160 Underlying Factors Contributing to Sleep Health Among Middle-aged and Older Adults

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A65-A65
Author(s):  
Rebecca Lorenz ◽  
Varun Chandola ◽  
Samantha Auerbach ◽  
Heather Orom ◽  
Chin-Shang Li ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Older Adults ◽  
Depressive Symptoms ◽  
Implementation Science ◽  
Poor Sleep ◽  
Middle Aged ◽  
Sleep Health ◽  
Sampling Procedures ◽  
The Impact

Abstract Introduction Although poor sleep is not inherent with aging, an estimated 50-70 million adults in the US have insufficient sleep. Sleep duration is increasingly recognized as incomplete and insufficient. Instead, sleep health (SH), a multidimensional concept describing sleep/wake patterns that promote well-being has been shown to better reflect how sleep impacts the individual. Therefore, focusing on the underlying factors contributing to sleep health may provide the opportunity to develop interventions to improve sleep health in middle-age and older adults. Methods Data from the 2014 wave of the Health and Retirement Study (HRS) were used. Sample size was restricted to those who completed an additional questionnaire containing sleep variables. A derivation of the SH composite was constructed using eight selected sleep variables from the HRS data based on the five dimensions of sleep: Satisfaction, Alertness, Timing, Efficiency, and Duration. Total score ranged from 0-100, with higher scores indicating better SH. Weighting variables were based on complex sampling procedures and provided by HRS. Machine learning-based framework was used to identify determinants for predicting SH using twenty-six variables representing individual health and socio-demographics. Penalized linear regression with elastic net penalty was used to study the impact of individual predictors on SH. Results Our sample included 5,163 adults with a mean age of 67.8 years (SD=9.9; range 50-98 years). The majority were female (59%), white (78%), and married (61%). SH score ranged from 27-61 (mean=50; SD=6.7). Loneliness (coefficient=-1.92), depressive symptoms (coefficient=-1.28), and physical activity (coefficient=1.31) were identified as the strongest predictors of SH. Self-reported health status (coefficient=-1.11), daily pain (coefficient=-0.65), being middle-aged (coefficient=-0.26), and discrimination (coefficient=-0.23) were also significant predictors in this model. Conclusion Our study identified key predictors of SH among middle-aged and older adults using a novel approach of Machine Learning. Improving SH is a concrete target for health promotion through clinical interventions tailored towards increasing physical activity and reducing loneliness and depressive symptoms among middle-aged adults. Support (if any) This study was supported by National Heart, Lung, and Blood Institute (NHLBI) UB Clinical Scholar Program in Implementation Science to Achieve Triple Aims-NIH K12 Faculty Scholar Program in Implementation Science

scholarly journals The Impact of Work Hours on Depressive Symptoms among Koreans Aged 45 and Over

2021 ◽  
Vol 18 (3) ◽  
pp. 853
Author(s):  
Juyeong Kim ◽  
Eun-Cheol Park
Keyword(s):  
Older Adults ◽  
Depressive Symptoms ◽  
Social Services ◽  
Depression Scale ◽  
Epidemiological Studies ◽  
Work Hours ◽  
Middle Aged ◽  
Health Studies ◽  
Males And Females ◽  
The Impact

Background: Given the documented importance of employment for middle-aged and older adults’ mental health, studies of the association between their number of work hours and depressive symptoms are needed. Objectives: To examine the association between the number of work hours and depressive symptoms in Korean aged 45 and over. Methods: We used data from the first wave to fourth wave of the Korea Longitudinal Study of Aging. Using the first wave at baseline, data included 9845 individuals. Depressive symptoms were measured using the 10-item Center for Epidemiological Studies Depression scale. We performed a longitudinal analysis to estimate the prevalence of depressive symptoms by work hours. Results: Both unemployed males and females aged 45–65 years were associated with higher depressive symptoms (β = 0.59, p < 0.001; β = 0.32, p < 0.001). Females working ≥ 69 h were associated with higher depressive symptoms compared to those working 41–68 h (β = 0.25, p = 0.013). Among those both middle-aged and older adults, both males and females unemployed were associated with higher depressive symptoms. Those middle-aged female working ≥69 h were associated with higher depressive symptoms. Conclusions: An increase in depressive symptoms was associated with unemployed males and females working ≥69 h compared to those working 41–68 h. Although this association was found among middle-aged individuals, a decrease in depressive symptoms in both sexes was associated with working 1–40 h. Depressive symptoms should decrease by implementing employment policies and social services to encourage employers to support middle-aged and older adults in the workforce considering their sex and age differences.

