Subscales encompassed “enhancement’; “progression’; “social pressure’; “distraction/compulsion’; “altruism’; “fear of missing out’; and “resale”. The scale showed good criterion and construct validity  and monthly self-reported spend (r = 0.38)), and good internal validity . Parallels with, and divergence from, motivations for related activities of gaming and gambling, and alignment with broader theoretical models of motivation, are discussed.Qualitative studies have identified a diverse array of motivations for purchasing items within video games through chance-based mechanisms . Given that some individuals—particularly those at risk of disordered gaming and/or gambling—are prone to over-involvement with loot box purchasing, it is important to have a reliable, valid means of measuring the role of different motivations in driving purchasing behaviour. Building on prior qualitative research, this paper reports the development and validation of the “RAFFLE” scale, to measure the Reasons and Facilitators for Loot box Engagement. A 23-item, seven-factor scale was developed through cognitive interviews ( Video gaming  is an activity in which over half of the UK population are estimated to take part, generating billions of pounds in annual revenue . Within It is important to understand not just who buys loot boxes and what co-occurring problems they may be experiencing, but also the reasons why people purchase them. Identification of the motivations for engaging in addictive behaviours has the potential to inform educational messaging and interventions ,11. FurtA small number of studies have begun to unpick the motivations for purchasing loot boxes. In an online survey, Zendle and colleagues  asked a These two studies evidence a wide range of motivations for engaging with loot boxes; some closely aligned with gaming involvement , and others more closely aligned to gambling-related motives —as might be expected, given the established links between both gambling and gaming, and loot box purchasing. Notably, purchasers reference both external variables—such as events and promotions, and exciting sounds and animations—and intrinsic drivers, centring around fulfilling desires or satisfying needs, such as the desire to be competitive, or to experience excitement. This is consistent with the literature on motives for both gaming and gambling, which are influenced by external drivers such as advertisements and promotions ,17 and tWhile these qualitative studies provide a detailed account of individuals’ varied reasons for purchasing loot boxes, to date, there is no existing, validated scale to measure motivations quantitatively. Despite parallels with gaming and gambling, existing scales for these activities are not directly transferable to loot box purchasing; a unique activity that merits a bespoke scale. Factors such as gaming as a means of “violent catharsis”  or for “This scale was developed and validated in three phases, described below: (1) cognitive interviews, to refine items developed from qualitative interview data; (2) an online survey analysed by exploratory factor analysis and an expert opinion panel, to refine scale content, identify factor structure and test internal consistency and construct validity; and (3) a large-scale validation survey analysed by confirmatory factor analysis, to test factor structure, internal consistency and construct validity.This study was conducted to refine the content and wording of the initial pool of items, which were developed via in-depth qualitative interviews (reported in ). The aiTwenty-five gamers aged between 16 and 44, who had bought loot boxes at least once, were purposively recruited from across the UK to ensure a range of demographic characteristics including age, sex, ethnicity and geographical region. Nineteen males and six females with a mean age of 25.4 years (SD = 6.93), took part between July and August 2020. Participants were recruited from rural and urban areas across the North, Midlands, and South of England, and while the sample was primarily White British in ethnicity, there were participants of Black, Asian and mixed ethnicities. A further 15 experts by experience  attended one of 5 online stakeholder involvement workshops.An initial pool of 55 items was developed by an expert panel of 9 academic researchers (authors of this paper), based primarily on findings from 28 in-depth qualitative interviews (reported in ), and suAfter being presented with an online information sheet and providing digital informed consent , participants were interviewed remotely (due to COVID-19 restrictions) by researchers LLN or SGS, with interviews lasting approximately 1 h, on average. They were presented with each of the 55 candidate scale items, and explicitly instructed to “think aloud” and explain their thought process in responding to the item. This approach was coupled with verbal probing techniques to enquire about language and clarity, and the item’s perceived relevance and importance (as per established cognitive interviewing protocols ). For inAfter each 4–6 interviews, meetings with the wider research team took place where items were refined iteratively based on feedback from batches of interviews, with all changes to the wording, however minor, logged in a new column of the tracking spreadsheet. Subsequent batches of participants reviewed the revised items in an iterative process, to ensure that after being adapted in light of participant feedback, newly revised items were member-checked, rather than simply assuming that the research team’s changes had effectively addressed issues.We were able to draw on the literature on motivations for gaming and gambling to give a broad sense of what kinds of subscales or “domains” our candidate items might fall into; for instance, we expected that there would be motivations around fun or excitement; around social factors; and around in-game progression. However, motivations for loot box purchasing specifically have only been studied in a few recent studies, and our qualitative work indicated that they are not identical to motives for either gaming or gambling alone, and are nuanced and overlapping. For instance; buying loot boxes to try and obtain items that will allow you to catch up with friends, is a novel and loot-box specific motivation, and it was unclear whether this would load onto a “social” or a “progression/competition” related factor. Therefore, we utilised an “exploratory factor analysis” for the first phase of validation, to examine the scale’s factor structure and identify possible subscales in a data-driven manner without pre-imposing an a priori theoretical framework. This phase also facilitated item reduction, identifying poorly performing and/or redundant items for removal, and provided an opportunity to test the scale’s internal consistency and construct validity.n = 11) or both (n = 7) attention checks  were removed due to risk of non-serious responding, leaving 485 participants. Their mean age was 31.66 ; 50.7% were female and 49.3% were male.Between 17 November 2020 and 27 November 2020, 503 participants were recruited through Prolific Academic. Those who incorrectly answered one (Participants were asked to rate the frequency of each reason for buying loot boxes of the 39 longlist items (right-hand column of r were <0.63).Participants who wished to complete the survey were informed that the study required them to respond to all items. All participants complied with these instructions; therefore, there were no missing data. All scores were within 1.1 standard deviations from the mean, and most variables had skewness indices within the “acceptable” range of −2 to +2; ; see TabWe used principal axis factoring with direct oblimin rotation . All 39 items were entered with “eigenvalues over 1” as the initial extraction technique. All communalities were >0.2.n = 7 academics with expertise in measurement science, gambling and gaming) further informed decisions about order of item removal through a combination of numeric ratings and qualitative comments. Item removal proceeded until a stable solution  was reached. At this point, eigenvalues and the scree plot were examined, and considered alongside interpretability of the factor structure, to determine whether the number of extracted factors was optimal.Items were removed stepwise, beginning with the “poorest’—based on “loadings” on the pattern structure matrix, and/or cross-loading. Candidates for removal were identified through loadings >0.3 on more than one item, and/or <0.2 difference between loadings , with thDescriptive statistics for the 39 original candidate items are presented in Factor 1, “enhancement”, which explained the most variance (over a quarter) and had the strongest reliability, contained items relating to enjoyment/enhancement. Some items reference enjoyment of opening the boxes and how that makes the opener feel, while some refer to enjoying or valuing the items within the boxes, but the common thread is the seeking of pleasure as a motivating force.Factor 2, “progression”, contained items related to the desire to progress within a game—i.e., to come back from a failure or defeat; to overcome a hurdle; or to gain an advantage of some kind to facilitate progression.Factor 3, “social pressure”, contained items relating to (negative) social pressure, referring to fear of being shamed or left out, and to direct pressure from peers.Factor 4, “distraction/compulsion” contained items relating to feeling compelled to purchase (“can’t stop” and “urge”) and to being motivated by a desire to take one’s mind off their life or relieve boredom.Factor 5, “altruism”, comprised just two items which referred to altruistic motives, specifically, the supporting of good causes and of games developers.Factor 6, “fear of missing out” was comprised of three items referring to concerns about missing out on something—from items in a collection to an in-game event or a special offer.Factor 7, “resale”, was comprised of two items, both of which were concerned with the desire to obtain items to sell on, either for real or in-game currency.As part of a