Although not routinely established during cardiopulmonary resuscitation (CPR), video-assisted CPR has been described as beneficial in the communication with emergency medical service (EMS) authorities in out-of-hospital cardiac arrest scenarios. Since the influence of video quality has not been investigated systematically and due to variation of quality of a live-stream video during video-assisted CPR, we investigated the influence of different video quality levels during the evaluation of CPR performance in video sequences.Seven video sequences of CPR performance were recorded in high quality and artificially reduced to medium and low quality afterwards. Video sequences showed either correct CPR performance or one of six typical errors: too low and too high compression rate, superficial and increased compression depth, wrong hand position and incomplete release. Video sequences were randomly assigned to the different quality levels. During the randomised and double-blinded evaluation process, 46 paramedics and 47 emergency physicians evaluated seven video sequences of CPR performance in different quality levels .p = 0.306). In the subgroup analysis, correct classification of increased compression depth showed significant differences depending on video quality (p = 0.006). Further, there were significant differences in correct CPR classification depending on the presented error (p < 0.001). Allegedly errors, that were not shown in the video sequence, were classified in 28.3%, insignificantly depending on video quality. Correct evaluation did not show significant interprofessional differences (p = 0.468).Of 650 video sequences, CPR performance was evaluable in 98.2%. CPR performance was correctly evaluated in 71.5% at low quality, in 76.8% at medium quality, and in 77.3% at high quality level, showing no significant differences depending on video quality  Registered on 2018-08-21. Sudden cardiac arrest is the most immediate life-threatening medical condition. Despite all advances in prevention and treatment, it accounts for approximately half of all deaths from cardiovascular disease , and is By introducing dispatcher-assisted telephone CPR (T-CPR), which contains structured instruction on how to perform CPR, the probability of surviving cardiac arrest has considerably increased , 7–10. HDespite technical inventions and revolutionary advantages over the last decades, like the ubiquitous coverage of high-speed internet connectivity and mobile multifunctional smartphones, the predominant way of communication with emergency authorities has remained the traditional audio contact. Video-assisted CPR (V-CPR) is a new possible way of instructing bystanders during CPR , 12. AltHowever, to date there is no priority for mobile data used for emergency calls, hence livestream video quality can vary due to reduced data transfer rates or bad mobile internet coverage. The necessary quality of a video in order to allow accurate judgement of CPR efforts has not been studied so far. Furthermore, in published research, there is heterogeneity concerning the qualification of the evaluating person.The aim of this study was to determine the necessary video quality in order to be able to evaluate CPR quality. In addition, we wanted to investigate whether there is an influence of the evaluator’s profession on evaluation of CPR quality in a video sequence.This prospective, randomized, controlled, parallel group and double-blind simulation trial was conducted in the University Hospital of Cologne and the EMS Department of the Fire Brigade of Kerpen, Germany. The study was approved by the Ethics Committee of the University Hospital Cologne  and was registered at the German Clinical Trial Register (Registration number DRKS00015297). Data was collected from September 2019 to February 2020. Written and informed consent was obtained from each participant prior to inclusion.CPR was simulated using a standardized CPR training manikin . Video sequences were recorded with a Tamron objective  on a Nikon D750  with a resolution of 1920 × 1080 pixels per inch and 30 frames per second, to obtain a video with maximum quality. The camera was placed laterally of the manikin on a tripod of 1.80 m height, facing the manikin and the helper performing CPR from approximately 2.5 m of distance. A metronome  was set to 110 bpm (or the corresponding lower/higher frequency) to ensure guideline-conformant compression frequency, and the internal compression depth indicator of the manikin was used to ensure sufficient compression depth. In the final video sequences, neither metronome nor compression depth indicator was visible respectively audible. Seven high quality video clips of 12 s each were recorded, showing either guideline-conformant optimum performance compression-only CPR, or a typical error often made during CPR  to obtain three quality levels: high (resolution 1920x1080ppi), medium (resolution 320x240ppi) and low (resolution 128x96ppi), all with a frame rate of 30fps  the dependency of the seven ratings conducted by the same evaluator by assuming an intraclass correlation coefficient of 0.8 and b) a drop-out rate of 10%. When recruiting a total of 100 evaluators, the actual power of a chi-square test to detect a difference in proportions of 25% was estimated as 81% . For each evaluator separately, error types and qualities were randomly assigned to video sequences, such that each evaluator had to judge each quality at least twice. For random assignment of error types and qualities, the software “R”  was employed. Descriptive analyses present numbers and percentages for categorical variables. For statistical analyses, categories out of the seven CPR performances were formed: low and high compression rate were summarized in the category “compression rate”, low and increased compression depth in the category “compression depth” and finally hand position and thorax release. Where exact Fisher’s test could not be calculated due to limited computational capacities, Chi-square tests were applied to investigate associations between categorical variables. Missing data were not imputed. As this is an exploratory study, no correction for multiple testing was performed. In total, 651 video sequences were presented to the evaluators, including 322 video sequences being evaluated by 46 paramedics and 329 video sequences analyzed by 47 emergency physicians .Errors presented in the video sequences were evaluated correctly in 71.5% at low quality, in 76.8% at medium quality and in 77.3% at high quality . Likewise, correct CPR performance (p = 0.363), low and high compression rate , superficial compression depth (p = 0.619) and incomplete release (p = 0.198) did not show significant differences depending on video quality. In contrast, 13.8% of increased chest compressions at low quality, 50% at medium quality and 42.9% at high quality level were classified correctly, revealing a significant difference in different video quality stages (p = 0.006).Regarding the classification of the specific errors depending on the different video quality levels, results are shown in Fig. p < 0.001; Fig. Further analyses focussed on the frequencies of correct error classification depending on the presented error. Thereby, we found a significant difference in correct classification depending on the presented error (n = 8]), superficial compression depth and incomplete thorax release , high compression rate, increased compression depth and wrong hand position .Correct CPR was falsely evaluated as “incorrect” in 41.3% of all videos and thus evaluators attributed errors to the correct CPR performance. Correct CPR performance was falsely classified as low compression rate .In 12 of 650 video sequences (1.8%), a CPR category  was marked as “not evaluable”. In case of low video quality, evaluators indicated a CPR category to be not evaluable in 6 video sequences (0.9%), 1 video sequence (0.2%) in a medium quality level and in 5 high quality video sequences (0.8%), revealing no significant difference depending on video quality level (p = 0.422), the presented error was of significance for the indication of additional errors (p < 0.001). Lowest frequency of additional allegedly errors was found in video sequences of correct CPR performance .As shown in Table p = 0.468). Significant interprofessional differences were only seen in low quality video sequences, where 78.7% (n = 85) of the presented errors were correctly evaluated by emergency physicians, but only 64.2% (n = 68) by paramedics (p = 0.023). In medium and high quality, interprofessional evaluation showed no significant differences (p = 0.873 and p = 0.419 respectively). However, by analysing the results of emergency physicians and paramedics separately, there was a significant influence of the video quality level on correct errors classification by paramedics (p = 0.021) but not by emergency physicians (p = 0.8). For paramedics evaluating a video sequence, increased chest compressions in a low video quality were significantly less frequently recognized, compared to medium and high quality levels . Further, video quality had a significant influence on detection of incomplete thorax release after compression evaluated by paramedics (p = 0.045) but not by emergency physicians (p = 0.393). It was most frequently detected at a high quality level (92.3%), followed by a medium quality level (73.3%) and low quality level (50%). In both professional groups, the presented error in a video sequence had a significant influence on correct error classification . Finally, there was no interprofessional difference regarding indicating “CPR performance not evaluable” (p = 0.415) and indicating further errors (p = 0.728).This study was not powered to find differences in the evaluation of different professions. However, our results reveal some interesting findings regarding interprofessional differences between paramedics and emergency physicians. Summarizing all quality stages, correct classification of the presented errors was achieved in 73.9% for paramedics and in 76.5% for emergency physicians, showing no significant interprofessional difference  and framerates up to 60fps become available, albeit not routinely implemented in video calls yet. Although this study did not show appropriate level of statistical power and sensitivity in regard to the interprofessional differences, results do not show a significant interprofessional difference in the evaluation of V-CPR. It can be carefully suggested that a variety of medical emergency professions can serve as dispatchers for V-CPR, since both professional groups identified over 70% of the CPR performances correctly. Furthermore, it must be kept in mind that our evaluators were confronted with video-assisted CPR for the very first time. None of them had any training with this technology. It can be hypothesized that any training may lead to further improved evaluation of V-CPR .Furthermore, data in both simulated and actual patients have shown that even experienced clinicians are not very good at determining whether compressions are being one properly – this applies for “remote” observers as well as for those that are physically present at the time –27. JudgThis study was conducted with a resuscitation mannequin made for education and evaluation of medical personal, which is only a substitute for real patients. In accordance with the current European Resuscitation Council (ERC) Guidelines , we confResults of this study show that video quality has no significant impact on the evaluation of CPR in a video sequence. In fact, even low video quality leads to an acceptable evaluation of CPR performance. Except of the parameter “increased compression depth”, not video quality but the presented error was of significance for correct CPR classification.


