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Cigarette smoking cessation is considered a most important intervention to reduce COPD progression . The dynamics of TD is shown in Fig 8 as cigarette smoking cessation occurs after different days of CS exposure (S = 1.67) with the parameters in Table A in S1 File. Our simulations demonstrate that when early smoking cessation happens before 920 days of CS exposure, TD falls to the baseline and COPD is prevented (Fig 8). Smoking cessation starting after day 920 leads to the reduction of TD to some extent, but COPD and inflammatory responses persist (Figure C in S1 File). Our results are qualitatively consistent with experimental and clinical observations [64–66]. Analysis of positive feedback loops will elucidate further these effects of smoking cessation as discussed later.
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| 100.0 |
As mentioned above, although CS is the major risk factor for COPD, only about 20–30% of chronic smokers are susceptible to the disease, suggesting that susceptibility of smokers to COPD varies [1, 7, 9]. This implies that parameters governing the dynamics of TD as well as other important network elements in the model can change among smokers with different levels of COPD susceptibility. Importantly, effects of cigarette smoking cessation can be different among smokers with variable susceptibility . While quitting smoking can prevent COPD in some patients (i.e., reversible susceptible smokers), smoking cessation fails to slow or stop COPD progression in others (severely susceptible smokers) [2, 66].
|
review
| 66.44 |
Our global sensitivity analysis shows that the TD outcome is sensitive to a subset of parameters including k13, k14, and k15 and dTD in Eq 12. Fig 9 illustrates how variations in k13 affect the transitions from resistant, reversible to severely susceptible smokers. Here, the CS dose (S = 1.67) is the same as that in the case shown in Figs 2–4. While k13 is varied, dTD = 3.4×10−3 (1/day) and the values of the other parameters in Table A in S1 File are used. The results show that when k13 is less than 2.6×10−2 ml/(cell day),TD remains at a low-level (<30%), exhibiting a COPD resistant feature seen in Fig 9(A). While k13 lies in a value range of 2.6×10−2 and 0.31 ml/(cell day), COPD occurs but smoking cessation leads to TD decreasing to the baseline shown in Fig 9(B). In this case, COPD is reversible . When k13 is larger than 0.31 ml/(cell day), TD at the steady state is reduced to some extent but still remains at a high level (>30%) after smoking cessation at day 2500. A COPD patient in this case is severely susceptible . Interestingly, for a severely susceptible smoker whose k13 is relatively large, the M1-induced destruction of lung tissue predominates. In this case, M1 is sufficient for the progression of COPD, consistent with experiments in mice [24, 25].
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| 100.0 |
(a) k13< 2.6×10−2 ml/(cell day) corresponds to resistant smokers (TD<30%), while k13≥2.6×10−2 ml/(cell day) is associated with susceptible smokers. (b) Effects of smoking cessation after 2500 days of CS exposure. 2.6×10-2ml/(cell day) ≤ k13 < 0.31ml/(cell day) corresponds to reversible susceptible smokers and COPD is reversible. k13≥0.31 ml/(cell day) is associated with severely susceptible smokers. In this case, COPD is not reversible.
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| 99.94 |
A well-recognized example for severely susceptibility is the severe deficiency of α-1 antitrypsin, which is the major inhibitor of neutrophil elastase. The severe α-1 antitrypsin deficiency is present in only 1–2% of individuals with COPD . As neutrophil elastase functions to degrade macrophage elastase inhibitor of metalloproteinase-1, the severe α-1 antitrypsin deficiency significantly increases the tissue destruction activity of macrophage elastase . Therefore, the overall effect of the severe α-1 antitrypsin deficiency is to significantly enhance the M1-induced TD generation , corresponding to the case where k13 adopts a relatively large value for a severely susceptible smoker (Fig 9).
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study
| 100.0 |
Similar results were obtained by varying k14 and k15 shown in Figures D-E in S1 File, respectively. Intriguingly, the changes in some parameters such as kI6,M1 in Eq 10, which do not affect directly on the TD outcome, also lead to similar results for TD dynamics, shown in Figure F in S1 File. Here, our modeling results demonstrate that similar TD outcomes can result from variations of different parameters. As will be discussed later, these parameters may be associated with different mechanisms of CS-induced immune responses in COPD progression, implying that similar COPD phenotypes can be caused by different mechanisms (endotypes). This finding could be important for developing therapeutic methods to treat COPD, as also discussed below.
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study
| 99.94 |
To identify important network elements for CS-induced COPD, in silico knockout simulations for the proinflammatory elements, e.g., M1, DC, Th1, Th17, CD8+T cells and TNF-α, IL-6, IFN-γ, and IL-17 were conducted in the following discussion. Deletion of a network element in the model was performed by setting all parameters of the element and the rate to zero . Here, the parameters used in the simulations are listed in Table A in S1 File [dTD = 2.9×10−3 (1/day)] and the CS dose (S = 1.67) is the same as that shown in Figs 2–4. The results for the time courses of TD, and Iα, I6 and I17 are presented in Fig 10.
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| 100.0 |
(a) TD dynamics, (b) Iα dynamics, (c) I6 dynamics, and (d) I17 dynamics. In silico knockouts of M1 (red dashed line), DC (red circles), Th1 (black stars), Th17 (black plus), CD8+T (black squares), TNF-α (blue dash-and-dot line), INF-γ (black asterisk), IL-6 (blue triangles), and IL-17 (black cross) [wild type is denoted by WT (black solid line)].
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other
| 99.4 |
As seen in Fig 10, M1 knockout results in significant reductions of TD and proinflammatory signals to rather low levels despite continuous CS exposure. M1 not only secretes proinflammatory cytokines such as TNF-α, IL-6 and IL-12, but also causes tissue damage as discussed above (Fig 1). Our results are in line with experiments demonstrating that M1 is a determinant for CS-induced COPD .
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| 100.0 |
DC knockout leads to the low-level outcomes of TD and the adaptive immune signals such as I17 shown in Fig 10. The innate immune signals, e.g. Iα and I6, are reduced partially but still remain at a certain level. Upon deletion of Th1 there are no significant changes in the CS-induced TD and proinflammatory outcomes. This result is in agreement with clinical data for low Th1 population density , and mice experiments showing that CS induced significantly the production of Th17 rather than Th1 . Deletion of Th17 or CD8+T cells leads to a significant reduction of TD so that the progression of COPD is halted seen in Fig 10(A). However, the CS-induced proinflammatory signals such as Iα and I6 are reduced but still maintain at a relatively high level compared to those in silico experiment of M1 knockout. Different from the CD8+T deletion, the knockout of Th-17 leads to a reduction of I17 to the baseline, as shown in Fig 10(D). These results are in line with experiments in mice [70, 71].
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| 100.0 |
TNF-α knockout simulations show that there is no significant reduction in TD and other proinflammatory signals (Fig 10). This result is in line with clinical data and pharmaceutical studies discussed previously [6, 18, 19]. The IL-6 deletion allows the adaptive immune signals, T17, T8, I21 (data not shown) and I17 to maintain at the baseline level, and results in a significant reduction of TD (<10%) at the steady state (Fig 10), whereas innate proinflammatory signals such as Iα maintains at a relatively high level (blue triangles in Fig 10). While the IL-17 knockout leads to the results similar to those in the deletions of Th17 and CD8+T, the IFN-γ deletion results in TD (at the steady state) at a relatively high level (~32%, black asterisks in Fig 10).
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To investigate the importance of the positive feedback loops shown in Fig 1 in COPD progression, a feedback loop breaking approach is applied in the present study. Here, four aforementioned positive feedback loops, M1→TD→M1 (Loop 1), IL-6→Th17→ IL-17→TD→IL-6 (Loop 2), M1→IL-12→Th1→IFN-γ→M1 (Loop 3), and IL-6 ┫Treg→IL-10 ┫Th17→IL-17→TD→IL-6 (Loop 4), are explored by setting, e.g., k3 in Eq (A), kI6, TD in Eq (J), and kI12, M1 in S1 File to zero, leading to the breaking of Loops 1–3 on TD→M1, TD→IL-6, and M1→IL-12, respectively. We set KTg, I6 in Eq 6 to a very large value, e.g., 104 to break Loop 4 on IL-6 ┫Treg in the calculation. Simulations were performed using our model with the parameters in Table A in S1 File (dTD = 2.9×10−3/day). The results reveal that breaking Loop 1, 2, or 4 allows TD to remain less than 10% (Fig 11), indicating that COPD does not occur. However, breaking Loop 3 has no profound effects on COPD progression (cyan line in Fig 11). This finding is consistent with virtual knockout of Th1 discussed above.
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Loops 1, 2 and 4, which all include the TD node, can amplify the immune response to CS and enhance the TD outcome when they are activated. As Loop 1, 2 or 4 is necessary for COPD progression under the condition given in Table A in S1 File discussed above, we also carried out simulations to investigate whether one of these loops is sufficient for COPD when other positive feedback loops are broken. Here, k3 = 1.9×106 cell/(ml day), kI6,TD = 22.0pmol/(cell day), and KTg,I6 = 2.3 pmol/L are used for Loop 1, 2, and 4, respectively. The other parameter values are given in Table A in S1 File (dTD = 2.9×10−3/day). The results show that activation of Loop 1, 2, or 4 can lead to COPD [Fig 12(A)]. Again, Loop 3 alone is not able to drive COPD progression by modification of kI12, M1.
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(a) Loop 1, 2, or 4 alone causes CODP while Loop 3 does not. (b) As Loop 1 is activated, M1 (red solid line) predominates over M2 (red dashed line). While Loop 4 is activated, both M1 (blue solid line) and M2 (blue dashed line) are relatively low. (c) In the case where Loop 1 is activated, Tg is predominant over T8 or T17. (d) The activation of Loop 4 leads to the predominance of T8 and T17 over Tg.