comprehensive online survey reported elsewhere , a sample of UK-based loot box purchasers completed the refined scale developed in study 2, and a CFA was conducted to test the fit of the factor structure identified in study 2 in a larger sample, and to provide another opportunity to examine the scale’s internal consistency and construct validity.Between 8 March 2021 and 31 March 2021, 3063 participants were recruited from Prolific Academic. After the removal of 71 response sets due to failing one or more attention checks—54 who either did not complete all questions and/or revoked consent, and 220 who stated at screening that they did not purchase loot boxes, but later gave a non-zero answer to monthly spend—2718 remained. Of these, 1495 had experience of purchasing loot boxes. Of these, 51.5% were female, 47.6% male, and 0.9% non-binary/third gender. Mean age was 31.16 (SD 9.93) years (range: 18–69 years).Participants who bought loot boxes completed the 23-item scale developed during validation survey 1. Response categories were identical to those used in validation survey 1, and again we administered the RLI  and quesConfirmatory factor analysis was performed with MPlus , using dX2 = 18,948.52, df = 253, p < 0.0001), indicating divergence of what was observed from the predicted model. However, due to this metric’s sensitivity to sample size, this is typical in large samples—where other metrics are more informative [Fit indices are summarised in ormative .According to the Standardised Root Mean Square Residual value—which Kline deems to be the most appropriate metric for Likert-type response scales—the scale’s structure was a good fit . The scr = 0.394).Cronbach’s alpha for the entire scale was very good (0.84), and for the subscales was good (0.69 to 0.79), with the exception of the “resale” motives subscale—which fell just under the acceptable threshold of 0.6, which is not unusual for a subscale comprised of only two items—furthermore, inter-item correlations were within the optimal range (p < 0.001) between overall RAFFLE scores, and scores on each subscale, and both of the measures of loot box involvement . As summarised in r = 0.10 for “altruism” subscale and RLI score) to moderate  and strong . The total RAFFLE score which had a 0.63 correlation with RLI and 0.38 correlation with spend—indicating strong criterion validity.As in validation study 1, we observed highly significant correlations . With rThe aim of this work was to develop and validate a scale to measure reasons and facilitators for purchasing chance-based items within video games. We were motivated by the lack of pre-existing scales specific to loot box purchasing, despite the existence of multiple scales to measure gaming and gambling motives.Following previous qualitative work that identified a broad array of factors that drive people to purchase loot boxes , our cogMost of these subscales have strong parallels with those seen in existing measures of motivations for other activities. “Enhancement” is one of the most universal: factors to do with fun, excitement, or recreation have been identified across numerous studies of motivations for both gambling ,37,38,39In contrast, the second factor, “progression” has many parallels in the literature on gaming motivations, where motives to do with achievement , competiFactor 3, “social pressure” has parallels with social motives for gaming and gambling. However, this factor encompassed negative social pressure, with items referring to fear of being shamed or left out, and to direct pressure from peers. Social motivations in the wider literature often encompass more positive factors, such as opportunities for teamwork or for forming relationships as motivators for gaming ,47 and gFactor 4, which we named “distraction/compulsion”, contained items relating to feeling compelled to purchase (“can’t stop” and “urge”). This was the most strongly correlated of all the factors with RLI scores, as might be expected given the fact that two of the four items pertain to feeling out of control of purchasing. While the RLI items focus primarily on indicators of current harmful engagement, however (such as playing longer than intended and putting off other activities), the distraction/compulsion subscale items focus on the feelings that drive purchasing. In particular, desire to relieve boredom and take one’s mind off one’s day-to-day life loaded onto this factor. Motivations around boredom, escapism and coping with day-to-day life have been frequently observed in the contexts of both gaming ,52,53 anFactor 5, labelled “altruism”, consisted of only two items, referring to purchasing loot boxes to support good causes and games developers. While they are not as universally reported as other motives, charitable motives have been identified within the gambling literature , and “suFactor 6 was comprised of three items referring to how purchasing was driven by concerns about missing out on something—from items in a collection to an in-game event or a special offer; and was named “fear of missing out”. Zendle and colleagues accurately predicted that the “limited time” nature of many loot boxes could be an important factor in their appeal , and feaFinally, factor 7, labelled “resale”, was comprised of just two items, both of which were concerned with the desire to obtain items to sell on, either for real or in-game currency. The parallels with monetary gambling motivations—identified in many studies of gambling motives—are clear ,36,64. TIn summary, the RAFFLE comprises seven subscales, which have both parallels with, and distinctions from, motivations for gaming and gambling. Many of the RAFFLE’s subscales can be understood through the lens of self-determination theory, and/or as sources of “gratifications”. Both intrinsic motivations  and extrinsic motivations  of the individual are represented. Importantly, however, a subscale offering insights into the impact of more externally located influences (such as limited-time promotions) also emerged. All these subscales correlated significantly with multiple measures of engagement with loot boxes, but the “distraction/compulsion” subscale was most robustly linked. This measure of the extent to which a person is driven by feelings of compulsion, or a desire to escape boredom or their day-to-day life, may have clinical utility as an indicator of the risk of problematic involvement, and future research to test this possibility would be valuable, particularly given the concise nature of the subscale.People with lived experience of loot box engagement were consulted throughout the development of the RAFFLE, ensuring the items were clear and meaningful to the target audience, and large, diverse samples from across the UK gaming community were recruited for each stage of the development and validation of the scale. Items were developed through comprehensive qualitative interviews, supplemented by insights from the literature and from an expert opinion panel, and two stages of validation were conducted. Although the model fit in the confirmatory factor analysis phase was (very) slightly below accepted standards on some measures, the resultant scale had good reliability , and very good criterion validity (indicated by strong and significant correlations with other measures of loot box engagement). Two of the subscales, likely due to being comprised of only two items each, had low reliability as evidenced by Cronbach’s alpha values of below 0.7; however, arbitrary cut-offs for these values are of debatable utility , and we While online data collection for studies 2 and 3 enabled us to collect data from a large number of participants in a relatively short space of time, it is important to note that this inevitably meant relying upon self-report measures of problematic involvement , rather than utilising “gold standard” structured clinical interviews to evaluate participants” clinical characteristics, such as gambling disorder. In addition, the sample was comprised solely of UK adults, meaning that we cannot be sure how well the RAFFLE will perform in children or different geographical locations.It is important to note the possibility that the complexity of the factor structure identified within the current studies is, in part, the product of large sample sizes with associated high statistical power. Scales developed with large samples can run the risk of identifying more factors than can be practically replicated with smaller samples in future work. It will be important to examine, in future work, whether the 23-item scale developed here consistently produces a seven-factor structure, or whether a simpler, higher order factor structure emerges.The RAFFLE is, to our knowledge, the only validated scale for measuring the strength and nature of the diverse array of drivers of loot box engagement. We have presented preliminary evidence that some subscales correlate more strongly with problematic involvement: in particular, we predict that the distraction/compulsion subscale may prove to be a useful brief measure of risk; given its robust correlation with RLI scores. Whereas the RLI measures current risky involvement, it is possible that the distraction/compulsion subscale may play a useful pre-emptive role in predicting future risk. Future work should seek to confirm these hypotheses, uncovering how the psychology of loot box motivations relates to harm. Mediational analyses would be particularly beneficial to more fully understand the nuances of any relationships between the different types of motivation and risk of over-engagement. Future work could also explore whether there are differences in motivations for loot box purchasing across game types, in the same way that play motivations can vary across games ; and wheWith many countries—including the UK, USA, Australia and Spain—currently investigating legislation for loot boxes, such studies have the potential to inform policy, educational and treatment interventions, enabling such interventions to be appropriately tailored to minimise any harms experienced by those prone to over-engagement with loot boxes in video games.


