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CUSTOM TARGETS Custom shapes and sizes are available. SUPPORT If you have questions on which target style fits your need call us today to discuss! Follow via Facebook Mail To Yelp about it! In addition, the type of goal that the participants set in the intake survey was recorded.

Finally, participants filled out a posttest survey presented in Multimedia Appendix 1 in which we especially assessed the perceived impact of the campaign on their walking, biking, and sports performance, as well as their perception of their capability to perform the prescribed tasks ie, self-efficacy.

The first set of statistical analyses focused on the evaluation of dropouts. A participant was labeled as a provisional dropout if they had not visited the app during a specific wave and was therefore assumed to have lost interest ie, dropped out during the previous wave. Several multiple regression models were fit to determine whether the number of dropouts changed over time and were different per treatment.

In particular, we tested for significant second-order interaction effects of time ie, the wave number and treatment. The second set of analyses focused on the evaluation of engagement levels of the participants.

To evaluate treatment differences, further analyses were performed on participants who actually had an opportunity to receive exposure to the treatment.

Hence, from the entire data set, a subset was derived preserving the combination of a particular participant and wave only if they had ever checked the app during that wave and if they had participated for a duration of at least two waves because during the wave in which a participant signed up, they were not actually receiving a treatment yet.

We tested whether significant second-order interaction effects existed among these variables. In all models we allowed random intercepts for both individuals and the governmental organizations they were part of. The final model was selected based on the Akaike information criterion [ 37 ].

The Akaike information criterion estimates the relative quality of statistical models for a given set of data. The measure rewards goodness of fit and includes a penalty for increasing the number of predictors ie, to prevent overfitting because increasing the number of predictors generally improves the goodness of the fit.

In addition, a third set of analyses zoomed in on the experimentally controlled tasks ie, the longer walk, the longer bike ride, and the sports sessions to evaluate treatment differences at the level of individual activity types. Specifically, for each activity type, a hierarchical linear model was built to predict the number of times a participant registered a task for that particular activity type.

In addition, we tested whether significant second-order interaction effects existed among these variables. The final model was again selected based on the Akaike information criterion [ 37 ].

Finally, the fourth set of analyses focused on the evaluation of subjective measures that were derived from a posttest survey.

This final set of analyses was performed on a subset of the data set that only included participants who filled out the posttest survey and were using the mHealth app in more than one wave. A set of 3 separate linear models was used to estimate the perceived impact of the campaign on walking performance, biking performance, and sports performance.

To obtain the final models, a backward elimination selection procedure was used [ 38 ]. Backward elimination starts with all predictors included in the model, with variables subsequently being eliminated one at a time. In total, unique participants joined the study, and they were randomly assigned to a treatment: 82 Of the participants, 83 These data are summarized in Figure 3 , which displays a cohort diagram that details the number of participants engaged in different study phases.

Table 3 displays sample demographics based on the results of the posttest survey, which was filled out by Gender, age group, and personality scores are displayed for the entire sample as well as per treatment.

The demographic characteristics in the control group and treatment group are distributed similarly. Hence, it is assumed that these groups were comparable at baseline.

Figure 4 displays the decrease in the number of participants who visited the mobile app during a given wave. The number of participants who joined the campaign for the first time during a given wave are displayed in green. The number of participants who dropped out during a specific wave are displayed in red.

The number of participants who checked the mobile app during a specific wave, although they dropped out during an earlier wave ie, reclaimed users are displayed in yellow. No significant differences in dropout rates between treatments could be detected. In addition, no significant interaction effect between time ie, the wave number and treatment was detected.

Hence, it is assumed that dropouts were spread equally over treatments. a Posttest personality scores were not available for the participant who was not assigned a treatment.

The complexity parameters of the dynamic tasks that the control participants were assigned are presented in Table 2. However, the complexity parameters for the treatment group were different for each individual in that group and were only determined at the start of a new wave.

The mean SD , minimum, and maximum values of the 3 complexity parameters are displayed per wave in Table 4. Of the participants, 10 5. In addition, of these participants, 55 Figure 5 displays the number of days participants visited the app on average per wave per treatment. Figure 6 displays the number of days participants visited the app on average per type of goal they set.

From the second set of statistical analyses, it was found that the number of days participants visited the app dropped over time ie, —1. No significant difference between treatments was detected, although it did matter whether participants completed the intake survey.

Finally, no significant interaction effects were detected; all treatments were affected equally by the impact of time. Figure 7 displays the average number of activities participants registered per treatment. Figure 8 displays the average number of activities participants registered per type of goal they set.

Multimedia Appendix 3 displays an overview of the number of times a particular suggested task was registered per organization. Moreover, from the second set of statistical analyses, it was found that the number of activities participants registered decreased over time ie, —0.

Finally, no significant interaction effects were detected; again, all treatments were affected equally by the impact of time ie, relative wave number.

The third set of analyses zoomed in on the experimentally controlled tasks ie, the longer walk, the longer bike ride, and the sport sessions to evaluate treatment differences at the level of individual activity types Figure 9. For each activity type, a hierarchical linear model was built to predict the number or times a participant registered a task for that particular activity type.

No significant predictors were found for estimating the number of longer bike rides a participant registered. When zooming in on the perceived impact on performance of individual activity types ie, walks, bike rides, and sports sessions , no significant predictors were found for estimating the perceived impact on walk performance Figure Nevertheless, for both the control and treatment groups, the perception of capability diminished over time ie, —0.

The aim of this study is to evaluate the impact of personalized goal setting in a gamified health promotion program on participant engagement levels. Our results show that engagement with the program inevitably dropped over time, both in the personalized condition and in the control condition.

Although this pattern is common in digital health promotion programs [ 39 ], several factors may be relevant for explaining this tendency in this particular context. Hence, a great proportion of our sample seemed to be still in the precontemplation or contemplation phase, phases in which they were actually not yet planning for a more active lifestyle.

Still, the participants who had set themselves a goal ie, by completing the intake survey were more engaged than those who had not. In particular, these participants visited the app more frequently and also registered more of the healthy tasks they were prescribed.

Hence—as proposed by Goal-Setting Theory—setting a goal is in itself a motivating task [ 33 ]. Nevertheless, improvement goals—which are arguably more difficult to achieve than maintenance goals—did not seem to be significantly more motivating in general than maintenance goals.

This finding seems to contradict both Flow Theory and Goal-Setting Theory, which propose that difficult—but still attainable—goals are more engaging than easier goals [ 31 , 33 ]. Then again, it should be noted that the descriptive means were mostly in the expected direction ie, improvement goals were more engaging than maintenance goals and the impact of improvement goals was actually significantly larger for promoting sports sessions: if a participant explicitly expressed a need to improve their current performance, they perceived their sports performance to be improved significantly.

Finally, the impact of the personalized treatment on engagement levels seemed to be generally limited. However, descriptive means were mostly in the expected direction ie, personalized goals were more engaging than generically suggested goals. The seemingly limited impact of personalized goal setting may be explained by the actual strategy for personalizing the set of tasks.

Moreover, we found that personalizing the suggested minimum number of sports sessions did stimulate participants to perform significantly more sports sessions, as well as significantly improved their perception of their sports performance.

Upon close examination of this complexity parameter, we found that it can be characterized as a frequency parameter, whereas the parameters for personalizing walks and bike rides are typically characterized as intensity parameters. A frequency parameter defines how many times a particular activity should be performed in a given time frame, whereas an intensity parameter defines how a particular activity should be executed eg, for how long and how far.

We are unaware of context-specific factors that could have influenced this effect. However, we cannot claim generalizability yet either. Hence, our personalization strategy may have suggested tasks that were perceived as too difficult or too easy by our target users, thereby potentially compromising self-efficacy and engagement with the program [ 30 , 31 ].

The execution of this study was subject to several limitations. First, participants could take part without completing the intake survey. As a result, it was unknown in the case of some participants whether they explicitly choose not to set goals for themselves or whether they actually did aim to maintain or improve their current capability levels.

Second, participants may have felt that the number of points they were awarded for their activities, which affected their position on the social leaderboard, was unfair. Although, objectively speaking, this tailoring strategy makes the whole competition actually more fair, we received reports from several participants perceiving it as unfair that they had to seemingly expend more effort than their colleagues to be awarded the same number of points.

Third, an additional design choice that participants may have perceived as unfair was the decision to reward walks and bike rides on a per-trip basis, instead of, for example, on a daily aggregate basis.

As a result, participants who went out for multiple shorter walks may not have been sufficiently rewarded for their effort.

Then again, our aim was to promote activities with a minimum duration of 10 minutes, but perhaps it is worthwhile exploring this trade-off in more depth. Fourth, the study outcomes were largely based on self-reported measures.

Although participants could automatically ie, objectively prove their engagement with a certain task using Google Fit, Strava, or a built-in GPS-based activity tracker, they were also allowed to manually ie, subjectively claim that they had engaged in a certain task.

