Sentiment analysis is the automated coding of emotions expressed in text. Sentiment analysis and other types of analyses focusing on the automatic coding of textual documents are increasingly popular in psychology and computer science. However, the potential of treating automatically coded text collected with regular sampling intervals as a signal is currently overlooked. We use the phrase "text as signal" to refer to the application of signal processing techniques to coded textual documents sampled with regularity. In order to illustrate the potential of treating text as signal, we introduce the reader to a variety of such techniques in a tutorial with two case studies in the realm of social media analysis. First, we apply finite response impulse filtering to emotion-coded tweets posted during the US Election Week of 2020 and discuss the visualization of the resulting variation in the filtered signal. We use changepoint detection to highlight the important changes in the emotional signals. Then we examine data interpolation, analysis of periodicity via the fast Fourier transform (FFT), and FFT filtering to personal value-coded tweets from November 2019 to October 2020 and link the variation in the filtered signal to some of the epoch-defining events occurring during this period. Finally, we use block bootstrapping to estimate the variability/uncertainty in the resulting filtered signals. After working through the tutorial, the readers will understand the basics of signal processing to analyze regularly sampled coded text.The online version contains supplementary material available at 10.3758/s13428-022-01917-1. Language is the most successful communication tool, enabling the propagation and accumulation of human culture .Treating text as signal entails the extension of signal processing tools, frequently employed in engineering, time-series analysis, and audio analysis to regularly sampled content-coded text. It affords several advantages overlooked by traditional methods, notably the following applications, which we cover in this manuscript as its main contribution: the denoising of time-series data, interpolating the data, detecting breakpoints, estimating periodicity, and obtaining uncertainty measures of such estimates through circular bootstrapping. Removing noise using signal analysis techniques is performed in most if not all studies using the electroencephalogram  may lead to overfitting  to demonstrate how such techniques operate in practice. In case study A, we examine emotional signal trajectories during the United States presidential election week of 2020, focusing on noise reduction using finite impulse response (FIR) filtering. In case study B, we analyze periodicity and patterns of changes in personal value signals during the turbulent period of November 2019 to October 2020 in the United States  using fast Fourier transform (FFT) and FFT filtering, after relying upon data interpolation for the imputation of missing values. We finally discuss the limitations of the methods used in case studies.This tutorial will use the following fundamental signal analysis and processing techniques: FIR filtering, interpolation, changepoint detection, FFT, FFT filtering, and bootstrapping. We have chosen these methods because of their widespread diffusion in signal processing applications, their relative simplicity, and the relevance of a tutorial for the treatment of coded text as signal , FIR filtering generates an output time series in which each value is computed based upon (1) itself and 2) preceding and (3) subsequent values in the input signal, depending upon a predefined sliding window length and filter type. A popular example is the moving average filter for which the weights along the window can be configured . The advantage of triangular windows is to place a higher weight on the observation at the center of the window and linearly decrease the weights to observations further from it. On the other hand, all observations within the window are averaged without weighting when a flat window is used. FIR filtering distinguishes the underlying pattern in a time series from the random fluctuations by reducing the latter. Although it effectively filters the noise, a disadvantage of FIR filtering is a resulting shortening of the output time series . Polynomial is another type of interpolation, where a given signal is interpolated by the polynomial that fits the data points in the signal.It allows filling in missing values in a sampled signal or resampling such signal to a higher temporal  resolution. There are several interpolation methods, of which N . From these two basic parameters (fs and N), other parameters of the measurement can be determined, including the Nyquist frequency fn , which indicates the theoretical maximum frequency that can be determined by the FFT . For example, if a signal is measured at a sampling rate of 100 Hz , then only frequency components up to 50 Hz can be theoretically estimated. This means that the sampling frequency must be at least double the highest frequency of interest in the signal  from its original domain  to the frequency domain, as the sum of periodic sinusoidal waves with different frequencies. The FFT allows us to decompose a signal in its frequency components, like a prism that separates white light into different colors: one feeds in a signal, and it gives back the cosine and sine functions that, when added together, reconstruct the signal. This tool is employed in many fields, including vibration analysis, audio engineering, image processing, medical imaging, and time-series analysis. Two parameters are relevant for the FFT: (1) the sampling rate or sampling frequency fs of the measuring system, which is the number of samples collected in a given time , and (2) the total number of samples  Holton, . So, if For instance, in Fig. The oscillations of a pendulum provide an intuitive example of the amplitude and the frequency of a signal. In this example, the amplitude corresponds to the maximal distance from the point of equilibrium for each of its oscillations. Thus, the amplitude is highest when the pendulum is raised and released. It then decreases until the pendulum is steady. At that point, the amplitude is 0. The frequency of the oscillations of the pendulum can be defined as the number of times the pendulum crosses the point of equilibrium in a defined period.In the realm of social media analysis, FFT analyses could, for instance, also be used to examine whether there exist patterns in posting frequency. One could imagine that social media users would post less during working hours, except for the weekends. If this were the case, the FFT analysis would show a stronger amplitude at the frequencies corresponding to one day and one week. Such an application complements the use we gave it in case study B as an illustrative example.Based on the inverse relationship between the FFT and IFFT, it is possible to attenuate or remove some of the frequency components in the signal. For instance, if after applying the FFT to the signal some of the resulting frequencies are removed, and the IFFT is applied, a filtered version of the original signal is obtained, which does not contain information about the discarded frequency component. This type of processing is commonly used to attenuate underlying noise in signals .There are various FFT filters, including low-pass, high-pass, band-pass, and band-block. A low-pass filter blocks all frequency components above the predefined cutoff frequency (the amplitudes of frequencies above this cutoff are set to 0). An example application is the removal of movement artifact in electrocardiogram signals. In contrast, a high-pass filter removes all frequency components below the cutoff frequency. High pass filters are used in speakers for the removal of low frequency noise. A band-pass filter only allows frequencies to pass the filter within a chosen range, which is determined by the lower and upper cutoff frequencies. Bandpass filters are used in antennas as a mean to reduce the influence of different artifacts on the signal. There is another type of FFT filter called threshold filter. The signal components are not removed according to their frequencies but their amplitudes . An important advantage of FFT filtering is that the resulting output signal is not shortened, as opposed to FIR filtering. For more details on FFT filtering, see Bevelacqua , an applruptures Python toolbox  from a population by treating the sample as though it were the population. By computing the parameters of interest for each of the generated signals, it is possible to determine the empirical distribution of these parameters, from which confidence intervals and standard errors can be defined, allowing to characterize the uncertainty of the estimated parameters .A limitation of the classical bootstrap is that it performs poorly for time series (where each data point depends on previous data points) as it cannot replicate the correlation in the data. To incorporate this temporal dependency, other methods have been proposed, including the block-bootstrap, which is based on splitting the time series into consecutive non-overlapping blocks and using bootstrapping on each block. This resampling technique is mainly used when the data, or the errors in a model, are correlated. A crucial step in the analysis is the determination of the optimal block length, for which various automatic algorithms have been proposed; for further technical details, see Patton et al.  and PoliThe first requirement for coded text to be treated as a signal using FFT is that the measurement occasions should ideally be equally spaced  withPython 3 (included by default in Google Colab) for data analysis andGoogle Drive for hosting the data files.The case studies for this tutorial present the use and benefits of treating text as signal to remove noise from longitudinal coded textual data and examine patterns of change in such data. Here are the requirements for following the tutorial:https://colab.research.google.com). Python is a widely used programming language with probably the most comprehensive signal analysis capabilities that efficiently extend the content discussed in this tutorial to more advanced features. Code written in Python is generally understandable for people with experience with other languages . We used Google Colab notebooks in this tutorial to simplify things for users who do not have a Python integrated development interface (IDE) installed on their system, and because the use of notebooks has attested pedagogical value  collected in mid-November 2020. The tweets used to build the datasets for this study were collected and preprocessed as follows: We set our code to retrieve the timelines  of 24,000 Twitter users located in one of the 160 most populated US counties. These users were selected using stratified sampling from a larger pool . It comprises aggregated personal values (see below) from 7,917,884 coded tweets  posted between November 1, 2019, and October 31, 2020. Since December 2019, COVID-19  has become the most immediate global concern worldwide  and one global (COVID-19 pandemic) epoch-defining event  are coded for each processed