Riccardo Finotello
email: riccardo.finotello@cea.fr

Mohamed Tamaazousti

Jean-Baptiste Sirven

HyperPCA: a Powerful Tool to Extract Elemental Maps from Noisy Data Obtained in LIBS Mapping of Materials -Supplementary Material

Technical Background

We briefly recall the definitions and properties of the Principal Components Analysis (PCA) and the Discrete Wavelet Transform (DWT) used in the main paper.

Principal Components Analysis

Let Y ∈ R n×p be a data matrix with zero sample mean whose vector components y (i) ∈ R p for i = 1, 2, . . . , n represent the observed data. The principal components (PCs) are an orthonormal basis of vectors w (i) i=1,2,...,p representing the directions of the lines which best fit the data. PCA [START_REF] Pearson | On lines and planes of closest fit to systems of points in space[END_REF] is a statistical and unsupervised machine learning tool to compute the PCs and use them to "rotate" the data in a suitable basis, where the sample variance is the largest.

Mathematically, PCA is a linear map realized as the right multiplication between matrices:

PCA: R n×p → R n×p Y → Y = Y W (1)
where W ∈ R p×p contains the PCs as columns, such that W T W = 1, that is W is an orthogonal transformation W ∈ O(p). By its definition, the first component w (1) ∈ R p of W is the direction which maximizes the variance of the transformed data. Let y (i) (1) = y (i) • w (1) for i = 1, 2, . . . , n be the rotated components of the new data vectors, then the PCA computation translates into the maximization problem:

w (1) = arg max ||w||=1 1 n -1 n i=1 y (i) (1) 2 = arg max w w T Cw w T w , (2)
where C = 1 n-1 Y T Y ∈ R p×p is the sample covariance matrix of the data. The last line is known as Rayleigh quotient. The solution to the extremization problem is given if w [START_REF] Pearson | On lines and planes of closest fit to systems of points in space[END_REF] is the eigenvector of C corresponding to the largest eigenvalue (see the Rayleigh-Ritz theorem [START_REF] Horn | Matrix analysis[END_REF]). Other components w (k) for k > 1 can be computed through the iterated application of this procedure after the deflation of the original matrix:

Y (k) = Y - k-1 s=1 Y w (s) w T (s) . (3)
The k-th PC w (k) ∈ R p is given by the eigenvector associated to the largest eigenvalue of

C (k) = 1 n -1 Y (k) T Y (k) . (4)
In general, the PCs are represented by the orthonormal basis of the eigenvectors of C, ordered by explained variance, that is the value of the corresponding eigenvalues.

In the main paper, we start from individual spectra x (i) ∈ R p for i = 1, 2 . . . , n. The procedure is then as follows:

1. the mean intensity x = 1 n-1 n i=1 x (i) ∈ R p is computed across all spectra, i.e. column-wise, and it is subtracted to the original data vectors. Vectors y (i) = x (i) -x ∈ R p for i = 1, 2, . . . , n are defined, and the corresponding centred matrix Y = y (i) i∈ [1,n] is constructed; 2. in order to estimate the directions of maximal variance, eigenvalues and eigenvectors of the sample covariance matrix C = 1 n-1 Y T Y ∈ R p×p are then computed and collected into the diagonal matrix of the eigenvalues Λ ∈ R p×p and the eigenvectors matrix W ∈ R p×p . Each column vector w (i) of the orthonormal matrix W is the eigenvector associated to i-th eigenvalue; 3. the data matrix is projected onto its loadings, L = Λ

1 2 W T ∈ R p×p , and scores, S = Y W Λ -1 2 ∈ R n×p , such that Y = S L.
The newly constructed representation contains the PCs as columns of the scores, ordered by the fraction of total variance explained, and the projection coefficients as rows of the loadings.

Wavelet Transforms

In signal processing, a Fourier Transform (FT) is able to extract the frequency content of a signal, but it loses information related to the localization of the frequency in the original domain. A Short-Time FT (STFT) can instead be used to partially reinstate the ability to localize the frequency components by scanning the signal with a window function w:

F STFT [f | w] (ω, t) = R dτ f (τ)w(t -τ)e -iωτ .
(

) 5
The window considered is such to always consider intervals of the same length in the original domain: the number of sampled data points used to scan higher and lower frequencies is the same, thus losing the ability of a multi-level resolution. Wavelets are square-integrable real function of real variables ψ ∈ L 2 (R) which have been introduced to adapt the width of the slice of the domain to the frequencies which are analysed (see [START_REF] Daubechies | Ten Lectures on Wavelets[END_REF][START_REF] Williams | Introduction to wavelets in engineering[END_REF] for more details):

F WT [f | ψ] (s, t) = R dτ f (τ)ψ t -τ s , ( 6
)
where s is the scale factor to adjust the extension of the window, and t represents the localization parameter.

Given their ability to adjust the size of the scanned portion of the domain, wavelets can be used to construct multi-level representations of a signal f ∈ L 2 (R) at various levels of resolution. A multi-level basis of wavelets is built by considering a sequence of vector spaces:

{0} ⊂ • • • ⊂ V -1 ⊂ V 0 ⊂ V 1 ⊂ • • • ⊂ L 2 (R), (7)
such that i∈Z V i is dense in L 2 (R), and

i∈Z V i = {0}. Moreover, g(x) ∈ V i ⇔ g(2x) ∈ V i+1 . Thus, V m = span{g(2 m x -k), k ∈ Z},
where the overline indicates the closure of the set. An orthonormal basis of V m can be defined as the family of functions

ψ m,k (x) = 2 m 2 ψ(2 m x -k) k∈Z , (8)
where m is also referred as the scale parameter. At any given value of m, a square integrable function f can be approximated in the space V m by projection onto the newly defined basis:

P m [f ] (x) = ∞ k=-∞ a m,k ψ m,k (x), (9)
where {a m,k } k∈Z are the Approximation Coefficients (ACs) of f . Let now W m be the orthogonal complement of V m such that V m = V m-1 W m-1 , and let

φ m,k (x) = 2 m 2 φ(2 m x -k) k∈Z (10)
be a basis of W m . We then have the relation

P m [f ] = P m-1 [f ] + Q m-1 [f ] , (11)
where Q m is the projection operator onto W m :

Q m [f ] (x) = ∞ k=-∞ d m,k φ m,k (x). (12)
Coefficients {d m,k } k∈Z are the Detail Coefficients (DCs) needed in order to move from one level of decomposition to the next. Relation (11) can be chained leading to:

P m [f ] t = P m-t [f ] + k n=1 Q m-t [f ] . (13)
The equation shows the decomposition of the original signal at a given scale m in terms of lower scales, indexed by t: the original decomposition at scale m can be entirely recovered by considering the decomposition at level m -t together with all the DCs from scale m -t to m. In this sense, no information is lost.

A Discrete Wavelet Transform (DWT) is a particular wavelet transform in which the signal S and the basis functions are discretely sampled, and possibly finite. At a given scale m, they effectively act as passband filters on the original signal with the DCs, representing the projection (9), being constructed by a high-pass filter h m,k [n] = h m [n -k] related to φ m,k , and the ACs, representing (12), being the result of the convolution with a low-pass filter m,k

[n] = m [n -k] connected to ψ m,k : d m,k = d m [k] = +L q=-L S[q] h m,q [k], a m,k = a m [k] = +L q=-L S[q] m,q [k], (14)
where L may be theoretically very large. The procedure can then be repeated on the ACs to further increase the frequency resolution as in (13). As for each successive iteration of the filter bank the frequency domain is halved as well as the original domain, the effect of the DWT is to subsample the signal at each scale m. According to the length of the support of S and the wavelet profile considered, the number of decomposition layers which can be computed changes.

Determination of the Signal-to-noise Ratio in Synthetic Datasets

As shown in Section 3.1 of the main text, synthetic spectra are generated from the theoretical distribution in local thermodynamical equilibrium, with the addition of stochastic noise. Wavelength channels which do not contain any signal, i.e. pure noise channels, are defined as:

x [n] (i) = |β N |, i = 1, 2, . . . , n, n = 1, 2, . . . , n λ , ( 15
)
where β is uniformly distributed in the interval [0, β max ], and N follows a standard normal distribution. In order to determine the signal-to-noise (SNR), we need to compute the expected value and the population variance of x

[n] (i) in the pure noise channels of the synthetic spectra. This is possible through the computation of the moments of |β| and |N |:

E[|β|] = E[β] = 1 β max βmax 0 dt t = β max 2 , ( 16
)
Var(|β|) = Var(β) = 1 β max βmax 0 dt (t -E[β]) 2 = β 2 max 12 , (17)
and

E[|N |] = 1 √ 2π +∞ -∞ dt |t| e -t 2 2 = 2 π +∞ 0 dt t e -t 2 2 = 2 π +∞ 0 du e -u = 2 π , (18)
Var(|N |) = 1 √ 2π +∞ -∞ dt (|t| -E[|N |]) 2 e -t 2 2 = Var(N ) -E[|N |] 2 = 1 - 2 π . ( 19
)
Finally, given the fact that the probability density functions of |β| and |N | are respectively independent, we can compute the moments of the distribution of |β N |:

E[|β N |] = E[|β|] E[|N |] = β max √ 2π , Var(|β N |) = E β 2 N 2 -E[|β N |] 2 = E β 2 -E[β] 2 + E[β] 2 E N 2 -E[N ] 2 + E[N ] 2 -E[|β N |] 2 = Var(β) + E[β] 2 Var(N ) - β 2 max 2π = β 2 max 1 3 - 1 2π . ( 20
)
The SNR is defined as

Var(|β N |) - 1
2 , thus:

SNR = 1 β max 6π 2π -3 2.40 β max . ( 21
)

Performance Review of the Proposed Algorithm

In the principal text we showed that HyperPCA performs better than standard PCA. The application of simply one of the two techniques, either DWT or Sparse Kernel-PCA (kSPCA), to the data does not achieve the same results. In this additional section, we show that the combined action of the DWT and the application of kSPCA delivers the best outcome: we study separately the contributions of the two techniques we introduce, and we show how they compare to the current methods and the proposed technique. Namely, we show results using: a. standard PCA applied to the DWT of the original data, b. kSPCA applied directly to the original data, c. kSPCA and PCA applied to the Continuous Wavelet Transform (CWT) of the spectra. As the comparison with standard PCA applied to the data was already provided, showing these results enables a better comparison with alternative treatments.

The expected natural outcome of the application of PCA on the DWT of the original data is the retention of a high degree of noise which might prevent the complete separation of the elements in the dataset: the DWT indeed acts as an initial, weak denoising filter, but the lack of the kernel suppressing small entries in the covariance matrix retains most of the background noise. At the top of Figure 1 we show the PC showing Ca in the "granite" dataset of Section 3.1.3 in the main text. Noise is indeed evidently present in the loading vector, and it prevents the complete separation of Ca from the background distribution. This is a feature which is visible also in other datasets, as it is a general trait of the underlying physical nature of the data. The DWT of the original data is not enough to lead to an improvement of the standard PCA.

We then consider kSPCA applied directly to the data, without any kind of sparsity filter. At the bottom of Figure 1, we show the fifth PC computed on the "granite" dataset. By comparison with Figure 14 in the main text, the PC shows the Al contribution correctly, but the quality of the score matrix is worsened by the remaining noise component. The detection of the element is possible, as the application of the kernel lowers the detection threshold of signal. The lack of a sparse basis to appropriately express the signal, does not allow the kSPCA to reach the same quality as HyperPCA.