Introduction: Older adults, who already have higher levels of social isolation, loneliness, and sedentary behavior, are particularly susceptible to negative impacts from social distancing mandates meant to control the spread of COVID-19. We sought to explore the physical, mental, and social health impacts of the pandemic on older adults and their coping techniques.Materials and Methods: We conducted 25 semi-structured interviews with a sub-sample of participants in an ongoing sedentary behavior reduction intervention. Interviews were recorded and transcribed, and iterative coding was used to extract key themes.Results: Most participants reported an increase in sedentary behavior due to limitations on leaving their home and increased free time to pursue seated hobbies . However, many participants also reported increased levels of intentional physical activity and exercise, particularly outdoors or online. Participants also reported high levels of stress and a large decrease in in-person social connection. Virtual connection with others through phone and video was commonly used to stay connected with friends and family, engage in community groups and activities, and cope with stress and social isolation. Maintenance of a positive attitude and perspective gained from past hardships was also an important coping strategy for many participants.Discussion: The COVID-19 pandemic and associated social distancing measures have impacted older adults' perceived levels of activity, stress, and social isolation, but many leveraged technology and prior life experiences to cope. These themes could inform future interventions for older adults dealing with chronic stress and isolation. The COVID-19 pandemic has impacted people across the globe, prompting the implementation of public health restrictions at the federal, state, and local level in an attempt to slow the spread of the virus and resulting disease. Older adults aged 65 or over with chronic conditions are at the highest risk for contracting and dying from a severe case of COVID-19, in addition to being at an increased risk for social isolation, loneliness, and high levels of sedentary behavior , 7.The short-term and long-term impacts of these restrictions on the health and well-being of older adults with chronic conditions is unclear. Early studies suggest that older adults are engaging in less physical activity and are more sedentary than before the pandemic, and that the biggest challenges presented by the pandemic are social constraints and activity restrictions , 9. In aClinicalTrials.gov (NCT03739762). All interviews were conducted between June and August of 2020.The study was conducted at Kaiser Permanente Washington Health Research Institute located in Seattle, WA. Effective early March, 2020, Washington State began implementing state-mandated restrictions and business closures, and encouraging residents to stay at home, limit social gatherings, and maintain physical distance when in public spaces . All res2 or above. Primary outcomes of interest are thigh-worn accelerometer-based sedentary behavior metrics and blood pressure measured at 6-months. Participants  are recruited from Kaiser Permanente Washington membership panels in King County, WA and are considered eligible if they are between age 60–89; have a body mass index between 30 and 50 kg/m2; self-report 6 or more hours of daily sitting time; are able to stand from a seated position without assistance; are able to walk one block; are fluent in English; have continuous enrollment within KPWA in the prior 12 months; and do not have indications in their medical record of long-term nursing care, palliative care, hospice care or a cancer diagnosis, deafness/significant hearing loss diagnosis, dementia, or a serious mental health disorder in the prior 24 months. Eligible and consenting participants who successfully complete all baseline study measurements are then randomized to receive a sitting reduction intervention (termed I-STAND) or a healthy living focused attention control condition. The I-STAND intervention is built on our team's prior work . To maximize diversity, we also oversampled eligible participants of color and those with self-reported high blood pressure or diabetes at baseline. Eligible participants were contacted by phone or email and invited to participate in a 30–45 min telephone interview. Invitations continued until terature , had beeTo reduce response bias, interviews were conducted by two team members who had no prior contact with the interview participants during the study. The interviewers (M.A.G.H. and J.D.) followed a semi-structured interview guide with open-ended questions and follow-up prompts. Interview questions focused on participants' experiences with the ongoing COVID-19 pandemic and included questions such as: “What impacts did you notice while social distancing measures were actively in place for our community?”; “Did you notice any specific impacts to your: mental health, physical activity levels, sitting time each day, diet or the foods you chose to eat each day, sleep patterns, or overall quality of life?”; “What has been helping you cope with the pandemic and related social distancing measures?”; “What impacts did the pandemic have on your overall engagement with the HART study, such as your ability to check in with your coach, meet goals, and make progress on your healthy behaviors?”; “Are there changes to your life that you think may not return to ‘normal' for a longer time?”. Interviews averaged 30 min (range = 15–54 min). Each call was audio-recorded and then transcribed for analysis. Participants were compensated for participation in the parent trial, but not compensated for completing the qualitative interview.Each interview transcript was independently coded by at least two members of a three-person coding team. Transcripts were divided among two primary coders who collectively coded all transcripts and were the same individuals who conducted the in-depth interviews (M.A.G.H. and J.D.). M.A.G.H.'s background is in anthropology and public health and J.D.'s background is in psychology and public health. Both received qualitative methods training with supervision from investigators experienced in qualitative methods. They also had training in motivational interviewing from the lead researcher (D.E.R.). The primary coders were assisted by D.E.R., who has a background in clinical psychology and public health, including training in qualitative methods. Coding was performed using an inductive thematic approach  from theN = 25 participants from the greater Seattle, WA area met study timepoint eligibility criteria and agreed to a one-time phone interview, and saturation was reached. Of the HART participants contacted, only one participant refused an interview due to scheduling conflicts. A total of I don't even want to go to the grocery store. I do every three weeks or something, but it's just that fear because I really don't want to get it.” (P22). As a natural consequence of limiting time away from home, most participants reported a general decrease in social and physical activities due to the need to stay home and, often, feelings of increased isolation. “Initially I was home all the time, I didn't go anywhere… It has been much more isolating for me.” (P4). Many participants mentioned a distinct fear of leaving their home or going out, particularly in the early months of the pandemic. Leaving the house, even for essential reasons caused worry about exposure to the COVID-19 virus. Consequently, many equated staying home with staying safe. As time progressed and more information was available about the virus, many have adapted and began going out more in ways they deemed safe, typically engaging in mitigation measures like social distancing, mask use, and interacting in outdoor settings. “And the more they started to learn about the virus and how you contracted…the more active I became.” (P7).As a result of the pandemic, most participants reported a large increase in the amount of time they spent at home. When they did leave their home, most reported being very intentional about their trips, limiting excursion to essential needs  and making those trips efficient to limit time spent away from home. For some essential services, like grocery shopping, many participants mentioned using delivery services or having others shop for them, at least some of the time. “…I didn't want to take this time out of my life to not travel and not see my family.” (P25). One person delayed retirement because they would not be able to volunteer or travel, as planned. Despite this, some participants did report taking road trips, but generally limiting that travel to destinations closer to home and/or to second homes. “I did go down to the Oregon coast for six days, so at that point where our beach home is, there wasn't so much issues with the pandemic.” (P4).COVID-19 disrupted many participants' travel plans for leisure, to visit family, or for specific events like a grandchild's high school graduation, reunions, or travel for funerals. Many participants found these disruptions upsetting and disappointing, viewing it as a missed opportunity. “I was working. I lost my contract - I do contract work - due to COVID.” (P18). Among those who were not retired and had continued employment, most transitioned to remote work-from-home arrangements. Responses to this transition were mixed among the portion of our sample that still worked full or part time. Some people appreciated the flexibility of working from home and appreciated the lack of commuting, while others reported finding it undesirable, less ergonomic, and challenging to incorporate into their home environment. “…So working from home, I hate it, I absolutely hate it. My condo is not conducive to it.” (P11). Many people also noted that in-person work had helped keep them active such as by taking stairs, visiting coworkers at their desks, or doing active work activities, all of which have ceased in a work-from-home environment. “At work I've always tried to make a point of moving more, taking the stairs, things like that… But when I got home, and I don't hear anybody talking, it's completely quiet, I'm using equipment that's less ergonomically sensible yet I get so focused on my work that I just stay and do it and I don't move…” (P19).Work, whether paid or volunteer, was disrupted for many participants. For many, work typically provided a sense of purpose and encouraged them to get up each day, making these interruptions very disruptive to their daily lives. COVID ended most people's volunteer work, and several people reported losing some or all of their paid work. “I just got told I'm being furloughed because the state has lost so much money, so I'm losing 20% of my salary.” (P14). However, others felt financially secure or even able to save money. “I've been able to save money. I'm not paying for gas, not paying for this and that…right now they're paying me my full salary, even though I don't work fulltime.” (P11). Respondents also voiced uncertainty about their future job security, income, and the economy. “I think the biggest impacts of COVID over time are going to be financial recovery…” (P19).A few participants reported experiencing some level of financial hardship due to the pandemic. “[Golf]'s one thing I do all the time, 3-4 days a week, but we still social distance when we're playing. I try to make a point of trying to be six feet away.” (P9). Reported masking habits differed between people and situations. Some wore masks all the time when not in the home, while others only wore them in populated