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. 2023 Nov 8;25:e48754. doi: 10.2196/48754

Table 3.

Pooled mean estimates of highest accuracy by several factors.

Group Studies, Na Sample size, N Accuracy (%), range Pooled mean accuracy, mean (95% CI) Heterogeneity measures Test for subgroup differences (P value)





τ2 Q (P value) I2 (%)
Algorithms .07

Support vector machine 7 21,413 0.50-0.99 0.82 (0.67-0.94) 0.0520 819.0 (<.001) 99.3

Random forest 6 22,132 0.56-0.99 0.83 (0.68-0.94) 0.0426 1187.6 (<.001) 99.6

Decision tree 4 21,785 0.70-0.99 0.87 (0.68-0.98) 0.0585 1164.3 (<.001) 99.7

Multilayer perceptron 3 504 0.71-0.87 0.81 (0.70-0.90) 0.0087 8.3 (.02) 75.8

Logistic regression 2 93 0.70-0.71 0.71 (0.61-0.80) 0.0000 0.0 (.98) 0.0

XGBoost 2 1239 0.55-0.67 0.62 (0.50-0.73) 0.0070 12.7 (<.001) 92.1

Long short-term memory networks 2 10,695 0.67-0.69 0.67 (0.66-0.69) <0.0001 1.2 (.27) 17.7

Ensemble model 2 605 0.91-0.94 0.92 (0.89-0.94) 0.0003 1.4 (.24) 28.6

K-nearest neighbor 2 61,022 0.62-0.99 0.88 (0.32-1.00) 0.1672 15.0 (<.001) 93.4
Aims of AIb .33

Detectionc 33 143,800 0.50-0.99 0.84 (0.72-0.91) 0.2857 62,108.0 (<.001) 99.9

Prediction 7 6109 0.55-0.81 0.72 (0.66-0.78) 0.0082 117.9 (<.001) 94.9
Status of WDd .91

Commercialc 27 130,279 0.55-0.99 0.82 (0.68-0.91) 0.3345 28,205.8 (<.001) 99.9

Noncommercialc 11 16,610 0.67-0.95 0.85 (0.71-0.92) 0.0471 1363.3 (<.001) 99.4
WDs .12

Musec 6 279 0.71-0.88 0.77 (0.67-0.85) 0.0000 9.0 (.11) 46.2

Empatica E4c 5 121,048 0.86-0.99 0.97 (0.00-0.99) 1.0715 1722.8 (<.001) 100

Fitbit 3 393 0.56-0.89 0.70 (0.45-0.89) 0.0453 52.4 (<.001) 96.2
Data sources .59

WD-basedc 27 141,516 0.50-0.99 0.81 (0.64-0.90) 0.3498 59,871.8 (<.001) 99.9

WD-based and othersc 13 8393 0.67-0.95 0.86 (0.75-0.92) 0.0552 622.7 (<.001) 98.4
Data types .48

Activity datac 8 18,619 0.67-0.94 0.88 (0.62-0.96) 0.1133 1041.3 (<.001) 99.7

Activity data and othersc 12 7675 0.55-0.95 0.78 (0.57-0.90) 0.1492 573.6 (<.001) 99.1

EDAe data and othersc 9 122,650 0.71-0.99 0.92 (0.55-0.99) 0.6718 5870.6 (<.001) 99.9

EEGf datac 6 279 0.71-0.88 0.78 (0.67-0.85) 0.0000 9.0 (.11) 46.2
Reference standards .80

STAIb,g 8 398 0.58-0.88 0.73 (0.61-0.82) 0.0087 19.5 (.006) 61.7

CIDIh 2 529 0.55-0.94 0.77 (0.33-1.00) 0.1099 117.5 (<.001) 99.1

DAMSi 2 296 0.56-0.89 0.75 (0.39-0.98) 0.0691 27.6 (<.001) 96.4
Validation methods .41

K-foldc 24 129,113 0.55-0.99 0.86 (0.70-0.94) 0.3875 24,618.6 (<.001) 99.9

Hold-outc 10 18,959 0.50-0.92 0.76 (0.57-0.87) 0.0303 901.7 (<.001) 99.3

Leave-one-out 2 582 0.56-0.74 0.66 (0.49-0.82) 0.0141 7.1 (.008) 86.0
All studiesc 40 149,909 0.50-1.00 0.82 (0.71-0.89) 0.2713 75,900.5 (<.001) 99.9 N/Aj

aMany studies were included more than once in each meta-analysis given that they assessed the performance of more than one algorithm.

bAI: artificial intelligence.

cAccuracy was pooled using the multilevel meta-analysis method.

dWD: wearable device.

eEDA: electrodermal activity.

fEEG: electroencephalogram.

gSTAI: State-Trait Anxiety Inventory.

hCIDI: Composite International Diagnostic Interview.

iDAMS: Depression and Anxiety Mood Scale.

jN/A: not applicable.