The comparison between scores and loadings in Figure 1 shows that the action of the DWT is mostly aimed at modifying the line profile and to highlight the presence of physical signal with respect to the noise. The modification of the eigenvalue distribution puts in evidence the physical line by acting directly on the signal-to-noise ratio, which in the case of kSPCA is almost doubled with respect to the the application of DWT only, which nonetheless has a noise filtering effect, too.

The application of PCA or kSPCA over the CWT of the data opens a different kind of discussion. In this section, we show the results obtained using the best fitting wavelet profile, a Mexican hat continuous wavelet, on the "granite" data. As a general trait, we notice two shortcomings to this approach: a. the creation of strong artefacts, not present in the original dataset, b. the strong modification of the profiles of the emission lines.

In the presence of strong spectral interference, imposing a continuous wavelet profile, negatively impacts neighbouring emission lines, creating strong artefacts, which completely spoil the readability of the loading vectors, as standard PCA on the CWT shows in Figure 2. Imposing the profiles of the continuous wavelet has greatly modified the shape, which becomes rather difficult to interpret as emission spectra. Notice, for instance the characteristic "wells" forming at the base of the lines. Even the coupling of CWT with the kSPCA approach, shown in Figure 3, suffers from the same drawbacks: in this case, though the score maps present good spatial information, the extracted loading vectors are difficult to read and present too many artefacts to be considered physical.

As shown, the various components of HyperPCA play a complementary role in the outcome. The DWTs are able to provide a sparse representation of the input and, at the same time, reduce part of the high frequency noise, as byproduct of the procedure, while retaining the physical information present in the datasets. At the same time, the kernel-based sparse PCA method helps to distinguish noise components from genuine signal, when the latter is sparse in a certain basis. Only the combination of both aspects in HyperPCA has the ability to automatically distinguish the sources of different signals and to disentangle the background noise, thus behaving as an unsupervised Artificial Intelligence, rather than a simple geometrical transformation of the input.

Small Elemental Map

In this section we highlight a specific effect of HyperPCA addressing the high dimensionality of LIBS mapping datasets. We show that the use of the kernel function is able to solve the theoretical phase transition issue in cases where the number of variables (wavelength channels) is greater than the number of available samples (spectra). In this case, it can be shown that PCA does not correctly reconstruct the directions of maximal variance [START_REF] Baik | Phase transition of the largest eigenvalue for non-null complex sample covariance matrices[END_REF], which ultimately leads to meaningless PCs.

Fe l

Fe l Fe l The proposed dataset is a small LIBS map taken on a 316L stainless steel sample. In Figure 4 we show the reference elemental distributions as 200 µm × 150 µm images, taken at the SEM in a different location with respect to the LIBS map location, for the main elements. The average spectrum recorded during the LIBS mapping experiment is shown in Figure 5. The maps are mostly homogeneous on very small scales and different positions, and thus it is fair to assume the same composition all over the surface. Table 1 shows the relative concentration of the elements in the sample, estimated by EDS.

As the LIBS analysis of the sample is unable to provide the same resolution to detect possible substructures in the sample, we focus on a different aspect of the data analysis. We also do not provide the line intensities charts as they are not relevant to LIBS mapping in this case. We use a 3 µJ laser pulse on a 25 × 25 matrix on the surface, with a resolution of 10 µm in both directions. Craters are 3 µm both in diameter and depth on average. We use a grating with 2400 grooves/mm, centred at 391.914 nm. The goal of the analysis is to show that HyperPCA can extract meaningful information even below the theoretical threshold for extraction associated to PCA: in this case, the data matrix X ∈ R p×n λ is such that n λ > p, that is the number of columns in the dataset exceeds the number of rows. By RMT arguments, it is possible to show that the PCA reconstruction of the PCs is not reliable, and the distance between the true and extracted eigenvectors increases, when the signal-to-noise ratio β -1 √ q = n λ p [START_REF] Baik | Phase transition of the largest eigenvalue for non-null complex sample covariance matrices[END_REF][START_REF] Paul | Asymptotics of sample eigenstructure for a large dimensional spiked covariance model[END_REF]. As in this case q > 1, we may expect issues in the reconstruction of the loading vectors.

In Figure 6 we show the most readable PCs extracted using the standard PCA. The loadings are all affected by a higher noise rate, even though the average spectrum in Figure 5 shows a strong signal presence: PC-1 is mostly unusable for element recognition tasks, while the line profiles in PC-3 and PC-4 present some positively reconstructed lines together with poorly resolved profiles. Although most of the lines in this wavelength range correspond to either Fe or Cr, some of them are almost undistinguishable and hardly resolved. As anticipated, the score matrices do not provide any information on the map, due to the homogeneity of the sample with LIBS mapping resolution.

In Figure 7 we show PCs extracted with HyperPCA as a proof of concept of the proposed pipeline. We use a kernel parameter α = 5.0 × 10 2 and the first level of the bior3.5 wavelet filter banks. We impose a HT of 0.99 times the largest value of the power spectrum of the DCs. Even from the first ranked PC, the impact of the noise components on the resolution of the lines is reduced, thus increasing the quality of the loading vector associated to the largest eigenvalue of the covariance matrix: the profiles of the lines are generally better resolved than in standard PCA. Moreover, the absence of noise components in the first PCs is such to enable the resolution of principal and subdominant emission lines which may create spectral interference, such as the Fe lines in PC-3 at 388.63 nm and 390.65 nm, and PC-6 at 387.86 nm and 390.65 nm. The readability of the PCs is ensured also at low rank, such as PC-38 in the figure, where Cr at 391.92 nm is resolved free of Fe interference, thus also restoring some predictive power to the score map. Even though the quality of the score matrix is reduced by its small size, as the PC isolates Cr, the map can give an indication of the distribution of the element nonetheless. The ratios of the explained variance are also better estimated, such that a better assignment of the reconstruction confidence can be assigned to each PC.

HyperPCA can thus deal also with extreme cases in which the experimental framework may impose strong restrictions, such as small maps, or noisy data, usually defined in RMT as a spiked model where the separation between the largest eigenvalue is not strongly distinct from the bulk. Such situations are usually connected to, but not limited to, cases in which the transition threshold n λ = p is crossed, in which case the noise gains a larger importance with respect to signal. The use of the kernel approach ensures the ability to go beyond the theoretical bounds of standard PCA, thus restoring the readability of the PCs. In addition, beyond the case of LIBS mapping, it is interesting to note that the situation where the number of channels exceeds the number of spectra (n λ > p) is very frequent in conventional LIBS, where PCA is often applied to explore small datasets. As shown in this section, standard PCA is therefore limited if the signal-to-noise ratio is low. HyperPCA provides a relevant alternative to extract more meaningful information.

Complementary Figures

  PCA on the DWT of the "granite" dataset.Ca l(b) kSPCA on the "granite" dataset (Ca emission line).

Figure 1 :

 1 Figure 1: Different analyses of the "granite" dataset.

Figure 2 :

 2 Figure 2: PCA on the CWT of the "granite" data.

Figure 3 :

 3 Figure 3: kSPCA on the CWT of the "granite" data.

6

 6

Figure 4 :

 4 Figure 4: Distributions of the main elements in the 316L stainless steel.

Figure 5 :

 5 Figure 5: Preprocessed average and 1σ spectra with an example of single-shot spectrum of the 316L stainless steel sample.

Figure 6 :Figure 7 :

 67 Figure 6: Reconstruction of the 316L stainless steel sample using standard PCA.

Figure 8 :Figure 9 :Figure 11 :Figure 12 :Figure 13 :Figure 14 :Figure 15 :

 891112131415 Figure 8: NiAu spectra in LTE.

Figure 16 :

 16 Figure 16: "Granite" spectra in LTE.

Figure 17 :

 17 Figure 17: First PC of the "granite" dataset computed using HyperPCA: the noise present in the dataset deeply spoils the first PC of both PCA and HyperPCA rendering it mostly unusable.

Figure 18 :Figure 19 :Figure 20 :

 181920 Figure 18: Example spectra in the "granite" dataset which illustrate the difficulty in extracting the signal due to the high noise level.

Table 1 :

 1 Concentration of elements in the stainless steel.

		concentration
	Fe	0.68
	Cr	0.17
	Ni	0.10
	Mo	0.02
	C	0.02
	Si	0.01


Mathieu Balaguer
email: mathieu.balaguer@irit.fr

Timothy Pommée

Julien Pinquier

Jérôme Farinas

Virginie Woisard

Florence Sordes

the constitution of social circles (ECSC)' in patients treated for cancer of the upper aerodigestive tract

Keywords: social circles, oncology, assessment, speech, communication

Background

The International Classification of Functioning, Disability and Health developed by the World Health Organization in 2001 [START_REF]International Classification of Functioning, Disability and Health : ICF[END_REF] suggests looking beyond the impairment to the functional (activity limitations) and psychosocial consequences (participation restrictions) of pathologies. In this classification, personal and environmental factors can impact functional and psychosocial levels. Social support is one of these environmental factors. Social circles (family, friends, caregivers, etc.) have a facilitating or degrading role on the dynamics of activities and participation. Moreover, care strategies now require considering activity limitations and participation restrictions. Caregivers are increasingly involved in the therapeutic protocol, and therapeutic strategies must be adapted to the chronicity of the disease. In 1995, Wilson [START_REF] Wilson | Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes[END_REF] presented a bio-psycho-social model through which the different levels involved in functional and psychosocial dynamics can be clarified, and any causal relations that may exist between biological and physiological factors, symptomatic status, functional status, general health perceptions and overall Quality of Life (QOL) can be established. Overall QOL is defined as overall general well-being and personal development assessed objectively and subjectively in physical, material, social and emotional dimensions, weighted by a set of personal values [START_REF] Felce | Quality of life: Its definition and measurement[END_REF][START_REF] Karimi | Health, Health-Related Quality of Life, and Quality of Life: What is the Difference?[END_REF]. Health-related QOL refers to how one functions in life and perceives well-being in the physical, mental, and social domains of health specifically [START_REF] Hays | Measurement and Modeling of Health-Related Quality of Life[END_REF][START_REF] Torrance | Utility approach to measuring health-related quality of life[END_REF][START_REF] Ebrahim | Clinical and public health perspectives and applications of health-related quality of life measurement[END_REF]. Therefore, the characteristics of individuals and their environment, such as the patient's values or social support system, can modify functional status and the general perception of health, which are intermediate factors between biological and physiological variables and QOL. Many subjective tools have been developed to allow patients to self-assess their symptomatic or functional status (e.g. Voice Handicap Index [START_REF] Jacobson | The Voice Handicap Index (VHI)[END_REF] or Speech Handicap Index [9]) and the perception of QOL (EORTC QLQ-C30 and H&N35 [START_REF] Bjordal | Quality of Life in Head and Neck Cancer Patients: Validation of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-H&N35[END_REF][START_REF] Groenvold | Validation of the EORTC QLQ-C30 quality of life questionnaire through combined qualitative and quantitative assessment of patient-observer agreement[END_REF] and FACT-G [START_REF] Cella | The Functional Assessment of Cancer Therapy scale: development and validation of the general measure[END_REF]). However, the characteristics of the patient's social environment are less explored in healthcare contexts.