public areas such as stores and parks, but not when walking around their neighborhood. “I wear my mask and I keep my distance when I'm in public, but if I walk my dog [around the block] I don't wear a mask.” (P5). A small portion of our sample voiced that they didn't like wearing them at all and avoided going places to avoid masking. Participants frequently voiced appreciation of people who wear masks in public, as well as a fear of and annoyance with people who don't. Most participants mentioned businesses they used regularly  shutting down entirely. Furthermore, many participants mentioned that those businesses that remained open typically had rules for entry such as requiring masks, social distancing, temperature checking, or restricted hours of operation.Participants overwhelmingly reported adhering to social distancing recommendations and wearing masks outside their home. People reported intentionally minimizing in-person contact, maintaining space between themselves and others when engaging in in-person contact at work or in public, and keeping in-person contact focused on doing outdoor activities like walking or golfing. “…I have literally gotten depressed…As the months and weeks pass by, it's kind of like, what is going to happen? You just kind of wonder if it will end or if they will find a way to manage it better.” (P7). Most participants indicated elevated and chronic stress from concern about their health, health of family and friends, finances, general disruption to daily life, as well as the divisive political climate. Furthermore, the ongoing nature of these stressors led to fatigue. “I often feel more down because I don't see - none of us does - see an end to this style of living that we have to do now.” (P1). A small number of participants reported lethargy and feelings of de-motivation with the pandemic. Interestingly, for a small portion of our sample, a shift to work-from-home and an end to commuting has meant a decrease in stress. “My blood pressure has gone down, now that I work from home, which I find really interesting.” (P11).Most participants voiced experiencing low mood, anxiety, stress, fear, and/or anger due to the pandemic. These feelings stemmed from high levels of uncertainty and major changes to their routines. These impacts were accentuated by the fact that activity restrictions meant more limited options for distraction. “We stayed home and ate at home a lot. We have a favorite local restaurant that we had frequented, and they offered curbside pickup, so we took advantage of that…” (P6). Most participants were eating at home more often, and grocery shopping habits have changed noticeably for many people. People reported shopping less frequently or ordering online, making it more challenging to keep fresh fruits and veggies easily accessible. “I don't go out very often to shop for groceries so…it's harder to have fresh things all the time.” (P12). Some participants noticed an increase in snacking between meals and more food cravings for less healthy food. “I just felt a craving for, not so much sweets but dark chocolate. I've had different food cravings and not all of them healthy.” (P5). Reports of weight gain as a consequence of stress, boredom, and diet changes were also common. “Since March I've gained ten pounds, being inside. Even though I'm walking, I'm, out of boredom, looking for a ten o'clock snack and a two o'clock snack…” (P8).Eating habits were noticeably changed for most participants. Most people interviewed were not dining out at restaurants, though many reported still getting take-out to support local businesses. “I started in January with a personal goal of ten thousand steps a day and so far I have averaged that every month…having the extra time to just go out whenever the weather looked was great versus having to wait until I was home from work.” (P10). Several participants reported taking up home and yard improvement projects they might not have otherwise done. A small portion also reported doing virtual exercise classes or exercise videos . Other participants reported that their physical activity routines were disrupted by the pandemic. Participants noted that the loss of normal activities and schedules generally meant moving less. Those going to gyms could no longer go. Many lost socially supported physical activities like walking groups or in-person classes. “…I don't walk unless I have somebody to walk with because I just like that social interaction…” (P4). Some reported they were too fearful of COVID to even leave the house to get outside. “…For a little while I was afraid to leave, to go outside. I didn't know if you got it from the air…” (P7).Respondents reported large changes to their normal physical activity patterns. Some reported having more free time to engage in physical activity. Many reported being able to exercise the same amount as pre-pandemic or more, particularly through outdoor neighborhood walking. “Especially in the beginning…I felt like I was sort of a walking zombie. Well, sitting zombie.” (P7). Many reported more TV watching and pursuing seated hobbies as an escape and coping mechanism. “I watch television more than I ever have before, as an active escape mechanism.” (P19). Activities like running errands were now done virtually while sitting on a computer rather than by leaving the house or walking around stores as they were before. “So those early months I was seated a lot, looking at the computer trying to figure out how do I sign up for Instacart and how do I get the Costco app…” (P7).Most participants noted a large increase in sitting time being stuck at home. “under that kind of stress, you're not going to be sleeping well…it was kind of a vicious circle where you didn't sleep well, you're tired the rest of the day, all the problems start to magnify themselves because you're not rested enough to think clearly.” (P7). However, a similarly small number noted that their sleep had improved due to changes in their work schedule or structure. “I got better sleep and that was important, because before I was only getting maybe 5-6 hours and I work a 4/10 schedule, so I get better sleep now.” (P2).Surprisingly, changes in sleep were not commonly reported by those interviewed. A few participants noted sleep disturbance and difficulty sleeping due to stress and worry about the pandemic. “…Several participants reported knowing a friend or family member who had COVID, including one who had an aunt who died from COVID-19. One participant reported personally having COVID-19 infection but had recovered at the time of the interview.I couldn't do a lot of things that I've been doing for years. That was playing competitive badminton three times a week, I couldn't do that. I couldn't get up early and go volunteer in Seattle…” (P2). This decrease in in-person social interaction resulted in reported feelings of social isolation and distress for many, though a small number of others reported not being bothered by it or even preferring limited social time. Some people did mention that they were still choosing to engage in in-person interactions with a limited network, albeit in different ways than normal . “We have not had anyone in our home except our daughter, she's in our bubble of protection. She doesn't live with us. And we're very strict about masking and following the rules.” (P24). Some participants specifically commented that, as older adults, they feel as if they are “losing time” to travel and see loved ones that they won't get back. “…at my point in time in my life, assuming I have 20 more years left, I'm losing time. I didn't want to take this time out of my life to not travel and not see my family. So it's a little sad in that respect.” (P25).Because most participants were intentionally limiting visitors to their home and their own trips out of the house, they were not seeing friends, family, and people in the community as often as they did pre-pandemic. Many also mentioned specific loss of social engagement from prior regular activities such as exercise groups and classes, work and volunteer work, religious services, and community-facing errands. “I haven't been able to travel to see my grandchildren in D.C., there's a new baby coming and certain religious ceremonies that happen, and if it's a boy I can't be there.” (P4).Participants reported missing out on seeing parents, children, and grandchildren due to social distancing and travel resections including missing milestone family events like funerals, graduations, and the birth of new babies. “We use Facebook and email and web so that keeps us in touch without having to be face to face, and that's worked fine for us.” (P6). For many, the use of web-based video chats was a new skill, but many reported using it to attend religious services, group meetings , friend or family gatherings, happy hours, scholarly presentations, and online classes. “We've tried to learn the new technology as much as possible so we can at least try to stay connected through different types of social media and using Zoom and stuff, to visit that way, and that's been really helpful.” (P7). Participants noted that while these virtual media help them stay connected to friends and family, it is often not as fulfilling as in-person interaction. However, for some virtual connection was easier to fit into their lives than in-person meetings and has afforded unique opportunities to share experiences they wouldn't have otherwise . Some participants noted that they thought their shift toward more virtual connection with friends and family may be a change that is carried forward beyond the pandemic, in place of some face-to-face visits. In addition to this large shift to virtual connection, some participants did report engaging in limited in-person connection in outdoor settings and with a limited group of individuals, as described in the In-person social engagement section above. Several participants also reported living with a partner who was supportive and provided a safe social connection. “Well, I have an amazing husband, he seems to always be cheerful so it helps.” (P25).Because they were staying at home more and in-person social engagement is greatly reduced, many people reported transitioning social activities to virtual modes like phone calls, web-based video chat , email, web messaging , and texting to help them cope and stay connected to others. “I love mysteries, TV mysteries…That's my main coping mechanism. And of course shopping on Amazon.” (P22). Many participants also reported spending time going online to browse, shop, connect with others, attend choir practice, or take classes . “But now we're listening a lot to online presentations by scholars and activists, especially on Black Lives and the shameful history of America.” (P24). Many participants reported doing more gardening, tending to plants, or doing major yard and home improvement projects. “I started to watch all these home improvement shows on TV, so I got these ideas and now I'm getting four new appliances next week and new flooring for my kitchen.” (P2). Many also reported reading and one person engaged in an online book club. Several participants also mentioned becoming activists around politics and racial justice as means to work toward a more hopeful future.People reported engaging in activities and hobbies like watching television, doing crafts , and baking. “Getting out of the house and walking around the neighborhood, we did that every day, twice a day, and that gave you some sense of normality too.” (P20). Because they were not getting as much daily life movement and many regular exercise options were