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Developing a Sleep Algorithm to Support a Digital Medicine System: Noninterventional, Observational Sleep Study

Developing a Sleep Algorithm to Support a Digital Medicine System: Noninterventional, Observational Sleep Study

Historically, sleep parameters were assessed primarily through sleep diaries and polysomnography (PSG); however, each of these has limitations for daily utilization [1,2]. Sleep diaries may be helpful at identifying changes in daily sleep habits and activity patterns, but as most studies highlight, these reports rely on subjective responses [1,2,5,11]. PSG requires instrumentation that may be better utilized within a dedicated sleep facility and is not designed for continuous monitoring [1,2].

Jeffrey M Cochran

JMIR Ment Health 2024;11:e62959

Efficient Screening in Obstructive Sleep Apnea Using Sequential Machine Learning Models, Questionnaires, and Pulse Oximetry Signals: Mixed Methods Study

Efficient Screening in Obstructive Sleep Apnea Using Sequential Machine Learning Models, Questionnaires, and Pulse Oximetry Signals: Mixed Methods Study

In this study, we used 2 extensive polysomnography datasets, the Sleep Heart Health Study (SHHS) [20,21] and polysomnography records from the Sleep Medicine Center of Taipei Veterans General Hospital (TVGH), for the training, testing, and validation of both models. We justified using datasets from different geographic areas to enhance the robustness and generalizability of their machine learning models across diverse populations, addressing potential biases and improving the study’s external validity.

Nai-Yu Kuo, Hsin-Jung Tsai, Shih-Jen Tsai, Albert C Yang

J Med Internet Res 2024;26:e51615

Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review

Accuracy of Fitbit Charge 4, Garmin Vivosmart 4, and WHOOP Versus Polysomnography: Systematic Review

Polysomnography (PSG) is the gold-standard method for objectively assessing sleep. PSG records signals of brain activity, eye movements, and muscle tone, as well as audio and video, enabling it to classify sleep stages [9]. However, PSG may not be ideal for monitoring sleep in particular settings, as it is expensive, labor-intensive, and time-consuming; requires various equipment and technical expertise; and is impractical for long-term use or in-home environment settings [10,11].

An-Marie Schyvens, Nina Catharina Van Oost, Jean-Marie Aerts, Federica Masci, Brent Peters, An Neven, Hélène Dirix, Geert Wets, Veerle Ross, Johan Verbraecken

JMIR Mhealth Uhealth 2024;12:e52192

Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study

Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study

Polysomnography (PSG) has been widely accepted as the gold standard for sleep assessment. However, disadvantages include high cost, invasiveness, and difficulty in measuring continuous sleep over long periods [6-8]. Recently, more user-friendly sleep devices have been developed to overcome these problems. For instance, home-based single-channel electroencephalogram (EEG)—the Zmachine Insight Plus—provides algorithm-based sleep staging [9].

Keita Kawai, Kunihiro Iwamoto, Seiko Miyata, Ippei Okada, Hiroshige Fujishiro, Akiko Noda, Kazuyuki Nakagome, Norio Ozaki, Masashi Ikeda

J Med Internet Res 2023;25:e51336

Integrative Approaches to Sleep Management in Skin Disease: Systematic Review

Integrative Approaches to Sleep Management in Skin Disease: Systematic Review

Primary sleep outcome measurements include the PROMIS (Patient Reported Outcome Measurement Information System) parent-proxy score, the PROMIS patient score, and a polysomnography-based wake after sleep onset [11]. Melatonin supplementation, given its suppressive effects of autotaxin, has also been attempted as a treatment for sleep disturbance in children with AD [35]. In a double-blinded RCT of 73 children and adolescents, 3 mg of melatonin daily was compared to a placebo.

Vishnutheertha A Kulkarni, Isaiah Mojica, Vahram Gamsarian, Michelle Tahjian, David Liu, Tjinder Grewal, Yuyang Liu, Torunn E Sivesind, Peter Lio

JMIR Dermatol 2023;6:e48713

Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study

Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study

Some studies compared CSTs and alternative tools available for sleep analysis, such as electroencephalography headbands [4] or subjective sleep diaries [12] (without employing the gold standard polysomnography), which failed to validate the consistency between CSTs and polysomnography. Chinoy et al [11] compared the performance of 7 CSTs with polysomnography (Fatigue Science Readiband, Fitbit Alta HR, Garmin Fenix 5 S, Garmin Vivosmart 3, Early Sense Live, Res Med S+, and Sleep Score Max).

Taeyoung Lee, Younghoon Cho, Kwang Su Cha, Jinhwan Jung, Jungim Cho, Hyunggug Kim, Daewoo Kim, Joonki Hong, Dongheon Lee, Moonsik Keum, Clete A Kushida, In-Young Yoon, Jeong-Whun Kim

JMIR Mhealth Uhealth 2023;11:e50983

Three Contactless Sleep Technologies Compared With Actigraphy and Polysomnography in a Heterogeneous Group of Older Men and Women in a Model of Mild Sleep Disturbance: Sleep Laboratory Study

Three Contactless Sleep Technologies Compared With Actigraphy and Polysomnography in a Heterogeneous Group of Older Men and Women in a Model of Mild Sleep Disturbance: Sleep Laboratory Study

Polysomnography (PSG) is considered the gold standard for evaluating sleep, but longitudinal implementation of PSG at scale is not feasible because of the cost and burden it imposes on the user. Rest-activity monitoring (actigraphy) through a wrist-worn wearable device is currently the most widely used alternative for monitoring sleep in real-world settings.

Kiran K G Ravindran, Ciro della Monica, Giuseppe Atzori, Damion Lambert, Hana Hassanin, Victoria Revell, Derk-Jan Dijk

JMIR Mhealth Uhealth 2023;11:e46338

Predicting the Risk of Sleep Disorders Using a Machine Learning–Based Simple Questionnaire: Development and Validation Study

Predicting the Risk of Sleep Disorders Using a Machine Learning–Based Simple Questionnaire: Development and Validation Study

Diagnosis of insomnia is typically based on clinical symptoms rather than sleep parameters measured by overnight polysomnography (PSG), although PSG can identify potential underlying causes of insomnia-related symptoms [9,10]. OSA is characterized by repeated episodes of partial or complete upper airway obstruction during sleep, which can lead to sleep fragmentation and poor sleep quality. In-laboratory overnight PSG is the gold standard for diagnosing OSA [11].

Seokmin Ha, Su Jung Choi, Sujin Lee, Reinatt Hansel Wijaya, Jee Hyun Kim, Eun Yeon Joo, Jae Kyoung Kim

J Med Internet Res 2023;25:e46520