document.Tweets in dataset A were coded using the Linguistic Inquiry and Word Count 2015 , openness to change , self-transcendence , and conservation   to set up a Google Colab notebook for the uses of the tutorial, (2) to perform FIR filtering on aggregated coding of textual data (for denoising the data), including the use of different settings of the filtering function (to introduce the reader to these settings), (3) to plot the resulting signals with different settings and in comparison with the unfiltered data, and (4) to examine automatically detected changes in the signal trajectories using changepoint detection. This allows the visualization of patterns in different filtered signals and illustrates the benefits of the methods. Additionally, changepoint detection will be used to present an automated analysis of abrupt changes in the signal.CaseStudyA.ipynb from their Google Drive, and when prompted to select an application, choose Google Colab to open the file.Step 1. Granting access to Google drive from Colab, installing and importing packages. We first import the modules and functions necessary for this case study . Then, we allow Google Colab to access Google Drive and load the dataset for case study A :For these purposes, we investigate the emotional signal trajectories during the US presidential election week of 2020 (see Supplement Running the cell can be done by clicking on the Play icon on its left or holding Shift and pressing Enter (Shift + Enter) while the cursor is in the cell. Running other cells can be done by repeating the Shift + Enter combination. Readers can run the cells as they read, or all at once.Step 2. Centering and aggregating. We center the relevant variables by user_id (in Cell 2). The resulting variables now represent change within users around their mean. We also aggregate these data by day, hour, and quarter-hour to obtain equally spaced observations with sufficient granularity. We will refer to these data as emotional signals. Finally, we create a date and time vector, which will be helpful later for plotting emotional variation through time. The centering procedure below can be used in other projects in which there are several observations per grouping variable (here the Twitter user).After running Cell 1, a prompt appears: “Go to this URL in a browser: <URL>; Enter your authorization code: <input field>”. After the code from the URL is pasted in the required field on Google Colab, Google Colab has access to Google Drive and the dataset is loadedStep 3. Function declaration. We now define in Cell 3 our FIR filtering function, which we name FIR. The function takes a signal, the type of the sliding window, and its length as arguments. This function can use several types of sliding windows. We also define a function for the filter parameters, filter_parameters, and functions for displaying and formatting the figures: PlotFormatter and Plot_filtered_data. For the sake of brevity, we do not reproduce these functions here.Step 4. Plotting the FIR-filtered emotional signals. Positive and negative emotions. In Cell 4, we plot the emotional signals  filtered using window sizes of 12 and 24 . We chose these somewhat arbitrary values based on the length of the period of interest and because we were interested in changes that span several hours rather than short-lived changes. Denoising the data with such granularity, therefore, was deemed appropriate. We set the window type as "triangle" . Moreover, we apply a changepoint detection algorithm to the negative emotion signal (Panel C) to automatically identify shifts in the trajectory of this signal. All results are depicted in Fig. p < 0.05) for the filtered data with a window size of 12. For the data filtered with a window size of 24, the significance was achieved only on November 2 and 3. The highest values occurred on November 1 and 7, as the presidential election outcome became more concrete.In Fig. There was also an interesting variation in negative emotions, with an increase by the end of November 1, a strong dip reaching more than three standard deviations below the average for the considered period on November 3 , shortly thereafter followed by an increase reaching three standard deviations above this average on November 4 . Note that these variations were statistically significant for both filtered signals. These patterns could not be seen using the unfiltered signal see Fig. . There wStep 5. Plotting the FIR-filtered emotional signals. Anger, sadness and anxiety. In step 5 (Cell 5), we plot the FIR-filtered signals for anger, sadness, and anxiety in Fig. Figure Changes in anger, sadness, and anxiety  can be observed in Fig. After having been granted access to Google Drive from Colab and installing and importing the required packages for case study A, we explained how to load the dataset and the procedure that the reader should follow for centering variables and aggregating the data  to achieve equally spaced observations with enough granularity. Then, we defined the FIR filtering function, executed the FIR filtering of the emotional signals, and plotted the FIR-filtered emotional signals using different settings for window sizes and FIR filter types. We included a comparison of the results with and without the use of FIR so the readers could visualize the benefits of the method. We also used changepoint detection to automatically estimate shifts in the signal trajectories.In case study B, we will examine the use of interpolation , the FFT , and FFT filtering  to personal value-coded tweets from November 2019 to October 2020. This will allow us to examine the variation in the filtered signal in relation to epoch-defining events occurring during this period. We will also perform circular bootstrapping on the resulting signals (in order to estimate their uncertainty). The learning objectives of case study B are (1) to perform data imputation using signal interpolation within counties , followed by aggregation over counties  to examine the periodicity in personal value signals in these data ; (3) to obtain data filtered from relatively high signal frequencies for which the explained variation goes beyond the interest of this analysis; and (4) to use circular bootstrap to obtain measures of the uncertainty of the resulting filtered signals. These steps will allow the reader to understand the application of FFT and related methods for the analysis of signals from repeatedly coded text. Notably, the reader will learn to determine at which frequency there is a more important change in the data, and how to filter out the impact of frequencies above those of interest.We treated personal values coded from text as signal and examined their periodicity and how these evolved over 12 months. The original data for this case study are voluminous and would take a long time to process. Therefore, we already performed the aggregation by county, hour, and day. The unit of analysis is hence the hour, within-day and within-county. Due to the limited number of users for which the timelines were downloaded within each county, missing values were observedStep 1. Granting access to Google drive from Colab, importing packages and loading the dataset. As we did in case study A, before we load the dataset for case study B, we first need to allow Google Colab to access Google Drive . We also want to proceed to the importation of the necessary modules and functions for the case study.Readers should first open the Colab notebook for this case study (CaseStudyB.ipynb).Step 2. Function declaration. We declare a few functions in Cell 2, which, for the sake of brevity, are not reproduced here:to_wide creates a data frame containing the data of one variable, arranged by time (date and hour) in rows and county in columns.Interpolate will interpolate the data over one column using linear interpolation.Interp160 will iteratively apply the function Interpolate to the data of all counties, stored for each in one column.FFTgraph will plot the power (magnitude squared) of the frequencies of a signal.FFTfilter will perform low-pass FFT filtering of a signal (denoising) at the user-specified threshold.plotWithinTimeRange will plot observations within a user-specified time range for two variables.PlotFormatter1, PlotFormatter2 and PlotFormatter3 will be used to apply different formatting operations to graphs.Step 3. Interpolate the data. The functions Interpolate and Interpolate160 we defined above require a wide dataset, where data for each county are contained in a different column in chronological order. Therefore, we first arrange the data in such form before interpolation using the to_wide function. This function requires a vector indicating the measurement occasions on which to operate (named Date_time here). We performed all this in Cell 3 and checked that there were no missing values left in the data, which is the case.to_wide function we provide in the notebook can be used for such purpose in other projects.Step 4. Aggregation of over counties. We now average the data over the counties (Cell 4) to obtain a single time series for each variable, representing each county equally.When using signal analysis, we recommend against using methods of imputation that do not consider the local, i.e., temporal, context of the data to be imputed, as it can be expected that such imputation methods will lead to a distortion of the signal. Further, these methods make assumptions that are challenging to meet for frequently sampled textual data . One example is that tweets are less regularly posted during the night, potentially leading to more missing values at the aggregate level at that moment. Beyond social media analysis, interpolating the data can be used in projects featuring data that have been regularly sampled but for which some observations are missing. We note that transforming a dataset from a long to a wide format is often a necessary step for longitudinal data analysis. The Step 5. Plotting the power of the frequencies in the signals . Spectral plots provide information on the signal spectrum, i.e., which frequency has higher power. The signal is more strongly affected by frequencies with higher power. This hence provides information on the periodicity of the data. Let us examine the frequencies in each resulting variable using an FFT. In Cell 5, we produce the spectral plots of the personal value signals  is in all cases around 1 (x-axis), meaning that there is important circadian  periodicity in the signal of all personal values. The peak at 0 corresponds to a constant offset of the signal. One can also observe a smaller secondary peak at the x-axis value of 2, meaning that there is also a 12-hour periodicity in the signals. The values of the power spectrum for frequencies larger than 6, corresponding to a 4-hour periodicity, are small and so we can assume that there are no important cycles in the signals shorter than 4 hours. Note that the power spectrum is plotted in the range of frequencies up to 12, which is the highest (Nyquist) frequency that can be analyzed for this data, i.e., corresponding to periodic cycles of 2 hours or longer.see Fig.  at a samx-axis.Step 6. Removing circadian periodicity and plotting the self-transcendence signal. The