Repeated stimulus causes a specific suppression of neuronal responses, which is so-called as Stimulus-Specific Adaptation (SSA). This effect can be recovered when the stimulus changes. In the auditory system SSA is a well-known phenomenon that appears at different levels of the mammalian auditory pathway. In this study, we explored the effects of adaptation to a particular stimulus on the auditory tuning curves of anesthetized rats. We used two sequences and compared the responses of each tone combination in these two conditions. First sequence consists of different pure tone combinations that were presented randomly. In the second one, the same stimuli of the first sequence were presented in the context of an adapted stimulus (adapter) that occupied 80% of sequence probability. The population results demonstrated that the adaptation factor decreased the frequency response area and made a change in the tuning curve to shift it unevenly toward the higher thresholds of tones. The local field potentials and multi-unit activity responses have indicated that the neural activities strength of the adapted frequency has been suppressed as well as with lower suppression in neighboring frequencies. This aforementioned reduction changed the characteristic frequency of the tuning curve. Neural adaptation is a common phenomenon which has been extensively observed in the mammalian sensory area such as visual ,2, auditResearch on adaptation and change detection goes back to evoked potential signals in mismatch negativity (MMN) studies, which indicated this ability in human , monkey Tuning curve is an important response curve, which expresses the relation between responses of sensory neurons to peripheral stimuli, and is assumed as a criterion for the evaluation of neurons function in different brain areas. In auditory system, tuning curve identifies the response of one or some neurons for sound stimuli at different frequencies-intensity combinations. The frequency, which creates responses in minimum intensity, is called Characteristic Frequency (CF). The studies declared different types of tuning curves  in auditory cortical and subcortical regions .Most previous studies of SSA in different auditory cortical and subcortical neurons have described the adaptation effects to repetitive sounds in an oddball design with two different probability occurrences. Recent research  reportedThe objective of this study was to determine the relationship between responses of a sound stimulus in a control sequence and an adapted sequence. For this purpose, we considered SSA in a more complex pattern with a paradigm, which is consisted of pure tones with different frequency and intensity combinations. We designed a paradigm comprised of two audio sequences and used multiunit recording techniques to explore the effects of adaptation on the auditory tuning curves in rat primary auditory cortex. We found that adaptation shifts the auditory tuning curve toward higher-level intensities. We compared each frequency response in two sequences. The response to the adapter stimulus was decreased significantly. However, the response of the neurons to the neighboring frequencies was not decreased significantly. Hence, in a sequence, the adaptation to a specific stimulus around CF causes tuning curve to shifts toward the higher threshold and changes the CF.All experiments were carried out in Iran Neural Technology Center (INTC) at Iran University of Science and Technology (IUST) with the approval of, and using methods conforming to the standards of the ministry of health and medical education. The animals are maintained in the cages in facilities of the Neural Technology Center. The animal care and use committee of Neuroscience Research Laboratory, Iran University of Science & Technology approved all surgical procedures and experimental protocols in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All surgery was performed under urethane pentobarbital anesthesia, and all efforts were made to minimize suffering. At the end of experimental period, the animal was scarified under ether anesthesia.The data were recorded from the left auditory cortex of 15 adult male and female Wistar rats weighing 250–350 gr. Anesthesia was performed using an intramuscular injection of urethane  and supplementary doses  as required. Urethane is a proper drug for anesthetized recording because it has less effect on the neural activities in comparison with other pentobarbital as well as stable longtime remaining anesthesia , that leA linear multi-electrode array, consisting of four tungsten electrodes  with 200 um distance were used for extracellular recording. The electrodes were directed into the cortex using a Microdrive . The LFP and MUA recordings were performed using the USB-ME64-PGA recording system . Furthermore, an on-line data visualization was controlled with the Multichannel software ‘MC-Rack’. The recorded raw signals of each channel (with 10 kHz sampling rate) were initially pre-amplified by an eight-Channel Miniature Preamplifier (MPA). Next, the amplified signals were filtered between 1–5000 Hz and then were amplified again with the gain of 1000 with Programmable Gain Amplifier (PGA) device. Finally, in order to display and record the data for off-line analyses, the signals were transmitted to the computer.All used stimuli were pure tones with duration of 50 ms, 5 ms rise-fall time (linear ramps) and 300 ms Inter-Stimulus Interval  that were generated using MATLAB. Generated audio events were stored in a memory card (MMC) and were played simultaneously with a trigger pulse that was send to the digital input of recording system in order to represent the onset of stimulus. Experiments were conducted in a shielding room. The pure tones were presented through an insert phone (ER3) that was placed into the rat’s right ear canal by a foam. This insert phone covers the human hearing frequencies band (20 Hz–20 kHz), so we used the same band of frequencies for this study.In order to detect the selective neurons a Broad Band Noise (BBN) bursts were presented with 300 ms duration (frequency between 1–20 kHz) and 500 ms ISI at amplitude of 50 dB. Considering all four electrodes, only the channels which their average LFP amplitude were large enough, have been recorded. Then, different frequency-level combinations of pure tones were presented . Various arrangements of 11 frequencies /f1 = 44%) Control sequence (C). In the first sequence, 20 selected combinations (five selected frequencies in four levels) were presented randomly. Each frequency-level combination was presented in 30 trials. LFP and MUA responses of all combinations were averaged across trials and then the tuning curve (control condition) was achieved.Adapting sequence (A). In the second sequence, to find the adaptation effects on the tuning curve, the middle frequency (f3) was selected as the adapted frequency. This frequency at the level of 60 dB was considered as the adapter and the other combinations were assumed its neighborings. In this sequence, the adapter tone was presented four times per each combination. Audio tones were presented in a quasi-random sequence. Therefore, the adapted stimulus occupied 80% of all tone probability in this sequence. By considering this sequence, the adapting condition tuning curve can be achieved. The pure tones were presented in 30 trials and in a random sequence. According to the recent experiments, 25 presentations of each stimulus were enough to estimate an average response with a reasonable signal to noise ratio . In ordeLFP and action potentials were recorded from four channels simultaneously. The data were analyzed using Matlab. For LFP, the raw data was passed from a low pass filter with cutoff frequency of 300Hz. Then all responses were aligned on the stimulus onset, and their baselines were corrected. In order to modify the baseline as close as possible to the response onset, the average response of the first 5 ms duration from the stimulus onset was subtracted from it. The first latencies were always longer than 8 ms, . MaximumFor automatic spike-detection from raw signals, a threshold should be set. This can be set, as a multiple of the standard deviation of the signal . In thisThe resulted spike trains were aligned regarding the stimulus onset, smoothed with a 10 ms Hamming window, and averaged.i) and A(fi) represent the response strength of a frequency fi in control and adapting sequences  respectively. The AI was used for both LFP and MUA comparison. It measures the difference of each frequency response in control and adapted sequences. For LFP analyses, the difference between maximum and minimum of LFP response during 0–80 ms after onset was measured. It identified the response strength of the neurons faced to each stimulus. In terms of firing rate, the response strength of each frequency fi was considered as the firing rate value of first 80 ms from the onset. Therefore, the AI indicates how neural responses are changed in the desired two sequences.In order to quantify the adaptation effects, the percentage of difference between response activities of control and adapting conditions was measured. The adaptation changes were characterized by a criterion that was called Adaptation Index (AI) in an analysis similar to SSA index (SI) that was employed in , 13. ThiThe FRA of each site was separately mapped for two sequences. In order to do better comparison of the variation of the tuning curves in two sequences, the border of tuning curves were achieved by defining a threshold. This threshold is the minimum response of each frequency and declares the limited area of responses. This border usually is defined as mean spikes-per-presentation ± 1 SD , 15]. T. T15]. TExtracellular recordings from primary auditory cortex of urethane-anesthetized rats show that adaptation to specific stimulus near preferred or CF causes a reduction in neural response and dwindle FRA. The results demonstrate that the suppression of neural activities was specifically significant in the response of the adapter frequency.All data were recorded from primary auditory cortex (A1) of 15 rats (n = 167 recording sites). Local Field Potential (LFP) and Multi-unit Activity (MUA) were simultaneously collected. Similar to the other sensory systems, the responses of auditory cortex neurons were reduced due to repetitive stimulus. In the current study, we considered this repetition effect on the auditory tuning curves. Because of some system constraints, we considered only neurons in A1 which response to low frequency tones. The neurons with CF between 200 Hz—20 KHz were selected for recording.For each recording site, the control and adapting sequences  were presented. The response of each frequency-amplitude combination was compared in the two conditions. The onset of responses was aligned and the responses were averaged across trials for time interval between-10 ms to 90 ms after the stimulus onset. mean plus standard deviation of responses in different combinations) and determined the border of the responses. In order to compare the neural activities in the two aforementioned states, the tuning curves of neural activities have been considered. For each sequence (control and adapting sequences), analyses were performed based on the LFP and MUA and consequently two tuning curves were characterized. LFP voltage change and spike firing rate have been used in order to measure the FRA and the tuning curve. Considering LFP signals, voltage difference was calculated during 80 ms after stimulus onset in each frequency-level combination and it was assumed as the response strength. This quantity was averaged across all trials for each combination. Moreover, it mapped in a contour consisted of different frequency and level responses. The same calculation was performed by considering the firing rate of this duration (80 ms) in each combination. In LFP variation and firing rate were used to enumerate the difference between responses of the two conditions. The Adaptation Index (AI) quantified the difference between responses of control and adapted sequences. It indicates the adaptation value and refers to the normal distance between responses of each stimulus in two control and adapted conditions. It was calculated for responses of each frequency at the level of 60 dB in two conditions . In eachFor finding the relation between adaptation and CF, we investigated the isointensity curve at the adapter level. In order to specify the adaptation effect on the neighboring frequencies, six frequencies were considered just at the adapter level (60 dB). For each frequency, the firing rate was calculated as the response strength in two conditions. In the previous section, the neural analyses were illustrated in a single site with two different adapters. In this section, the population results of 167 sites in A1 neurons are reported. The difference between these two conditions was evaluated by statistical measurement; Wilcoxon signed-rank test. In all parts, the difference between the two distributions has been compared with three p-values . Respectively, the significance of this difference has been indicated with one star, two stars, and three stars.Considering the neural populations, the responses of adapted frequency (f3) and four neighboring frequencies  were aligned to each other in the two sequences. First, the variation of LFPs and value of spike counts for two conditions (control and adapting) were compared. This comparison indicated a significant difference in both LFP and MUA analysis, which was arisen in adapted response. In addition, a reduction in neighboring frequencies was seen but not conspicuous. This reduction is not significant but occurred in all neighboring frequencies. A narrow bar is employed in The significant suppression in adapter can be relevant to the special decrease founded in different auditory pathway from A1, IC, MGB, which is called SSA. Moreover, the response reduction in neighboring stimulus after the adaptation processes can be explained by the cross frequency adaptation concept that mentioned the effects of each stimulus on neighboring stimulus responses.In addition, to show the effect of adaptation better, the isointensity curves of the two sequences in the neuronal population were used. In order to elucidate this effect, the spike counts of all neurons were normalized to the maximum firing rate. The responses of each neuron to the adapted and neighboring frequencies were characterized  were calculated. The FRA has been achieved with both LFP and MUA separately. For characteristic tuning curve in population, first all of the responses were normalized. Then LFP variations and spike counts considered as the strength of each stimulus.The results indicate a suppression of neural responses of stimuli in adapting sequence and show a decrease in the response area. In the other words, in the adapting sequence, neurons respond to the frequencies in higher levels in compared to that frequencies in control condition sequence. This decrease is significant in the adapted frequency. Therefore, this adapter causes the tuning curves to shift toward the higher thresholds. The adaptation process changes the CF of the auditory tuning curve. Considering the neuronal population, it has elucidated that the tuning curve has been changed from one peak curve to multi peak curve. This concept is found conspicuously in the MUA tuning curves too .The neural response of adapted sequence tuning curve was subtracted from the control one in order to quantify the FRA changes. This analysis has been performed in the case of adapted and four neighboring frequencies at three different intensities . The conThe effect of frequent presentation (adaptation) on a specific stimulus has been reported in different parts of auditory system ,15,16,26The results suggest that adaptation to a specific frequency in a stream of audio events shifts the auditory tuning curves toward the higher-thresholds. This shift is uneven, since the adapter response was decreased more substantial than the neighboring stimuli. Therefore, the neural responses were suppressed in a sequence consisting of an adapter compared to their responses in a control sequence. This suppression changes the CF and the shape of the tuning curve.According to a recent study , three cThis study has been conducted to find out the effects of adaptation to a single stimulus  on the neighboring stimulus responses. Therefore, we measured both LFP and MUA of 167 recording sites in the primary auditory cortex of anesthetized rats. The voltage variation of LFP and the firing rate of MUA were compared for responses of each frequency in two states. In the first state, the responses of frequency-level combinations in a random presentation were considered for finding FRA. In the next state, a sequence was the same as the first one, just with this difference that a special frequency with 60dB level was considered as the adapted frequency. It was presented with the probability of 80% of all sequence stimuli. We find out that the adaptation to a specific frequency reduces the neural response of the combinations in that sequence. This reduction was significant for adapter frequency in the adapter intensity (60dB), in one-step higher, and in one-step lower level . The resSeveral investigations have reported SSA in different parts of auditory pathway. Since a decade ago, the adaptation to a specific stimulus was indicated in the auditory cortex with an experiment of single unit recording. In addition, some similar behaviors have been extensively presented between MMN and adaptation to specific stimulus in a single neuron. This SSA increases the sensitivity of the neurons in front of unexpected changes. In next year’s, this phenomenon was exposed in other subcortical regions. It expresses a reduction in neural response of a repetitive stimulus in comparison with the rare presentation. Most of these studies considered the usual oddball paradigm that had been employed in MMN studies. In that paradigm, two probe stimuli (Standard and Deviant) were considered for investigation. In this paper, different combinations of pure tones have been considered in an adaptation paradigm to explore the effects of adaptation in the auditory tuning curves. The existence of different stimulus combinations affects the responses of these stimuli.The population results show a reduction in neuronal responses in both LFP and MUA analyses. This suppression was not significant for the neighboring frequencies . This should probably be occurred because of the various dynamic behaviors of the neurons in different layers of neocortex . Most ofOn the other hand, several combinations of pure tones were needed to investigate tuning curves. Then, in order to have an adapter in seq.2, the probability of neighboring stimuli in seq.2 become less than their probability in seq.1. In fact, the tones in low probability presentation cause large responses . Then beThe results show that adaptation to a specific frequency affects the neighboring frequencies; however, it depends on the adapter and its distance from the CF . FurtherIn this paper, we explored the effects of the adaptation to a specific frequency on the tuning curves of the auditory cortex. The auditory cortex is considered as a main part of auditory system in SSA phenomenon. According to the investigations, the tuning curves in A1 have several shapes . This prIn this study, the rats have been presented with two audio sequences as the stimulus while the neural activities have been recorded extracellularly from the area of primary auditory cortex. LFP and MUA results demonstrate that adaptation makes a shift on the tuning curve of A1 neurons toward the higher-level intensities. It elucidates that adaptation to one stimulus affects the neural responses of the neighboring stimulus. Generally, the population responses of all stimuli in the adapted sequence have been extensively suppressed. This reduction was significant in the response strength of adapter  which is likely relevant to the stimulus specific adaptation that is characterized by two-tones sequence in various studies. Moreover, it causes a reduction on the response of neighboring combinations (cross-frequency adaptation) but it is not significant. This effect depends on the adapter and its distance from CF.