In upper aerodigestive tract (UADT) oncology, many studies focus on the QOL of patients [START_REF] Murphy | Quality of life research in head and neck cancer: A review of the current state of the science[END_REF] in terms of associated impairments, treatment [START_REF] Rathod | A systematic review of quality of life in head and neck cancer treated with surgery with or without adjuvant treatment[END_REF] or peer-to-peer support [START_REF] Hatton | A systematic scoping review to map the evidence for peer-to-peer support in patients with Head and Neck cancer on quality of life[END_REF]. However, few consider the influence that the constitution of social circles around the patient can have on the relationship between cancer and QOL, with regards to social support [START_REF] Wang | The relationship between social support and quality of life: Evidence from a prospective study in chinese patients with esophageal carcinoma[END_REF] or social well-being [START_REF] Bilal | Quality of life associated factors in head and cancer patients in a developing country using the FACT-H&N[END_REF]. A social circle is defined as a set of people with connections, "common ground" and norms that are mutually recognized as shared [START_REF] Bidart | La vie en Réseau[END_REF]. Although the relationships within the different social circles and with QOL are complex as shown by the biopsychosocial models [START_REF] Wilson | Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes[END_REF], psychological, cognitive and general health factors (associated diseases, cancer risk factors and the functional impact of ENT cancer pathology on communication) must be included in the determination of activity limitations and participation restrictions. Psychological status is linked to the formation of social circles [START_REF] Kawachi | Social Ties and Mental Health[END_REF]. Weak social relationships increase the level of psychological distress and decrease the motivation to take care of oneself [START_REF] Kroenke | A conceptual model of social networks and mechanisms of cancer mortality, and potential strategies to improve survival[END_REF]. Social isolation increases the risk of depression, while this risk is decreased by social support [START_REF] Pearlin | Marital Status, Life-Strains and Depression[END_REF]. Fratiglioni [START_REF] Fratiglioni | Influence of social network on occurrence of dementia: a community-based longitudinal study[END_REF] showed that at the cognitive level, the risk of developing dementia is higher in isolated individuals and those who live alone and are unmarried. General health is perceived differently depending on the size of the formal support network and the individual's satisfaction with this network [START_REF] Payne | Assessing structure and characteristics of social networks among cancer survivors: impact on general health[END_REF]. For instance, the risk of colds is increased in individuals with a reduced social network [START_REF] Cohen | Social ties and susceptibility to the common cold[END_REF]. Even exposure to the risk factors of ENT cancer is linked to the individual's social network. Smoking [START_REF] Cohen | Social ties and susceptibility to the common cold[END_REF] and alcohol consumption [START_REF] Mowbray | Social networks and alcohol use disorders: Findings from a nationally representative sample[END_REF] are also higher in individuals with reduced social circles. With regards to functional limitations, perceived social support affects patients' ability to communicate with their peers [START_REF] Eadie | Role of Psychosocial Factors on Communicative Participation among Survivors of Head and Neck Cancer[END_REF]. Speech and communication also impact on social dynamics and social isolation is positively correlated with withdrawal from situations involving communication due to poor speech intelligibility, especially for laryngectomized patients [START_REF] Danker | Social withdrawal after laryngectomy[END_REF]. Finally, socially isolated women report a lower QOL (age-adjusted models and multivariate-adjusted models) [START_REF] Kroenke | Social networks, social support mechanismes, and quality of life after breast cancer diagnosis[END_REF]. In ENT oncology specifically, social support is positively correlated with speech-related QOL [START_REF] Karnell | Assessing head and neck cancer patient outcome domains[END_REF].

Therefore, it seems to be essential to consider the constitution of social circles of patients in oncology. Social circles can have an impact on functional limitations which include communication skills on one hand, and QOL on the other.

Various questionnaires are available to assess social network. For example, there are questionnaires on supportive behavior in anxiety situations in university students [START_REF] St-Jean-Trudel | Elaboration and validation of a questionnaire measuring social support in situations of anxiety with a population of university students[END_REF], on the satisfaction and availability of social support in healthy adults (such as the SSQ6) [START_REF] Bruchon-Schweitzer | Le questionnaire de soutien social de Sarason (SSQ6). Une adaptation française[END_REF], and on social adjustment [START_REF] Weissman | The Assessment of Social Adjustment[END_REF]. Another focuses on the frequency with which different relationships are used to fulfill the functions of three behavioral systems; attachment, care and affiliation, in 200 adolescents [START_REF] Furman | Methods and Measures: The Network of Relationships Inventory: Behavioral Systems Version[END_REF]. Other questionnaires, such as Blackstone's Communication inventory, provide information about the social environment of people with communication needs but are not developed as tests [START_REF] Blackstone | Social Networks: A communication inventory for individuals with severe communication challenges and their communication partners[END_REF]. Finally, one questionnaire measures the frequency of participation and satisfaction with social behaviors of an adult psychiatric population in different types of daily activities [START_REF] Zanello | Validation du questionnaire de fonctionnement social (QFS), un autoquestionnaire mesurant la fréquence et la satisfaction des comportements sociaux d'une population adulte psychiatrique[END_REF]. However, these questionnaires have two limitations. On one hand, they are not validated in a clinical context in diseased populations, except for the Social Functioning Questionnaire [START_REF] Zanello | Validation du questionnaire de fonctionnement social (QFS), un autoquestionnaire mesurant la fréquence et la satisfaction des comportements sociaux d'une population adulte psychiatrique[END_REF]. On the other hand, none of them specifically evaluates the constitution of social circles in terms of the size of each circle, the frequency of relations with each circle, and the satisfaction with the frequency and quality of exchanges with each circle. However, not only does the constitution of social circles influence subjects' communication needs, but it also contributes to their psychosocial dynamics in terms of interpersonal relationships [START_REF] Sprecher | Social Networks and Change in Personal Relationships[END_REF].

The objective of this study is to develop a questionnaire that assesses the constitution of social circles in a population treated for UADT cancer and to analyze the construct and criterion validity in patients in this population.

Methods

Design

This is a cross-sectional observational questionnaire validation study, with controls. The study protocol was approved by the Committee for the Protection of Persons (CPP: Ouest IV, 19/02/2020, reference 11/20_3) within the framework of the ANR RUGBI project 1 . This study was conducted in two steps: development and then validation of the Evaluation of the Constitution of Social Circles (ECSC).

Development of the ECSC

An expert committee was formed to develop the ECSC, which included two speech therapists, a phoniatrician qualified in language sciences, two computer science researchers and a social scientist/psychology researcher (see supplementary material). The experts were selected according to their academic disciplines which provide complementary perspectives on the social and clinical aspects of social circles and on data processing. All the experts are also involved in multiple research projects related to QOL in their respective fields of expertise. Multiple focus groups were led with these experts to reach a consensus on two subjects: (1) the definition of social circles and (2) the criteria to be evaluated for each social circle.

Defining social circles

The definition provided by Bidart et al. [START_REF] Bidart | La vie en Réseau[END_REF] was given to the experts initially: a social circle is a set of people, with connections, "common ground" and norms that are mutually recognized as shared. Based on that definition, six social circles were selected by the expert committee and defined in French. Three were part of the patient's close network: the private circle (people with whom one lives every day), family (family who is not necessarily seen on a daily basis but with whom regular contact is maintained), and friends (people who are liked, whom one knows well, and with whom one has close regular contact). The other three circles are more distant individuals: acquaintances (people one has met, with whom one has regular contact but no special relationship, or about whom one knows little), caregivers and community (people one knows, whom one greets, with whom one shares common interests but have no special relationship), and strangers. These definitions are close to those proposed by Blackstone [START_REF] Blackstone | Interaction with the partners of AAC consumers: Part I -Interaction[END_REF], with clarification of some circles. The Family circle proposed by Blackstone is segmented into two separate circles to differentiate between the everyday family and close family who are not seen on a daily basis. The other circles are very similar: Blackstone's Friends, Acquaintances, Paid professionals and Unfamiliar partners correspond respectively to circles of Acquaintances, Caregivers and community, and Strangers according to the ECSC.

Choice of ECSC criteria

Four evaluation criteria, based on the QFS questionnaire model [START_REF] Zanello | Validation du questionnaire de fonctionnement social (QFS), un autoquestionnaire mesurant la fréquence et la satisfaction des comportements sociaux d'une population adulte psychiatrique[END_REF] in terms of frequency and satisfaction assessment, were also defined by the expert committee. The QFS assesses social functioning in eight dimensions of daily living (activities, daily tasks, hobbies, family and intimate relationships, extrafamilial relationships, financial and administrative management, general health and community life). For each of these dimensions, two questions are asked: one related to the frequency of participation in these dimensions, the other to satisfaction with the participation. Each question is constructed on a Likert-like scale, from 5 to 0 (for frequency, 5 = every day to 0 = never, for satisfaction, 5 = very satisfied to 0 = very unsatisfied). The objective of the questionnaire was to be able [START_REF]International Classification of Functioning, Disability and Health : ICF[END_REF] to quantify the size and frequency of contact of each social circle and (2) to establish the satisfaction with the exchanges with the members of these different circles. Four evaluation criteria were chosen by the experts: two related to the constitution of social circles (number and frequency), the other two related to the satisfaction with the exchanges and contact with the members of these social circles. Therefore, the first criterion concerns the number of people considered to be part of the circle (see column A, supplementary material). The second criterion concerns the frequency of contact (column B). The third concerns overall satisfaction with the frequency of contact (column C). The last criterion concerns overall satisfaction with the exchanges with the people in the different circles (column D).

Scoring procedures for the ECSC criteria

Two of the four evaluation criteria were measured on a frequency scale (number of people in each circle and the frequency of contact). The number of people in each circle (column A) was assessed for the private, family, and friend circles. No quantification was requested for the other social circles due to the difficulty in achieving reliability. For the private circle, four responses were proposed: 0, 1, 2 to 4, or 5 and more. For family and friends, five responses were proposed: 0, 1, 2 to 4, 5 to 9, or 10 and more. The score assigned to this criterion corresponds to the first value of the proposed response (i.e. 0, 1, 2, 5, or 10). The frequency of contact (column B) was measured for all six circles on a five-point scale: every day (score = 5), several times a week (4), several times a month (3), several times a year (2), once a year or less [START_REF]International Classification of Functioning, Disability and Health : ICF[END_REF].

The other two criteria were related to perceived satisfaction and were measured for all six social circles. The satisfaction criterion regarding the frequency of contacts with each social circle (column C) was evaluated on an 11-point Likert scale, ranging from -5 (frequency estimated as too low), to +5 (frequency estimated as too high), with 0 considered a neutral value. The more the value deviates from 0, the lower the satisfaction (frequency too low if leaning towards the negative, or too high if leaning towards the positive). The last criterion, which is related to the satisfaction of exchanges with each social circle (column D), was evaluated on an 11-point Likert scale, from -5 (very unsatisfactory exchanges), to +5 (very satisfactory exchanges), with 0 for neutral. Note that, unlike column C, the change in value is linear. For the two criteria related to satisfaction, the score assigned for the evaluation corresponds to the value of the item checked by the participant.