Music videos contain a great deal of visual and acoustic information. Each information source within a music video influences the emotions conveyed through the audio and video, suggesting that only a multimodal approach is capable of achieving efficient affective computing. This paper presents an affective computing system that relies on music, video, and facial expression cues, making it useful for emotional analysis. We applied the audio–video information exchange and boosting methods to regularize the training process and reduced the computational costs by using a separable convolution strategy. In sum, our empirical findings are as follows: (1) Multimodal representations efficiently capture all acoustic and visual emotional clues included in each music video, (2) the computational cost of each neural network is significantly reduced by factorizing the standard 2D/3D convolution into separate channels and spatiotemporal interactions, and (3) information-sharing methods incorporated into multimodal representations are helpful in guiding individual information flow and boosting overall performance. We tested our findings across several unimodal and multimodal networks against various evaluation metrics and visual analyzers. Our best classifier attained 74% accuracy, an f1-score of 0.73, and an area under the curve score of 0.926. Emotion is a psycho-physiological response triggered by the conscious or unconscious perception of external stimuli. There is a wide variety of factors associated with emotion, including mood, physical feeling, personality, motivation, and overall quality of life. Emotions play a vital role in decision making, communication, action, and a host of cognitive processes . Music vA number of studies have attempted to show how music carries human affective states  and boosWith the rise of the Internet and social media, users are engaging more than before with multimedia content in order to communicate and explore their affective states. In this paper, we focused on analyzing music and image sequences produced by either the music industry or random social media users. Music videos are among the most circulated types of content on the Internet. Emotion recognition has already been employed by music streaming services, the video game industry, the advertising industry, the television industry, and the music industry for audio–video content matching and synchronization . EmotionDeep learning technology makes our daily lives more convenient across diversified domains. Numerous success stories have rapidly spread in the domains of animal sounds ,12,13,14(a)We extended and improved an existing music video dataset  and prov(b)We trained unimodal and multimodal architectures with music, video, and facial expressions using our supervised data. The networks were designed using 2D and 3D convolution filters. Later, the network complexity was reduced by using a novel channel and separable filter convolution.(c)An ablation study was conducted to find a robust and optimal solution for emotion classification in music videos. Music was found to be the dominant source for emotion-based content, and video and facial expressions were positioned in a secondary role.(d)The slow–fast network strategy  was applThis article seeks to enhance and improve a supervised music video dataset . The datWe overcame the difficulties of input processing for multimodal data of large dimensions. Different networks were trained and interpreted by analyzing different information sources individually and jointly. The network performance was evaluated visually and statistically by using different evaluators.The remainder of this article is organized as follows. In Different approaches to the analysis of emotions in music have been taken in the past by using data-driven explorations. At the time of writing, the authors were not aware of any research on deep learning that leveraged affective computing of music videos from scratch. Some unimodal architectures were studied well by using diverse datasets in the past. In this section, we discuss some existing techniques for affective computing of music, video, and facial expressions.Music emotion recognition (MER) is one research sub-domain of music information retrieval (MIR) that focuses on the study of the characteristics of music and their correlation with human thinking. The audio can be dominant information for high-level semantics, such as emotion, in multimedia content. Lopes et al.  showed tVideo is an ordered sequence of images that include several visual clues for affective computing. While there is no deep-learning-related research on the visual representation of emotion in music videos, several studies ,41,42 haFacial expressions can provide information about humans’ feelings and thoughts, and they play a crucial role in interaction and decision making. Facial expressions can have universal qualities  and haveThe wide proliferation of multimedia content that is posted online is increasingly pushing researchers away from conventional unimodal architectures and towards complex multimodal architectures. Some multimodal architectures ,61 have A number of emotion representation frameworks have been proposed in the last decade. The categorical model  describeThe 2D valence–arousal framework is the most widely used framework for emotion representation in music. While this approach overcomes the problem of categorical limitations and ambiguities in search tags, the categories are vague, unreliable, and not mutually exclusive . The catWe categorized the dataset into six distinct classes based on their corresponding emotional adjectives. The “Excited” class usually includes positive emotions. The visual elements of the “Excited” class include images of a smile on a face, movement of arms, dancing scenes, bright lighting, and coloring effects. The audio components of this class include high pitch, large pitch variations, uniform harmony, high volume, low spectral repetition, and diverse articulations, ornamentation, and vibrato. The visual features of the “Fear” class reflect negative emotions via a dark background, unusual appearance, wide eyes, open mouth, a visible pulse on the neck, elbows directed inward, and crossed arms. Common visual elements in the “Tension” class are fast-changing visual scenes, crowded scenes, people facing each other, aggressive facial expressions with large eyes and open mouths, and fast limb movements. The audio elements in the “Tension” and “Fear” classes include high pitch, high tempo, high rhythmic variation, high volume, and a dissonant and complex harmony. The visual elements in the “Sad” class are closed arms, a face buried in one’s hands, hands touching the head, tears in eyes, a single person in a scene, a dark background, and slow-changing scenes. The “Relaxation” class includes ethnic music and is visually represented with natural scenes in slow motion and single-person performances with musical instruments. The acoustic components of the “Sad” and “Relaxation” classes include slow tempo, uniform harmonics, soft music, and low volume. The “Natural” class includes mixed characteristics from all five other classes. The data samples included in each class reflect diversity in music genres, culture and nation of origination, language, number of music sources, and mood. Five coworkers were involved in the construction of the new dataset.The raw music video data needed to be processed in an acceptable form prior to being entered into the neural network. Our processing of the dataset of music, video, and facial expressions followed a number of steps for each individual data sample. The music network was trained on the real (magnitude) and imaginary (phase angle) components of the log magnitude spectrogram. The magnitude of the log Mel spectrogram was kept to one channel, while the phase angle representation was placed in another in order to preserve both.i was converted into mono and then subsampled with a window size of 2048, a sampling rate of 22,050 Hz, and a time shift perimeter of 512 samples. The sampling rate varied for the slow–fast network, in which xi in the slow path was sampled at a rate of 32,000 Hz, while the xi in the fast path had a sampling rate of 8000 Hz. Fast-Fourier Transform (FFT) was then applied to each window to transform the xi from a time-domain representation into a time–frequency (T-F) representation ). A total of 128 non-linear Mel scales that matched the human auditory system were selected from across the entire frequency spectrum. The log Mel spectrogram offers two advantages over waveform audio; first, it reduces the amount of data that the neural network needs to process compared with waveform representation, and second, it is correlated with human auditory perception and instrument frequency ranges [For this work, a 30-second audio waveform xy ranges .i = {v23nn that were extracted. For the video/face network, 64 frames were taken in a distributed fashion. The video data were processed in a similar way to the audio data by varying the frame rate in the slow (eight frames) and