An innovative analytical method is proposed in order to select sR values optimizing the electromagnetic wave absorption either in low-gigahertz or low-terahertz frequency range. The frequency spectra of the absorption, reflection and transmission coefficients are computed in the range up to 4 THz by using different values of sR. Then, realistic sR values of chemically doped graphene monolayers over PET substrates are considered. The designed absorbers are characterized by an absorption coefficient with a peak value of about 0.8 at the first resonant frequency of 1.1 THz, and a 1.4 THz bandwidth centered at 1.5 THz with reflection coefficient below - 10 dB. Moreover, the optical transmittance of the proposed absorbers are computed by means of the optical matrix theory and it is found to be greater than 86% in all the visible ranges.A new, transparent, metal-free absorber, based on the use of multilayer graphene/dielectric laminates (GLs), is proposed for applications in the low-terahertz frequency range. The designed absorber has a total thickness of around 70 µm and consists of a front matching dielectric layer followed by a GL, a dielectric spacer and a back GL. The laminates are periodic structures constituted of graphene sheets separated by 50-nm-thick polyethylene terephthalate (PET) interlayers, while the matching layer and the spacer are one-quarter-wavelength thick and made of PET. The GLs are modeled as homogeneous-equivalent single layers (ESLs) characterized by their sheet resistances  In the last decade, the exponential growth of wireless communications and data traffic has fueled the research in technologies operating at terahertz (THz) and low-THz frequency ranges . MoreoveIn this context, graphene, which is a two-dimensional honeycomb structure of carbon atoms, has gained a lot of interest due to its exceptional electrical, thermal, mechanical and optical properties . Current2O3 is presented in  and [Φsp] are the transfer matrices of the ith laminate iL and of the PET layers (either the spacer or the front matching layer).The overall transfer matrix [Φ] of the absorber structure sketched in Li] is given by:ig] is the short-line transfer matrix of the ith single graphene layer with conductivity iσ(ω):int] is the transfer matrix of the PET dielectric interlayer:The transfer matrix  of the PET spacer and matching layer is given by Equation (14) replacing αint with αsp = 2 πtsp/Sλ.The transfer matrix  can be computed using matrices [ΦiESL]SL instead of matrices [ΦiL] without losing accuracy.The reflection ressions :(29)R=|1dBR and the absorption coefficient A are obtained from R and T as:Finally, the reflection coefficient in decibel iN and dc conductivity iσ of the graphene layers. In order to obtain the optimum values of the sheet resistances, a new analytical formulation is developed on the basis of previously obtained results [The absorption performances of the proposed transparent screens depend strongly on the laminate-sheet resistances that are functions of the number  results .iL is modeled as the corresponding homogeneous ESLi represented by the SL transfer matrix [ ΦiESL]SL expressed in Equation (26), in which the off-diagonal coefficients are i in nanometric scale. Moreover, the equivalent effective conductivity iNiσ. Therefore, the transfer matrix [ ΦiESL]SL assumes the following simplified form.siG can be written as:Each laminate tsp = λsr/4 in which fr, being sp]r at the resonance frequency assumes the following form:r is computed as:The thickness of the spacer and matching layer sketched in inYr and the transmission coefficient Tr. These quantities are expressed as functions of the sheet conductances sG1 and sG2. It yields:The coefficients of matrix (37) are used to evaluate the corresponding input admittance inYr and Tr respectively equal to 1/η0 and T*, where T* is a target value of the transmission coefficient at frequency fr, it is possible to obtain the optimum laminate-sheet conductances. It yields:T* < 0.5. Notice that the latter equations can be used also in the low-gigahertz frequency range, as discussed in  being the overall ray transfer matrix of the considered structure  of the graphene laminate sketched in iN graphene sheets separated by iN PET interlayers is computed as:gM] and [Mint] are the ray transfer matrices of the graphene layer and of the PET interlayer. In particular, [gM] isThe ray transfer matrix [tg and (Mint] is obtained from Equations (49) and (50) replacing the graphene complex refractive index tg with the PET complex refractive index tint.WithM] of the absorber structure sketched in Msp] of the PET spacer and matching layer is obtained from matrix [Mint] replacing tint with tsp.Finally, the overall ray transfer matrix [L1 and L2 are computed by means of Equations (40) and (41) assuming εr = 3 and *T equal to 0.1, 0.07 and 0.05. The obtained values of the corresponding sheet resistances siR = 1/siG are reported in The sheet conductances of the laminates iL is modeled through the ESLi-short-line transfer matrix of Equation (26) in which σie(ω) is given by Equation (28) where iNiσ is the sheet conductance Gis of the ith laminate. The scattering time τ and the spacer thickness tsp are assumed equal to 0.1 ps and 0.035 mm, respectively. The computed frequency spectra of R, T, A and dBR are represented in *T.The reflection, transmission and absorption coefficients of the absorbers are computed in the frequency range 0.01–4 THz using Equations (29), (30), (32) and (33) combined with Equation (11). Each laminate A increases slowly as *T decreases and shows a peak value at 1.1 THz, i.e., the first resonant frequency, pretty close to 1.24 THz given by Equation (45) considering εr = 3. Furthermore, the transmission coefficient T at the resonant frequency assumes values close to the target values *T, i.e., 0.1, 0.07 and 0.05.It is worth noticing that the absorption coefficient L2 as backside reflector. If the backside reflector is made of a PEC layer, i.e., if *T = 0 and s2R = 0, it yields sR1 = 125 Ω/sq. The computed frequency spectra of coefficients R, T, A and of dBR are reported in The previous calculations are carried out considering the laminate inY of the multilayer screen is computed by using Equation (31) with τ = 0.1 ps and spt = 0.035 mm. The obtained frequency spectra of the real and imaginary parts of inY are shown in *T.For the sake of completeness, the input admittance inY are equal to (1/η0) = 2.65 ms and zero, respectively, according to the optimal absorbance conditions assumed to obtain the analytical expressions of the sheet conductances.At a first resonance frequency of 1.1 THz the real and imaginary parts of 2) laminate, sandwiched between two PET dielectric layers. A reflection coefficient of -18 dB was obtained at about 1.1 THz when the graphene-SiO2 laminate was electrically doped by a 0.02 V/nm electrostatic field bias, giving rise to a laminate-sheet resistance of about 123 Ω/sq [L2 is replaced by a PEC layer.It is interesting to compare the reflection coefficient computed when a PEC is considered as a backside reflector with the absorbance performance of the adaptive absorber described in . This ab3 chemically doped graphene layers, characterized by a dc conductivity of 8.33 ms and a sheet resistance of 120 Ω/sq [sR1 = 135 ms and sR2 = 31 ms corresponding to * =T 0.07 in L1 and four doped graphene layers in the back laminate L2.The design of a feasible absorber requires the choice of doped graphene layers characterized by suitable values of sheet resistances in order to obtain laminates having effective sheet resistances close to the ones reported in 120 Ω/sq , should R, T, A and dBR of the designed absorber are computed assuming τ = 0.1 ps and tsp = 0.035 mm, as in the previous applications, and using the overall transfer matrix Equation (11) in which each laminate is represented by the multilayer (ML) matrix of Equation (12) or by the ESL-short-line matrix of Equation (26) obtained considering the sheet resistances of the doped graphene layers and the theoretical ones evaluated in the case T* = 0.07  and (51). In particular, a sensitivity analysis of Topt with respect to the number of graphene layers N2 of the back laminate L2 and to the interlayer thickness tint is carried out. Topt spectra of the back laminate having tint = 50 nm and N2 ranging between 2 and 5, while Topt spectra of the corresponding overall structures, i.e., also considering the matching layer, the laminate L1 and the spacer. On the other hand, Topt spectra of the back laminate consisting of five doped graphene layers (N2 = 5) and interlayer thickness tint varying from 50 nm to 200 nm. Similarly, Topt of the corresponding overall structures, i.e., also considering the matching layer, the laminate L1 and the spacer.The optical transmittance Topt is always greater than 85%. Moreover, when the PET interlayer thickness is one order of magnitude lower than the considered wavelength, Topt can be easily computed by means of the approximated expression Equation (8). As the PET interlayer thickness increases, Topt cannot be computed by means of Equation (8), as shown in It can be noticed that Topt of the designed screen (with N2 = 4) is around 88% in all the visible ranges.Finally, from The proposed absorber structure consists of a front matching PET layer followed by a graphene/PET laminate, a PET spacer and a back graphene/PET laminate. The matching layer and the spacer have one-quarter-wavelength thickness. The structure is extremely simple since it does not include complex frequency-selective surfaces or metamaterials. Moreover, the designed absorbers have a total thickness of 70 µm and they are transparent and metal free, since no metal is used, also for the backside reflector.The absorption performances are evaluated through numerical calculations in the low-terahertz frequency range and both graphene conductivity and PET permittivity are considered as frequency-dependent quantities. A homogenization procedure is applied to represent the periodic graphene-PET structures as equivalent short-line single layers.3 chemically doped graphene layers are considered in order to design feasible absorbing screens with laminate-sheet resistances comparable with the theoretical ones. The computed frequency spectra of the absorption, reflection and transmission coefficients in the frequency range up to 4 THz prove the good performance of the designed absorbers and the utility of the proposed design method. In particular, the proposed screen has a 1.4 THz bandwidth centered at 1.5 THz with reflection coefficients below - 10 dB and an absorption coefficient with peak values around 0.8 at the first resonant frequency of 1.1 THz. Then, a sensitivity analysis is carried out to highlight the strong influence of the estimated scattering time on the screen performances.The optimum sheet resistances of the laminates are obtained analytically as functions of the interlayer relative permittivity and of the target transmission coefficient at the first resonance frequency. It is worth noticing that the analytical expressions of the optimum laminate-sheet resistances can be applied to both low-gigahertz or low-terahertz frequency ranges. Then, HNOFinally, the optical transmittance of the proposed absorber is computed by means of the optical matrix theory and we conclude that such transmittance is greater than 88% in all the visible ranges.