Validating the ECSC

Participants

To validate the ECSC, patients coming for consultation or hospitalization in an ENT rehabilitation service or in an ENT service were included. They were recruited by the ENT or medical staff. In addition, associations of laryngectomized patients were contacted to distribute the questionnaire to their members who met the inclusion criteria. The inclusion criteria were: being of legal age (at least 18 years old) and having been treated for cancer of the oral cavity, oropharynx, hypopharynx, or larynx (surgical treatment and/or radiotherapy and/or chemotherapy) for at least six months (stable disorders). Exclusion criteria were: patients with any other associated chronic disease, those who were participating in another research with an ongoing exclusion period, or those who the investigator was unable to provide with information and whose compliance could not be ensured due to impaired physical or psychological health (clinical judgement by the investigator). Controls were recruited on a voluntary basis. Adult subjects with no history of cancer treatment or, in general, no history of chronic disease, were invited to participate. There was no strict matching with the patient population, but attention was paid to the characteristics of the control subjects, in particular their age (for two groups of patients and controls close in age, this being a factor that can influence the constitution of social circles). All subjects (patients and controls) who could be included during the inclusion period (October 2019 -December 2020) were asked to participate in this study. The inclusion period corresponds to the inclusion period of the quality of life work package of the main study (RUGBI project).

Validation study

The validation study was conducted according to COSMIN recommendations [START_REF] Mokkink | The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes[END_REF].

Construct validity was examined by:

evaluating the inter-and intra-circle correlations of each item (structural validity); -comparing performance between patients and controls (clinical validity: hypothesis-testing, i.e, patients with UADT cancer are more likely to be impacted in the constitution of their social circles than healthy subjects).

In the absence of an accurate reference tool to measure the constitution of social circles, several questionnaires were used to examine the criterion validity. All were self-reported. Four items of the Social Functioning Questionnaire (QFS) [START_REF] Zanello | Validation du questionnaire de fonctionnement social (QFS), un autoquestionnaire mesurant la fréquence et la satisfaction des comportements sociaux d'une population adulte psychiatrique[END_REF] (Dimension IV. Family and Couple Relationships: Frequency, Satisfaction; Dimension V. Extra-Familial Relationships: Frequency, Satisfaction) as well as the QFS overall Frequency and Satisfaction scores, were compared to the scores of each ECSC item. The higher the QFS scores, the higher the frequency and satisfaction (same change in score as for the ECSC). In fact, the QFS assesses the frequency and satisfaction of exchanges in two social groups (Family/Couple which corresponds to the Private and Family circles, and extra-familial which may correspond to Friend, Acquaintance, Caregiver and community or Stranger circles). This construction of the QFS enables a comparison of all the items of the ECSC with the QFS. Note that only patients treated for oral cavity or oropharyngeal cancer were asked to complete the QFS due to a deviation in the protocol for laryngeal cancer patients. Due to the association between social isolation and depression [START_REF] Pearlin | Marital Status, Life-Strains and Depression[END_REF], the Hospital Anxiety and Depression Scale [START_REF] Zigmond | The Hospital Anxiety and Depression Scale[END_REF] was used. This 14-item questionnaire gives a sub-score of anxiety from seven items and a sub-score of depression from the seven other items. The higher the score, the more severe the symptomatology (inverse scoring for ECSC). A moderate correlation is expected between HAD and ECSC at least for the anxiety or depression sub-scores. The Phonation Handicap Index [START_REF] Balaguer | Validation of the French Versions of the Speech Handicap Index and the Phonation Handicap Index in Patients Treated for Cancer of the Oral Cavity or Oropharynx[END_REF] is a 15-item questionnaire that assesses speech-related symptomatology, speech impairment and psychosocial impact (inverse scoring for ECSC : the higher the score, the more severe the symptomatology). The EORTC QLQ-H&N35 is a cancer-specific healthrelated QOL questionnaire including a Speech dimension (three items, the higher the score the more severe the symptomatology, similar to ECSC scoring) [START_REF] Aarstad | EORTC Quality of Life Questionnaire Head and Neck (H&N)-35 scores from H&N squamous cell carcinoma patients obtained at diagnosis and at 6, 9 and 12 months following diagnosis predict 10-year overall survival[END_REF]. Both were used to analyze the relationship between speech and social support. As speech affects communication, a link between speech impairment and social circle formation is expected [START_REF] Karnell | Assessing head and neck cancer patient outcome domains[END_REF][START_REF] Karnell | Influence of social support on health-related quality of life outcomes in head and neck cancer[END_REF]. Finally, Global Health Status, Physical Functioning, and Role Functioning scores from the EORTC QLQ-C30 [START_REF] Bjordal | Quality of Life in Head and Neck Cancer Patients: Validation of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-H&N35[END_REF] were compared to the ECSC scores (inverse scoring). The links between QOL and the constitution of social circles [START_REF] Kroenke | Social networks, social support mechanismes, and quality of life after breast cancer diagnosis[END_REF][START_REF] Karnell | Assessing head and neck cancer patient outcome domains[END_REF] led us to use this cancer-specific QOL questionnaire with 30 items.

Statistical analyses

The statistical analyses were carried out using Stata 16. In all analyses, a level of significance at 5% was chosen. The correlation analysis was performed using the following thresholds [START_REF] Mukaka | Statistics corner: A guide to appropriate use of correlation coefficient in medical research[END_REF]: higher than 0.9 (very high correlation), 0.7 to 0.9 (high correlation), 0.5 to 0.7 (moderate correlation), 0.3 to 0.5 (low correlation), below 0.3 (negligible correlation). The correlation coefficients were presented with their p-value.

Results

Participants

Twenty-two patients treated for UADT cancer participated in this study (participation rate: 100%), most of whom were men (15/22, 62%). The mean age was 65.6 years. Nine patients (41%) were treated for laryngeal or hypopharyngeal cancer, 7 (32%) for oral cavity cancer, and 6 (27%) for oropharyngeal cancer.

Twenty-one controls were also included with a mean age of 60.8 years, including 12 women (57%) (Table 1). Patients and controls were not significantly different in terms of age (p=0.11, Mann-Whitney test) and gender (p=0.07, Chi-squared test).

Construct validity: structural validity

Inter-item correlations (Table 2) were mainly low to moderate. This result is consistent with the structure of our questionnaire which is not built with underlying dimensions. Nevertheless, a complementary analysis of inter-item correlations seems to reveal three groups of social circles, within which the correlations are higher. First, moderate to high significant correlations were found between the family and friend circles, particularly regarding the frequency of contact (Family B / Friends B correlation: rs=0.56, p=0.02), satisfaction with frequency (Family C / Friends C correlation: rs=0.77, p<0.01), and satisfaction with exchanges (Family D / Friends D correlation: rs=0.84, p<0.01).

Construct validity: clinical validity (hypothesis-testing)

Details of the scores for each item can be found in Table 3. Clinical validity was analyzed by testing the hypothesis that the constitution of the social circles of patients with UADT cancer is more likely to be impacted than that of healthy subjects. On average, patients treated for UADT cancer had smaller social circles than controls. Patients had fewer people in their private circle on average, although the difference is not statistically significant (1 vs. 1.43 for controls; p=0.24). The number of members in the family and friend circles was significantly lower in patients: an average of 3.41 members in their family versus 6.29 in controls (p<0.01), and 3.5 friends versus 6.43 for controls (p=0.01). Frequency of contact was significantly higher in controls in the private domain (p=0.01). For the family (p=0.85) and friend circles (p=0.43), frequency of contact was also higher in controls but the difference is not statistically significant. However, contact with acquaintances (p=0.65) and caregivers (p=0.46) was more frequent among patients (but the difference is not statistically significant). Satisfaction with exchanges showed higher median scores for controls in all social circles. On average, satisfaction with frequency was also higher for controls for private (with significance: p=0.03), acquaintance and stranger circles, but was just slightly higher for patients for the three other circles (family: 0.02-point difference; friends: 0.11-point difference; caregivers and community: 0.11-point difference). Finally, for all social circles, satisfaction with exchanges was higher in controls, although this difference is not statistically significant.

Criterion validity

Criterion validity was analyzed using correlation analyses (bivariate analyses).

The number of members of the private circle was moderately correlated with the frequency of contact (rs=0.64, p=0.02) and satisfaction with frequency in family circle (rs=0.54, p=0.07, not statistically significant) (Table 4).

The frequency of family contacts on the QFS was moderately correlated with the number of members in the family circle (rs=0.57, p=0.04) with statistical significance. Inversely, satisfaction with family relations on the QFS was moderately correlated with the frequency of family contacts, but this correlation is not statistically significant (rs =-0.56, p=0.07).

The correlation between the frequency of extra-familial relations and the size of the friend circle was significantly high (rs =0.73, p<0,01) and moderate for the frequency of contact with caregivers (rs =0.52, p=0.07, not statistically significant). Finally, the QFS satisfaction sub-score was moderately correlated (statistically significant) with the size of the family (rs =0.68, p=0.01) and friend (rs =0.59, p=0.03) circles. On questionnaires that assess associated impairments (Table 5), the anxiety sub-score had a low correlation with satisfaction with exchanges (family: rs=-0.46, p=0.03, friends: rs=-0.39, p=0.08, not statistically significant), as well as with the frequency of contact with family (rs=0.46, p=0.04, statistically significant) and satisfaction with frequency with friends (rs=-0.33, p=0.08, not statistically significant).

The depression sub-score of the HAD presented the strongest correlations in the private circle (size: rs=-0.30, p=0.16, satisfaction with exchanges: rs=-0.37, p=0.11) and the size of the family circle which was the only significant correlation (rs=-0.41, p=0.05). Satisfaction with exchanges in the private circle was highly correlated with the speech domain of the H&N35 (rs=-0.53, p=0.01), but had only a low correlation with the impairment (rs=-0.44, p=0.05) and functional (rs=-0.39, p=0.09, not statistically significant) sub-scores of the PHI. The frequency of contact with the family also had a low correlation with the speech domain of the H&N35 (rs=0.42, p=0.05) and the psychosocial score of the PHI (rs=0.32, p=0.14, not statistically significant). In addition, in the private circle, satisfaction with frequency had a low correlation with the functional (rs=-0.41, p=0.06) and psychosocial (rs=-0.30, p=0.18, not statistically significant) PHI sub-scores. Satisfaction with exchanges with acquaintances had a low correlation with the impairment (rs=-0.35, p=0.10) and psychosocial (rs=-0.33, p=0.13) sub-scores of the PHI. Finally, only satisfaction with exchanges in the private circle was moderately correlated with social functioning on the EORTC QLQ-C30 (rs=0.56, p=0.01). Although the correlations were not statistically significant, the EORTC QLQ-C30 "social functioning" was moderately correlated with the size (rs=-0.33, p=0.12) and frequency of contacts (rs=-0.41, p=0.06) in the friend circle.

The relation between ECSC scores and QOL assessed by the EORTC QLQ-C30 was low to moderate for the private circle. The size of the private circle and satisfaction with exchanges were systematically associated with overall QOL and specifically with physical and social functioning (rs ≥0.30, p≤0.18). The size of the family circle was moderately correlated with physical functioning (rs =0.58, p=0.04, significant correlation). Frequency of contact with friends showed a low correlation (rs =-0.37, p=0.28, not statistically significant) with social role. Finally, satisfaction with frequency and exchanges with strangers had a low correlation with overall QOL (r≤-0.34, p≥0.08, not statistically significant).

Discussion

Development of the ECSC

The questionnaire for the Evaluation of the Constitution of Social Circles respects the psychometric properties evaluated at this point in patients treated for UADT cancer.