This design choice could have introduced fraudulent activity registrations. This low response rate on the posttest survey may have introduced a selection bias in the fourth set of analyses of subjective measures.

Finally, this study evaluated the impact of our intervention on a particular target group ie, government staff within a specific context ie, the work environment. A follow-up study should better control how participants set goals for themselves ie, by means of the intake survey.

For example, participants could be required to complete the intake survey before they are allowed to engage in the gamified program.

It seems natural to set different goals for participants who are in the precontemplation or contemplation phase ie, the phase in which participants are not [yet] planning for a more active lifestyle and for participants who are already actively improving their lifestyle ie, participants in the action phase.

Perhaps these 2 groups need to be assigned a different gamified program altogether. In addition, future work should focus on evaluating different strategies for personalizing goal parameters. A particular opportunity is exploring in more detail the potential impact of personalizing the frequency parameters, rather than the intensity parameters.

Focusing on promoting activity frequency particularly satisfies physical activity guidelines, which suggest that frequently interrupting periods of sitting with short bouts of physical activity is essential to remain healthy because sitting for prolonged periods can in itself compromise health [ 9 ].

Does personalization based on frequency parameters also have a larger impact on engagement levels in general?

And if so, why? Finally, future work could explore the impact of allowing participants to add personalized goals for other types of activities too eg, healthy dietary intake. Although we have not yet been able to generalize our findings to support the claim that personalizing activity frequency fosters engagement levels better than personalizing activity intensity, we still suggest that practitioners focus on setting personalized goals based on activity frequency, in particular, because focusing on activity frequency implies performing physical activity more often instead of for longer duration or performing more intense physical activity.

This focus adheres especially well to physical activity guidelines, which suggest that frequently interrupting periods of sitting with short bouts of physical activity is essential to remaining healthy because sitting for prolonged periods can in itself compromise health [ 9 ].

Meanwhile, we encourage scholars to replicate our study setup to gain a deeper understanding of the potential impact of different strategies for tailoring health goals. To this end, we recommend that scholars also apply Goal-Setting Theory [ 33 ] and Flow Theory [ 31 ] when designing their studies.

Similarly, we encourage scholars to evaluate the relationship between strategies of adaptive goal setting and contextual factors eg, whether outcomes can be replicated with other target audiences. In this study, we evaluated a gamified program that was designed to promote engagement in physical activity with sedentary government staff.

Our aim is to investigate the impact of adaptive goal-setting strategies on end-user engagement levels with the program. In particular, through the program, study participants were stimulated to engage in a set of health-related activities eg, to go for a walk, run, or sports session.

Our results indicated that end-user engagement with the program inevitably decreased over time. However, compared with a control group, it was found that tailoring the frequency of suggested activities ie, as opposed to tailoring the intensity of activities does promote engagement in that activity ie, engaging in sports sessions.

This effect was reported to be especially strong in participants who expressed an intention to improve their health-related capabilities at the beginning of the program.

In fact, engagement was generally higher in participants who expressed an intention to improve their capabilities on at least one health dimension. Hence, when designing a gamified health promotion program, end-user engagement levels may be fostered by having end users explicitly state their current and desired capabilities and by setting health goals that tailor the suggested frequency of engaging in activities that constitute these goals.

This work is part of the research program Gamification for Overweight Prevention and Active Lifestyle , which is partly financed by the Netherlands Organization for Health Research and Development.

PVG, RN and AK were involved in the development of the GameBus mHealth platform. Editorial notice : This randomized study was only retrospectively registered. The authors explained that this is due to them not being aware that it was classified as a randomized controlled trial. The editor granted an exception of ICMJE rules for prospective registration of randomized trials because the risk of bias appears low.

However, readers are advised to carefully assess the validity of any potential explicit or implicit claims related to primary outcomes or effectiveness, as retrospective registration does not prevent authors from changing their outcome measures retrospectively.

Overview of the number of times a particular suggested task was registered per organization. Whether for professional training, competitions, or recreational use, our custom targets are designed to enhance your shooting experience. Torres Targets is dedicated to offering exceptional custom target solutions.

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The activity trackers that were supported included Google Fit, Strava, and a GPS-based activity tracker that was built into the native version of the GameBus app available for both Android and iOS devices. To prevent users from repeating a single task over and over, we set a maximum number of points that could be obtained per task per week, as well as a maximum number of times a task was rewarded per week with points Table 1.

Note that the sports session is rewarded X times per week, where X depends on the actual campaign wave. Note that therefore the number of points awarded per sports session needs to be calculated for a given wave by dividing 40 the maximum number of points awarded per week by X.

Figure 2 displays the exemplar sets of tasks that users in the control or treatment groups could be assigned through GameBus. The study was designed as a 2-arm randomized intervention trial.

The experimental setup was centered around setting the complexity parameters ie, the X values of the 3 dynamic tasks. In particular, the parameters to determine were as follows: 1 the minimum distance of a longer walk, 2 the minimum distance of a longer bike ride, and 3 the maximum number of rewarded sports sessions and consequently the number of rewarded points per sports session.

For the control group, the parameter values of the dynamic tasks were based on national guidelines. The Belgian guidelines for physical activity are based on the Australian activity guidelines [ 34 ]. These guidelines recommend a minimum of minutes ie, in line with the study by Long et al [ 8 ] of moderate-intensity activity per week, with each activity episode lasting at least 10 minutes.

In addition, these guidelines suggest regularly interrupting periods of sitting with short bouts of physical activity ie, in line with the study by Owen et al [ 9 ]. On the basis of these guidelines, it was agreed with the organizing committee to suggest tasks with a duration of 10 to 30 minutes, giving participants ample opportunity to engage in at least minutes of moderate-intensity activity per week.

In the intake survey, participants were asked to provide an estimation of 1 the number of steps they walked on a daily basis, 2 the number of kilometers they biked on a weekly basis, and 3 the number of sports sessions in which they participated on a weekly basis.

Furthermore, participants were asked whether they wanted to improve on any of these estimated numbers. If they wanted to improve their capabilities, they were asked to express depending on the dimension they aimed to improve the following: 1 the number of steps they wanted to walk on a daily basis, 2 the number of kilometers they wanted to bike on a weekly basis, and 3 the number of sports sessions they wanted to attend on a weekly basis.

The number of steps one could, and wanted to, walk per day was multiplied by 0. Hence, to personalize each parameter, we have used the formula that is displayed below, where i is a reference to the individual participant for whom the parameter value is calculated, t is the type of parameter eg, walking distance, biking distance, or number of sports sessions , W is the total number of waves of the campaign ie, 4 , and w is the wave number of a given wave:.

In addition, the value for capability was set by participants themselves ie, by means of the intake survey.

If a participant had not completed the intake survey, their capability was estimated to be their last performance for a particular activity type t. In case a participant had no recorded history on the activity type t , their capability was defined as the average performance of all other users on the activity type t.

Note that in case there was no history of any participant on the activity type t yet, that capability was defined as a fixed value eg, 1 km for t with regard to walking, 2 km for t with regard to biking, and 2 sessions for t with regard to engaging in sports.

However, if a participant had not completed the intake survey, their goal was derived by multiplying their capability with a fixed value of 1.

Finally, the different parameter values were capped by a predetermined minimum and maximum. The minimum and maximum for walking distance were 1 km and 10 km, respectively; the minimum and maximum for the distance of a bike ride were 2 km and For instance, if the aforementioned formula would suggest to reward 0 sports sessions, this final check would override that value, and instead allow a participant to claim points for their sports sessions twice per week.

Users were allowed to join and drop out at any moment throughout the campaign. Whenever a user joined the campaign, they would always be given a default set of tasks until the end of the then-active wave ie, the default set of tasks was displayed as the control treatment; Figure 2.

After the wave had ended and at the start of a new wave , a user would be allocated to either the control group or the treatment group and receive a new set of tasks accordingly. The control and treatment samples were stratified such that each sample included the same number of people who had set a goal to improve their current capabilities eg, new participants were immediately requested to express their current capabilities and goals through the intake survey.

By stratifying our samples, the control and treatment groups were likely to be comparable. Throughout the campaign we sent some email notifications to participants. In particular, upon registration, participants received a welcome email with a request to complete the intake survey.

In addition, a campaign email was sent at the start of each wave. These campaign emails included participation instructions as well as directions for obtaining technical support. Finally, at the end of the campaign, a closing email with a request to fill out the posttest survey was sent.

After 4 days, we sent out a reminder to fill out the posttest survey. Finally, some of the 7 organizations expressed some additional requests. In particular, 1 organization ie, the municipality of Wuustwezel expressed the need for some additional tasks eg, ones that were more specific than the catch-all task Share your healthiest moment of the week.