Parkinson’s disease (PD) is a common neurodegenerative disorder resulting in a range of mobility deficits affecting gait, balance and turning. In this paper, we present: (i) the development and validation of an algorithm to detect turns during gait; (ii) a method to extract turn characteristics; and (iii) the classification of PD using turn characteristics. Thirty-seven people with PD and 56 controls performed 180-degree turns during an intermittent walking task. Inertial measurement units were attached to the head, neck, lower back and ankles. A turning detection algorithm was developed and validated by two raters using video data. Spatiotemporal and signal-based characteristics were extracted and used for PD classification. There was excellent absolute agreement between the rater and the algorithm for identifying turn start and end (ICC ≥ 0.99). Classification modeling ) gave the best accuracy of 97.85% when trained on upper body and ankle data. Balanced sensitivity (97%) and specificity (96.43%) were achieved using turning characteristics from the neck, lower back and ankles. Turning characteristics, in particular angular velocity, duration, number of steps, jerk and root mean square distinguished mild-moderate PD from controls accurately and warrant future examination as a marker of mobility impairment and fall risk in PD. Parkinson’s disease (PD) affects 145,000 people across the UK . MovemenNavigating complex and unpredictable environments requires adaptations to stepping patterns and altering walking direction through turning. People with PD reportedly turn on average >60 times every hour . TurningInstrumented assessments of movement provide objective, quantifiable outcomes that offer increased sensitivity when compared to clinical scales and functional assessments ,16. EvalPrevious work has shown that turning characteristics, such as turn velocity, angle and jerk extracted from the lower back, are useful for PD classification ,28. PeopA variety of measurement outcomes may be extracted from IMUs, including those that are signal-based  and jerk) or spatiotemporal . Evaluating movement throughout the various phases of a turn  is important for enhancing our understanding of turning deficits in PD, but increases the number and dimension of the extracted features. Partial least square with discriminant analysis can overcome these issues, and may be an appropriate choice under such circumstances .Given the complexity of turning, measuring turning performance may be useful for the classification of PD. Identifying measurement outcomes that are sensitive to pathology and predict clinical endpoints is important for classification models. Therefore, the aims of this study were to: (i) optimize and validate a custom algorithm for detecting turns and phases within the turn; (ii) extract a range of turning characteristics using multiple IMUs worn on the upper and lower body; and (iii) investigate the contribution of turning characteristics and sensor location in the classification of PD. We hypothesized that characteristics associated with movement speed and smoothness and the number of steps would be important in the classification of PD. We anticipated that characteristics extracted from the upper and lower body segments would both be influential.ICICLE-GAIT is a collaborative study with ICICLE-PD, an incident cohort study  conducted between June 2009 and December 2011 . ParticiDemographics such as age, sex, height, mass, and body mass index (BMI) were obtained. Global cognition was assessed using the Mini-Mental State Examination (MMSE)  with higClinical measures were recorded for PD participants only. To assess the severity of PD motor symptoms, participants were classified according to the Hoehn and Yahr scale ranging from 0 (no symptoms) to 5 (wheelchair bound or bedridden unless aided) , as wellAs part of the gait assessment, participants completed four intermittent straight walks across an instrumented walkway. Participants initiated walking from behind a line of tape 1.5-metres from the walkway at a self-selected preferred pace until they passed another line of tape 1.5-metres beyond the end of the walkway . ParticiFive IMUs containing a tri-axial gyroscope, accelerometer and magnetometer  were attached to the participants head, neck , lower back (lumbar spine L5) and ankles , as shown in ® .For the purpose of this study, a turn was defined as a rotation of the IMU on the lower back ≥90 degrees about the vertical axis with a duration of 0.5–10 s. Gradual turns (defined as turns ≥10 degrees and ≤0.5 s in the same direction) were merged . Using t® was used to fuse the accelerometer, gyroscope and magnetometer data to estimate turn orientation. To detect possible movement in the body segments  around the vertical axis, zero-crossing of the angular velocity in the vertical direction was used. Based on the detected fluctuations in terms of yaw angles, gradual turns were combined for the head, neck and lower back IMU. The number of steps were quantified from the ankle IMUs when a turn angle of ≥30 degrees was detected around vertical axes.Turn orientation was estimated based on the integration of angular velocity about the vertical axis. Kalman Filtering  was used to estimate Euler angles to extract the orientation for all IMUs. For the upper body , static gyroscopic biases (due to factors such as temperature) and noise were removed with a compensation algorithm. This type of algorithm was chosen to avoid drift due to the integration . For theAlgorithm 1. Pseudo code for turning start and end detection.   02: for each IMU attachment #head(HD), Neck(C7), L5, Ankles   03:  store calibrated ω, a, m   04: end for   05: #Data Filtering: Noise & Drift Removal   06: Access ω   07: Compensation Algorithm: for HD, C7, L5   08: Access ω, a, m   09: Kalman Filtering (KF) Algorithm: for Ankles    10: #Every possible body segment rotation around VT   11: Access VT of ω   12: Zero-Crossing: for HD, C7, L5, Ankles   13: #Orientation Estimation   14: Integration ω VT to get Yaw: for HD, C7, L5   15: Sensor fusion with KF to Euler to Yaw: for Ankles   16: #Final Turn Detection and Estimation for HD, C7, L5   17: Yaw angle (ᴪ) extraction based on zero-crossing   18: for every ᴪ   19:   #Gradual Turns Combination   20:  if ᴪ > 10° and for next consecutive turns if intra turn   21:   duration < 0.5 s and ᴪ > 10° and same Direction   22:    Combine these gradual turns   23:  end   24:  #Turns from HD & C7   25:  if turn angle (θ) ≥ 30° and 0.5 s ≤ turn duration < 10 s   26:    Save turn start and end   27:    Save final gradual turn magnitude vector    28:  end   29:  #Turns from L5   30:  if turn angle (θ) ≥ 90° and 0.5 s ≤ turn duration < 10 s   31:    Save turn start and end   32:    Save final gradual turn magnitude vector    33:  end   34: end for   35: #Turn Transition and Estimation from Ankles    36: for every turn from L5   37:  Access the turn start and turn end from L5   38:  Access the turn direction from L5   39:  #Inner and outer turn detection to overcome direction biases   40:  if L5 direction is right   41:    Inner turn = RA   42:    Outer turn = LA   43:  else (L5 direction is left)   44:    Outer turn = RA   45:    Inner turn = LA   46:  end   47: end for   48: #Final turn transition from Ankles   49: for angles from Inner and Outer turn   50:  if inner turn angle ≥ 30°   51:    save the turn start and turn end   52:    save the turn magnitude vector   53:  end   54:  if outer turn angle ≥ 30°   55:    save the turn start and turn end   56:    save the turn magnitude vector   57:   end   58: end for #Turning features extraction based on turn start and end time   01: #Data AccessAll turns ≥90 degrees detected from the lower back IMU (L5) were considered for analysis. A 30 degree threshold was used for the head, neck, and ankles based on the lower quartile of the overall turn angle magnitude. To avoid the effect of turn direction on the ankle data, we organized ankle data according to whether they were inner  or outer  with respect to the turn. The start of a turn (turn start) and the end (turn end) were identified for each IMU, based on the algorithm in Algorithm 1.Data from 26 participants (10 PD and 16 controls) who were not included in the validation analyses were used for algorithm development and optimization , who had at least three turns  were reviewed by two independent raters. To be consistent across both groups (PD vs. CL), the first three turns were considered for validation. Raters were instructed to record the frame number of the turn start and turn end using predetermined definitions based on the following visible kinematic events. Turn start was defined as the frame at which the toe off occurs for the step which initiated a change in walking direction . Turn end was defined as the frame at which heel strike occurs for the step, which concludes the change in walking direction. Both raters were blinded to which group participants were in.The raters provided a number of additional annotations, including turn direction, and the presence and proximity of a researcher walking beside the participant. These outcomes were used for descriptive purposes only. We also asked the raters to nominate which group (PD or control) they believed the participant in the video belonged to. This information was compared to the results of the classification analysis .2) were computed. Signal-based turning characteristics were extracted from each IMU (accelerometer and gyroscope) in each axis . The number of steps to complete a turn was extracted from the ankle IMUs. Feature extraction was performed in MATLAB®.Based on the turn detection protocol described in 2 index) and the variance captured by the independent  and dependent variables  were assessed. Increasing the number of model components can increase the captured variance while reducing the overall predictive performance leading to unreliable models. The number of components was selected based on the cumulated Q2 index, which assesses the global fitness of the model. The cumulated Q2 index for each component should be > 0, with values close to 1 considered important [Algorithm 2. Pseudo code for turning features extraction.    