A first point of analysis concerns the scoring of criterion C (satisfaction with frequency). We chose to propose a scale ranging from -5 (frequency too low) to +5 (frequency too high), with an intermediate score of 0 (satisfactory). This score construction enables linear evaluation of frequency. But an adaptation of this score by transforming the raw scores into absolute values would give values ranging from 0 (satisfactory) to +5 (unsatisfactory). Therefore, an examination of this adapted score using absolute values would provide additional information by targeting the notion of satisfaction which would be complementary to the notion of frequency (evaluated by the raw score).

In our study, several results show a non-significant p-value because our small number of participants only provides a low power to highlight significant links. Therefore, we chose to analyze the value of the correlation coefficient as a priority because this indicator is stronger than the p-value and offers more perspectives for interpretation. The value of the coefficient gives the strength and direction of the relation, whereas the p-value only gives information on the significance of relation, considering the constraints due to our limited statistical power.

The analysis of structural validity shows that the items are homogeneous and globally are all related to the evaluated concepts of size, frequency and satisfaction with the constitution of social circles. However, a closer look shows that three groups of social circles appear. The first group includes the family and friend social circles (close relatives). In these two circles, satisfaction with the frequency of contacts and exchanges is linked.

A second group concerns non-close relatives, with a moderate to strong association between satisfaction with exchanges and frequency of contact for three social circles (Acquaintance, Caregiver and community and Stranger). Finally, the last group concerns the private circle, which shows the highest correlations between satisfaction with exchanges and frequency of contact in this circle and QOL or limitations on speech.

Consequently, these results seem to reflect a three-level dynamic concerning patients' interactions with members of their social circles. Major importance is accorded to the satisfaction with links with the private circle, a predominant role to the size of the family and friend circles, and less close interactions with the non-close relative social circle. However, family and friend circles remain very different, with specific interactions in each. Nevertheless, the tendency of family and friend circles to present statistical correlation indicators that encourage their grouping in the same social circle could be explained by the fact that in oncology, the links with friends are sometimes similar to those with close family or even much closer [START_REF] Soum-Pouyalet | Information and support programs for cancer patients' family and friends: A world survey[END_REF]. Therefore, a grouping of the family and friend social circles makes sense in the specific context of our study population, i.e. oncology.

The analysis of clinical validity shows a decrease in average scores in patients compared to controls. As expected, cancer pathology negatively altered the constitution of social circles [START_REF] Sprecher | Social Networks and Change in Personal Relationships[END_REF], although few results appear significant. This lack of statistical significance may be related to our sample size, resulting in a lack of power.

The inverse correlation between satisfaction with family relationships (QFS) and the frequency of family contacts (ECSC) might be explained by the fact that the family circle is smaller for patients and that contact with family members is more highly appreciated due to the lower frequency. This is consistent with the fact that patients are more isolated after cancer disease [START_REF] Adams | Cancer-related loneliness mediates the relationships between social constraints and symptoms among cancer patients[END_REF]. Therefore, it seems that patients have fewer people in their close circles, while more people are in contact with them without developing strong ties. The structuring of patient follow-up with medical care may increase relations with social circles that are less close to the patient.

Regarding criterion validity, the ECSC shows satisfactory correlations with the reference questionnaires. Laryngectomized patients did not complete the QFS. Therefore, the criterion validity analysis only included patients treated for oral or oropharyngeal cancer. However, the anatomical and structural changes induced by laryngectomy (tracheostomy and increased pulmonary secretions) could also modify contact and the quality of the patients' exchanges with their peers. The criterion validity of the ECSC with laryngectomy patients remains to be demonstrated. The correlations with QFS are moderate to high regarding the size of the Private, Family, and Friend social circles. Correlations are moderate on the satisfaction items of the ECSC and the QFS. While the private circle is best correlated with satisfaction with exchanges and overall satisfaction, only low correlations were found with depression and anxiety, contrary to other studies [START_REF] Pearlin | Marital Status, Life-Strains and Depression[END_REF].

Finally, the analysis of criterion validity highlights the need to assess each social circle. The Private, Family, and Friend circles are related to anxiety/depression scores, as well as functional performance (social on the EORTC QLQ-C30, and speech on the H&N35). The satisfaction of exchanges with acquaintances is linked to performance for the perceived speech impairment. Finally, overall QOL is linked to satisfaction with the frequency of contact and exchanges with strangers.

Limitations and perspectives

This study does have some limitations. First, due to our small sample size, the results of this study can only be considered as preliminary.

Although a priori there is no consensus on how to calculate sample size in validation studies for questionnaires [START_REF] Anthoine | Sample size used to validate a scale: a review of publications on newly-developed patient reported outcomes measures[END_REF], an increase in the sample size would improve the statistical power. The inclusion of a larger number of subjects would allow better generalization of the results, and would make it possible to ascertain the reliability of the results presented in this article. Moreover, increasing the sample size would also permit stratified analyses according to tumor location. Oral, oropharyngeal and laryngeal tumors have different consequences on subjects' capacity to communicate, and affect the phonemic or nasal aspects of speech or voice production. An analysis of the impact of voice or speech disorders according to tumor location on the constitution of social circles could then be carried out. Among the factors that can impact the constitution of social circles, age should also be considered. In fact, the constitution of social circles changes with age due to retirement, loss of loved ones, etc. [START_REF] Sprecher | Social Networks and Change in Personal Relationships[END_REF] A close link has been found between the constitution of social circles and age-related frailty, which can lead to social isolation [START_REF] Davies | The longitudinal relationship between loneliness, social isolation, and frailty in older adults in England: a prospective analysis[END_REF]. Therefore, beyond the clinical data, it seems relevant to analyze the results of the ECSC in our population according to age for early detection of frailty which can impact therapeutic follow-up.

We chose not to quantify the number of members among the acquaintance, caregiver and community, and stranger social circles because of the difficulties in providing an accurate or close to accurate quantification. However, further validation of the ECSC could include quantifying members to obtain an estimate of the size of each circle. This quantification could provide information about contacts made with peers at social activities without the establishment of close ties. The limits of the ranges in the ECSC should be carefully chosen so that the estimates are precise enough to be informative, but also broad enough to be reliable.

Focus on the development and examination of the validity of the ECSC would require complementary analyses of the reliability. First, the internal consistency of the items could be examined. Although the ECSC is not constructed in dimensions and therefore does not allow for analysis of the internal consistency by dimension, analysis of the global internal consistency of all the items on the questionnaire seems relevant. It would make it possible to ensure the relevance of the items and possibly to make adaptations, particularly in terms of item formulation.

Moreover, an analysis of test-retest reliability is necessary to ensure the stability of the results over time. Therefore, completion of the ECSC a second time could be proposed after a 7 to 14-day delay if no intervening event that could modify the results has occurred.

Clinical considerations

This study highlights the importance of social circles, especially those closest to the patient (private circle, family, and friends). In fact, the size of these circles is smaller compared to a control population. Moreover, the frequency of contact in the family and friend circles is related to perceived speech impairment (Participation PHI, H&N-35 Speech) and social functioning (EORTC QLQ-C30). The highest correlations, and therefore the strongest links, are between satisfaction with exchanges and perceived speech impairment and social functioning, as well as between global QOL and role functioning.

In this study, all the recruited patients had completed cancer treatment at least six months previously, to ensure stability of the disorders induced by the cancer or the treatment. The variety of treatments and care contexts results in different patient profiles. The objective of the ECSC is to be usable in all patients treated for head and neck cancer. Therefore, the inclusion of these different profiles seems entirely relevant. However, the medical context can impact the constitution of social circles and thereby induce different responses to the ECSC. Therefore, stratified analyses to examine the influence of the type of treatment and the time since treatment on the formation of social circles would appear to be important in order to anticipate the psychosocial repercussions of therapeutic strategies at early stages.

In terms of management, the caregiver and community circles are also essential to consider, as they seem to provide support in the maintenance of patient follow-up, and also in the exchanges between the patient and their circles. Furthermore, it seems important to support social circles at an early stage, at the time of diagnosis and throughout treatment, so that social isolation is minimized as much as possible, and so that the patient can rely on available resources daily (both in the private circle and from close relatives and caregivers). This support from social circles is also essential in the prevention of risk factors for the recurrence of cancer [START_REF] Cohen | Social ties and susceptibility to the common cold[END_REF][START_REF] Mowbray | Social networks and alcohol use disorders: Findings from a nationally representative sample[END_REF]. The use of the ECSC in routine clinical practice would provide an inventory of the size of the patient's entourage to be drawn up to identify potential caregivers each day (private circle), as well as to assess the possible supports available and the possible replacements for caregivers (family and friend circles). Moreover, combined with other functional and QOL questionnaires (for example, those already used in routine practice), it could help to explain more precisely each patient's perception of their disability (specifically in terms of communication on the PHI) or psychosocial dimensions of QOL. Therefore, use of the ECSC seems relevant from the very first stages of care (i.e. from diagnosis), but regular use during follow-up would allow the changes in the patient's social dynamics to be assessed. This is in line with the relevance of assessing the test-retest reliability of this questionnaire. Finally, research remains to be carried out on the influence of the constitution of social circles on the answers to the psychosocial dimension items of self-questionnaires. Some studies have shown that the evaluation of a psychosocial dimension in the Patients' Concerns Inventory [START_REF] Rogers | The development of a Patients Concerns Inventory (PCI) to help reveal patients concerns in the head and neck clinic[END_REF] seems to be transversal to the analysis of associated impairments [START_REF] Balaguer | The (head and neck) carcinologic handicap index: validation of a modular type questionnaire and its ability to prioritise patients' needs[END_REF]. Therefore, use of the ECSC would make it possible to weight responses to the psychosocial dimension of Patients Concerns Inventory questionnaires, to more precisely target patients' needs and complaints.

Although we explored the links between social circles and functional speech impairment, it would be relevant to study any link that may exist between social circles and the other functions of the aerodigestive tract. In fact, an alteration in the capacity to swallow could be linked to the constitution of social circles as such disorders often hinder food intake. Adaptation of food consistency and coughing sometimes lead to social withdrawal of the patient during mealtimes [START_REF] Ekberg | Social and Psychological Burden of Dysphagia: Its Impact on Diagnosis and Treatment[END_REF].

Finally, in terms of management, the constitution of social circles can be considered in different pathological contexts. Patients with speech disorders in ENT oncology or patients with neurological pathologies who present with dysarthria or aphasia will have different communication needs according to the strength of their social circles. Therefore, the therapeutic strategy should be adapted to the patient's context of communication so that it is as close as possible to their daily life. Moreover, in these pathological contexts, the extension of this study to the primary caregiver of an aphasic or a Parkinsonian patient would make it possible to evaluate the social dynamics in terms of the dyad and not only at the patient level, as well as allow satisfaction to be measured regarding the constitution of social circles in a bilateral patient/caregiver perspective.

Conclusion

The preliminary results of this study show that the ECSC presents adequate performances in terms of validity. More analyses on a larger sample, and a reliability study are still required to ensure greater generalizability of the results. The ECSC can be used in routine clinical practice to assess the social dynamics related to patients in terms of the constitution of their social circles. This would enable the adaptation of therapeutic strategies to the human resources available to the patient, and also to prevent social isolation and functional consequences.

Lastly, a broader study should be conducted on the influence of the constitution of social circles on other chronic pathologies, such as stroke and neurodegenerative pathologies.