not currently available, some participants also reported trying to get extra unstructured movement into their day to cope with the change. One person also started walking indoors in their home to get more daily steps.Physical activity, especially walking, was an important coping strategy for many participants, serving as a way for people to safely get out of the house, and do something that helped them stay healthy and spend time outdoors. Walking, home improvement projects and yard work were commonly reported ways to cope that had the additional benefit of allowing contact with nature and neighbors. “…My community has organized people to shop for you, they'll come pick up my credit card and my store card and take my list and grocery shop and then bring it back to me.” (P4). Additionally, when in public most participants noted intentionally social distancing and masking to minimize anxiety and infection risk. Many also reported avoiding places where people did not follow guidelines .Many participants acknowledged their high-risk status for COVID-19 infection and complications and noted fear about contracting the disease. To minimize risk of infection and help alleviate some anxiety and fear, many participants refrained from going out and preferred staying in to minimize risk of exposure to COVID-19. For many, this included trying to order groceries online, have someone else shop for them, or grocery shop infrequently. “A few participants noted that being in the HART study was helpful during the pandemic, offering an opportunity for social connection with study staff, structure and goals, and ideas for new coping mechanisms to try. One person got the idea to start meditating during COVID-19 from conversations with their study health coach and study materials.I am a person who takes it day by day, I don't get down. As hard as it is, there have been worse things that have happened in my life that caused me more anxiety and other issues than not seeing my grandkids. As hard as it is not having a regular routine. I have always had a positive outlook on life.” (P4). For some, spiritual and religious beliefs and engaging in religious services and groups remotely were also helpful to maintain connection and perspective to cope with the challenges of COVID-19. “I have a group of six gals, that we have been meeting online three times a week to talk and pray together. We're all Christians. Do a little Bible study. Just things to have some interaction. So that's been a good support group.” (P10).Many participants reported coping by maintaining a positive, one-day-at-a-time attitude. Reflecting on past hardships and practicing gratitude for current privileges helped provide perspective and resilience coping with the pandemic. “The COVID-19 pandemic and related public health mitigation measures have markedly changed nearly all facets of daily life and health for the older adults we interviewed. However, the resilience of this population was equally notable, particularly in their adaptability to new technologies and their ability to tap into past experiences to maintain a positive outlook despite hardships. The narratives shared by this sample provide great insight into opportunities for future intervention and research across areas of older adult health such as sedentary behavior, physical activity, mental health, and social engagement.Almost universally, participants in our sample reported perceived increases in sedentary behavior since the onset of the pandemic due to the loss of routine and increased time spent at home and engaging in sedentary activities like watching TV. The older adult population is already known to be the most sedentary age group , 19, accInterestingly, while some participants reported decreased physical activity as well as increased sedentary time, many participants reported doing more intentional physical activity. Activities like walking and gardening were commonly reported as coping strategies by our participants, as these activities helped them get outside the house, engage with neighbors and friends in safer ways, and maintain a sense of normalcy. Several participants also noted leveraging technology to engage in online exercise classes, an option that wasn't available pre-pandemic for most participants. This points to an opportunity for future research into optimal ways to incorporate virtual classes into older adult exercise programs, which could potentially expand program reach and maximize schedule flexibility for participants. Given the individual level variation in physical activity changes reported among older adults in our sample, future study to quantify the long-term impacts of the observed changes in physical activity is warranted.Negative impacts on mental health and social engagement were also prominent in our sample. Participants expressed high levels of uncertainty and fear related to the pandemic and related governmental policies, leading to high levels of stress and worry. Further, the need to restrict socialization and limit travel was distressing for many participants, challenging many of their traditional coping mechanisms. Consequently, participants reported large shifts in the way they were staying socially engaged with friends and family, leveraging technology, particularly video conferencing, in ways they hadn't prior to the pandemic. Similar themes of stress, loneliness and coping through virtual social engagement are supported by two recent mixed methods studies conducted at during the onset of the pandemic, suggesting these themes are persisting over the course of the pandemic , 12. HowIn general, the adaptability demonstrated in our sample to embrace new technological solutions and options to engage socially, exercise, and complete activities of daily living (like grocery shopping and banking) was notable. There are likely opportunities to leverage the new-found comfort in the older adult population with virtual connection. A 2017 Pew Research Institute report noted that internet use and comfort among seniors was steadily growing, with 67% of older adults age 65+ reporting regular internet use, including regular use of social media platforms among 34% of those age 65+ . Adding In addition to themes around technological adaptability, it was clear that positive attitude was a crucial coping mechanism among those interviewed. This manifested differently for various participants, with some adopting an attitude of persistence and others choosing to intentionally focus on gratitude for what they do have during this time of restriction. Drawing on the lifetime of prior stressors and challenges they had faced was a key source of resilience and strength that helped many to cope. Those participants that used this framework appeared to cope better with the frustrations and hardships of the COVID-19 restrictions, a phenomenon posited by Lind et al. , who sugThese findings must be interpreted in the context of several potential limitations. Like all qualitative studies, the findings may be unintentionally biased by the researchers. However, our team-based, iterative approach to analyzing and interpreting the data improved the rigor of our investigation. Social desirability bias may have resulted if participants felt compelled to describe their experiences during the HART study favorably. We attempted to reduce any perceived pressure by informing participants at the beginning of each interview that the study staff would not know the name of interview participants, that all feedback—even negative feedback—was valuable to the study team, and that participants were free to skip any question that they did not feel comfortable answering. Furthermore, participants were not offered additional incentives for completing the interview, and interviewers were neutral study team members who had no prior contact with interviewed participants. Generalizability of the results is limited by the use of a convenience sample of participants in the HART study. Participants who agreed to be interviewed may not be representative of the general population of older adults or of the larger HART study sample. Seasonality may also limit generalizability. We interviewed participants during the summer months (June–August), where outdoor recreation and socialization is more common and easier to facilitate. Due to the nature of our questions and pandemic-related activity restriction recommendations, perceived behavioral and health impacts of the pandemic may be experienced differently by participants in different seasons. Furthermore, our sample of older adults is predominantly white, highly educated, and all reside in and around the Seattle, WA area. Perspectives from this sample may not represent those of other older adult populations. Our sample also consisted of younger older adults (mean age = 68), who may be more tech savvy than the oldest old. Future studies should attempt to recruit a more diverse sample to see if qualitative experiences are shared across other populations.In sum, the older adults in this sample noted impacts from the COVID-19 pandemic on nearly all facets of daily life and activity, particularly noting profound increases in sedentary time, stress, and social isolation. However, these older adults also demonstrated resilience and adaptability by embracing new technology and drawing on a wealth of life experience to cope with those changes. These coping strategies, particularly more extensive leveraging of technological interventions and mindfulness and gratitude training, can inform future research interventions for older adults dealing with chronic stress or isolation.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Kaiser Permanente Washington Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.MAGH, JD, and DR contributed to the study design, execution, coded transcripts, and conducted qualitative analysis. MAGH and JD carried out all primary data collection. JC, EH, JM, KM, and SP aided in data interpretation. All authors contributed to and approved the final manuscript.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.