Furthermore, the municipality of Essen requested waves with a duration of 4 weeks each instead of a duration of 2 weeks each. For them, the social leaderboards were reset every 4 weeks ie, twice over the entire campaign. However, note that—and this applied to the participants from Essen too—the personal set of healthy tasks was still updated every 2 weeks.

In mHealth, engagement is most commonly captured by means of measures of app use [ 36 ]. Using the GameBus platform, the engagement of participants was repeatedly measured as follows: 1 the number of days a participant visited the app ie, the distinct days the participant opened the mobile app and 2 the number of activities a participant registered.

These variables complement each other because the former may be limited to passive engagement, whereas the latter requires active participation ie, performing the suggested tasks. Both measurements were recorded per participant per wave.

Hence, a record for a particular participant who joined the campaign only in the fourth wave would have a relative wave number of zero for that record. This relative wave number was used to model time in this study to ensure that time effects eg, novelty effects were equal among participants.

In addition, the type of goal that the participants set in the intake survey was recorded. Finally, participants filled out a posttest survey presented in Multimedia Appendix 1 in which we especially assessed the perceived impact of the campaign on their walking, biking, and sports performance, as well as their perception of their capability to perform the prescribed tasks ie, self-efficacy.

The first set of statistical analyses focused on the evaluation of dropouts. A participant was labeled as a provisional dropout if they had not visited the app during a specific wave and was therefore assumed to have lost interest ie, dropped out during the previous wave.

Several multiple regression models were fit to determine whether the number of dropouts changed over time and were different per treatment. In particular, we tested for significant second-order interaction effects of time ie, the wave number and treatment.

The second set of analyses focused on the evaluation of engagement levels of the participants. To evaluate treatment differences, further analyses were performed on participants who actually had an opportunity to receive exposure to the treatment. Hence, from the entire data set, a subset was derived preserving the combination of a particular participant and wave only if they had ever checked the app during that wave and if they had participated for a duration of at least two waves because during the wave in which a participant signed up, they were not actually receiving a treatment yet.

We tested whether significant second-order interaction effects existed among these variables. In all models we allowed random intercepts for both individuals and the governmental organizations they were part of. The final model was selected based on the Akaike information criterion [ 37 ].

The Akaike information criterion estimates the relative quality of statistical models for a given set of data. The measure rewards goodness of fit and includes a penalty for increasing the number of predictors ie, to prevent overfitting because increasing the number of predictors generally improves the goodness of the fit.

In addition, a third set of analyses zoomed in on the experimentally controlled tasks ie, the longer walk, the longer bike ride, and the sports sessions to evaluate treatment differences at the level of individual activity types.

Specifically, for each activity type, a hierarchical linear model was built to predict the number of times a participant registered a task for that particular activity type.

In addition, we tested whether significant second-order interaction effects existed among these variables. The final model was again selected based on the Akaike information criterion [ 37 ]. Finally, the fourth set of analyses focused on the evaluation of subjective measures that were derived from a posttest survey.

This final set of analyses was performed on a subset of the data set that only included participants who filled out the posttest survey and were using the mHealth app in more than one wave.

A set of 3 separate linear models was used to estimate the perceived impact of the campaign on walking performance, biking performance, and sports performance.

To obtain the final models, a backward elimination selection procedure was used [ 38 ]. Backward elimination starts with all predictors included in the model, with variables subsequently being eliminated one at a time. In total, unique participants joined the study, and they were randomly assigned to a treatment: 82 Of the participants, 83 These data are summarized in Figure 3 , which displays a cohort diagram that details the number of participants engaged in different study phases.

Table 3 displays sample demographics based on the results of the posttest survey, which was filled out by Gender, age group, and personality scores are displayed for the entire sample as well as per treatment. The demographic characteristics in the control group and treatment group are distributed similarly.

Hence, it is assumed that these groups were comparable at baseline. Figure 4 displays the decrease in the number of participants who visited the mobile app during a given wave.

The number of participants who joined the campaign for the first time during a given wave are displayed in green.

The number of participants who dropped out during a specific wave are displayed in red. The number of participants who checked the mobile app during a specific wave, although they dropped out during an earlier wave ie, reclaimed users are displayed in yellow.

No significant differences in dropout rates between treatments could be detected. In addition, no significant interaction effect between time ie, the wave number and treatment was detected. Hence, it is assumed that dropouts were spread equally over treatments.

a Posttest personality scores were not available for the participant who was not assigned a treatment. The complexity parameters of the dynamic tasks that the control participants were assigned are presented in Table 2.

However, the complexity parameters for the treatment group were different for each individual in that group and were only determined at the start of a new wave. The mean SD , minimum, and maximum values of the 3 complexity parameters are displayed per wave in Table 4.

Of the participants, 10 5. In addition, of these participants, 55 Figure 5 displays the number of days participants visited the app on average per wave per treatment. Figure 6 displays the number of days participants visited the app on average per type of goal they set. From the second set of statistical analyses, it was found that the number of days participants visited the app dropped over time ie, —1.

No significant difference between treatments was detected, although it did matter whether participants completed the intake survey. Finally, no significant interaction effects were detected; all treatments were affected equally by the impact of time. Figure 7 displays the average number of activities participants registered per treatment.

Figure 8 displays the average number of activities participants registered per type of goal they set. Multimedia Appendix 3 displays an overview of the number of times a particular suggested task was registered per organization.

Moreover, from the second set of statistical analyses, it was found that the number of activities participants registered decreased over time ie, —0.

Finally, no significant interaction effects were detected; again, all treatments were affected equally by the impact of time ie, relative wave number. The third set of analyses zoomed in on the experimentally controlled tasks ie, the longer walk, the longer bike ride, and the sport sessions to evaluate treatment differences at the level of individual activity types Figure 9.

For each activity type, a hierarchical linear model was built to predict the number or times a participant registered a task for that particular activity type. No significant predictors were found for estimating the number of longer bike rides a participant registered. When zooming in on the perceived impact on performance of individual activity types ie, walks, bike rides, and sports sessions , no significant predictors were found for estimating the perceived impact on walk performance Figure Nevertheless, for both the control and treatment groups, the perception of capability diminished over time ie, —0.

The aim of this study is to evaluate the impact of personalized goal setting in a gamified health promotion program on participant engagement levels. Our results show that engagement with the program inevitably dropped over time, both in the personalized condition and in the control condition.

Although this pattern is common in digital health promotion programs [ 39 ], several factors may be relevant for explaining this tendency in this particular context. Hence, a great proportion of our sample seemed to be still in the precontemplation or contemplation phase, phases in which they were actually not yet planning for a more active lifestyle.

Still, the participants who had set themselves a goal ie, by completing the intake survey were more engaged than those who had not. In particular, these participants visited the app more frequently and also registered more of the healthy tasks they were prescribed. Hence—as proposed by Goal-Setting Theory—setting a goal is in itself a motivating task [ 33 ].

Nevertheless, improvement goals—which are arguably more difficult to achieve than maintenance goals—did not seem to be significantly more motivating in general than maintenance goals.

This finding seems to contradict both Flow Theory and Goal-Setting Theory, which propose that difficult—but still attainable—goals are more engaging than easier goals [ 31 , 33 ]. Then again, it should be noted that the descriptive means were mostly in the expected direction ie, improvement goals were more engaging than maintenance goals and the impact of improvement goals was actually significantly larger for promoting sports sessions: if a participant explicitly expressed a need to improve their current performance, they perceived their sports performance to be improved significantly.

Finally, the impact of the personalized treatment on engagement levels seemed to be generally limited. However, descriptive means were mostly in the expected direction ie, personalized goals were more engaging than generically suggested goals.

The seemingly limited impact of personalized goal setting may be explained by the actual strategy for personalizing the set of tasks. Moreover, we found that personalizing the suggested minimum number of sports sessions did stimulate participants to perform significantly more sports sessions, as well as significantly improved their perception of their sports performance.

Upon close examination of this complexity parameter, we found that it can be characterized as a frequency parameter, whereas the parameters for personalizing walks and bike rides are typically characterized as intensity parameters.

A frequency parameter defines how many times a particular activity should be performed in a given time frame, whereas an intensity parameter defines how a particular activity should be executed eg, for how long and how far. We are unaware of context-specific factors that could have influenced this effect.

However, we cannot claim generalizability yet either. Hence, our personalization strategy may have suggested tasks that were perceived as too difficult or too easy by our target users, thereby potentially compromising self-efficacy and engagement with the program [ 30 , 31 ].

The execution of this study was subject to several limitations. First, participants could take part without completing the intake survey. As a result, it was unknown in the case of some participants whether they explicitly choose not to set goals for themselves or whether they actually did aim to maintain or improve their current capability levels.

Second, participants may have felt that the number of points they were awarded for their activities, which affected their position on the social leaderboard, was unfair.

Although, objectively speaking, this tailoring strategy makes the whole competition actually more fair, we received reports from several participants perceiving it as unfair that they had to seemingly expend more effort than their colleagues to be awarded the same number of points.