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other
| 98.44 |
It is of interest to note that Loops 1, 2 and 4 play important but different roles in the CS-induced immune response in COPD progression as shown in Fig 12. The activation of Loop 1 results in a marked predominance of M1 over M2, thus responsible for the innate immune response [Fig 12(B)]. In this case, Tg is predominant over T8 or T17 [Fig 12 (C)]. The activation of Loop 4, as well as Loop 2 (data not shown), drives Th17 and CD8+ to predominate over Treg, responsible for the adaptive immune response [Fig 12(D)]. In this case, both M1 and M2 are relatively low [Fig 12(B)]. These results demonstrate that there exist different cellular and molecular mechanisms (endotypes) in COPD progression. In particular, these different endotypes (mechanisms) can lead to similar TD (CODP) outcomes, highlighting the heterogenous nature of COPD. This finding would be of particular importance in treatment of COPD using personalized medicine and target therapy as discussed below.
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study
| 99.6 |
The molecular and cellular mechanisms of the CS-induced immune response in COPD progression are not completely understood. In particular, the issues regarding the dynamics of CS-induced immune response in COPD, the effects of cigarette smoking cessation, and the disease susceptibility remain to be elucidated [1–2]. As COPD is a chronic and progressive inflammatory disease whose dynamical time scale is usually very long (over 20 years of CS exposure ), it would be extremely difficult for real-time measurements in the clinic or the laboratory. The main objective of this paper is to use computer modeling and simulation to address these issues. As discussed above, the nodes in our network model, which bears a multiscale nature, represent the cytokines, immune cells and damaged tissues (TD) whose dynamics characterizes the progression of COPD. The interactions between these nodes are often highly nonlinear and can be described using the Hill functions. The population dynamics of the network elements are described by a set of DOEs with parameters, whose values are known or estimated from established literature. For those no experimental data are available, we performed the global sensitivity analysis to obtain order-of-magnitude estimates.
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study
| 99.94 |
The results in this work demonstrate that CS-induced COPD development is a multi-step process involving both innate and adaptive immune responses. In the early acute phase of CS exposure, innate immune response predominates. During the transition from the innate to the adaptive immunity, if M1 predominates over M2, the system proceeds to high-grade chronic inflammation and eventually toward COPD where the adaptive immunity play a dominant role (Figs 2–4). However, when M2 (Treg) is predominant over M1 (Th17 and CD8+ T), the acute inflammation turns into the low-grade chronic inflammation, and COPD does not occur (Fig 5–7).
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study
| 100.0 |
CS inhalation has been considered the major risk factor for COPD, but only about 20–30% chronic smokers develop the disease, suggesting that cigarette smokers have different levels of COPD susceptibility [1, 9]. Our simulations disclose that there exist three types of smokers according to their COPD susceptibilities, i.e., resistant, reversely susceptible and severely susceptible smokers. While long-term CS inhalation can cause just low levels of chronic inflammation in resistant smokers but without COPD, susceptible smokers can develop COPD eventually under the same CS exposure conditions as shown in Fig 9. After cigarette smoking cessation, COPD can be prevented in reversible susceptible smokers, but the disease cannot be fully reversed in severely susceptible smokers (Fig 9). The sensitivity analysis in this work has identified a subset of parameters in the model that govern the dynamical behaviors of COPD and describe the different disease susceptibilities of different smokers as shown in Fig 9 and Figures D-F in S1 File.
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study
| 100.0 |
The network model in this paper involves multiple immune cells, cytokines and lung tissues, forming multiple proinflammatory and anti-inflammatory/regulatory pathways with several positive feedback loops. The in silico knockout experiments performed in this study have identified several important proinflammatory elements including IL-6, IL-17 cytokines, and M1, DC, Th17, CD8+T cells. It is intriguing to note that despite high concentrations of TNF-α often found in COPD smokers, our present study shows that this cytokine may not play a significant role in the progression of COPD. This finding is consistent with biopharmaceutic and clinical studies [6, 18, 19]. The feedback loop breaking simulations demonstrate that Loops 1, 2 and 4, which all involve the TD node, play important but different roles in the COPD progression. Activation of Loop 1, which enhances the M1 and TD productions, can promote the M1/M2-type COPD while Loops 2 and 4 contribute to the (Th17+CD8+T)/Treg-type disease where IL-6 and IL-17 are key molecules for the disease progression. These results indicate that COPD can be heterogeneous and can result from different molecular and cellular mechanisms.
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study
| 99.94 |
The reason why inflammation persists in COPD patients even after long-term smoking cessation is currently unknown . The above loop breaking results may provide novel insight into the aforementioned smoking cessation effects. After cigarette smoking cessation, the disease remains if these positive feedback loops continuously work to cause the tissue damage. But when TD is reduced due to smoking cessation to such an extent that these positive feedback loops are unable to drive tissue damage further, COPD will be suppressed.
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| 99.44 |
The feedback loop breaking simulations are consistent with the in silico knockout experiments in this study, together identifying the network inflammatory determinants for CODP progression. For example, IL-6 is not only a product from Loop 1, but also a key component of both Loops 2 and 4. IL-6 is an important proinflammatory factor for synthesis of acute phase proteins such as C-reactive protein, which is associated with several acute and chronic inflammatory diseases including COPD [15, 73, 74]. This cytokine is also identified as a major regulator of the balance between Treg and Th17 (CD8+T) cells as seen in our above discussions. As IL-6 plays an important role in COPD progression, it has recently been recognized as a potential target for COPD [73, 74]. Another example is that IL-17 as well as Th-17 is identified as a key component for COPD. Targeting IL-17 and Th-17 has become a promising strategy for treatment of the disease . However, COPD is a complex and heterogeneous disorder, e.g. similar clinical phenotypes can come from different endotypes, which are associated with different molecular and cellular mechanisms, as shown in our above modeling analysis. It is critical to identify the molecular and cellular disease mechanisms, by which subtypes of COPD patients are defined. As such, future treatment options would target the identified endotypes using available anti-inflammatory drugs . For this purpose, specific biomarkers of these endotypes would be particularly useful. Our modeling study offers a possible approach to probe endotype biomarkers for COPD and provides novel insight into this personalized medicine strategy as discussed above.
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| 99.9 |
As we focus on the CS-induced immune response in the progression towards stable COPD in this work, other pathogenic mechanisms including exaggerated proteolytic activity, resulting from an imbalance between protease and antiprotease, and excessive oxidative stress from an oxidant-antioxidant imbalance are implicitly involved, for example, in the representation of the M1→TD process as discussed above. Disruption of the balance between cell death and repair is also included indirectly in the TD component. These highly coarse-grained representations can be extended in more detail. Indeed, such studies have been undertaken recently in references [76–77]. However, the adaptive immunity has not been investigated and the insight into the cellular and molecular mechanisms of COPD is incomplete in these studies .
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| 99.94 |
Although cigarette smoking is the main risk factor for COPD, other factors can also play an important role in disease development and progression . For example, as almost every component of the immune system undergoes age-associated changes, aging is a risk factor for developing COPD . An exacerbation of COPD (ECOPD) is defined as an acute event characterized by a worsening of the patient’s respiratory symptoms that is beyond normal day-to-day variations and leads to a change in medication, responsible for substantial COPD mortality . Viral or bacterial infections are the main causes of ECOPD, leading to an acute flare-up of inflammation in the lung with stable COPD. Interestingly, mechanical forces of lung tissues have been shown to contribute to COPD progression by allowing rupture of tissue elements which directly leads to increased airspaces and this progression can go on even after smoking cessation . It is important to point out that our current network model focusing on CS-induced COPD does not take these factors into account. Elucidating the roles of these factors in COPD progression is beyond the scope of this article. Possible extensions of the present work to incorporate these factors into the network model will be investigated in future studies.
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| 75.56 |
It is of great interest to note that COPD and lung cancer are frequently induced by cigarette smoking, but these two disorders show opposite phenotypes [81, 82]. While COPD is featured by excessive lung injury and airway epithelial cell death, lung cancer is caused by unregulated proliferation of epithelial cells . Numerous epidemiological studies have linked the presence of COPD with increased lung cancer incidence, however, the molecular and cellular links between these two diseases remain obscure [5, 82–84]. Our network modeling research along this line remains for future.
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| 92.06 |
In this work, we develop a network model for the dynamics of CS-induced immune response in COPD progression. Using this model, computer simulations are performed for the investigations of smoking cessation effects and susceptibility of smokers to COPD, and the identification of important network elements in COPD progression. Our modeling study identifies several positive feedback loops that play important but different roles in COPD progression. The computational results in this study are consistent with laboratory and clinical observations, providing novel insight into the cellular and molecular mechanisms of CS-induced COPD.
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study
| 99.94 |
Identifying and characterizing the anatomic architecture of the substantia nigra (SN) has important clinical implications for the evaluation of structural changes associated with neurodegenerative conditions, such as Parkinson’s disease (PD)123. The SN is subdivided into two histologically distinct regions, the ventral pars reticulata (SNr) and the dorsal pars compacta (SNc). The SNc is composed of neuromelanin (NM)-containing dopaminergic neurons, which are affected early in PD123.
|
review
| 97.0 |
Numerous attempts have been made to visualize substructure morphology of the SN and to assess the neurodegenerative changes using various magnetic resonance imaging (MRI) signal contrasts23. For example, iron-sensitive MR sequences have great potential to define the boundaries and shape of the SN234 as the local deposition of iron alters magnetic field inhomogeneities and appears hypointense in T2 or T2*-weighted images (T2*WI) due to the shortening of transverse relaxation times25. By taking advantage of region-specific iron content within the SN, the area of lower T2*-weighted signal intensity is assigned to the SNr based on the histological observation of elevated iron concentration in that region356. NM-sensitive T1-weighted fast spin echo technique in in vivo 3T MRI studies allows the visualization of the SNc via hyperintense areas23789. NM within the dopaminergic neurons is speculated to generate paramagnetic T1-shortening effects on combining with metals, such as iron and copper23710. While NM-containing dopaminergic neurons are densely distributed in the SNc, they form clusters of cells that penetrate deep into the caudal part of the SNr. Thus, it may not be easy to delineate the boundaries between the SNc and the SNr with the use of NM-sensitive T1-weighted MRI alone3.