Statements

Acknowledgement: The authors wish to acknowledge Clémentine Valiente and Anaïs Cance, SLP students, for collecting data.

Statement of Ethics:

All procedures were performed in accordance with the 1964 Helsinki declaration and its amendments or comparable ethical standards.

Study approval statement:

This study protocol was reviewed and approved by the Committee for the Protection of Persons within the framework of the ANR RUGBI project, approval number [CPP: Ouest IV, 19/02/2020, reference 11/20_3]. Consent to participate: Each subject was told in advance of the purpose of this study and was given an information sheet. Subjects gave their written informed consent (non-opposition).

Conflicts of Interest Statement: All authors declare that they have no conflict of interest.

Supplementary material

EVALUATION OF THE CONSTITUTION OF SOCIAL CIRCLES ÉVALUATION DE LA CONSTITUTION DES CERCLES SOCIAUX

For each group of people, please indicate:

-The number of people you consider to be part of the group (column A);

-The average frequency with which you have contact with these people (column B);

-How you rate the frequency of your contact with these people: from -5 "too low" (I don't meet them enough) to +5 "too high" (I see them too often) (column C);

-How satisfied you are with the quality of the exchanges you have with these people when you meet them: from -5 "very unsatisfied" to +5 "very satisfied" (column D).

Pour chaque groupe de personnes, veuillez indiquer vous concernant :

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Le

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  nombre de personnes que vous estimez faire partie du groupe concerné (colonne A) ; -La fréquence moyenne à laquelle vous avez des contacts avec ces personnes (colonne B) ; -Comment vous jugez la la fréquence de vos contacts avec ces personnes : de -5 « trop faible » (Je ne les vois pas suffisamment) à +5 « trop élevée » (Je les vois trop souvent à mon goût) (colonne C) ; -Si vous êtes satisfait.e de la qualité des échanges que vous avez avec ces personnes quand vous les voyez : de -5 « très insatisfait » à +5 « très satisfait » (colonne D). several times a month (plusieurs fois par mois) ☐ several times a year (plusieurs fois par an) ☐ once a year or less (une fois par an ou moins) ☐-5: too low (trop faible) ne vois pas forcément tous les jours mais avec qui j'ai des contacts étroits et réguliers. several times a month (plusieurs fois par mois) ☐ several times a year (plusieurs fois par an) ☐ once a year or less (une fois par an ou moins) ☐-5: too low (trop faible) je connais bien et avec qui je me sens bien, avec qui j'ai des liens privilégiés et en qui j'ai confiance. several times a month (plusieurs fois par mois) ☐ several times a year (plusieurs fois par an) ☐ once a year or less (une fois par an ou moins) ☐-5: too low (trop faible) qui je n'ai pas de relation privilégiée (que je connais peu). ☐ every day (tous les jours) several times a month (plusieurs fois par mois) ☐ several times a year (plusieurs fois par an) ☐ once a year or less (une fois par an ou moins) ☐-5: too low (trop faible) côtoie, à qui je dis « bonjour » et « au revoir », avec qui je partage des intérêts communs sans avoir avec eux de relation privilégiée. ☐ every day (tous les jours) several times a month (plusieurs fois par mois) ☐ several times a year (plusieurs fois par an) ☐ once a year or less (une fois par an ou moins) ☐-5: too low (trop faible) several times a month (plusieurs fois par mois) ☐ several times a year (plusieurs fois par an) ☐ once a year or less (une fois par an ou moins) ☐-5: too low (trop faible) very satisfied (très satisfait)

  1 software (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC.).For the descriptive analysis of the patient and control groups, frequency indicators were chosen for the qualitative variables (gender and socio-professional category). Age was described by mean and median as indicators of central tendency, and by standard deviation and interquartile range as indicators of dispersion. Frequency indicators were also used to describe the distribution of tumor location and treatment in patients.

Due to the sample size (n<30), nonparametric statistical tests were used. The characteristics of patients and healthy subjects were compared using a Mann-Whitney test (for age), and a Chi-squared test (for gender). The analysis of construct validity (structural validity) and criterion validity was based on Spearman's correlation coefficients. For clinical validity, the patient/control comparison was assessed with a Mann-Whitney test.

Table 1 :

 1 Characteristics of included subjects in each group (patients and controls) M, Pommée T, Pinquier J, Farinas J, Woisard V, Sordes F. Development and preliminary validation of the questionnaire 'Evaluation of the constitution of social circles (ECSC)' in patients treated for cancer of the upper aerodigestive tract. Folia Phoniatr Logop. 2022. https://doi.org/10.1159/000525352

	Patients (n=22)	Controls
		(n=21)

Balaguer

Table 2 :

 2 Correlation matrix between the scores of the ECSC items for analysis of construct validity (The number corresponds to a circle: 1-Private, 2-Family, 3-Friends, 4-Acquaintances, 5-Caregivers and Community, 6-Strangers. The letter corresponds to a criterion: A-number of members, B-regularity of contact, C-satisfaction with frequency, D-satisfaction with exchanges; in medium gray with white numbers: -0.70<rs<-0.50, in medium gray with black numbers: 0.50<rs<0.70, in dark gray: |rs|≥0.70) -in brackets, p-values of Spearman's correlation coefficients

		1A	1B	1C	1D	2A	2B	2C	2D	3A	3B	3C	3D	4B	4C	4D	5B	5C	5D	6B	6C
	1B	0.58 (0.02)	1.00
	1C	0.17 (0.52)	0.22 (0.41)	1.00
	1D	0.20 (0.45)	0.26 (0.33)	0.67 (<0.01)	1.00
	2A	-0.02 (0.94)	-0.37 (0.16)	0.15 (0.59)	0.21 (0.44)	1.00
	2B	0.23 (0.39)	0.03 (0.92)	-0.15 (0.57)	-0.22 (0.41)	-0.19 (0.48)	1.00
	2C	-0.08 (0.76)	0.04 (0.89)	0.74 (<0.01)	0.41 (0.12)	-0.14 (0.61)	0.31 (0.25)	1.00
	2D	0.11 (0.67)	0.20 (0.44)	0.55 (0.03)	0.65 (0.01)	0.04 (0.87)	0.25 (0.36)	0.69 (<0.01)	1.00
	3A	0.06 (0.82)	-0.25 (0.34)	0.27 (0.32)	-0.14 (0.61)	0.18 (0.52)	0.28 (0.29)	0.29 (0.28)	0.07 (0.79)	1.00
	3B	0.25 (0.36)	0.20 (0.47)	0.04 (0.89)	-0.21 (0.45)	-0.07 (0.80)	0.56 (0.02)	0.31 (0.24)	0.31 (0.25)	0.51 (0.04)	1.00
	3C	0.13 (0.63)	0.08 (0.77)	0.62 (0.01)	0.41 (0.11)	-0.21 (0.44)	0.24 (0.37)	0.77 (<0.01)	0.62 (0.01)	0.03 (0.90)	0.24 (0.37)	1.00
	3D	0.13 (0.62)	0.15 (0.57)	0.49 (0.05)	0.62 (0.01)	-0.17 (0.52)	0.34 (0.20)	0.72 (<0.01)	0.84 (<0.01)	0.11 (0.68)	0.31 (0.24)	0.74 (<0.01)	1.00
	4B	-0.20 (0.46)	-0.51 (0.04)	-0.28 (0.30)	-0.24 (0.37)	-0.20 (0.44)	-0.05 (0.86)	-0.08 (0.74)	-0.13 (0.64)	-0.07 (0.80)	-0.25 (0.34)	-0.05 (0.84)	-0.07 (0.81)	1.00
	4C	-0.30 (0.26)	-0.49 (0.05)	0.01 (0.98)	0.23 (0.38)	0.04 (0.88)	0.31 (0.24)	0.37 (0.16)	0.34 (0.20)	-0.28 (0.29)	-0.15 (0.57)	0.43 (0.09)	0.33 (0.22)	0.36 (0.18)	1.00
	4D	-0.16 (0.56)	-0.53 (0.03)	-0.30 (0.25)	-0.01 (0.98)	0.08 (0.77)	0.22 (0.42)	-0.08 (0.76)	-0.02 (0.93)	0.11 (0.68)	0.18 (0.50)	-0.03 (0.92)	0.03 (0.91)	0.47 (0.07)	0.54 (0.03)	1.00
	5B	-0.21 (0.43)	-0.21 (0.44)	-0.45 (0.08)	-0.30 (0.25)	-0.26 (0.33)	-0.17 (0.53)	-0.30 (0.26)	-0.14 (0.60)	-0.23 (0.38)	0.02 (0.93)	-0.07 (0.78)	-0.04 (0.87)	0.69 (<0.01)	0.06 (0.82)	0.32 (0.23)	1.00
	5C	-0.48 (0.06)	-0.57 (0.02)	-0.33 (0.22)	-0.06 (0.82)	-0.05 (0.85)	0.32 (0.23)	0.20 (0.46)	0.10 (0.70)	-0.16 (0.56)	0.03 (0.90)	0.13 (0.62)	0.17 (0.52)	0.45 (0.08)	0.83 (<0.01)	0.73 (<0.01)	0.28 (0.30)	1.00
	5D	-0.31 (0.24)	-0.38 (0.15)	-0.18 (0.51)	0.22 (0.41)	0.10 (0.71)	0.39 (0.13)	0.22 (0.42)	0.44 (0.09)	-0.12 (0.66)	0.27 (0.32)	0.24 (0.37)	0.47 (0.07)	0.18 (0.51)	0.71 (<0.01)	0.71 (<0.01)	0.21 (0.43)	0.77 (<0.01)	1.00
	6B	0.17 (0.53)	-0.14 (0.62)	0.22 (0.42)	0.25 (0.36)	-0.07 (0.78)	0.03 (0.92)	0.29 (0.27)	0.31 (0.24)	-0.15 (0.59)	0.00 (0.99)	0.50 (0.05)	0.47 (0.07)	0.44 (0.09)	0.34 (0.20)	0.14 (0.62)	0.44 (0.08)	0.15 (0.57)	0.26 (0.32)	1.00
	6C	-0.43 (0.09)	-0.58 (0.02)	-0.01 (0.97)	0.24 (0.37)	0.08 (0.77)	0.23 (0.40)	0.26 (0.34)	0.29 (0.28)	-0.24 (0.38)	-0.28 (0.29)	0.33 (0.21)	0.29 (0.27)	0.41 (0.12)	0.93 (<0.01)	0.51 (0.05)	0.13 (0.63)	0.75 (<0.01)	0.68 (<0.01)	0.26 (0.33)	1.00
	6D	-0.26 (0.32)	-0.42 (0.11)	0.13 (0.63)	0.35 (0.18)	0.11 (0.68)	0.11 (0.68)	0.24 (0.38)	0.37 (0.16)	-0.29 (0.27)	-0.28 (0.29)	0.45 (0.08)	0.27 (0.31)	0.27 (0.31)	0.87 (<0.01)	0.40 (0.13)	0.07 (0.80)	0.55 (0.03)	0.55 (0.03)	0.26 (0.32)	0.93 (<0.01)

Table 3 :