02: Access turn start and end timings with turning angle magnitude    03: #Spatiotemporal turning characteristics    04: for total turns detected    05:  Turn time = mean of (turn end time − turn start time)    06:  Turn angle = mean of turning angle magnitude vector    07:  minimum, maximum, and variability in turn time and angle vector    08:  #Full turn angular velocity    09:  Angular velocity = Turn angle/Turn time    10:   #Angular frequency (angular velocity) in start, mid & end of turn    11:  selection of 0.1 s in the start, mid, end of within turn    12:  mean & variability in     13:  mean & variability in     14:  #Direction of turn    15:  left turn if angle magnitude is negative or right otherwise    16:  #Number of turn/transitions    17:  length of start and end time vector    18: end for    19: #Signal based turning characteristics    20: Accessing the ω, a in VT, AP & ML directions    21: Detrend the ω, a    22: Data filtering: 4th order low-pass Butterworth filter at 20 Hz cut-off    23: Getting resultant magnitude of VT,AP & ML for ω, a    24: for each start, mid, and end of turn    25:  RMS for ω, a in VT, AP, ML & R #Turn overall, start, mid & end    26:  #Jerk: rate of change of a    27:  RMS, max, min, range of each turn jerk in VT, AP, ML & R #Turn     28:   overall, start, mid & End    29:  #Angular acceleration: rate of change of ω    30:  RMS, max, min, range of each turn angular acceleration in VT, AP,     31:   ML & R #Turn overall, start, mid & end    32: end for #classification modeling and statistical analysis    01: #Detected TurnsPartial least square with the discriminant analysis (PLS-DA) method was used for the classification of PD from controls ,43. The mportant . Classifmportant .AlgoritFor algorithm validation, the limit of agreement (LOA) expressed both as absolute values and as a percentage of the mean were used to assess agreement between the two raters and between the raters and algorithm, for the PD and control groups separately. Spearman’s rho assessed relative agreement while the intra-class correlation ) assessed absolute agreement between raters and algorithm. Predefined acceptance ratings for ICC were: excellent (>0.900), good (0.750–0.899), moderate (0.500–0.749) and poor (<0.500) ,47. RMS t-test) and non-parametric (Mann-Whitney U-test) tests were performed on each turning characteristic depending on the distribution (Shapiro-Wilk test). Statistical analyses were performed in MATLAB® and SPSS®. A p-value < 0.05 was considered statistically significant.For visualization of group differences, turning characteristics were converted to standardized Z-scores. To evaluate group differences, parametric (student p > 0.05). Balance confidence  was significantly reduced in PD compared to controls (p < 0.001). PD participants were of mild-to-moderate disease stage (Hoehn and Yahr II-III), and LEDD was, on average, 536 mg/day. No freezing episodes were observed during the assessment.The groups were well matched for age (52 to 89 years), height, mass, BMI and global cognition (MMSE) . The groups were well matched for age, height and mass, and had a mean UPDRSIII score of 40.4 ± 12.6.A researcher walked alongside 16 PD participants (62%) and 3 control participants (8%) for all walking trials. For 7 PD participants (27%) and 26 control participants (90%), they were unaccompanied during the walking trials. The remaining participants were accompanied on some, but not all, of the trials . Of those participants for whom a researcher was present, the researcher either was close by or within reaching distance for 15 PD (58%) and 2 control (5%) participants for all walking trials. For a small number of participants (3 PD and 8 controls) a researcher was close by some of the time . To understand whether the presence of a researcher influenced turn direction (right or left) the raters reported whether participants turned towards or away from the researcher. Four PD (15%) and 2 control participants always turned towards the researcher compared with 1 PD (4%) who always turned away. The majority of participants (14 PD and 2 control participants) for whom a researcher was present demonstrated no consistent turn direction with respect to the researcher (towards or away). Bar charts depicting these descriptors are provided in A good level of agreement was observed between the raters when identifying the turn start and end for PD and controls .For the control group, both raters had excellent rho (0.99) and an ICC of 0.99 for turn start and turn end. RMSE and LOA were lower for turn start compared to turn end.For the PD group, both raters had excellent rho (0.99) and an ICC > 0.98. RMSE was lower for turn start compared to turn end. LOA were lower for turn end compared to turn start.A total of 425 turning characteristics from the head, neck, lower back, and ankle IMUs were calculated. One-hundred-and-nine turning characteristics  were extracted from the head, neck, and lower back . Forty-nine turning characteristics  were extracted from each ankle. Signal-based characteristics were extracted from each axis of the gyroscope and accelerometer .p values for all characteristics are presented in The following turning characteristics were found for each of the IMU locations: PD turned more frequently to the right compared to controls (CL); turn duration was longer; and angular velocity was reduced throughout the turn  in PD.Turning characteristics from the head: PD participants had a more frequent head rotation higher compared to CL. From the signal-based turning characteristics, PD had lower RMS of acceleration and angular velocity overall, as well as during the start, mid, and end, compared to CL and this was true for all directions. PD participants had a lower RMS, and range of jerk in all directions. RMS of jerk at the start of the turn was slightly higher for PD . RMS of jerk at the end of the turn was lower for PD compared to CL in all directions.Similarly, RMS and range of angular acceleration were lower across the turn overall, as well as the start, mid and end in PD, in all directions.Turning characteristics from the neck: Similar to the head, people with PD demonstrated more frequent and smaller neck (C7) rotations compared to CL. PD had lower RMS of acceleration and angular velocity compared to CL in all directions. Similarly, RMS of jerk and angular acceleration were lower for PD compared to CL in all directions.Turning characteristics from the lower back: Fewer turns were detected for PD  compared to CL. The turn angle was smaller (lower maximum and higher minimum) and less variable for PD compared to CL. Turn duration was more variable in PD compared to CL. RMS of acceleration and angular velocity were lower for PD compared to CL in all directions. RMS (minimum and maximum) and range of jerk were lower for PD in all directions. RMS of jerk was lower during the turn start and higher mid turn in all directions for PD. However, at the end of the turn, the RMS of jerk was lower in ML and VT directions and higher in the AP and R directions, for PD. PD also had lower RMS of angular acceleration in all directions.Turning characteristics from the inner ankle: PD participants took more steps to complete the turn. The turn angle was smaller in PD compared to CL. Angular velocity was more variable in PD compared to CL RMS of acceleration, angular velocity, jerk, and angular acceleration were lower for PD compared to CL.Turning characteristics from the outer ankle: PD participants took fewer steps with the outer ankle compared to CL. Almost the same turn angle and turn duration was observed in both groups; however, turn angle was less variable in PD compared to CL, with higher angular velocity observed in PD. RMS of acceleration, angular velocity, jerk and angular acceleration were lower for PD compared to CL.2X) and dependent (R2Y) variables, overall model quality (cumulative Q2 index), and information about the number of selected components in the model is provided in For each PLS-DA model, the explained variance captured by the independent  and inner ankle resulted in optimal classification accuracy of 97.85% with a sensitivity of 95% and specificity of 100%. The model that provided balance in sensitivity (97.30%) and specificity (96.43%) included turning characteristics from the neck, lower back and lower body (inner and outer ankle) and achieved a classification accuracy of 96.77%. Using only the spatiotemporal characteristics, optimal classification was achieved when the model included the upper body  and outer ankle resulting in a classification accuracy of 96.77% with 94.59% sensitivity and 98.21% specificity. Using only the signal-based characteristics, optimal classification was achieved when the PLS-DA was trained using all sensor locations (upper and lower body) with a classification accuracy of 90.32% with 84% sensitivity and 95% specificity.Models considering only sensors on the upper body  resulted in a classification accuracy of 94.62% with 89% sensitivity and 98% specificity. Considering only sensors on the lower body (inner and outer ankle) resulted in a classification accuracy of 70% with a sensitivity of 49% and specificity of 84%. Full body (upper and lower) spatiotemporal characteristics offered improved classification accuracy (94.64%) compared to signal-based characteristics (90.32%). Signal-based characteristics derived from the lower back resulted in a maximum accuracy of 84%; adding the inner ankle characteristics improved classification accuracy to 94% ; and adding the head and neck improved accuracy by a further 2%. Spatiotemporal characteristics from the upper body  performed better than signal-based characteristics; however, optimal results were achieved when combined. In contrast, signal-based characteristics from the lower body (inner and outer ankle) performed better than spatiotemporal characteristics alone.Using a combination of spatiotemporal and signal-based characteristics, and relying on a single sensor location, the neck IMU provided the best classification accuracy of 89.25% compared to the lower back (81.72%). Using only spatiotemporal characteristics, the neck and lower back resulted in a classification accuracy of 84% and 83%, respectively. Using only the signal-based characteristics, the lower back resulted in a classification accuracy of 85% compared to the neck (81%). In the majority of models, turning characteristics derived from the inner ankle improved the classification accuracy compared to the outer ankle, the inclusion of which reduced sensitivity.The contribution and importance of individual turning characteristics in the classification models were quantified using the VIP in the PLS-DA . CharactImportant spatiotemporal turning characteristics: Average angular velocity was an important spatiotemporal characteristic from the neck and lower back. From the lower back, turn duration ; average angular velocity (mid turn); variability in angular velocity (turn start); and variability in the peak angular velocity were important. From the inner ankle, the number and duration of transitions/steps were influential. From the neck, angular velocity  and turn duration  were important.Important signal-based turning characteristics: The most influential characteristics were the RMS of angular velocity from the neck  and lower back .Similarly, in the middle of the turn, RMS of angular velocity for the lower back  and the neck  were important. From the inner ankle, RMS of angular velocity ; RMS, minimum, maximum, and range of jerk in the VT direction; and range of angular acceleration (R) were important. From the lower back, RMS of angular acceleration ; minimum angular acceleration (VT); RMS of acceleration ; and RMS of jerk  were important. From the neck, angular acceleration ; RMS of jerk (AP) had greater importance. From the outer ankle, the maximum jerk (R) was important.In the present study, we have demonstrated that wearable technology can be used to detect turns whilst walking accurately in older adults and people with PD, and turning characteristics collected from a range of upper and lower body locations can also accurately distinguish between people with PD and age-matched controls. To the best of our knowledge, this is the first study to validate a turning algorithm for detecting the start and end of turns and quantify a comprehensive set of turning characteristics from a variety of sensor locations for PD classification. The