fast (64 frames) branches of the slow–fast network.The serially binned images were collected in a distributed way to preserve the temporal information of the entire video sequence, as shown in https://opencv.org/ accessed on 7 June 2021) 4.2.0 version. The images were cropped and resized for the network input. Music video frames may or may not have a face in them, or may depict more than one face. We chose music videos that contained at least one face. In each video, video frame vt may contain one face {f1}, more than one face {f1, f2, f3, …, fm}, or no faces. After processing all of the video frames, we counted all of the frames containing faces. If the number of face frames was less than the face network input size, we repeated these frames until satisfying the requirement. Additional frames were discarded if the total face frames exceeded the neural network input. The repeated frames and discarded additional frames lost the temporal information of video sequences of faces. This is one reason for why our face network had a relatively small contribution to the overall decision making compared to the video and music networks.For the face network input, face areas were detected in each video frame by using the cascade classifier feature of OpenCV , as in . The cha0 in 1 to A3), which are shown in 3 was a slow–fast network designed to capture the spatial and temporal information of audio/video. The basic architecture of our proposed network and a detailed view of each block are shown in We propose four basic architectures for the music, video, and face emotion networks. The architecture AThe basic block of the unimodal architecture, which is shown in https://zenodo.org/record/4542796#.YCxqhWgzaUk accessed on 7 June 2021) and code (https://github.com/yagyapandeya/Supervised-Music-Video-Emotion-Classification accessed on 7 June 2021) used to produce these experiments are publicly available  curve. We define accuracy as the probability of correct classification within a dataset. Accuracy indicates better evaluations in a balanced dataset. The F-measure is the harmonic mean of precision and recall; when the precision increases, the recall decreases, and vice versa. The F-score handles imbalanced data and provides a measure of the classifier’s performance across different significance levels. A confusion matrix presents the number of correct and erroneous classifications by specifying erroneously categorized classes. A confusion matrix is a good option for reporting results in multi-class classification. The area under the ROC curve is a measure of how well a parameter can distinguish between a true positive and a true negative. An ROC is a probability curve that provides a measure of a classifier’s performance in two-dimensional space. The area under the ROC curve (AUC) measures the degree of separability across multiple classification thresholds. These evaluation metrics were used to evaluate the effectiveness of our system for emotional classification in music videos. The data  performed better with fewer parameters. Both positive and negative effects were found when using the squeeze-and-excitation blocks with the music networks.The musical analysis focused on the objective aspect of musicality. The neural network determined the changes in the spectral representations according to the emotional category. The unimodal architecture, which used only music information, performed the best compared to the face and video networks. The success of the network was related to the smooth changes in the musical patterns over time. Uncertainty in the music processing network, however, could arise due to the subjectivity of musical components and expressive techniques. We tested various music network architectures for emotion in music, and they are illustrated in 3) to capture the spatial and temporal information of videos with varying frame rates on each branch. The learned information of each branch was boosted and shared by using the MMTM block. The results of the video network were better than those of the face network, but not as good as those of the music network. The video network had a smooth temporal pattern that could not occur in the face network because a non-face frame would break the sequence. Compared to the music, the visual scenes abruptly changed according to time, which could affect the system’s performance. Uncertainties in the video network could occur with user-generated videos, which may not have industry standards of recording, camera movement, and focus. We used the slow–fast network architecture  with MMTM performed the best with the integrated architecture. The music network with 2D convolution (A0) was found to be better than the network with the rectangular filter (A1 and A2). The first row of 2 network with music, video, and facial expressions (eighth row) had the lowest number of parameters, but the performance was even lower when using the best unimodal music architecture. The A2 face network with SE was found to be effective in the integrated architecture with both music and video networks. For example, the A2 face network with the A1 music and video network (last row of 1 face network (penultimate row). In the overall analysis, the visual clues that used face expression were found to be supportive for the classifier, with a small increment in network complexity.The integrated network of music, video, and facial expressions was trained in an end-to-end manner by using supervised data. We validated the results of our experiments by using two visual evaluation methods: a confusion matrix and a ROC curve plot. The confusion matrix counted the number of samples in classes that were confused with each other. The confusion matrix in The ROC curves obtained when using our various multimodal architectures are shown in Human emotions can be connected to each other, and these connections also appear in music videos. We analyzed the correlation of our six emotional categories in music videos. The class-wise probability was computed by using the sigmoid function at the end of the neural network. The class correlation results of test samples from each class are illustrated in Despacito (https://www.youtube.com/watch?v=kJQP7kiw5Fk) (accessed on 7 June 2021) (7.41B views) and Gangnam Style (https://www.youtube.com/watch?v=9bZkp7q19f0) (accessed on 7 June 2021) (4.10B views) are shown in An emotional relationship between consumers and music videos was shown in ,87,88 toOur research proposes a new deep learning method and prepares a new dataset for emotion classification in music videos. The past studies were conducted for affective computing on low- and mid-level audio and video feature classification using conventional classifiers, such as SVM. No study has implemented a deep learning method that can collectively use audio, video, and facial expression information in a single network with end-to-end training.A previous study  attempteSome studies have proposed the use of a diverse dataset for affective computing of music videos. One study concerning emotional analysis of music that used a recurrent neural network with an SVM on top  achievedIn A number of possible multimodal architectures that use music-related audio and video information have also been studied for affective computing. The study in  used lowIn this research, first, we analyzed the unimodal representations of music, video, and facial expression information from music videos by using end-to-end training. The unimodal architectures were further integrated into a multimodal architecture to develop a robust and optimal classifier. Compared to past research, our dataset is more diversified; the networks were trained and tested on real data domains and not on features. We reduced the system’s complexity and enhanced the performance of the architecture by using novel convolution and information-boosting methods. The results were statistically evaluated by using various evaluation metrics.Affective computing enables AI systems to interpret human emotions. This area of computing is inherently interdisciplinary, though the analysis of emotions in music videos remains a particularly unexplored area within the field of computer engineering. Our system classifies music videos by using a dataset that was introduced for supervised training. Several unimodal and multimodal architectures have been proposed to analyze music, video, and facial expressions from scratch. Our proposed architectures, including the slow–fast network, were designed to use 2D and 3D convolution, as well as a novel separable channel and filter convolution. Our best multimodal architecture achieved 74.00% accuracy, an F1-score of 0.73, and an area under the curve (AUC) score of 0.923.Future researchers have space for improvement in terms of the performance, the dataset, the emotion representation framework, and the evaluation measures. In this study, we only included music, video, and facial expression networks. Music lyrics are another vital source of information that can be integrated in further studies for more accurate affective computing.