period of interest spans one year from November 1, 2019, to October 31, 2020. For example, we first examine the variation in the self-transcendence signal between November 15 and January 15, 2020, after removing circadian periodicity and higher frequencies, which we consider as noise because we are not particularly interested here in the changes happening at a faster time frame than a day. In other words, we remove circadian periodicity because variation related to more extended periods is blurred by substantial circadian variation. For this, we employ an FFT low-pass filter with a threshold of 0.5 (Cell 6). Remember that 1 represents circadian (24-hour) change, so 0.5 represents 48 hours . We then plot the resulting filtered signal and the unfiltered signal for comparison. Such low-pass filtering could be applied to coded textual data at the participant level, in order to remove unwanted noise prior to performing other analyses, as routinely performed in EEG studies for instance.We note that we could have used other types of coded text treated as signals for our analysis using the FFT; an example is the affective dimensions of the LIWC (see case study A). We also could have set the unit of analysis to the week, in which case the sampling frequency fs would have been equal to 24 * 7. This would simply have changed the values on the Looking at the orange line in the resulting plot  and self-transcendence (orange) in Fig. Around Thanksgiving, an increase can be observed in self-transcendence (two standard deviations above the average over the considered period) and openness to change (four standard deviations). An increase in self-transcendence is observed around Christmas, and the highest values for openness to change occurred near New Year’s Eve. The changes in self-transcendence observed in Fig. Around the time when the COVID-19 state of emergency was declared in all US states , conservation increased and reached around two standard deviations above its average for the 12 months. Values for the next month were globally higher than in the previous month. Values in self-transcendence were globally higher between March 15 and May 5 than they were between January 15 and March 15. An increase in both conservation and self-transcendence  can be observed at a time of civil unrest  due to police violence (murder of George Floyd). Overall, stronger variation in self-enhancement is observed from the beginning of the period of interest to the end of the first trimester of 2020 compared with the rest of 2020. The variation in self-enhancement was less markedly relatable to events occurring during the 12 months of investigation.Step 8. Change in the self-enhancement signals over 12-months: removing periodicity above 30 days. We are now interested in signal changes over a more extended period. In Cell 8, we want to examine the result of further filtering of the data, this time removing periodicity beyond 30 days . We use the following code to perform the necessary filtering steps and plot the resulting signal:In Fig. Steps 6–8) could be helpful, for example, for computing both univariate and multivariate linear  and nonlinear  associations between the studied signals to investigate whether they are related after adjusting for periodicity. Although covering these topics is beyond the scope of this tutorial, we provide the following links to Python toolboxes and libraries specialized in such tasks, for interested readers.5The FFT examples shown in the three previous subsections . The main aim of this section is to provide a tool for accessing the variability or uncertainty associated with the filtering approaches used in previous sections. To focus on a practical example, we will use the data shown in Fig. In order to preserve the temporal dependence in the residual vector, the bootstrap approach should take samples from the data in blocks rather than single observations, thus preserving the temporal dependence within each block. Therefore, we estimated the optimal block length from the residual vector in the second step. This algorithm produces optimal block lengths for the circular block bootstrap, the algorithm we use in this example. Hence, we next created 5000 copies of the original data corrupted with different noise realizations by adding 5000 versions of (circular-block) bootstrapped residuals to the denoised data. Finally, we denoised each of these 5000 signals, plotted in Fig. Summary. After granting access to Google Drive from Colab and importing the required package, we loaded the dataset into Google Colab. Then, we declared the relevant functions, interpolated the data, aggregated the data over counties, and averaged the data so each variable could be represented in a time series. Afterward, we used spectral plots to map out the power of the frequencies in the signals. Subsequently, we plotted the self-enhancement signal after removing circadian periodicity, visually showing the benefits of the FIR-filtered signal compared to the aggregated signal after interpolation. Then, we removed periodicity above one week and 30 days to examine the changes in the personal values and self-enhancement signals over 12 months. We then used block bootstrap to obtain and plot the confidence intervals.Psychologists and computer scientists have been increasingly interested in automatically processing large datasets consisting of textual documents  covered a single, major political event involving a progression of successive actions occurring at a short time scale (from minutes to hours) which received 24-hour live media coverage over one week. Case study A used a form of sentiment analysis  well known in the field. In contrast, the period covered in case study B (dataset B) contained multiple events consisting of successive subordinate events and actions occurring at longer time scales (from days to weeks and months). It focused on investigating large-scale changes in personal values, which is unprecedented. We proposed a series of steps to apply the signal analysis and processing techniques to both case studies. Readers can quickly reproduce the steps we presented on Google Colab using the provided notebooks and datasets.Researchers in psychology and computer science interested in analyzing automated coding of textual documents may want to apply the method we presented here to comparable datasets collected from social media  that have been previously analyzed using traditional techniques. This could be another feasible way to test the method and experience its advantages.Signal analysis methods developed during recent decades for characterizing and detecting nontrivial structures in regularly sampled and coded textual data can be used to reveal the nature of the underlying dynamics producing the data, and to investigate and predict the effects of short-term events on future outcomes. Our tutorial aimed to introduce basic concepts and illustrate two practical examples of its use. Additional analyses are possible by adapting related methods frequently used in econometrics Fishman, , environHaving precisely one observation per time point in an analyzed signal is a requirement common to the presented signal processing analyses. However, this can lead to loss of information if values are aggregated for that goal. One way to circumvent this limitation is by decreasing the period between the considered time points. Further, nothing prevents a researcher from applying the algorithms described in this tutorial to every included participant, should they have enough data at every time point. Outliers can influence values obtained by interpolation at either or both sides of the missing data. One limitation of the case studies we presented here, and in general, is that we cannot exclude the possibility that the overall observed patterns were influenced by periodicity over a larger time frame. However, more data would be needed to assess this. Null hypothesis significance testing (NHST) is not straightforward using the presented methods, because we obtained only one signal in the tutorial. Using bootstrapping, we could include 95% confidence intervals to estimate regions of significance. A simple method for NHST would be to categorize the time points in the filtered signals based on, e.g., month. Analyses of time series more in line with the aims of NHST were presented by Refinetti et al. .The tutorial is provided in Python, as this language includes powerful signal processing libraries for time-series analysis. However, similar analyses could potentially be implemented in other popular software such as R or MATLAB. We have chosen Python because of the availability of signal processing libraries, enhanced speed of execution, and the ease of use of Google Colab, which broadens the readership to non-Python users. We note that the interpretations of the patterns are more focused and less detailed than in a research paper, as this piece was intended to be a tutorial on the techniques. Because this is a tutorial and not a textbook, reviewing all existing methods relevant to text mining for psychologists and computer scientists was beyond the scope of this work. For other methods, the readers are invited to consult the following references: Weiss et al.  and IgnaESM 1(DOCX 98 kb)


The COVID Radar app was developed as a population-based surveillance instrument to identify at-risk populations and regions in response to the COVID-19 pandemic. The app boasts of >8.5 million completed questionnaires, with >280,000 unique users. Although the COVID Radar app is a valid tool for population-level surveillance, high user engagement is critical to the success of the COVID Radar app in maintaining validity.This study aimed to identify optimization targets of the COVID Radar app to improve its acceptability, adherence, and inclusiveness.The main component of the COVID Radar app is a self-report questionnaire that assesses COVID-19 symptoms and social distancing behaviors. A total of 3 qualitative substudies were conducted. First, 3 semistructured focus group interviews with end users (N=14) of the app were conducted to gather information on user experiences. The output was transcribed and thematically coded using the framework method. Second, a similar qualitative thematic analysis was conducted on 1080 end-user emails. Third, usability testing was conducted in one-on-one sessions with 4 individuals with low literacy levels.All 3 substudies identified optimization targets in terms of design and content. The results of substudy 1 showed that the participants generally evaluated the app positively. They reported the app to be user-friendly and were satisfied with its design and functionalities. Participants’ main motivation to use the app was to contribute to science. Participants suggested adding motivational tools to stimulate user engagement. A larger national publicity campaign for the app was considered potentially helpful for increasing the user population. In-app updates informing users about the project and its outputs motivated users to continue using the app. Feedback on the self-report questionnaire, stemming from substudies 1 and 2, mostly concerned the content and phrasing of the questions. Furthermore, the section of the