combination of characteristics measured at the neck, lower back, and inner and outer ankles offered optimal sensitivity and specificity.We found excellent agreement between the raters and the algorithm with higher error observed for the turn end for both groups. This was particularly true for controls, who turned faster (higher angular velocity). Turning speed may have influenced accuracy when identifying or detecting turn events. To validate the detection of the start and end of each turn, we defined kinematic events that could be detected through observation compared to the algorithm (which used velocity thresholds). However, the optimized algorithm was able to detect the small transitional rotation in the body accurately from the angular velocity, which is difficult to observe from the naked eye. Any small change in the body segment angle can be quantified, based on the zero crossing of angular velocity. Thus, using wearable IMUs is critical for the accurate and objective assessment of turning across a variety of body segments.Previous studies ,23 have For comparison with published turning algorithms used with PD ,23, PhamThis is the first study to investigate using turning characteristics from a combination of IMU locations for the classification of PD. Overall, classification with a single sensor ranged 70–89% accuracy, with 35–86% sensitivity and 84–92% specificity. The IMU on the neck gave optimal classification, followed by the lower back, inner ankle, head and outer ankle. The performance was further improved when turning characteristics were combined from other IMU locations; with a combination of the neck, lower back, head and inner ankle resulting in an optimal accuracy of 98% . Our findings are in line with previous work ,28. UsinThe turning characteristics which were most influential in the classification modeling were related to RMS of angular velocity (radians/second), angular velocity (turn angle/turn duration), turn duration, jerk, angular acceleration, number of steps/transitions per turn, and RMS of acceleration. The results from this study are in line with previous findings  confirmiImportant turning characteristics reported in the present study are based on the model trained on characteristics derived from the neck (C7), lower back (L5) and lower body (inner and outer ankles), which resulted in optimal balance in sensitivity (97.30%) and specificity (96.43%) with an accuracy of 97%. In terms of body segment coordination during turning, older adults without PD sequentially rotate the head, followed by trunk and pelvis to turn ,51,52. ISeveral turning characteristics from the inner ankle, such as turn duration, angular velocity, jerk, angular acceleration, the number of steps required to complete the turn were important. From the outer ankle, the only characteristic of importance was the maximum jerk. A reduction in segment rotation can influence stepping strategy during turning . The numPrevious work has validated algorithms for turn detection based on video-derived annotations comparing the number of turns, turn phase and turn duration. In the present study, we validated an algorithm for detecting turns through identifying turn start and turn end. Developing robust means for detecting the start and end of a turn is important for subsequently derived turn characteristics. We selected thresholds based on the study setting; however, thresholds can be adjusted to capture a range of turn behaviors. Further work is required to determine appropriate thresholds for a variety of settings. Thresholds used in the lab may not be appropriate for use in the community where smaller turns may be required and thresholds may need to be adjusted for different sensor arrays and locations ,12,13,19In this study, we present turn data using a semi-structured protocol during which turning behaviors were covertly assessed. The turn angular displacement was set (180 degrees) and initiation of each turn was prompted by passing a line on the floor. Turn area, turn direction (right or left) and turn strategy were not controlled; therefore, a range of turning behaviors were included in the present analysis. A variety of strategies may be used when turning  ; howeverA strength of the present study is the use of a multi-sensor array to quantify turning characteristics for PD classification. We have shown that turning characteristics from the neck, lower back, inner and outer ankles resulted in optimal balance in sensitivity and specificity. Previous studies attached the sensor on the lower back , pelvis In this article, we demonstrated that turning during gait can be accurately identified with inertial measurement units, and turn characteristics from both the upper and lower body are important in the classification of PD. Turning characteristics extracted from the head, neck, lower back and inner ankle gave maximum accuracy of 97.85%, with the optimal balance in sensitivity (97.30%) and specificity (96.43%) achieved using turn characteristics from the neck, lower back, and inner and outer ankles. Characteristics related to movement speed (such as angular velocity), smoothness (such as jerk) and the number of steps taken to complete a turn were highly influential in the classification of PD. Further work is warranted to test the clinical utility of turning metrics captured in both laboratory and community-based settings.


Neurotrophin signaling via receptor tyrosine kinases is essential for the development and function of the nervous system in vertebrates. TrkB activation and signaling show substantial differences to other receptor tyrosine kinases of the Trk family that mediate the responses to nerve growth factor and neurotrophin-3. Growing evidence suggests that TrkB cell surface expression is highly regulated and determines the sensitivity of neurons to brain-derived neurotrophic factor (BDNF). This translocation of TrkB depends on co-factors and modulators of cAMP levels, N-glycosylation, and receptor transactivation. This process can occur in very short time periods and the resulting rapid modulation of target cell sensitivity to BDNF could represent a mechanism for fine-tuning of synaptic plasticity and communication in complex neuronal networks. This review focuses on those modulatory mechanisms in neurons that regulate responsiveness to BDNF via control of TrkB surface expression. Nerve growth factor (NGF) was the first member of the neurotrophin family that was discovered as a survival factor for distinct populations of neurons during a critical period of development. When brain-derived neurotrophic factor (BDNF) and the other members of the neurotrophin family were identified, it became apparent that the responsiveness of BDNF-sensitive neurons was also qualitatively different from NGF-dependent neurons. These differences can be explained by differential target cell responsiveness to BDNF rather than differential expression of the corresponding TrkB receptor. In the present article, we aim to provide an overview about some historic aspects that first pointed to differences between NGF and BDNF, and recent findings on molecular mechanisms how target cell sensitivity to BDNF signaling is regulated by TrkB cell surface expression. These mechanisms could explain differential responsiveness of developing neurons to NGF and BDNF.Work by Viktor Hamburger and others has shown that many types of neurons are generated in excess in the embryonic nervous system of vertebrates and that only subpopulations that are supported by neurotrophic factors are maintained (reviewed by Purves and Lichtman 2A-R) depend on the presence of TrkB  and spinal motoneurons . First, neuronal activity triggers Ca2+ entry via AMPA and NMDA receptors (a). Ca2+ stimulates adenylyl cyclase activity, thus causing an elevation of cAMP levels which trigger PKA and PI3K activity (b). This leads to the mobilization of a rapidly available intracellular reserve pool of TrkB (c). TrkB containing vesicles are transported in a microtubule-dependent manner to distinct sites at the cell surface where they integrate, such as dendritic spines. In the dendritic target area, TrkB is transported in an actin-dependent manner via Myosin Va and associates with PSD-95 for guided integration into the PSD (d). TrkB is then able to bind BDNF and gets activated via autophosphorylation at C-terminal tyrosine residues (e).In summary, these studies provide evidence that cAMP-mediated TrkB surface translocation in dendritic spines but also axon terminals involves several critical steps Fig. . First, Elevated levels of cAMP play a central role in the translocation of TrkB to distinct cell surface regions within neurons. Thus, it is not surprising that G protein–coupled receptors (GPCRs) were proposed as key activators and potential central physiological regulators for the effects observed in cultured neurons after cAMP treatment , phospholipase C (PLC), or L- and N-type Ca2+ channels  via stimulation of phospholipase C-beta (PLC-β) activity    constitute the largest and most diverse family of transmembrane receptors , or mannose (Man) modifies the core oligosaccharide (Moremen et al. Glycan structures on newly synthesized glycoproteins are crucial for protein secretion. N-glycosylation is the most frequent form of posttranslational modification in the ER (Moremen et al. An unconventional secretory pathway from the ER to the cell surface that is independent of the Golgi apparatus has recently been described (Hanus et al. Taken together, N-glycosylation appears as a modulatory mechanism for retention or export of Trk receptors from the ER. While TrkA and insulin receptors require N-glycosylation for surface export, the same condition retains TrkB in the ER, leading to the conclusion that N-glycosylation has individual effects on distinct Trk receptors. Notably, TrkB export from the ER after EGF-mediated transactivation is a very fast process and occurs within seconds. For this reason, it appears unlikely that this mechanism involves processing in the ERGIC and Golgi. This observation rather favors the idea of a direct ER export to the cell surface (Puehringer et al. The development and function of neuronal networks, especially correct communication and adequate responses to incoming synaptic signals, crucially depend on neurotrophin signaling. TrkB and TrkA differ in the way how they induce cell death in the absence of corresponding ligands (Nikoletopoulou et al. The regulation of TrkB cell surface expression has many advantages for the function of BDNF/TrkB signaling in the context of synaptic plasticity. Changes in subcellular cAMP could act in a highly local manner and thus fine-tune individual synapses (Maiellaro et al. So far, it remains elusive whether the simultaneous activity of different afferents is required to induce BDNF-/TrkB-mediated LTP in such circuits. LTP then would be induced in a spatio-temporal manner only when both afferents are active. On the other side, such a scenario also would require a highly precise mechanism for removal of TrkB from the cell surface, and it is not clear whether this occurs only after ligand-induced endocytosis or also by other mechanisms. Alterations of such control mechanisms could cause aberrant synaptic plasticity and network communication and thus might be involved in numerous neuropsychiatric diseases.


