Nature exposure has been renowned for its positive physiological and psychological benefits. Recent years have seen a rise in nature immersion programs that make use of Guided Forest Therapy walks in a standard sequence of sensory awareness activities to expose participants to natural environments in a safe but effective manner. The study aimed to compare the efficacy of guided versus unguided nature immersion, upon three dependent variables of mood, nature connectedness and heartrate. 51 participants were assigned to either guided or unguided nature immersion. Nature connectedness (Connectedness to Nature Scale, CNS), Environmental Identity Scale, EID short form) and mood (Positive and Negative Affect Scale, PANAS) were assessed before and after nature immersion, while heart rate was tracked continuously by a wristwatch heart rate tracker throughout the 2-h experience. Demographics and general health practice (GHP) information were also collected. A mixed model ANOVA revealed that nature connectedness and mood (but not heart rate) improved post-immersion for all participants. Comparing the guided/unguided conditions, there were no significant differences in the change in nature connectedness, mood or heart rate. Comparing within the five segments within the standard sequence in the guided condition, the third and fifth segments revealed a significantly lower heart rate compared to the baseline heart rate.
Background
With the challenges that aging populations pose to healthcare, interventions that facilitate alleviation of age-related morbidities are imperative. A prominent risk factor for developing age-related morbidities is immunosenescence, characterized by increased chronic low-grade inflammation, resulting in T-cell exhaustion and senescence. Contact with nature and associated physical activities have been shown to boost immunity in older adults and may be promoted in the form of Horticultural Therapy (HT). We aimed to examine the effects of HT on immunosenescence.
Methods
We conducted a randomized controlled trial with 59 older adults assigned to either the HT intervention or waitlist control group. Older adults in the HT intervention group underwent HT intervention program over six months. Venous blood was drawn at baseline and at the 3 rd and 6 th month from the commencement of this study. For participants who attended all three blood collection time points (HT: n=22, waitlist: n=24), flow cytometry analysis was performed on whole blood samples to evaluate the kinetics of lymphocyte subsets over the intervention period, revealing the composition of CD4+ and CD8+ subsets expressing exhaustion markers – CD57, CTLA4, and KLRG1. Enzyme-linked immunosorbent assays were employed to measure changes in plasma IL-6 levels.
Results
HT is associated with increased numbers of naïve CD8+ T cells and fewer CTLA4-expressing terminally differentiated effector CD4+ and CD8+ memory T cells re-expressing CD45RA (TEMRA). Furthermore, IL-6 levels were reduced during HT, and the frequencies of naive and TEMRA CD8+ T cells were found to be associated with IL-6 levels.
Conclusion
HT is associated with a reduction in the levels of biomarkers that measure the extent of T-cell exhaustion and inflammaging in older adults. The positive effects of HT on T-cell exhaustionwere associated with the reduction of IL-6 levels.
Forecasting of the solar irradiance and load demand are essential for system level control and components coordination in the supervisory controller of an off-grid hybrid energy system. This paper presents the analysis of the predictions for solar irradiance and load demand using two different Single Exponential Smoothing forecasting approaches. Both approaches perform prediction based on hourly basis. The first approach uses the current day data while the other uses the previous day data. Comparison between the two approaches is carried out, and the forecast results show that the Single Exponential Smoothing forecast models utilizing the previous day data achieved higher accuracy as compared to the one using the previous hour data.
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