Forensic Scientists increasingly face challenges when testifying about pattern evidence. “Pattern evidence” here denotes fingerprint evidence, firearms, tool marks, footwear, tire impressions, handwriting, bite marks, etc. Critics question whether experts using only subjective information can provide the court with reliable information. Detractors maintain that objective data, meaning statistics, is necessary for the expert to form an opinion in court cases. Some critics have used the depreciatory term “junk science” to suggest that certain forensic science disciplines have little or no validity and should no longer be used.This short commentary offers a different assessment and argues for the value of pattern evidence in court cases. Suggesting that knowledge without statistics to assess its worth is a rather narrow point of view. To propose that without statistics, evidence interpretation has little probative value to assist a judge or jury in reaching a finding fails to consider how conclusions are made. It also suggests that any sort of expert opinion without objective data should not be used in court.The question of admissibility of expert evidence goes beyond the use or even the availability of objective data to support an expert's opinion. Many experts offer their conclusions in legal matters based on their professional training, knowledge, skills and experience. Often that knowledge is based on empirical data. Consider, psychologists who offer opinions about syndrome evidence, or forensic pathologists who offer opinions on the manner and means of death. Medical practitioners base diagnoses on practical experience, which may not have much statistical data to support conclusions. Practitioners would call that professional expertise. If there were the requirement that statistics had to be provided for pattern evidence cases, then all expert testimony would be held to that same requirement. The very purpose of expert witnesses is to assist judges and juries to understand matters beyond their ken. Opinion testimony would rely solely on the availability of objective data and subjectivity would limited or perhaps not be permitted.The defense bar, law school professors, academics and statisticians make the claim that statistical data is essential to quantify opinion testimony; that without statistics, opinion evidence should not be admitted in court to support a conclusion. While these stakeholders certainly have every right to make their claims, it is only the trial court judge who is the “gatekeeper” and who decides what is or is not admissible. That is not to say that statistics are not important, but where statistical information is limited, opinion evidence based on subjectivity ought not be dismissed out of hand. Rather the information should be presented to the court to make decisions on the weight and admissibility of the evidence.In the United States, courts have long wrestled with the question of who is an expert and what is expert testimony. The current rules about expert testimony  are found in the Federal Rules of Evidence (FRE) .A witness who is qualified as an expert by knowledge, skill, experience, training, or education may testify in the form of an opinion or otherwise if:(a)the expert's scientific, technical, or other specialized knowledge will help the trier of fact to understand the evidence or to determine a fact in issue;(b)the testimony is based on sufficient facts or data;(c)the testimony is the product of reliable principles and methods; and(d)the expert has reliably applied the principles and methods to the facts of the case.FRE Rule 702 under Testimony by Expert Witnesses states:An expert may base an opinion on facts or data in the case that the expert has been made aware of or personally observed. If experts in the particular field would reasonably rely on those kinds of facts or data in forming an opinion on the subject, they need not be admissible for the opinion to be admitted. But if the facts or data would otherwise be inadmissible, the proponent of the opinion may disclose them to the jury only if their probative value in helping the jury evaluate the opinion substantially outweighs their prejudicial effect.Further, FRE Rule 703 under the Bases of an Expert:Notes of Advisory Committee on Proposed Rules [The ed Rules  go into Strengthening Forensic Science in the United States: A Path Forward [In 2009, the National Academy of Sciences (NAS) issued it landmark report about forensic science,  Forward . The repA result of the NAS report was the creation of the Organization of Scientific Area Committees (OSAC) for Forensic Science  or OSAC Forensic Science in Criminal Courts: Ensuring Scientific Validity of Feature-Comparison Methods [More recently, The President's Council of Advisors on Science and Technology (PCAST) issued a report entitled  Methods .The report focused on a number of issues. The principal attention concerned the reliability of pattern evidence. It challenged the validity of evidence where feature comparative methods are used to associate items of evidence. Examiners observe patterns found within the evidence which may associate an item of evidence to another item or a particular source. The most common types of pattern evidence are fingerprints, but other types of pattern evidence, such as firearms and tool mark evidence, tire impression and footwear evidence, handwriting evidence, bite mark evidence, were also highlighted. In addition, the report also discussed DNA cases where mixtures of biological material were found, such as in the case of multiple assailant sexual assault cases.PCAST highlighted the importance of statistics to inform the trial judge about the significance of the evidence and by extension the degree of importance to which it should be given by a jury. It reported that in some cases, there is insufficient data to reliably make a conclusion about the connection between an item of evidence collected at a crime scene and a reference sample, for example a shoe impression in soil at a burglary scene and a suspect's shoes and points out that there is insufficient information to ascribe a statistical value to the significance of an examiner's observations.Kumho Tire Co. v. Carmichael case summarizes a key point:The Daubert “gatekeeping” obligation applies not only to “scientific” testimony, but to all expert testimony. Rule 702 does not distinguish between “scientific” knowledge and “technical” or “other specialized” knowledge, but makes clear that any such knowledge might become the subject of expert testimony. It is the Rule's word “knowledge,” not the words (like “scientific”) that modify that word, that establishes a standard of evidentiary reliability. Daubert referred only to “scientific” knowledge because that was the nature of the expertise there at issue. Neither is the evidentiary rationale underlying Daubert's “gatekeeping” determination limited to “scientific” knowledge. Rules 702 and 703 grant all expert witnesses, not just “scientific” ones, testimonial latitude unavailable to other witnesses on the assumption that the expert's opinion will have a reliable basis in the knowledge and experience of his discipline. Finally, it would prove difficult, if not impossible, for judges to administer evidentiary rules under which a “gatekeeping” obligation depended upon a distinction between “scientific” knowledge and “technical” or “other specialized” knowledge, since there is no clear line dividing the one from the others and no convincing need to make such distinctions.However, the issue here is not only the presence or lack of statistics to inform judges of their gatekeeping responsibility. A footnote from the Certainly, objective data about any sort of expert opinion evidence is helpful, but the absent that information should not necessarily exclude the information from a jury.The reader might well think that the information in this commentary does not really cover much new ground. However, this writer suspects that new forensic examiners as well as new prosecutors may not have considered this issue, and may get caught up in an evidentiary hearing with the goal of excluding certain physical evidence. So, it is certainly worth considering. In addition, judges often lack expertise in scientific and technical subjects and need assistance in understanding how all this fits together.The admissibility of expert evidence has yet to be fully resolved. Likely, there will be additional court decisions that will provide direction to trial court judges and forensic experts. For now, all we can do is to wait and see how this all plays out.