Occupational therapy (OT) is a vital service that supports older adults’ ability to age in place. Given the barriers to accessing care, video telehealth is a means of providing OT. Even within Veterans Health Administration (VHA), a pioneer in telehealth, video telehealth by OT practitioners to serve older adults is not well understood.This study examines VHA OT practice using video telehealth with older veterans using an implementation framework.A web-based national survey of VHA OT practitioners conducted between September and October 2019 contained a mix of mostly closed questions with some open-text options. The questions were developed using the Promoting Action on Research Implementation in Health Services model with input from subject matter experts. The questions gathered the extent to which VHA OT practitioners use video telehealth with older veterans; are comfortable with video telehealth to deliver specific OT services; and, for those using video telehealth with older veterans, the barriers, facilitators of change, and perceived benefits of video telehealth.P<.001), instrumental activities of daily living (P=.004), home safety (P<.001), home exercise or therapeutic exercise (P<.001), veteran or caregiver education (P<.001), durable medical equipment (P<.001), assistive technology (P<.001), education and work (P=.04), and wheelchair clinic or seating and positioning (P<.001). More than half  of those using video telehealth reported at least one barrier, with the most frequently endorsed being Inadequate space, physical locations and related equipment. Most  respondents using video telehealth reported at least one facilitator, with the most frequently endorsed facilitators reflecting respondent attitudes, including the belief that video telehealth would improve veteran access to care  and willingness to try innovative approaches .Of approximately 1455 eligible VHA OT practitioners, 305 participated (21.0% response rate). Most were female  occupational therapists  with a master’s degree  and 10 years or fewer  of VHA OT practice. Less than half  had used video telehealth with older veterans, and users and nonusers of video telehealth were demographically similar. When asked to rate perceived comfort with video telehealth to deliver OT services, participants using video telehealth expressed greater comfort than nonusers, which was significant for 9 of the 13 interventions: activities of daily living (Most VHA OT survey respondents had not used video telehealth with older veterans. Users and nonusers were demographically similar. Differences in the percentages of respondents feeling comfortable with video telehealth for specific OT interventions suggest that some OT services may be more amenable to video telehealth. This, coupled with the primacy of respondent beliefs versus organizational factors as facilitators, underscores the need to gather clinicians’ attitudes to understand how they are driving the implementation of video telehealth. Veterans Health Administration (VHA), the largest integrated health care system in the United States, has been using telehealth since the 1990s to provide care to a broadly dispersed veteran population. VHA provides care to veterans who served in military, naval, or air services. Approximately 60% of US veterans are enrolled in VHA care, including more than 90% of those who incurred a service-related disability. The median age of veterans is 65 years, including a large portion of rural veterans . To ensuVideo telehealth for specialty care such as occupational therapy (OT) has historically been underdeveloped; of an estimated 1.5 million total VHA OT encounters in fiscal year 2018, less than 1% were delivered using telehealth . This isLack of evidence for OT video telehealth has resulted in a gap in knowledge about how best to integrate video telehealth into OT practice in response to the COVID-19 pandemic .To optimize the integration of video telehealth solutions, various contextual factors must be considered, according to the Promoting Action on Research Implementation in Health Services (PARIHS) framework. PARIHS was developed as an evaluative framework to support the systematic integration of research findings and intervention strategies into clinical care, thereby enhancing the quality and efficacy of health services . ClinicaGiven this knowledge gap, this study sought to gather OT practitioners’ experiences with and perspectives on video telehealth to serve older adults. Specifically, we sought to ascertain the extent to which VHA OT practitioners use video telehealth to serve older veterans; VHA OT practitioners’ comfort with video telehealth to deliver specific OT services; and, for those using video telehealth with older veterans, perceived barriers, facilitators of change, and benefits of video telehealth. The aim of this study is to identify barriers and facilitators related to the successful implementation of video telehealth to ensure equitable distribution of this service to older adults.To gather data, a survey was conducted with a volunteer sample of OT practitioners enrolled in VHA’s national internal OT email listserv groups from a network of 1243 health care facilities, including 170 VA medical centers and 1063 outpatient sites. Inclusion criteria were being either an occupational therapist or OT assistant and treating older veterans, who were defined as those aged 65 years or older. No other exclusion criteria were included. The survey flow and inclusion criteria are shown in Here, we present survey details in accordance with the Checklist for Reporting Results of Internet E-Surveys checklist . Survey Items were reviewed for face and content validity by 5 subject matter experts in OT, telehealth, and geriatrics care and revised based on feedback. Before the survey launch, 5 VHA OTs (separate from above) pretested the survey, which involved completing the survey draft via the REDCap (Research Electronic Data Capture) link, followed by cognitive interviews conducted by the first author using predetermined verbal probes . Probes not comfortable to very comfortable. An option of not sure was provided. We collapsed the 4-point comfort scale into 2 categories—comfortable and not comfortable—as there were insufficient data to retain the 4 categories. The not sure option was excluded, as it was found to be uninterpretable. The comfort item, which was not required, was provided to all respondents whether they used video telehealth or not. Six additional checklist items were completed only by those using video telehealth and addressed the perceived barriers (2 items), facilitators (2 items), and benefits (2 items). A complete list of barriers, facilitators, and benefits is given in other and none and an open-text option whereby respondents could write in additional entries via short open-ended responses. For all checklist items, respondents could select more than one option. Respondents were able to review their answers using the back button. Responses were anonymous; however, as each participant was not sent an individualized survey link, respondents could potentially complete the survey more than once.The final survey included 17 survey items . ParticiThe survey was conducted in September and October 2019. Practitioners were invited to participate by emailing a survey link to the VHA OT provider listserv and posting on the VHA’s web-based forum for OTs. An anonymous URL link only available on the VA intranet was used. Survey respondents had to be logged into an active VA network account to respond to the survey. The survey was kept open for 4 weeks, with 3 reminder emails and forum posts sent before the survey closed. The email invitation and survey overview specified that participation was voluntary, anonymous, and confidential and that those who agreed to participate agreed to these conditions. Survey data were collected and managed using the REDCap electronic data capture tools hosted at VHA. REDCap is a secure web-based app designed to support data capture for research studies . Survey t tests were used for continuous variables. Statistical significance was set at P<.05. For the purposes of analysis, the categorical item, VHA years of practice, was divided into 2 segments of nearly equal size: 10 years or less and 11 years or more. Prefer not to answer responses were excluded from analysis, as this option lacked specificity recommended for demographics questions [Survey data were exported from REDCap into Excel (Microsoft Corporation) and imported into R for analysis. To statistically examine baseline differences in demographics and differences between perceived comfort for those using video telehealth or not, chi-square or Fisher exact tests (when cell counts were <5) were used for categorical variables and two-tailed uestions . RuralitShort open-ended responses to barriers, facilitators, and benefits items were analyzed using conventional content analysis . Two cliOverall, from approximately 1455 eligible VHA OT practitioners, 305 participated (21.0% response rate). The survey flow is shown in Less than half  of survey respondents used video telehealth with older veterans. There were no statistically significant differences between respondents using video telehealth and those not using video telehealth according to demographic characteristics. The sample characteristics using video telehealth are shown in comfortable and not comfortable—by using video telehealth. Respondents who used video telehealth were more likely to express comfort using video telehealth, which was true for 9 out of 13 interventions, except for leisure, social participation, rest and sleep, and sensory or cognitive strategies. More respondents were comfortable with the idea of using video telehealth for these 9 interventions than were not comfortable with them. This was true among both those who had and had not used video telehealth; however, the comfortable versus not comfortable difference was greater for users. Mean comfort ratings with confidence intervals are given in user=69; nnonuser=67), IADL , home safety , home exercise or therapeutic exercise , wheelchair clinic or seating and positioning , durable medical equipment provision or follow-up , veteran and/or caregiver education or training , education and work , and assistive technology provision or follow-up . No significant relationships between comfort and use of video telehealth were found for the interventions of sensory or cognitive strategies , social participation , leisure , and rest and sleep .The 9 interventions showing this statistically significant relationship, with sample sizes for users versus nonusers of video telehealth in parentheses , were ADL  of respondents reporting barriers, whereas lack of leadership support was the least frequent, selected by 8% (6/74) of respondents. More than a quarter  indicated that they encountered no barriers. Of the 74 respondents, 23 (31%) provided short, open-text other comments, which were then categorized as one of the listed barriers or as novel. Most open-text comments expanded on the barriers selected from the list provided. For example, some described challenges related to technology, such as decreased connectivity or veterans’ lack of technical ability.none. Most  respondents using video telehealth reported at least one facilitator. The most frequently endorsed facilitators reflected respondent