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study
| 99.9 |
Recently, ultra-high field seven Tesla (7T) MRI has provided detailed morphological information of the SN with improved spatial resolution and T2*-contrast and has opened up the possibility for more accurate identification of PD pathology345611. Specifically, postmortem 7T T2*WI, in combination with histological correlations and in vivo data, can directly depict the pockets of high signal intensity in the dorsal SN corresponding to nigrosome 1, which is known to be the structure most vulnerable to degeneration in PD1311. In PD, the loss of hyperintense nigrosome 1 and the expansion of signal hypointensity were each identified as a result of the loss of dopaminergic neurons and an increase in iron content within nigrosome 135611. More recently, 3T susceptibility-weighted imaging (SWI) was used to detect nigrosome 1 by dorsolateral nigral hyperintensity, which is absent in neurodegenerative parkinsonism, including PD1213, progressive supranuclear palsy, and multiple system atrophy14.
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review
| 99.75 |
In vivo MRI studies have reported an increase in overall iron accumulation without specifying the form of stored iron in the SN11. Ferric iron, whose strong paramagnetic properties alter MR signal contrast, is also known to bound to NM and ferritin411. The resulting NM-iron complex is one of the main iron compounds in SN dopaminergic neurons15. From the MR perspective, the paramagnetic NM-iron complexes may be detected by T2*-weighted iron-sensitive MR sequences as well as NM-sensitive T1-weighted imaging (T1WI), but their efficacy can change with an increasingly magnetic field and requires thorough validation. We hypothesize that NM would significantly contribute to the T2*-weighted hypointensity observed in the SN with increasing magnetic field strength. Similarly, the SN regions with a high NM content, such as nigrosome 1, may not appear fully hyperintense in T2*WI from the high magnetic field of 7T.
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study
| 100.0 |
In this study, we investigated the association between postmortem T2* imaging at 7T and the histological features in non-specific pathology brain samples to identify the contributions of various histological components to the T2* signal loss in the SN. T2* maps for an unbiased quantification of distinct physical tissue properties16 were generated and directly correlated with quantitative histology, such as densities of NM and iron pigments17.
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study
| 100.0 |
This study was approved by the Pusan National University Yangsan Hospital Institutional Review Board and the Ulsan National Institute of Science and Technology Institutional Review Board in accordance with the guidelines of the Helsinki Declaration. All methods were carried out in accordance with the approved guidelines.
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other
| 99.94 |
Postmortem midbrains were obtained from a 40-year-old male subject (subject I) and a 70-year-old female subject (subject II), neither with a history of neurological disease. Each subject joined the Pusan National University Anatomical Donation Program and signed the informed consent. The brain tissue was fixed and preserved in 4% neutral buffered formaldehyde solution for at least 2 months (5 and 3 months, respectively), a time period that facilitates a constant tissue T21819. The postmortem interval before fixation was less than 24 hours. Each brain sample was sectioned into 1.5-cm-thick slices containing the rostrocaudal extent of the SN parallel to a plane bisecting the mammillary body and the superior colliculus4 and was transected in the midsagittal plane to provide one-half (right side) of the SN for the analysis. The SN was normally pigmented without gross abnormalities. All accessible blood vessels were carefully removed to prevent susceptibility artefacts from intravascular iron20.
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study
| 99.94 |
MR images were acquired using a 7T MRI system (Bruker Biospec preclinical scanner, Ettlingen, Germany). In order to avoid susceptibility artefacts at the tissue-air interface, the tissue block was inserted into a syringe tube and immersed in a 4% formaldehyde solution. The tube containing the tissue block was then inserted into a magnetic resonance coil (radiofrequency transceiver volume coil, diameter 40 mm). All sequences were acquired along a transverse slice plane parallel to the block face of the rostral SN. To obtain optimal SN contrast, MR images were acquired with the following parameters. A multi-gradient echo sequence was performed to obtain T2*WI and T2* maps with repetition time (TR) = 2000 ms and echo time (TE) = 3.1~40 ms (10 TEs, increment = 4.1 ms), FOV = 35 × 35 mm, matrix size = 256 × 256, in-plane resolution = 136 × 136 μm, slice thickness = 0.5 mm, number of slices = 20, flip angle = 30°, scanning time = 25 min. Corresponding T2* maps were generated by linear fitting from a semi-log plot of decaying signal versus TE using MATLAB (R2013a, The MathWorks, USA). T1WI was acquired using a fast spin echo sequence (TR/TE = 700/8.0 ms, FOV = 35 × 35 mm, matrix size = 256 × 256, in-plane resolution = 136 × 136 μm, slice thickness = 0.5 mm, number of slices = 20, flip angle = 90°, scanning time = 96 min). A magnetization transfer contrast pulse (flip angle = 117°, 1500 Hz off-resonance, scanning time = 96 min) was also applied to obtain NM-sensitive T1WI.
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study
| 100.0 |
After MRI scanning, the tissue blocks were taken out of the tube and immersed in a 4% formaldehyde solution. Tissue blocks were placed in a solution of 30% sucrose in phosphate-buffered (PB) saline for several days until they sank to the bottom of the vessel. They were then removed from the sucrose bath, frozen in powdered dry ice, and stored at −80 °C. The tissue block was trimmed and sectioned at a thickness of 50 μm parallel to the block face. The histological sections were stored in individual wells in a PB solution containing 0.1% sodium azide at 4 °C. Of ten serial histological slides corresponding to each MRI slice (thickness 0.5 mm), four adjacent slides were stained serially with Perl’s Prussian blue (which is sensitive to ferric iron) without a counter stain, Kluver-Barrera (KB) (luxol fast blue stain for myelin with Nissl counterstain for neurons), tyrosine hydroxylase (TH) (to identify dopamine cells and fibres), and calbindin D28K (to subdivide the SN), using methods previously described121. The sections for Perl’s iron staining were selected to visualize NM, which appear as unstained brown pigments11.
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study
| 99.94 |
Perl’s reaction was performed in a 1:1 mixture of 20% potassium ferrocyanide and HCL. For KB staining, sections were incubated in 0.1% luxol fast blue MBS solution and counterstained in 0.1% cresyl violet acetate solution. For the TH immunohistochemical staining, the sections were incubated in the primary antibody (rabbit anti-tyrosine hydroxylase, AB152, Millipore Corporation, Temecula, CA) at a dilution of 1:500 in 1% normal goat serum, followed by biotinylated goat anti-rabbit immunoglobulin G (1:200 dilution). Immunohistochemical staining for calbindin D28K was performed using the rabbit anti-Calbindin D-28K (1:500 dilution, AB1778, Millipore Corporation, Temecula, CA) and biotinylated goat anti-rabbit immunoglobulin G (1:200 dilution). All slides were scanned using Olympus Slide virtual microscopy (Olympus, Japan) with a spatial resolution of 0.6836 μm2 per pixel (100×) and stored in lossless compression formats (Olympus virtual slide image format.vsi and JPEG 2000.jpx).
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other
| 99.8 |
Careful consideration was required to find accurate corresponding histological slides for each MRI slice. Since the block face of the tissue was used to determine both the direction of MRI acquisition and the histology slide cutting, this information was used to simplify a transformation model between 3D MRI volume and a stack of 2D histology images. We modelled the z-direction transformation (perpendicular to the block face) as a global translation and the xy-plane transformation (parallel to the block face) as a slice-by-slice 2D rigid transformation to account for possible misalignment during staining and scanning. Based on this transformation model, an up-sampled 3D volume of T1WI (68.36 μm2 per pixel) was then registered to a stack of down-sampled 2D KB staining images (68.36 μm2 per pixel) to maximize mutual information (MI)22. In Supplementary Figure S1, the original T1WI (C-I) was registered to the KB stain image (A) to obtain the aligned T1WI (C-II). Up-sampling was done with a cubic B-spline interpolation and a non-negativity constraint23; this image registration code with the above transformation model was implemented using the MATLAB Image Processing Toolbox (The Mathworks, USA). The resulting registration information was used to align other MRI images (e.g., T2*WI) to the corresponding KB staining images since all MRI images with different contrasts were obtained without moving the target tissue. In Supplementary Figure S1, the aligned T2*WI (D-II) was obtained by registering the original T2*WI (D-I) to the KB stain image (A) using the same transformation information obtained from the T1WI to KB image registration. Lastly, TH, Perl, and calbindin staining images (10×, 6.836 μm2 per pixel) were registered to the closest KB staining image (10×, 6.836 μm2 per pixel) using a MI-based rigid image registration. Since image features of each stained image are quite different from one another, we used thresholded binary stained images of KB, TH, Perl, and calbindin to perform image alignments so that no strong internal correlation between images would affect the results of image alignment. In Supplementary Figure S1, the aligned binary Perl stained image (B-II) was obtained from the original Perl stained image (B) that was automatically thresholded to generate the binary image (B-I) using red channel information, which has the largest contrast between the Prussian blue-stained tissue and the white slide background. It was then registered to the binary image (A-I) generated from the KB stained image (A) using red channel information, which also has the highest contrast between the KB blue-stained tissue and the white background. Similarly, the aligned Perl stained image (B-III) was made using the same transformation, in which all three channels of information from the original Perl stained image (B) was aligned to the original KB stained image (A). For the TH and calbindin stained images, similar procedures were applied so that the registered stained images to the KB stained image were obtained. We visually confirmed that our co-registration method between the various MR images and histology images worked well for small tissues cases, like the SN. However, more sophisticated methods such as the work of Adler et al.24 may be required for imaging larger tissue samples.