The revised reinforcement sensitivity theory (rRST) of personality has conceptualized three main systems: the behavioural approach system (BAS), behavioural inhibition system (BIS), and fight-flight-freeze system (FFFS). Research links greater relative left-frontal activity with BAS-related tendencies and impulsivity and greater relative right-frontal activity with “withdrawal” motivation that included both BIS and FFFS. Although rRST has addressed the separation of FFFS and BIS, much of personality neuroscience research does not indicate which system is related to right frontal activity. We administered the Reinforcement Sensitivity Theory of Personality Questionnaire (RST-PQ) to measure the BAS and its facets , BIS, and the withdrawal FFFS. We examined the association of RST-PQ traits with resting electroencephalogram (EEG) alpha-asymmetry in female participants (N = 162) by considering the influence of experimenter’s gender. In the total group, that included two subgroups with experimenters of different gender, BAS-impulsivity was related to greater left- than right-frontal activity, and FFFS, but not BIS, was related to greater relative right-frontocentral activity. These associations remained significant for the subgroup with a young same-sex experimenter, but not with opposite-sex experimenter. The BAS was conceptualized to embody approach- and reward-related responses to signals of reward. The BIS was thought to control goal-directed behaviour to aversive stimuli and signals of punishment and was described as the system of anxiety. This theory also mentioned a less delineated fight-flight system (FFS) that was responsible for fear. In this original view, what is less apparent is the hidden complexity in and between these systems, which makes any attempt to provide a psychometric description of them far from ordinary and prone to confusion. Thus, the revised RST rRST4; developed the FFS further into a fight-flight-freeze system (FFFS), which was clearly distinct from the BIS and was primarily responsible for controlling all forms of aversive stimuli, including frustrating nonreward, fighting threats, fleeing in active avoidance, or freezing to avoid predators’ interest. In the rRST, the BIS becomes responsible for resolving all forms of goal conflict, including the co-activation of the BAS and the FFFS. Although the newer classes of RST measures have conceptualized the separation of the FFFS and the BIS, most of them still conceive of the BAS as a unitary dimension. However, there is compelling evidence that the BAS is multidimensional, both on the basis of empirical evidence6 and on theoretical grounds7. According to Corr4, behavioural activation processes consist of a number of cognitive operations involving working memory and executive controls, such as identifying the biological reinforcer, planning behaviour, executing the plan, and physically grasping the final biological reinforcer8. A popular RST questionnaire, showing excellent reliability and validity data, is Carver and White5 BIS/BAS scales. This instrument includes one scale to measure BIS and three scales to measure BAS functioning . The BIS/BAS questionnaire has been widely employed in electroencephalography (EEG) research, using resting EEG alpha activity to relate the relative right-frontal activation with BIS scores and the relative left-frontal activation with BAS scores.The original Reward Sensitivity Theory (RST) of personality10. The relative activation in homologous areas of the left and right hemispheres is typically calculated by subtracting the resting EEG alpha power of the cortical area of interest in the left hemisphere from that of the right hemisphere11. Original research on the BIS/BAS and resting alpha asymmetry found inconsistent results12. Two original studies observed that individual differences in the BAS related to greater left- than right-frontal activation at resting baseline14. One of these studies also reported that individual differences in the BIS related to greater right- than left-frontal alpha activity during baseline14, whereas the other found no relationship between the BIS and asymmetric frontal activity13. Psychobiological models of approach, avoidance, and inhibition systems have mainly highlighted the BAS and FFFS systems as major descriptors of personality and temperament22. Research investigating asymmetrical activation of the frontal cortex with approach/avoidance behaviour has shown that approach/BAS is associated with greater relative resting left-frontal activity at baseline21, e.g.29, for review see30. Although the relationship between the BAS and left-frontal activity appeared to be more replicable than the relationship between the BIS and right-frontal activity, a greater relative right-frontal cortical activation is found to be associated with conflict-related avoidance/BIS, although this support has received less empirical attention33. However, despite evidence linking trait frontal activity with the FFFS, many studies have failed to replicate the finding that withdrawal is associated with greater trait or state right-frontal activity34.Alpha band power is inversely related to cortical activation, since alpha frequency desynchronizes in response to activation33, deriving BIS and FFFS subscales from the original Carver and White5 BIS/BAS scales, demonstrated that the BIS subscale, but not the FFFS, related to greater relative right-frontal activity, while a measure of impulsivity related to less relative right-frontal activity.From the perspective of RST, the major problem with BIS/BAS scales is the lack of separation of the FFFS and the BIS, which may account for inconsistent findings in past research when relating the BIS scale to resting frontal alpha activity. Recent study findings suggest that these mixed results may be due to items in the BIS scale assessing both the FFFS and revised-BIS. In particular, Neal and Gable35 developed the Reinforcement Sensitivity Theory Personality Questionnaire (RST-PQ) to facilitate future research, specifically on rRST and, in general, on approach-avoidance theories of personality. This questionnaire employed theoretically motivated thematic facets to cover the defensive space, comprising the FFFS  and the BIS , allowing the important separation between these two systems and the important distinction of reward sensitivity and impulsivity. The four sub-scales of the BAS – Reward Interest (BAS-RI), Goal-Drive Persistence (BAS-GDP), Reward Reactivity (BAS-RR), and Impulsivity (BAS-I) – allow this tool to test the instrument’s unique predictive power and redundancies. The first two BAS facets characterize the early stages of approach (“anticipatory pleasure” or “hope”), the latter two the behavioural and emotional “excitement pleasure attack” as the final biological reinforcer is reached. According to Corr36, this four-factor model updates Carver and White5 three-factor model of trait approach behaviour. According to Corr37 it is especially important to separate BAS-RI, BAS-RR and BAS-I. This distinction is since the first facet concerns the individual disposition to identify the biological reinforcer, the second the individual differences in emotional response to reward, and the third is tailored to reflect the final action for the capture of the desired object4.Very recently, Corr and CooperIn the present study, we administered the RST-PQ to a group of female participants to examine the link between the BIS and FFFS scales and asymmetry in frontal alpha activation.34, we expected that the BIS, as measured by the RST-PQ, would be related to relative right-frontal activity. In addition, considering the recent findings of a link between impulsivity and less relative right-frontal activity33 we predicted that greater BAS-I would be related to enhanced left-frontal activity.Based on a body of previous research linking personality traits of regulatory control to right-frontal activity38. The authors speculated that a link between alpha-asymmetry and the BAS emerged only among male participants when the experimenter was an attractive female. This hypothesis was also advanced by Schneider and collaborators, who failed to find a significant correlation between alpha asymmetry and personality for adolescents39. It also seems consistent with the significant association of BAS with frontal alpha asymmetry found in a study of our own among female participants with a male experimenter25. These observations call into question the hypothesis that the BAS is driven by patterns of resting cortical asymmetry and suggest the capability model of frontal EEG asymmetry and personality40 to account for inconsistent EEG asymmetry findings across studies. This view suggests that individual differences in affective tendencies will emerge as the product of the interaction between the innate capabilities of the individual and the situational context41. Thus, given the above-mentioned evidence, a further aim of the present study was to test the hypothesis that the situation in which female participants were undergoing a resting EEG assessment by a young male experimenter  would uncover a robust association of approach-oriented traits with greater resting EEG alpha asymmetry. In contrast, when female participants were interacting with a young female experimenter, we expected an association of withdrawal motivation traits with resting EEG asymmetry in favour of the right frontal cortex.A meta-analysis of studies examining the relation of the BAS with frontal alpha asymmetry yielded that this association is less robust than usually assumedCorrelations among RST-PQ personality traits and descriptive statistics are shown in Table Correlations between alpha asymmetry scores and personality measures were performed across the recording sites of interest for the Tot-Group, FF-Group, and FM-Group. The BAS-GDP, BAS-RI, BAS-RR, and BIS traits were all unrelated to alpha asymmetry scores of interest  and the FF-Group  = 6.85, p < 