 3 Details of ESCS scores for patients and controls, and p-value (Mann-Whitney test) for analysis of clinical validity

						PATIENTS					CONTROLS			p-
				N	Mean	Standard dev.	Perc. 25	Perc. 50	Perc. 75	N	Mean	Standard dev.	Perc. 25	Perc. 50	Perc. 75	value
		Number		22	1	0.87	0	1	2	21	1.43	1.08	1	1	2	0.24
	Private	Regularity contact Satisfaction with frequency of	20 20	4.15 -0.55	1.46 1.82	4 -1	5 0	5 0	21 21	4.95 0.62	0.22 1.32	5 0	5 0	5 0	0.01 0.03
		Satisfaction with exchanges	19	1.95	2.95	0	2	5	21	2.81	2.52	2	4	5	0.47
		Number		22	3.41	3.03	2	2	5	21	6.29	3.50	2	5	10	<0.01
	Family	Regularity contact Satisfaction with frequency of	21 22	3.10 -0.5	1.09 2.46	3 -2	3 0	4 0	21 21	3.14 -0.48	1.01 2.25	2 -2	3 0	4 0	0.85 0.95
		Satisfaction with exchanges	21	1.38	3.06	0	2	4	21	2.92	2.44	3	3	4	0.10
		Number		22	3.5	2.79	2	2	5	21	6.43	3.37	5	5	10	0.01
	Friends	Regularity contact Satisfaction with frequency of	21 22	2.91 -0.68	1.18 2.63	2 -2	3 0	4 0	21 21	3.1 -0.57	0.54 2.09	3 -2	3 0	3 0	0.43 0.80
		Satisfaction with exchanges	21	0.95	3.53	-2	2	4	21	2.48	2.4	1	3	4	0.24
		Regularity contact	of	22	3	1.11	3	3	4	19	2.74	0.99	2	3	4	0.43
	Acquaintances	Satisfaction with frequency	22	-0.05	1.94	0	0	0	19	0.05	1.87	0	0	0	0.65
		Satisfaction with exchanges	22	0.82	1.87	0	0	2	19	1.58	2.24	0	1	4	0.22
		Regularity contact	of	22	3.5	1.01	3	3.5	4	19	3.26	0.73	3	3	4	0.46
	Caregivers-Community	Satisfaction with frequency	22	0.27	1.42	0	0	0	19	0.16	0.90	0	0	0	0.99
		Satisfaction with exchanges	22	0.96	1.79	0	0	2	19	1.21	2.04	0	0	3	0.71
		Regularity contact	of	19	3.05	1.51	2	3	5	17	3.47	1.33	3	4	4	0.40
	Strangers	Satisfaction with frequency	18	-0.39	1.88	0	0	0	17	0.18	0.81	0	0	0	0.46
		Satisfaction with exchanges	18	-0.44	1.79	0	0	0	17	0.53	1.28	0	0	0	0.07

Table 4 :

 4 Correlations between ECSC and QFS items for analysis of criterion validity (dark gray |rs|≥0.70; medium gray 0.50≤|rs|<0.70; light gray 0.30≤|rs|<0.50) -in brackets, p-value of Spearman's correlation coefficients; in bold, p-values lower than 0.05

						QFS
				Family/Couple-Freq.	Family/Couple-Satisf.	Extra familial rel. -Freq.	Extra-familial rel. -Satisf.	Overall frequency	Overall satisfaction
		Number		0.64 (0.02)	0.54 (0.07)	0.35 (0.23)	0.43 (0.16)	0.30 (0.32)	0.36 (0.22)
		Regularity contact	of	0.15 (0.65)	0.40 (0.24)	0.23 (0.48)	0.17 (0.64)	-0.06 (0.85)	-0.29 (0.38)
	Private	Satisfaction with frequency	0.36 (0.27)	0.44 (0.20)	-0.21 (0.54)	0.39 (0.30)	0.00 (1)	0.28 (0.39)
		Satisfaction with exchanges	0.37 (0.28)	0.17 (0.65)	-0.21 (0.54)	0.39 (0.30)	0.16 (0.65)	0.51 (0.13)
		Number		0.57 (0.04)	0.39 (0.20)	0.33 (0.27)	0.58 (0.04)	0.32 (0.28)	0.68 (0.01)
		Regularity contact	of	0.26 (0.40)	-0.56 (0.07)	0.06 (0.85)	-0.66 (0.02)	0.24 (0.44)	-0.04 (0.90)
	Family	Satisfaction with frequency	0.18 (0.54)	0.14 (0.67)	-0.03 (0.93)	0.12 (0.70)	0.04 (0.91)	0.15 (0.63)
		Satisfaction with exchanges	0.39 (0.20)	0.26 (0.43)	-0.21 (0.51)	0.42 (0.19)	0.11 (0.71)	0.42 (0.17)
		Number		0.43 (0.14)	0.42 (0.17)	0.73 (<0,01) 0.42 (0.19)	0.35 (0.24)	0.59 (0.03)
		Regularity contact	of	-0.09 (0.77)	-0.17 (0.60)	0.49 (0.10)	0.05 (0.88)	-0.15 (0.63)	-0.20 (0.52)
	Friends	Satisfaction with frequency	0.35 (0.23)	0.21 (0.49)	-0.11 (0.70)	0.15 (0.62)	0.05 (0.87)	0.00 (0.98)
		Satisfaction with exchanges	0.47 (0.12)	0.14 (0.66)	0.06 (0.83)	0.31 (0.35)	0.31 (0.32)	0.47 (0.12)
		Regularity contact	of	-0.36 (0.22)	-0.13 (0.67)	0.19 (0.52)	0.00 (1)	-0.04 (0.89)	-0.13 (0.65)
	Acquaintances	Satisfaction with frequency	0.23 (0.44)	0.00 (0.99)	-0.22 (0.46)	-0.06 (0.85)	-0.04 (0.89)	0.00 (0.99)
		Satisfaction with exchanges	-0.02 (0.93)	0.27 (0.38)	0.01 (0.96)	0.56 (0.05)	-0.41 (0.16)	0.09 (0.75)
		Regularity contact	of	-0.22 (0.46)	-0.06 (0.85)	0.52 (0.07)	0.10 (0.74)	0.14 (0.63)	-0.05 (0.85)
	Caregivers-Community	Satisfaction with frequency	-0.01 (0.97)	-0.08 (0.78)	0.13 (0.66)	0.00 (1)	-0.07 (0.80)	0.10 (0.74)
		Satisfaction with exchanges	0.18 (0.55)	-0.04 (0.90)	-0.05 (0.87)	0.30 (0.33)	-0.05 (0.85)	0.24 (0.41)
		Regularity contact	of	0.24 (0.48)	0.13 (0.71)	0.05 (0.88)	0.22 (0.54)	0.07 (0.82)	0.02 (0.95)
	Strangers	Satisfaction with frequency	0.27 (0.45)	-0.07 (0.85)	-0.27 (0.45)	-0.13 (0.73)	0.07 (0.84)	0.00 (1)
		Satisfaction with exchanges	0.22 (0.53)	0.03 (0.92)	-0.30 (0.39)	-0.09 (0.82)	0.01 (0.98)	-0.21 (0.56)

Table 5 :

 5 Correlations between ECSC items and associated deficit scales for analysis of criterion validity (dark gray |rs|≥0.70; medium gray 0.50≤|rs|<0.70; light gray 0.30≤|rs|<0.50) -in brackets, p-value of Spearman's correlation coefficients; in bold, p-values lower than 0.05 Pommée T, Pinquier J, Farinas J, Woisard V, Sordes F. Development and preliminary validation of the questionnaire 'Evaluation of the constitution of social circles (ECSC)' in patients treated for cancer of the upper aerodigestive tract. Folia Phoniatr Logop. 2022. https://doi.org/10.1159/000525352

				HAD	EORTC QLQ-H&N35		PHI			EORTC QLQ-C30
			Anxiety	Depression	Speech	Impairments	Activities	Participation	Social functioning	Global QoL	Physical functioning	Role functioning
		Size	-0.06 (0.76)	-0.30 (0.16)	-0.03 (0.87)	-0.13 (0.53)	-0.06 (0.76)	-0.14 (0.52)	0.20 (0.36)	0.35 (0.10)	0.51 (0.01)	0.30 (0.17)
		Regularity of contact	-0.15 (0.51)	0.10 (0.67)	0.04 (0.84)	-0.09 (0.69)	-0.04 (0.84)	-0.08 (0.73)	-0.01 (0.94)	0.06 (0.78)	0.18 (0.44)	0.17 (0.47)
	Private
		Satisfaction with frequency	-0.26 (0.25)	-0.26 (0.27)	-0.39 (0.08)	-0.22 (0.33)	-0.41 (0.06)	-0.30 (0.18)	0.25 (0.27)	0.14 (0.55)	0.16 (0.49)	0.32 (0.18)
		Satisfaction with exchanges	-0.25 (0.30)	-0.37 (0.11)	-0.53 (0.01)	-0.44 (0.05)	-0.39 (0.09)	-0.26 (0.28)	0.56 (0.01)	0.36 (0.12)	0.31 (0.18)	0.42 (0.07)
		Size	-0.24 (0.27)	-0.41 (0.05)	-0.10 (0.64)	0.13 (0.54)	0.07 (0.75)	0.15 (0.49)	-0.02 (0.91)	0.04 (0.83)	0.58 (<0.01)	0.12 (0.57)
		Regularity of contact	0.46 (0.04)	0.25 (0.27)	0.42 (0.05)	0.20 (0.37)	0.23 (0.30)	0.32 (0.14)	-0.08 (0.71)	-0.09 (0.69)	-0.08 (0.72)	-0.29 (0.21)
	Family
		Satisfaction with frequency	-0.25 (0.26)	0.02 (0.90)	0.03 (0.88)	0.02 (0.89)	-0.09 (0.66)	-0.01 (0.96)	0.005 (0.97)	0.07 (0.75)	-0.06 (0.77)	0.11 (0.63)
		Satisfaction with exchanges	-0.46 (0.03)	-0.12 (0.59)	-0.13 (0.55)	-0.27 (0.22)	-0.27 (0.23)	-0.18 (0.41)	0.18 (0.42)	0.11 (0.63)	0.26 (0.25)	0.18 (0.43)
		Size	-0.04 (0.84)	-0.11 (0.59)	-0.02 (0.90)	-0.009 (0.96)	-0.13 (0.55)	-0.07 (0.73)	-0.33 (0.12)	0.24 (0.26)	0.23 (0.29)	-0.002 (0.99)
		Regularity of contact	-0.04 (0.86)	0.18 (0.41)	0.19 (0.40)	-0.03 (0.87)	-0.06 (0.79)	-0.02 (0.91)	-0.41 (0.06)	-0.04 (0.84)	-0.10 (0.66)	-0.37 (0.10)
	Friends
		Satisfaction with frequency	-0.33 (0.13)	-0.05 (0.81)	0.02 (0.92)	-0.02 (0.91)	-0.14 (0.52)	-0.11 (0.61)	0.10 (0.65)	-0.09 (0.67)	-0.11 (0.62)	0.15 (0.49)
		Satisfaction with exchanges	-0.39 (0.08)	-0.13 (0.56)	-0.07 (0.75)	-0.18 (0.42)	-0.16 (0.47)	-0.09 (0.66)	0.09 (0.67)	0.13 (0.54)	0.04 (0.84)	-0.009 (0.96)
		Regularity of contact	0.11 (0.61)	0.03 (0.86)	0.004 (0.98)	-0.14 (0.52)	-0.02 (0.92)	-0.20 (0.34)	0.16 (0.47)	0.08 (0.69)	-0.10 (0.64)	-0.09 (0.67)
	Acquaintances	Satisfaction with frequency	-0.04 (0.83)	0.05 (0.82)	0.30 (0.17)	0.20 (0.36)	0.26 (0.23)	0.23 (0.30)	0.21 (0.33)	-0.17 (0.42)	-0.05 (0.79)	-0.007 (0.97)
		Satisfaction with exchanges	-0.10 (0.65)	-0.02 (0.92)	-0.05 (0.81)	-0.35 (0.10)	-0.26 (0.24)	-0.33 (0.13)	0.27 (0.21)	0.11 (0.62)	0.07 (0.73)	0.007 (0.97)
		Regularity of contact	-0.08 (0.70)	-0.11 (0.62)	-0.06 (0.76)	-0.01 (0.94)	0.07 (0.74)	-0.08 (0.72)	0.02 (0.90)	-0.12 (0.57)	-0.01 (0.94)	-0.02 (0.92)
	Caregivers-Community	Satisfaction with frequency	0.02 (0.93)	0.13 (0.55)	0.29 (0.18)	0.25 (0.25)	0.26 (0.23)	0.31 (0.15)	0.09 (0.67)	-0.03 (0.86)	-0.10 (0.63)	0.06 (0.77)
		Satisfaction with exchanges	-0.18 (0.40)	-0.15 (0.49)	-0.01 (0.95)	-0.17 (0.42)	-0.07 (0.74)	-0.03 (0.86)	0.30 (0.16)	-0.02 (0.91)	0.06 (0.77)	-0.05 (0.80)
		Regularity of contact	-0.28 (0.24)	-0.13 (0.57)	0.05 (0.81)	-0.14 (0.56)	0.18 (0.45)	-0.10 (0.67)	0.16 (0.49)	-0.02 (0.92)	-0.003 (0.98)	-0.07 (0.75)
	Strangers	Satisfaction with frequency	-0.08 (0.75)	-0.004 (0.98)	0.20 (0.41)	0.11 (0.64)	0.14 (0.55)	0.09 (0.72)	0.26 (0.29)	-0.34 (0.15)	-0.11 (0.65)	-0.24 (0.35)
		Satisfaction with exchanges	-0.13 (0.60)	-0.02 (0.92)	0.12 (0.61)	0.03 (0.87)	0.06 (0.78)	-0.01 (0.96)	0.28 (0.25)	-0.41 (0.08)	-0.07 (0.75)	-0.14 (0.57)