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study
| 100.0 |
Neuron-occupied areas (Supplementary Figure S2, A-II and B-II) were extracted from 2D KB and TH stained images (Supplementary Figure S2, A-I and B-I, respectively, 100×, 0.6836 μm2 per pixel) using black and brown colour information for the quantification of Nissl-positive neurons and TH-positive neurons, respectively. Black colour information in blue KB stained sections was extracted by thresholding blue channel information and dark brown colour information in TH-stained sections was extracted by manually selecting the brown-colour-range of the synthetic image from all three colour channels (red channel × max image intensity2 + green channel × max image intensity + blue channel). The number of pixels occupied by neurons for each 10 × 10 pixel block was then recorded to generate a density map (% occupied by neurons), as shown in Supplementary Figure S2, D-II (TH case only). Similarly, iron pigments (Supplementary Figure S2, C-III) and unstained pigmented NM (Supplementary Figure S2, C-II) were also extracted from 2D Perl-stained images (Supplementary Figure S2, C-I, the same two figures) to obtain iron and NM density maps (blue and brown, respectively). Blue-coloured iron information was extracted by thresholding the synthetic image that emphasizes the blue colour (blue channel – red channel/2 – green channel/2) and brown-coloured NM information was obtained by thresholding the sum image of all colour channels to distinguish brown-coloured pigments from the white-coloured background. These density images (10×, 6.836 μm2 per pixel) were then registered to the closest KB-stained image (10×, 6.836 μm2 per pixel) using the previously obtained alignment information. In Supplementary Figure S2, the TH-positive neuron density image (D-II), ranging from 0 to 100%, was registered to the closest KB-stained image to yield the aligned density image (E-II) using the same warp transformation information from the original TH-stained image (D-I) to the aligned image (E-I). Next, all registered MR images and density map (neuron, NM, and iron) images (10×, 6.836 μm2 per pixel) were down-sampled to smaller images after twice blurring using a 10 × 10 moving average kernel so that low resolution images (683.6 μm2 per pixel) were obtained for a voxel-wise statistical analysis. Histograms of the T2* distribution were generated for the respective thresholded binary stained images of iron pigments (blue), NM (brown), and reference tissue regions from the Perl-stained images (10×, 6.836 μm2 per pixel). Mean and standard deviation of each T2* distribution was obtained and compared to one other.
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study
| 100.0 |
Voxel-wise correlation analyses were performed between the MR images and the density maps derived from the histology images. No further comparisons between MR and histology images were performed when tissue damage or MRI boundary image artefacts were visually distinct or when the correlation values of neuronal density between KB, TH, and Perl stains were low from possible co-registration error. With these criteria, six sequential slides from each subject were studied. These included the caudal to rostral areas of the SN. Three different polygonal regions of interest (ROIs) were manually selected (Supplementary Figure S3): ROI-whole SN contains the whole SN based on KB staining corresponding to the bulk of SN hypointensity on the T2*WI; ROI-SNc contains the SNc based on TH staining within the ROI-whole SN including the A9 cell group, and ROI-SNr contains the SNr, which is obtained by subtracting ROI-SNc from ROI-whole SN. For voxels within ROIs, direct correlations between the T2* values and the density of neurons, NM, and iron pigments were evaluated.
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study
| 100.0 |
Pearson’s linear correlation (Spearman’s rank correlation in parentheses) coefficient tables (5 × 5 matrix each) were constructed for histological variables, the Nissl-positive neuron density, TH-positive neuron density, NM density, and iron pigment density, for the ROI-whole SN using the MATLAB Statistics Toolbox (The Mathworks Inc., USA) for each subject. Pearson’s linear partial correlation (Spearman’s rank partial correlation in parentheses) coefficients were also obtained to measure the degree of association between T2* values and each single histological variable with the effects of the other histological variables removed (NM with the effect of iron pigments removed in ROI-SNc, iron pigments with the effect of NM removed in ROI-whole SN, ROI-SNc, and ROI-SNr) using the MATLAB Statistics Toolbox. The coefficients of multiple correlations were calculated for each subject to study the association between the T2* values and all histological variables from the above correlation coefficient tables. From our multiple correlations study, we found that more than one variable, including the Nissl-positive neuron density, TH-positive neuron density, and NM density did not improve the coefficient of multiple correlations due to very high correlations among these three variables (see Supplementary Table S1). Therefore, only three variables (T2* value, NM density, and iron pigment density) were used for this study. Furthermore, to measure the independent contribution of iron and NM in T2* contrast, multiple regression β values for each subject were calculated with y = β0 + β1x1 + β2x2, where y, x1, and x2 correspond to is T2* values, NM, and iron density values, respectively.
|
study
| 100.0 |
The ex vivo SN imaging protocol was optimized for high resolution histological comparisons and enhanced SN-associated MRI contrasts. For the T2*WI, an echo time of TE = 15.4 ms yielded the best MRI visual contrast among the 10 different TEs, ranging from 3.1 to 40 ms (Supplementary Figure S4). All MRI scans displayed visible contrasts between the SN and surrounding structures. The T1WI images presented more distinct boundaries than the T2* weighted sequences, which appeared as arch shapes between the SN and the crus cerebri. However, the magnetization transfer T1WI could not visually depict NM-related contrasts in the SNc as previously reported with 3T MRI23789.
|
study
| 100.0 |
In both subjects, the ventral hypointense layers visible in the T2*WI images extended anteriorly to the crus cerebri (Fig. 1). This signal hypointensity was more prominent in the medial aspect. The boundaries between the SNr and the SNc were difficult to draw regardless of the imaging sequence. We could not find a signal-intensity difference leading to a delineation of these areas. The lateral SN showed hyperintensity relative to the medial areas in the T2*WI sequence.
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study
| 99.94 |
The T2*WI showed clusters of hypointense foci within the SN (Figs 1 and 2). Various shapes, including patches or linear streaks, were commonly identified at the exit level of the third cranial nerve fibres. These foci were much more hypointense and had lower T2* values than those seen in the hypointense layers between the SN and the crus cerebri.
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study
| 100.0 |
The line of demarcation between the SN and the crus cerebri (Fig. 1) was determined by KB staining. We found that hyperintense areas in T1WI corresponded exactly to the extent of the SN, as delineated by histology. However, the ventral hypointense layers on the T2*WI were not coextensive with the SN, but instead extended partially into the posterior part of the crus cerebri and overlapped with the densely myelinated portions. In these areas, iron pigments stained using Perl’s technique were detected in the absence of TH-positive cells or NM (Fig. 2).
|
study
| 100.0 |
Co-registration of MRI and histology data allowed us to identify clusters of pigmented NM revealed as prominent T2* hypointense in the background of the relative isointense signal (Fig. 2-C,D). Furthermore, MRI contrast in the dorsolateral SN revealed that high TH and low calbindin content was primarily generated by NM-pigmented neurons rather than Perl-positive iron particles (Fig. 2-F,a,b). Some hypointense areas reflected bundles of myelinated fibres in the ventrolateral SN (Fig. 2-A). Among midbrain dopaminergic cell groups, the T2* hypointense foci corresponded well to the clusters of NM-containing A9 nigral cell groups (Fig. 2). Similar signal loss was not prominently observed in the other dopaminergic cell groups located in the ventral tegmental (A10) or retrorubral (A8) areas, where there was a lesser amount of NM. The clusters of NM-containing neurons exhibited qualitatively better contrast in the images of the older subject (Fig. 2-II).
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study
| 100.0 |
The histological assessment showed that Nissl-positive or TH-positive neurons within the SN were colocalized with pigmented NM. The densities of these histological components were positively correlated with one other but not necessarily with the density of the iron pigment (Supplementary Table S2). Significantly reduced (P < 0.0001) mean T2* values were observed for both iron pigments and NM with respect to the reference SN tissue region as shown in the T2* histogram for both subjects (Fig. 3). Iron pigments and NM showed similar mean T2* values. We observed that the T2* values were well correlated with NM in the ROI-SNc (Table 1). The Pearson’s partial correlation coefficient was higher for subject II (r = −0.47 for subject I, P < 0.0001 and r = −0.65 for subject II, P < 0.0001). T2* values were also significantly correlated with iron pigments within the ROI-whole SN, ROI-SNc, and ROI-SNr in both subjects (Table 1; P < 0.0001). Note that both Pearson’s linear correlations and nonparametric Spearman’s rank correlations yielded similar results. Coefficients of the multiple correlation (R) between T2* values, iron pigments, and NM were 0.56 and 0.70 for subject I and subject II, respectively. When β1 is for NM and β2 is for iron in each subject, respectively, subject I was found to have β0 = 10.83, β1 = −0.43, and β2 = −8.05 while subject II had β0 = 11.95, β1 = −0.83, and β2 = −12.64. Both cases had higher β-values for iron than NM. Considering that β1 indicates the contribution of NM to T2* and β2 is the contribution of iron to T2*, the relative contribution of iron to T2* is higher than the contribution of NM to T2* by a factor of 18.7 and 15.2 times for subjects I and II, respectively. In other words, one particle of iron confers about −10 (ms) to the T2* value of corresponding region, while 15 particles of NM confer about −10 (ms) to the T2* value of the corresponding region. However, mean T2* values in Fig. 3 were similar in both iron and NM pigments since the number of NM particles was much larger than that of iron particles from Perl-stained slides.
|
study
| 100.0 |
We identified one of the main histological components that contribute to T2*-sensitive contrast by using the co-registration of postmortem MRI and histological data. Signal loss on T2*WI may be attributable to paramagnetic macromolecules, both NM and iron pigments, within the SN4. These quantitative associations suggest that NM may be one of the determinants of T2* signal loss in the SNc.
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study
| 100.0 |
Correlations between the MRI data and histology is required to elucidate the nature of the MRI contrast. Histologically validated high-resolution MRI may enable a more precise definition of the boundaries and substructures of the SN. In previous studies, the overall correlation was calculated either based on a visual comparison between MRI and histological sections or with atlases obtained from different subjects25. However, visual comparison is prone to misalignment error. Recently, more precise postmortem high-resolution MRI and histological correlations from the same sections were performed using semi-automatic co-registration methods based on manually placed landmarks or block-face images1121. In this study, we used a fully automated MI-based image registration method to align the MRI and histology images with a new transformation model (2D rigid transformation + 1D translation) rather than a fully 3D rigid transformation model, to account for possible transformations that happen during slice preparation. In addition, our image registration used thresholded binary histology images such that a localized registration-induced bias would not be introduced in the correlation studies between T2*-related MR images and histology images (except for the T1WI to KB-stained image registration).