attitudes, including the belief that video telehealth would improve veterans’ access to care  and willingness to try innovative approaches . Organizational facilitators, such as leadership support, were reported to a lesser degree. Adequate space, physical locations and related equipment was the least selected facilitator, which is in concordance with inadequate space being the top barrier. Other and none were rarely reported.Reported facilitators, which included both organizational factors and practitioner beliefs, are given in none. Top-ranked benefits related to access, with 94% (87/92) of respondents reporting benefits of remediating veteran distance from the medical center or difficulty getting to the medical center. The impact of video telehealth on efficiency, as indicated by the ability to serve more veterans or to see veterans more often, was reported to a lesser degree . Short open-ended entries primarily elaborated access benefits, with respondents indicating increased opportunities through video telehealth, such as wheeled mobility specialists, to collaborate with other team members.Most VHA OTs who responded to the survey had not used video telehealth with older veterans, with those using video telehealth demographically similar to those not using video telehealth. Differences in comfort with video telehealth for specific OT interventions suggest that some OT services may be more amenable to video telehealth. This, coupled with our finding that respondent beliefs were more pronounced than organizational factors as facilitators, suggests the importance of clinicians’ attitudes in the implementation of video telehealth.This is the first study to provide insights into the state of OT video telehealth with older adults, a population of heightened interest because of changing demographics and their increased risk of complications and infections related to COVID-19 . Before Although most respondents were not, at the time of the survey, using video telehealth with older adults, users and nonusers were demographically similar. Given that the highest rated facilitators to and benefits of video telehealth by users included clinicians’ attitudes toward video telehealth, such as the belief that video telehealth would increase access to care, emphasis on perceived benefits could help encourage OT practitioners hesitant to try video telehealth. However, we did not ask those using video telehealth about attitudinal barriers, such as perceived harm or negative impact of video telehealth in terms of decreased privacy or limitations of what can be clinically done in video telehealth. Thus, it is difficult to draw further conclusions about clinicians’ attitudes toward video telehealth from these data.Regarding respondent comfort using video telehealth for specific areas of OT practice, differences between users and nonusers indicate that using video telehealth may enhance comfort with video telehealth. However, the causal relationship between respondent comfort and use of video telehealth is not clear, that is, Does the use of video telehealth enhance comfort or do those who are more comfortable with the technology opt to use video telehealth? This relationship should be examined in future studies.not comfortable with video telehealth suggest potential practitioner knowledge gaps about certain areas of OT practice, incongruity between practice and application in video telehealth, and potential limitations of video telehealth. Respondents were less comfortable with the use of video telehealth for sensory or cognitive strategies. This warrants further study, as it is not clear (as specific intervention examples were not provided) what sensory or cognitive strategies respondents were thinking about when they answered this question. Rest and sleep was another practice area that had higher uncomfortable ratings. As a newer area of OT practice [Interventions receiving higher ratings of practice , there iIn addition, lower comfort for leisure and social participation is noteworthy, given the strongly established role of OT in these areas . SeveralThe findings suggesting that certain OT interventions may be more amenable to a video telehealth platform than others warrant further investigation as to clinician decision making around video telehealth. Both those using video telehealth and those not yet using it felt comfortable with the idea of using video telehealth to provide veteran or caregiver education and training. This may reflect either increased comfort with the use of video telehealth to support interventions relying primarily on verbal engagement or the ubiquity of educational strategies to accompany OT interventions. Relatedly, high percentages of feeling comfortable in using video telehealth for home safety is an interesting finding, given that video telehealth home safety evaluations are complex and may require a caregiver or the patient to ambulate through the home while carrying a portable computing device . SimilarGiven that older adults may have less confidence in operating technology and more mobility limitations, OT interventions delivered through video telehealth, particularly more dynamic interventions such as home safety evaluations, should be optimized to meet older adults’ needs. Identifying strategies to train and prepare veterans to participate in OT-delivered video telehealth  may facilitate the implementation of video telehealth. In addition, certain populations may have complex care needs, which hamper their ability to participate in video telehealth. Caregiver assistance, particularly for adults who have cognitive impairment or are at risk of falls, may also be needed. Promoting eHealth literacy and co-designing interventions to match technology with older users’ needs will optimize telehealth delivery ,52.Perceived benefits, which primarily focused on increased access, corroborate VHA’s organizational mission to use video telehealth to increase access to care. Access was partly related to travel distance; however, open-ended responses suggested that access was more broadly conceptualized to include the ability for more timely care and for more care coordination. For example, practitioners noted that video telehealth allowed them to involve different members of the care team. Older adults often manage multiple chronic conditions that require ongoing intervention by several clinicians. Therefore, video telehealth may increase opportunities for interdisciplinary collaboration to address care needs. This may be even more relevant at times such as during the global pandemic when video telehealth is virtually the only option for face-to-face care. Similarly, these findings raise factors relevant to health care systems that aim to integrate video telehealth OT services.Given the dynamic nature of many OT interventions, an important organizational consideration is the inclusion of technical support for both OTs and older adults. Technical support as an organizational component of video telehealth may be more critical for OT than other, more stationary video telehealth encounters. Mental health video telehealth, for example, consists of mostly verbal exchange, whereas OT interventions may involve veterans working on a cooking task in the kitchen or transferring in and out of the bathtub. This raises potential problems around bandwidth and lost visual or audio that may require the involvement of technical support, in addition to the aforementioned safety concerns.Barriers and facilitators reveal additional organizational considerations in the delivery of OT services using video telehealth, beyond the aforementioned need for technical support. Lack of physical space (the most frequent barrier and least reported facilitator) may reflect the fact that OTs are often treating in shared spaces such as rehabilitation gyms, unlike mental health clinicians who usually have private offices. This highlights the need to consider infrastructure and privacy in the implementation of video telehealth for OT services; however, allowing practitioners to deliver video telehealth from home would lessen space demands. This study also has implications for clinician education and training to ensure that interprofessional trainees are prepared to offer telehealth to older adults ,54. Of nThis study had several limitations. Regarding survey design, we did not ask practitioners using video telehealth to reflect on barriers such as potential harm, safety risks, disruptions related to video telehealth, increasing workload, or necessary time and training to familiarize themselves with technology, which limits the scope of our findings. As we cannot demonstrate causality between comfort and use of video telehealth, more in-depth surveys or qualitative interviews with OTs may elucidate perceived primary causal issues for comfort as well as perceived barriers and facilitators. The lack of description for certain OT interventions listed in the survey  results in difficulty interpreting some comfort ratings. Nonrespondent bias may also constrain generalizability, as practitioners may have felt pressured to participate or those with a strong interest may have been more likely to participate in the survey. We did not collect data on age, and although years of practice is informative, it is not a proxy for age. Finally, we did not ask whether video telehealth was conducted into the home or between major medical centers and satellite clinics, thereby limiting what conclusions can be drawn regarding video to home, a main telehealth strategy in the post-COVID-19 landscape.On the basis of our findings, the following are some key implications for implementation of video telehealth in delivering OT services to older adults. Implications reflect the myriad contextual factors vital to ensuring that video telehealth meets the needs of both OT clinicians and patients:Perspectives of early OT adopters of video telehealth, including perceived facilitators, may inform those not yet using video telehealth.The benefit of video telehealth in increasing access to care may encourage increased use of video.Gathering practitioner decision making around the use of video telehealth for specific OT interventions will optimize delivery to clients who face access barriers, increasing the reach of extant providers while potentially saving resources such as clinic space.OT practitioners may have unique infrastructure needs, including dedicated private spaces and need for technical support, in the provision of services using video telehealth.Video telehealth with older adults as a service delivery model is rapidly expanding, with VHA at the forefront. Early adoption of video telehealth by VHA OT practitioners appears to be driven, in some measure, by clinician experiences and attitudes; however, institutional barriers remain. As the pandemic offered a model of veterans and some clinicians participating in video telehealth from their own homes, institutional barriers such as limited space may be less of a concern in the post-COVID era. Expansion of video telehealth to deliver services to older adults will involve identifying ways to maximize the video telehealth platform through adaptation and tailoring of interventions to provide client-centered care. There is a need for more evidence on video telehealth OT strategies for older adults, which COVID-19 and resulting OT rapid practice change may expedite.