app allowing users to compare their symptoms and behaviors to those of their peers was found to be suboptimal because of difficulties in interpreting the figures presented in the app. Finally, the output of substudy 3 resulted in recommendations primarily related to simplification of the text to render it more accessible and comprehensible for individuals with low literacy levels.The convenience of app use, enabling personal adjustments of the app experience, and considering motivational factors for continued app use  were found to be crucial to procuring and maintaining a population of active users of the COVID Radar app. Further, there seems to be a need to increase the accessibility of public health tools for individuals with low literacy levels. These results can be used to improve the this and future public health apps and improve the representativeness of their user populations and user engagement, ultimately increasing the validity of the tools. Since December 2019, the world has been battling the SARS-CoV-2 (COVID-19). In the Netherlands specifically, the first case was identified in February 2020 and has resulted, to date, in >2.6 million confirmed cases and 19,000 deaths .Since the start of the pandemic, mobile apps have been implemented for a variety of goals such as risk assessment and decision-making as well as self-management and self-monitoring of symptoms . SymptomIn the Netherlands, a smartphone-based surveillance app, the COVID Radar app, was developed. The goal of the app was to predict topographical future COVID hot spots with the help of self-reporting of COVID symptoms, enabling the possibility for local or national governments to intervene, for instance, by informing the public or implementing local restrictions. The app enables frequent and anonymous voluntary self-reporting of COVID-19–related symptoms and behaviors . The app was developed during the first COVID-19 wave in the Netherlands in the spring of 2020. Among other functionalities, the app contains a short monitoring questionnaire (≥20 questions) asking users to self-report their COVID-19–related symptoms, social distancing behaviors, COVID-19 status, and vaccination status. The app additionally provides users feedback on their reported social distancing behavior  and symptoms by enabling them to compare these to the mean values nationwide as well as within their specific geographic region. Since the launch of the app in April 2020, it boasts of >8.5 million completed questionnaires, with >280,000 unique users, of whom >13,000 completed the questionnaire at least twice a week on average. The COVID Radar app can be considered a citizen science project, as it involves the public in the scientific processes, in this case the collection of large-volume longitudinal symptom and social distancing data across the Netherlands , but I’m also doing it for myself and for my own awareness.When discussing the motivation to keep using the app, several participants discussed the potential of integrating motivational tools in the app to increase adherence. Particular suggestions included repeated exposure to the research goals and the broader aim behind the COVID Radar; for example, by means of in-app updates. Other suggestions related to the potential addition of serious gaming elements, such as enabling participants to collect digital badges based on the number of times they completed the questionnaire:But you could also use badges, you know. I have another app in which I earn badges for 5 days, 10 days, and 50 days. But then you have to make some kind of dashboard available in the COVID Radar where one can see if one has completed the questionnaire. I know that I am very motivated by those kinds of streaks.Most participants in the focus groups became aware of the app through a direct or indirect personal connection to the LUMC or the city of Leiden. More efforts in terms of national publicity campaigns using social media were suggested to reach more citizens. Another suggestion was to advertise the app in geographic areas with relatively low user engagement using pamphlets or local media.Most feedback on the self-report monitoring questionnaire concerned the content and phrasing of the questions. Several participants pointed out that some questions left room for interpretation by app users, which may lead to biased answers. More specifically, the question “Yesterday, how many people came within 5 meters of you, outside the house?” received extensive feedback. Most participants considered this question challenging or even too difficult to answer, as they did not feel capable of accurately estimating this number:I ride my bike almost daily. How many people do you come across within 5 meters? A street is 5 meters, so every time I ride my bike I am almost sure that I can fill in that 50 people came closer than 5 meters to me.The question “Are you completely vaccinated against COVID-19?” also received extensive feedback. Some participants wondered why there was only an interest in whether they were completely vaccinated, instead of allowing users to indicate how many times they had received vaccination.Regarding the additional information accompanying the questions, the provided information did not always clarify questions or doubts the participants had concerning the respective question. For example, there were numerous questions regarding whether certain locations, such as work offices, shops, and supermarkets, should be categorized as public places.“Are you experiencing any COVID symptoms?”) which, if answered positively, resulted in a pull-down menu in which they could indicate which specific symptoms were present.Concerning the length of the questionnaire, some participants found it too long, although most participants reported that filling in the questionnaire took less than a minute. Relatedly, some participants struggled with their motivation to answer the same questions repeatedly. One suggestion was to enable the possibility of indicating whether the participant experienced changes in COVID symptoms, as these were found to be relatively steady in comparison with behavioral measures . More specifically, participants suggested creating a shortened version of the questionnaire in which the symptom complaints of COVID-19 were combined into one question  presented in the Radar section were another frequently mentioned subject of potential improvement. Lack of contrast and nonmatching colors in the legends and figures were described as hindering to understanding by the app users.Participants who did not complete the questionnaire daily reported that the push messages motivated them to completing the questionnaire more frequently:I am very pleased with the reminders… The reminders definitely encourage me to fill in the questionnaire.In contrast, participants who completed the questionnaire daily reported that the push messages created confusion. The messages made them question whether or not they had already completed the questionnaire on that day. They also reported the reminders to be a source of irritation when they were certain they had already completed the questionnaire that day. A potential solution offered by the participants was to create the possibility of personalizing push notifications, allowing them to be set at a user-defined time and frequency, thereby helping them to create a routine.A total of 1080 emails were analyzed; approximately one-third of the emails were coded as irrelevant, as they comprised either duplicate mail, spam mail, mail commercials, or updates from the COVID Radar Facebook or Instagram accounts. Most emails contained questions from app users on how to interpret or answer a specific question from the self-report questionnaire  literacy levels, for example, by using language level A2 or B1 according to the Common European Framework of Reference for Languages  and by cAlthough the feasibility and acceptability of using smartphone apps for continuous monitoring of disease seems evident from the literature -20, motiAnother potential strategy to increase user engagement and motivation is the incorporation of persuasive technology such as gamification: the use of gaming elements such as challenges and rewards to a nongame environment. Although still in its early stages, preliminary evidence supports the use of gamification to increase use and user engagement in internet interventions focusing on  health and behavior change . HoweverThe main limitations of substudies 1 and 2 pertain to sample bias; it is likely that the participants in the focus groups were above-mean motivated app users with more positive attitudes toward the project and the app. Likewise, email feedback is likely to be sent more frequently by highly engaged app users, whereas less motivated users or users with negative evaluations or experiences with the app may have stopped using the app and were thus not involved and reached by substudies 1 and 2. Another potential limitation is the selection bias based on the online nature of the interviews: because the focus groups were conducted using Zoom, possible participants who were not yet familiar with this program might have decided against participation for this reason. To reduce this bias, we offered a practice round to participants who were not yet experienced in using Zoom. One participant took advantage of this opportunity. NS was available for technical assistance, which was not requested during the 3 focus group interviews. However, we cannot claim that other potential participants with a lower digital literacy were not deterred from participation. However, the participants in substudy 3 had not previously used the app; therefore, their feedback on the textual features of the app was unbiased by previous experience with the app. Furthermore, the results of substudies 1 and 3 may be limited in terms of generalizability because of the relatively low number of participants in these substudies . The use of web-based interviewing further comprised the ability to fully assess body language. However, the interviews were conducted via videoconference, so facial expressions could be read and interpreted by the interviewer. Therefore, it is not believed that this biased our conclusions regarding the focus groups.A self-report population-based surveillance mobile app seems useful in national research on symptoms and social distancing behavior. The main motivations of citizens to use such an app are related to altruism, collectivism, and wanting to help science in developing and improving mobile self-report apps in the context of public health surveillance tools. Convenience of app use, enablement of personalization of the app, and motivational factors are key to procuring and maintaining a population of active users engaging with an app such as the COVID Radar. When designing such an app and developing content, it is important to consider its accessibility for individuals with low literacy levels. These results are not only critical to the optimization of the COVID Radar app, ultimately increasing its acceptability, inclusiveness, and adherence, but are also relevant in the broader context of citizen science projects and public health surveillance tools during the pandemic.