0.0001, R2 = 0.30; β = 0.51, p < 0.0001), whereas for the FM-Group, this relation failed to reach significance  = 0.47, p > 0.05, R2 = 0.05; β = 0.02, p > 0.05). A similar regression was also significant for frontal asymmetry in both the Tot-Group  = 4.34, p = 0.0004, R2 = 0.14; β = 0.35, p = 0.0002) and the FF-Group  = 6.99, p < 0.0001, R2 = 0.30; β = 0.50, p < 0.0001) but not in the FM-Group  = 0.58, p > 0.05, R2 = 0.06; β = 0.04, p > 0.05). In addition, FFFS scores were near the significance level as predictors of frontocentral asymmetry for the Tot-Group  = 1.95, p = 0.07, R2 = 0.07; β = −0.15, p = 0.07) and were a significant predictor for the FF-Group  = 2.21, p < 0.05, R2 = 0.12; β = −0.22, p = 0.037), but again, for the FM-Group, this relation was not significant  = 0.34, p > 0.05, R2 = 0.04; β = −0.06, p > 0.05). In the above models, only BAS-I and the FFFS were significant predictors of alpha asymmetry scores  or the BIS (conflict-related avoidance). Using the RST-PQ, which provides separate measures for the BIS and the FFFS, rather than supporting a single BIS scale, the current results indicate that the relationship between greater right-frontal activity and the FFFS scale may be driven by individual difference in pure avoidance/escape.Alpha asymmetry, calculated at conventional scalp sites, showed that higher BAS-I was highly related to greater relative left frontopolar and left-frontal activation. This relation was significant for both the total group, of which only approximately one-third of the participants had a male experimenter, and for the FF-Group, in which the experimenter was a young female. In addition, for the FF-Group, we also found a negative association of the FFFS trait with frontocentral asymmetry. This association for the total group of participants showed a trend towards significance (p = 0.07), whereas for the FM-Group, in which the experimenter was a young male, we failed to find any significant relationship between alpha asymmetry and personality measured by RST-PQ traits. Importantly, these results are the first to link BAS-I and FFFS measures of RST-PQ with greater resting left-frontal activity and right frontocentral activity, respectively. Additionally, our findings indicate that these links, obtained in female participants, are more robust when the experimenter is a young female. On the whole, the present findings support previous work establishing the connection between trait impulsivity (reduced regulatory control) and greater relative left-frontal activationet al.45, which showed that 60% of the variance in resting frontal activity is a result of trait influences, and 40% of the variance was due to occasion-specific fluctuations, but measurement errors were negligible46. However, it is important to note that we observed a robust personality-hemispheric asymmetry in the presence of the same-sex experimenter but not in the presence of opposite-sex experimenter, as observed by Kline and colleagues47 and the evidence provided in the review by Wacker, et al.38. According to these authors, individual differences in affective tendencies are dependent upon the interaction between the situational context and the innate capabilities of the individual41. This view has been supported by findings reported in a recent study, in which EEG asymmetry in male participants was correlated with their BAS scores only when the EEG recording was carried out by an attractive female experimenter, i.e., in a situation where approach motivation was highly salient but did not emerge without that contextual stimulus38. In contrast, the present study had only female participants, and the relation between personality and EEG alpha asymmetry was not significant in the case of opposite-sex experimenter, i.e., in a situation where approach motivation was prominent. This may have induced a potential greater embarrassment felt by female participants in presence of a male experimenter. In addition, since we did not collect sexual orientation of the participants we cannot generalize that all participants experienced an approach motivation, thus variability in sexual orientation may contribute to mask the effect. Considering that this state of embarrassment is variable across female participants, we think that much of the variance in resting frontal activity was more dependent on the extent to which each female participant perceived the young male experimenter as an attractive person. In this case, it may be that situational variables were particularly strong and may overwhelm the influence of a trait relationship with the resting EEG measure. This is not surprising, considering that nearly half of the variance on resting EEG asymmetry of the variance is due to state influences48.In terms of the influence of sex concordance between participants and experimenters in the relation of personality traits and EEG alpha asymmetry, our findings indicate that sex concordance may influence EEG resting activity during recording in the laboratory. However, the current results indicate that the association of the BAS-I trait to greater left frontopolar and frontal activity was sufficiently robust to be significant in the whole group of female participants  had a young female experimenter and 57  a young male experimenter.One hundred and sixty-nine right-handed female psychology students participated for course credit. Due to technical problems with EEG recordings, the data sets of seven participants could not be completed. The remaining 162 female participants defined the sample of the present study . Hand preference was assessed using the Italian version of the Edinburgh Handedness Inventory35. The RST-PQ consisted of 71 statements and measures three major systems: (1) the FFFS, related to fear; (2) the BIS, related to anxiety; and (3) the BAS, measuring four behavioural approach facets: BAS-GDP ; BAS-RI ; BAS-RR ; BAS-I . Cronbach’s α values for the BIS and the FFFS, respectively, were 0.86 and 0.78. Alpha values for the BAS-GDP, BAS-RI, BAS-RR, and BAS-I were, respectively, 0.87, 0.75, 0.78, and 0.76.The participants completed the RST-PQEach participant was seated in an electrically shielded EEG booth, and electrodes were applied to measure EOG and EEG. Participants accomplished 8 min of baseline resting EEG recording, half with eyes open and half with eyes closed, in a counterbalanced order across participants. They were instructed to remain still and to inhibit blinks or eye movements during each recording period. During the eyes-open condition, participants had to fixate on a white cross presented in the centre of a computer monitor that was 1.2 m distant from their head.51. The ocular correction procedure on EEG data was performed using the method by Gratton, et al.52. After ocular correction, data were again visually inspected for appropriate removal and correction of artefacts. All epochs were extracted through a Hamming window, which was specified to diminish the signal on 20% at the beginning and on 20% at the end of the epoch, to prevent spurious estimates of spectral power. After artefact detection, all available artefact-free EEG epochs  were extracted from both the eyes-closed and eyes-open trials and subjected to conventional spectral analyses in accordance with Sutton and Davidson14 method.Scalp EEG was recorded using a 32-channel tin electrode stretch Lycra cap . Electrode placement was based on the 10–20 system, with a ground electrode mounted between FPz and Fz. Prior to the placement of electrodes, the expected electrode sites on the participant’s scalp and face were cleaned with an alcohol solution. Electrode impedances were kept under 5 kΩ, with homologous sites held within a difference of 1 kΩ. EEG activity was referenced online to the left earlobe. Bipolar horizontal and vertical EOG were recorded, respectively, from the epicanthus of the right and the left eye and from the supra- and infra-orbital positions of the right eye using standard tin electrodes. EEG was recorded using a 40-channel NuAmp acquisition system  set in DC mode, with a gain of 200 (100 for eye channels), a bandpass of 0.01–100 Hz (Butterworth zero phase filter with 24 dB/octave roll off), and an online notch filter at 50 Hz. The signals were sampled at 1000 Hz. After data acquisition was accomplished, the continuous EOG and EEG recordings were visually inspected, and each portion of the EEG data showing ocular, muscular, or technical artefacts in any channel was rejected for this and all simultaneously recorded channels14. Each consecutive epoch overlapped by 50%.Power spectra were calculated using Fast Fourier Transform (FFT). Prior to spectral analysis, an off-line linked-ears (A1 + A2) reference was computed to facilitate comparisons with previous work e.g.53. Following previous research, alpha asymmetry measures were calculated as the right minus left difference of ln power values of homologous scalp sites of interest, such that higher scores indicated greater left-sided cortical activity. The following five alpha asymmetry scores were calculated: frontopolar (Fp2–Fp1), frontal (F4–F3), frontocentral (FC4–FC3), central (C4–C3), and parietal (P4–P3). These measures served for correlation analyses with personality measures. The artefact screening, re-referencing, and spectral analysis were performed with Brain Vision Analyzer 2.0 , and further computation of asymmetry measures was performed using SAS version 9.4.The band of interest was the conventional alpha band (8–13 Hz), and power values were averaged across all epochs. Power values were natural-logarithm (ln) transformed to normalize the dataTo test the influence of the experimenter’s gender on EEG alpha asymmetry, separate correlation analyses were performed in the following three groups of participants: (1) total group (N = 162), wherein 57 participants had a male experimenter (Tot-Group); (2) group (N = 105) with a female experimenter (FF-Group); (3) group (N = 57) with a male experimenter (FM-Group).


