Table 2.

Summary of Studies

Study	Category	Participants [Data Source]	Devices	Collected Data	Machine Learning Algorithms	Input Features (X)	Output (Supervised) (Y)	Output (Unsupervised)	Performance	Application to Asthma Management
Chen A, 202023	Technology development	11 healthy adults	2 wireless wearable sensors: the abdominal respiration (Sensor1), the chest respiration (Sensor2)	Respiratory behaviors	Random Forest	100 data points sliding window, 1200 data slices per individual	4 postures: Standing, laying on the back, laying on the left, laying on the right	-	Accuracy = 99.53% (individual classifier)	Monitor sleeping posture and respiratory behavior
Vatanparvar K, 202024	Technology development	131 individuals (age not specified): asthma = 69, COPD = 9, asthma and COPD = 13	Smartphone (Samsung Galaxy Note 8)	1 minute of voluntary cough	Gaussian Mixture Model, neural networks	5380 sound samples of coughs	Coughing individual	-	Sensitivity = 90.30%, specificity = 96.39%, accuracy = 93.34% (NeTrain with cough embeddings)	Passive monitoring of coughs
Prinable J, 202025	Technology development	9 healthy adults	Pulse oximeter, portable sleep diagnostic (Alice PDx)	Raw PPG trace, SPO2, pulse rate, and relative tidal volume (RTV)	Deep learning (LSTM)	45 recordings, 4 features each: PPG, band-passed PPG, SPO2, pulse rate	-	Inspiration time, expiration time, respiratory rate, inter-breath intervals (IBI), and the inspiration-expiration ratio (I:E)	Relative bias <4% (apart from I:E ratio)	Passive monitoring of breathing
Adhi Pramono R.X, 201926	Technology development	Unknown individuals from multiple repositories47	Unknown devices	Cough sounds	Logistic regression	43 recordings. Frequency bands of interest: B-HF and B-01. The spectral features: HFMaxratio, MinMaxratio, and LQMAXratio	2 classes: cough, non-cough	-	Sensitivity = 90.31%, specificity = 98.14%, F1-score = 88.70%, positive predictive value = 88.47%, Matthews Correlation Coefficient (MCC) = 87.46%	Passive monitoring of coughs
Chen H, 201927	Technology development	126 individuals (all ages, infants to elderly) (including asthma and COPD) [ICBHI Scientific Challenge]48 and unknown individuals [R.A.L.E lung sounds]49	Digital stethoscopes	Respiratory sounds	SVM, Extreme Learning Machine (ELM), KNN	240 recordings, 2 features extracted from Enhanced Generalized S-Transform (EGST): mean and standard deviation of EGST coefficients	2 classes: wheezing, normal respiratory	-	Sensitivity = 100%, specificity = 99.27% (ELM, SVM, KNN)	Active monitoring of wheeze
Li K, 201928	Technology development	30 adults (age 19-48) [HARuS]50 and 14 children (age 5-15) with asthma [BREATHE]51	[HARuS] waist-worn smartphone (Samsung Galaxy S II) and [BREATHE] wrist-worn smartwatch (Motorola Moto 360 Sport)	Triaxial accelerometry and gyroscopic data	XGBoost (Gradient Boosted Trees), SVM, random forest	2000 inspiratory and expiratory segments of sounds, 6 features per window of signal: arithmetic mean, SD, median absolute deviation, minimum, maximum, and entropy	6 physical activities: [HARuS] 6 activities: standing, sitting, lying, walking, walking downstairs, walking upstairs; [BREATHE] 6 activities: standing, sitting, lying, walking, walking on stairs, running	-	[HARuS] Accuracy rate = 91.06% (GGS), [BREATHE] accuracy rate = 79.4% (GGS)	Activity recognition of smart watches
Azam M.A, 201829	Technology development	50 individuals (age not specified) with COPD, asthma, bronchitis, and pneumonia	Smartphone (Samsung Galaxy S3)	Airflow 25 cm in front of mouth	Bag-of-Features, SVM	255 breathing cycles, 5 features extracted from instantaneous envelop (IE) and instantaneous frequency (IF)	2 classes: normal, Adventitious Signal (AS)	-	F1-score = 75%, accuracy rate = 75.21 (complete cycle)	Active monitoring of breathing sounds
Adhi Pramono R.X, 201930	Technology development	Unknown individuals from multiple repositories47	Unknown devices	Cough sounds	Logistic regression	43 unique recordings, 4 features each: Linear Predictive Coding (LPC) coefficient, tonality index, spectral flatness, and spectral centroid	2 classes: cough, non-cough	-	Sensitivity = 86.78%, specificity = 99.42%, F1-score = 88.74%	Passive monitoring of coughs