Fusarium oxysporum f. sp. cubense (Foc) symptoms. A total of 117,814 bacterial and 17,317 fungal operational taxonomy units (OTUs) were identified in the rhizosphere, roots, and corm of the host plant. Results revealed that bacterial and fungal microbiota present in roots and corm primarily emanated from the rhizosphere. The composition of bacterial communities in the rhizosphere, roots, and corm were different, with more diversity observed in the rhizosphere and less in the corm. However, distinct sample types i.e., without (asymptomatic) and with (symptomatic) Fusarium symptoms were the major drivers of the fungal community composition. Considering the high relative abundance among samples, we identified core microbiomes with bacterial and fungal OTUs classified into 20 families and colonizing distinct plant components of banana. Our core microbiome assigned 129 bacterial and 37 fungal genera to known taxa.We assessed the diversity, structure, and assemblage of bacterial and fungal communities associated with banana plants with and without  Rhizospheric and endophytic microbiota have been shown to significantly improve plant health and development ,2. BenefMusaceae spp.) are cultivated throughout the humid tropics and sub-tropics. In sub-Saharan Africa (SSA), bananas are an important component of the diet and a crucial source of income for many small holder farmers. Disease pressure is the major threat threatening banana productivity in the region [Fusarium oxysporum f. sp. cubense (Foc), lack of pesticides, and appropriate resistant varieties encouraged the search for alternatives to sustain productivity. Foc race 1 is present everywhere in SSA hampering dessert banana (of the Musa subgroup AAB) productivity [Bananas  race 1. Symptomatic banana plants were selected based on the morphological symptoms including yellow coloration of leaves and maroon color lines inside the pseudostem. In addition, we demonstrated the composition and assemblage of the naturally occurring microbiome in the rhizosphere, root, and corm samples. Our study is the first to describe an inventory of bacterial and fungal communities associated with the components of asymptomatic and symptomatic banana plants infected by Foc. In addition, we describe core microbiomes in the rhizosphere, root, and corm and established a model for studying banana microbiota.To elucidate the role of the microbiome in plant growth and disease control, it is necessary to investigate the microbiome dynamics and distribution in different plant components. Thus, the present investigation was conducted to study the microbiome (both bacteria and fungi) structure and mode of associations with asymptomatic and symptomatic banana plants infected by Musa subgroup AAB) is a popular dessert banana cultivar in east Africa that is susceptible to Foc. The rhizosphere, roots, and corm of asymptomatic and symptomatic plants were targeted for sampling. Symptomatic banana plants exhibited yellowing leaves with brown streak discoloration inside the pseudostem and corm, and asymptomatic plants had green leaves with no discoloration inside the pseudostem and corm  of fungi was amplified . AmplifiThe library was loaded onto the Illumina platform at a concentration of 10–20 pM for cluster generation and sequencing. The template fragments were sequenced in both the forward and reverse direction through Paired-End sequencing on the Illumina platform.DNA (chimeras) sequences were filtered using usearch61 algorithm (de novo mode) and generated in silico using the FLASH tool with a minimum 10 bp overlap to recreate the V3–V4 region. All the samples were pooled in a single file, processed, and clustered to facilitate resemblance among different plant components and locations. The taxonomic names were assigned with UCLUST algorithm. OTUs for bacterial and fungal communities correlated with their abundances, were organized into a BIOM file. QIIME was used for downstream processing and intra-sample analysis or Alpha (α) and Beta (β) diversity calculations to analyze species richness. We used non-metric multidimensional scaling (NMDS) and principal coordinate analysis (PCoA) plots to visualize differences in bacterial and fungal communities composition among samples.Differences in microbial community richness among rhizosphere, root, and corm of banana were evaluated with chaos1 estimator and the Shannon diversity index. The Bray–Curtis dissimilarity matrix was calculated for bacterial and fungal communities and used for PCoA analysis with QIIME.We investigated core and non-core microbial communities (both for bacteria and fungi) in combination of sample type and location with MySQL query over the filtered OTU table. Further, we evaluated the relative abundance of each OTU in different samples and taxonomical histograms were plotted.The sequence data were deposited in NCBI with SRA accession PRJNA493905 and PRJNA494050.We characterized the bacterial and fungal communities associated with each plant component , location, and sample types (asymptomatic and symptomatic). A total of 16 million reads, with an average of 0.9 and 0.5 million reads per sample type, were generated for 16S and ITS2, respectively. A total of 4.6 million reads from 16S and 2.5 million reads from ITS2 were clustered at ≥97% sequence identity after the removal of low-quality reads via the UCLUST algorithm. A total of 117,814 bacterial and 17,317 fungal operational taxonomy units (OTU) were recovered.We analyzed the variables such as plant component, location, and sample types (asymptomatic and symptomatic). Among the rhizosphere, root, and corm, a specific association was observed for both bacterial and fungal communities among the sample types. The rhizosphere significantly impacted the bacterial communities regardless of plant components. However, the location had a significant effect on fungal communities . High vaRhizobiales, Xanthomonadales, and Burkholderiales were the most dominant bacterial orders associated with each plant component in both locations for asymptomatic and symptomatic sample types. Among the fungi, Hypocreales was the most dominant order followed by Agaricales, Incertae_sedis, Sordariales, Mortierellales, and Eurotiales in both locations for asymptomatic and symptomatic sample types  in Kilimanjaro and Chthoniobacteraceae (18.36%) and Sphingomonadaceae (13.34%) in Arusha; both were highly abundant in sample types. In case of fungal communities, Nectriaceae were found to be preferential colonizers and represented 23% of the relative abundance for both locations, followed by Plectosphaerellaceae (17.14%), Mortierellaceae (9.54%), and Trichocomaceae (8.42%) of sample types.Members of Hyphomicrobiaceae were more abundant in the rhizosphere, and Pseudomonadaceae in the roots and corm in both locations of sample types. For fungal communities, Mortierellaceae was observed with the highest relative abundance in the rhizosphere, Nectriaceae in roots, and Plectosphaerellaceae in corm of both locations of sample types. Notably, a small group of bacterial and fungal communities were not specifically accommodated by any particular plant component. This includes core bacterial communities associated with Chthoniobacteraceae and Sphingomonadaceae, and core fungal communities were associated with Trichocomaceae and Auriculariaceae.Bacterial communities classified as members of Bacillus, Pseudomonas, and Trichoderma  that is associated with promoting plant growth. The rhizosphere and root samples also included fungi belonging to Aspergillus and Trichoderma, which are known for their biocontrol attributes. Bacterial communities such as Pseudomonadaceae, which were supposed to be in high relative abundance in rhizosphere, root, and corm, have been poorly explored with respect to their plant growth promoting attributes in bananas. Other groups such as Hyphomicrobiaceae and Sphingomonadaceae have never been explored for their plant growth promoting traits. Regarding fungal communities, Trichoderma was among the highly abundant groups identified in the rhizosphere, root, and corm whose plant growth traits are known. More than 92.8% of bacterial groups that were identified have never been explored. In the case of fungal communities, very few genera (<1%) have been explored from any host plant  samples were with a lower bacterial and fungal diversity than the asymptomatic samples. This may be due to the disease stress, which is directly linked to the decreased value of available carbon for microbes in the rhizosphere, responsible for specific microbial groups selection, and thus regulating their community compositions. Banana plant resistance and Fusarium aggressiveness in different plant components also contribute to bacterial and fungal prevalence. In both locations, bacterial and fungal OTU counts were maximum in the rhizosphere followed by root and corm in sample types (We demonstrated that the rhizosphere had the greatest diversity of bacterial and fungal communities in the banana plant  but thatle types . Host plle types . Our resle types . NotablyBacillus, Pseudomonas, and some free-living diazotrophs such as Azospirillum and Bradyrhizobium which contribute to efficient nutrient acquisition, promote plant growth, and control soil-borne diseases. Pseudomonas and Bacillus, commonly found in the banana rhizosphere are known to promote growth, reduce stress, and act as biocontrol agents against diverse Fusarium-infected plant diseases [Aspergillus which has already been reported for its contribution to plant growth and induced resistance against phytopathogens. Beneficial interactions of fungal communities with host plant roots are also known to improve nutrient acquisition [A fraction of the core bacterial and fungal communities identified in the present study have previously been reported, but a larger proportion have not been reported. Some core bacterial and fungal communities were known to stimulate plant growth and promote health in various crops. We infer that these beneficial microbial communities are positively correlated with banana health. These include diseases . Host pldiseases ,24,25. Wuisition ,28,29,30Our study revealed that banana plants harbor rich and diverse bacterial and fungal communities, which decreases from the rhizosphere to the roots and corm. Rhizosphere, roots, and corm components impacted bacterial communities, and different sample types influenced fungal communities distribution. Besides the high taxonomic and functional diversity in bacterial communities, we observed about 92.8% of the total relative abundance. By comparing communities in other crops, we state that several of the communities discovered in banana could serve as key nodes for plant growth and health.