The Internet, and particularly YouTube, has been found to be and continues to develop as a resourceful educational space for health-related information. Understanding physical literacy as a lifelong health-related outcome and facilitator of an active lifestyle, we sought to assess the content, exposure, engagement, and information quality of uploaded physical literacy videos on YouTube. Two researchers collected 300 YouTube videos on physical literacy and independently coded each video’s: title, media source of upload, content topics related to physical literacy, content delivery style, and adherence to adapted Health on the Net Foundation Code of Conduct (HONcode) principles of information quality. Physical literacy videos that focused on physical activity and behaviors were the strongest predictor of high quality ratings, followed closely by videos covering affective domains  of physical literacy. The content delivery method was also important, with videos utilizing presentations and testimonials containing high quality information about physical activity. Thus, providers of physical literacy and health-related online video content should be aware of and adhere to the expected quality standards. As health information expectations and ethical standards increase, the Internet, and specifically YouTube, has the potential to enhance video resources, virtual networking opportunities, as well as the sharing, dissemination, accumulation, and enrichment of physical literacy information for all. Regular physical activity is a critical component of healthy living, particularly as it pertains to weight management, preventing chronic disease, and promoting psychological well-being ,2. It isAccording to Whitehead , the affAt present, scholarly conversations continue as to how to best implement, endorse, and teach physical literacy in educational settings and sport/physical activity programs ,12. For When considering physical literacy from a health perspective, both as an outcome and antecedent to an active and healthy lifestyle, it becomes a worthwhile endeavor to examine and understand how physical literacy information is shared and disseminated on the Internet, and in particular, among social media users. Social media has been found to be a popular and increasingly-accessible resource for health-related information . YouTubeAs the Internet and social media allow for asynchronous and synchronous transmission and dissemination of information, it is critical for proponents of physical literacy  to be aware of how physical literacy is portrayed online, especially through popular social media which is a common learning forum for youth health and wellness . Given tNevertheless, as posited by Dudley et al. , “develoMoreover, as noted by Casey, Goodyear, and Armour , among twww.youtube.com), with 1.8 billion registered users [Is the information quality of YouTube videos on physical literacy associated with viewer attitudes?Does the quality of physical literacy information in the YouTube videos vary based on media source?Do the content properties in the physical literacy YouTube videos influence the (a) quality of information and (b) viewer attitudes?Does the content delivery style of the physical literacy YouTube videos influence the (a) quality of information and (b) viewer attitudes?While the aforementioned discussion demonstrates the recent  investigation of social media platforms as health-related, information resources, little attention has been directed towards evaluating physical literacy videos on YouTube. For instance, although YouTube has been investigated as a source of information for a number of health-related topics ,18,44,45ed users , is the ed users . The plaed users  and is ued users ,50. YouTed users . Furthered users ,16,18. TAccording to Petrescu , on averFor each video, we wanted to know which properties of physical literacy were addressed and in which context, athletic or educational; as well as the media source that uploaded the YouTube videos and for which target audience they were intended. To address these questions, the following details were recorded for each retrieved video: title, media source of upload, date of upload , duration , content topics related to physical literacy , the style of content delivery , and adherence to adapted Health On the Net Foundation (HONcode) principles of video quality (measured in frequency of principles followed).As it concerned the media source of upload, we assigned each video to one of seven total categories: (a) educational institution ; (b) government agency/organization ; (c) news media outlet; (d) non-profit organization; (e) physical education-based/development program ; (f) professional organization ; and (g) user-generated content (UGC).We were also interested in the quality of physical literacy information in the YouTube videos. Efforts to best determine and measure the quality of information via ethical guidelines for internet-derived health information, such as self-regulatory codes of ethics, rating tools, third-party reviewers, and both accreditation and certification systems, have been numerous and diverse in their approaches ,54. Yet,In accordance with each of the eight HONcode principles and subsequent adaptations , we usedAdditional metrics collected included total video view counts, viewer engagement , and number of posted viewer comments. As per YouTube’s “Creator Academy” platform, a total of 18 example categories  are listed for content creators to organize and optimize the viewer reach of their videos. Thus, coders screened the listed category for each video  and evaluated the relevance of the selected category per the video’s content topic.As it concerns the categorical organization of both content topics and the delivery of said content, the authors collaborated to devise a definitive list for each set of categories. In drawing upon Edwards et al.  systematn = 22), were not in English (n = 37), and duplicate videos (n = 91) were subsequently excluded. Whenever disagreement arose between both coders as to whether a particular saved video did not meet the inclusion criteria, resolution was met by either common consensus through discussion or by way of a third member of the research team. In all, a total of 150 unique videos were saved and utilized for data analysis.In order to satisfy the inclusion criteria of this study, video content had to be audible in the English language and pertinent to physical literacy content topic. Both coders were informed of the respective criteria for locating and henceforth evaluating each video. For each video that satisfied our inclusion criteria, both coders saved the video to a YouTube playlist. As previously indicated, a maximum of 150 videos can be saved on a single playlist, which allowed the coders to save a total of 300 videos. Of those selected, all videos with content that did not relate to the aims and scope of this study (c = 0.90. This metric was selected as it can serve as a robust measure between two coders when observing continuous variables [Interrater reliability of codes was established to confirm an appropriate level of consensus between the two coders. To determine interrater reliability via Cohen’s Kappa (ĸ), or coefficient of agreement, 40 videos were randomly selected and coded by two independent researchers. A subsample size of 40 was deemed appropriate for conducting reliability analyses  and the ariables ,62.U test was conducted to determine associations between video quality of information and viewers’ attitudes, via “likes” (Research Question 1). Similarly, Fisher’s exact test was conducted to determine associations between media source of the YouTube videos and adherence to the adapted HONCode principles (Research Question 2). Binary logistic regression analyses were run to examine the influence of physical literacy YouTube video content properties on the quality of information (Research Question 3a) and viewer attitudes (Research Question 3b). Likewise, binary logistic regression analyses were run to determine the potential influence of the content delivery style of the physical literacy YouTube videos on the quality of information (Research Question 4a) and viewer attitudes (Research Question 4b).IBM SPSS software version 25  was utiln = 122; 81.3%), followed closely by psychosocial and attitudinal concepts , and behavioral aspects . Affective components of physical literacy were present in the majority of the YouTube videos , while the cognitive constructs were discussed in less than half of the videos . Over half of the videos addressed these physical literacy domains in the context of unstructured play and physical activity  with only 28 (18.7%) considering physical literacy in the context of structured sport and physical activity. The physical education space was the predominant context for 43.3% (n = 65) of the videos. Of the remaining 85 YouTube videos in which the information was presented outside the context of physical education, 38 (44.7%) were solely within the space of unstructured play, while one (1.2%) video focused only on structured sport. Thirty (35.3%) of these videos did not communicate information within the context of structured or unstructured play, while 16 (18.8%) videos considered both contexts.Demographic data and frequencies can be found in n = 47; 31.3%) and non-profit organizations . Nearly one-third of the YouTube videos were uploaded by a government agency/organization , while educational institutions posted only eight videos (5.3%). Regarding the audiences of the YouTube videos, over half  portrayed physical literacy as a lifelong journey and were inclusive of both youth and adults. Forty nine (32.7%) videos specifically addressed physical literacy in youth, while only 12 (8%) were directed solely towards an adult population. Only five (3.3%) of the YouTube videos were judged to not have an intended audience.The media sources uploading these physical literacy videos to YouTube were diverse. The two largest media sources of content comprised almost two-thirds of the videos: user-generated , leaving the remaining 86 videos (57.3%) to be rated low quality. Principle #2, which requires videos to be clear in their mission to supplement and not replace information from certified/qualified educators and sources, was achieved by the most videos . Principle #3, requiring videos to provide information and instruction in line with the most commonly accepted and understood conceptualizations of physical literacy, was the second most achieved , while Principles #1 and #4, which required source information  and credentials , were each followed by nearly half of the videos. For further detail on the HONCode principles see U test revealed a significant difference in the number of viewer likes given to low versus high quality YouTube videos, U = 3,377.50, z = 2.424, p = 0.015, r = 0.20, such that high quality videos, or those adhering to 4 or more adapted HONCode principles , received more “likes” than low quality videos .Less than half of the YouTube videos were rated as being of high quality . While this was just above half of the videos posted by such social media accounts, the largest proportion of high-quality videos were posted by professional associations . The vast majority of user-generated YouTube videos on physical literacy were of low-quality .Due to 4 of the 14 cells (28.6%) containing less than the expected count of 5, Fisher’s exact test was conducted. The analysis revealed a statistically significant difference in adherence to the adapted HONCode principles based on the media