With rapid global urbanization, the importance of understanding relationships between the changing environment and wellbeing is being increasingly recognized. However, there is still a lack of understanding of how long-term residential environment exposure affects subjective wellbeing under the dual changes of geographical environment and residential location. Based on a survey of the elderly (people over 60 years old) in Guangzhou, China, this study analyzes the effect of long-term residential environment exposure over 25 years on subjective wellbeing in later life in the context of residential mobility. The study found that subjective wellbeing in later life is not only related to the current residential environment but also the cumulative exposure to the long-term residential environment. The relationship between long-term residential environment exposure and subjective wellbeing in later life tends to be stable with the increase of cumulative time, especially the cumulative years over 15 years. Considering the importance of residential mobility history, the study further analyzes the moderating effects of relocation frequency and residential location. Relocation frequency can strengthen the positive effect of residential environment on subjective wellbeing and weaken the negative effect of residential environment on subjective wellbeing, which confirms the existence of residential self-selection. In addition, the direction of effect of residential environment on residents who move between living in the urban center and the periphery is consistent with that of residents who have always lived in the urban center, while the effects of the residential environment on residents who have always lived in the urban center and those who have always lived in the urban periphery are related in different directions. The conclusion of this study can provide guidance for individuals’ residential choice and governance of the urban environment to improve wellbeing. With the emergence of social models of health, the attention of health geographers has shifted from medically centered concepts of the body and disease to broader concepts of health experienced by individuals . RecentlThe value of linking the urban environment and wellbeing outcomes is being increasingly recognized; however, the myriad relationships are far from being understood scientifically ,6. The eHealth geography research based on individual spatiotemporal behavior becomes the key to understanding the urban environment and individual quality of life . SpatiotIn the process of rapid urbanization in China, the urban environment has undergone tremendous changes, and the spatial differences have continued to increase. At the same time, since the housing market reform, residential mobility has become more and more common . IndividResidential mobility has become more and more common, especially intraurban residential mobility ,18. UnliResidential mobility is affected by the broader social and economic environment, such as housing policy and the real estate market . During As a material entity, residential space supports people’s daily life, and the residential environment is the foundation of subjective wellbeing. An individual’s subjective wellbeing may be the product of decades of environmental exposure. Previous studies on residential environment rarely considered the variability and long-term nature of the residential environment in the life course and focused on the relationship between residential environment and subjective wellbeing in the same time period without considering that living environment in the early stage of the life course will affect subjective wellbeing in later life. Studies have pointed out that long-term environmental exposure is more important than current exposure because the residential environment may have a continuous impact over time ,29,30. RThere is a growing recognition that the residential environment is not static but changes dynamically over time . Over thMost of the existing studies have examined the short-term effects of residential environment but ignored the complexity of long-term residential environment on subjective wellbeing in later life. The accumulation of early life experiences may have an impact on later life, especially in old age. Population aging is a global social phenomenon. The elderly in China, defined as over 60 years old, have experienced significant social transformation in their lifetime, and their residential environments and choices are affected by their specific social backgrounds.Although residential context has profound effects on individuals’ subjective wellbeing, people can change their subjective wellbeing by moving to a new location. Research on the effects of long-term residential environment exposure on subjective wellbeing cannot ignore people’s life experience of moving between different residential environments. Traditional cross-sectional research does not distinguish between the short-term impact of the residential environment (for movers) and the long-term impact (for stayers), which may lead to biased results. Therefore, the relationship between long-term residential environment exposure and subjective wellbeing needs to consider the impact of residential mobility history.Residential mobility has generally been viewed as a negative and stressful event, which is substantiated by numerous studies . People In China’s big cities, there are significant spatial differences in the urban environment. The central area of a city is a high-density area of population activity, while the peripheral area is in a relatively closed state with less social differentiation . There aBased on the analysis and review above, this paper proposes a conceptual framework to examine and compare the relationship of long-term residential environment exposure and subjective wellbeing under residential mobility . There aHypothesis 1.Subjective wellbeing in later life is related to the long-term residential environment exposure.Hypothesis 2.This relationship may be moderated by residential mobility history such as relocation frequency and residential location.2 in 1992 to 1249 km2 in 2016, and the urbanization rate increased from 69.40% in 1992 to 86.06% in 2016. The urban space has undergone large-scale expansion and reconstruction in a short period of time, resulting in dramatic changes in the characteristics of the living environment and continuous reorganization of the social structure, which has a profound impact on residents’ lives.Guangzhou, located in the south of China, is a mega city. The socialist market economic system implemented in 1992 further accelerated the city’s urban expansion and urbanization movement. The built-up area of Guangzhou increased six-fold in 25 years from 206 kmThe data of this study are based on a questionnaire survey of the elderly conducted in Guangzhou from October to December 2016. We adopted stratified random sampling survey method. Firstly, based on the data of the sixth Census of Guangzhou, the neighborhoods were clustered, and then, a total of 46 neighborhoods were selected from various types of neighborhoods as sample neighborhoods to conduct the random sampling survey of the elderly. More details of the survey are in Su et al. (2021) . A totalSubjective wellbeing is an individual’s self-evaluation of their own life quality. In colloquial terms, it can simply be considered self-appraisal of whether one is happy or not . At presResidential environment, in this study, is the social and physical environment of the residential neighborhood. Based on literature review and data availability, the population density and the proportion of migrants and a highly educated population (undergraduate and above) in the neighborhood are used as proxies for residential social environment. These variables of residential social environment are derived from data of the Fourth Census (1990), the Fifth Census (2000), and the Sixth Census (2010) of Guangzhou. The neighborhood boundaries in this study are defined based on administrative units created by each census. For physical environment variables, satellite images of Guangzhou were obtained through the Geospatial Data Cloud Platform of the Computer Network Information Center of the Chinese Academy of Sciences, with a spatial resolution of 30 m. Image data with less cloud cover were selected for analysis, and images with higher cloud cover were selected from adjacent years for replacement. To represent the residential physical environment, the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the normalized difference built-up index (NDBI) were obtained through band calculation. NDVI is a numerical indicator that uses the visible and near-infrared bands of the electromagnetic spectrum, which can effectively capture neighborhood green space . NDWI waAssessment of long-term residential environment exposure was based on residential location over the past 25 years. The questionnaire recorded each participant’s residential address and residence time (start/end), which allowed us to accumulate the residential environment variables year by year so that the dual changes of residence and environment could be taken into account at the same time.The survey obtained the residential mobility trajectory of the elderly in the sample from 1992 to 2016, including the time and detailed address of each relocation. The number of times