Efficacy of an m-Health Physical Activity and Sleep Intervention to Improve Sleep Quality in Middle-Aged Adults: The Refresh Study Randomized Controlled Trial

2020 ◽  
Vol 54 (7) ◽  
pp. 470-483 ◽  
Author(s):  
Anna T Rayward ◽  
Beatrice Murawski ◽  
Mitch J Duncan ◽  
Elizabeth G Holliday ◽  
Corneel Vandelanotte ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sleep Quality ◽  
Controlled Trial ◽  
Poor Sleep ◽  
Middle Aged ◽  
Poor Sleep Quality ◽  
Sleep Health ◽  
Wait List Control ◽  
Improve Sleep Quality ◽  
Middle Aged Adults

Abstract Background Poor sleep health is highly prevalent. Physical activity is known to improve sleep quality but not specifically targeted in sleep interventions. Purpose To compare the efficacy of a combined physical activity and sleep intervention with a sleep-only intervention and a wait-list control, for improving sleep quality in middle-aged adults without a diagnosed sleep disorder. Methods Three-arm randomized controlled trial (Physical Activity and Sleep Health (PAS), Sleep Health Only (SO), Wait-list Control (CON) groups; 3-month primary time-point, 6-month follow-up) of 275 (PAS = 110, SO = 110, CON = 55) inactive adults (40–65 years) reporting poor sleep quality. The main intervention component was a smartphone/tablet “app” to aid goal setting and self-monitoring physical activity and/or sleep hygiene behaviors (including stress management), and a pedometer for PAS group. Primary outcome was Pittsburgh Sleep Quality Index (PSQI) global score. Secondary outcomes included several self-reported physical activity measures and PSQI subcomponents. Group differences were examined stepwise, first between pooled intervention (PI = PAS + SO) and CON groups, then between PAS and SO groups. Results Compared with CON, PI groups significantly improved PSQI global and subcomponents scores at 3 and 6 months. There were no differences in sleep quality between PAS and SO groups. The PAS group reported significantly less daily sitting time at 3 months and was significantly more likely to report ≥2 days/week resistance training and meeting physical activity guidelines at 6 months than the SO group. Conclusions PIs had statistically significantly improved sleep quality among middle-aged adults with poor sleep quality without a diagnosed sleep disorder. The adjunctive physical activity intervention did not additionally improve sleep quality. Clinical Trial information Australian New Zealand Clinical Trial Registry: ACTRN12617000680369; Universal Trial number: U1111-1194-2680; Human Research Ethics Committee, Blinded by request of journal: H-2016-0267.

scholarly journals Equity-Specific Effects of Interventions to Promote Physical Activity among Middle-Aged and Older Adults: Development of a Collaborative Equity-Specific Re-Analysis Strategy

2019 ◽  
Vol 16 (17) ◽  
pp. 3195 ◽  
Author(s):  
Gesa Czwikla ◽  
Filip Boen ◽  
Derek G. Cook ◽  
Johan de Jong ◽  
Tess Harris ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Social Inequalities ◽  
Interdisciplinary Collaboration ◽  
Middle Aged ◽  
Convenience Sample ◽  
Individual Level ◽  
Specific Effects ◽  
Analysis Strategy ◽  
The Impact

Reducing social inequalities in physical activity (PA) has become a priority for public health. However, evidence concerning the impact of interventions on inequalities in PA is scarce. This study aims to develop and test the application of a strategy for re-analyzing equity-specific effects of existing PA intervention studies in middle-aged and older adults, as part of an international interdisciplinary collaboration. This article aims to describe (1) the establishment and characteristics of the collaboration; and (2) the jointly developed equity-specific re-analysis strategy as a first result of the collaboration. To develop the strategy, a collaboration based on a convenience sample of eight published studies of individual-level PA interventions among the general population of adults aged ≥45 years was initiated (UK, n = 3; The Netherlands, n = 3; Belgium, n = 1; Germany, n = 1). Researchers from these studies participated in a workshop and subsequent e-mail correspondence. The developed strategy will be used to investigate social inequalities in intervention adherence, dropout, and efficacy. This will allow for a comprehensive assessment of social inequalities within intervention benefits. The application of the strategy within and beyond the collaboration will help to extend the limited evidence regarding the effects of interventions on social inequalities in PA among middle-aged and older adults.

scholarly journals Substitutions of physical activity, sedentary behaviour and sleep: associations with mental health in middle-aged and elderly persons

2021 ◽  
pp. jech-2020-215883
Author(s):  
Amy Hofman ◽  
Trudy Voortman ◽  
M. Arfan Ikram ◽  
Annemarie I Luik
Keyword(s):  
Mental Health ◽  
Physical Activity ◽  
Older Adults ◽  
Depressive Symptoms ◽  
Sedentary Behaviour ◽  
Vigorous Physical Activity ◽  
Population Based ◽  
Elderly Persons ◽  
Accelerometer Data ◽  
Middle Aged