A training plan, or an exercise prescription, is the point where we translate sport and exercise science into practice. As in medicine, good practice requires writing a training plan or prescribing an exercise programme based on the best current scientific evidence. A key issue, however, is that a training plan or exercise prescription is typically a mix of many interacting interventions  that additionally change over time due to periodisation or tapering. Thus, it is virtually impossible to base a complex long-term training plan fully on scientific evidence. We, therefore, speak of evidence-informed training plans and exercise prescriptions to highlight that only some of the underlying decisions are made using an evidence-based decision approach. Another challenge is that the adaptation to a given, e.g. endurance or resistance training programme is often highly variable. Until biomarkers for trainability are identified, we must therefore continue to test athletes, clients, or patients, and monitor training variables via a training log to determine whether an individual sufficiently responds to a training intervention or else re-plan. Based on these ideas, we propose a subjective, pragmatic six-step approach that details how to write a training plan or exercise prescription that is partially based on scientific evidence. Finally, we advocate an athlete, client and patient-centered approach whereby an individual’s needs and abilities are the main consideration behind all decision-making. This implies that sometimes the most effective form of training is eschewed if the athlete, client or patient has other wishes. Physicians, physiotherapists, lifestyle and sports coaches, as well as fitness trainers all have one thing in common: they prescribe interventions to change the human body. The aims of physicians are to prevent, treat or ideally cure diseases by prescribing medical treatments. In contrast, sports coaches and fitness trainers aim to improve athletic performance, fitness and/or health by writing training plans or prescribing exercise often combined with nutritional recommendations. In doing so, physicians and sport practitioners can choose from a myriad of possible interventions including resistance or endurance exercise training, a Mediterranean, vegan or protein-rich diet or, in the case of medicine, drugs or surgery. But how can one select those interventions that most likely to achieve the desired treatment effects or adaptations in an individual?For many years, practitioners simply followed their opinion or the opinion of experts published in books or on the internet plus perhaps information from some random scientific papers. For example, “experts” have recommended that endurance can be best improved by training at the lactate threshold or that sets with 8–12 repetition maximum are “optimal” for muscle hypertrophy, despite a paucity of research supporting these beliefs. In medicine, however, the advent of evidence-based medicine has changed medical decision-making , the one without the ginseng developed severe shortness of breath, while the one who took the ginseng breathed evenly and smoothly” [“moothly” .Whilst this intervention study provided evidence for beneficial effects of ginseng, the evidence is low quality: the breathing measurement is subjective, the subjects probably differed at baseline, there was only one subject per group and the researcher probably had a conflict of interest as he seemed enthusiastic about ginseng. This highlights that scientific evidence is not automatically a truth but needs to be interpreted critically. Over time, scientists learned to design more robust intervention experiments as researchers improved scientific methods and reduced bias, and other sources of error.Since Ben Cao Tu Jing’s historical experiment, researchers have used many study types to investigate the effect of a given medical treatment or exercise on an outcome. The most important difference is the use of observational studies, where the investigator just observes the effect of a treatment but does not control the treatment, versus experimental studies where the experimenter administers the treatment or the intervention. A key step forward was the advent of randomized control trials (RCTs) with additional control measures such as blinding and placebos as advocated by Archie Cochrane, who deemed these designs a superior alternative to observational studies and anecdotal opinions .the conscientious, explicit, and judicious use of the current best evidence in making decisions about the care of individual patients” [“atients” .So how has the refinement of studies that test the effectiveness of an intervention led to “evidence-based practice”? The actual term “evidence-based medicine”, or more broadly “evidence-based practice”, was first used in the 1990s by researchers at McMaster University and has been especially linked to David Sackett who, from 1994 to 1999, led the Centre for evidence-based Medicine at the University of Oxford. In a review titled “Evidence based medicine: what it is and what it isn’t,” Sackett and colleagues define evidence-based medicine as …conscientious, explicit, and judicious use” mean that we do not necessarily need to do exactly what the evidence says; rather, we have the option to make alternative, subjective decisions if we feel that there is a good reason to do so.By that definition, David Sackett and colleagues instruct evidence-based practitioners to do two things: First, gather all of the currently available scientific evidence of sufficient quality and second, interpret and apply that evidence. Importantly, the terms, “Several issues must be considered when judging the available evidence. First, not all scientific data is of equal quality. Single pieces of evidence include subjective “expert” opinion, case reports and series, non-randomised control trials, and randomised control trials. On the path towards becoming an evidence-based practitioner, we must, therefore, develop the skill of critically interpreting scientific evidence. To aid this, e.g. Greenhalgh has published book “How to read a paper” .current best evidence for an intervention decision as there may be dozens of observational or experimental studies in relation to a single intervention such as the use of aspirin for treating headaches. This demand has led to systematic reviews and meta-analyses as two new publication types that support evidence-based practice as they aim to synthesize the current best evidence for treatment decisions. These forms of evidence also comprise the tip of the so-called evidence pyramid .Evidence-based medicine has now become the gold standard in medical decision-making, i.e. in deciding how to treat a patient. That said, evidence-based medicine is not without issues, as summarized by Greenhalgh et al. . One sucp values < 0.05 used for accept-or-reject-hypothesis conclusions), which has been termed “dichotomania”. The issue is that we should never conclude ‘no difference’ or ‘no relationship’ in an intervention simply because a p value is above a given threshold of “statistical significance” [p values simply indicate how probable an effect is and should be considered just one of several decision-making criterions. It can be argued that magnitude-based statistics such as effect sizes and confidence intervals are potentially more valuable for deciding whether, e.g. the adaptation to a training intervention is likely more than just measurement variability and biologically meaningful [A second problem is that statistical analyses often employed in the applied sciences are inherently flawed from a decision-making standpoint. A primary problem here is the focus on null hypothesis statistical testing ” [the ACSM’s new Position Stand contains all the flaws that were pervasive in their previous Position Stand” [In 2002, the American College of Sports Medicine published a position stand on resistance training for novice, intermediate and advanced exercisers. The position stand makes specific recommendations about how many repetitions, sets and intensities are necessary to trigger certain adaptations . This pof data)” . This nen Stand” . In summIn summary, these three examples show that there are sometimes issues with the evidence base for current training recommendations. This does not always imply that these training recommendations are ineffective or that future training recommendations must all be based on near-perfect evidence, as such evidence does often not exist. To us, the “lesson learned” is to honestly state the quality of the evidence and to avoid exaggerating evidence claims.In this section, we will now discuss how to apply evidence-based practice to writing a training plan or exercise prescription. When writing a training plan or prescribing exercise, trainers or coaches interact with athletes, clients or patients. In line with the concept of patient-centred care , we advoExercise training interventions typically include a mix of many different exercises (e.g. 10 exercises for different muscles in a resistance training plan), with variable exercise intensities and durations . In addition, these interventions change over time due to periodization or tapering, and are combined with other interventions such as nutrition (e.g. a protein-rich diet or carbohydrate loading). Whilst it is possible to base some training plan decisions on the current best scientific evidence, it is virtually impossible in a complex, months-lasting training plan to base every decision on scientific evidence. Also, there will probably be no studies comparing the planned, complex intervention mix to a matched control intervention. For example, assume we aim to train a natural, female bodybuilder for 20 weeks, with 20 resistance exercises, nutritional and supplement recommendations, with a bulking and cutting period. Whilst we can easily find answers to questions such as “What are the general guidelines for maximizing muscle hypertrophy?” with scientific evidence , 45, we For this reason, we use the terms evidence-informed training plan or evidence-informed exercise prescription to highlight that only some of the underlying decisions were made with an evidence-based approach, whilst other decisions are based on personal experience. So how can we write an evidence-informed training plan or exercise prescription? Fig. 2max of 60 ml/min/kg , a pace of 4 m/s at 2.5 mmol/L lactate [2 [Step 1 and 2 define the overall goal and break it down into smaller factors or goals. For example, if the overall goal in step 1 was a sub 3 h marathon in April 2021, then the sub-goals could be a V̇O lactate  and a BMctate [2  at the tctate [2  or the tIn an evidence-informed approach, some decisions should be based on the best available evidence. How to do this is proposed in the green box in Fig. 2max [In the yellow box in Fig. 2max , or to tFinally, note that the six-step plan is a subjective approach to planning training or prescribing exercise and that there are no studies investigating whether this approach as an intervention works better than alternative approaches.In summary, training plans and exercise prescriptions are typically a complex mix of interventions where realistically only some of the underlying decisions can be based on the best, current scientific evidence whilst the remaining decisions are based on unsystematic evidence or experience resulting from practice, academic courses or coaching qualifications.In this current opinion article, we have suggested a pragmatic six-step strategy detailing how an evidence-informed training plan or exercise prescription could be written. This six-step training plan strategy is a subjective proposal not based on direct research. It also has not been compared to other ways of planning training or prescribing exercise. An added complication is that training responses to a given training programme can vary greatly and hence practitioners need to integrate a personalization strategy to identify non-/poor responders early on. Until biomarkers for trainability are identified, the only way to identify a non-/poor responder is to test and to monitor key training variables to see whether an athlete, client or patient responds to the training intervention. This is, of course, current practice. Moreover, we advocate an athlete, client or patient-centred approach which means following the athlete’s, clients or patient’s wishes even if there are more effective interventions. The trainer’s/coaches’ role is to ensure that the athlete, client or patient can make informed decisions.Finally, training plans and exercise prescriptions are the points where sport and exercise science knowledge is translated into sport and exercise practice. In our view, methods of how to plan training or prescribe exercise and how to base some of the decisions on scientific evidence should be taught as part of sport and exercise science degrees in the same way as evidence-based, patient-centred and personalized/precision medicine is taught to medical students.