There is an increased interest in the use of multi-criteria decision analysis (MCDA) to support regulatory and reimbursement decision making. The EVIDEM framework was developed to provide pragmatic multi-criteria decision support in health care, to estimate the value of healthcare interventions, and to aid in priority-setting. The objectives of this study were to test 1) the influence of different weighting techniques on the overall outcome of an MCDA exercise, 2) the discriminative power in weighting different criteria of such techniques, and 3) whether different techniques result in similar weights in weighting the criteria set proposed by the EVIDEM framework.A sample of 60 Dutch and Canadian students participated in the study. Each student used an online survey to provide weights for 14 criteria with two different techniques: a five-point rating scale and one of the following techniques selected randomly: ranking, point allocation, pairwise comparison and best worst scaling.The results of this study indicate that there is no effect of differences in weights on value estimates at the group level. On an individual level, considerable differences in criteria weights and rank order occur as a result of the weight elicitation method used, and the ability of different techniques to discriminate in criteria importance. Of the five techniques tested, the pair-wise comparison of criteria has the highest ability to discriminate in weights when fourteen criteria are compared.When weights are intended to support group decisions, the choice of elicitation technique has negligible impact on criteria weights and the overall value of an innovation. However, when weights are used to support individual decisions, the choice of elicitation technique influences outcome and studies that use dissimilar techniques cannot be easily compared. Weight elicitation through pairwise comparison of criteria is preferred when taking into account its superior ability to discriminate between criteria and respondents’ preferences.The online version of this article (doi:10.1186/1478-7547-12-22) contains supplementary material, which is available to authorized users. Regulatory authorities face a complex decision task when considering new drugs or medical technologies for access and reimbursement. They need to take into account multiple aspects of a new technology and weigh their impact on the value of the innovation. Important aspects include: evidence on clinical benefits and risks, effects on quality of life, costs to the health care system, severity of disease, the context of use and equity . On a loBecause these decisions are complex if made unaided, decision makers typically use heuristic or intuitive approaches to simplify them . Thus, tIn recent years, multiple initiatives were undertaken to improve the decision making process in the policy decision making setting. Multi-Criteria Decision Analysis (MCDA) was proposed as a way to overcome some of these problems . MCDA, aIn an extensive project coordinated by the European Medicines Agency (EMA)(IMI-PROTECT), the use of multi-criteria decision analysis (MCDA) has been proposed as an approach to perform quantitative risk-benefit modeling and help decision-makers construct the values that are fundamental to effective decisions . RecentlThe actual use of MCDA techniques to support decisions in health care is increasing . One sucBuilding on an open source philosophy, the EVIDEM framework is collaboratively developed and regularly updated based on the practical experience and methodological issues identified in pilot testing and implementation. Important areas for further study that were identified were: the decision to use the five point rating scale to weight criteria, whether or not a hierarchy of decision criteria should be included to simplify weighting, and whether the choice of the weight elicitation technique had an impact on the measured value of healthcare interventions. While some other MCDA methods include very specific recommendations on the weight elicitation technique and put specific demands on the hierarchy of criteria, the EVIDEM framework aims to offer a more flexible approach , 16.The mixing and matching of weight elicitation techniques to meet the specific demands of the decision context is common practice. Criteria for selecting a weighting technique include: perceived face validity in the decision context, cognitive effort required from the respondent, and difficulty in analysis. Although these issues are important, the first question to be answered is whether choice of method would influence the validity of the results of the analysis and its overall impact on the outcome of the MCDA exercise. The fact that there is no golden standard for weighting, i.e. no measure of a “true” weight is available, challenges validity testing of weighting techniques. In this study we assume that the main aim of weighting in MCDA is to estimate the respondent’s priorities in satisfying different decision criteria. Therefore, an important aspect in the choice of weighting technique is its ability to differentiate between criteria that are considered important and those which are not. The objectives of this study were to test: 1) the influence of weights elicited with different techniques on the overall outcomes of a multi criteria decision analysis, 2) whether different weight elicitation techniques have equal discriminative power in weighting different criteria, and 3) whether different techniques result in similar weights in weighting the criteria set proposed by the EVIDEM framework.To familiarize respondents with the decision context, a health care decision problem was presented. Respondents were asked to take the perspective of a health policy maker having to decide on the reimbursement of one of multiple health innovations, with limited funds available.“in making the decision, you have to take into account multiple criteria. The ability of the health innovations to satisfy these criteria differs, and sometimes these criteria are conflicting . Therefore it is important to know how you prioritize these criteria in your decision.” After the decision problem was introduced, respondents were asked to indicate, for 14 different criteria, their relative importance in deciding on reimbursement of a health innovation from a societal perspective (Appendix A).Then respondents were introduced to the concept of multiple criteria decision making: The five-point rating exercise is a technique in which all criteria are rated on a five-point scale (RS) ranging from (1) not important to (5) very important. The five-point RS was the initial technique proposed in the EVIDEM framework .The ranking (RA) exercise is a technique in which criteria are ranked from most important to least important. Ranking is a commonly used method to prioritize criteria in simple-multi-attribute utility theory (SMART) and often combined with point allocation (PA) where a budget of 100 points is allocated over criteria to reflect their relative importance –20.The pairwise comparison (PC) technique compares criteria on a reciprocal numerical rating scale ranging from 9 (strong preference for criteria A) to 9 (strong preference for criteria B). If criteria are considered equally important, a score of 1 is given. This technique is used in the analytic hierarchy process (AHP) .In best worst scaling (BWS), subsets of four criteria are presented, and respondents are asked to select the most important and least important from the set. This is performed 12 times and all criteria are presented equally often. This method is known as best worst scaling (BWS) case 1 , 23.There are many weighting techniques in MCDA. We selected four different weight elicitation techniques based on diversity in formatting and their frequency of use :The fiveRanking, rating and point allocation are direct methods to assess criteria importance. For each criterion, one value judgment is required Figure . In contTo study the effect of introducing this variation in the hierarchical ordering of criteria, the five-point rating scale, which easily accommodates both hierarchical as well as nonhierarchical ordering, was used to elicit preferences in both conditions. This resulted in five different ways to estimate criteria weights.Respondents were asked to use the five-point rating scale (RS_nH) and either: (1) ranking (RA) and rating through point allocation (PA) (n = 15), (2) pairwise comparisons , (3) best worst scaling  or (4) the five point rating scale with criteria ordered in a hierarchical structure  to elicit criteria weights to state the perceived importance of the 14 criteria. The order of the five point rating scale and the other technique was randomly varied among respondents. After using both techniques, respondents were asked which of the two techniques they preferred. At the end of the survey, respondents were asked to provide some background characteristics.This study was conducted at the University of Twente in the Netherlands and the University of Montreal, Canada. The study was approved by the Board of Ethics of the University of Montreal and exempted from ethical approval in the Netherlands. Students from the health oriented courses at both universities were invited to participate in the study via mass e-mail sent to the university student accounts of the studies health sciences, biomedical engineering and technical medicine. At the time of the study about 200 students attended the courses that were targeted. Exactly how many students were reached is unknown, as the e-mail was sent by university personnel to ensure privacy of the students. After three weeks, a reminder was sent.Enrollment in this pilot study was stopped on pragmatic grounds (limited time and funding available) after 60 students completed the survey. All the data was analyzed with PASW Statistics 18 and Stata 12.0. Criteria weights were calculated for each technique and descriptive analysis  was performed.wi) obtained in this study with performance scores (vi) obtained in a previous study [The influence of criteria weights on the overall value estimate of interventions was explored by calculating the overall value (V) that would result from multiplying the normalized criteria weights  of the weighting techniques: 1) the mean of the weight distance between the 1st and 3rd most important criterion within each respondent for all methods; 2) the mean of the weight distance between most and least important criterion within each respondent for all methods; and 3) the mean of the weight distance between the weight for each criterion and the weight for each criterion if no method was used to prioritize criteria . To test whether the techniques enable prioritization in criteria based on the weights which are elicited with the different techniques, we used a one sample t-test to evaluate whether the difference in weights between the 1st and 3rd and the most and least important criterion were significantly different from zero. If no prioritization of weights occurs, all weights would be equal (thus the difference between the most and least important weight were zero).a. First, we plotted the weights per criteria averaged over all respondents that had the same combination irrespective of the order of weighting techniques against the difference between these mean weights per criteria separately for each second weighting technique. Second, we plotted the same outcomes but then for each respondent individually. These Bland-Altman plots allowed us to study any systematic difference between the elicitation methods  on a group level and individually.We used the correlation of the across-respondents means and the mean within-respondent correlation to evaluate whether weights elicited with different methods resulted in similar weights, as proposed by Nickerson . To visuOf the 200 students invited to participate in the study, 88 started the online survey. 