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study
| 100.0 |
The distribution of iron in the SN has been localized to NM-containing neurons, microglia, and astrocytes. NM is the principal iron storage site and plays an important role in intraneuronal iron homeostasis in dopaminergic neurons of the SN26. By visual comparison, we found that the paramagnetic hypointense foci with decreased T2* values within the dorsolateral SN corresponded well to the clusters of NM-containing neurons, although not accompanied by sufficient amounts of stainable iron pigments.
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study
| 100.0 |
It is likely that NM-bound iron causes paramagnetic T2 shortening effects4. Our results also suggest that NM is one of main sources of a T2*-sensitive contrast in the SN at 7T. The correlation of T2* shortening with the density of NM and the negligible T2* changes in the less-pigmented dopaminergic neurons of the ventral tegmental (A10) and retrorubral (A8) areas support this hypothesis20. However, we are unable to provide histological evidence of the amount of iron bound to NM leading to T2* contrast because ferric iron deposits in NM-containing neurons are not usually detectable by Perls’ staining15.
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study
| 100.0 |
We observed some individual differences in the contribution of NM and iron to T2* signal loss. The multiple correlation analysis showed that the contribution of NM and iron to T2* signal loss was greater in the older subject. This may be explained by postmortem evidence for age-related increases in these two histological variables3262728. The content of NM-iron complexes is known to increase with age15262728. NM deposits in the extracellular space were also observed in the more aged SN1526. Indeed, the presence of significant amounts of NM released from dying neurons is a feature of PD1526. Unfortunately, the staining techniques used in this study were not able to differentiate between intraneuronal and extraneuronal NM. Future investigations will be pursued to further explore these results with a larger, age-controlled sample.
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study
| 100.0 |
Besides macromolecules (neurons, pigmented NM) and iron pigments, other local tissue components can influence T2* contrast, such as water content changes, microvasculature, as well as the tissue properties of the postmortem brain29. We found hypointense areas indicating bundles of myelinated fibres, particularly in the ventrolateral SN. Previous in vivo studies have shown vascular contrasts corresponding to Duvernoy’s India ink-stained atlas images. They suggested that the non-heme iron visualized in T2*WI or SWI are most likely due to the ferritin associated with the vascular network430. In contrast to this hypothesis, we could not find clear vessel-related contrast within the postmortem SN. Although its feasibility is questionable, further correlative analyses of in vivo MR images with postmortem histological sections from the same subject are needed to confirm the presence of vascular signals.
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study
| 100.0 |
Recent studies have shown that the line of demarcation between the SN and the crus cerebri in T2 imaging is ambiguous25. We observed iron-related signals with a band-like appearance in the SNr, which extended anteriorly to the posterior crus cerebri. Histologically, the hypointense areas on the T2*WI overlapped with densely myelinated fibres delineated by KB staining. Additionally, the margins of the SN facing the crus cerebri were very well correlated with the calbindin-stained fibres, which coincides with the striato-pallido-nigral pathway331. At the cellular level, the Perl staining for iron showed abundant iron pigmentation, presumably in the oligodendrocytes, which are known to stain more strongly for iron than any other cell type34. Therefore, it is reasonable to infer that the hypointense area posterior to the crus cerebri is the transitional part of the SNr. The T1WI hyperintensity located in the same area corresponded exactly to the extent of the SN as delineated by myelin-stained histology without overlapping with the adjacent crus cerebri. This is likely due to the fact that the MR T1 shortening effect from iron is weaker and more localized than those from blurring the T2* effect in this area4, especially at a high field strength. Recent studies also indicate that the boundaries of the SN derived from the NM-sensitive MRI and T2WI/SWI were spatially incongruent2129. This discrepancy between different MRI contrasts should be considered in interpreting the resultant delineations of the SN29.
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study
| 100.0 |
As with any postmortem study, the effects of tissue fixation must be considered19. The MRI properties of postmortem tissue can change as a result of decomposition and chemical fixation. T2 values have been observed to be lower in fixed postmortem brain than in vivo for both white and grey matter18. The postmortem brain tissue samples in this study were fixed for at least two months, as required for T2 values to stabilize1819. The overall distribution of signal abnormalities are similar to those obtained in previous postmortem MRIs, which showed more laminar structure in the SN compared to in vivo scans611. T1-weighted sequencing with the magnetization transfer contrast pulse used here did not create an NM-specific contrast in the SNc1011. The reduction of T1 contrast at the high field strength and the proton microenvironment may have limited tissue T1 contrast due to changes in the penetration of the tissue by the fixative2032.
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study
| 100.0 |
We identified the NM-related T2* hypointense signal with the co-registration of 7T MRI and histology that accounts for the steps in histology slice preparation. Metal-bound NM macromolecules can alter the magnetic field uniformity and cause paramagnetic hypointense signals in T2*WI and decreased T2* values within the SN4. Nigral T2* imaging can reflect the cellular density of NM-containing neurons at 7T. Future investigations with increased sample sizes and advanced MRI techniques are necessary to generalize our observations, including in vivo verifications611.
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study
| 100.0 |
The cardiometabolic health of persons with a severe mental illness (SMI), such as schizophrenia, other psychotic or bipolar disorders, is alarming with obesity rates of 45-55% and type 2 diabetes rates of 10-15% . This is up to four times higher than in the general population of comparable age . The increased risk in SMI patients is associated with their illness (negative and depressive symptoms lead to disinterests in and lower levels of autonomous motivation towards physical activity [2, 3]), their treatment (antipsychotic medication, inadequate somatic treatment) and lifestyle factors (e.g. lack of exercise, unhealthy diet, smoking) [1, 4].
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review
| 99.9 |
In mental as well as in general health care, SMI patients may receive insufficient attention for their physical condition . In the Netherlands, screening of somatic and mental health on a regular base is now obligatory for SMI patients according to the multidisciplinary guideline . However, somatic screening results indicating increased risk of negative health outcomes are seldom translated into (adequate) treatment . General practitioners working with these patients may lack knowledge of this specific population. On the other side, psychiatrists and other mental health professionals may lack knowledge and expertise in addressing lifestyle issues. Due to their knowledge on the SMI population and the frequent contacts, mental health nurses (MH nurse) are assumed to be the most adequate persons to address lifestyle behaviour change in SMI patients. Therefore, evidence-based lifestyle tools that provide MH nurses with knowledge, techniques and practical skills to stimulate patients in behaviour change are needed.
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review
| 99.7 |
Lifestyle interventions have been shown to be effective in the reduction of body weight and cardiometabolic risk factors such as waist circumference, triglycerides and fasting glucose in adults with SMI [9, 10]. However, the quality of studies on the effectiveness of lifestyle interventions in the SMI population is rather low, samples are small and results are inconsistent [10, 11], although one well-designed relatively large intervention RCT has recently been published . Systematic reviews on lifestyle interventions in different populations indicate that, to be effective, a lifestyle intervention should contain at least three key components: exercise, diet and behavioural therapy . Behavioural therapy strategies that enhance individual behavioural change include improving self-management skills such as tailoring information to the individual, identifying (lifestyle) areas for improvement, goal setting, making action plans, giving personalized feedback to reinforce new behaviours and using social and environmental strategies to support change [13, 14]. However, most of these techniques have a limited effect, and only work well for patients who are motivated . An approach to deal with unmotivated patients or patients who are not ready to change yet, is the motivational interviewing (MI) approach of Miller and Rollnick combined with the stages-of-change from the transtheoretical model of Prochaska and DiClemente . MI is a patient-centred counselling approach that targets behaviour change by addressing intrinsic motivation. MI seems more effective than traditional methods in targeting lifestyle change . It has been shown to be effective in improving weight status, Body Mass Index (BMI) and cholesterol levels of overweight and obese adults and of clients in a broad range of other domains [19, 20]. According to the stages-of-change from the transtheoretical model, patients’ level of motivation and self-efficacy to change is reflected in one of the five stages of change [21, 22]: the precontemplation, contemplation, preparation, action or maintenance stage, ranging from no intention to change till the motivation to maintain behaviour change. Treatment (or intervention) should adapt to a patient’s stage-of-change in order to increase intrinsic motivation for behaviour change . A combination of action planning with feedback and a motivational stages-of-change approach is believed to be effective in behavioural change in SMI patients . In addition, mental care is nowadays more rooted in the community and therefore more depending on SMI patients’ peers, families and environment. Therefore, peer and family support is considered an essential component for successful intervention implementation.
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review
| 99.9 |
In the Lifestyle Interventions for severe mentally ill Outpatients in the Netherlands (LION) trial, we propose a patient-centred multidimensional intervention using a web tool consisting of several of the above described successful intervention components, e.g. raising awareness of own lifestyle behaviours, goal setting, addressing motivation to change, personalized feedback, integrating support of friends and family and searching for healthy lifestyle activities in local communities (e.g. local sport clubs). An advantage of the intervention is that the tool addresses patients’ level of motivation (stage-of-change) to change diet and physical activity levels and that nurses are trained in motivational interviewing. This combination makes the intervention suitable for patients who do not seem motivated to change their lifestyle, indicating the intervention is considered eligible for more or less every patient. Due to the feasible character of the intervention, this trial will aim for a large sample size (~N = 250). Another unique feature of this trial is the pragmatic character of the intervention. Often, lifestyle interventions are implemented by external staff in strictly controlled conditions, recruiting the most motivated patients [8, 24]. In regular care however, staff with different levels of expertise will need to implement the intervention with available resources (e.g. time, budget), a high workload with competing priorities, and working with patients who may be unmotivated [8, 13]. This trial will show outcomes with high external validity of a lifestyle intervention implemented in a real-world care setting.