NCBI Taxonomy is the main taxonomic source for several bioinformatics tools and databases since all organisms with sequence accessions deposited on INSDC are organized in its hierarchical structure. Despite the extensive use and application of this data source, an alternative representation of data as a table would facilitate the use of information for processing bioinformatics data. To do so, since some taxonomic-ranks are missing in some lineages, an algorithm might propose provisional names for all taxonomic-ranks.Ord_Ornithischia) or interpolating parent nodes when needed (e.g. Cla_of_Ornithischia), both examples given for the dinosaur Brachylophosaurus lineage. The new hierarchical structure was named Taxallnomy because it contains names for all taxonomic-ranks, and it contains 41 hierarchical levels corresponding to the 41 taxonomic-ranks currently found in the NCBI Taxonomy database. From Taxallnomy, users can obtain the complete taxonomic lineage with 41 nodes of all taxa available in the NCBI Taxonomy database, without any hazard to the original tree information. In this work, we demonstrate its applicability by embedding taxonomic information of a specified rank into a phylogenetic tree and by producing metagenomics profiles.To address this issue, we developed an algorithm that takes the tree structure from NCBI Taxonomy and generates a hierarchically complete taxonomic table, maintaining its compatibility with the original tree. The procedures performed by the algorithm consist of attempting to assign a taxonomic-rank to an existing clade or “no rank” node when possible, using its name as part of the created taxonomic-rank name (e.g. http://bioinfo.icb.ufmg.br/taxallnomy.Taxallnomy applies to any bioinformatics analyses that depend on the information from NCBI Taxonomy. Taxallnomy is updated periodically but with a distributed PERL script users can generate it locally using NCBI Taxonomy as input. All Taxallnomy resources are available at  Any biological data are tightly linked to taxonomic data and several bioinformatics analyses depend on taxonomic information to achieve their objectives. Metagenomics, clinical forensic medicine, and other fields rely on fully-annotated taxonomic data to identify and group organisms present in a sample, often summarizing the results to a taxonomic-rank such as family, order, class, or phylum. Furthermore, any discussion made from evolutionary analyses refers to the taxonomic classification proposed so far. Taxonomic information can be obtained from several taxonomic databases, like the Catalogue of Life , which pMicrocystis aeruginosa (NCBI:txid:1126), had no taxon with the Class rank. When our group has started developing Taxallnomy, Arabidopsis did not have Class and pig did not have Order ranks. However, if we look further in the taxonomic lineages, we find some taxa without taxonomic-rank, denoted as “no rank” or “clade” taxa, included in the tree to add phylogenetic information to the taxonomy base, pointing out monophyletic groups. Those might be useful nodes to be borrowed to represent preliminary added taxonomic-ranks. When that is not possible, the interpolation of new nodes without affecting the original hierarchy would be the solution.The taxonomic classification comprising the NCBI Taxonomy follows the phylogenetic taxonomy scheme with the topology reflecting a consensus of views from taxonomic and molecular systematic literature  and the M. aeruginosa and several non-assigned classes of cyanobacteria are present they will all be counted as “NULL” in a computational database such as MySQL, therefore grouping non-related counts. For such analyses, a hierarchically complete taxonomic tree incorporating “all” taxonomic-ranks could be of great benefit. Thus, in this work, we developed an algorithm that carefully takes the taxonomic tree provided by NCBI Taxonomy and generates a hierarchical taxonomic tree in which all lineages have the same depth and all hierarchical levels corresponding to a taxonomic-rank, thus it can be handled as a table of 41 columns. Additionally, the table can regenerate the original tree with all nodes if desired for the computational analyses. The final database was named Taxallnomy because it provides taxonomic names for all taxonomic-ranks in a lineage comprised in NCBI Taxonomy. Taxallnomy thus programmatically proposes provisional names for all gaps in taxonomic-rank in NCBI Taxonomy, favoring bioinformatics analysis and maybe inspiring curators on proposing the appropriated names for novel taxonomic-ranks. The procedure acts in such a way that does not harm the NCBI Taxonomy classification schema. Names of the proposed ranked taxa are generated by appending prefixes to existing node names, so they will not be mistaken as unappropriated novel taxa, which might be created after appropriated nomenclature by taxonomists. Taxallnomy is in a tab-delimited format, making it easy to access all members of a given clade . Users can access and explore the hierarchical structure of the Taxallnomy database at its website at http://bioinfo.icb.ufmg.br/taxallnomy. Instructions to access the data programmatically through API or to produce the Taxallnomy database in a local machine are also available at the Taxallnomy website. Local production is very simple and grants the use of updated information, processed straight from updated NCBI Taxonomy.These issues may be due to the uncertainty or conflict amongst experts on the classification of this group and turn to make hierarchical taxonomic-ranks of NCBI Taxonomy incomplete, or the experts are not missing some taxonomic-ranks in some lineages. Because of that, a simple query regarding the taxonomic-ranks, such as “How many distinct taxa of class rank are represented in this data?” could become a difficult task. For instance, if the class for Dump files with taxonomic information provided by the NCBI Taxonomy FTP server (ftp.ncbi.nih.gov/pub/taxonomy/) were used for the construction of the Taxallnomy database. Specifically, the dump files containing the parent–child relationship (nodes.dmp) and the taxa names (names.dmp), which are available in both older (taxdump) and newer (new_taxdump) versions of NCBI Taxonomy, were used to generate Taxallnomy tables. The results presented in this work were obtained using dump files downloaded on November 11, 2020, although the Taxallnomy website is kept up to date.Homo sapiens, Mammalia, Bacteria) and a numeric identifier  associated to, and they correspond to the nodes of the tree. Each taxon is connected to a single node of a level above (parent taxon), except for the root node which is positioned on the top of the tree. Furthermore, a taxon may be connected to one or more nodes of a level below (child taxon); when a taxon is not connected to any child taxon, it is referred to as leaf taxon (or leaf node). Each taxon may or may not (e.g. clade) have one of the 41 taxonomic-ranks assigned to it (Table Mycrocystis class). Finally, some taxa in the tree were considered in NCBI Taxonomy as unclassified taxa, which contain the term unpublished, unidentified, unassigned, environmental samples, or incertae sedis on its name, and therefore their children are not subjects for taxonomic classification, although their parents are.Here we present common terms used when referring to the hierarchical structure of NCBI Taxonomy. The taxonomic tree of NCBI Taxonomy consists of several taxa organized in a hierarchical data structure. All taxa have a name (e.g. Homo sapiens (NCBI:txid9606) lineage, for the sbCla_Theria, the Subclass taxonomic-rank was assigned to the “no rank” Theria (prefix sbCla_); moreover, in Tri_of_Homo, the Tribe rank was assigned to an interpolated new node, parent of Genus Homo (prefix Tri_of_); furthermore, in sbSpe_in_Homo sapiens, the Subspecies rank was assigned to a created node to be a child of species Homo sapiens (prefix SbSpe_in_). Therefore, prefixes make reference to the procedure, and thus the created names will differ from NULL in bioinformatics analyses, and will never be mistaken by taxonomy experts, otherwise, they might suggest a position in the tree for putative creation of an actual taxonomic-rank. Moreover, since Taxallnomy has a format of a table, obviously bioinformaticians might make use of only the most commonly used taxonomic-ranks, selecting a few columns to classify the data.The algorithmic challenge that we proposed for this work is to fill in the blanks on the taxonomic lineage considering the taxonomic-ranks. To accomplish this, we created an algorithm that performs one of these operations: (1) assigns missing taxonomic-ranks, with the appropriated name and txid to available currently unranked taxa (presently named clades or “no rank” taxa) throughout the taxonomic tree, if possible, or (2) creates nodes to add the missing taxonomic-rank taxa, carefully interpolating them in the hierarchy without affecting it, and naming it accordingly to its child, or even (3) creates children taxa when the lineage does not present them but others do so. In all cases prefixes will distinguish Taxallnomy additions from the original names i.e. no completely original name will be confused with actual taxonomic-rank name, e.g. in the The first approach is to map existing nodes that are unranked taxonomically to assign taxonomic-ranks to them, which will append the prefix Cla_, Ord_, Fam_, etc. The algorithm begins evaluating all unranked taxa by moving through the hierarchical levels of the taxonomic tree. For each unranked taxon found, the algorithm evaluates if some of the 41 taxonomic-ranks occurring in NCBI taxonomy . Also, the hierarchical level of the NA is referred to as the first level (L1). The first condition evaluated by the algorithm is the existence and the number of levels that are redundant with the L1 (RL). We consider that levels following the L1 are redundant if all nodes on it are (1) unranked and (2) have the same candidate ranks as the NA, and if (3) the level above it is the L1 or a redundant level without leaf nodes. If the number of candidate ranks (CR) in the NA is less than the number of levels that need a rank (L1 plus consecutive redundant levels), there are not enough ranks to assign to the nodes on those levels. In this case, one option is to assign the lowest rank level among the candidate ranks to the NA and leave some of its descendant nodes unranked. However, we opted to leave the NA without a rank so that the unranked taxa of further levels could have a rank assigned. This procedure was chosen as this could result in a more unranked taxa with a rank assigned, favoring the dichotomization of the final tree. In subtree 1  has one candidate rank (taxonomic level 2), and the level following the L1, which is composed of nodes C1 and D1, meets all conditions established to be a redundant level (RL). Since the number of candidate ranks in the NA (CR = 1) is not sufficient to rank the nodes on L1 and further redundant levels (L1 + RL = 2), the algorithm leaves the node B1 without a rank, allowing the nodes of further level (C1 and D1) to have a taxonomic-rank assigned.For a better understanding of the assignment rules, consider the subtrees in Fig.  n = {1, ,…,5}. AlIf the previous condition is not true then the next condition evaluated by the algorithm is the existence of unranked taxa in the subtree in which the number of candidate ranks on it is equal to or less than the number of consecutive unranked taxa in the path between it and the NA. If a node in this condition is found  and “E5” . To fulfill this gap, the algorithm creates between them a node (node “a”) with Subphylum rank assigned to it. This type of node is referred to as type 2 and is named using the abbreviation of the assigned rank