Blended web-based and face-to-face (F2F) treatment is a promising electronic health service because the strengths of one mode of delivery should compensate for the weaknesses of the other.The aim of this study was to explore this compensation by examining patients’ user experience (UX) in a blended smoking cessation treatment (BSCT) in routine care.Data on patients’ UX were collected through in-depth interviews (n=10) at an outpatient smoking cessation clinic in the Netherlands. A content analysis of the semantic domains was used to analyze patients’ UX. To describe the UX, the Hassenzahl UX model was applied, examining 4 of the 5 key elements of UX from a user’s perspective: (1) patients’ standards and expectations, (2) apparent character (pragmatic and hedonic attributes), (3) usage situation, and (4) consequences .BSCT appeared to be a mostly positively experienced service. Patients had a positive-pragmatic standard and neutral-open expectation toward BSCT at the treatment start. The pragmatic attributes of the F2F sessions were mostly perceived as positive, whereas the pragmatic attributes of the web sessions were perceived as both positive and negative. For the hedonic attributes, there seemed to be a difference between the F2F and web sessions. Specifically, the hedonic attributes of the web sessions were experienced as mostly negative, whereas those of the F2F sessions were experienced as mostly positive. For the usage situation, the physical and social contexts were experienced positively, whereas the task and technical contexts were experienced negatively. Nevertheless, the consequential appeal of BSCT was positive. However, the consequential emotions and behavior varied, ultimately resulting in diverse combinations of consequential appeal, emotions, and behavior .This study provided insights into the UX of a blended treatment, and the results support the expectation that in a blended treatment, the strengths of one mode of delivery may compensate for the weaknesses of the other. However, in this certain setting, this is mainly achieved in only one way: F2F sessions compensated for the weaknesses of the web sessions. As a practical conclusion, this may mean that the web sessions, supported by the strengths of the F2F sessions, offer an interesting approach for further improving the blended treatment. Our theoretical findings reflect the relevance of the aspects of hedonism, such as fun, joy, or happiness in the UX, which were not mentioned in relation to the web sessions and were only scarcely mentioned in relation to the F2F sessions. Future research should further investigate the role of hedonistic aspects in a blended treatment and whether increased enjoyment of a blended treatment could increase treatment adherence and, ultimately, effectiveness. Health care is undergoing a sea change driven by the progress in digital technology . One of One of the main elements clarifying the individual’s use of services in general  and eHeavia a user interface and F2F communication in counselling sessions.Usually, the term UX refers to products, services, and objects that a person interacts with through a user interface . HoweverAlthough a number of studies have examined the blended treatment , little features of a product or service, a process is triggered, in which the user constructs the UX  are selected and combined by the treatment developers independently of the patients that ultimately follow the treatment. Since the features are not constructed by the users, the product features only play a minor role in this study. In turn, the focus is placed on the patients’ response to the treatment’s features to explore the UX from the user’s perspective in a narrower sense. This means that the UX from a user’s perspective is built based on only four of these five key elements: (1) the patient’s standards and expectations, (2) the apparent character, (3) the usage situation, and (4) the consequences. In the following paragraphs, each key element is described and illustrated by examples of how it applies to the blended treatment in this study.features of a product or service refer to its content, presentation, functionality, and interaction [via a Web-based smoking diary). More details about the study intervention are provided below in the Methods section.The eraction ,24. The eraction  that comstandards and expectations are based on their experiences with the other services [The patients’ services ,24 with pragmatic and hedonic attributes [utility  and usability  of a service, such as BSCT. Hedonic attributes of BSCT refer to stimulation , identification , and evocation .When confronted with a service, an apparent character is constructed by the user. The apparent character is a cognitive structure representing tributes ,24. Pragtechnical, task-related, physical, and social contexts. These situations are different between patients and over the course of the treatment, especially for the Web-based sessions. For example, filling in a smoking diary while being on your own in a silent surrounding may result in different consequences than doing this in the living room with a partner and children around you.The usage situation moderates the consequences of the apparent character ,24 and rappeal, emotions, and behavior [The fit of the apparent character and the usage situation leads to three consequences: behavior ,24. For As UX has been shown as an important factor in explaining the behavior of a user in general , and patBSCT is a clinician-led intervention  which coThe current study is a substudy of the LiveSmokefree study—a single-center randomized controlled noninferiority trial with parallel group design, which examines the effectiveness of BSCT as compared with F2F treatment. The inclusion criteria for the LiveSmokefree study were (1) aged 18 years or older, (2) willing to quit smoking, (3) current daily smoker (at least one cigarette a day), and (4) speaking/reading/writing Dutch.A purposive sample (n=10) of the participants from the blended arm of the LiveSmokefree study  that hadBoth the LiveSmokefree study and this substudy on patients’ UX were approved by the accredited Medical Research Ethics Committee Twente (P14-37/NL50944.044.14). The LiveSmokefree study was registered in the Dutch Trial Registration (NTR5113).Qualitative data about the patients’ UX was collected by in-depth semi-structured interviews. The interview guide  was deveThe interviews were conducted by the first author (LS) between October 2016 and March 2017. Because LS is not a Dutch native speaker, he was supported by trained Dutch research assistants to avoid possible ambiguities and linguistic misunderstandings. On the date of the interview, the interviewees were picked up from the waiting area of the SRP and led to a neutral meeting room. After receiving permission for audio recording, the interviewer read a written introduction, which emphasized that the patient was invited to recall and describe (“tell stories”) their UX. After this briefing, a general stimulus  was used to start. Interviews followed a detailed written interview guide , but werThe audio-recordings were transcribed verbatim by trained research assistants following the guidelines for data preparation and transcription, as described by McLellan et al , and werThe data regarding the patients’ age, sex, education level, internet skills, nicotine dependence, and counselor  were acqThe data about adherence and smoking status  were acqBased on the semi-structured interview guide, content analysis was used to analyze all the interviews. The codebook was developed by two research team members , building on the interview guide and the research goals related to the clinical setting  ). The coAfter coding, all coded Dutch quotes were paraphrased in English by LS and collected in a table . ApplyinIn the following, the patient characteristics are presented first. Then, the positive and negative statements for each key element are described. As far as possible, this is done first for BSCT in general and then for the F2F and Web sessions. It is to be noted that the analysis and presentation methods were clarified after the interview phase, and the statements were not always available in every area.Patients’ characteristics are shown in In general, the patients approached BSCT mostly with a positive-pragmatic standard and a neutral-open expectation. None of the patients had followed a blended treatment or a Web-based treatment before. Therefore, their standards and expectations were based mainly on earlier experiences with F2F sessions, with earlier stop smoking attempts, and with ICT use in general. Only one patient (#34) used health-apps . However, most of the patients (7/10) had received F2F counseling before, participated in a group therapy (#34), or were familiar with mindfulness .F2F sessions, positive standards predominated. Patients said, for example, that “Human touch is important” (#75), quitting is easier with F2F support , F2F treatment is “ideal,” and it “adapts to your competencies” (#12). One patient, however, considered that it “can be hard if you dislike the counselor” (#14).For earlier stop smoking attempts, patients had the standard that the quitting success may depend on themselves , on missing support .Building amongst others on ICT-use in general, while being familiar with using ICT , the majority of patients showed a pragmatic standard “Computer is a tool” ; “I am not a computer freak” (#12); “Computer is not my way” (#10); or “I am neither a forerunner nor a left behind” (#25). Only one patient (#34) reported that he “personalizes his mobile.” Most patients also emphasized that they do not prefer computer-mediated communication over F2F communication because it “leads to misunderstandings” (#53), “it is easier to cheat online” , “it is easier to do sloppy” (34), “online information is not as important as written on paper” (#25), or “I do not trust internet information” (#509). Three of the patients  reported that they use mobile devices  more often, for example, “I use the laptop less since I have a tablet” (#25) or “I prefer mobile over PC” (#27).For BSCT in general, most patients  described their expectations as “neutral” or “not clear,” while some  emphasized to expect support from BSCT, saying, for example, that they want the counselor to be “a driving force” (#10) or that they expect “to get more grip on smoking cessation” (#34). One patient (#14) remarked that BSCT “is new and sounds interesting.”Referring to pragmatic attributes (usability and utility) were experienced mostly positive while the hedonic attributes , especially for the Web sessions, tended to be negative.While being confronted with BSCT and moderated by their standards and expectations, the apparent character of BSCT that the patients constructed, seemed to be both positive and negative. The usability and utility) were experienced as good. However, some patients also criticized pragmatic aspects of BSCT, especially of the Web sessions, which indicated possibilities for further improvements.BSCT’s pragmatic attributes , “there have been no problems” (#509), “everyone was kind” (#34) “everything was clear and easy to use” (#53), “all was quite logical” (#14), the “treatment was picked up well” , “BSCT parts connected to each other” , and that “the intervals between sessions were fine” (#53). One patient (#14) reported “less travelling”  as an advantage of BSCT, while another patient (#10) found that “still having to travel to the hospital at all” is a disadvantage. Further negative aspects of usability reported were the “long waiting list” before treatment start  (Note: regular waiting time before treatment start was around two months), “the long waiting times” in the waiting area before start of a F2F session (#14), that it was “not clear where to turn to outside the office hours” (#14), that “intervals between sessions were too long” , and that “not everything was explained in detail” (#25) and that the patient was “surprised about the order of the sessions” (#25).Most patients experienced the usability of the F2F sessions was experienced as “easy” (#25) or as “easier than web” (#27). Yet some patients criticized “that the counselor did not have enough time”  or that the sessions were “slow and time consuming” .The usability ofWeb sessions as “easy to use,” while three patients  reported the opposite (“not easy to use”). The patients criticized that the Web sessions were “too time demanding” , there was “a lot of repetition” , they “did not get immediate response” (#14), they “did not receive online assignments” (#27), and the “login would have been easier if you do not have to remember your password” (#27). Furthermore, two patients  reported that they did the smoking registration on paper before doing it on the Web because “it was simpler” (#106). However, this was “double work” (#106). Yet the patients liked “to be notified about new Web content automatically” (#75), that “emails and phone calls raised awareness” (#34), that “filling in forms online was handy” (#34), and “online saved time” (#25).Six patients , experienced the utility of BSCT in general, the patients experienced the utility as positive, finding that “all BSCT parts were helpful—some more, some less” (#53), BSCT “matched my quitting process” (#53), “all has been discussed” (#106), “there was progress” (#106), BSCT “offered support” (#27), or “Web only would not have offered what I needed” (#75).With regard to the utility of the F2F session was experienced as positive by most patients (7/10) also. Patients reported that F2F “offered flexibility” (#75) as “I could talk to the counselors about all of my problems” (#509), “all has been discussed” (#14), that with F2F “it was easier to ask questions” (#14), F2F “you got direct answers” (#14), F2F “stimulated more than web” (#10), and F2F “with medication was better than medication only” (#106). The counselors “reinforced” , “stimulated” , “offered support” , “shared good metaphors” (#53), and “explained everything very well” (14). Three patients experienced the F2F session as not useful, saying that the counselors “did not offer enough support” , “did not reinforce” (#12), “did not motivate” (#12), “did not discuss all alternatives” (#34), and “asked too much questions” (#27).The utility of the Web sessions, there were both positive and negative experiences. Some patients had a predominantly negative experience saying that “reporting via Web was too time demanding” (#509), that Web “offered too much information” (#106), that Web “did not match my quitting process” , that “a computer does not answer” (#14) and that Web “does not work for me” (#75). Furthermore, ideas for improvement were reported, such as “an App would be better than web” (#34) and other services should be included, such as “short reinforcements via WhatsApp, emails, in-between sessions, video instructions, helpdesk, chat support, short instructions” (#34) and “audio information” (text to speech) (#27). However, patients also reported positive experiences saying that the Web “offered support in difficult moments” (#53), Web “offered tips” (#53), and “it was good to have information available online” .For the stimulation, identification, and evocation), BSCT was experienced both positively and negatively. While some patients felt stimulated by BSCT, others reported being demotivated. Especially for the Web sessions, most patients reported low identification. Also, the Web sessions evoked mostly negative comparisons and induced several ideas for improvements.For the hedonic attributes , to “discuss costs of smoking” (#12), to “think” , to “dig deeper” (#509), or to “look back” (#75). Patients also reported that the carbon monoxide measurements during the F2F sessions stimulated quitting . On the other hand, patients reported that “BSCT did not offer new things” (#34) or was “not interesting” (#14), and that certain interventions  were “not new” (#25). Furthermore, patients were demotivated by “always the same questions” (#27), by “digging too deep” (#27), and by contradictory goals (quitting smoking vs weight reduction) (#27).For F2F sessions, patients said that the “counselor had no impact” . However, some patients  reported that they were reinforced by the counselors to use the Web sessions.For the Web sessions, one patient said, that Web “broadened your awareness” (#75), whereas the majority of patients reported no or low stimulation saying that “online won’t get through to me” , “online exchange with the counselor did not affect extraordinary” , and to be demotivated by the Web sessions  or computer use (#106).For the BSCT in general patients could identify linking to individual features, such as “perseverance” or “self-control.” However, for the Web sessions, most patients reported low identification. The ones showing higher identification with the Web sessions did this by referring to personal contact with the counselor. Patients found it easier with the F2F sessions than the Web sessions of BSCT.For BSCT in general, patients reported that BSCT linked to individual features, such as “perseverance” (#75), “self-control” (#75), “the ability to work based on reading and writing” (#75), “IT-skills” (#10), and “age” (#10). However, one patient (#27) reported that she “felt treated like a child” and that she “lost her rhythms.”Related to F2F sessions patients reported that these “felt more familiar” (#106), that patients liked “the F2F sessions the most” (#53) and “talking to the ladies” (#10)  refers to the female counselors).For the Web sessions, most patients reported low identification, saying, “I don't feel like it much” (#106) or “not to like online” , that “online is not my style” , to “prefer on paper” (#25), or being “too stupid for IT” (#10). One patient (#75) showed a higher identification with the Web sessions, emphasizing “Web I did for myself,” “I know why I did Web,” and “I understood the process.” In turn, she criticized saying that “online did not give the opportunity to make it more personal” (#75). Three patients reported that the Web parts supported their personal contact with the counselor, mentioning that via Web parts “I had contact with her” and “they knew something about me” (#509), that “during the F2F sessions it became clear that the counselor reads the Web content“ (#25), that “I had the idea that it is used on the other side” (#53), and that “you knew there is someone behind it” (#34). In turn, three patients reported that “you didn’t know who has written the content” (#15), that “computer did not talk to you” , and that “you did not get the feeling that there is a human being on the other side” (#12).For the Web sessions, the patients reported several negative comparisons, such as “Web was like handling a machine, because you are not sitting opposite to each other” (#106), Web sessions were like “bookkeeping” , like “a manual” (#53), like “filling in tax forms” (#10), and like “paper” (#27).For the technical context had a negative impact on the UX. Especially the Web sessions depended on the technical factors, which were criticized. Furthermore, referring to the task context of BSCT in general, some patients reported not having enough time for the treatment. Both the physical and the social context were described as mostly positive.For the usage situation, mostly the Web sessions, criticizing by saying that Web “did not work on iPad” . Although the patients had been informed at start of the treatment that the software for the Web session could not be used on tablet computers, they would have preferred to use tablets because the “Tablet is always on, Laptop not”  and tablet “is more comfortable” (#10), or because they  moved from laptop to tablet during BSCT. Furthermore, for the use of computers for the Web sessions, the patients criticized by saying that they “had to start up the laptop, which takes time” .For the technical situation, the patients referred to the Referring to tasks, patients reported not to have enough time for the BSCT “because of other tasks” (#509) or “because of family tasks” (#106), or to feel “sometimes stressed—sometimes relaxed” (#27).F2F sessions, the patients reported little about the physical usage situation, mentioning only “that I live close to the hospital” (#25) and “that the treatment took place in the old building, which was not a nice place”  (Note: Between the patients treatment and the interviews the department moved to a new building).For the Web sessions, the patients shared more information about the physical usage situation reporting that they did the Web sessions at “my own home office” , in a “hobby room upstairs, which is a nice place” (#10), “upstairs, where it is quite hot in the summer” (#14), “with the laptop at the dining table with wife and children around me” (#53), “in the kitchen” (#106), and “with laptop lying on the bed in the sleeping room” (#509).For the BSCT in general, most patients reported feeling supported by the family, saying that everyone “supported”  and “complimented” (#53), that “family motivated stopping” (#106) and “nearly no one in our family smokes” (#14), that “my partner stimulated” , “offered incentives” , “accompanied” (#14), “gave feedback on better health conditions” (#53) and “does not smoke” (#509), and that “children supported” (#27), “children were positive about quitting” (#53) and “my son also quit” (#14). One patient said he (#25”) “lives alone” and “did not tell much about BSCT”; she reported that “everyone was sceptic of the quitting success.” One patient (#27) reported that “her partner did not support,” “questioned the Web sessions,” and broke “the agreement to smoke outside only.”For the social situation during For friends and colleagues, the patients reported that “none of my friends smoke” (#10), “no one smokes inside” (#14), and that “colleagues also have positive experiences with cessation treatment” (#53). Furthermore, one patient emphasized that he “stimulates others to quit smoking” (#10).BSCT in general had a positive appeal, while emotions  varied. Again, there was clear distinction between the F2F sessions and the Web sessions. Similar to the emotional consequences, the behavioral consequences  also varied, ultimately resulting in diverse combinations of consequential appeal, emotions, and behavior.Overall, BSCT in general, appealed to be “good.” The patients reported that BSCT was a “mix of talking and reading” (#14) and it “offered variety” (#75). The “shared information both F2F and Web was fine” (#106) and “Web only would not have been so easy” (#53). F2F sessions and Web sessions were “quite different” (#34); “sometime F2F was better—sometimes Web was better” (#14) and “Web was an extension of F2F” (#53). One patient (#27) emphasized the medical treatment saying “Champix was good.”For six patients , F2F sessions mostly appealed to be “good.” The patients reported that the F2F sessions were “fine” (#509) or “finer than web” (#106) and that the F2F sessions were “most important” (#53) or “most important at treatment start” (#34). One patient (#12) emphasized “that only F2F touches your heart” and that he would go for F2F “100% in all facets.” However, one patient (#27) said that the F2F sessions were “whiny.” For the counselors, one patient (#27) described her counselor as “nice,” while another patient (#34) said that his counselor had a “stiff posture” and that she was “annoying,” “pedantic” and “cumbersome.”The Web sessions, the majority of patients reported a negative appeal, saying that the Web sessions “yielded nothing” , were “a lot” , “cumbersome” (#106), “boring” , “tiring” (#27), “nonsense” , and “dead” (#10). However, one patient (#75) said that “Web was nice” while others—also referring to positive appeal—reported that the Web sessions could be done “comfortable at home” (#34) and that Web was “a serious matter” (#25), although she would not go for “Web only.”For the BSCT in general, some not. Again, there was a distinction between