18 respondents did not finish the survey, and their results were excluded from the analysis. Background characteristics are not available for respondents that did not complete the survey. The sample was over representative of females, younger and highly educated individuals compared to the average population , the most and least important criteria (0.156) and the average absolute difference between weighted criteria importance, and equal weights (0.034). These differences are smallest with five point rating scale in a hierarchical ordering. For all techniques, the difference in weights between most and least important criteria are significantly different from zero  on an individual level, as is the difference in weights of the first and third ranked criterion (p < 0.05). This indicates that all techniques enable significant discrimination in criteria importance compared to no weighting of criteria.The results of this study indicate that agreement across groups of respondents using the same techniques is high. The highest across respondent correlation was found between five point rating scale and best worst scaling (0.97), and the lowest correlation is found between five point rating scale and ranking 0.71) . This indicates that the ability of an individual to give consistent weights with different techniques is lower and is dependent on the combination of techniques.If preferences are stable and are not influenced by the experience of the respondent, for instance with weight elicitation or familiarity with the decision context, the order in which weighting techniques were presented in the survey would not result in a systematic bias in the criteria weights elicited with that technique. Figure Respondents were also asked which technique they preferred. The majority of respondents (74%) preferred pair-wise comparisons or ranking combined with point allocation (69% of the respondents) to the five point rating scale. 87% of respondents preferred five point rating scale over best-worst scaling. Hierarchical weighting with five point rating scale is preferred (66%) to non-hierarchical weighting.The aim of weighting in MCDA is to quantitatively reflect the relative importance of multiple criteria in order to value healthcare intervention. Earlier research has shown that decision makers choose techniques based on their fit to the decision context, and cognitive and time demands in collecting and analyzing their results . Given tThe first objective of this study was to test the influence of weights elicited with different techniques on the overall outcome of the decision analysis. In agreement with earlier studies, in this study mean differences in outcome are negligible at the group level. However, the overall value of the innovation was judged differently on the individual level with pairwise comparisons and five-point rating scale showing the largest difference in overall value estimates between individuals.The second objective of this study was to compare the ability of techniques to discriminate between criteria which is reflected in the range in weights between different criteria. Weight range is important because it reflects the ability of the weighting technique to distinguish between criteria of high and low importance and thus to prioritize outcomes in estimating the value of health innovations . AbsolutThe third objective of this study was to test whether the choice for a different weighting technique would result in different weight estimates for the criteria. Across respondents, weights elicited with different techniques have high correlation, indicating they measure the same underlying perceived importance. Across respondents, mean comparability of weights elicited with different techniques is relevant if the intended application involves value means as in program evaluation and decision analysis. However, when weights elicited within one respondent with different techniques are compared, correlation is only moderate. Mean within respondent comparability of weights is important if the intended application requires individual level values as in clinical decision making.In the practical context, the results of this study have several implications. The EVIDEM framework considers 14 core criteria. This reflects the reality of health care decisions at the local, national or even global level are influenced by many criteria , 29, 30.There are some limitations that should be taken into account when interpreting the results of this study. There was an effect of order of the weight elicitation technique in the survey. With all techniques, except for combinations that include best-worst scaling, respondents discriminate less the second time they are asked to provide weights. One reason could be that preferences are constructed during the elicitation process . A studyFor MCDA to gain wider use as a decision support tool in regulatory and reimbursement decision making clear guidance on the choice of techniques is needed. Although uniformity in techniques would be beneficial, there are many issues to consider in choosing a weighting technique, such as theoretical foundation, its fit to the decision context and its efficiency in collecting and analyzing preferences. The results of this study show that based on its ability to discriminate between criteria weights in a decision setting with many criteria, pairwise comparison is the preferred technique to elicit weights. However, the results of this study also show that on an aggregate level, the differences in weight as a result of technique have limited impact on the overall outcome of the analysis, and thus the choice of weighting technique has limited impact on the comparability of results of MCDA in a group setting.Alternatively, if the aim of the analysis is to compare individual preferences, for instance of different members within decision panels, the results of this study indicate the use of different weight elicitation techniques will result in considerable differences in weight estimates and outcome of the analysis, making uniformity in weight elicitation technique more important.aA Bland-Altman plot, also known as a Tukey Mean difference plot, is a well-recognized method to study agreement between measurements. In a Bland-Altman plot the mean of two measurements on the same object is plotted against the difference between these two measurements (this is the relative bias). If the points of this plot are scattered randomly above and below the x-axis, then there is no consistent bias of one measurement over the other. If the difference between the measurements deviates from zero there is no agreement between the measurements. A significant bias can be shown with a paired t-test. Moreover, the standard error of these differences represents the magnitude of error between the two measurements. If the differences between the two measurements are not clinically important, the two methods may be used interchangeably. Points outside the limits of agreement  are considered outliers. A linear trend of the differences for higher values or lower values indicates that one method overestimates high values and under-estimates low values.Disease severity: Severity of the health condition of patients treated with the proposed intervention  with respect to mortality, disability, impact on quality of life, clinical course .D1: Size of population: Number of people affected by the condition (treated or prevented by the proposed intervention) among a specified population at a specified time; can be expressed as annual number of new cases  and/or proportion of the population affected at a certain point of time .D2: Clinical guidelines: Concurrence of the proposed intervention  with the current consensus of a group of experts on what constitutes state-of-the-art practices in the management of the targeted health condition; guidelines are usually developed via an explicit process and are intended to improve clinical practice.C1: Comparative interventions limitations (unmet needs): Shortcomings of comparative interventions in their ability to prevent, cure, or ameliorate the condition targeted; also includes shortcomings with respect to safety, patient reported outcomes and convenience.C2: Improvement of efficacy/effectiveness: Capacity of the proposed intervention to produce a desired  change in signs, symptoms or course of the targeted condition above and beyond beneficial changes produced by alternative interventions.I1: Improvement of safety & tolerability: Reduction in intervention-related health effects that are harmful or undesired compared to alternative interventions.I2: Improvement of patient reported outcomes: Capacity of the proposed intervention to produce beneficial changes in patient-reported outcomes (PROs)  above and beyond beneficial changes produced by alternative interventions; also includes improvement in convenience to patients and adherence to treatment course.I3: Public health interest : Risk reduction provided by the proposed intervention at the population-level .T1: Type of medical service : Nature of the clinical benefit provided by the proposed intervention at the patient-level .T2: Budget impact on health plan (cost of intervention): Net impact of covering the intervention on the budget of the target health plan . This represents the differential between expected expenditure for the proposed intervention and potential cost savings that may result from replacement of other intervention(s) currently covered by the health plan. Limited to cost of intervention .E1: Cost-effectiveness of intervention: Ratio of the incremental cost of the proposed intervention to its incremental benefit compared to alternatives. Benefit can be expressed as number of events avoided, life-years gained, quality-adjusted life-years gained, additional pain-free days etc.E2: Impact on other spending : Impact of providing coverage for the proposed intervention on other expenditures  such as hospitalization, specialist consultations, adverse events, long-term care, disability costs, lost productivity, caregiver time, equipment maintenance cost etc.E3: Completeness and consistency of reporting evidence: Extent to which reporting of evidence on the proposed intervention is complete  and consistent with the sources cited.Q1: Relevance and validity of evidence: Extent to which evidence on the proposed intervention is relevant to the decisionmaking body  and valid with respect to scientific standards  and conclusions (agreement of results between studies). This includes consideration of uncertainty .Q2: Additional file 1: Figure S1: Hierarchy of criteria. (DOC 180 KB)


People with low socioeconomic status (SES) and some ethnic minorities are often underrepresented in lifestyle programmes. Therefore, a lifestyle programme was developed especially targeting these groups. Developing this lifestyle programme and designing an intervention study to test the effectiveness of this programme was an informative process in which several obstacles were encountered and choices had to be made. Study protocols, however, rarely describe these obstacles encountered in the protocol design process, and it is not always clear why researchers made certain choices. Therefore, the aim of this article is to describe both the final MetSLIM study protocol and the considerations and choices made in designing this study protocol.The developed MetSLIM study has a quasi-experimental design, targeting 30- to 70-year-old adults with an elevated waist circumference, living in deprived neighbourhoods, of Dutch, Turkish or Moroccan descent. The intervention group participates in a 12-month lifestyle programme consisting of individual dietary advice, four group sessions and weekly sports lessons. The control group receives written information about a healthy lifestyle and one group session provided by a dietician. The study contains an elaborate effect, process and economic evaluation. Outcome measures are, among other things, change in waist circumference and the other components of the metabolic syndrome.Matching the preferences of the target group, such as their preferred setting, has implications for the entire study protocol. The process evaluation of the MetSLIM study will provide insight into the consequences of the choices made in the MetSLIM study protocol in terms of reach, acceptability and delivery of the programme, and the effect and economic evaluation will provide insight into the (cost)effectiveness of the lifestyle programme in order to reduce waist circumference among individuals with low SES of different ethnic origins.NTR3721 .Netherlands Trial Register  Lifestyle intervention studies have shown that the development of cardiometabolic diseases such as type 2 diabetes mellitus (T2DM) can be prevented or postponed by the combination of a healthy diet and increased physical activity ,2. FurthIn order to decrease the burden of lifestyle-related morbidity, effective lifestyle interventions are needed for low SES individuals and ethnic minorities. Therefore, a research project was started with the aim of developing a lifestyle intervention study especially targeted at these groups. The project focuses on the adaptation of a lifestyle intervention study named SLIM (Study of Lifestyle intervention and Impaired glucose tolerance Maastricht). The SLIM study was a randomized controlled trial, designed to study in a university setting the effectiveness of a diet and physical activity intervention on glucose tolerance in persons with impaired glucose tolerance . The intThe adaptation of the SLIM study protocol into the new study protocol, called MetSLIM, was an informative process in which several choices were made. Study protocols rarely describe obstacles encountered in the protocol design process, and it is not always clear why researchers make certain choices. Transparency, by sharing considerations and choices made, can, however, help other researchers to design their study protocol. Therefore, the aim of this article is to describe the design of the MetSLIM study and the considerations and choices made in adapting the SLIM study protocol to the needs of individuals with low SES of different ethnic origins.The protocol design process was supported by gathering information on the current health status and preferences of the target group; consulting health professionals, researchers and a communications expert; and assessing possibilities in the local community setting. The elements of the original SLIM study protocol and the considerations about maintaining or changing those elements for the MetSLIM study protocol are described in more detail in Tables The objective of the MetSLIM study is to evaluate the (cost-)effectiveness of the adapted lifestyle programme to reduce elevated waist circumference and improve other components of the metabolic syndrome (MetS) in individuals with low socioeconomic status of Dutch, Turkish and Moroccan origin. It was decided to focus on these groups as they are three of the largest ethnic groups in the Netherlands . Change The MetSLIM study is a quasi-experimental 12-month intervention study. The intervention study is designed for execution in a community setting. In order to prevent spill-over and to have the opportunity to recruit enough participants, the study will be executed in two cities. Two Dutch cities with sufficient potential to recruit the target population have been identified on the basis of the location of the two involved universities, the presence of low SES neighbourhoods and the number of Turkish and Moroccan citizens . InterveThe sample size calculation was estimated based on the change in waist circumference as an outcome of SLIM after one year (mean difference between intervention and control group 2.1 cm) , and theApplicants are eligible to participate if they fulfil the following criteria , local health professionals and other local contacts. Recruited participants will also be asked to inform friends or family members about the study and to ask them to participate in the study, if they meet the study criteria. Potential participants will fill in a screening questionnaire to check for the inclusion and exclusion criteria.The lifestyle programme will last for 12 months. It will consist of four group meetings, four hours of individual dietary advice and weekly sports lessons provided in the neighbourhood. The first group meeting is an introduction/kick-off meeting, guided by the researcher, in which participants get to know the dietician, the sports instructor and other study participants. The other three group meetings are about nutrition and are guided by the dietician. The topics of these meetings include comparing products/reading labels, dealing with social occasions and making affordable choices in the supermarket. The four hours of individual dietary advice will be divided over a flexible number of consultations in order to suit the needs of the participants. Participants will receive dietary advice from an ethnicity-matched dietician and information leaflets from the Netherlands Heart Foundation and the Netherlands Nutrition Centre on the benefits of healthy nutrition and increased physical activity. If these information leaflets are available in Turkish or Arabic, participants will be provided with leaflets in the language of their choice.The physical activity lessons will be set up especially for the study participants. The lessons will be offered for men and women separately. Female and male sports instructors will be involved to provide gender-matched physical activity lessons. Participants will also be allowed to bring a friend or family member to increase social support.Participants will receive the results of their anthropometric measurements, blood glucose and total cholesterol concentrations, and their physical activity levels after the baseline measurements as well as after the end measurements. The participants’ GPs will receive anthropometric values, blood values and urine values.At one group meeting, guided by a dietician, control group participants will receive general advice about a healthy diet. At the end of this meeting, participants will receive information leaflets on the benefits of healthy nutrition and increased physical activity. If these information leaflets are available in Turkish or Arabic, participants will be provided with leaflets in the language of their choice. Like in the intervention group, control group participants and their GPs will receive the results of the measurements.The researcher will make an appointment with  participants at a location close to their home to measure their anthropometrics and blood pressure. Subsequently, the participant will receive a referral letter for the medical laboratory in their neighbourhood to hand in a urine sample and to have blood taken for testing. In addition, participants will be asked to fill in several questionnaires. Participants can choose to complete these questionnaires in Dutch or in their mother tongue. They will be asked whether they prefer to fill in the questionnaires themselves at home or with a research assistant speaking their mother tongue. All measurements, except the process evaluation measures, will be performed at baseline and after 12 months.Blood samples will be taken after at least 10 hours of fasting to measure fasting glucose, HLD cholesterol, LDL cholesterol, total cholesterol, triglycerides, HbA1c, fasting insulin, liver function enzymes, creatinine and uric acid. Fasting spot urine samples will be collected to measure creatinine and microalbumin. Blood pressure will be measured six times, with two minutes rest in-between, in a seated position, with the Omron 705CP. The mean will be calculated from the last five measurements. Anthropometric measurements will be taken, including body weight, waist circumference, hip circumference, body fat percentage and height. Height will be measured to the nearest 0.1 cm. Waist circumference will be determined midway between the lowest rib and the iliac crest, and measured to the nearest 0.5 cm. Hip circumference will be measured to the nearest 0.5 cm at the widest portion of the buttocks. Waist and hip circumference will both be measured twice. Body weight and body fat percentage will be measured with the Tanita BC-418 .To evaluate changes in physical activity level, participants will fill in the validated Short QUestionaire to Assess Health enhancing physical activity (SQUASH) . A questTo evaluate changes in diet, an ethnic-specific Food Frequency Questionnaire (FFQ) will be administered .A questionnaire has been developed to gain insight into determinants of behaviour. Questions to measure barriers to, and reasons for, healthy eating and physical activity are based on questions used in the Pan-EU Survey . Items tQuality of life will be assessed with the SF-36 questionnaire .A process evaluation guide has been developed on the basis of items described in the literature -46, inclCosts and effects of the intervention programme will be compared with costs and effects of the control programme. The economic evaluation consists of a cost-effectiveness analysis and cost-utility analysis, and will be done from a societal and a healthcare perspective. A time horizon of 12 months will be used. Change in waist circumference will be used as clinical outcome for the cost-effectiveness analysis, and quality-adjusted life years (QALYs) for the cost-utility analysis. QALYs will be assessed with the EuroQoL instrument (EQ-5D-5L) ,48. Healt-test or chi-squared test. For the economic evaluation, the incremental cost-effectiveness ratio will be calculated on the basis of the differences in costs and effects between the intervention and the control programme. Bootstrapping will be used to calculate confidence intervals around costs and effects. A cost-effectiveness acceptability curve will be constructed from which it can be judged whether the intervention is cost-effective given a range of cost-effectiveness thresholds. The cost-effectiveness analyses will be complemented with sensitivity analyses for critical assumptions.For the effect evaluation, the intention-to-treat method will be followed. Changes in effect outcomes will be compared between the intervention and the control group. Analyses will be adjusted for age, gender, ethnicity and other possible confounders. For the process evaluation, both quantitative and qualitative data will be collected. Interviews will be analysed using a thematic approach. Quantitative data will be described by means and frequencies. Characteristics of the responders versus non-responders, and of the completers versus drop-outs, will be analysed by means of an independent sample This article provides a detailed description of the MetSLIM study protocol, which is based on the SLIM study protocol. Furthermore, this article gives insight into the obstacles encountered in developing the MetSLIM study targeting low SES individuals of different ethnic origins. Adaptations to the original SLIM study protocol were considered necessary in order to overcome practical barriers that hinder the target group’s participation; to suit the  needs of the target group; and to make it feasible to perform the study in a local (community) setting. The main adaptations regarding the lifestyle programme, which will be offered to the intervention group, are: 1) additional group meetings about price concerns and social occasions with regard to a healthy diet; 2) ethnicity-matched dietician; 3) gender-matched sports instructor; 4) all activities in the participants’ own neighbourhood; and 5) activities for women and men separately. These adaptations are expected to be relevant for both the recruitment and retention of participants and for the successful delivery of the lifestyle programme .A strict comparison between the effects of the adapted and the original lifestyle programme will be difficult. The target groups of SLIM and MetSLIM vary more than just in socioeconomic status and ethnicity. MetSLIM will include persons at a younger age and with an elevated waist circumference instead of impaired glucose tolerance, and excludes persons who use medication for cardiometabolic diseases. Consequently, the study population of the two studies could differ in health status, and this might influence both the interest in participating in a lifestyle programme, either positively or negatively ,51, and A strength of the adaptation from the SLIM study protocol to the MetSLIM study protocol is that we involved the target group,  professionals and other researchers, and checked possibilities in the local setting while designing the MetSLIM study protocol. This enabled the creation of a study protocol that takes into account both the needs of the target group and what is actually possible in the local setting. In the end, however, researchers made the final decisions in the design of the MetSLIM study protocol. Although practically challenging, it could have been useful to involve the target group and health professionals in this decision making as well, in order to take into account the balance between evidence-based concerns and the acceptability or applicability of the intervention ,53. The The current article illustrates, next to a detailed description of the MetSLIM study protocol, several considerations that should be taken into account when a study protocol is being adapted or developed for individuals with low SES of different ethnic origins. Transparency, by sharing these considerations and choices made in the development of a study protocol, can help other researchers and health professionals to create appropriate strategies for (testing the effectiveness of) lifestyle interventions for this target group. Recruitment for the MetSLIM study started in January 2013 and data collection is expected to finish in June 2015. The process evaluation of the MetSLIM study will provide insight into the consequences of the choices made in the adapted study protocol in terms of reach, acceptability and delivery of the programme, and the effect and economic evaluation will provide insight into the (cost)effectiveness of the adapted lifestyle programme to reduce waist circumference among individuals with low SES of different ethnic origins.