BackgroundPhysical activity, sedentary behaviour and sleep are potential risk factors of mental health disorders, but previous studies have not considered the dependency between these activity domains. Therefore, we examined the associations of reallocations of time among older adults’ physical activity, sedentary behaviour and sleep with depressive and anxiety symptoms using compositional isotemporal substitution analyses.MethodsWe included 1943 participants (mean age 71 years, SD: 9; 52% women) from the population-based Rotterdam Study. Between 2011 and 2016, we collected accelerometer data (mean duration 5.8 days, SD: 0.4) on physical activity, sedentary behaviour and sleep and self-reported data on depressive symptoms and anxiety.ResultsA reallocation of 30 min more moderate-to-vigorous physical activity was associated with a −0.55 (95% CI −1.04 to −0.06) points lower depressive symptoms score when replacing sleep and a −0.59 (95% CI −1.06 to −0.12) points lower score when replacing sedentary behaviour, but not when replacing light physical activity (−0.70, 95% CI −1.63 to 0.24). No associations were found for anxiety.ConclusionReplacing sedentary behaviour or sleep with more moderate-to-vigorous physical activity was associated with less depressive symptoms, suggesting that mainly intensive types of physical activity are important for middle-aged and older adults in relation to depressive symptoms.

scholarly journals Explainable Machine-Learning Predictions To Support Personalized Cardiology Strategies

2021 ◽  
Author(s):  
De Rong Loh ◽  
Si Yong Yeo ◽  
Ru San Tan ◽  
Fei Gao ◽  
Angela S Koh
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Older Adults ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Left Ventricular ◽  
Physical Parameters ◽  
Activity Levels ◽  
Physical Activity Levels ◽  
The Impact

Abstract Aims A widely practiced intervention to modify cardiac health, the effect of physical activity on older adults is likely heterogeneous. While machine learning (ML) models that combine various systemic signals may aid in predictive modeling, the inability to rationalize predictions at a patient personalized level is a major shortcoming in the current field of ML. Methods and Results We applied a novel methodology, Shapley Additive Explanations (SHAP), on a dataset of older adults n = 86 (mean age 72 ± 4 years) whose physical activity levels were studied alongside changes in their left ventricular (LV) structure. SHAP was tested to provide intelligible visualization on the magnitude of the impact of the features in their physical activity levels on their LV structure. As proof of concept, using repeated K-cross validation on the train set (n = 68), we found the Random Forest Regressor with the most optimal hyperparameters, which achieved the lowest mean squared error. With the trained model, we evaluated its performance by reporting its mean absolute error and plotting the correlation on the test set (n = 18). Based on collective force plot, individually numbered patients are indicated on the horizontal axis, and each bandwidth implies the magnitude (i.e., effect) of physical parameters (higher in red; lower in blue) towards prediction of their LV structure. Conclusions As a tool that identified specific features in physical activity that predicted cardiac structure on a per patient level, our findings support a role for explainable ML to be incorporated into personalized cardiology strategies.

scholarly journals Age Differences in Estimating Physical Activity by Wrist Accelerometry Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3352
Author(s):  
Mamoun T. Mardini ◽  
Chen Bai ◽  
Amal A. Wanigatunga ◽  
Santiago Saldana ◽  
Ramon Casanova ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Older Adults ◽  
Random Forest ◽  
Energy Expenditure ◽  
Physical Activities ◽  
Learning Models ◽  
Middle Aged ◽  
Activity Types ◽  
Machine Learning Models

Accelerometer-based fitness trackers and smartwatches are proliferating with incessant attention towards health tracking. Despite their growing popularity, accurately measuring hallmark measures of physical activities has yet to be accomplished in adults of all ages. In this work, we evaluated the performance of four machine learning models: decision tree, random forest, extreme gradient boosting (XGBoost) and least absolute shrinkage and selection operator (LASSO), to estimate the hallmark measures of physical activities in young (20–50 years), middle-aged (50–70 years], and older adults (70–89 years]. Our models were built to recognize physical activity types, recognize physical activity intensities, estimate energy expenditure (EE) and recognize individual physical activities using wrist-worn tri-axial accelerometer data (33 activities per participant) from a large sample of participants (n = 253, 62% women, aged 20–89 years old). Results showed that the machine learning models were quite accurate at recognizing physical activity type and intensity and estimating energy expenditure. However, models performed less optimally when recognizing individual physical activities. F1-Scores derived from XGBoost’s models were high for sedentary (0.955–0.973), locomotion (0.942–0.964) and lifestyle (0.913–0.949) activity types with no apparent difference across age groups. Low (0.919–0.947), light (0.813–0.828) and moderate (0.846–0.875) physical activity intensities were also recognized accurately. The root mean square error range for EE was approximately 1 equivalent of resting EE [0.835–1.009 METs]. Generally, random forest and XGBoost models outperformed other models. In conclusion, machine learning models to label physical activity types, activity intensity and energy expenditure are accurate and there are minimal differences in their performance across young, middle-aged and older adults.