Third, an additional design choice that participants may have perceived as unfair was the decision to reward walks and bike rides on a per-trip basis, instead of, for example, on a daily aggregate basis. As a result, participants who went out for multiple shorter walks may not have been sufficiently rewarded for their effort.

Then again, our aim was to promote activities with a minimum duration of 10 minutes, but perhaps it is worthwhile exploring this trade-off in more depth. Fourth, the study outcomes were largely based on self-reported measures.

Although participants could automatically ie, objectively prove their engagement with a certain task using Google Fit, Strava, or a built-in GPS-based activity tracker, they were also allowed to manually ie, subjectively claim that they had engaged in a certain task.

This design choice could have introduced fraudulent activity registrations. This low response rate on the posttest survey may have introduced a selection bias in the fourth set of analyses of subjective measures. Finally, this study evaluated the impact of our intervention on a particular target group ie, government staff within a specific context ie, the work environment.

A follow-up study should better control how participants set goals for themselves ie, by means of the intake survey. For example, participants could be required to complete the intake survey before they are allowed to engage in the gamified program. It seems natural to set different goals for participants who are in the precontemplation or contemplation phase ie, the phase in which participants are not [yet] planning for a more active lifestyle and for participants who are already actively improving their lifestyle ie, participants in the action phase.

Perhaps these 2 groups need to be assigned a different gamified program altogether. In addition, future work should focus on evaluating different strategies for personalizing goal parameters.

A particular opportunity is exploring in more detail the potential impact of personalizing the frequency parameters, rather than the intensity parameters.

Focusing on promoting activity frequency particularly satisfies physical activity guidelines, which suggest that frequently interrupting periods of sitting with short bouts of physical activity is essential to remain healthy because sitting for prolonged periods can in itself compromise health [ 9 ].

Does personalization based on frequency parameters also have a larger impact on engagement levels in general? And if so, why? Finally, future work could explore the impact of allowing participants to add personalized goals for other types of activities too eg, healthy dietary intake.

Although we have not yet been able to generalize our findings to support the claim that personalizing activity frequency fosters engagement levels better than personalizing activity intensity, we still suggest that practitioners focus on setting personalized goals based on activity frequency, in particular, because focusing on activity frequency implies performing physical activity more often instead of for longer duration or performing more intense physical activity.

This focus adheres especially well to physical activity guidelines, which suggest that frequently interrupting periods of sitting with short bouts of physical activity is essential to remaining healthy because sitting for prolonged periods can in itself compromise health [ 9 ].

Meanwhile, we encourage scholars to replicate our study setup to gain a deeper understanding of the potential impact of different strategies for tailoring health goals. To this end, we recommend that scholars also apply Goal-Setting Theory [ 33 ] and Flow Theory [ 31 ] when designing their studies.

Similarly, we encourage scholars to evaluate the relationship between strategies of adaptive goal setting and contextual factors eg, whether outcomes can be replicated with other target audiences.

In this study, we evaluated a gamified program that was designed to promote engagement in physical activity with sedentary government staff. Our aim is to investigate the impact of adaptive goal-setting strategies on end-user engagement levels with the program.

In particular, through the program, study participants were stimulated to engage in a set of health-related activities eg, to go for a walk, run, or sports session. Our results indicated that end-user engagement with the program inevitably decreased over time.

However, compared with a control group, it was found that tailoring the frequency of suggested activities ie, as opposed to tailoring the intensity of activities does promote engagement in that activity ie, engaging in sports sessions.

This effect was reported to be especially strong in participants who expressed an intention to improve their health-related capabilities at the beginning of the program. In fact, engagement was generally higher in participants who expressed an intention to improve their capabilities on at least one health dimension.

Hence, when designing a gamified health promotion program, end-user engagement levels may be fostered by having end users explicitly state their current and desired capabilities and by setting health goals that tailor the suggested frequency of engaging in activities that constitute these goals.

This work is part of the research program Gamification for Overweight Prevention and Active Lifestyle , which is partly financed by the Netherlands Organization for Health Research and Development. PVG, RN and AK were involved in the development of the GameBus mHealth platform.

Editorial notice : This randomized study was only retrospectively registered. The authors explained that this is due to them not being aware that it was classified as a randomized controlled trial. The editor granted an exception of ICMJE rules for prospective registration of randomized trials because the risk of bias appears low.

However, readers are advised to carefully assess the validity of any potential explicit or implicit claims related to primary outcomes or effectiveness, as retrospective registration does not prevent authors from changing their outcome measures retrospectively. Overview of the number of times a particular suggested task was registered per organization.

Edited by L Buis; submitted org , Skip to Main Content Skip to Footer. Evaluating the Impact of Adaptive Personalized Goal Setting on Engagement Levels of Government Staff With a Gamified mHealth Tool: Results From a 2-Month Randomized Controlled Trial Evaluating the Impact of Adaptive Personalized Goal Setting on Engagement Levels of Government Staff With a Gamified mHealth Tool: Results From a 2-Month Randomized Controlled Trial Authors of this article: Raoul Nuijten 1 ; Pieter Van Gorp 1 ; Alireza Khanshan 2 ; Pascale Le Blanc 1 ; Pauline van den Berg 3 ; Astrid Kemperman 3 ; Monique Simons 4.

Article Authors Cited by 8 Tweetations 6 Metrics. Original Paper. Raoul Nuijten 1 , MSc ; Pieter Van Gorp 1 , PhD ; Alireza Khanshan 2 , MSc ; Pascale Le Blanc 1 , Prof Dr ; Pauline van den Berg 3 , PhD ; Astrid Kemperman 3 , PhD ; Monique Simons 4 , PhD 1 Department of Industrial Engineering, Eindhoven University of Technology, Eindhoven, Netherlands 2 Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands 3 Department of the Built Environment, Eindhoven University of Technology, Eindhoven, Netherlands 4 Department of Social Sciences, Wageningen University and Research, Wageningen, Netherlands.

Corresponding Author: Raoul Nuijten, MSc Department of Industrial Engineering Eindhoven University of Technology Groene Loper 3 Eindhoven, AE Netherlands Phone: 31 Email: r.

nuijten tue. mHealth ; health promotion ; physical activity ; personalization ; adaptive goal setting ; gamification ; office workers.

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Both TRAF2 and TRAF3 serve as negative regulators of non-canonical NF-κB signaling pathways and target NIK for constant ubiquitination and degradation [ ]. Loss of this quaternary inhibitory complex can lead to increased NIK protein accumulation and constitutive activation of the non-canonical NF-κB signaling pathway [ ].

Zhang B. Modeling these genetic events in mice, Zhang B. demonstrated a key oncogenic role for the non-canonical NF-κB pathways in DLBCL pathogenesis [ 27 ]. Most DLBCL tumors developed in their mice model resembled ABC-DLBCL [ 27 ]. Thus, NIK appears to be an attractive new therapeutic target for ABC-DLBCL treatment, particularly for patients with ABC-DLBCL that are refractory to bortezomib or to the BCR pathway inhibitor ibrutinib.

Of interest, proteasome inhibitors such as bortezomib or carfilzomib, can also block the constant ubiquitination and degradation of NIK, thereby upregulating the non-canonical NF-κB signaling pathways.

In addition, targeting both arms of NF-κB signaling may also improve the therapeutic outcome in patients with newly diagnosed high-risk DLBCL displaying mutations in both canonical and non-canonical NF-κB pathways [ 12 , 18 , 19 , 27 , ].

Dual targeting of NF-κB pathways has been successfully demonstrated for multiple myeloma in vitro and in a xenograft model [ , ].

Combination therapy simultaneously targeting NIK and IKKβ as a main kinase of the canonical NF-κB pathway , either using the selective NIK inhibitors AM or AM and a small molecule IKKβ inhibitor MLX [ ] or the promising dual inhibitor of NIK and IKKβ, PBS [ ], showed significant anti-multiple myeloma activity, associated with apoptosis and inhibition of both NF-κB pathways in tumor cells in vitro [ , ] and in a mouse xenograft model of human multiple myeloma [ ].

Recent preclinical study demonstrated that the thalidomide-like drug lenalidomide is preferentially suppressing the proliferation and survival of ABC-DLBCL subtypes with minimal effects on non- ABC-DLBCL [ 90 , 91 ].

Thalidomide-like immunomodulatory agents such as lenalidomide or pomalidomide, are clinically important drugs for multiple myeloma and other B-cell malignancies [ — ].

IRF4 overexpression has been shown to enhance NF-κB activation and BCR signaling [ 90 , 91 ]. The lenalidomide-mediated reduction of IRF4 requires the E3 ubiquitin ligase complex coreceptor protein cereblon CRBN [ 90 , 91 ].

CRBN a substrate receptor of the Cul4-Rbx1-DDB1-CRBN E3 ubiquitin ligase complex, is a direct target of the immunomodulatory drugs thalidomide, lenalidomide and pomalidomide [ , ].