F2F sessions and Web sessions, but not as clear as for the appeal.Emotional consequences varied—some patients were satisfied with BSCT in general, three patients reported to be satisfied  or “thankful” (#106). Furthermore, referring to negative emotions about BSCT in general, patients reported “feeling abandoned, left alone” (#12), “tension and the need to relax physically” (#75), and “contradictions between quitting smoking and weight reduction” (#27). One patient said that the F2F sessions and Web sessions stimulated “the same moods” (#25). The mood during the F2F sessions was “good” , while Web sessions were experienced as “unpleasant” (#27) and “making me nervous” (#34). One patient reported to feel “guilty because I did not stick to appointments” (#27).While two patients  said that they were not satisfied with adhered to BSCT in general, doing both the F2F sessions and the Web sessions. One of them (#14) said he “could have stopped after four sessions” because he was “sure not to need it in the future.” However, he continued BSCT “to do the counselors and researchers a favor.” Five patients  reported that they found the Web sessions “sloppy.” Furthermore, one patient (#27) mentioned that she “forgot about some of her sessions.”During the interviews, three patients  reported that they BSCT in general  was 6.5 (IQR 5.50-11.75). Based on a 60% threshold for both the F2F sessions and the Web sessions [Based on the auxiliary data , medium sessions , three pquitting. One (#106) mentioned that “I had no problems because I had medication (Champix)” and “I threw away my last shags.” The other one (#53) mentioned that he told himself “Never again!” and “Enough!” (Basta!), and that “he saved money for the holidays with his family.” Two patients  reported that they reduced smoking during BSCT.Based on the interviews and the auxiliary data, four patients  reported successful The variety of consequential appeals, emotions, and behavior could be summarized in three types of combinations: “positive,” “negative,” and “mixed” consequences.Three patients  experienced “positive” consequences. BSCT appealed to be good and they felt “satisfied”/“thankful,” adhered to the treatment and quit smoking.On the contrary, another three patients  experienced “negative” consequences: The Web sessions appealed negative  and BSCT in general resulted in negative emotions (abandoned/not satisfied). Ultimately, they did not adhere to the treatment and did not quit smoking.Mixed consequences: Three  of the four remaining patients did not quit smoking, while one (#10) did. Interestingly, BSCT in general appealed “good” to the nonquitters  while—for the quitter (#10) at least, the Web sessions appealed to be “nonsense.” Although two of the nonquitters  reported negative emotions (“tension”/“not satisfied”), these two patients at least partly adhered to BSCT . In turn, the third nonquitter (#27) reported positive emotions (“satisfied”) but did not adhere at all.To the remaining quitter (#10), although the Web sessions appealed to be “nonsense” and he did not adhere to BSCT in general, he reported positive emotions (“satisfied”) and ultimately quit smoking.This study aimed to provide insight in the UX of a blended treatment. In the light of this study, the expectation that the strength of one mode of delivery can compensate for the weaknesses of the other in blended treatment, can be partially supported because the F2F sessions compensated for the weaknesses of the Web sessions so that BSCT in general was mostly experienced positively.Our study described the UX of a BSCT using Hassenzahl’s key elements of UX from a user’s perspective ,24. OverAlthough patients’ pretreatment expectations toward BSCT were neutral and the Web sessions appealed negative, overall BSCT in general appeared to be positively experienced afterwards. This is in line with an evaluation study (n=7) by Kooistra et al  of a bleInterestingly, although the hedonistic gap made the Web sessions appeal negatively, the overall BSCT was experienced positively. This could support the assumption that in blended treatment the strength of one mode  may compensate for the weaknesses of the other  ,5. This We mainly found that the F2F sessions compensated for the weaknesses of the Web sessions. Yet three patients reported that the Web sessions influenced their personal contact with the counselor positively. Although the Web sessions mostly had a low identification and a negative appeal, the Web sessions supported the F2F sessions because these patients felt more related to the counselor. However, even though the Web sessions may have supported the F2F sessions, it should be noted that none of the patients indicated that the Web sessions compensated for the F2F sessions. It remains undecided if this is because there was no need for compensation as the F2F sessions were overall positive, or that the Web sessions were not able to compensate. It should also be taken into account that the routine care in the hospital was not Web based, the patients were of older age, and did not have an affinity for the internet , and the patients’ preferences for modes of delivery were not taken into account as they were not free to choose for BSCT because they were included in a randomized controlled trial. These factors may additionally explain the low positive impact of the Web sessions.The emotional and behavioral consequences varied, ultimately resulting in three types of combinations of appeal, emotions , and behavior : “positive,” “negative,” and “mixed.” These types can be used to work on UX profiles that can support further development of blended care and improve the matching between the treatment and patient .Further work needs to be done to investigate how the integration of F2F and Web treatments can be carried out to ultimately increase the effectiveness and efficiency of a blended treatment. This study provides a hint to explore this question by emphasizing the relevance of hedonic attributes in the UX. Even if the UX was predominantly positive because the hedonistic gap in the area of the Web sessions was compensated relatively easily by the F2F sessions, this does not mean that BSCT cannot be further improved to increase adherence and long-term abstinence. Hedonism could be a starting point for this. Further research on the following questions could be useful:Could the hedonistic gap in the Web sessions be not only due to the mode of delivery, but also the concrete content of the Web sessions? Perhaps, it was precisely the interventions that the patients experienced as nonhedonistic, which were the part of the Web sessions. This was neither explicitly considered in the treatment design nor asked for in detail in the interviews. However, this might have been the case because more standard exercises and messages could be offered on the Web more easily. A stronger involvement of patients in the early design stages of the Web sessions may help to prevent the hedonistic gap.May hedonism play a less prominent role in the health care context than in the other domains? Patients tend to approach a health problem with a pragmatic-neutral expectation, such as “What’s important is that it works. As long as it helps, I can also accept that it is unpleasant.” Consequently, hedonistic aspects, such as fun, enjoyment, pleasure, and aesthetics may not be expected in the first place, and therefore, may not be missed. Moreover, this may be compensated relatively easily by positive experiences with the counselors. However, if hedonism was less important in health care, it would contradict our conclusion that it should receive more attention.Could both scientific research and clinical practice use insights from persuasive systems design ,37, “nudHow do the apparent character and the consequential appeal and emotions relate to the quitting behavior? On the one hand, apparently a negative appeal  may lead to consequential combination of negative appeal, emotions, and behavior . On the other hand, it is also possible to distinguish between diverse episodic UXs ultimately leading to a cumulative UX , for exaThe data and model used in this study provided a rich insight into the UX of a blended treatment for smoking cessation in an ambulant clinical setting. Though this study yielded valuable knowledge for the understanding and improvement of BSCT and the matching of patients and treatment, limitations should be noted when interpreting the findings. First, the sample of patients used in this study was a purposive sample that was intended to represent the heterogeneity of the patients of an outpatient cessation clinic. Hence, it is uncertain, if the rather small sample (n=10) is representative of the population referring to characteristics, such as sex, age, internet skills, or educational level, and if the thematic saturation was reached with this sample size. It should also be considered that patients did not choose BSCT on their own but were randomly assigned to it because they participated in a randomized controlled trial. However, the high degree of consensus in the findings may indicate generalization of our main conclusions. Second, the interviews were conducted retrospectively. Conducting additional interviews at treatment start and during treatment could have offered a more valid insight in the process of patients’ UX construction . Third, the software that was used for the Web sessions was developed around 2005, which may have led to technical incommodities , which may have negatively impacted the UX. This assumption is based on the fact that patients often stated that they would have liked to do the Web sessions on their mobile device. We assume that a newer mobile device–compatible software with similarly good pragmatic attributes as the previous Flash-software could have also improved the hedonistic attributes and thus have led to more positive consequences. Fourth, the interviews were conducted with the first patients that followed the new blended version of the smoking cessation treatment. At that time, the treatment still had some teething problems, such as being new for the originally F2F counselors. We did not integrate the counselors’ views on the uptake of BSCT, and therefore, we cannot compensate for bias through inadequate treatment fidelity. Fifth, as long-term abstinence is the goal of a smoking cessation treatment, prolonged follow-up analysis of patients’ UX could reveal a different picture. For example, some patients may continue using the Web-based modality and benefit from this at a later stage, resulting in a UX that would be more in favor of the Web-based treatment. Conversely, relapse to smoking at a later stage may lead to a negative adjustment of the UX of the blended treatment. Sixth, we could not elaborate further on which specific parts of the Web sessions were experienced positively or negatively as we did not ask for these in detail in the interviews. Seventh, the study interventions are selected and combined by the researchers and treatment developers without considering the individual patients who ultimately followed the treatment. This resulted in a rather inflexible approach of blending  to allow for comparisons with the F2F treatment as usual in the randomized controlled trial (LiveSmokefree study) . This inThis study provides insight into the key elements of the UX of a blended treatment for smoking cessation and supports the expectation that in a blended treatment, one mode of delivery may compensate for the weaknesses of the other. However, in this certain setting, this could be mainly achieved in only one way: F2F sessions compensated for the weaknesses of the Web sessions. As a practical conclusion, this may mean that the Web sessions supported by the strength of the F2F sessions, offer an interesting approach for further improving the blended treatment in this specific context. Our theoretical findings reflect the relevance of the aspects of hedonism, such as fun, joy, or happiness in UX , which w