followed by the preposition “of” and the original name of its first ranked descendant node. For the node “a”, since it has the node “E5” as the first ranked descendant node, it is named as “spPhy of E5”. Human’s tribe, for example, is proposed to be Tri_of_Homo, which Homo is a Genus stated in the original database.The taxonomic tree has portions where two consecutive taxa do not have consecutive ranks. In this case, the algorithm creates nodes between them and assigns the created nodes with ranks that are missing. For instance, we could observe in subtree 5 of Fig. 5” is a leaf node of Phylum rank (level 5). Since “E5” is a leaf node, all ranks after Phylum are missing in this lineage. In this case, Taxallnomy will visit these nodes and create nodes to fulfill those missing ranks. The node “b” in subtree 5 . These nodes are referred to as taxa of type 3. They are useful in cases when there are, for example, subspecies declared in the database. For instance, Sus scrofa (NCBI:txid9823) has over 60 thousand proteins deposited, but only around 1.5 thousand are assigned to one of its 11 subspecies. Therefore, most of those entries are “NULL” for the Subspecies rank in the original database; but, by creating the node of type 3, all of them are treated to have a node of Subspecies rank named “sbSpe in Sus scrofa”. Another usage of nodes of type 3 is on metagenomics analysis  was a leaf taxon of Species rank that did not have a Genus rank in its lineage. However, its lineage contained an unranked taxon named Beringia (NCBI:txid1037069), which had the Genus rank appropriately assigned by the algorithm. Similarly, Nocardia argentinensis ATCC 31,306 (NCBI:txid1311813), in the same version of NCBI Taxonomy, was a “no rank” leaf taxon, which did not have a node with Species rank in its lineage, but it contained an unranked taxon named Nocardia argentinensis (NCBI:txid1311812). The current algorithm also appropriately assigned the Species and Subspecies ranks to the nodes N. argentinensis and N. argentinensis ATCC 31306, respectively. However, depending solely on these rules incurs some obvious errors, like in those unranked leaf taxa which do not have nodes with Species and Genus rank in its lineage. For instance, Rosodae (NCBI:txid721787) is a “no rank” leaf taxon that has a parent node with Subfamily rank (level 21). According to the algorithm, Rosodae could have ranks ranging from Tribe (level 22) to Isolate (level 41), and, based on the rank priority, it would be assigned to Genus rank, which is not a proper rank for it. To correct this situation, special rules were added to the algorithm to have the Species and Genus ranks assigned to an unranked taxon. We established that the Species rank assignment to an unranked taxon should occur only if, among its ascendant nodes, there is a node of Genus rank in the original database. On the other hand, an unranked taxon should have the Genus rank assigned if there are nodes of Species rank among its descendants in the original database. Moreover, the assignment of both ranks to an unranked taxon should not occur if a node has terms in its name that identify it as an unclassified entry. With these rules, the unranked taxon Rosodae mentioned before has the Tribe rank assigned instead of Genus rank.Species and Genus are ranks with high frequency Table , thus boHomo sapiens).The primary identifier of each node comprising the Taxallnomy tree is the Taxonomy ID provided by the NCBI Taxonomy database. However, since the Taxallnomy algorithm assigns ranks to nodes and creates new nodes, we formulated a code that properly identifies them. The Taxallnomy code consists of three digits added as a decimal number in the Taxonomy ID of each node. The first two digits indicate the taxonomic-rank in to which it was assigned. It goes through the code "01" to "41", in which the first code ("01") refers to the Superkingdom rank and the last one ("41") refers to the Isolate rank. The third digit ranges from 1 to 3 and indicates the approach used by the algorithm to create/modify a node. The codes 1, 2, and 3 refer respectively to taxa of type 1, type 2, and type 3. For instance, in the taxon code 6072.031, 6072 corresponds to the NCBI Taxonomy ID (Eumetazoa) and 031 is the code added by the Taxallnomy algorithm, indicating that it is a node of type 1 created on Subkingdom rank. Using the Taxallnomy name convention, the name of this node will be “sbKin Eumetazoa”. Furthermore, taxa originally ranked in the NCBI Taxonomy database has the code 000 included . For experiencing Taxallnomy, one can access the website and add, for example, this list of seven Genus-TxIDs: 9030, 8500, 8507, 28376, 8468, 643744, 436494; or type and add, one by one, their taxon names: Gallus, Crocodylus, Sphenodon, Anolis, Chelonia, Brachylophosaurus, and Tyrannosaurus. This will generate the complete hierarchical subtree comprising those taxa in which, as one reads this, some order and class ranks might be missing yet. It is worth mentioning that those unranked taxa which had no rank assigned by the algorithm could also be displayed in the tree, demonstrating that Taxallnomy does not harm the NCBI Taxonomy hierarchy.Users can query the Taxallnomy database and download the results using its web interface at https://sourceforge.net/projects/taxallnomy). The Taxallnomy database comprises five main tables named “lin”, “lin_name”, “tree_complete”, “tax_data”, and “rank”. The first two tables have the taxonomic lineages that comprise the Taxallnomy tree. The tables have a column containing the NCBI Taxonomy ID (txid), which is the primary key column of the tables; and 41 columns representing the 41 taxonomic-ranks found in the NCBI Taxonomy database. In the “lin” table, the taxonomic-rank columns are filled with taxonomic codes, whereas, in “lin_name, those columns are filled with taxonomic names. The table “tree_complete” contains all parent–child relationships in the Taxallnomy database that make the hierarchical structure complete. Two other hierarchically incomplete versions of the tree table are also available in the Taxallnomy data source; one is the table “tree_all”, which includes the unranked taxa that did not have a rank assigned, and the other is the table “tree_original”, which has the same hierarchical structure as the one provided by the NCBI Taxonomy database. In the “tax_data” table, users can find information about each taxon comprising the tree, such as its scientific name, common name, and rank level. Finally, the “rank” table contains information about the ranks comprising the taxonomic tree, such as name, level, priority order, and abbreviation.Users with high demand can also find all necessary files to have a copy of the Taxallnomy database in a local MySQL database at the Taxallnomy SourceForge page  were not included.Taxallnomy database consists of a total of 9,875,550 nodes, in which 9,183,606 (92.99%) of them are nodes created by the Taxallnomy algorithm or unranked taxa that had a rank assigned. Among them, 170,742 (1.86%) are of type 1, 4,225,358 (46.01%) of type 2 and 4,787,506 (52.13%) of type 3. Moreover, the number of unranked taxa used to create the nodes of type 1 corresponds to 99.85% of all unranked taxa found in the original tree . The number of leaf taxa totalized 728,071, of which 68.18% are from Eukaryota, 8.51% from Bacteria, 0.13% from Archaea, and 23.38% from Virus or Viroids Superkingdoms. In these counting, unclassified taxa  and Microcystis aeruginosa (NCBI:txid1126) have eight and 10 taxa on their lineages, respectively, same taxonomic-ranks, even the species rank , might be displayed in different level of the profile.Metagenomics analyses heavily rely on taxonomic data and information about taxonomic-ranks. After the taxonomic annotation performed by software like MEGAN , MG-RASTAll these issues can be resolved by using a hierarchically complete taxonomic tree provided by the Taxallnomy database. To exemplify this, we took a metagenomics sample collected from a tropical freshwater reservoir in Brazil (projectID on MG-RAST:mgp13799) and generated its taxonomic profiles using taxonomic sources from NCBI Taxonomy and Taxallnomy. For this, we submitted the reads to MEGAN for taxonomic annotation and retrieved the taxonomic lineage from both databases for each taxon that appeared in the annotation process. Then, we assembled all taxonomic lineages retrieved in a spreadsheet and generated, for instance, pie charts for the ranks Kingdom, Phylum, and Class  and,Homo sapiens is placed in the family Hominidae, we could assert that Homo sapiens is more closely related to any species within this family than to any other species which is not Hominidae.The main criticism faced by the rank-based classification is the lack of an absolute definition of each rank since there are no well-established criteria for the rank assignment process . For thiTaxonomic information provided by NCBI Taxonomy  is a valSince the Taxallnomy algorithm could create new nodes (nodes of type 2 and 3) or assign a rank to a preexisting one (nodes of type 1) along with the hierarchical structure, another task performed by the algorithm is to create adequate names for those nodes. Besides the existence of a nomenclature rule to name taxa of a given rank, it would be a complex task to adopt them, since different taxonomic groups have different nomenclature rules –61. So, There is no comprehensive method to address the problem of the lack of a complete hierarchical rank classification, but some solutions have been practiced. The most common and simplest one is the elimination of the “no rank” taxa throughout the lineage , 51, 56.Besides the creation and nomination of new nodes based on ranked taxa, a remarkable feature performed by the Taxallnomy algorithm is the rank assignment of a “no rank” taxon. Taxa with “no rank” status are spread throughout the tree and usually are discarded by users or software that require a hierarchically complete tree, which results in the loss of information. In this work, we show that we could take advantage of the “no rank” taxa to fulfill lineages with missing ranks and assist in generating a completely hierarchical taxonomic tree. It is worth mentioning that keeping the “no rank” taxa in the final tree as many as possible is important to preserve the groups already structured by the taxonomic tree. Thus, the current algorithm performs the rank assignment procedure to assign ranks to as many “no rank” taxa as possible. By this, the algorithm has assigned a rank to more than 99% of all “no rank” taxa without disarranging the rank hierarchy already established by the ranked taxa. In the algorithm, we also established a priority scale among the ranks Table  to help Several bioinformatics analyses and tools rely on the taxonomic information provided by NCBI Taxonomy. However, working with or querying data by taxonomic-rank is not trivial because of the absence of some ranks in the taxonomic lineages and the presence of taxa without a rank throughout the taxonomic tree. In this work, we address this issue by developing an algorithm that takes the taxonomic tree from NCBI Taxonomy and makes it hierarchically complete according to the taxonomic-ranks. The final tree was named Taxallnomy, and it has 41 hierarchical levels corresponding to the 41 taxonomic-ranks that comprise the NCBI Taxonomy. From the Taxallnomy database, the user can retrieve the complete taxonomic lineage with 41 nodes, all of them with a taxonomic-rank, to all taxa available in the NCBI Taxonomy. Taxallnomy applies to any bioinformatics analyses that depend on the information from NCBI Taxonomy.