The AI in the patients gradually decreased after continuous rehabilitation, but remained higher than that of healthy participants.This study aims to determine if the relative displacement between the extensor digitorum communis (EDC) tendon and its surrounding tissues can be used as an adhesion index (AI) for assessing adhesion in metacarpal fractures by comparing two clinical measures, namely single-digit-force and extensor lag . The Fisher–Tippett block-matching method and a Kalman-filter algorithm were used to determine the relative displacements in 39 healthy subjects and 8 patients with metacarpal fractures. A goniometer was used to measure the extensor lag, and a force sensor was used to measure the single-digit-force. Measurements were obtained twice for each patient to evaluate the performance of the AI in assessing the progress of rehabilitation. The Pearson correlation coefficient was calculated to quantify the various correlations between the AI, extensor lag, and single-digit-force. The results showed strong correlations between the AI and the extensor lag, the AI and the single-digit-force, and the extensor lag and the single-digit-force ( Injuries to the hand, wrist, and fingers account for more than one million emergency department visits by workers annually in the United StatesMany methods have been reported for the quantitative assessment of the extent of tendon adhesion, including measuring the work output of the digital flexion at maximal digital flexion78Currently, no method is available for noninvasively evaluating the gross extent of adhesion while identifying the location on the tendon suspected of the formation of adhesion. A method that provides information about the extent of adhesion at both the macro and micro levels can assist clinicians in making better-informed decisions in rehabilitation processes and thereby improve the quality of health care. However, ultrasound imaging is an established method for the accurate diagnosis of tendon pathology121314A recent study using ultrasound found that the total displacement of the surrounding tissue of the FDP tendon constituted 22% to 53% of the total displacement of the FDP tendon in 11 healthy human control subjects181920The present study aims to test the hypothesis that the relative displacement between the extensor digitorum communis (EDC) tendon and its surrounding tissues can be used as an adhesion index (AI) for determining the extent of adhesion in patients with metacarpal fractures and identifying the locations of suspected adhesions. Emphasis is placed on verifying whether this index is correlated with extensor lag and single-digit-force, which are two clinical measures currently used for evaluating the gross extent of such adhesionThe Institutional Medical Ethics Committee of National Cheng Kung University approved this prospective cross-sectional study, and written informed consent was obtained from each participant. The study was conducted between December 25, 2014 and November 20, 2015. The experimental methods were carried out in accordance with approved guidelines and regulations22The following inclusion criteria were applied when recruiting patients suffering from metacarpal fractures: (a) aged 20–65 years and (b) able to fully understand and cooperate in the experiment. The exclusion criteria were (a) a history of related hand injuries or surgeries, (b) a history of rheumatoid arthritis, diabetes mellitus, or osteoporosis, or (c) the presence of central nerve injuries. The presence of central nerve injuries was adopted as an exclusion criteria because patients with central nerve injuries such as cerebral vascular accident and traumatic brain injury tend to have symptoms of hemiplegia; this would have been disadvantageous to the present study because measurements of the single-digit-force of the injured finger were normalized by comparing them to those of the contralateral normal finger. To evaluate the performance of the proposed AI for assessing the change in the extent of adhesion, ultrasound image sequences and measurements of the single-digit-force and extensor lag were acquired from each patient twice (hereinafter referred to as the first and second measurements) with an interval of 2 weeks (during which continuous rehabilitation was applied). In total, 8 patients were included, which entailed 1 index finger, 4 ring fingers, and 3 little fingers being assessed.The inclusion and exclusion criteria for the healthy participants were the same as those for the patients, except that a requirement for a normal range of finger motion was added to the inclusion criteria for the healthy subjects. A total of 39 healthy participants were included and ultrasound image sequences were obtained from each of them. The distribution of their digits was matched according to the distribution in the patients. Thus, 2 index fingers, 31 ring fingers, and 6 little fingers were assessed. The healthy participants were tested only once . However, 10 randomly chosen healthy participants were tested twice to evaluate the reliability of the proposed AI by calculating the intra-class correlation coefficient. Tests of extensor lag and single-digit-force were not applied to the healthy subjects .The age, sex, dominant hand, and digit involved in the experiment of each patient and healthy individual were recorded and a chi-squared test was performed to ensure the homogeneity of the two groups. The duration of injury of each patient was also recorded for descriptive statistics.General principles for the postoperative care of metacarpal fracturesThe rehabilitation guidelines complied with the general principles prescribed in textbooks22Protection with splint, Controlled stabilized joint motion (passive ROM), Scar management4 weeksGentle progressive mobilization (active and passive ROM), Stretch and tendon gliding exercise, Scar management6 weeksStrengthening, Activities of daily lives training, Resistance exercise8 weeksStrengthening, Dexterity training12 weeksThe EDC tendon is usually considered as one muscle with four tendons because of the incomplete separation between the tendons’ muscle bellies within the forearmIn the intrinsic plus position, which is also known as the safe position, the metacarpophalangeal joints are flexed at 70–90° and the interphalangeal joints are fully extended. While in the intrinsic minus position (claw posture), the metacarpophalangeal joints are extended and the interphalangeal joints are flexedAll of the ultrasound images were acquired at 18 FPS using a Terason T3000 device  with a multifrequency transducer . All of the obtained image sequences were exported as uncompressed AVI files and imported into MATLAB software  for additional analyses. Each image sequence typically contained 200 images.R and the covariance matrix of the white sequence of Q were set at 1 and 0.005, respectively.The EDC tendon and its surrounding tissue were identified by a single clinician . An example of how the EDC tendon and its surrounding tissue were identified in a B-mode ultrasound image obtained from a healthy individual is shown in The displacement estimate of each reference template represented the displacement of the center pixel of that reference template. By maximal overlapping of the reference windows, the displacement of all pixels along the tendon and its surrounding tissue could be obtained .axial direction in each group (the tendon and the surrounding tissue) were first averaged to obtain the displacement of the specific lateral location (hereinafter referred to as the x-location) in each group. The adhesion image A(x) was then obtained by calculating the ratio of the total displacement of the tendon to its surrounding tissue at locations along the x direction:To calculate the AI, all optimized displacement estimates in the S(x) and T(x) are the averaged total displacements of the surrounding tissue and the tendon, respectively, at location x along the tendon. Typically, approximately 600 