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other
| 99.6 |
The pilot study seems promising: after three months intervention, patients receiving the multidimensional lifestyle intervention (N = 20) lost on average three kilograms of body weight compared to care-as-usual (CAU) controls, performed more physical activity and rated their general well-being as better than patients receiving CAU (N = 20) . Patients mentioned as enabling factors the role of nurses in stimulating a healthy lifestyle, and that more physical activity made them feel better, which enabled them to change other lifestyle factors as well. The intervention was well appreciated by patients and staff.
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study
| 99.94 |
The aims of current pragmatic trial are to test whether a 12-month multidimensional lifestyle intervention, including aspects of increased awareness of own lifestyle and related risks, motivation, self-management, diet, exercise, and a supportive environment, is (cost-)effective in reducing cardiometabolic health and decreases depressive and negative symptoms. Also, barriers and facilitators in implementing the intervention on nurse and patient level will be explored.
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other
| 98.8 |
The primary research question is:Is a 12-month multi-dimensional lifestyle approach including a web tool for SMI patients effective in improving or stabilising abdominal obesity (waist circumference) and other cardiometabolic risk factors in SMI patients after six and twelve months intervention compared to care as usual?
|
study
| 99.7 |
Is a 12-month multi-dimensional lifestyle approach including a web tool for SMI patients effective in improving or stabilising abdominal obesity (waist circumference) and other cardiometabolic risk factors in SMI patients after six and twelve months intervention compared to care as usual?
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other
| 98.8 |
Secondary research questions are:2.Is a 12-month multi-dimensional lifestyle approach including a web tool for SMI patients effective in reducing depressive and negative symptoms in SMI patients after six and twelve months intervention compared to care as usual?3.Is a 12-month multi-dimensional lifestyle approach including a web tool for SMI patients aimed at improving or stabilising abdominal obesity (waist circumference) and other cardiometabolic risk factors in SMI patients after six and twelve months intervention compared to care as usual, cost-effective?4.What barriers and facilitators on nurse and patient level affect implementation of the 12-month multi-dimensional lifestyle approach?
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study
| 99.9 |
Is a 12-month multi-dimensional lifestyle approach including a web tool for SMI patients aimed at improving or stabilising abdominal obesity (waist circumference) and other cardiometabolic risk factors in SMI patients after six and twelve months intervention compared to care as usual, cost-effective?
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other
| 99.6 |
We hypothesize that this 12-months multi-dimensional lifestyle approach will improve cardiometabolic risk factors compared to patients who receive care as usual. Specifically, we expect the intervention to reduce waist circumference (WC), Body Mass Index (BMI) and Metabolic Syndrome Z-score (MS Z-score) after six and twelve months intervention because we expect that patients will try to increase their physical activity levels and improve their dietary habits. We expect that, through the intervention, patients will increase levels of physical activity and experience improvements in self-management skills and thereby improving self-efficacy [3, 14, 26], leading to a decrease in depressive and negative symptoms (i.e. lower depressive and negative symptoms scores). We hypothesize that the intervention will be cost-effective as costs will be relatively low (training of staff) while the physical and mental health of SMI patients will improve. Improvements in health, due to the increased self-management and increased exercise, might lead to less psychotropic drug use, such as antidepressants and anxiolytics [27, 28]. Finally, we will explore what barriers and facilitators on patient and nurse level have an influence on intervention implementation.
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study
| 99.94 |
The Lifestyle Interventions for severe mentally ill Outpatients in the Netherlands (LION) trial is a pragmatic single-blind multi-site cluster randomised controlled trial (RCT). Details are described below according to the SPIRIT 2013 statement . The study was approved by the Medical Ethical Committee of the University Medical Center Groningen. The trial is registered in the Dutch Trial Registry NTR3765 (www.trialregister.nl, 21 December 2012).
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other
| 99.9 |
Mental health care for severe mentally ill (SMI) outpatients is organized by a Flexible Assertive Community Treatment (FACT) team with MH nurses. FACT means that patient care is outreaching, takes place in the community (patients’ own environment) and ranges from high intensive (24 h) treatment to low intension support – for a detailed description of FACT, see [30, 31]. Patients living in sheltered facilities receive a combination of housing and services in the community. 21 FACT and eight sheltered facility teams serving SMI patients of five mental health care organizations in the North of the Netherlands, an area covering 2.8 million inhabitants, are invited for this study. Per team, nurses are invited to participate. All teams per organization are matched based on caseload size, mean age of patients, mean duration of admission of patients, most frequent diagnosis and location (urban or rural). After matching, teams are randomly allocated to the control or intervention arm by means of a random number generator by a researcher of the research team not involved in training of staff and recruitment of patients (see Fig. 1 for flowchart of the study). To minimise spill-over, randomisation is on team level, although inclusion of study participants and statistical inference are on patient level.Fig. 1Flowchart of the LION-study
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study
| 99.94 |
The study population consists of community-dwelling SMI patients and SMI patients living in sheltered facilities. Of this population, approximately 75% of the patients is diagnosed with a psychotic disorder, 15% with bipolar disorder and approximately 10% with complex personality disorders. In the North of the Netherlands, SMI patients are invited for annual Routine Outcome Monitoring screenings as part of standard care, consisting of a physical examination, a lab test and psychosocial measures. Patients are invited for the LION study when ROM screening outcomes indicate at least one of the following risk factors for metabolic syndrome: waist circumference > 88/102 cm (females/males); fasting glucose > 5.6 mmol/L or HbA1c > 5.7%; BMI > 25 kg/m2. Exclusion criteria are being pregnant, a BMI < 19 kg/m2, being primarily diagnosed with Korsakov syndrome or having a physical impairment which makes daily physical activity impossible. When patients are eligible for the study, they receive a detailed information letter from their case manager and, if willing to participate, sign informed consent.
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study
| 100.0 |
The main objective of this trial is to detect an abdominal weight loss having public health significance. Previous work has indicated that 5-10 cm reduction in waist circumference (WC) is considered a realistic guideline with a high probability of health benefits . For power calculations, we assumed 10% dropout rate. To include 250 participants, a 10% extra will be needed resulting in a total of 275 patients. Under these assumptions, and assuming an SD of 16.3 cm based on pilot data from this population, for two-sided 0.05-level tests of the null hypothesis, the study should provide approximately 80% power for detecting a difference of 5.8 cm in WC at 12 months between intervention and control groups. In addition, the study will have the same power to detect a reduction of 0.6 mmol/L in plasma glucose, given an SD of 1.7 mmol/L.
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other
| 99.7 |
After patients have signed informed consent, the research coordinator creates a web tool account for the patient. Hereafter, patient and nurse start the intervention by using the web tool ‘Traffic Light Method for somatic screening and lifestyle’ (TLM). The web tool is used during regular care visits, which take place, on average, once every two weeks. During the first visit, patient and nurse map out lifestyle behaviour in the web tool; during later visits they update progress (follow-up) reports (details below). Filling in a follow-up report each biweekly care visit in the follow-up phase is estimated to take 15 min. Six months after start of the intervention, the six-months measures take place. Hereafter, patient and nurse start again with the lifestyle behaviour screening and creating a lifestyle plan, followed by the follow-up phase until the end of the trial (12-months measure).
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other
| 99.8 |
The intervention in this trial is a 12-month multidimensional, patient-centred lifestyle intervention, including use of the web tool ‘Traffic Light Method for somatic screening and lifestyle’ (TLM), which supports behaviour change in various phases. The five most important features of the intervention are presented in Table 1.Table 1Five important features of the multidimensional lifestyle intervention using a web tool in the LION studyFeatureDescription1Patients’ readiness for behaviour change is not a prerequisite for starting the intervention. Nurses encourage behaviour change by making use of the stages-of-change of the transtheoretical model and motivational interviewing .2Patient-centeredness: patients decide if and what behaviour he/she wants to change, creates his/her own lifestyle plan with realistic goals and support. The tool can also be used by patients themselves to enhance self-management.3Because diet and physical activity are key components of a healthy lifestyle, these components are combined with behavioural change counselling; for an intervention to be effective, these three ingredients should be included .4Active support of the patient’s family and friends, incorporated in the lifestyle plan.5Nurses are trained to not only support patients in their behaviour change but also work behind the scenes to create a healthier environment: organise accessible exercise opportunities, raise team support for a healthier lifestyle in patients and share up to date lifestyle knowledge with the team, and raise awareness among other health care professionals (e.g. GP’s) of the increased cardiovascular risk of most SMI patients.
|
other
| 98.5 |
The 12-month intervention will be delivered by MH nurses. Before the start of the study, nurses will receive one day of training on (a) basic components of motivational interviewing and the stage of change model , (b) side effects of psychotropic medication, (c) lifestyle of and risks for SMI patients, (d) working with TLM, and (e) environmental factors that affect effectively working with TLM (e.g. health behaviour of staff members themselves or the availability of unhealthy products in the home environment) – see Meijel (2015) for more details. In addition, the study protocol will be explained. After three months, an evaluation session is planned to discuss obstacles with the tool, obstacles in motivating patients to participate and to recollect study protocol. Trained LION nurses are, due to the nature of the intervention, not blind for study allocation.