Wildlife pedigrees provide insights into ecological and evolutionary processes. DNA obtained from noninvasively collected hair is often used to determine individual identities for pedigrees and other genetic analyses. However, detection rates associated with some noninvasive DNA studies can be relatively low, and genetic data do not provide information on individual birth year. Supplementing hair DNA stations with video cameras should increase the individual detection rate, assuming accurate identification of individuals via video data. Video data can also provide birth year information for individuals captured as young of the year, which can enrich population‐level pedigrees. We placed video cameras at hair stations and combined genetic and video data to reconstruct an age‐specific, population‐level pedigree of wild black bears during 2010–2020. Combining individual birth year with mother–offspring relatedness, we also estimated litter size, interlitter interval, primiparity, and fecundity. We used the Cormack‐Jolly‐Seber model in Program Mark to evaluate the effect of maternal identity on offspring apparent survival. We compared model rankings of apparent survival and parameter estimates based on combined genetic and video data with those based on only genetic data. We observed 42 mother–offspring relationships. Of these, 21 (50%) would not have been detected had we used hair DNA alone. Moreover, video data allowed for the cub and yearling age classes to be determined. Mean annual fecundity was 0.42 . Maternal identity influenced offspring apparent survival, where offspring of one mother experienced significantly lower apparent survival  than that of offspring of four other mothers . We video‐documented cub abandonment by the mother whose offspring experienced low apparent survival, indicating individual behaviors  may scale up to affect population‐level parameters . Our findings provide insights into evolutionary processes and are broadly relevant to wildlife ecology and conservation. We installed video cameras at hair stations and combined genetic and video data to reconstruct an age‐specific, population‐level pedigree of wild black bears during 2010–2020. Combining individual birth year with mother‐offpsring relatedness, we also estimated reproductive parameters and evaluated the effect of maternal identity on offspring survival. Maternal identity influenced offspring survival, where offspring of one mother experienced significantly lower apparent survival  than that of offspring of four other mothers  We could video‐capture individuals that visited hair stations but did not deposit hair, provided individuals were close enough to cameras to trigger them. This method assumes individuals can be accurately identified via video data. For wild black bears, we successfully identified individuals through meticulous scrutiny of video data (not still photos) cross‐referenced with individual genetic identities determined from hair DNA concurrently collected at hair stations in view of video cameras . Advances in automated facial recognition software  reconstruct a population‐level pedigree with individual birth years, (2) estimate natality, litter size, interlitter interval, primiparity, and fecundity using genetic data integrated with video data, and, (3) evaluate the effects of maternal identity on offspring apparent survival using combined genetic and video data and compare the resulting model rankings and parameter estimates with those based on only genetic data.22.1Pseudotsuga menziesii), Ponderosa pine (Pinus ponderosa), subalpine fir (Abies lasiocarpa), and quaking aspen , serviceberry , huckleberry (Vaccinium globulare), arrowleaf balsamroot , hawthorn (Crataegus spp.), sweet cicely (Osmorhiza spp.), cow parsnip (Heracleum lanatum), thimbleberry (Rubus parviflorus), strawberry (Fragaria spp.), currant (Ribes spp.), horsetail (Equisetum spp.), clover (Trifolium spp.), and other forbs and grasses (Poaceae spp.). Other large mammals on MPG Ranch include elk (Cervus canadensis), white‐tailed deer (Odocoileus virginianus), mule deer (Odocoileus hemionus), and mountain lions (Puma concolor). Prior to 2009, black bear hunting had occurred on the study site for decades. Beginning in 2009, black bear hunting was prohibited on the study site and on ~4000 ha of adjoining private lands. Black bear hunting occurred on nearby lands held in block management by The Nature Conservancy and on some nearby public lands. Deer and elk hunting occurred on the Ranch.We conducted our study on MPG Ranch , a 6,191‐ha conservation property located in the Northern Sapphire Mountains in western Montana, USA  and placement of video stations were consistent. A station was defined as one or more video cameras aimed at a unique feature, such as a rub post, rub tree, wildlife trail, or water source. We often placed multiple video cameras at different angles at a single station to maximize individual identification. When multiple cameras captured an individual at a single station during a single event, we used video data from all cameras to identify the individual, but only one video was counted as a capture event. We placed cameras ~0.3–1.0 m off the ground to view black bear characteristics such as chest blazes, facial features , genitals, and other body traits . Cameras recorded for 1‐min intervals at up to 60 frames per second and were operational 24 h per day. We checked all video cameras every 2–3 weeks during summers 2010–2014 and replaced camera batteries at least 4 times annually. During 2015–2020, we checked cameras and replaced camera batteries every 6 weeks during March‐November, annually.We used the following video camera systems to observe and detect bears: Stealth Cam model STC‐G42NG, Stealth Cam model STC‐DVIRHD , Reconyx XR6 UltraFire , Bushnell model 1197678 , and Browning models 8FHD‐P and BTC‐7A . During 2010, we placed video cameras at 23 stations to determine which areas were most frequented by bears. We added three video stations during 2011 and 33 additional video stations during 2012. We adjusted the location of some video stations in 2013 to maximize the detection of black bears and we removed four unproductive video stations. During 2013–2020, the number  at the 16 rub trees because bears were already using those trees for rubbing. Importantly, the initial goal of our camera‐based research was to document individual bear behaviors, so video and hair stations were placed in areas that were known to be frequented by bears, rather than in a grid pattern . All DNA extractions were performed at either MPG Ranch or the U.S. Forest Service Rocky Mountain Research Station (RMRS), Missoula, MT. RMRS performed all individual identity analysis on purified extracts using a panel of nine microsatellite loci, including G1A, G10D, G10B . Individual identity analyses were performed on purified extracts using the same methods as that described using hair DNA. Throughout the field season, we followed the University of Montana's Covid‐19 Guidelines for Field Research. Our protocol for handling bears was approved by the Institutional Animal Care and Use Committee of Montana Fish Wildlife and Parks (IACUC #: FWP02‐2020).In summer 2020, we began a pilot study for another research project that included live‐capture and collaring black bears on the study site. We collected blood and hair samples from captured bears, which we used to determine genetic identity and paternity and to test maternity determinations based on video observations. We immobilized non‐cub bears using telazol , administered with a dart pistol or pole syringe. Each immobilized bear was weighed, sexed, PIT‐tagged, measured, and ear‐tagged. For cubs and non‐yearling bears, we pulled a premolar for age determination via cementum analysis . We attached Vertex Plus GPS collars  to bears that weighed ≥55 pounds. Each collar band bore unique symbols to aid in bear identification upon recapture. We collected blood from most captured individuals and placed a few drops on FTA blood collection cards. In the lab, we punched three 3.00 mm holes into the blood‐soaked filter paper using a Harris Micro‐Punch and DNA was extracted from the FTA paper using the DNeasy2.7https://cervus.software.informer.com/3.0/, accessed 30 March 2021). We used the same method to determine paternity. Not all adult males provided genetic data, so we did not sample all candidate fathers. However, every male for which we had genetic data was tested for possible paternity of every genetically identified bear in our study site. In 2016, we observed the death of one cub via video data and we obtained her genetic identity based on tissue samples that we collected during the necropsy.We created a population‐level pedigree with individual birth years for wild black bears in our study site. For many wild species, including black bears, parental care by the mother makes it easy to identify maternity based on observation  of monitored adult females following Garrison et al.  divided by the number of adult females monitored during year x.We estimated litter size based on the earliest observations of complete litters after den emergence Miller, . We estin et al. . Althoug2.9During some video‐capture events of family groups, mothers may have been in view of cameras while cubs were not. Therefore, we estimated the cub detection rate by calculating the proportion of video‐capture events during which all littermates of each multi‐cub litter were observed together during year 1. To estimate cub detection rate, we included only multi‐cub litters that were known to have survived year 1 so that the number of cubs that should have been present during video captures was known.2.102.10.1n = 56) and placement of video stations were consistent during this period. During 2013–2020, we documented the number of capture events for each offspring, each year. For example, if a cub was born in 2013, we documented the number of capture events during the cub's first year and all subsequent years, through 2020 . We used the Cormack‐Jolly‐Seber model  and offspring recapture probability (p). To evaluate the effect of maternal identity on offspring φ, we grouped offspring individuals into maternal identity groups. There were eight maternal identities  and 40 encounter histories , so it was important to minimize the total number of estimable parameters. We also evaluated models that included time‐dependent and constant φ and p. We considered the intercept‐only model (φ {.