A(x) locations were measured along the tendon. An example adhesion image obtained from a healthy subject is shown in where A(x) locations that were larger than 0.6. The methodology for finding the percentage of locations that were above this threshold was selected because of its accuracy in determining the extent of the total coverage area on the tendon that exhibited adherence.On the basis that this ratio ranges were reported from 0.22 to 0.53 in healthy human subjectsExtensor lag is a common sequela of metacarpal fractures that occurs when the active extension is smaller than the full passive extension. Because adhesion may decrease the gliding amplitude of the EDC tendon, it may reduce the active extension range of motion of the digits. A previous study found that metacarpal fractures frequently lead to extensor tendon dysfunction through adhesionThe function of a tendon is the transfer of force generated by a muscle to a connected bone so as to induce movement. Therefore, the magnitude of the single-digit-force was used as an outcome measure for evaluating the functionality of the tendon. A full-bridge thin-beam force sensor  was used to calculate the single-digit-force by measuring the voltage change induced by the load. The single-digit-force was measured when the patient used their proximal phalangeal to exert a maximal voluntary force on the sensor under the hook position ; this ac2931r > 0.7 or r < −0.7. The intraclass correlation coefficient was calculated to determine the test–retest reliability of the proposed AI. A value above 0.75 was considered to indicate an acceptable reliability. Because of the low sample size, the Mann-Whitney test and Wilcoxon signed-rank tests were used to compare the AIs of healthy subjects to those of patients as well as the respective AIs of separate pairs of measurements. Two-tailed tests were used for all statistical analyses (α = 0.05). In addition, in clinical observations, the limitation of the joint motion and restrained force exertion of the hand have usually been described as having an association with tendon gliding conditions. However, the relationship among these independent factors with respect to tendon gliding and adhesion has not been quantitatively explored using different measurements such as length, goniometry, and force. This study applied the correlation test, which is a straightforward method for examining the relationship among the three variables  and investigating the ability of the proposed AI to assess the recovery status of the patients. In addition, data obtained in the first and second measurements were combined and correlated to explore the relationship among the variables. In other words, for demonstrating the recovery status of the patient at different time points each measurement of AI, extensor lag, and single-digit-force in the present study was considered independent.Statistical analysis was performed using SPSS software . Descriptive statistics were used to summarize demographic and duration-of-injury information. Because a preliminary Shapiro-Wilk test demonstrated that the variables conformed to a normal distribution, Pearson’s correlation coefficients were calculated to quantify the correlations among the AI, extensor lag, and single-digit-force. A correlation was considered relevant when r = 0.718, −0.849, and −0.741; P = 0.002, P < 0.001, and P = 0.001, respectively).The results obtained by analyzing 16 ultrasound recordings as well as the measurements of the normalized single-digit-force ratio and the extensor lag are listed in P = 0.569, 0.269, 0.196, and 0.219, respectively) . Typically, the AI was smaller in the second measurement of the patients  than in the first measurement . An example adhesion image obtained from a patient before (first measurement) and after (second measurement) rehabilitation is shown in The results obtained by analyzing 39 ultrasound recordings from the 39 healthy individuals are listed in ctively) . Figure This study found that the AI was strongly correlated with two other clinical measures, the single-digit-force and the extensor lag, and thereby confirms the hypothesis that the proposed AI can be used to evaluate the extent of adhesion in patients with metacarpal fractures. Specifically, the extent of adhesion indicates the extent of the coverage area on a tendon exhibiting adherence. However, the correlation between the AI and the extensor lag was slightly weaker than that between the AI and the single-digit-force. This could be caused by the extensor lag being a less sensitive clinical measure than the single-digit-force is. From an anatomical perspective, tendons connect bone and muscle with parallel fibers and the biomaterial to transmit tensile forces from muscle to bone and support digit movement. The digit force is not fully transmitted after an adhesion has formed. The obstruction of the force transmission by adhesions has been confirmed by the biomechanical model proposed in previous researchThe AI of the healthy participants was observed to be lower than that of the patients. One patient exhibited an AI that was similar to the typical value found in the healthy participants, which was probably because of that patient having already finished his rehabilitation and visiting the hospital for a follow-up session. The clear difference in the AI of the healthy participants and that of the patients also suggests that it may be valuable information for clinicians to use in evaluating the completion patients’ rehabilitation process.Decreased tendon gliding severely affects hand function and presents a challenge to clinicians. The primary goal of rehabilitation is the maintenance of maximal tendon gliding and range of motion. According to the rehabilitation protocol, active and passive mobilization are implemented during the 4–8 week period following surgery. When a suspected adhesion develops, it may be necessary for a patient to undergo resistance exercise to address the limited tendon gliding. Therefore, the ability of the proposed AI to identify the location of the suspected adhesion by observing the adhesion image is an essential development for clinicians. Furthermore, a potential use of the proposed AI is for the injection of antiinflammatories and lubricants to reduce postsurgical adhesionSeveral limitations of the present study should be addressed. First, the study sample was small (involving only eight cases over 1 year); this was partly because of the application of strict inclusion criteria. Nevertheless, the AI was found to be strongly correlated with the two additional clinical measurements used for evaluating the extent of adhesion; moreover, clear and statistically significant differences in the AI among healthy individuals, overall patients, and the first and second measurements of the patients were observed. Consequently, this limitation did not adversely affect the results obtained in this study. Second, the intra- and interrater reliabilities were not considered. However, because the EDC tendon is very close to the surface of the hand, it can be visually identified, making it relatively easy to locate compared to other tendons in the human body. Third, previous studies have found that joint positions, movement velocity, and single-digit motion influence the extent of relative displacement between the tendon and its surrounding tissue213536In conclusion, this study has demonstrated that the relative displacement between the EDC tendon and its surrounding tissue provides valuable information for evaluating the extent of adhesion and differentiating the locations of suspected adhesion formation in patients with metacarpal fractures. The proposed AI has potential for clinical application as a diagnostic tool for rehabilitation.How to cite this article: Lai, T.-Y. et al. A Novel Adhesion Index for Verifying the Extent of Adhesion for the Extensor Digitorum Communis in Patients with Metacarpal Fractures. Sci. Rep.6, 31102; doi: 10.1038/srep31102 (2016).