|
study
| 99.8 |
The Traffic Light Method (TLM) is a web tool originally developed as a practical tool for nurses and patients in one Dutch mental health care organization (GGz Centraal) and further advanced by a small spin-off company (Charly Green, Bilthoven, The Netherlands). It is based on the current state of the art of effective interventions and (inter)national guidelines on healthy lifestyle behaviour. During development, it was extensively reviewed by experts from the field in a Delphi panel and its use was optimized in a pilot study at GGz Centraal . The web tool is, after registration, available online (www.leefstijlinbeeld.nl; for a preview, see Fig. 2a and b).Fig. 2 a and b Preview of the web tool Traffic Light Method (TLM). Legend: a the starting page of the lifestyle behaviour screening representing the domains discussed in the Traffic Light Method (TLM) web tool; b examples of questions in the dietary domain within the lifestyle behaviour screening with built-in features to increase awareness (colouring according to risk profile) and knowledge (green bars presenting healthy reference values according to (inter)national guidelines)
|
study
| 99.94 |
a and b Preview of the web tool Traffic Light Method (TLM). Legend: a the starting page of the lifestyle behaviour screening representing the domains discussed in the Traffic Light Method (TLM) web tool; b examples of questions in the dietary domain within the lifestyle behaviour screening with built-in features to increase awareness (colouring according to risk profile) and knowledge (green bars presenting healthy reference values according to (inter)national guidelines)
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other
| 99.9 |
The TLM consists of two parts: (I) a lifestyle behaviour screening followed by creating a lifestyle plan and (II) a follow-up phase. In the lifestyle behaviour screening, the patient, together with a nurse, answers questions on several health and lifestyle related domains, see Table 2 for an overview of these domains. The Traffic Light Method displays a risk profile with all lifestyle behaviours in green, orange or red, depending on the level of risk. The patient creates, while being coached by the nurse, a lifestyle plan containing maximum three attainable lifestyle goals. During the subsequent follow-up phase, nurse and patient will systematically evaluate the patient’s progress in achieving the lifestyle goals described in the lifestyle plan. This will be done biweekly during regular care visits for approximately 15 min. In order to enhance and stimulate behaviour change, several techniques are built in the web tool. The aims of the lifestyle behaviour screening and the follow-up phase are presented in Table 3.Table 2Domains and subdomains in web tool Traffic Light Method (TLM)DomainSubdomains(a) General medical information1. Physical measuresa 2. Measures from lab testa 3. Physical diseases and handicaps4. Rating own health(b) Use of medication1. Satisfaction with medication use2. Somatic medication3. Psychiatric medication4. Freely available medication(c) Dietary habits (last 7 days)1. Satisfaction with own dietary behaviour2. Rating own dietary behaviour3. Assessing stage-of-change for dietary behaviour change4. Assessing dietary habits(d) Physical activity (last month)1. Satisfaction with own physical activity2. Rating own physical activity3. Assessing physical activity with SQUASH questionnaire4. Assessing stage-of-change for physical activity behaviour change5. Sedentary behaviour(e) Use of stimulants1. Disadvantages of dependence on substances2. History of substance abuse3. Use of alcohol4. Smoking behaviour(f) Other lifestyle factors1. Personal hygiene2. Relaxation3. Sleep behaviour4. Computer behaviour5. Social environment(g) Sexuality1. Condom use2. Sexually transmitted diseases(h) Lifestyle planb a Measures are taken from the Routine Outcome Monitoring screening conducted within two months prior to the web tool assessment. b Only available for participants in the intervention group Table 3Aims for the lifestyle behaviour screening and the follow-up phase in the lifestyle interventionAimDescription of aim per phaseLifestyle behaviour screening phase1Identify unhealthy lifestyle behaviours. The tool uses a traffic light principle for a clear visible presentation of possible health risks related to certain lifestyle behaviours, with green colours representing behaviours with low or no health related risk and red colours representing behaviours with high health related risks (see Fig. 2b).2Increase patient’s and nurse’s knowledge of healthy lifestyle behaviours. The tool provides direct feedback on what healthy behaviours are according to (inter)national guidelines and gives additional information to increase patient’s and nurse’s knowledge on healthy lifestyle behaviours (see Fig. 2b).3Create awareness. Patients are challenged to discuss identified risk factors and nurses support patients in deciding what lifestyle behaviours to change. Nurses use MI and stages-of-change techniques to assist patients in identifying their problems and overcoming ambivalence or resistance to behaviour change. It is supported by regularly classifying the patient’s current stage-of-change.4Create a lifestyle plan with concrete and reachable goals. Based on the lifestyle anamnesis and discussion with the nurse, patients set maximum three goals to achieve according to the criteria of S.M.A.R.T.-goals . The nurse’s role is to support patients in setting realistic goals. Patients explore which interventions are available and seem attractive, and what is needed to reach goals. Active self-management of patients is encouraged, support of family and friends is explored and, when available and deemed necessary, incorporated in the plan.Follow-up phase6Evaluating lifestyle goals systematically on a regular basis. During every regular care visit, a new follow-up file is uploaded and filled in by patient and nurse. By doing this, continuity is ensured and this repetitive character will lead to more sustainable behaviour change.7Barriers and facilitators in achieving lifestyle goals are indicated. Patient and nurse discuss which factors are helpful in achieving goals and which factors limit achieving goals in order to increase the success of achieving the goals in the following period. Again, nurses use motivational interviewing techniques and the stages-of-change of the transtheoretical model.
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review
| 87.06 |
All information entered in the web tool can be printed as a personal booklet for the participant to share the information and his/her lifestyle plan with friends and family or to use healthy lifestyle information in daily life (such as when doing groceries, preparing food).
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other
| 99.94 |
To increase the degree of implementation of the intervention, an implementation strategy was defined consisting of several components: 1) establish support from organizational management, 2) involve team management, 3) train MH nurses in using the web tool, motivational interviewing and the stage of change model, 4) plan a meeting with MH nurses and the trainer three months after training, 5) plan regular visits of research team one every three months, 6) send out newsletters to keep teams and nurses informed and involved.
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other
| 99.9 |
Patients in the control group participate in ROM screenings and results are discussed with the patient as part of standard care. Because data on lifestyle behaviours will be gathered from the lifestyle anamnesis part in the web tool, patients in the control group fill in the questions in the anamnesis part of the tool, but in blanc version in the web tool or on paper version; they do not receive any feedback or information via colours or education rules. In addition, patients in the control condition do not set up a lifestyle plan and therefore have no biweekly follow-up sessions. Nurses in the control group are instructed to give care as usual. This implies medical problems are tackled immediately according to protocol, while lifestyle guidance is more or less provided when patients wish to (based on ROM screenings).
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other
| 99.8 |
Measurements are performed on patient and staff level. An overview of all measurements at baseline, six and twelve months is given in Table 4.Table 4LION trial measurement overviewBaseline6 months12 monthsMeasurements on patient level Routine Outcome Monitoring General informationBirth year, gender, diagnoses, year of first psychosisXMedication useXX Physical measuresHeightXXXWeightXXXWaist circumferenceXXXBlood pressure (systolic, diastolic, pulse)XXX Lab testLipids (Total cholesterol, LDL-cholesterol, HDL-cholesterol, triglycerides)XXXGlucose metabolism (glucose, HbA1c)XXX Psychological measuresb CDSSXXPANSSXXHoNOSXXMANSAXX Cost-effectivenessa Dutch care consumption questionnaireXXXSF6DXXX Web tool TLM Lifestyle habitsDaily physical activity (SQUASH)XXXFood frequency questionnaire (adapted to patient population)XXX Additional measure by research assistantPhysical activity (pedometers) and body fatnessc XXXMeasurements on staff level General informationBirth year, gender, level of education, number of years working in psychiatry, functionX Staff questionnaireKnowledge on diet and physical activity, attitude towards lifestyle changes in patients, self-efficacy in addressing lifestyle issues with patientsXXDaily physical activity (SQUASH)XXFood frequency questionnaireXX a Measures are not part of standard ROM screening but added to ROM screening for the purpose of this study. b The conducted psychosocial measures within the ROM protocol could vary per team, not all teams conduct every psychosocial measure. c Only conducted by one of the five health care organisations (GGZ Friesland)
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study
| 99.75 |
a Measures are not part of standard ROM screening but added to ROM screening for the purpose of this study. b The conducted psychosocial measures within the ROM protocol could vary per team, not all teams conduct every psychosocial measure. c Only conducted by one of the five health care organisations (GGZ Friesland)
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other
| 99.56 |
Most measures on patient level are conducted during the ROM screening, which is part standard care and of the scientific ongoing PHAMOUS (Pharmacotherapy Outcome and Monitoring Survey) cohort . In mental health care organisations in the North of the Netherlands, it is routine care that ROM trained nurses invite patients annually for a ROM screening including somatic and psychosocial measures. Data of these measurements are reported in patients’ record forms and discussed with the patient. These data are stored in a large database and anonymized data are available for scientific research. This method was approved by the Medical Ethical Committee of the University Medical Center Groningen. For the LION study, data of two regular ROM screenings will be used for baseline and 12-months measures. An additional, short version of the ROM screening is scheduled six months after start of the intervention and patients will receive a small fee (€5,00/£4,30) for participation. ROM nurses carrying out the assessments are blinded for study allocation.
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study
| 99.94 |
The physical measurements include waist circumference, height, weight, pulse and systolic and diastolic blood pressure. Patients visit a (hospital) laboratory that collects a blood sample, if possible in fasting state, for levels of lipids (total cholesterol, LDL-cholesterol, HDL-cholesterol and triglycerides [all in mmol/L]) and glucose metabolism (glucose [mmol/L], HbA1c [%]). Measurements are taken following standard ROM protocols. Waist circumference (in cm) is measured in duplicate using a flexible nonstretching tape halfway between the iliac crest and lowest rib in standing position at the end of an expiration. Body weight is measured by calibrated scales (Seca, model 813) in light clothing without shoes or jackets. Measurements for height (in cm) will be available from multiple measurements of ROM nurses. The highest height will be used unless patients wear shoes, then the highest height without shoes is used. Pulse and systolic and diastolic blood pressure are measured after 5 min’ rest in sitting position, using a blood pressure monitor (BOSO medicus control).
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other
| 99.06 |
During an interview, trained nurses administer positive and negative symptoms with the PANSS (Positive and Negative Syndrome Scale ) and depressive symptoms with the CDSS (Calgary Depression Scale for Schizophrenia ). Prior to the interview, patients fill in the MANSA, a self-report questionnaire about patients’ Quality of Life and uncertainties can be discussed during the interview. The HoNOS (Health of the Nations Outcome Scale ) is an observation scale of psycho-social functioning and is scored by the case manager or team.