Thalidomide-like drugs directly bind to CRBN and promote the recruitment of its common substrates such as transcription factors Aiolos and Ikaros to the E3 ubiquitin ligase complex, thus leading to substrate ubiquitinylation and degradation [ ] and subsequent repression of IRF4 and SPIB [ 90 , 91 ].

Repression of IRF4 and SPIB by lenalidomide induces a lethal type I interferon response in ABC-DLBCL by augmenting interferon β IFNβ production [ 90 ]. IRF4 and its regulatory partner SPIB prevent IFNβ production by repressing IRF7 in ABC-DLBCLs [ 90 ].

However, due to their high toxicities, IFNα and -β have not yet been accepted as clinically useful agents in patients with aggressive B-cell lymphoma. A recent study performed by Hagner P. Surprisingly, CC emerges with features that differentiate it from family member of thalidomide analogs.

The anti-lymphoma activity of CC was independent of the cell of origin and observed in both ABC- and GCB-DLBCL cell lines, in contrast to the ABC-subtype selective activity of lenalidomide [ ]. CC has therefore been suggested to belong to a new class of drugs: pleiotropic pathway modifiers [ , ].

These novel properties make CC potentially clinically active in the GCB- subtype of DLBCL in which its predecessor, lenalidomide, has only limited or even no activity [ ].

At least three possibilities have been suggested to explain the differential activity of CC and lenalidomide [ , ]. First, CC may promote the recruitment, ubiquitination and degradation of specific and unique substrates to mediate some of its biological effects distinct from lenalidomide [ ].

Secondly, Aiolos and Ikarus, both known co-repressors of ISG transcription may act independently of IRF4 and interferon secretion in GCB- and type-3 DLBCL [ ]. Moreover, other potential immunomodulatory mechanisms for its activity in GCB -DLBCL likely do exist and may impact the nonimmune environment in vivo , in patients as well [].

CC has already demonstrated clinical activity as single-agent in DLBCL [ — , ]. Of interest, Shi C. recently demonstrated that proteasome inhibitors such as bortezomib and carfilzomib can block Ikaros degradation by lenalidomide in multiple myeloma, when concomitantly added to the lenalidomide treatment [ ].

These data suggest that administration of thalidomide-like agents concurrent with or shortly after proteasome inhibitor administration might be ineffective or at least strongly reduce the efficacy of thalidomide-like agents in DLBCL.

Constitutive STAT3 activation has been recently correlated with poor overall survival in patients with ABC-DLBCL subtype treated with R-CHOP [ — ]. Inhibition of constitutive STAT3 activity sensitizes resistant B-cell NHL cells to chemotherapeutic cytotoxic drugs, including CHOP, cisplatin, fludarabine, adriamycin, and vinblastine [ , ].

STAT3 is persistently phosphorylated pSTAT3-Y in most ABC-DLBCL in an autocrine and paracrine manner from the tumor microenvironment [ — ]. Inactivating STAT3 in ABC-DLBCL cells inhibits cell proliferation and triggers apoptosis in vitro [ — ]. Inhibition of IL10R signaling with an anti-IL10R-blocking antibody induced dose-dependent cell death in all tested ABC-DLBCL cell lines and primary DLBCLs [ , ].

In preclinical in vitro studies, inhibitors of PI3K, such as LY selectively targeted PTEN- deficient GCB-DLBCL cells [ , ]. In addition, inhibition of target of rapamycin complex 1 mTORC1 or PI3K blocks proliferation and induces cell death in BCR-subtype of ABC-DLBCL [ , , ]. Autophagy can also serve as a protective mechanism to survive from chemotherapeutic-induced genotoxic stress [ ].

Secondly, the weak activity of rapamycin analogues can also be explained by their mTORC1-selective inhibitor activity. Both everolimus and temsirolimus target only the mTORC1 but not mTORC2. mTORC2 is generally considered to be unaffected by rapamycin and produces resistance at least partly via the induction of upstream receptor tyrosine kinase signaling and phosphorylation of AKT on S, a critical regulatory site that stimulates maximal activity of this important survival kinase [ — ].

A preclinical study performed by Mortensen D. provided preliminary evidence that CC can strongly inhibit the growth of GCB-, ABC- and type-3 DLBCL cell lines associated with high mTORC1 and mTORC2 activity in vitro [ ]. Of interest, these data suggest that ABC-DLBCL with high IRF4 tend to be less sensitive towards CC [ ].

A previous preclinical study showed that OSI markedly diminished proliferation and induced apoptosis in a variety of lymphoid cell lines and induced tumor regressions in B-cell lymphoma xenograft models [ ]. Moreover, using in vitro screening, Ezell S. Taken together, despite their modest activity as single-agents both everolimus and temsirolimus might be targeted as a clinical strategy for re-sensitization to R- CHOP based chemotherapy [ , , ].

Programmed death 1 PD-1 is an inhibitory receptor expressed on the surface of T cells that functions in conjunction with receptor ligands, PD-L1 and PD-L2 to physiologically limit T-cell activation and proliferation [ ].

Its ligands, PD-L1 and PD-L2, are expressed on antigen-presenting cells [ ]. Binding of PD-L1 or PD-L2 to its receptor inhibits T-cell activation and counterbalances T-cell stimulatory signals, thus primarily limits the T-cell response in peripheral tissues [ ].

The sustained expression of PD-1 and the receptor ligands result in T-cell exhaustion and immune escape [ , ]. This mechanism has been adopted by tumors to prevent antitumor activity in tumor-infiltrating lymphocytes that are present in the tumor microenvironment [ ]. PD-L1 expression is either driven by direct oncogenic signaling or upregulated on the tumor cell surface via induction by IFNγ or other inflammatory cytokines, as occurs in the course of the normal immune response [ ].

There are also efforts to combine anti-PD1 agents with other drugs [ ] Additional file 1 : Table S2. The polycomb-group oncogene product enhancer of zeste homologue 2 EZH2 is a histone methyltransferase and plays a key role in transcriptional repression through chromatin remodeling [ ]. The YF mutation in EZH2 results in altered histone-lysine methyltransferase activity [ , ].

The EZH2YF mutation can cooperate with c-MYC to accelerate lymphomagenesis in animal models and is implicated in drug resistance [ ]. In addition, EZH2 cooperates with BCL2 and BCL6 to create the GCB phenotype and induce B-cell lymphomas through formation and repression of bivalent chromatin domains [ 77 , 79 ].

Several recent preclinical studies demonstrated that potent and selective S-adenosyl-methionine-competitive small molecule inhibitors of EZH2 such as E EPZ , GSK or CPI eliminate tumor growth in GCB-DLBCL models with activating EZH2 mutations [ 77 , — ].

GSK is selectively targeting the activating oncogenic mutant form of EHZ2 [ ]. GSK affected the viability of mutant EZH2-containing GCB-DLBCL cells in vitro and in mouse xenograft models with EZH2 mutations in vivo but not of wild-type WT EZH2-containing GCB-DLBCL cells or in mouse WT-EZH2 xenograft models [ ].

On the other hand inhibitors such as CPI are broadly efficacious also in GCB-DLBCL models with wild-type EZH2 [ ]. Moreover, a recent preclinical study provided evidence for synergistic anti-tumor activity of the EZH2 inhibitor E EPZ and glucocorticoid receptor agonists in models of GCB-DLBCL in vitro [ ].

BCL2 is frequently overexpressed in both GCB- and ABC-DLBCL, albeit the mechanisms of BCL2 upregulation are different between GCB- and ABC-DLBCL [ 75 , , ].

Recent studies have suggested that overexpression of BCL2 remains a negative predictor of outcome after rituximab-based chemotherapy mainly in GCB-DLBCL [ , ] while MCL1 mainly contributes to chemotherapy resistance in ABC-DLBCL [ ]. On the other hand, in presence of c-MYC overexpression, BCL2 overexpression also contributes to a decreased survival of ABC-DLBCL after rituximab-based chemotherapy [ ].

Small-molecule BH3 mimetics include the clinically relevant agents ABT, ABT navitoclax , ABT and GX obatoclax [ — ]. ABT and its oral derivative A bind to BCL2, BCL-W and BCL-XL, but not to MCL1, BFL1 or A1 [ , , ], whereas GX, a pan-BCL2 inhibitor also binds to and inactivates MCL1 [ , ].

However, on-target BCL-XL inhibition by ABT and GX led to dose-dependent thrombocytopenia and posed a barrier to maximizing the activity of these agents [ ]. Moreover, a recent preclinical study suggests that patients may eventually develop ABTresistant disease by up-regulating the expression of MCL1 and BFL1 [ ].

