Methods

Design

This study leverages real-world, overnight, video recordings collected from an ongoing, prospective, observational, four-night, home sleep apnea study (ClinicalTrials.gov Identifier: NCT05376475) and controlled-setting video recordings from a cross-sectional study, which is open-access and can be found online.5A core outcome set was not used in the design of this study.

Participants

Participants were recruited Canada-wide by the research team via various social media platforms. All data collection was completed within the participants’ own homes.
Participants for the real-world study were eligible to take part if their American Society of Anesthesiologists Physical Status (ASA PS) class was II or lower, they had a low-risk singleton pregnancy, were in the third trimester (between 28 weeks and 0 days through 40 weeks and 6 days gestation, inclusive, determined by first-trimester ultrasound), were aged 18 to 50 years, had a 2.4 GHz Wi-Fi network in their home, and slept in a bed at night (i.e., not a reclining chair or similar). Participants’ bed partners were eligible to participate if their ASA PS class was II or lower and they slept in the same bed as the pregnant participant. Exclusion criteria for both the participant and their bed partner included non-English speaking, reading, or writing and ASA PS class III or higher.
For the participant eligibility criteria of the controlled-setting study, please refer to the publication.5

Interventions

Eligible participants and their bed partners provided voluntary, written, informed consent to participate. Basic demographic data was collected from each participant and their participating bed partner. Each participant used a home-surveillance camera to record a video of themselves and their bed partner sleeping overnight for a total of four nights. See Appendix A for additional details regarding interventions in the real-world study.
For the interventions in the controlled-setting study, see the publication.5

Outcomes

The main outcomes for this study were the precision and recall of the model for detecting thirteen pre-defined body positions (seeDataset Development , below). The outcome of the real-world study pertaining to the present study was the sleeping position of the pregnant participant and bed partner. See Appendix A for additional details. For the outcomes in the controlled-setting study, see the publication.5

Sample Size

For the real-world study, we selected a target sample size of N=60 couples (see Appendix A ). Recruitment for the real-world study is ongoing, and the present study uses a subset of this data. In the controlled-setting study, the target sample size was twenty.5

Statistical Methods

We assessed the normality of continuous variables via the Shapiro-Wilk test at a 0.05 significance level and indicated deviations from normality in our results. Statistical analyses were conducted using the R Statistical Software package (Version 4.2.2).6

Dataset Development

All video recordings were reviewed manually by two trained reviewers and frames (images) were extracted at specific time points. The reviewers manually and independently annotated the extracted frames using an open-source annotation tool, LabelImg (see Appendix A ).7Following our previous work,5body positions were classified into twelve position classes, including: left recovery, left lateral, left tilt, supine, supine thorax with left pelvic tilt, supine thorax with right pelvic tilt, supine pelvis with left thorax tilt, supine pelvis with right thorax tilt, right tilt, right lateral, right recovery, and sitting up at the edge of the bed (see Figure A.1 in Appendix A ). In addition, we added a prone position class for a total of thirteen body position classes.
Since pillows can impact sleeping positions, our model (SLeeP AIDePt-2) was designed to detect pillows. This involved identifying pillows – such as head pillows, body pillows, pregnancy pillows, and wedge pillows – in various locations on the bed. As a result, we thoroughly reviewed the controlled-setting dataset and annotated the presence of pillows in those frames as well (see Appendix A ).

Model Development and Evaluation

We framed the detection of sleeping positions and pillows as a multi-participant, multi-class, classification problem where state-of-the-art deep Convolutional Neural Networks were used. In this regard, we used YOLOv5s (You Only Look Once, version 5s) with 7.5 million parameters,8,9including pre-trained weights from the COCO (Common Objects in Context) dataset and fine-tuned it on our annotated dataset to make predictions of classes. The experiment was implemented using the PyTorch framework. The model was trained using stochastic gradient descent with momentum, an initial learning rate of 0.01, a batch size of 32, and early stopping criteria.10,11The training was conducted under the Linux virtual machine running on Google Colab with an NVIDIA A100 (SXM4) 40GB GPU.
Our dataset was split into five non-overlapping folds, and we then trained, validated, and tested SLeeP AIDePt-2 by five-fold cross validation. In each loop of the cross-validation, three folds served as the training set, one fold was the validation set, and one fold was the testing set. Five loops were completed so each fold was given a turn to be the validation set and testing set. The weights that achieved the highest mean average precision (mAP) on the validation set were saved for each loop. Finally, performance evaluation was completed using the best weights on the testing set for each loop, and the following standard performance measures were calculated for each class: precision (analogous to “positive predictive value” in the clinical realm), recall (analogous to “sensitivity”), and average precision (AP), which is the area under the precision-recall curve (similar to how AUROC is the area under the receiver-operator curve), at standard intersection over union (IoU) values.
See Appendix A for more details regarding our rationale for using YOLOv5s and disabling flip augmentation; use of k-fold cross validation, early stopping criteria, a weighted random sampler; and definitions of standard performance measures, allocation of the study participants to the five folds, and the training, validation, and testing set assignments for each loop.

Patient and Public Involvement

Patients and public were not involved in the design of this study; however, this rationale for this study was informed by the experiences of pregnancy loss parents, who have brought maternal sleeping position to the attention of researchers and clinicians internationally.12