC, Summers B, Wilhelm S. Digital biomarkers of social anxiety severity: digital phenotyping using passive smartphone sensors. J Med Internet Res. May 29, 2020;22(5):e16875. [CrossRef] [Medline]
    139. Sverdlov O, van Dam J, Hannesdottir K, Thornton-Wells T. Digital therapeutics: an integral component of digital innovation in drug development. Clin Pharmacol Ther. Jul 2018;104(1):72-80. [CrossRef] [Medline]
    140. Makin S. The emerging world of digital therapeutics. Nature. Sep 2019;573(7775):S106-S109. [CrossRef] [Medline]
    141. Alliance for Advancing Bioelectronic Medicine. Building a bioelectronic medicine movement 2019: insights from leaders in industry, academia, and research. Bioelectron Med. Jan 31, 2020;6(1):1. [CrossRef]
    142. Pavlov VA, Tracey KJ. Bioelectronic medicine: preclinical insights and clinical advances. Neuron. Nov 2, 2022;110(21):3627-3644. [CrossRef] [Medline]
    143. Sevcencu C. Single-interface bioelectronic medicines-concept, clinical applications and preclinical data. J Neural Eng. Jun 6, 2022;19(3):031001. [CrossRef] [Medline]
    144. Peeples L. Core concept: the rise of bioelectric medicine sparks interest among researchers, patients, and industry. Proc Natl Acad Sci U S A. Dec 3, 2019;116(49):24379-24382. [CrossRef] [Medline]
    145. Henson P, Wisniewski H, Hollis C, Keshavan M, Torous J. Digital mental health apps and the therapeutic alliance: initial review. BJPsych Open. Jan 2019;5(1):e15. [CrossRef] [Medline]
    146. Dang A, Arora D, Rane P. Role of digital therapeutics and the changing future of healthcare. J Family Med Prim Care. May 2020;9(5):2207-2213. [CrossRef] [Medline]
    147. Seshadri DR, Bittel B, Browsky D, et al. Accuracy of the apple watch 4 to measure heart rate in patients with atrial fibrillation. IEEE J Transl Eng Health Med. 2020;8:2700204. [CrossRef] [Medline]
    148. Seshadri DR, Bittel B, Browsky D, et al. Accuracy of apple watch for detection of atrial fibrillation. Circulation. Feb 25, 2020;141(8):702-703. [CrossRef] [Medline]
    149. Namwongsa S, Puntumetakul R, Neubert MS, Chaiklieng S, Boucaut R. Ergonomic risk assessment of smartphone users using the rapid upper limb assessment (RULA) tool. PLoS One. 2018;13(8):e0203394. [CrossRef] [Medline]
    150. Mallinson K. Smartphone revolution: technology patenting and licensing fosters innovation, market entry, and exceptional growth. IEEE Consumer Electron Mag. Apr 2015;4(2):60-66. [CrossRef]
    151. Gupta S, Mahmoud A, Massoomi MR. A clinician’s guide to smartwatch “interrogation”. Curr Cardiol Rep. Aug 2022;24(8):995-1009. [CrossRef] [Medline]
    152. James J. The smart feature phone revolution in developing countries: bringing the Internet to the bottom of the pyramid. Inf Soc. May 19, 2020;36(4):226-235. [CrossRef]
    153. Park J, Heo S, Park K, et al. Research on flexible display at Ulsan National Institute of Science and Technology. npj Flex Electron. Nov 13, 2017;1(1):9. [CrossRef]
    154. Choi M, Bae S-R, Hu L, Hoang AT, Kim SY, Ahn J-H. Full-color active-matrix organic light-emitting diode display on human skin based on a large-area MoS backplane. Sci Adv. Jul 2020;6(28):eabb5898. [CrossRef] [Medline]
    155. Yokota T, Fukuda K, Someya T. Recent progress of flexible image sensors for biomedical applications. Adv Mater. May 2021;33(19):e2004416. [CrossRef] [Medline]
    156. Sekitani T, Someya T. Stretchable, large-area organic electronics. Adv Mater. May 25, 2010;22(20):2228-2246. [CrossRef] [Medline]
    157. Li S, Peele BN, Larson CM, Zhao H, Shepherd RF. A stretchable multicolor display and touch interface using photopatterning and transfer printing. Adv Mater. Nov 2016;28(44):9770-9775. URL: https://onlinelibrary.wiley.com/toc/15214095/28/44 [CrossRef]
    158. Kim EH, Han H, Yu S, et al. Interactive skin display with epidermal stimuli electrode. Adv Sci. Jul 3, 2019;6(13):1802351. [CrossRef]
    159. Tajima R, Miwa T, Oguni T, et al. Truly wearable display comprised of a flexible battery, flexible display panel, and flexible printed circuit. J Soc Info Display. Oct 21, 2014;22(5):237-244. [CrossRef]

    ‎
    AI: artificial intelligence
    CMOS: complementary metal-oxide-semiconductor
    ECG: electrocardiogram
    EEG: electroencephalogram
    EGG: electroglottogram
    EMG: electromyogram
    IMU: inertial measurement unit mHealth: mobile health
    OLED: organic light-emitting diode
    PPG: photoplethysmogram
    PSA: pressure-sensitive adhesive

    Edited by Lorraine Buis; submitted 07.05.23; peer-reviewed by Chatchawal Wongchoosuk, David Parry; final revised version received 08.11.23; accepted 20.12.23; published 22.01.24.

    Copyright

    © Wonki Hong. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 22.1.2024.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.