source, Fisher’s exact test = 39.36, x2  = 49.73, p < 0.001. The full model explained between 28.2% (Cox & Snell R square) and 37.9% (Nagelkerke R square) of the variance in quality, per the HONcode principles, correctly classified 72.7% of the videos. df = 1, 95% CI: 1.17–18.29, p = 0.029), with an odds ratio (OR) of 4.62. Videos containing affective content were also found to be a significant predictor (OR = 3.6) of a video being rated as high quality .Binary logistic regression revealed that the physical literacy properties of the YouTube videos had a significant association with the quality of the videos, Binary logistic regression revealed no significant findings regarding the influence of the physical literacy content properties and viewer attitudes.x2  = 42.56, p < 0.001. The full model explained between 24.7% (Cox & Snell R square) and 33.2% (Nagelkerke R square) of the variance in quality, per the HONcode principles, correctly classifying 73.3% of the videos. df = 1, 95% CI: 5.13–36.5, p < 0.001). Delivering content via testimonial style was also a significant predictor of a high quality video , with an OR of 4.5.Binary logistic regression revealed that the delivery style on the YouTube videos’ content had a significant impact on the quality of the videos, df = 1, 95% CI: 1.07–5.87, p = 0.035).The full model did not significantly explain any influence of the content delivery style of the physical literacy YouTube videos on viewer attitudes. However, videos delivering content through demonstration were found to be the strongest predictor, with an OR of 2.5, of viewers attitudes to “like” a video organization, as well as goals and intentions of their activities, play, and movement without the restrictions of developmental plans that may inhibit their expression and enjoyment . The valGiven the breadth of one’s lifelong progression towards physical literacy, a number of topics were presented in the YouTube videos on physical literacy. Being a construct largely focused on movement and one’s ability to engage with the physical environment through a variety of movement forms, competencies and patterns ,69, it wThe least focused on elements of physical literacy, although still present in over half of the videos, were those of affect and cognition. While not as salient as psychosocial and health-related behaviors, it is critical that video content focus more on the development of one’s confidence, motivation, and especially self-esteem, as a lack any or all of these constructs may hinder one’s disposition towards lifelong physical activity . LikewisIntended as a code of ethics, the comprehensive set of principles put forth by the HONcode benefits both users and managers of online content regarding objective, verifiable, ethical, transparent and trustworthy medical and health-related information. However, not even half of the videos were deemed to be of high quality, as providers of information may not have shared their health and education qualifications for providing such information or share the source from which they collected their information. Yet, the intended mission of many videos was simply to educate viewers, and as such, it was made clear that physical literacy information was being communicated in accordance with commonly accepted definitions or understandings and as a supplement, not a replacement, to the advice of a qualified physical education or health source. Regardless, as a result of these findings, it is suggested that developers and uploaders of YouTube videos on physical literacy better source their information and be more transparent with their credentials via disclaimers espousing the adherence to the HONcode.Regarding the quality of information, it was discovered that the greatest proportion (85%) of high-quality videos belonged to professional associations, despite the largest number of high quality videos being posted by non-profit organizations. Further, the lowest number of high-quality videos were of the user-generated variety, who conversely, were also responsible for the highest number and greatest proportion of low-quality videos. This suggests not only the value that viewers place on reputable sources of information, but also the preference and trust they may have towards reputable professional and non-profit organizations. Given the scarcity of high-quality and excess of low-quality videos posted by individual YouTube users, it was promising to see the potential apprehension viewers might have towards sources of information coming from individuals and non-credentialed groups or organizations. Thus, when users are being instructed or advised to use YouTube for additional information, it is recommended that they be directed to physical literacy videos from reputable sources that post high quality videos.Surprisingly, educational institutions posted only eight (5.3%) videos. This is troubling given the knowledge and research capabilities of people and working groups within such spaces. Furthermore, as a legitimate health outcome and antecedent, physical literacy serves as a cross-disciplinary interest in a number of related areas of study. However, it is rebranded so as to better fit the purpose and understanding within each discipline , adding As it concerns the availability of online teaching and learning opportunities, as well as the sharing of knowledge and resources, there are several findings that speak to best practices for content and delivery of physical literacy videos on YouTube. Regarding the quality of videos, providing content in either a formal or informal presentation has the greatest association with adherence to the HONcode principles. As such, health-related information disseminated in this format is judged to be the most trustworthy and in accord with, yet not disparately impacted by, common physical literacy knowledge and trends. Similarly, physical literacy videos with personal testimonies could reach viewers in a more open and honest way, such that they are more likely to be deemed high quality than other content delivery methods. However, YouTube viewers were more than twice as likely to “like” videos in which the information was delivered as a demonstration or instruction. This is understandable given the large focus (122 videos) on physical abilities and FMS in this study’s sample; as it is likely easier to understand and learn movement skills through visualizations including role models.Lastly, videos that focused on the affective and behavioral components of physical literacy were judged as having the highest quality. Perhaps this has to do with the expertise warranted to speak appropriately and applicably on these components. This is not meant as a value judgment on the remaining core themes , but ratHaving to utilize YouTube’s integrated search engine, there is a chance that relevant videos on the website were not included in this study. Likewise, not being privy to the procedures by which engagement and evaluative metrics are determined and measured, we had to rely on YouTube’s controlled information in our assessment of physical literacy videos. Additionally, the information shared by uploaders of the videos was not always readily available or indicative of intentions leading to the creation and sharing of the physical literacy videos. Some of this limitation can be mitigated by collecting data on more videos and for a longer period of time. Other user metrics could also be collected to provide a more holistic view of user perceptions and quality of the uploaded videos. A longitudinal study in which viewer metrics are analyzed over a period of time could provide researchers and practitioners with better understandings of how and why videos trend, are sustained, and/or not viewed. Further, while this study was not limited to YouTube videos uploaded or viewed by users in a particular country, the use of the English language as inclusion criteria likely served to keep relevant, non-English speaking videos from being accounted for and coded.From a design standpoint, several of the content and delivery coding procedures could serve to limit the interpretation of results. First, the coding of the content delivery style was not mutually exclusive. For instance, developers of a video could utilize both animation and presentation styles to send their message. Similarly, when considering the context of the videos , both styles of play could be present in the videos, which could also be set in both environments. Thus, it is important to delve further into the role of structured vs unstructured play in both athletic and education contexts. Additionally, simply coding data collected from uploaded YouTube videos provides a limited perspective on site as an information resource for physical literacy. Future studies would benefit from taking a behavioral approach and collect self-reported data from watchers of the uploaded videos and users of YouTube to gain a stronger sense of their perceptions of the video website as an information resource.Although well accepted by many organizations, the instrument is not without its shortcomings. As a self-regulatory tool that relies on codes of ethics, the HONcode may be limited in the sense that “self-regulation does not deter the unscrupulous, those who mostly need to have their ethical standards raised  (p. 236)As researchers and practitioners alike, we must continue to concern ourselves with the quality of health-related information available on the Internet, particularly YouTube, and other social media outlets. As it relates to online information resources and videos for physical literacy, it is critical to keep in mind who is watching these videos, how the information is being conveyed, in what context is it delivered, if is it influenced by a particular type of curricula , and whether or not consideration was made towards certain socio-demographic factors and inclusivity. Lastly, what are the social determinants of physical literacy for youth? While the aforementioned list of considerations is by no means an exhaustive list, it challenges us to be mindful of the many intersecting factors that may not only be impacting one’s ability to become physically literate, but also the content, delivery, and quality of the resources intended to foster physical literacy in the first place. This study revealed that the content and content delivery method were most important in quality evaluations. Physical literacy videos that focused on physical activity and behaviors were the strongest predictor of high quality ratings, followed closely by videos covering affective domains  of physical, literacy. The content delivery method was also important, with videos utilizing presentations and testimonials containing high quality information about physical literacy. Thus, providers of physical literacy and health-related online video content should be aware of and adhere to the expected quality standards.The Internet is a resourceful educational space for health-related information and content ,83. Unde