} p {.}) to be the null model. We used the CJS model rather than other more complex models because the CJS model had the least number of estimable parameters (only φ and p).To model offspring apparent survival, we included video and genetic data collected during 2013–2020 because the number  p . We estimated the overdispersion parameter (c) and, in the case of overdispersion, we adjusted c‐hat accordingly. We used Akaike's  to rank models in terms of their ability to explain the data. Models with Δ QAICc values <2.0 were considered to have substantial support  per bear, per year into single data points for each bear and year from 2013 to 2020. We used the CJS model to estimate φ and p, and to evaluate the effects of maternal identity on φ. We considered the intercept‐only model (φ {.} p {.}) to be the null model, we evaluated the goodness‐of‐fit of the saturated model, we used QAICc to rank models, and we considered models with Δ QAICc values <2.0 to have substantial support.We performed a second offspring 33.1During 2010–2020, we documented 9,241 video‐capture events of an estimated 94 individual black bears  of those hair samples, from which we identified 54 unique individual bears  visiting stations to switch out SD cards and to collect hair samples, annually. Analyzing video data and cross‐referencing video captures with genetic captures required ~400 h (~50 staff days), annually.3.2φ analyses. Of the 210 hair depositions by bears, 203 (97%) were concurrently video‐documented. After removing redundant hair samples, our sample size for estimating individual identification accuracy was 134 successfully genotyped hair samples matched with 134 video‐capture events. Of the 134 genetic‐video sets, we accurately matched the video identification with the genetic identification 130 times (97%). Previously, we also demonstrated that we accurately identified yearling bears across time , our video‐based identifications correctly matched the genetic‐based identifications.We collected 210 hair samples that successfully genotyped of the 41 bears that were included in our 3.3We live‐captured 13 individuals during our pilot study in 2020 and determined genetic identities for all 13 from blood or hair DNA collected during the handling process. Eight of those 13 bears had previously left hair DNA so we already knew their genetic identities. Three live‐captured bears were yearlings (with distinct traits) that we had previously observed with their mothers (who had distinct traits), but who had not previously left hair DNA. For all three, we obtained genetic identities, confirmed the mother–offspring relationships that we had estimated based on video observations, and evaluated paternity using DNA analyses. Two live‐captured bears were subadult males that we had neither previously observed via video nor detected via hair DNA on our study site. Neither subadult male was the offspring of any female or male for which we had genetic identities.3.4When constructing wild pedigrees, founders and immigrants are assumed to be unrelated and non‐inbred , genetic analyses confirmed the presumed mother–offspring relationship based on video observation  sired nine cubs in seven litters with three different mates  were produced at 2‐year intervals, two litters were produced at 1‐year intervals, and one litter was produced at a 4‐year interval . We observed 13 interlitter intervals for six of the eight females that bore cubs; most litters  and estimated p was 0.64  fathered genetically identified cubs. Costello et al.  similarln = 6 cubs in 4 litters) and Bears M2 and M5 each fathered three genetically identified cubs born during 2015–2016. In 2018, however, two different adult males (Bears M21 and M15) each fathered three genetically identified cubs and one adult male (Bear M14) sired one genetically identified cub in 2019, whereas Bears M2 and M5 sired none. Beginning in spring 2018, we observed that the largest adult male (Bear M2) sustained a leg injury that caused him to limp throughout 2018. The injury may have made it difficult for Bear M2 to roam widely, an effective strategy for finding and mating with receptive females  sired all genetically identified offspring  did not leave hair DNA. In addition to the expected individual variability in genetic sampling  was 0.93, so partial litters were observed during 7% of video‐capture events of family groups. For each family group, the date of the first observation of a partial litter always occurred either after or on the same date we first observed the entire litter together Table . For allUsing only noninvasive hair DNA and video data, we estimated fecundity rate, one of the two cornerstones of population biology , indicating she was likely in relatively good condition during late fall 2018 , which were rarely used by a few researchers who followed strict protocols to minimize wildlife disturbance.https://mpgcloud.egnyte.com/dl/DtocMNnaOv). Other studies have reported black bear cub abandonment  was 0.64, which was much lower than the estimated p using combined genetic and video data (p = .96). Also, the sample size of encounter histories used to model offspring φ decreased from 40 (using combined genetic and video data) to 24 (using only genetic data). Moreover, the encounter histories included in the φ analysis using only genetic data were truncated for 21 of 24 (88%) offspring. For example, Bear F7 (easily identified because she had a distinct chest blaze) was video‐captured multiple times annually during 2013–2020. Therefore, Bear F7’s annual encounter history (using combined genetic and video data) included nine captures, one for each year, 2013–2020. However, Bear F7 was genetically captured only twice during that same period. Thus, Bear F7’s annual encounter history (using only genetic data) included only two captures. The smaller sample size and truncated encounter histories were likely the reason that the effect of maternal identity on offspring apparent survival was not detected when we used only genetic data. The top‐ranked model from φ analyses using only genetic data was the null model, where all other models  ranked very low and had extremely low to zero model weight. Alternatively, the top‐ranked model from φ analyses using combined genetic and video data included the effect of maternal identity on offspring apparent survival.Integrating video data with genetic data increased the probability of detecting individuals, which helped inform the population‐level pedigree, reproductive parameter estimates, and models of offspring annual apparent survival. Regarding the latter, the estimated 2) and our sampling intensity was relatively high (~2.5 stations/km2). Many individuals in our study were captured multiple times at multiple stations annually. Therefore, our sampling intensity may have been excessive for estimating demographic parameters ; Data curation ; Formal analysis (lead); Investigation ; Methodology ; Project administration ; Software (lead); Supervision ; Validation (lead); Visualization ; Writing – original draft (lead); Writing – review & editing . Alan B. Ramsey: Conceptualization ; Data curation (lead); Formal analysis ; Funding acquisition (supporting); Investigation ; Methodology ; Project administration (lead); Resources (supporting); Software ; Supervision (lead); Validation ; Visualization ; Writing – original draft (supporting); Writing – review & editing . Carly Muench: Data curation ; Formal analysis ; Investigation ; Methodology ; Writing – original draft (supporting); Writing – review & editing . Kristine L. Pilgrim: Conceptualization ; Data curation (supporting); Formal analysis ; Investigation ; Methodology ; Project administration (supporting); Software ; Supervision ; Validation ; Writing – original draft (supporting); Writing – review & editing . Cory Engkjer: Conceptualization (supporting); Data curation ; Formal analysis ; Investigation ; Methodology ; Software ; Validation ; Writing – original draft (supporting); Writing – review & editing . Philip W. Ramsey: Conceptualization ; Funding acquisition (lead); Investigation ; Project administration (lead); Resources (lead); Supervision (lead); Validation ; Visualization ; Writing – original draft (supporting); Writing – review & editing .