ABT venetoclax , a second-generation orally available derivative of ABT that selectively targets BCL2 is currently under evaluation in clinical trials of B-cell NHL [ , , ]. ABT has greater than fold selectivity for BCL2 over BCL-XL [ , ]. Preclinical and early clinical studies demonstrated that ABT inhibits the growth of aggressive c-MYC-driven mouse B-cell lymphomas and human BCL2-dependent B-cell lymphoma tumors in vivo without causing thrombocytopenia [ , ].

Of interest, a recent study provided preliminary evidence that normal, untransformed mature B cells may also be sensitive to ABT, both in vitro and in vivo [ ]. A preclinical study showed that single-agent ABT had only modest antitumor activity against most DLBCL lines and resulted in compensatory upregulation of MCL1 expression [ ].

B-cell lymphoma protein BCL6 overexpression inhibits apoptosis induced by chemotherapeutic agents in DLBCL [ 32 ]. BCL6 is overexpressed in both GCB- and ABC-DLBCL, albeit through different mechanisms [ 32 ].

Recent studies demonstrated that HSP90 forms a complex with BCL6 and inhibition of HSP90 with the drug PU-H71, a purine scaffold HSP90 inhibitor destabilizes BCL6 and selectively kills BCL6-positive DLBCL cells in vitro and in vivo [ ].

Subsequent studies demonstrated that small molecule inhibitors, including the retro-inverted BCL6 peptide inhibitor RI-BPI, 79—6 that directly antagonize BLC6 function by disrupting the BCL6-corepressor complexes via binding in the lateral groove of the BCL6 BTB domain and thereby selectively inhibiting the interaction with nuclear receptor co-repressor BCOR, NCOR1 and NCOR2 proteins [ , , ].

The small-molecule inhibitor mediated disruption of the activity of BCL6, can be selectively toxic towards high-risk BCL6-dependent BCR-subtypes of GCB and ABC-DLBCL in vitro and potently suppressed GCB-DLBCL tumors in a DLBCL xenograft mouse model in vivo through reactivating pro-apoptotic genes repressed by BCL6 [ , , ].

RI-BPI mediated inhibition of BCL6 also induces the expression of EP, resulting in acetylation and activation of TP53 and concomitant acetylation and inactivation of HSP90 [ ]. SIRT1 expression is associated with poor prognosis in DLBCL [ ].

Several HDAC inhibitors HDACi are already approved for clinical use or in clinical trials [ ]. HDACi and sirtuin inhibitors can target both GCB- and ABC-DLBCL, albeit through different mechanisms [ , ].

Various HDACi and sirtuin inhibitors can repress GCB-DLBCL as a result of their inhibition of the BCL6 oncogene [ — ].

Inhibition of both HDACs and SIRT1 results in the accumulation of acetylated BCL6 [ ]. Acetylation of BCL6 inhibits the ability of BCL6 to recruit HDAC-containing SMRT co-repressor complexes [ ]. Thus, inhibition of HDACs and Sirtuins in BCL6-positive GCB-DLBCLs and to a minor extend in ABC-DLBCL results in the accumulation of inactive acetylated BCL6 and eventually in cell cycle arrest and apoptosis [ , ].

Moreover a recent preclinical and clinical study demonstrated that combined sirtuin and pan-HDAC inhibition synergistically kills DLBCLs with a preference for GCB-DLBCL [ ].

Combined treatment of DLBCL cells with HDACi such as vorinostat in combination with the Sirtuin inhibitor niacinamide produced synergistic cytotoxicity in vitro and in vivo by inhibiting BCL6 and activating TP53 [ ].

Acetylation of p53 strongly stimulates its pro-apoptotic activity [ ]. In addition, a study performed by Gupta M. demonstrated that HDACi such as LBH can effectively suppress STAT3 in ABC-DLBCL [ ]. Inhibition of HDACs leads to increased acetylation of STAT3, dephosphorylation of pSTAT3 Y , nuclear export of STAT3 to the cytoplasm and blocks survival of ABC-DLBCL cells [ ].

Inhibition of SIRT1 has also been shown to induce dephosphorylation of pSTAT3 Y , nuclear export of STAT3 to the cytoplasm and thereby inactivation of STAT3 [ ]. Despite an encouraging activity of the HDACi vorinostat in DLBCL was noted in a phase I trial study [ ], subsequent clinical phase II trial studies of oral vorinostat in relapsed DLBCL showed only limited activity against relapsed DLBCL [ ].

High c-MYC expression correlates with inferior clinical outcome in R-CHOP-treated DLBCL patients [ , , ]. C-MYC-driven gene over expression has been suggested to confer resistance to rituximab immunotherapy [ , , , ].

Recent preclinical studies demonstrated that the transcriptional coactivator and bromodomain and extra terminal BET protein BRD4 is required for transcriptional amplification of the c-MYC oncogene [ — ]. BRD4 occupies a small set of exceptionally large super-enhancers associated with genes including the c-MYC oncogene [ , ].

Another preclinical study recently demonstrated that the new BET bromodomain inhibitor OTX affects pathogenetic pathways in preclinical B-cell tumor models, including GCB-, ABC-and type-3 DLBCL-NOS [ ]. OTX showed strong anti-proliferative activity in a large panel of cell lines derived from mature B-cell lymphoid tumors [ ].

As already mentioned above, inhibition of BRD2 and BRD4 by CPI and JQ1 could also inhibit the oncogenic NF-κB activity and kill ABC-DLBCL cells [ ]. Surprisingly, this effect seems to be independent of c-MYC since ectopic provision of c-MYC did not rescue ABC-DLBCL cells from JQ1 toxicity [ ].

Remarkably, Eμ- Brd2 transgenic mice developed predominately ABC-like DLBCLs [ ]. Confirming previous preclinical studies [ , ], Boi M. also showed that BET bromodomain inhibition by OTX increases rituximab sensitivity in DLBCL cells [ ].

The first results from a phase I study with the orally available BET bromodomain inhibitor OTX have been reported with clinical responses in both leukemia and B-cell lymphoma patients in the absence of major toxicities [ , ].

Of interest, a recent study performed by Lu J. showed that JQ1 and OTX can lead to BRD4 protein accumulation over time in Burkitt lymphoma BL cells and incomplete c-MYC suppression in vitro [ ]. Whether these effects may also occur in DLBCL remain to be investigated.

Remarkably, three recent studies provided a novel strategy, which can circumvent these limitations by generating bifunctional phthalimide or VHL-ligand-conjugated versions of JQ1 and OTX [ — ]. and colleagues designed a hetero-bifunctional proteolysis targeting chimera PROTAC , ARV, by connecting the small-molecule BRD4 binding moiety OTX to the E3 ligase cereblon binding moiety of pomalidomide that recruits BRD4 and BRD2 to the E3 ubiquitin ligase cereblon, leading to fast, efficient, and prolonged degradation of BRD4 and BRD2, effective suppression of c-MYC signaling, inhibition of cell proliferation and apoptosis induction in BL in vitro [ ].

In the second study, Winter G. generated a bifunctional thalidomide-conjugated version of JQ1, termed dBET [ ]. Treatment of acute myeloid leukemia AML cells with dBET1 induced highly selective cereblon-dependent protein degradation of BET family members in vitro and in vivo , resulted in transcriptional downregulation of MYC, induction of antiproliferative responses in leukemia cells in vitro and delayed proliferation and leukemia progression in mice, without toxicity, thus underscoring the potential clinical utility of this approach [ ].

Moreover, treatment with dBET1 or ARV induced a superior apoptotic response compared with JQ1 or OTX, respectively in primary AML or BL cells [ ]. Pharmacologic destabilization of BRD4 in vivo also resulted in improved anti-tumor efficacy in a human leukemia xenograft compared to JQ1, highlighting the potential superiority of BET degradation over BET bromodomain inhibition [ ].

Moreover a third study using a BET-specific PROTAC, termed MZ1, that tether JQ1 to a ligand for the E3 ubiquitin ligase VHL, demonstrated preferential degradation of BRD4 over BRD2 and BRD3 at low concentrations and did not observe any degradation of JQ1-specific off-targets by MZ1, thus point to a more BRD4-selective pharmacological profile of BET specific PROTACs compared with pan-selective BET inhibitors JQ1 or OTX [ ].

Taken together, cereblon-based PROTACs may therefore provide a better and more efficient strategy in targeting BRD4 and BRD2 than traditional small-molecule inhibitors [ , ].

This chemical strategy for controlling target protein stability of BRD proteins may also have broad implications for therapeutically targeting previously intractable proteins in DLBCL [ ].

Using a mouse model of c-MYC-driven B-cell lymphomagenesis, a recent study uncovered the mechanism by which c-MYC coordinates the nexus between nucleotide metabolism and protein biosynthesis. The authors found that the single rate-limiting enzyme, phosphoribosyl-pyrophosphate synthetase 2 PRPS2 , promotes increased nucleotide biosynthesis in c-MYC-transformed cells [ ].

