Publications by Author: Marino, Miguel

Objectives. To examine whether workplace interventions to increase workplace flexibility and supervisor support and decrease work–family conflict can reduce cardiometabolic risk. Methods. We randomly assigned employees from information technology (n = 555) and long-term care (n = 973) industries in the United States to the Work, Family and Health Network intervention or usual practice (we collected the data 2009–2013). We calculated a validated cardiometabolic risk score (CRS) based on resting blood pressure, HbA1c (glycated hemoglobin), HDL (high-density lipoprotein) and total cholesterol, height and weight (body mass index), and tobacco consumption. We compared changes in baseline CRS to 12-month follow-up. Results. There was no significant main effect on CRS associated with the intervention in either industry. However, significant interaction effects revealed that the intervention improved CRS at the 12-month follow-up among intervention participants in both industries with a higher baseline CRS. Age also moderated intervention effects: older employees had significantly larger reductions in CRS at 12 months than did younger employees. Conclusions. The intervention benefited employee health by reducing CRS equivalent to 5 to 10 years of age-related changes for those with a higher baseline CRS and for older employees. Trial Registration. ClinicalTrials.gov Identifier: NCT02050204. (Am J Public Health. 2023;113(12):1322–1331. https://doi.org/10.2105/AJPH.2023.307413)

AbstractObjectives To evaluate the effects of a workplace-based intervention on actigraphic and self-reported sleep outcomes in an extended-care setting. Design Cluster randomized trial. Setting Extended-care (nursing) facilities. Participants \US\ employees and managers at nursing homes. Nursing homes were randomly selected to intervention or control settings. Intervention The Work, Family, and Health Study developed an intervention aimed at reducing work-family conflict within a 4-month work-family organizational change process. Employees participated in interactive sessions with facilitated discussions, role-playing, and games designed to increase control over work processes and work time. Managers completed training in family-supportive supervision. Measurements Primary actigraphic outcomes included total sleep duration, wake after sleep onset, nighttime sleep, variation in nighttime sleep, nap duration, and number of naps. Secondary survey outcomes included work-to-family conflict, sleep insufficiency, insomnia symptoms, and sleep quality. Measures were obtained at baseline, 6 months, and 12 months postintervention. Results A total of 1522 employees and 184 managers provided survey data at baseline. Managers and employees in the intervention arm showed no significant difference in sleep outcomes over time compared with control participants. Sleep outcomes were not moderated by work-to-family conflict or presence of children in the household for managers or employees. Age significantly moderated an intervention effect on nighttime sleep among employees (P = .040), where younger employees benefited more from the intervention. Conclusion In the context of an extended-care nursing home workplace, the intervention did not significantly alter sleep outcomes in either managers or employees. Moderating effects of age were identified where younger employees' sleep outcomes benefited more from the intervention.

Objectives: We validated actigraphy for detecting sleep and wakefulness versus polysomnography (PSG).

Design: Actigraphy and polysomnography were simultaneously collected during sleep laboratory admissions. All studies involved 8.5 h time in bed, except for sleep restriction studies. Epochs (30-sec; n = 232,849) were characterized for sensitivity (actigraphy = sleep when PSG = sleep), specificity (actigraphy = wake when PSG = wake), and accuracy (total proportion correct); the amount of wakefulness after sleep onset (WASO) was also assessed. A generalized estimating equation (GEE) model included age, gender, insomnia diagnosis, and daytime/nighttime sleep timing factors.

Setting: Controlled sleep laboratory conditions.

Participants: Young and older adults, healthy or chronic primary insomniac (PI) patients, and daytime sleep of 23 night-workers (n = 77, age 35.0 ± 12.5, 30F, mean nights = 3.2).

Interventions: N/A.

Measurements and Results: Overall, sensitivity (0.965) and accuracy (0.863) were high, whereas specificity (0.329) was low; each was only slightly modified by gender, insomnia, day/night sleep timing (magnitude of change < 0.04). Increasing age slightly reduced specificity. Mean WASO/night was 49.1 min by PSG compared to 36.8 min/night by actigraphy (β = 0.81; CI = 0.42, 1.21), unbiased when WASO < 30 min/night, and overestimated when WASO > 30 min/night.

Conclusions: This validation quantifies strengths and weaknesses of actigraphy as a tool measuring sleep in clinical and population studies. Overall, the participant-specific accuracy is relatively high, and for most participants, above 80%. We validate this finding across multiple nights and a variety of adults across much of the young to midlife years, in both men and women, in those with and without insomnia, and in 77 participants. We conclude that actigraphy is overall a useful and valid means for estimating total sleep time and wakefulness after sleep onset in field and workplace studies, with some limitations in specificity.

Citation: Marino M; Li Y; Rueschman MN; Winkelman JW; Ellenbogen JM; Solet JM; Dulin H; Berkman LF; Buxton OM. Measuring sleep: accuracy, sensitivity, and specificity of wrist actigraphy compared to polysomnography. SLEEP 2013;36(11):1747-1755.