161 Sleep, Emotion, and Physical Activity in Older Adults Who Engage in Resonant Breathing Biofeedback

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A65-A66
Author(s):  
Noor Nasseri ◽  
Hanna Lagman ◽  
Katharine Simon ◽  
Jing Zhang ◽  
Sara Mednick
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Depressive Symptoms ◽  
Sleep Quality ◽  
Emotion Dysregulation ◽  
Depression Symptoms ◽  
Poor Sleep ◽  
Emotional Wellbeing ◽  
Sleep Habits ◽  
Coherence Level

Abstract Introduction Resonant breathing (RB) biofeedback increases rhythmic heart-respiration coherence patterns and has been associated with improved emotional wellbeing, physiological health, and sleep quality (Lehrer et al, 2000). Sleep quality declines with age, which leads to emotion dysregulation, cognitive impairment, and poor physical health (Crowley, 2011). However, limited research has investigated the sleep characteristics of older adults who practice RB-biofeedback. Therefore, our study investigates this population’s sleep characteristics, emotional stability, and physical health. Methods Thirty-one healthy participants (24 Female; M=54.68 years, SD=9.74) who self-identified as RB-biofeedback experts completed a series of online questionnaires assessing history, frequency, and duration of practice, sleep (habits and quality), physical activity (frequency, duration, and intensity), and mood (depression symptoms). They also reported their typical coherence level achieved, which is a numerical composite value associated with the heart rhythm’s uniform sine-wave pattern at approximately .1HZ (McCraty et al., 2010). Results Using bivariate correlations, we found that poor sleep quality was positively correlated with stress (r = .954, p = .001), poor sleep hygiene (r = .591, p &lt; .001), severe sleepiness (r = .518, p = .003), emotion dysregulation (r = .511, p = .004), depressive symptoms (r = .089, p &lt; .001), and negatively correlated with subjective happiness (r = .511, p &lt; .003). Severe sleepiness was negatively correlated with older adults’ enhanced physical fitness (r = .612, p &lt; .001), and poor sleep hygiene was positively correlated with depressive symptoms (r = .503, p = .004). We found no significant correlations between coherence level, mood, physical activity, or sleep measures. Conclusion We found significant associations between healthy sleep habits and emotional wellbeing. Those with better sleep quality and more positive sleep habits also had fewer depression symptoms. Moreover, those categorized as more athletic reported lower levels of severe sleepiness, suggesting that physical activity may be a protective factor for sleep in older adults. We did not find a relation between coherence level and sleep, or physical activity. These null results may be due to the high expertise level of the subject sample. Future studies should compare results to older adults who do not practice RB-biofeedback. Support (if any) Undergraduate Research Opportunity Program

The impact of the 24-hour movement spectrum on vascular remodeling in older men and women: a review.

2021 ◽  
Author(s):  
Jennifer S. Williams ◽  
Emily C. Dunford ◽  
Jem L. Cheng ◽  
Kevin Moncion ◽  
Sydney E. Valentino ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Resistance Training ◽  
Arterial Stiffness ◽  
Endothelial Function ◽  
Aerobic Training ◽  
Poor Sleep ◽  
Cerebrovascular Function ◽  
Increased Risk ◽  
The Impact

Aging is associated with increased risk of cardiovascular and cerebrovascular events, which are preceded by early, negative remodeling of the vasculature. Low physical activity is a well-established risk factor associated with the incidence and development of disease. However, recent physical activity literature indicates the importance of considering the 24-hour movement spectrum. Therefore, the purpose of this review was to examine the impact of the 24-hour movement spectrum, specifically physical activity (aerobic and resistance training), sedentary behaviour, and sleep, on cardiovascular and cerebrovascular outcomes in older adults, with a focus on recent evidence (<10 years) and sex-based considerations. The review identified that both aerobic training and being physically active (compared to sedentary) are associated with improvements in endothelial function, arterial stiffness, and cerebrovascular function. Additionally, there is evidence of sex-based differences in endothelial function: a blunted improvement in aerobic training in postmenopausal women compared to men. While minimal research has been conducted in older adults, resistance training does not appear to influence arterial stiffness. Poor sleep quantity or quality are associated with both impaired endothelial function and increased arterial stiffness. Finally, the review highlights mechanistic pathways involved in the regulation of vascular and cerebrovascular function - specifically the balance between pro- and anti-atherogenic factors, which mediate the relationship between the 24-hour movement spectrum and vascular outcomes. Finally, this review proposes future research directions: examining the role of duration and intensity of training, combining aerobic and resistance training, and exploration to sex-based differences in cardiovascular and cerebrovascular outcomes.

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