Respiratory diseases mainly spread through interpersonal contact. Class suspension is the most direct strategy to prevent the spread of disease through elementary or secondary schools by blocking the contact network. However, as university students usually attend courses in different buildings, the daily contact patterns on a university campus are complicated, and once disease clusters have occurred, suspending classes is far from an efficient strategy to control disease spread. The purpose of this study is to propose a methodological framework for generating campus location networks from a routine administration database, analyzing the community structure of the network, and identifying the critical links and nodes for blocking respiratory disease transmission. The data comes from the student enrollment records of a major comprehensive university in Taiwan. We combined the social network analysis and spatial interaction model to establish a geo-referenced community structure among the classroom buildings. We also identified the critical links among the communities that were acting as contact bridges and explored the changes in the location network after the sequential removal of the high-risk buildings. Instead of conducting a questionnaire survey, the study established a standard procedure for constructing a location network on a large-scale campus from a routine curriculum database. We also present how a location network structure at a campus could function to target the high-risk buildings as the bridges connecting communities for blocking disease transmission. Because respiratory diseases are mainly spread through interpersonal contact , class sThe concept of a social network is useful for understanding the complexity of interrelationships among people ,8. A socFurthermore, people usually gather together because of similar personalities, preferences and life circles, leading to the formation of communities . Social The critical links, also called weak links, are the connections that act as bridges, connecting two independent communities  Figure . The linPhysical interpersonal contacts always occur in geo-referenced spaces . BecauseTherefore, this study aims to understand the spatial structures among department buildings based on the students’ class-taking behaviors. A spatial interaction network model is constructed to represent the spatial intensity of student flow between buildings. Geo-referenced community structures of department buildings are identified as possible life circles at a university campus. By identifying these community structures among buildings, including the critical links and high-risk buildings, we were able to understand the role of critical links among communities and to use these findings to implement major prevention strategies targeting high-risk buildings as high priority of intervention for blocking pathogen transmission through on-campus contact.Class activities cause long duration face-to-face contacts within indoor environment and could increase the risk of respiratory disease transmission ,3,4. TheOur dataset covered 17,513 students, 3214 courses, 144,700 registration records, 6059 classes, the locations of 135 department buildings, and the relevant information for these departments. We summarized the data into several data points, including the total number of students who took classes in each building, the flow volume of students moving between each department building, and the geographical distance between each building.Students who attend courses in nearby buildings may share a similar life circle on the campus. In other words, students could have higher probability of contact with each other if the course-taking buildings are more nearby. To capture potential contact relationships between the departments, we set out to establish a standard procedure to construct a geo-referenced location network based on the students’ class-taking locations and the spatial interactions among department buildings. We used the spatial interaction model to measure the level of potential interaction between each pair of buildings. In this study, the “attractions” were the total number of students who took classes in each building, and the “impedance” was the squared distance between the two buildings (see Equation (1)): G indicated the level of spatial interaction between building i and j. iM and jM were the total number of students enrolled in at least one class in building i and j, while ijd was the Euclideandistance between the two buildings.The interaction between two buildings was restricted by the existence of students moving between the two buildings. For example, if no students from department A took a class in building B, the interaction should be zero. We investigated the number of students moving between each pair of department buildings, resetting the level of spatial interaction for pairs of buildings without any class activity to zero. Moreover, to capture the possible routes of disease transmission, stronger spatial interactions between the buildings could be regarded as having a higher possibility of involvement in pathogen circulation. Therefore, we reserved stronger spatial interactions in the first quarter as the links, while nodes represented department buildings in the spatial interaction network model.A and B, link−AB appears in the shortest path between each pair of nodes i and j, and A community was formed by a group of department buildings with tight connections, which network structure would accelerate the transmission rate because the interaction between the nodes within a community would be more intensive because students could reach each other by several alternative paths. A critical link appears in the shortest path (the most simple and direct path connecting two nodes) that links each pair of nodes, implying that such a link can control the delivery of messages between nodes ,17. “EdgLinks with greater edge betweenness play more important role as “bridges”. If all the bridges were removed, the network would be separated into several “islands”, . In social network analysis, these “islands” are called communities. Communities are composed of tightly connected nodes that have only a few links to other communities. Our study adopted the GN algorithm which was developed in 2002 for identifying communities. This algorithm repeats the process of removing the link (or links) with the current highest edge betweenness to progressively reveal the underlying community structure . When a The study developed two network-based epidemic control strategies to assess the impact of location network structure on disease transmission, including blocking geo-referenced critical links between communities and the removal of high-risk buildings from the network. Blocking the critical links and removing bridge nodes would break the connection among communities. It could increase the degree of separation of the location network and decrease interactions among department buildings.After the communities were identified, the links connecting two nodes from different communities were identified as critical links a. Our stk, k appears in the shortest path between each pair of nodes i and j, and We also defined the high-risk buildings as the nodes (buildings) with a high node betweenness centrality . Like edi and node j, and Removal of the high-risk buildings aimed to increase the degree of separation by isolating or monitoring high-risk buildings from the contact network c. The deBy reserving the stronger spatial interactions of the first quarter as the links of the network, some of the department buildings with minor interactions became isolated. There were 68 department buildings remaining in the location network, with eight identified communities . As showIn general, the two major communities were separated by the main avenue: the southern community (C2) and the northern community (C3). The other six communities were located at the margins of the campus, each adjacent to different side entrances to campus. As shown in The critical links (weak links) in this study indicated that some students chose to attend classes in buildings in different communities, and both of the buildings (the origin building and the destination building) had a higher total number of students. University students construct complicated contact relationships on campus by taking classes in different department buildings. Once a respiratory disease outbreak occurs, some control measures, such as class suspension, are not sufficient to stop the spread of an infectious disease. Our study proposes a network-based epidemic control strategy with the design of a location network and spatial interaction models. Analysis of location networks takes the perspective of a topology structure to distinguish the spatial patterns of the network and to implement control measures when the sources of infection are unknown. Furthermore, preventions based on the community structures built in this study could capture the principle of neighboring life circles, which means that students of the same life circle have higher rates of contagion due to closer connections. This concept could reflect the nature of the spreading patterns of respiratory disease. We will further discuss some issues of the aspects of constructing contact networks, identifying community structures and implications to epidemic control strategies.Collecting social contact data involves issues regarding the data sources and sampling. Therefore, rather than using a questionnaire survey, student registration records from a curriculum database was used to construct the location network, not only because course-taking is one of the most typical activities at universities but also because class enrollment records are routine for each student, which means the contact relationships could be derived without additional cost and effort. In addition, by using the student’s class enrollment records, we collected the student population characteristics without using student IDs, thus maintaining students’ anonymity in our study. Using the student’s class enrollment records precluded the potential problems of data sampling or privacy issues.Methodologically, contact relationships are always embedded in geo-spaces. However, most network studies have concentrated on topological structures of the network and ignored the properties of geo-referenced locations in the network model ,10,11. P2 and includes 10 colleges and 44 departments. The department buildings are widely spread over the campus. Students have a lot of flexibility in terms of class choice, which means they can select courses based on their own interests and preferences. Based on these characteristics, the contact structures within the campus are complicated in terms of the spatial scale, heterogeneities of curriculum and students, similar to many other comprehensive universities around the world. This study established the standard procedure for establishing a campus geo-referenced location network, including data collection, derivation of contact relationships from a curriculum database, construction of a network model and geographic analysis and visualization of communities and critical links, which can be applied to other similar universities and colleges around the world to implement strategies of spatial targeting against the transmission of severe respiratory diseases.The major campus of the comprehensive university used in this study covers an area of 1.1 kmIn this study, one of the proposed network-based control measures was blocking critical links between communities. From the perspective of network topology, we should remove the critical links from the network until there is no connection between communities . BlockinHowever, another key question in disease control is the issue of how to stop the disease spread within a community at the initial stage of an epidemic. To block disease spread by increasing network separation, nodes acting as “bridges” with high betweenness should be blocked. Targeting the buildings with high betweenness instead of with high connections is more effective ,34. In tThere are still some limitations to this study. First, temporal fluctuations of class activities are not incorporated in the study design. Different semester year may have different course-taking patterns. Temporal fluctuations of spatial interactions among department buildings could provide more information on dynamics of the location network. Second, the data used in this study focused on class activities because of the characteristics of disease transmission, including indoor environment, long-duration of contacts, close-proximity among individuals. Other locations or buildings such as dining halls, dormitories, and libraries could also be potential destinations. However, those movements are not daily routines for college students as the class-taking schedule and the flow data of students (to or from) these locations would be difficult to be collected. The flow among these locations would not incorporate in the study design and it could cause missing important bridge nodes at campus. The comprehensive mobility data warrant further investigation. Third, the study covered only the structure of students’ flows, which implied that other kinds of people, such as faculty members and staffs, were not incorporated. Our study captured the class activities of the students, which are the majority on a university campus, however, the mobility patterns of faculty members could be another critical link among locations because they could teach courses at different classroom buildings at one day. These data is worth collecting in further studies for constructing a more comprehensive location network.Understanding the spatial interactions among locations is one of important clues to blocking respiratory disease transmission on campus. In this study, by constructing the location network, we captured the concept of spatial interactions and analyzed the structures of the network. Rather than conducting a questionnaire survey, this study used a routine curriculum database to collect the class activities, derive spatial relationships among classroom buildings, construct a network model and identify of geo-referenced communities, critical links and risk buildings. Location networks analysis takes the perspective of a topology structure to distinguish the spatial patterns of the network for implementing control measures when the sources of infection are unknown. Furthermore, preventions based on the community structures could capture the concepts of neighboring life circles, which means that students of the same life circle have higher rates of contact due to their closer connections. The high-risk locations such as the bridges connecting communities could be spatially targeted as first priory of intervention in response to a disease outbreak. We believe the framework is also appropriate to be applied to other similar universities and colleges around the world to implement strategies of spatial targeting against the transmission of severe respiratory diseases on campus.