Infante C, 201731	Technology development	87 individuals (age not specified): COPD = 7, asthma = 15, allergic rhinitis = 11, asthma and allergic rhinitis = 17, COPD & allergic rhinitis = 4, healthy = 33	Custom-built electronic stethoscope and Android application	Voluntary coughs recorded from the trachea (30 seconds), standard auscultation lung sound data, peak flow meter reading and clinical questionnaire	Logistic regression with L1 penalization (LASSO)	4 features: Zero Crossing Irregularity, Rate of Decay, Kurtosis, Variance	2 set of labels, diagnosis; cough type (wet or dry).	-	Sensitivity = 100%, specificity = 87%, AUC = 94% (Wet vs dry) Sensitivity = 35.7%, specificity = 100%, AUC = 67.8% (classifying unhealthy patients with cough type)	Active monitoring of coughs
Taylor T.E, 201832	Technology development	20 healthy adults	Inhaler Compliance Assessment (INCA) audio recording device, pneumotachograph spirometer	Audio recording of placebo Ellipta inhalation, inhalation rate	Linear regression, power law regression	15 inhalations per person, acoustic envelope of the inhaler inhalation	Flow rate: peak inspiratory flow rate (PIFR), volume or inspiratory capacity (IC), and the inhalation ramp time (Tr)	-	Accuracy = 90.89% (power law model)	Measure correct inhaler technique
Purnomo A.T, 202133	Technology development	Unknown individuals	FMCW radar	5 to 15 seconds of chest displacement breathing waveforms	XGBoost (Gradient Boosted Trees)	4000 breathing waveforms. [set 1] breathing wave form; [set 2] 8 features: mean, median, maximum, variance, standard deviation, absolute deviation, kurtosis, and skewness; [set 3] MFCC feature extraction	5 classes: normal breathing, deep and quick breathing, deep breathing, quick breathing, holding the breath	-	Precision > 80%, sensitivity > 70%, F1-score > 75% (for all classes, MFCC feature extraction)	Active monitoring of breathing
Zhang O, 202034	Attack prediction	2010 individuals (age >16) with severe and persistent asthma [SAKURA]52	Paper diary	Daily questionnaire: PEF, morning symptoms, evening symptoms, reliever inhaler usage, asthma sleep wakening	Recursive feature elimination, PCA, random under-sampling, random over-sampling, SMOTE, logistic regression, naïve Bayes, decision tree, perceptron	728,535 daily records, 432 features, 9 basic features	2 classes: exacerbation event, no exacerbation	-	Sensitivity = 90%, specificity = 83%, AUC = 85% (logistic regression)	Attack prediction from daily diary and PEF
Tsang K.C.H, 202035	Attack prediction	554 adults with asthma [AMHS]53	Smartphone (BYOT)	Daily and weekly questionnaire: symptoms, healthcare usage, medication usage, triggers encountered, PEF	Decision trees, logistic regression, naïve Bayes, and SVM	2659 periods, 25 features per 14-day period before unstable event, 6 basic features	2 classes: stable, unstable period	-	Sensitivity = 86.6%, specificity = 72.5%, AUC = 87.1% (naïve Bayes)	Attack prediction from daily diary
Tinschert P, 202036	Attack prediction	79 adults with asthma	Smartphone (Samsung Galaxy A3) application based on MobileCoach	ACT, Pittsburgh Sleep Quality Index, Nocturnal cough frequencies (manually labelling audio recordings from the smartphone’s built-in microphone)	Mixed-effects regressions, decision trees based on recursive partitioning analysis	2291 nights, 7 combinations of Pittsburgh Sleep Quality Index and Nocturnal cough frequencies	ACT score	Prediction of exacerbation risk in the next 7 days	56% < balanced accuracy < 70%	Attack prediction from sleep quality
Tenero L, 202037	Attack prediction	38 children (age 6-16): persistent asthma = 28, control = 10	Electronic nose (Cyranose 320)	VOCs in exhaled breath, spirometry	PCA, penalized logistic model	1 recording per person, 32 e-Nose nanosensors	4 classes: control (CON), controlled asthma (AC), partially controlled asthma (APC) or uncontrolled asthma (ANC)	6 most important sensors, 5 principal components	Sensitivity = 79%, specificity = 84%, cross-validated AUC = 80%	Asthma control prediction from exhaled breath
Finkelstein J, 201738	Attack prediction	Adults with asthma	Peak flow meter connected to laptop	PEF, daily questionnaire: symptoms, medication usage, trigger exposure, sleep	Naïve Bayes, adaptive Bayesian network, SVM	7001 records, 147 features, 21 basic variables x 7 days	2 classes: high-alert, no-alert PEF zone on day 8	-	Sensitivity = 100.0%, specificity = 100.0%, accuracy = 100.0% (adaptive Bayesian network)	Attack prediction from daily diary and PEF
