From the Guidelines
The "OPTIMISE your health" trial protocol will measure sleep health in middle-aged office workers with Type 2 Diabetes Mellitus (T2DM) by utilizing wrist-worn accelerometers to objectively assess sleep duration, efficiency, and timing, as this approach is supported by the most recent guidelines on facilitating positive health behaviors and well-being to improve health outcomes in diabetes 1.
Sleep Measurement Approach
The trial protocol will employ wrist-worn accelerometers, such as ActiGraph GT3X+, to measure sleep metrics over a 7-day period. Participants will wear these devices 24 hours/day for 7 consecutive days, with concurrent sleep logs to validate sleep periods. The accelerometer data will be processed using validated algorithms to extract sleep metrics, including:
- Total sleep time
- Sleep efficiency
- Sleep onset/offset times
- Sleep fragmentation This approach is based on the latest standards of care in diabetes, which emphasize the importance of consistent, uninterrupted sleep for improving health outcomes in individuals with T2DM 1.
Expected Outcomes
The trial anticipates improved sleep efficiency and duration associated with reduced sitting time and increased physical activity among middle-aged office workers with T2DM. This relationship is likely mediated through improved glycemic control, reduced inflammation, and enhanced mood states that accompany increased physical activity. The analysis will control for confounding variables, including:
- Age
- Sex
- BMI
- Diabetes duration
- Medication use By using objective sleep measurement, the trial will provide stronger evidence for the relationship between movement behaviors and sleep health in this population, compared to relying solely on self-reported measures.
Rationale
The approach is supported by the latest guidelines, which highlight the negative impact of long (≥8 h) and short (≤6 h) sleep durations on A1C levels, as well as the importance of regular sleep patterns for maintaining good glycemic control 1. Additionally, the guidelines note that irregular sleep patterns, such as those associated with evening chronotypes, can lead to poorer glycemic levels and increased susceptibility to inactivity. By measuring sleep health objectively, the trial will be able to assess the impact of reducing sedentary behavior and increasing physical activity on sleep outcomes in middle-aged office workers with T2DM.
From the Research
Measurement of Sleep Health
The 'OPTIMISE your health' trial protocol will measure sleep health in middle-aged office workers with Type 2 Diabetes Mellitus (T2DM) who reduce sedentary behavior and increase physical activity through various methods, including:
- Actigraphy, which is a methodology for recording and analyzing activity (movement) from small, computerized devices worn on the body 2
- The Pittsburgh Sleep Quality Index (PSQI), a self-report measure of sleep quality 3
- Sleep diaries, which provide a record of sleep patterns over a period of time 4
Validity of Sleep Measures
The validity of these sleep measures has been established in various studies, including:
- A study that found actigraphy to be reliable for evaluating sleep patterns in patients with insomnia, and for studying the effect of treatments designed to improve sleep 5
- A study that found the PSQI global score to be a valid measure of sleep quality in healthy midlife women, performing better than two-factor or three-factor models 3
- A study that found estimates of dim light melatonin onset (DLMO) from sleep markers derived from actigraphy to be suitable for limited research purposes 4
Limitations of Sleep Measures
However, there are also limitations to these sleep measures, including:
- Actigraphy may not be as accurate as polysomnography (PSG) for determining some sleep measurements, and may be susceptible to masking effects 5
- The PSQI global score may not differentiate poor sleep quality from depression, and may require further clinical assessment and research 3
- Estimates of DLMO from sleep markers derived from actigraphy may not provide precise estimates of DLMO, and may exhibit proportional bias 4