Remarkably the levels of PRPS2 are specifically increased in c-MYC-driven lymphomagenesis and this upregulation tightly correlates with eukaryotic translation initiation factor 4E eIF4E expression, which was also directly induced by c-MYC [ ].

Moreover, the authors also demonstrated that loss of PRPS2 in the Prps2 knockout mouse background is synthetically lethal in c-MYC-transformed human and mouse cell lines, but knockout of Prps2 did not affect wild-type cells or mice [ ].

Together, PRPS2 might be a promising and effective druggable target in c-MYC-driven subtype of DLBCL-NOS. By profiling genome-wide DNA methylation at single-base pair resolution in thirteen DLBCL diagnosis-relapse sample pairs and in a GCB-DLBCL cell line, Pan H.

identified a relapse-associated DNA methylation signature based on consistently differentially methylated regulatory elements between diagnosis and relapsed pairs [ ]. This signature was linked with specific genes and pathways, including the pro-apoptotic TGF-β-receptor-SMAD pathway suggested to play an important role in relapse of DLBCL [ ].

These data confirm a previous study indicating that methylation aberrations of TGF-β receptor activity pathway-associated genes might be involved in relapse and chemoresistance in DLBCL [ ]. Pan H. demonstrated that prolonged exposure to low-dose DNMT inhibitors DNMTi reprogrammed chemoresistant GCB- and ABC-DLBCL cells to become doxorubicin sensitive in vivo [ ].

Recurrent hypermethylation of the promoter region and reactivation of SMAD1 was a critical contributor and required for chemosensitization [ ]. Both studies are in line with a previous report demonstrating that escaping from TGF-β-SMAD5-mediated growth inhibition through microRNAmediated inhibition of SMAD5 is critical to relapse of DLBCL [ , ].

Antibody drug-conjugates ADCs , in which cytotoxic drugs are linked to antibodies targeting antigens on tumor cells, represent promising novel agents for the treatment of malignant B-cell lymphomas. ADCs use antibodies to deliver a potent cytotoxic compound selectively to specific antigen expressing malignant cells, thereby maximizing drug delivery while limiting bystander effects of traditional cytotoxic agents, thus improving the specificity and efficacy of chemotherapeutic agents.

Over the past several years, the use of ADCs as targeted chemotherapies has successfully entered clinical practice. Most of ADC developed for B-cell malignancies target CD19 or CD22 [ , ].

CD19 is the earliest differentiation antigen of the B-cell lineage and uniformly expressed on all types of B-lymphocytes and the vast majority of B-cell malignancies but not on other normal cells, thereby representing an attractive target in B-cell malignancies, including DLBCL [ , ].

CD19 is a transmembrane protein that forms a signaling complex together with CD21, CD81 and CD, which decreases the threshold for the activation of B cells mediated by the BCR [ ].

SAR is an anti-CD19 antibody conjugated to the cytotoxine Maytansine DM4, a potent inhibitor of tubulin polymerization and microtubule assembly [ , ]. SGN-CD19A is an affinity-optimized monoclonal anti-CD19 antibody linked to the microtubule disrupting agent monomethyl auristatin E MMAE [ ].

MEDI, an affinity-optimized and afucosylated monoclonal anti-CD19 antibody with enhanced antibody-dependent cellular cytotoxicity Additional file 1 : Table S7. Inotuzumab-ozogamicin is an affinity-optimized monoclonal anti-CD22 antibody linked to the DNA damaging toxin N-acetyl-γ-calicheamicin dimethyl hydrazide CalichDMH [ ].

Promising anti-tumor responses were observed in early stage clinical trials, where inotuzumab ozogamicin was administered either as single-agent or in combination with rituximab [ ].

The observed activities were in one study less than that seen with other standard salvage regimens for transplant eligible patients with DLBCL [ , ]. Ongoing phase II trials in CD22 expressing DLBCLs are examining inotuzumab-ozogamicin as part of chemotherapy combination regimens Additional file 1 : Table S7.

PE38 exerts its cytotoxic effect on cells by mono-ADP-ribosylating elongation factor 2, thereby inhibiting protein synthesis and leading to cell death [ ]. Brentuximab-vedotin is a human CDspecific antibody-drug conjugate, which consists of the chimeric monoclonal anti-CD30 antibody SGN conjugated to the synthetic microtubule disrupting agent monomethyl auristatin E MMAE [ ].

After binding to CD30 on the tumor cell surface, brentuximab-vedotin internalizes leading to release of MMAE via proteolytic cleavage and induction of cell-cycle arrest and apoptosis [ ].

CD30, part of the tumor necrosis factor TNF receptor family, is an ideal target for ADC-based therapy in DLBCL. Ongoing phase I and phase II trials in CD30 expressing DLBCLs are examining brentuximab-vedotin after autologous stem cell transplantation, as part of chemotherapy combination regimens Additional file 1 : Table S7.

In two recent clinical phase I studies, Pfeifer M. Pinatuzumab-vedotin is an anti-CD22 ADC and polatuzumab-vedotin an anti-CD79B ADC that are both conjugated to the microtubule-disrupting agent MMAE [ ].

Preclinical experiments unexpectedly showed that both pinatuzumab-vedotin and polatuzumab-vedotin are highly active and induced cell death in the vast majority of ABC- and GCB-DLBCL cell lines [ ], suggesting that both can be used effectively in DLBCL subtypes without the need for sophisticated molecular testing [ , ].

Another potential approach to target chemotherapy refractory DLBCLs are chimeric antigen receptor-modified autologous T cells CAR T cells targeted specifically to antigens expressed by B-cell malignancies. T cells that are genetically modified to express chimeric antigen receptors CARs recognizing the B cell-associated CD19 or CD20 molecules have emerged as a clinically feasible, potentially potent therapeutic modality and appears to be safe [ — ].

CARs are fusion proteins made up of antigen recognition moieties and T-cell activation domains [ , — ]. The CAR T cell based immunotherapy approach serves as a form of adoptive T-cell immunotherapy [ , ].

For a detailed description of CAR T-cell based therapies, the readers are referred to the recent excellent reviews [ , , ]. Studies of CAR T cells have mainly been performed in multiple myeloma, chronic lymphocytic leukemia and acute lymphocytic leukemias [ — ].

Initial studies on patients with relapsed DLBCL treated with anti-CD20 or anti-CD19 CAR T cells were not very successful, most likely due to a cellular anti-transgene immune response in some of the patients [ , ]. Moreover previous studies of anti-CD19 CAR T cells showed that multiple patients with indolent B-cell malignancies had specific depletion of normal B cells and lengthy remissions [ — ].

However, interim results of an ongoing study performed by Kochenderfer J. on heavily pre-treated patients showed the first patients, which obtained complete remissions CRs in chemotherapy-refractory DLBCL after receiving anti-CD19 CAR T cells [ , ].

Using a significantly changed anti-CD19 CAR T cell production process and modified clinical treatment protocol four of the seven evaluable patients with DLBCL obtained CRs, two obtained PRs, and one had stable disease SD after infusion of CAR T cells [ ] Additional file 1 : Table S8.

Infusion of anti-CD19 CAR T cells was associated with significant but only transient toxicity [ ]. Moreover, a preliminary report of an ongoing pilot study NCT evaluating the efficacy and safety of anti-CD20 CAR T cells in patients with chemotherapy refractory advanced DLBCL showed that five out of six evaluable patients experienced objective responses in this pilot trial [ ] Additional file 1 : Table S8.

Unfortunately, several reports already demonstrated that a small proportion of patients with B-cell malignancies had a relapse during therapies with novel experimental agents, such as ibrutinib, lenalidomide or bortezomib [ , — ].

However, the molecular mechanisms of resistance are poorly understood. A subsequent study demonstrated that the observed acquired resistance to ibrutinib in CLL was due at least in part to recurrent mutations in BTK and phospholipase Cγ2 PLCγ2 genes [ ].

A cysteine-to-serine mutation in BTK at the binding site of ibrutinib was identified in five patients and three distinct mutations in PLCγ2 were identified in two patients [ ]. Functional analysis showed that the CS mutation of BTK confers resistance to ibrutinib by preventing irreversible drug binding.

The RW, and LF mutations in PLCγ2 are all potentially gain-of-function mutations that allow autonomous B-cell-receptor activity that is independent of BTK [ ].

PLCγ2 is one of the key regulators of the B-cell receptor signaling pathway [ ]. Thus, the investigation of the molecular mechanisms underlying the observed secondary resistance to novel agents is of great importance. Molecular profiling of advanced cancer patients participating in targeted therapy trials will be important to identify mutational signatures that may predict for drug sensitivity and guide rational patient specific drug combinations.

It is increasingly apparent that differences in the local tumor microenvironment TME affect survival of patients with DLBCL after treatment with chemotherapeutic regimens [ , , ].