Castner J, 202039	Attack prediction	43 adults (working aged women) with poorly controlled asthma	Fitness tracker (Fitbit Charge), activity monitor (Actigraph GT3X+), spirometer (Vyntus), spirometer (MicroDiary), home monitor (Hobo Data Logger)	ACT, Mini Asthma Quality of Life Questionnaire (AQLQ), trait emotionality PANAS-X questionnaire, Consensus Sleep Diary, asthma control diary (ACD), physiologic and environmental sensors, medical record review, and spirometry.	Generalized linear mixed models	900 daily scores, [set 1] 8 features; [set 2] 10 features	[set 1] self-reported asthma-specific wakening; [set 2] FEV1	-	[set 1] AUC = 77% (sleep wakening) [set 2] AUC = 83% (FEV1)	Measure sleep disruption using fitness tracker
Khasha R, 201940	Attack prediction	96 individuals (age >5) with asthma	Weather reports, Air quality, questionnaires, medical records	140 variables about patient demographics, lung function, symptoms, environmental factors, medical history	Ensemble learning, multinomial logistic regression, SVM, random forest, extreme gradient boosting, KNN, decision tree, Gaussian naïve Bayesian, rule-based classifier created from clinical knowledge	2870 daily records, 35 selected variables	3 classes: well-controlled, not well-controlled, very poorly-controlled levels	-	Sensitivity = 88.3%, precision = 89.4%, specificity = 94.9%, neg pred value = 94.3%, accuracy = 92.7% (Ensemble Learning 2)	Asthma control prediction using health records and weather reports
Van Vliet D, 201741	Attack prediction	96 children (age 6-18) with asthma	NIOX analyzer (NIOX MINO), 5 liter inert bag with a resistant free valve (Tedlar bag), spirometer (ZAN 100®)	ACQ, GINA respiratory symptom score, online FeNO assessment, collection of exhaled breath (VOCs), dynamic spirometry	Random forest, PCA	574 chromatograms, 7 VOCs	Exacerbations	Most important VOCs, separation of children with and without exacerbation, possible difference in co-factors between samples of children with an exacerbation 14 days after sampling and those without an exacerbation after 2 months	Sensitivity = 88%, specificity = 75%, AUC = 90% (attack after 14 days)	Attack prediction from exhaled breath and home monitoring
Huffaker M.F, 201842	Attack prediction	16 children (age 5-18) with persistent asthma	BCG accelerometer-based passive bed sensor (Murata Technologies SCA11H)	Heart rate (HR), respiratory rate (RR), HR variability HRV, calculated RR variability (RRV), relative stroke volume (SV), HR percentile based on age, RR percentile based on age, movement, relative Q (= SV × HR), VO2	Random forest	891 nights, 16 features, 8 basic features	Report of asthma symptoms	-	Sensitivity = 47.2%, specificity = 96.3%, accuracy = 87.4%	Attack prediction from sleep and bed monitoring
Tibble H, 202043	Patient clustering	211 children (age 6-15) with asthma attack54	Electronic inhaler monitoring devices	Medication dose taken	PCA, K-means, decision trees	35,161 person-days of data, 5 features: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days	-	3 clusters: poor adherence, moderate adherence, good adherence	-	Characterize asthma patients by adherence
Tignor N, 201744	Patient clustering	334 adults with asthma [AMHS]53	Smartphone (BYOT)	Daily questionnaire: symptom diary, medication, triggers encountered	Probability based imputation with consensus clustering (PIC) method (utilize k-means)	1 recording per person. [Cluster formation] daily symptoms; [Characterize formation] 10 features: 4 clinical features, 3 demographic features, 3 trigger features	-	3 clusters: high day symptom rate, medium day symptom rate, low day symptom rate	-	Subtyping asthma patients for personalized alerts based on triggers

Abbreviations: AUC, Area under the ROC curve; BYOT, Bring your own technology; COPD, Chronic Obstructive Pulmonary Disease; FeNO, Fractional exhaled nitric oxide; GINA, Global Initiative for Asthma; LSTM, Long short-term memory; PCA, Principal Component Analysis; PPG, Photoplethysmogram; SVM, Support Vector Machine; VOCs, Volatile organic compounds.