Pharmacogenetics and biomarkers are becoming normalised as important technologies to improve drug efficacy rates, reduce the incidence of adverse drug reactions, and make informed choices for targeted therapies. However, their wider clinical implementation has been limited by a lack of robust evidence. Suitable evidence is required before a biomarker’s clinical use, and also before its use in a clinical trial. We have undertaken a review of five pharmacogenetic biomarker-guided randomised controlled trials (RCTs) and evaluated the evidence used by these trials to justify biomarker inclusion. We assessed and quantified the evidence cited in published rationale papers, or where these were not available, obtained protocols from trial authors. Very different levels of evidence were provided by the trials. We used these observations to write recommendations for future justifications of biomarker use in RCTs and encourage regulatory authorities to write clear guidelines.  The growing field of pharmacogenetics, which studies the effect of genetic biomarkers on the likelihood of treatment response or adverse drug reactions (ADRs) , offers HLA-B*57:01 allele has significantly reduced the incidence of severe ADRs associated with abacavir [Many drugs are withdrawn from the market due to lack of efficacy and/or ADRs ,12,13, aabacavir , and hasabacavir . Althougabacavir , reductiwww.bigted.org, provides information about each to guide those designing such a trial [While the US Food and Drug Administration (FDA) lists over 200 drugs with pharmacogenetic information included in their labels , their w a trial . However a trial —without  a trial ,42. With this in mind, we undertook a literature review with the aim of reviewing sources of evidence used to justify five previously published pharmacogenetic BM trials. These were chosen to represent different pharmacogenetic biomarker applications. We explored the nature and extent of previous evidence on the association of the included biomarkers with treatment response that had been used to justify their inclusion. We were not concerned with the findings of the trials, instead focusing purely on the evidence cited to justify the inclusion of biomarker(s) within their design. Indeed, we acknowledge that other trials will have been conducted since the publication of the trials included in our review which will have added to the evidence base on the use of the drugs under study. In light of our findings, we also reflected on and provided recommendations on how such evidence should be compiled by those planning future BM trials. TPMT genotyping helped prevent ADRs associated with azathioprine [To allow us to explore in detail the evidence compiled for each trial, we limited our review to five recently published BM trials. These were chosen carefully to ensure that they were representative of the available trials and spanned a range of different biomarker applications. We felt it important to not only include trials using biomarkers in a way that has been well-characterised , but also those incorporating biomarkers for less well-characterised purposes . The five chosen trials used biomarkers for prevention of ADRs , improvihioprine ,47. A sehioprine . The thihioprine . A fourthioprine ,45,48. Thioprine . For each trial, we identified each piece of evidence referenced in the introduction section of a protocol or design paper associated with the trial, and extracted details of the publication year , study dTPMT genotyping to prevent adverse reactions to azathioprine in patients with inflammatory disease [TPMT status and the implications of this on dosing prior to commencing azathioprine treatment. Patients in the non-genotyping arm received standard azathioprine dosing. TARGET (ISRCTN30748308) began recruitment in 2005 and investigated the use of  disease ,53. The TPMT.TARGET used a biomarker strategy design without biomarker assessment in the control arm , EvidencThe most recent evidence was an expert opinion by Seidman, 2003 . A 2002 CYP2C9 and VKORC1 and dosed according to an algorithm that included both genetic and clinical factors. The control group received a standard dosing regimen guided by clinical factors only. The EU-PACT study NCT01119300) was a large, single-blind, randomised European trial of genotype-guided dosing of warfarin 300 was a,69,70,71CYP2C9, VKORC1, and CYP4F2 genes on warfarin dosing. Also cited were editorials [This trial also used a biomarker strategy design without biomarker assessment in the control arm . The pubitorials ,77, costitorials ,79, and itorials . No prevThe SHIVA trial (NCT01771458) was a French proof-of-concept histology-agnostic phase II trial using an enrichment design  that recThe total evidence cited in the protocol ranged from 1998 to 2011 . Three RThe paper reporting on the results of this trial included an ‘Evidence before this study’ box . This deSLCO1B1 genotype guided statin therapy (GGST) trial (NCT01894230) investigated the utility of using genotyping to increase adherence to statins and promote lower cholesterol in patients with cardiovascular disease and a history of statin-induced side effects [The  effects ,45,48. P effects . The tri effects . This trial cited a large number of references ranging from 2002 to 2015 . Five seThe authors cited one systematic review  and thren = 174,149 participants from 27 RCTs [The meta-analyses by the CTTC group were both done on the same large data set of  27 RCTs ,128. EacCYP2D6-guided dosing of opioids to manage pain [CYP2D6 genotyping. Patients with poor metabolizer, intermediate metabolizer, or ultra-rapid metabolizer phenotypes were recommended different opioids to those with an extensive  metabolizer phenotype. Those in the control group did not receive CYP2D6-guided recommendations. Pain questionnaires were completed at baseline, 2, 4, 6, and 8 weeks. The trial is completed but results have not yet been published.This trial (NCT02664350) used a biomarker strategy design without biomarker assessment in the control arm, and recruited patients with solid tumours and metastases to investigate age pain . PatientThe authors cited evidence ranging from 1998 to 2017 . The oldThe trials in our review all used different approaches to gathering evidence for justifying biomarker inclusion, and there does not appear to be a standard approach to doing so. Of the trials examined, all cited evidence from within 3 years of their publication . The oldThe evidence types used included systematic reviews/meta-analyses, RCTs, qualitative research, guidelines, recommendations, editorials, and case studies. The traditional ‘evidence pyramid’ is often used to rank evidence types, with meta-analyses and systematic reviews at the top, and case studies and in vitro evidence near the base . HoweverTo explore the type and extent of evidence compiled to justify including biomarkers in previous BM trials, we have referred to the references in the trial design paper or protocol. This represents a relatively straightforward method of assessing the evidence for a biomarker’s inclusion in a trial, however has some inherent limitations. First, this method will not necessarily capture the entire evidence base upon which inclusion of the biomarker was justified, since the authors may not have provided a complete and accurate snapshot of the evidence they explored and used. Second, journal rules on the amount of references in a paper and word count restrictions could mean that the references included do not represent the totality of evidence used. Similar restrictions on references and word counts may limit the representation of the literature in protocols.While the ideal level of evidence is a well-conducted meta-analysis/systematic review of good quality RCTs, including a rigorous assessment of their quality, this is not always available or feasible. In particular, where a biomarker is very new, there may be limited previous evidence to underpin its use. This evidence may take the form of case series or previous case studies. If this is the only evidence available, then this may be the ‘best’ evidence to justify including the biomarker in a trial. It would be important to consider, in such circumstances, whether the proposed RCT would be premature and that the science should first of all be allowed to mature. It may be that different standards of evidence may be necessary for different biomarker types ,145. ForFurther, some conditions have existing diagnostic or treatment guidance algorithms that do not use biomarkers but have good clinical utility. In these scenarios, adding a biomarker to the algorithm might provide a low value of information compared to a biomarker used in a condition where a good clinical algorithm is not available. Therefore, authors might consider prioritizing the development of biomarkers for conditions that do not have sufficient clinical prediction methods for diagnosis or guiding treatment. It is also important to ensure that genetic biomarkers are not subject to higher evidentiary requirements than other types of biomarkers. This ‘genetic exceptionalism’ and the higher burden of evidence for genetic tests has been shown to be a barrier to implementation ,30,148. -Systematic review before embarking on a trialWith these factors in mind, our recommendations for all biomarker-guided trials consist of two essentials .-SystemWe would recommend an initial systematic review is undertaken prior to the start of any trial. The Lancet journal now requires all research papers to include a ‘Research in Context’ panel that shows the evidence available prior to the study, and how the authors searched for this information . This isRegardless of the type of evidence identified in the systematic review, we recommend that the quality of that evidence is assessed when justifying including the biomarker, and we suggest that design-specific tools are used for this purpose  . SeveralWhen synthesising evidence already existing from previous studies, it is also important to consider the age and ethnicities of the populations of the previous studies compared to the proposed trial’s population to ensure that the evidence is relevant. Many studies (94% in one review ) imply g-Guidelines are requiredFurther, if the systematic review reveals a sufficient number of previous RCTs or observational studies, authors should consider conducting a meta-analysis to assess the current evidence quantitatively. This would help ascertain whether there was sufficient uncertainty surrounding the current evidence to justify the planned RCT. An example of where this could have been implemented is in the fifth trial we examined . AuthorsGiven the lack of standardisation across BM trials in terms of how inclusion of biomarkers are justified, we recommend that guidelines are developed to aid researchers in compiling and presenting evidence to justify their inclusions. This will not only ensure that sufficient evidence exists prior to embarking on a BM trial, thus avoiding waste of resources, but will also serve as a useful guide to those planning a BM trial and provide transparency in the trial report. p-value, and odds ratio. The score is determined by PharmGKB curators [The Clinical Pharmacogenetics Implementation Consortium (CPIC) provides guidelines for the implementation of pharmacogenetics . The guicurators .Whilst these guidelines are for implementation of biomarkers into clinical practice in a patient who has a known genotype, a similar approach could be developed for justification of use in a RCT. We located one paper that discussed the incorporation of biomarkers into early phase clinical trials , but we Finally, the conclusions and recommendations above are based on the assumption that a BM trial is indeed required. It is possible that when compiling the evidence to justify inclusion of a biomarker in a trial that it is so overwhelmingly in favour of the biomarker’s clinical utility that it may be unethical to restrict its use to a randomised trial. This loss of clinical equipoise is something important to consider and indeed clinical implementation may be recommended and accepted without the need for a BM trial in such cases.