of residential mobility and residential location during this period were used to analyze the impact of residential mobility history on the relationship between long-term residential environment exposure and elderly subjective wellbeing. Taking the outer ring road of Guangzhou as the boundary, within the ring road is regarded as the central area, and outside the ring road is regarded as the peripheral area. According to the residential location, the sample of elderly people was divided into three types: those who have always lived in the urban center, those who have always lived in the urban periphery, and those who have moved between living in the center and the periphery. In addition, gender, age, education, marital status, and self-rated health have been widely discussed in the study of subjective wellbeing ,53. TherThe dependent variable is the current subjective wellbeing, which is a five-category ordinal variable, so the ordered logistic regression model (Ologit) was used to study the association between long-term residential environment exposure and subjective wellbeing at the individual level. The Ologit models are specified as follows:P/1 − P is the occurrence ratio, that is, the ratio of the occurrence probability of the event (y ≤ j) to the non-occurrence probability of the event (y > j). The odds ratio (OR) indicates the influence of independent variables on the change in the probability of occurrence of dependent variables.This equation is the occurrence probability of events in the Ologit model, and The study first constructed the Ologit models of cumulative exposure to the residential environment and elderly subjective wellbeing over the past 25 years (1992 to 2016). Results are presented in p < 0.001). Unlike cities in western countries, many cities in China are characterized by high population density. Large concentrations of population may lead to social disorder and exert pressure on public facilities and housing, which are not conducive to subjective wellbeing [p < 0.01). This may be because living long term among people with a higher education level is more conducive to owning and accumulating social capital [p < 0.001). Since China’s transition to a market economy and the reform of the housing system, residential mobility has become more and more common. In areas with a high proportion of migrants, people obtain social support and job opportunities through friends, acquaintances, and fellow villagers, forming a community with supportive characteristics [In the social environment of residential neighborhood, long-term exposure to high population density is significantly related to lower subjective wellbeing in later life . Green space can promote daily physical activities, increase social interaction opportunities, and relieve negative emotions caused by life pressure and thus increase residents’ subjective wellbeing [p <0.01). This may be because, on the one hand, living for a long time in the humid environment of Guangzhou, where the summer is hot and rainy, and the air is humid, is likely to bring certain health risks, such as asthma, rheumatism, and arthritis [p < 0.001). Rapid urbanization and urban sprawl in China have brought high building densities in cities, resulting in smaller and more crowded urban outdoor spaces. At the same time, high building density leads to a series of urban problems such as space congestion, tension in the use of public facilities, and environmental deterioration, reducing subjective wellbeing [In terms of the physical environment of residential neighborhoods, the elderly with long-term exposure to green space have higher subjective wellbeing  Moderating effect of relocation frequencyIn order to more clearly and intuitively understand the moderating effect of relocation frequency on the relationship between long-term residential environment exposure and subjective wellbeing in later life, the study divides relocation frequency into high relocation frequency (2 and above) and low relocation frequency (0 and 1) based on the median and draws a schematic diagram of the moderating effect see . From Fi(2) Moderating effect of residential locationResidential location is a categorical variable, so the study adopts grouping regression, which overcomes the possible result bias caused by simple regression without distinguishing the types of moderating variables and improves the accuracy of the test.In China’s market economy transformation and housing reform, institutional and market factors play an important role in housing choice and quality of life . Based oThe results show there is no evidence that hukou and work unit are related to the subjective wellbeing of the elderly, while the market factors reached statistical significance see . The higSubjective wellbeing in later life is not only related to current residential environment exposure but also to long-term residential environment cumulative exposure. Cumulative residential environment exposure in the last 24 years, 23 years, 22 years, etc., to the latest year was calculated according to the same method. The odds ratios of residential environment and subjective wellbeing were also computed to better understand the relationship between environment exposure and subjective wellbeing from different cumulative exposure times . The resThe study found that relocation frequency strengthens the positive relationship between long-term residential environment exposure and subjective wellbeing in later life but weakens the negative relationship between them. This may be due to the existence of residential self-selection. According to the theory of residential self-selection, residents choose their residence according to their socioeconomic attributes and attitude preferences ,65. SincThe relationship between the cumulative exposure of residential environment and subjective wellbeing in later life is affected by the long-term residential location. Residents who live in the urban center and those who have always lived in the urban periphery are affected differently by the geographical environment, which may be due to the differences in environmental habits and perceptions formed over a long period of time. For the residents who have moved between the center and periphery, the relationship between their subjective wellbeing and residential environment is more consistent with the residents who have always lived in the urban center. This may be due to the urbanization movement in recent decades, which has caused residents in the urban peripheral area to move to the central area and move closer to the lifestyle of urban center residents . The geoTaking Guangzhou, China, as a case city, this study explores the relationship between long-term residential environment exposure over 25 years and subjective wellbeing in later life and the possible moderating effect of residential mobility history. There are three main conclusions. First, subjective wellbeing in later life is not only related to the current residential environment but also the cumulative exposure to the long-term residential environment. The directions of the relationship between residential environment variables and subjective wellbeing are not affected by cumulative time in the direction (positive or negative) but differ in the intensity. The relationship between long-term residential environment exposure for 15 years or more and subjective wellbeing is relatively stable. Second, the relationship between residential environment and subjective wellbeing is moderated by relocation frequency. Relocation frequency can strengthen the positive relationship between a highly educated population, green space, and subjective wellbeing and weaken the negative relationship between population density, building density, and subjective wellbeing. This is consistent with the theory of residential self-selection. Last, the relationship between long-term residential environment exposure and subjective wellbeing in later life is heterogeneous due to the impact of residential location. Although there are differences in the intensity of effects between residential environment and subjective wellbeing of the elderly between the residents who have always lived in the urban center and those who have moved between the center and periphery, the direction of effects (positive effect or negative effect) is relatively consistent. The subjective wellbeing of residents who have always lived in the urban center and those who have always lived in the urban periphery is related to the residential environment in different directions.However, the study has some limitations. First, this is a cross-sectional study. Although we consider long-term residential mobility trajectory over 25 years and environmental changes, the causal inference between environment and subjective wellbeing is limited because the individual attributes and subjective wellbeing in this study are cross-sectional data. Thus, future research is needed to examine an explicit causal relationship between geographical environment and subjective wellbeing using a longitudinal design. Second, the study is carried out in the context of China. The elderly in the study have experienced the institutional transformation of the country, which has a specific historical background and unique characteristics. Whether the conclusions of this study are applicable to other countries and regions needs further verification.