Balaguer M, Pommée T, Pinquier J, Farinas J, Woisard V, Sordes F. Development and preliminary validation of the questionnaire 'Evaluation of the constitution of social circles (ECSC)' in patients treated for cancer of the upper aerodigestive tract. Folia Phoniatr Logop. 2022. https://doi.org/10.1159/000525352

1 https://www.irit.fr/rugbi Balaguer M, Pommée T, Pinquier J, Farinas J, Woisard V, Sordes F. Development and preliminary validation of the questionnaire 'Evaluation of the constitution of social circles (ECSC)' in patients treated for cancer of the upper aerodigestive tract. Folia Phoniatr Logop. 2022. https://doi.org/10.1159/000525352

Funding Statement: This study was funded by the Hospitals of Toulouse, and by the French National Research Agency [RUGBI project, Grant ANR-18-CE45-0008].

Data Availability statement: Data and the database are available on request from the corresponding author.


SEMI-VIRTUAL DYNAMIC TESTS OF HYBRID SYSTEMS COUPLING SOLAR THERMAL AND PV PANELS WITH HEAT PUMPS Summary

Keywords: performance test, hardware-in-the-loop, solar thermal, PV, PVT, heat pump, electricity selfconsumption

Despite the huge primary energy consumption associated with heating and cooling (H&C) demand in EU building stock, the share of installed renewable H&C solutions is still marginal (i.e. 5%). In order to speed up a transition towards the widespread application of renewable H&C in buildings, innovative solutions must be designed to compete against traditional solutions. SunHorizon project aims to unlock the potential for a userfriendly and cost-effective solution based on an optimized design and combination of commercial innovative solar technologies (thermal or/and PV) and Heat Pumps (HP). This paper aims to present how are performing experimentally two hybrid concepts, out of four in the whole project, that are coupling solar thermal and PV panels with heat pumps to satisfy thermal and electricity energy demand of residential buildings in Riga (Latvia) and Piera (Spain). Following hardware-in-the-loop approach, specific short test sequences are developed first for each of the two Technology Packages (TP) that allow for extrapolation of the measurements to annual seasonal performance figures including electricity self-consumption in addition to renewable heating and cooling indicators.

Introduction

Buildings are responsible for 41% of EU energy consumption and H&C equals 55% of the total EU energy demand (ODYSSEE, 2018). This demand is mostly met by fossil fuels, with natural gas having the main share, while renewable energy sources (RES) remain marginal (5%). However, according to the EC's H&C Strategy, the H&C sector still accounts for 59% of total EU gas consumption, and almost half of EU's buildings have boilers with an efficiency rate below 60% (IEA, 2018). Nevertheless, RES are becoming increasingly common and socially accepted. Therefore, to speed up a transition towards RES-based H&C in buildings, innovative solutions must be designed to compete against traditional solutions with equivalent or lower investment costs and payback times, while guaranteeing similar or better levels of comfort. Both residential and tertiary new buildings types are expected to become highly-efficient. Currently, solar panels and HP are the most common and socially accepted RES Based H&C solutions. Bringing together existing and mature HP and solar technologies guarantees a simple energy system nevertheless efforts are still required to optimize their coupling, as they are used to be designed and operated often separately. Solar and HP can form an integrated solution with several key advantages: high RES share and energy efficiency, low electricity and primaryenergy demand, low CO2 emission. HP are well suited to low-temperature needs as for well-insulated residential and tertiary buildings. In parallel solar thermal and PV reduces also the thermal and electricity demand in non-renovated envelopes. SunHorizon project aims to demonstrate innovative and reliable HP solutions (thermal compression, adsorption, reversible) which, properly coupled and managed with advanced solar panels (thermal, PVT, PV), provide H&C to residential and tertiary building with lower emissions, energy bills and fossil fuel dependency. Four different TPs are being developed and demonstrated all across EU climates (i.e. Germany, Spain, Belgium and Latvia) and building typologies (small and large-scale residential and tertiary buildings).

In this work, the wide SunHorizon scope above is restricted on two approaches, TP2 and TP4, which generate solar heat and electricity to save non-renewable energy consumption in residential context. TP2 is focused on areas with predominating heating demand and will be installed and run in-situ in Riga (Latvia) from August 2021. TP4 can meet both heating and cooling demands rather in Mediterranean areas and will be operated insitu in Piera (Spain). The Sunhorizon objective is here to validate the technologies integration into TP2 and TP4 concept through the semi-virtual testing approach, essentially control aspects, with respect to the relevant operating conditions to be met afterwards on the real demo sites. The main new challenge of the test sequence elaboration compared to previous work is to address the electricity balance between PV production and both system and building consumptions in the one hand, and the challenging transition between heating and cooling seasons while keeping the sequences as short as possible.

Concepts, test method and setup

The preliminary TRNSYS simulation work [START_REF] Chèze | Four Innovative Solar Coupled Heat Pump Solutions For Building Heating And Cooling[END_REF] led to the design and sizing of TP2 and TP4 concepts and it's been used to develop the related dynamic system test conditions.

The TP2 concept relies on 58m² Dualsun solar PVT panels, Boostheat 20kW thermal compression gas fired heat pump GUE=2.0 at A7/W35, 0.2/1.3 m3 cold/hot tanks and smart electric heater from PV electricity excess by Ratiotherm. The TP2 system was integrated in simulation in Riga (LV) demonstration context: residential house 34.5MWh space heating annual demand (SH), 2.9 MWh domestic hot water (DHW), electricity consumption 4.4 MWh. The estimation of annual green house gas emissions savings (fsav,GHG) through TP2 is 51% compared to existing gas boiler.

The TP4 concept is developed from BDR Thermea products, separate 4 m² solar thermal flat plate and 10 m² PV panels with harmonized roof integration, air source reversible 6kW COP=3.4 at A2/W35 heat pump, 200/150L buffer/DHW tank, and global controller managing heating/cooling and PV electricity balance at system and building level. The system is integrated in simulation in Piera (SP) demonstration context: residential house 5.4 MWh SH and 1.2 MWh cooling SC demand, 1.2 MWh DHW, 2.3 MWh electricity consumption. The estimation of fsav,GHG is 53% for TP4 in Piera compared to existing oil boiler and 4m² solar DHW heating system, also considering extra comfort gain through new cooling supply.

The semi-virtual test is a global system test approach considering the real-time strong interactions between the building, the local energy systems and controllers, the environment and the users. In his PhD thesis work, (Sayegh, 2020) developed a new approach and automated tool called TYPSS to define a climate sequence short enough to determine with the full dynamic models the real-time tested system performances that are then extrapolated to the full year. This approach came after previous works following similar philosophy around the elaboration of short sequence test for dynamic thermal system test as in (Albaric et al., 2010), [START_REF] Chèze | Comparative Dynamic Performance Tests of Two Real Technology Packages for Buildings Heating System Retrofit[END_REF] or [START_REF] Menegon | Direct characterisation of the annual performance of solar thermal and heat pump systems using a six-day whole system test[END_REF]. In the SunHorizon work, the TYPSS tool was used together with the TP2 and TP4 TRNSYS simulations to elaborate short test sequences that are relevant with respect to the two different demonstration sites and environments. Fig. 1 illustrates the various criteria involved in the TYPSS methodology to design a short sequence representative of one-year operation, looking at annual profile evolutions and integrated criteria. On the left, electricity consumption criterion 1, we can see that the summer increase in electricity consumption from the grid is well catch by the selected sequence: it reveals the actual balance between PV electricity generation and overall building consumption increase through cooling operation of the HP, from the selfconsumption strategy developed in TP4 controller. With regard to the short 10-days sequence challenge, on the right figure representing the comparison between annual and sequence of energy cumulated criteria, we notice that the deviation of every criteria doesn't excess 7% except for the cooling load. It was decided an acceptable trade-off considering the short sequence duration requirement and higher priority given to the overall electricity consumption of the system. An illustration of the architecture of TP4 is presented in Fig. 2, together with a picture of the indoor system parts integrated in dynamic thermal sytem test bench laboratory.

Conclusions

This work managed to develop relevant short 10-days test sequences for each system and demonstration environment, as customized building type, climate and users behaviors (DHW and specific electricity consumption). The test sequences allowed the extrapolation of the measurements during the 10-days tests to annual performance figures. The execution of both TP2 and TP4 tests revealed some tricky issues around the configuration of the controllers to achieve expected behavior, in particular for the TP2 system which is combining the components and controllers from two manufacturers, Ratiotherm and Boostheat. This testing experience allows issuing recommendations for the future reliable installations on-site in August 2021. In addition to the experience in fast-customized test sequence development and testing of TP2 and TP4 concept, the deviations of the measured performance figures compared to the simulated ones are discussed in this work.

The SunHorizon project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement N. 818329.

Fig. 1 :

 1 Fig.1: annual/test sequence comparison of HP electricity profile (left) and all integrated criteria (right)

Fig. 2 :

 2 Fig.2 : TP4 concept and outlook of real system integrated in semi-virtual test bench