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other
| 99.9 |
LION trained nurses assess lifestyle habits using the lifestyle behaviour screening part in the web tool TLM. Items in the TLM physical activity and nutritional domain serve both a measurement purpose as well as an intervention purpose. Daily physical activity is assessed using the Dutch validated SQUASH questionnaire . Nutritional habits are estimated using a semi-quantitative food frequency questionnaire (FFQ) with items based on a screening questionnaire for healthy eating habits of the Netherlands Nutrition Center according to the Dutch guidelines for a healthy diet and adapted to this population. The questionnaire will be used to assess changes in dietary habits on food group level. It is not specifically validated in SMI patients and can and will not be used to derive quantitative estimates of total energy, macro- or micronutrient intake.
|
study
| 99.94 |
The stages-of-change for physical activity behaviour change and for dietary behaviour change are assessed based on the five phases of the stage-of-change model . These stages indicating whether a patient is in the precontemplation (not ready to change), contemplation (thinking about possible change), preparation (preparing to change), action (carrying out changed behaviour) or maintenance phase (maintaining changes behaviour).
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other
| 99.8 |
Care consumption is estimated with the Dutch care consumption questionnaire , which is adapted to the context of the current study. Use of medication is derived from patient record forms. Quality adjusted life years (QALYs) will be the primary outcome measure in the cost-effectiveness analysis. In order to estimate QALYs, utility scores will be derived from the SF12, using the SF6D algorithm [42, 43].
|
study
| 99.94 |
All patients of one organisation (GGZ Friesland) are invited to wear a pedometer (Yamax SW200 ) for at least seven days, reporting the total steps per day in a diary. A trained research assistant measures patients’ body fat percentage in standing position by bioelectrical impedance analysis (BIA) in triplicate using a single-frequency bioimpedance analyzer (Model BIA 101, AKERN Srl, Italy) [46, 47]. In order to calculate the body fat percentage using a formula, height and weight are measured in accordance with previously described methods.
|
study
| 99.9 |
Staff members receive an online questionnaire at baseline and after the intervention is finished to gather information on, among other things, own lifestyle behaviours, attitudes towards lifestyle and process evaluations as potential determinants influencing intervention implementation. Data on birth year, level of education and experience are only collected at baseline.
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other
| 99.7 |
Attitudes, self-efficacy and frequency of performing lifestyle related activities. The questionnaire also addresses staff members’ attitudes toward lifestyle coaching for patients, rate their self-efficacy in lifestyle coaching and rate how often they perform lifestyle related activities with/for patients and the difficulty they experience with these activities . Questions on attitude and self-efficacy are based on the ACE-model which describes the relationship between a persons’ attitudes, social influences and self-efficacy, and their behaviour. Questions are adapted to fit the study design and patient group [50, 51]
|
study
| 99.94 |
Staff members’ daily physical activity is assessed with the SQUASH questionnaire and their diet is assessed using a semi-quantitative food frequency questionnaire (FFQ) with items based on a screening questionnaire for healthy eating habits of the Netherlands Nutrition Center according to the Dutch guidelines for a healthy diet .
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other
| 99.9 |
Variables will be presented as mean ± standard deviation (SD), and if not normally distributed as median [25th-75th percentiles], or N (%) for frequencies. Missing data are handled differently based on amount and type of missingness. If missing data can be well predicted by regression methods, multiple imputation will be considered. Otherwise, interpolation or replacement by study mean or median will be preferred over complete case analyses.
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other
| 89.6 |
The primary outcome is the change in waist circumference (in cm) over time (from baseline to six and from baseline to twelve months) comparing the intervention group to the control group. This will be analysed with multilevel linear mixed models using teams as cluster. Analysis will be based on intention-to-treat principle. In per-protocol analyses, the intervention effect on cardiometabolic risk factors will be studied as described above, comparing participants with different degrees of intervention adherence to controls. Secondary study outcomes are analysed according to the same principles and techniques as the primary outcome. A priori sensitivity analyses are foreseen for participants’ age, gender and type of housing. In additional analyses, we will test whether the 12-months lifestyle intervention changes the level of motivation (stage-of-change) for changing diet of physical activity levels. An alpha of 0.05 is considered statistically significant.
|
study
| 99.8 |
Given the disturbingly high levels of metabolic diseases in SMI patients, and the associated risks of premature death , it is of high importance to develop lifestyle interventions that can effectively be implemented in regular care. The current study investigates whether a multidimensional lifestyle intervention using a practical lifestyle tool for mental health nurses to improve their knowledge, skills and expertise regarding healthy lifestyle behaviours and behaviour change in severe mentally ill patients, influences cardiometabolic risk factors of SMI patients in their caseload. The Traffic Light Method (TLM) tool aims for patients to increase lifestyle behaviour awareness and knowledge, improve self-management (setting lifestyle goals, receiving systematically feedback) and to involve friends and family in achieving lifestyle goals. The primary outcome of the study is waist circumference, considered the best predictor of abdominal fatness and cardiovascular disease , and other cardiometabolic risk factors. These measures are strongly associated with a range of negative health outcomes, such as type 2 diabetes, stroke and cardiovascular disease [53, 54].
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study
| 99.94 |
The motivational interviewing approach is a major strength of this intervention as it enables inclusion of all patients, regardless of their motivation to change their lifestyle behaviours. Addressing the (lack of) motivation as part of the intervention has been proven successful in improving medication adherence in persons with schizophrenia , altering substance (ab)use and weight loss in (overweight and obese) adults [19, 20]. Because the intervention is implemented during regular care visits by their own MH nurse, large numbers of patients can benefit from the intervention.
|
study
| 46.8 |
In the intervention, the patient is taking the lead in creating a lifestyle plan and determining his/her lifestyle goals. Therefore, every patient is able to direct the lifestyle intervention in such a way that it contributes to his/her specific recovery wishes. This fits well within the recovery approach in which patients take control over their own recovery process and decide themselves which (lifestyle) behaviours they wish or need to change in order to recover . In the field of mental health, the recovery approach fits well because of the person-centeredness, focus on improving quality of life besides solely reducing impairments of the mental illness and the acknowledgment of multiple possible pathways to recovery.
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other
| 83.3 |
Another advantage of using the TLM web tool is that it systematically addresses a broad range of lifestyle behaviours instead of solely focussing on diet or physical activity. This gives patients the option to choose which lifestyle behaviour they wish to change. In addition, health professionals have expressed that lifestyle interventions should at least include the following lifestyle topics: “(1) healthy eating; including buying healthy foods on a budget, cooking skills and recipes, (2) the risks of weight gain and how to monitor weight, (3) exercise; what is available, physically possible, affordable and accessible, (4) dental hygiene, (5) substance misuse and (6) physical health monitoring such as blood checks” (p. 402) . All mentioned components are present in TLM, therefore it can be considered a complete and comprehensive lifestyle intervention tool.
|
review
| 78.44 |
A last strength of the intervention is that the web tool constitutes an objective source of information that draws the attention of both patient and MH nurse to unhealthy lifestyle behaviours by presenting a risk profile and showing related healthy options. The nurse coaches the patient in the behavioural changes he/she wishes to make using MI and the stages-of-change techniques. Therefore, the MH nurse will not impose unwanted lifestyle advices, which is a benefit for the professional relationship between nurse and patient.
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other
| 99.9 |
The study design for this trial has several advantages. First, by using regular ROM screenings and structurally inviting all patients with screening outcomes indicating at least one cardiometabolic risk factor, patient selection bias is minimal. Second, the intervention is feasible for a large number of patients (e.g. also unmotivated patients are eligible, implementation during regular care visits), leading to a large and highly representative study sample to be included. Third, follow-up of less motivated patients is feasible because of the routine ROM screenings, which will be performed routinely in all patients. Fourth, data collection is based on existing Routine Outcome Monitoring screenings infrastructures, which has several advantages: ROM nurses are well trained, baseline and follow-up measures are conducted by the same nurse, and additional time and costs of patients, nurses and researchers are limited. The ROM data collection covers a wide scope of measures, i.e. several physical measures and multiple psychosocial measures and, for this trial, only had to be extended with cost-effectiveness measures. Finally, the pragmatic character of the study, in which MH nurses carry out the intervention in hectic real word health care settings, will result in realistic and achievable intervention outcomes, representing outcomes with high external validity [8, 13].
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other
| 97.9 |
Some potential risks for bias might be expected. First, although patient selection bias should be minimal, it is still possible that it is difficult for nurses to include patients that are unmotivated to change their lifestyles in a lifestyle intervention study. Motivated patients are expected to be more easily included, leading towards patient selection bias based on the level of motivation. Second, because patients determine which lifestyle behaviours they wish to change, it is possible that they target other somatic health outcomes (e.g. dental health, sleeping patterns) than the primary study outcome waist circumferences and other cardiometabolic risk factors. In this case, stating that the intervention does not seem effective in reducing the primary outcome might be a biased conclusion when patients wished to change other health outcomes and might have been successful in addressing these changes. Third, although ROM is implemented on all sites, it is possible that not all sites have the capacity to conduct all measures, leading to missing values. In addition, ROM nurses cannot collect blood samples themselves but send patients an invitation to visit a (hospital) laboratory. Although ROM nurses convince and remind patients to do so, patients might be reluctant to go to the laboratory, also leading to possible missing values.
|
study
| 99.94 |
In the period between obtaining funding and preparing the study, unexpected large changes in the organisation of mental health care took place. Budgets were restrained and care delivery shifted from specialists towards general mental health care, leading to necessary adjustments in study design. The initial sample size was estimated based on 64 nurses all including 10 patients leading to a target sample of 640 patients. The Medical Ethics Committee advised us to plan an extra 20% inclusion to account for clustering of the data, yielding a target sample of 768 patients. However, due to increased workload, inclusion of twelve patients per nurse seemed unfeasible. To compensate, we planned to train more nurses so that less patients per nurse need to be included, and we furthermore broadened inclusion criteria so that patients in sheltered living facilities could be included as well. As we now had many clusters (teams and nurses) and relatively few patients per nurse, it was not necessary anymore to account for clustering of the data in calculating the sample size. The funding agency (ZonMw) and the Medical Ethics Committee have approved the adjusted study design and adjusted final target sample size of 275 patients.
|
study
| 99.94 |
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