In recent years, multi-omic studies have enabled resolving community structure and interrogating community function of microbial communities. Simultaneous generation of metagenomic, metatranscriptomic, metaproteomic, and (meta) metabolomic data is more feasible than ever before, thus enabling in-depth assessment of community structure, function, and phenotype, thus resulting in a multitude of multi-omic microbiome datasets and the development of innovative methods to integrate and interrogate those multi-omic datasets. Specifically, the application of reference-independent approaches provides opportunities in identifying novel organisms and functions. At present, most of these large-scale multi-omic datasets stem from spatial sampling  or case-control studies . We believe that longitudinal multi-omic microbiome datasets are the logical next step in microbiome studies due to their characteristic advantages in providing a better understanding of community dynamics, including: observation of trends, inference of causality, and ultimately, prediction of community behavior. Furthermore, the acquisition of complementary host-derived omics, environmental measurements, and suitable metadata will further enhance the aforementioned advantages of longitudinal data, which will serve as the basis to resolve drivers of community structure and function to understand the biotic and abiotic factors governing communities and specific populations. Carefully setup future experiments hold great potential to further unveil ecological mechanisms to evolution, microbe-microbe interactions, or microbe-host interactions. In this article, we discuss the challenges, emerging strategies, and best-practices applicable to longitudinal microbiome studies ranging from sampling, biomolecular extraction, systematic multi-omic measurements, reference-independent data integration, modeling, and validation. Advances in the study of microbial communities have highlighted their important role in natural processes, including those considered as ecosystem services for humankind . ComplexOverall, longitudinal multi-omics will enhance our understanding of microbial community dynamics, which could potentially bring about positive outcomes in biomedicine, biotechnology, and for the environment. However, various aspects must be considered when conducting longitudinal multi-omic microbiome studies, ranging from experimental design, bioinformatic processing, modeling, and validation. In this article, we explore challenges, considerations, and potential solutions for such studies, based on recent advances and reports , which aIntegration of multi-omic microbiome datasets has been routinely performed, with notable instances, including studies on type-1 diabetes , cancer absence of evidence is not evidence of absence” (de novo assemblies of sequencing data (MG and MT), subsequently complemented by additional omics  or limited  (n), e.g., a cohort of patients  , one shopatients . Equidispatients , and thuIt is crucial to limit potential biases linked to longitudinal data, e.g., in extended time-series; samples are stored for long periods, while multiple personnel may be involved in sample collection, handling, storage, and documentation. Hence, clear guidelines and standardization must be established, as they are key factors that potentially affect downstream processes and overall outcome .Biomolecular extraction from a single sample is ideal over multiple extractions from subsamples . AdvantaBatch effects are often overlooked in omic studies , but canA potential effective experimental measure for minimizing and elucidating batch effects is the inclusion of mock/control samples during both the extraction and high-throughput measurements . SamplesLongitudinal and multi-omic studies yield large datasets, where data processing and analyses are typically time and resource intensive. These rich datasets may be reused to study multiple aspects of a given microbial system . TherefoMicrobiomes may be studied from a gene-centric perspective , which rde novo assembly and binning). Features  appear in varying quantities, in different timepoints of longitudinal meta-omic studies. It is challenging to link and track features from one timepoint to another without any given point of reference. Therefore, the construction of what we term as “representative longitudinal catalogues”  of MAGs/genes, provides a non-redundant representative base to link features from the different longitudinal samples . The outde novo assemblers  compositional, e.g., provided as relative abundances, which require specific considerations when selecting statistical analyses , (ii) hiidistant . Imputatidistant .Initial exploration of the microbiome dynamics can be assessed through ordination analyses, where high dimensional population structure data are visualized in a two-dimensional space to observe the trajectory of the samples and the behavior of the system, i.e., metastability, cycles, and alternative states . Then, cExtensive literature of statistical and mathematical frameworks for multi-omic and/or longitudinal microbiome data is currently available. For instance, More recently, the emergence of studies which track microbiome dynamics of cohorts over time, i.e., multiple individuals/sites , necessiThe validation of the models remains one of the most challenging issues. Mathematical models combined with culture of synthetic microbial communities are commonly utilized to study mechanisms behind host-microbiome interactions . It is aLongitudinal microbiome studies combined with integrated multi-omic measurements provide unprecedented opportunities to study microbial community dynamics, both structurally and functionally. In tandem with evolving high-throughput technologies, e.g., long-read sequencing , these sSMA and SN outlined the manuscript and coordinated the writing process. LdN, SN, and SMA prepared the figure. All authors contributed to the writing, reviewing, and editing of the manuscript. All authors approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The contributory factors and symptoms for presbyopia progression have not been fully determined. The purpose of the study was to compare presbyopia progression in subjects with short and normal tear breakup time and to explore the severity of common ocular symptoms associated with presbyopia progression.  We conducted a clinic-based, retrospective, cross-sectional study. Inclusion criteria were bilateral phakic patients aged 40–69 years with best-corrected distance visual acuity better than 20/30, and exclusion criteria were the use of glaucoma eye drops, any disease affecting vision, or history of ocular surgery. We measured the binocular near add power and compared the results using Kaplan-Meier analysis. Association between near add power and ocular symptoms was explored. P = 0.043; Cox-Mantel test). Eye fatigue was most severe in the group with an add power of 1.25-2.00 D. Near add power was correlated with hyperopia, astigmatic errors, and anisometropia.  There were 1411 participants (mean age of 50.1 years). There were no significant differences in age, intraocular pressure, spherical equivalent, astigmatism, or anisometropia between the sexes. Kaplan-Meier analysis indicated that women with short tear breakup time reached the endpoint (near add power of +3.00 D) significantly earlier than those with normal tear breakup time ( This study suggests an exacerbation of presbyopia progression in women with short tear breakup time. Eye fatigue was most severe in those with an add power of 1.25-2.00 D. Presbyopia is a consequence of aging with the loss of accommodation, and its progression predominantly depends on progressive lens hardening and decreased ciliary muscle mobility . Many miDry eye (DE) is a common age-related eye disease in those of presbyopic age and more prevalent in women than men . DE is aThe aim of this study was to investigate the effect of DE-related parameters on near vision. We focused on BUT, which may affect vision, especially in middle- to older-aged DE patients. We achieved this by measuring the near add power  in outpatients, comparing the progression of presbyopia between these patients stratified by sex. The associations between near add power and DE-related symptoms and ocular parameters were also explored.This study was a clinic-based, retrospective, cross-sectional study involving healthy subjects attending the Tsukuba Central Hospital and Otake Clinic Moon View Eye Center. The Institutional Review Board and Ethics Committee of the Tsukuba Central Hospital  and Kanagawa Medical Association  approved the study, and it was carried out in accordance with the Declaration of Helsinki. The need for consent was waived by the Institutional Review Board. This study was a retrospective chart review since patient interview and ocular examinations for analysis were routinely performed in the participating eye clinics. The Institutional Review Board and Ethics Committee of Keio University School of Medicine approved this study  to permit authorship for authors (KN and MA) who were appointed at the Keio University School of Medicine.Participants aged 40 to 69 years with bilateral phakic eyes and best-corrected visual acuity above 20/30 were included. Individuals were excluded if they used antiglaucoma or anticataract eye drops or had vitreoretinal disease, any ocular surgery in the previous month, or acute ocular disease in the previous two weeks.R, Nidek Co., Ltd., Gamagori, Japan), autorefractometry , slit-lamp biomicroscopy, funduscopy, and intraocular pressure measurements . Binocular near add power was measured by a blinded examiner at a distance of 30 cm using a Bankoku near-acuity chart  or an automatic optometry system  [Board-certified ophthalmologists examined all patients and excluded subjects with major age-related eye diseases, including cataract, glaucoma, and macular diseases. Ophthalmological evaluation of participants consisted of best-corrected visual acuity  , 37. Aft, Japan) . BUT was, Japan) . ExaminaPatients were asked questions to determine the presence or absence of six common DE-related symptoms: dryness, irritation, pain, eye fatigue, blurring, and photophobia. These questions were selected from items on the Dry Eye-Related Quality-of-Life Score (DEQS) questionnaire  and weret-test and chi-squared test, as appropriate. Near add power with an endpoint of +3.00 D was compared between short BUT (≤2 mm) and normal BUT (>2 mm) patients using Kaplan-Meier analysis and the Cox-Mantel test. Regression analyses were performed to explore the correlation between near add power and ophthalmological parameters. To assess the association between near add power and common ocular symptoms, near add power was classified as low , moderate , or high . Data are presented as the mean ± standard deviation (SD) or as percentages where appropriate. All analyses were performed using StatFlex , with P < 0.05 considered significant.The sample size was calculated with a 0.05 margin of error and 95% confidence interval. Effect size was derived from a measured value in the current study. An effect size of 0.505 was identified in near add power with an appropriate total sample size for comparison of men and women being 34. Patient demographics and ophthalmological parameters were compared between sexes using the P < 0.001, chi-squared test). All symptoms were more prevalent in women than in men , but not in men  and with short BUT (P = 0.723).There were 1411 participants . There were no significant differences in age, intraocular pressure, spherical equivalent, astigmatic errors, anisometropia, near add power, and Schirmer test value between the sexes, whilst BUT, corneal staining score, and tear strip meniscometry were worse in women . BUT wasn in men . A Kaplaβ = −0.040, P < 0.001), astigmatic errors , anisometropia , and corneal staining score , adjusted for age and sex  and visual acuity and near add power were examined by five certified orthoptists with national licensure. The presence of an intra- and interindividual error rate should have been assessed , 48; how