Traditional fatigue fracture theory and practice focus principally on structural design. It is thus too conservative and inappropriate when used to predict the high-cycle fatigue life of dies used for metal forming, especially cold forging. We propose a novel mean stress correction model and diagram to predict the high-cycle fatigue lives of cold forging dies, which focuses on the upper part of the equivalent fatigue strength curve. Considering the features of die materials characterized by high yield strength and low ductility, a straight line is assumed for the tensile yield line. To the contrary, a general curve is used to represent the fatigue strength. They are interpolated, based on the distance ratio, when finding an appropriate equivalent fatigue strength curve at the mean stress and stress amplitude between the line and curve. The approach is applied to a well-defined literature example to verify its validity and shed light on the characteristics of die fatigue life. The approach is also applied to practical forging and useful qualitative results are obtained. Low-cycle fatigue (LCF) in metal forming is deeply related to die life during hot metal forming . To the Notably, most studies on HCF fractures have focused on structural design. They are thus inclined to conservatively predict fatigue life. For example, the Goodman–Haigh diagram ,7, whichFew researchers have studied die fatigue fracture during cold metal forming. Knoerr et al.  employedFu et al.  systematR = −1) are optimal for testing, particularly for metal forming. However, many tests are conducted under conditions where stresses equivalent to the normal stress condition (R = −1) are required. Several schemes and traditional MSCDs or fatigue limit diagrams are used to define the equivalent stresses, including the Goodman–Haigh, Gerber, Soderberg, Morrow, Walker, Dietman, Mason–Halford, and Smith–Watson–Topper diagrams, and variants thereof [Simple tension and/or compression tests, combined with three- or four-point bending tests using various combinations of mean stress and stress amplitude, have traditionally been conducted. However, the stress conditions under a fully reversed stress  is near the yield line of the MSCD or fatigue limit diagram.The mean stress effect, and modeling thereof, remain of major interest. Most researchers used Goodman–Haigh diagrams, which are conservative (to promote the design of safer structures) and focus on negative hydrostatic pressure in most cases. The die stress during cold metal forming is unlike that in a structural situation, because the die is designed to fracture after a defined number of production cycles and the principal cause of material plastic deformation is compressive stress. The experimental literature  shows thThere are several well-known stress measures  for deteIn this paper, we propose a general MSCD or fatigue limit diagram characterized by one or two parameters, i.e., the exponents of the mean stress per unit tensile strength, which not only combine flexibly the Gerber and Goodman–Haigh diagrams, but also modify them. We also extend the diagram to the negative mean stress range using an additional parameter. We use the general MSCD to predict the HCF life of the dies of a nut cold forging process.Variation in die stress during cold forging by an automatic multi-stage cold forging machine was examined for a typical industrial process. Some of the stages for manufacturing an automotive nut are shown in μ  was assuThe implicit elastoplastic finite element method with tetrahedral MINI-elements ,8,37 wasWe now discuss the characteristics of the basis stress during cold forging with prestressed and non-prestressed dies. We considered the signed effective stress in the analysis  just forWhen the prestresses are very high, as for the nut, the initial mean stress begins further from the origin than in the more common case, as can be seen from the comparison in However, the shrink ring experienced the opposite effects, as can be seen in On the contrary, the stress paths of the punch which were not prestressed were in the opposite direction of those of the prestressed cases of die inserts . The patR = −1) that its standard specimen can withstand for a specified number of cycles  without any fatigue fracture. However, actual engineering stresses of die in metal forming are multiaxial and the mean stress is not usually zero. In practical terms, an equivalent stress corresponding to the multiaxial stress is useful for predicting the engineering fatigue life from the S-N curve.For high accuracy, the fatigue strength of a material should be obtained by the maximum value of fully reversed stress  to the tensile strengths on the axis of mean stress. The results of experiments on aluminum alloys and steels are shown in In comparison, the Gerber parabola provides a relatively good fit with the experimental data obtained when the mean stress is positive . Most reThe curved diagram seems to better describe the equivalent fatigue strength when the mean stress is positive and the material is not so hard . ApparenA similar curve can be used to calculate the equivalent stress . They predicted the fatigue life of a die used for automatic multi-stage cold forging of a simple flange bolt, using the traditional Goodman–Haigh diagram. Moreover, they used seven combinations of N and equivalent stress amplitudes on the Tanrikulu and Karakuzu  experimeIn accordance with Tanrikulu and Karakuzu, we used a transverse rupture stress of 2900 MPa, rather than the tensile strength, when comparing our new approach with the traditional one. We also used their compressive yield strength (3500 MPa). First, after setting quin law .N in this study was calculated using the following formula of assumed linearity . This emphasizes the critical influence of the die material exponent Next, we used a combined Goodman–Haigh and Gerber diagram to solve the same problem. We used the following values: R = −1) and at R = −2.44). We assessed the effect of a negative mean stress on the equivalent fatigue strength on the We used the data in the fifth and eighth columns of We thus assumed that the slope of line DE in material . Figure We applied the presented MSCD diagram and the Goodman–Haigh diagram to predict qualitatively the HCF life span of the die insert marked by S in R = 0.1 [R = 0.1 . This isCompared to the former model, the potential HCF fracture region of the latter model was clearly concentrated in a limited region. This effect, and the HCF life, were qualitatively similar to the experimental data cf. b,c. ThisWe investigated the characteristics of die stresses developing during cold forging, with an emphasis on the shrink-fitting used to prestress dies prior to cold forging. We found that all the It was believed that the use of a traditional MSCD is inappropriate for cold forging, based not only on our new data on die mechanics, but also on experimental literature data. To the contrary, our MSCD is general and flexible. It includes the Goodman–Haigh and Gerber diagrams as special cases. We used two material fatigue parameters for the MSCD in the first quadrant. Both were exponents of the mean stress per unit tensile strength. One refers to the fatigue strength curve or line, and the other to the line for the tensile yield line.A straight line was used to extend the MSCD of the first quadrant to the second quadrant, based on experimental literature data. Its slope of the example, which is a fatigue property, was relatively steep, indicating that the zero-slope assumption of the modified Goodman–Haigh diagram may lead to considerable error when calculating HCF life, especially that of a prestressed die used for cold forging.Our fatigue limit diagram was numerically tested under various conditions, and its validity and flexibility were verified. The tests revealed the importance of the second MSCD quadrant when predicting the HCF life of prestressed dies which has been neglected by structural engineers. The tests emphasized that fatigue tests on die materials should include at least the negative mean stress case. This determines the slope of the equivalent fatigue line, which is most important when predicting the HCF life of prestressed dies used during metal forming.


During restoration of poorly fitting complete dentures (CDs) in edentulous patients, liners are used to reconstruct the concave surfaces of CDs with a new base material. These relining materials are classified into resilient liners (RLs) and non-resilient liners (NRLs), but the clinical effects of these liners and their selection criteria remain unclear. The purpose of this study is to evaluate the clinical efficacy of relining mandibular CDs using RL and NRL and to conduct a follow-up study.The study is currently being conducted at eight centers, and a parallel-group randomized controlled trial (RCT) is underway. One hundred thirty-two edentulous patients with poorly fitting mandibular CDs will be assigned to two groups based on whether they will receive RL or NRL. Participants will have an RL or NRL applied for relining their CDs using an indirect method of dynamic impressions. Data will be recorded at 1 week and 3, 6, and 12 months after denture delivery. The primary outcome will be assessment of the patients’ general satisfaction by using a 100-mm visual analog scale (VAS). Secondary outcomes will be measured as patient-reported outcomes, including food intake status and oral hygiene-related quality of life. Masticatory performance and the number of sore spots on the oral mucosa will also be recorded. Comparisons between the two groups and within-subject comparisons of pre- and post-intervention measurements will be conducted.For dentists and prosthetic researchers in Japan, this RCT will provide information on the clinical efficacy of RL materials in comparison to RNL in CD wearers. The new evidence regarding the use of RL materials in an aging population will also be useful to dentists in other countries in their routine clinical practice.This clinical trial has been registered at the University Hospital Medical Information Network (UMIN) Center (UMIN000041950). The aging of the global population, especially in developed countries, has led to an increase in the number of edentulous patients , 2. In dRandomized clinical trials (RCTs) have been conducted to investigate the clinical effects of newly fabricated CDs with RL materials in edentulous patients. These trials reported that the cushioning effect reduced pain and pressure ulcers during occlusion, improved patient satisfaction and masticatory performance, and reduced the number of denture adjustments –13. HoweThis trial is being organized by the Japanese Society for Denture Care. The trial has been developed by KK, the sponsor of the study, and conducted at eight centers . The trial is designed as a multicenter randomized parallel trial comparing patient satisfaction with mandibular CDs using RLs and that with mandibular CDs using NRLs, with a superiority test as the framework of this trial. Several coordinators are in close contact with the investigators at each university. Each university has an investigator and a dentist as the operator. The coordinator randomly assigns and manages the schedules of participants and investigators. The investigator obtains documented informed consent from the potential participant. They are then trained to evaluate the results of the clinical trial before measuring the data. The dentists who provided the denture treatment are not allowed to be investigators for this trial. This trial has been registered with the University Hospital Medical Information Network (UMIN) Center (UMIN000041950) on 10 October 2020.The coordinating center (TCC), data monitoring committee (DMC), and the steering committee (TSC) have been established for this clinical trial.TCC consists of the chief coordinator (NH) and the coordinators , who are in constant contact with each other. The role of the center is to allocate patients and study design, to oversee the collection, management, analysis, and interpretation of data, and to submit the results to the steering committee.The DMC consists of dentists who are not involved directly in this clinical trial as well as experts other than dentists. The role of the DMC is to conduct an audit of progress (report submission) to the coordinating center every 2 months and report the results to the steering committee.TSC consists of the coordinator, the investigators and dentists from each university, and members of the DMC. The role of this committee is to audit the progress of the trial, report adverse events, deliberate and decide on important changes to the study protocol including study discontinuation, and have final authority over the trial. TSC is scheduled to meet four times a year. In addition, the contents of TSC meetings are always reported to the sponsor, and the sponsor is obligated to report annually to the funder (the Japan Society for the Promotion of Science) on progress and results to date.The trial will be performed at the dental hospitals of eight universities in Japan. The recruitment and treatment protocols of the present trial have been approved by the ethical review committee of the following universities: Tokyo Medical and Dental University (Register No. D2020-058), Nihon University School of Dentistry at Matsudo (Register No. EC2-042), Tsurumi University (Register No. 1840), Kanagawa Dental University (Register No. 733), Aichi Gakuin University (Register No. 612), Tokushima University (Register No. 3855), Nagasaki University (Register No. 21041910), and Kagoshima University (Register No. 200313).The inclusion criteria are as follows: (1) edentulous patients who wanted to have their mandibular CDs repaired and (2) patients who provided written informed consent for this study. The exclusion criteria are as follows: (1) patients with physical or mental problems, (2) patients who cannot hear or read Japanese, (3) patients with dietary restrictions, and (4) patients with oral dyskinesia.α = 0.05 and β = 0.8, based on the standard deviation of the overall satisfaction obtained in a previous study  [The general satisfaction of patients will be measured as the 100-mm visual analog scale (VAS) score after 3 months of relining. The use of an RL material will be judged to be clinically effective when the VAS score of patients having CDs with RL is 15 mm higher than that of patients having CDs with NRL. Thus, the difference between the means of the two groups is set to 15 mm. The sample size is calculated using L: 31.4) . HoweverStratification is performed at each university hospital Fig. . The assA dynamic impression is made using a tissue conditioner on the CD to be indirectly relined. The dynamic impression should be recorded when the patient does not complain of pain during mastication. Considering the surface properties of the tissue conditioner, an interval of approximately one week between uses is appropriate.A super-hard plaster  is poured on the dynamic impression surface to make a plaster model, which is flasked for denture processing.The flask is opened, the tissue conditioner is peeled off from the mucosal surface of the model, and the mucosal surface of the denture base is trimmed to create space for the reline layer. The thickness of the reline layer is approximately 2 mm.After applying the adhesive material according to the manufacturer's instructions, the CD is lined with the relining material (RL or NRL), cured, morphed, and polished. The relined CD is then delivered to the patient. Post-denture delivery follow-up appointments will be scheduled, and any necessary adjustments will be carried out.The intervention for this trial will be the application of an RL material  or an NRL material  to the mandibular CD. This trial uses an indirect lining method with the application of dynamic impression. Tissue conditioner  will be used for recording the dynamic impression. All prosthetic procedures will be performed by a prosthodontist with at least 5 years of experience. In principle, the indirect method should be performed by a dental technician. The technique used is as follows:The basic patient information recorded will include age, sex, years of denture use, and number of dentures fabricated. In addition, treatment difficulty index measurements , 15 and In addition, to comprehensively understand the clinical effect of CDs with RL material, the Oral Health Impact Profile for edentulous subjects (OHIP-EDENT) , masticaThe trial schedule is outlined in Table After receiving informed consent from the patient, the investigator requests a group allocation to the coordinating center. Once the patient has been assigned to a group, a dentist is selected to be in charge. At that time, basic patient information is collected and patient satisfaction and OHIP-EDENT are assessed.The dentist-in-charge adjusts the jaw-type complete denture in use, if necessary, and schedules the next appointment.The investigator evaluates the patient's masticatory performance and food intake. The dentist performs a dynamic impression with tissue conditioner to initiate the intervention.The dentist-in-charge places the denture in use once the pain is gone.The dentist-in-charge places the mandibular CD with a resilient liner in the mouth.The investigators measures the location and number of mucosal pressure ulcers, patient satisfaction, OHIP-EDENT, and chewing ability.The investigators evaluates patient satisfaction, chewing ability, OHIP-EDENT, and food intake.The investigator, him or herself a dentist, will carefully monitor the occurrence of adverse events. If an adverse event occurs, appropriate measures will be taken and, in principle, the patients will be observed until the event disappears. The type of adverse event, date of occurrence , details of treatment , course of the adverse event, and details of any causal relationship with the resilient liner will be promptly reported to the steering committee. Observations will continue up to 1 year after the application of the resilient liner and will continue throughout the study period.In this study, there is no anticipated harm and compensation for trial participation.If any of the following discontinuation criteria are met during the study period, that individual patient will be discontinued promptly at the discretion of the principal/participating investigator: (1) when the subject withdraws consent, (2) when the subject moves to a new hospital due to relocation or stops coming to the hospital for other reasons, (3) when a subject develops symptoms that make it undesirable to start the study, (4) if it is discovered after the start of the study that the subject does not meet the selection criteria or violates the exclusion criteria, and (5) when it is determined that continuation of the study is not desirable for the patient for any other reason.When the decision is made to terminate the study, the coordinators first report the decision to the steering committee, and if a change in protocol is involved, a breach report is also submitted. The steering committee then notifies the sponsor and the coordinators of each university.The study coordinators and dentists at each university will identify the patients using registration numbers and initials during the duration of data preparation and handling. They will also take into consideration the protection of patient confidentiality. All of the personnel involved in this study will not be permitted to divulge, without justifiable cause, any confidential information about the individuals involved in the study.The collected data from each university will be gathered and stored at the coordinating center . The chief coordinator will review the collected data, after which the coordinator will perform an interim analysis and then a final analysis. Based on the results of the interim analysis and the status of data accumulation, the coordinating center will decide on the formal termination of the trial.All materials used in the study will be stored for 3 years after the completion or discontinuation of the study. In principle, access to the final clinical trial data is limited to the coordinator, but any data required to support the protocol can be supplied on request. This trial does not involve collecting biological specimens for storage.Normality will be confirmed by the Kolmogorov–Smirnov test, and either parametric or nonparametric tests will be performed.t-test or Mann–Whitney U test, depending on the distribution of the outcome.Between-group comparisons will be made using Student’s Within-group comparisons will be performed using the paired t-test or Wilcoxon's signed-rank test, depending on the distribution of the outcome.Chi-square test will be used to test proportions.The significance level will be set at 5% on both sides, and p-values will be adjusted by Bonferroni’s correction to account for multiplicity when more than three groups are compared.All the analyses will be performed in accordance with the intention-to-treat principle, and various strategies to account for any post-randomization missing data will be contemplated. The statistical analyses will be performed as follows:Subgroup analyses will also be performed among the different subpopulations defined by each of the multiple baseline characteristics of the participants in order to study whether the effects of the intervention vary across patient characteristics.The efficacy of single-implant mandibular overdentures to stabilize CDs in edentulous patients has been noted recently . This foIn clinical dentistry, the supplementary use of RL materials in the postoperative management of CD wearers has already been recognized –26. SincIn Japan’s rapidly aging society, many elderly CD wearers are unable to visit the dentist's office, despite the fact that poor denture quality can affect their quality of life. The application of RL materials can help some of these patients. However, the lack of sufficient evidence on RL materials has made dentists hesitant to recommend or apply them.This clinical study has several limitations. First, both the patient and the dentist cannot be blinded to the dental treatment due to its nature. In this study design, it is not possible to conduct a double-blind study. Second, the results obtained have limited generalizability because the sample includes only patients who are able to visit the hospital. Since the external validity of the evidence is lacking as the society further ages, we believe it is necessary to conduct a clinical trial in a new population.The RCT will provide dentists and prosthetic educators evidence regarding the effectiveness of RL materials for edentulous patients. We believe that the evidence will liberate dentists from the conventional notion that RL materials cause more harm than benefit, and will yield useful information for selecting treatment options for edentulous patients with ill-fitting CDs.The trial study period is January 2021–March 2024 (registration period until September 2023). At the time of manuscript submission , patient recruitment is ongoing.The obtained data will be presented at relevant scientific conferences and submitted to academic journals to help develop diagnostic and therapeutic guidelines for the selection and use of resilient liners.