Electronic health  literacy was conceptualized in 2006 as the ability of internet users to locate, evaluate, and act upon web-based health information. Now, advances in eHealth technology have cultivated transactional opportunities for patients to access, share, and monitor health information. However, empirical evidence shows that existing models and measures of eHealth literacy have limited theoretical underpinnings that reflect the transactional capabilities of eHealth. This paper describes a conceptual model based on the Transactional Model of Communication (TMC), in which eHealth literacy is described as an intrapersonal skillset hypothesized as being dynamic; reciprocal; and shaped by social, relational, and cultural contexts.The objective of our study was to systematically examine eHealth literacy definitions, models, and measures to propose a refined conceptual and operational definition based on the TMC.Walker and Avant’s concept analysis method was used to guide the systematic review of eHealth literacy definitions (n=10), rating scales (n=6), models (n=4), and peer-reviewed model applications (n=16). Subsequent cluster analyses showed salient themes across definitions. Dimensions, antecedents, and consequences reflected in models and measures were extracted and deductively analyzed based on codes consistent with the TMC.Systematic review evidence revealed incongruity between operational eHealth literacy included in definitions compared with literacies included within models and measures. Theoretical underpinnings of eHealth literacy also remain dismal. Despite the transactional capabilities of eHealth, the role of “communication” in eHealth literacy remains underdeveloped and does not account for physical and cognitive processing abilities necessary for multiway transactions.The Transactional Model of eHealth Literacy and a corresponding definition are proposed. In this novel model, eHealth literacy comprises a hierarchical intrapersonal skillset that mediates the reciprocal effect of contextual factors  on patient engagement in health care. More specifically, the intrapersonal skillset counteracts the negative effect of “noise” (or impediments) produced by social and relational contexts. Cutting across health and technology literacies, the intrapersonal skillset of eHealth literacy is operationalized through four literacies that correspond with discrete operative skills: (1) functional ; (2) communicative ; (3) critical ; and (4) translational . Electronic health  is increasingly being ingrained within the health care system and patient engagement experience. eHealth facilitates productive collaborations among informed patients, proactive health care professionals, and responsive health care systems to coordinate care for positive health outcomes ,2. AlongThe Transactional Model of Communication (TMC)  posits tThe TMC can be extended to interpersonal computer-mediated communication (I-CMC). I-CMC occurs remotely with technology  through diverse message channels  and sources  . In the Noise-inducing factors interfere with information transmission and accessibility among communicators, ultimately hindering their ability to access, understand, and transmit meaning to one another . Noise-iTo understand patients’ capacity to successfully use and benefit from eHealth, the concept of eHealth literacy was initially coined in 2006. eHealth literacy was defined as the ability to locate, evaluate, understand, and act upon health information from electronic sources ,15. DespThe purpose of this study is to propose a theoretical blueprint for defining and conceptualizing eHealth literacy in the transactional era of eHealth. Per the fundamental assumptions of the TMC, this study operationalizes eHealth literacy as an intrapersonal skillset grounded in counteracting the effect of noise during transactional interactions across computer-mediated platforms. In the context of I-CMC, the TMC is appropriate to form the basis of our proposed model because we aim to describe the communicative element of eHealth literacy and understand how underlying eHealth operational skills function in the larger context of computer-mediated transactions. In this study, we applied a concept analysis method, which is a rigorous method in which empirical literature is systematically surveyed to refine the operationalization of a construct ,21. The purpose ; (2) context ; and (3) ability . The same queries were conducted in Google Scholar to identify gray (or unpublished) literature. The final sample consisted of articles that (1) were published between 2006 and 2017 ; (2) were in the English language; (3) included the terms “eHealth,” “social,” “media,” “Web 2.0,” or “digital health” in the title or abstract; and (4) presented information on the concept, definition, or measurement of skills related to social media, digital health, and electronic health record use. A series of keywords were combined with the Boolean operator (“AND”) and entered into 3 electronic databases . In each search query, a combination of 3 terms was entered to reflect the following: (1) Walker and Avant’s  concept competence, contextual factors, action , object of interest, and objective. The attributes of eHealth literacy conceptual models and measures were extracted and entered into a descriptive table where congruent components were identified; furthermore, antecedents  and consequences (dependent variables) were extracted.An inductive analysis of eHealth literacy definitions was performed to identify thematic clusters from peer-reviewed and gray literature. Consistent with a separate concept analysis of health literacy , an induCompetence is characterized as a set of skills and knowledge, predominantly referred to as “the ability.” Influential Factors that determine the said ability are characterized as the interplay between contextual factors  coupled with situational factors . CompetenceAbility Skills (6)Set of skills & knowledge (3)Foundational skillset (4)Contextual factorsCultural, social, and situational frame (8)Interplay between social and individual factors in using technology (9)Dynamic and context-specific individual and social factors and technological constraints (10)To use information and communication technologies ActionTo locateFind Seek Search Retrieve (3)Gather (7)Acquire To understandComprehend Understand Process (7)To evaluateAppraise Evaluate the veracity (2)Discern the quality (2)To communicate To create (10)To translateIntegrate (1)Apply Use knowledge (8)Object of interestKnowledge InformationGeneral Health Quality (2)Emerging (5)eHealth (8)Online (7)Text (6)ObjectiveFor health (4)To address or solve a health problem To make informed decisions about health (2)To improve or enable health and health care (5)To treat various health concerns (6)To maintain or improve the quality of life throughout the lifespan Actions are thematically clustered according to operational skills, including the capacity to locate, understand, evaluate, exchange, and apply or translate health information. The Object of Interest, or the purpose of performing the actions, includes obtaining knowledge from high-quality Web-based health information. Finally, the Objective of obtaining the object of interest is generally for the purposes of health enhancement or to maintain or improve the health-related quality of life throughout the lifespan.Since 2006, 4 models and 6 measurement instruments of eHealth literacy have been published; their purposes, guiding theoretical frameworks (if applicable), and dimensions used in describing the concept and measurement have been presented in Norman and Skinner’s  Lily ModGilstad  adapted Unlike Norman and Skinner  and GilsKayser et al  applied Conceptual models have attempted to extend Norman and Skinner’s  Lily ModSynthesized together, these 4 models suggest that eHealth literacy is an intrapersonal skillset shaped by diverse contextual factors influencing the user and the situation prompting the eHealth interaction.Other measurement instruments assessed motivation or readiness to use eHealth. Through formative focus groups with older adults with chronic diseases, Koopman et al  derived Most recently, measurement instruments have been developed to account for the social features of eHealth. Seçkin et al  identifiPersonal Determinants influencing eHealth literacy include income and education [Relational Determinants include social influences or norms and alleviated linguistic and cultural barriers to health information [Knowledge Determinants include the type and amount of health information preferred and the amount of pre-existing knowledge about the health concern [Technological Determinants include motivation to use technology for health [Functional (or basic), health, and technology literacies are fundamental to eHealth skills ,29,36,37ducation ,24,36-39ducation , age [18ducation -42, mariducation , and headucation . Relatioormation ,29. Know concern ,24,30,40r health ,36,42, ar health ,43, the r health ,18, freqr health ,41,44, aThe consequences of eHealth literacy primarily comprise intrapersonal factors, which have a residual effect across socioecological contexts. The most salient intrapersonal consequence includes a change in the degree of patient engagement ,29,45. PThis synthesis and review of eHealth literacy definitions, models, and measures posits that the multidimensional construct is a reciprocal intrapersonal skillset influenced by the interaction between user- and task-oriented factors, which drive patient engagement, empowerment, and informed decision making. This is consistent with the theoretical underpinnings of the TMC . AccordiExisting definitions and models do not capture the transactional nature of eHealth literacy, specifically regarding information exchange, knowledge application, and message generation when communicating with other users. The most recent eHealth literacy frameworks depict eHealth literacy according to the intersection of user attributes, perceived motivation or control, and experiences using eHealth ,47. ThiseHealth literacy is proposed:The following definition of The ability to locate, understand, exchange, and evaluate health information from online environments in the presence of dynamic contextual factors and to apply the knowledge gained across ecological levels for the purposes of maintaining or improving health.locate, understand); (2) interaction with other users through mediated platforms ; and (3) assessment  and action  for health advancement. Second, the proposed definition acknowledges that eHealth literacy is highly contextual as it varies according to the interplay between user- and task-oriented factors. Third, this definition highlights the affordances of technology, which assist lay end users to access and exchange health information using electronic tools. Based on the synthesis of the literature, proficiency in using a technology or a Web-based environment does not solely determine one’s eHealth literacy; rather, it is the capacity of the users to achieve their intended eHealth goals when encountering noise that challenges the successful use of technology and transaction of health information. Finally, consistent with interpersonal communication literature and the TMC [This definition builds upon previous eHealth literacy definitions and extends the concept to the context of the TMC. First, operational skills comprise and correspond with the central aspects of eHealth : (1) intge); and  assessme the TMC ,6, this Through this updated definition of eHealth literacy, we extend beyond functional behaviors related to technology readiness and engagement. We consider eHealth literacy as a hierarchical skillset that not only allows users to engage with technology but also engages other users via technology for dynamic health information seeking and transactional exchanges across computer-mediated platforms. The contribution of our proposed definition lies in its ability to position current eHealth literacy literature through a translational health communication lens.Consistent with the definition above, Task-Oriented Factors include the channel in which the transaction occurs , the source or identity of the communicators , the language used to communicate , and the modality of the message . User-Oriented Factors, however, comprise factors that are central to the user, rather than to the situation or task. These factors include personal demographic information, including education, gender, and age. Relational support is described as the amount of support or perceived social norm in using eHealth. Consistent with prior eHealth literacy models ,30, a seThe degree of pre-existing knowledge about the health topic and the desire to obtain more information is also a user-oriented factor. Finally, there is a technological user-oriented factor, which is a general assessment of the users’ access, preference, and frequency of use. Consistent with the TMC , the intPhysical Noise can include external factors that hinder the eHealth experience, including cognitive or information overload due to a wide variety of multimedia or physical challenges with the technology used . Psychological Noise includes the affective response to the eHealth experience, including the urgency for the information or the nature of the search . Physiological Noise can be either temporal or permanent, meaning that it could be dexterity limitations due to a health condition or pain from a briefly debilitating migraine. Finally, Semantic Noise is the disagreement between meaning systems, including excessive use of scientific or wordy jargon from one or more communicators, as well as use of emojis or emoticons to transmit information. Ultimately, the degree of noise in a computer-mediated transaction is produced by the interaction between these task- and user-oriented factors.Noise-inducing factors in the TMC, as well as other interactional communication models, comprise physical, psychological, physiological, and semantic factors ,6,10. AlThe intrapersonal eHealth literacy  . WithoutFunctional, Communicative, Critical, and Translational eHealth Literacies. Existing eHealth literacy definitions, models, and measurement instruments include a high volume of literacies and minimal insight into their relationship with underlying skillsets. Empirical evidence has hinted that the scientific community should consider reeling eHealth literacy conceptualizations back to seminal 4-tiered operational behaviors or literacies prevalent in health literacy and general literacy literature. This has been shown in a recent library of research on eHEALS, the seminal model of eHealth literacy, positing that it is not a unidimensional measure; rather, it may be a measure of 2-3 constructs that assess eHealth awareness, Web-based health information seeking skills, and evaluation or application skills [In our model, health and technology literacies shape a multidimensional and hierarchical intrapersonal skillset, which comprises 4 eHealth literacies. These eHealth literacies are aligned with the gold-standard health literacy model ,54 to inn skills -57. The The eHealth literacies presented in According to Nutbeam , the defSufficient basic skills in reading and writing to be able to function effectively in everyday situations.pg 263Functional eHealth Literacy is defined as:Considering the technological context of functional health literacy, it is important to determine how well an individual can successfully read and write about health via a technological device. Therefore, Basic skills in reading and writing (typing) about health to effectively function on the internet.Communicative literacy is :Advanced cognitive and literacy skills, which together with social skills, can be used to participate in everyday activities to extract information and derive meaning from different forms of communication and to apply new information to changing circumstances.pg 264control, effectiveness in managing a situation to negotiate interpersonal problems and achieve a communicative goal; (2) collaboration, adhering to social norms to achieve an interaction goal; and (3) adaptability, acclimating to challenges by improvising communicative styles based on contextual and social cues. These principles of interpersonal communication are also consistent with the underlying elements of participatory media that foster collaboration, openness, participation, and apomediation (Web 2.0), which differs from more static and linear, one-way information seeking behaviors (Web 1.0) [Communicative eHealth Literacy is defined as:In its original conceptualization, communicative literacy was intended to assess patients’ communication skills when engaging with offline health care professionals ,54. eHeaWeb 1.0) . As suchThe ability to collaborate, adapt, and control communication about health with users on social online environments with multimedia.Critical literacy is defined as :Advanced cognitive skills, which together with social skills, can be applied to critically analyze information and to use this information to exert greater control over life events and situations.pg 264Critical eHealth Literacy is defined as:Through the lens of the TMC, critical eHealth literacy includes being aware of the type of health information that is communicated to and from Web-based users, as well as the source from which this information is presented. This includes not only source and information credibility but also entails evaluating the relevance of and risks related to sharing personal information with Web-based sources through diverse channels. In this model, The ability to evaluate the credibility, relevance, and risks of sharing and receiving health information on the internet.Translational eHealth Literacy is defined as:Developing a concept that acknowledges the dichotomy between “what people know” and “what people do” represents a fundamental gap in the health literacy literature . TranslaThe ability to apply health knowledge gained from the internet across diverse ecological contexts. Translational literacy is the highest cognitive level of eHealth literacy, meaning it is informed and built upon from all lower-level eHealth literacy dimensions .In this study, we systematically reviewed literature on eHealth literacy to provide an updated understanding of what we know about the construct in today’s more transactional era of eHealth. Unlike Griebel et al , who sugExisting eHealth literacy definitions include operational skills required for an eHealth end user to thrive on the internet . However, dimensions of existing models and measures are not intuitively aligned with the intrapersonal operational skills outlined in their corresponding definitions. Norman and Skinner’s  eHEALS wThe dimension of “communication” was significantly underdeveloped in eHealth literacy definitions, models, and measurement instruments reviewed. “Communication” was not integrated within definitions until 2011, and it first appeared in a conceptual model in 2014 as an outcome related to high eHealth literacy, not as an integral or defining element . CommuniThis study is not without limitations. Despite the rigorous extraction procedures and inclusion of gray literature, it is possible that not all eHealth literacy models, definitions, and measures were included because of the time frame of our literature extraction procedures. A qualitative approach was used to extract and analyze the literature in this study, which is prone to researcher bias . HoweverThe theoretical tenants from TMC that were used to derive the TMeHL were informed by results of previously published systematic reviews, theory-driven articles, measurement development studies, and empirically driven original research articles examining eHealth literacy in diverse populations. Published research informed development of TMeHL, but the model has not been subjected to any formal evaluation or hypothesis testing. Per recommendations by Griebel et al , future Existing eHealth literacy definitions, models, and measures do not account for the transactional nature of eHealth. Few have sufficient theoretical underpinnings. Prior to our contribution, researchers had yet to capture the “social” elements of eHealth with theoretical underpinnings from the perspective of transactional communication. This is primarily due to the high volume of overlapping and inconsistent literacies, as well as the underdeveloped nature of “communication” as an integral component of eHealth literacy. In addition, existing eHealth literacy definitions, models, and measures failed to adequately integrate “communication” as an essential component of eHealth literacy. Because of this, a refined eHealth literacy definition and model based on the TMC are proposed. There is a need to validate this model with key stakeholders in eHealth and test the assumptions of the model with eHealth experts and lay end users.