The local TME seems to be an essential player for the development and disease progression of DLBCL and to dictate lymphoma cell growth, response to therapy, as well as resistance of residual lymphoma cells to chemotherapeutic agents [ , ]. It is thought that specific niches within the DLBCL tumor microenvironment provide sanctuary for subpopulations of DLBCL cells through dynamic stromal cell-tumor cell interactions [ , ].

EMDR in resident DLBCL cells has been suggested to result in small foci of residual disease that subsequently develop complex genetic or epigenetic means of acquired resistance in response to the selective pressure of therapy [ , ].

However, the exact molecular mechanisms involved in EMDR are not yet fully understood in DLBCL and remain to be elucidated. The dynamic interplay between tumor cells and supportive fibroblast-like stromal cells, mediated through consists of extrinsic signals, which are generated by the lymphoma microenvironment and intrinsic factors encompassing signaling determinants of cell cycle and pro-survival pathways [ , , ].

For instance, it has been recently suggested that chronic and tonic BCR signaling is a central hub for the integration between the extrinsic B cell microenvironment and the intrinsic signaling pathways in B-cell lymphomas [ ].

For a detailed review about TME induced chronic BCR signaling in B-cell lymphomas the readers are referred to the recent excellent review [ ]. As already discussed, IL10 has been recently shown to enhance survival of primary DLBCL cell lines in vitro [ , ].

Both DTX3L and ARTD9 have been shown to be involved in drug resistance in HR- DLBCL associated with a R-CHOP chemotherapy-induced microenvironment gene expression signature [ , , ], see next sections.

For a detailed review about TME or environmental-mediated drug resistance the readers are referred to the recent excellent reviews [ , , ]. However, several recent studies from different labs identified several candidates, including STAT1, the E3 ubiquitin ligase DTX3L also known as B-lymphoma and BAL-associated protein BBAP and the B-aggressive lymphoma protein and mono-ADP-ribosyltransferase ARTD9 also known as BAL1 or PARP9 , which may be essentially required for the observed chemotherapy resistance in HR-DLBCL and also play a role in editing or inhibiting the host immune response against HR-DLBCL [ , , ].

These studies strongly suggest that STAT1, ARTD9 and DTX3L might serve as novel druggable targets in HR-subtype DLBCL [ , , ]. Moreover, a recent study using an Eμ- c-Myc driven B-cell lymphoma tumor mice model, demonstrated that the ARTD9-related ARTD8 also known as B-aggressive lymphoma protein BAL2 or PARP14 is overexpressed in mouse B-lymphoma cells and can facilitate c-MYC driven B-lymphoid oncogenesis [ ].

A schematic comparison of the domain architecture of the macrodomain containing ARTD PARP family members ARTD is presented in Fig. Both, ARTD8 and ARTD9 have been shown to interact with ARTD1 [ ]. Schematic comparison of the domain architecture of the macrodomain-containing ARTD family members ARTD7, ARTD8 and ARTD9 Domain architecture of the human diptheria toxin-like macro-domain containing mono-ADP-ribosyltransferases and ARTD family members ARTD7, ARTD8 and ARTD9.

The following domains are indicated: The diptheria toxin-like ART D domain is the catalytic core required for basal mono- ADP-ribosyltransferae ART activity.

The WWE domain is named after three conserved residues W-W-E and is predicted to mediate specific protein-protein interactions in ubiquitin- and ADP-ribose conjugation systems [ , , ]. The macrodomain can serve as ADP-ribose or O-acetyl-ADP-ribose binding module [ ]. Rimfire Rifle.

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Follow via Facebook Mail To Yelp about it! More training and appropriate resources should be provided to the nephrology workforce in conducting CGA, alongside a greater focus on fostering interdisciplinary collaborations with geriatricians to deliver personalized PD care to older patients following CGA [ 30 ].

Frailty screening and comprehensive geriatric assessment in older patients receiving peritoneal dialysis. The Frail and Elderly Patients on Dialysis FEPOD study demonstrated that frailty was associated with poorer QoL outcomes in older PD patients [ 31 ].

The recent ISPD guidelines highlighted the need for a person-centered approach with shared decision-making, defining life goals, and to maximize symptom burden control so to improve QoL outcomes of older PD patients living with frailty and multi-morbidities [ 14, 15 ]. Similarly, Kidney Disease Improving Global Outcomes KDIGO and SONG-PD statements also recommended similar care approaches in all PD patients including the older PD population [ 16, 17 ].

Specific assessment should be made on the need for assisted PD. Considerations of delivering a personalized, goal-directed peritoneal dialysis prescription strategy for older patients.

APD, automated peritoneal dialysis; CAPD, continuous ambulatory peritoneal dialysis; PD, peritoneal dialysis. APD may be more popular than CAPD as it frees up time in performing PD during daytime [ 34 ]. APD is also considered to be less demanding compared to CAPD in terms of patient workload.

However, APD may disrupt sleeping patterns, cause drain pain, and restrict movement during nighttime. APD may also be more costly than CAPD in many parts of the world although it has become more accessible over recent years [ 33, 35 ]. CAPD may better suit incremental PD prescription for patients with significant RKF.

Clinical outcomes such as patient survival, technique survival, and PD peritonitis rates have not been shown to differ between CAPD and APD [ 36, 37 ].

A goal-directed, incremental PD prescription strategy may be ideal in older patients with significant RKF to allow greater amount of free time off dialysis. Incremental PD prescriptions will need to be individualized, based on the degree of RKF, overall clearance, BP and volume control, and symptoms of patients.

They will need to be informed for the need to increase PD exchanges when RKF declines. Many older patients may need assisted PD from caregivers, family, or nursing home staff [ 40 ].

Compared to carrying out PD independently, assisted PD may be associated with better clinical outcomes in terms of PD technique survival mainly due to reduced risk of peritonitis , and PD-associated mortality [ 42 ].

The impact of reduced hand dexterity, visual, or hearing impairments causing PD-associated complications in older patients could be alleviated with assisted PD.

Assisted PD requires caregiver or nursing home staff to perform PD exchanges and exit site care for patients either in their home environment or in the nursing home with prompt reporting of any PD-related complications such as peritonitis or exit site infections to the dialysis unit [ 43 ].

PD nurse specialists will need to provide training and education for caregivers and nursing home staff to perform assisted PD, and to ensure these personnel observe the quality and safety standard in PD-performing techniques [ 45 ].

In terms of PD fluid prescription, the use of hypertonic glucose-containing PD solutions should be minimized in older people, due to the potential risk of worsening insulin resistance and glycemic control. The ISPD Adult Cardiovascular and Metabolic Guidelines recommended the use of icodextrin in high transporters and patients with uncontrolled volume overload [ 47 ].

In the recent ISPD guideline, a more relaxed recommendation on icodextrin use was given for better volume management for all patient groups including older individuals [ 14, 15 ].

However, icodextrin may not be readily accessible in many parts of the world including many low-income and low-middle income settings. In addition to using biocompatible PD solutions such as icodextrin as discussed in the previous section, renin-angiotensin-aldosterone system blockade through the use of angiotensin-converting enzyme inhibitors or angiotensin-receptor blocker ARB has been shown to be effective in preserving RKF for all PD patients including older people.

Furthermore, high dose furosemide was shown to be beneficial with improving urine volume and but not in the preservation of RKF [ 50 ]. Other strategies to preserve RKF may include avoiding nephrotoxic agents such as aminoglycosides and non-steroidal anti-inflammatory agents were possible [ 51 ].

PD patients are frequently complicated with hypertension. However, the relationship between BP and mortality is complex for PD patients, in that either extreme in BP may be associated with higher rates of mortality. Elevated pulse BP has been associated with an increased risk of all-cause and cardiovascular death in patients on PD over the medium to long term [ 52 ].

Recognition of this characteristic as a predictor of mortality suggests that one aim of anti-hypertensive therapy in PD patients should be to decrease elevated BP [ 52 ]. However, it has since been shown that hypotension may have greater implications on mortality compared with hypertension in older PD patients.

Another study by Udayaraj and colleagues [ 54 ] noted older PD patients with low BP at baseline were more likely to have underlying heart failure and cardiac co-morbidities and this may explain for greater risks of early mortality following PD initiation.

In general, BP management in older patients on PD should be individualized, and anti-hypertensive selection should be tailored to minimize risk of falls and symptoms due to postural hypotension [ 55, 56 ].

A prospective cohort study by Chan and colleagues [ 57 ] found that the number of medications but not daily pill load significantly predicted onset and progression of frailty, malnutrition, fall episodes, hospitalization, and mortality in PD patients.

Polypharmacy is particularly common among older PD patients, and regular medication reviews is indicated to reduce risks of medication-induced hypotension and other potential adverse effects.

Anemia is another common complication among older PD patients, given the consequences of untreated anemia on negative morbidity and mortality outcomes [ 58 ]. Increased emphasis being put on anemia management through intravenous iron and erythropoietin-stimulating agent ESA for the PD population is reflected from Peritoneal Dialysis Outcomes and Practice Patterns P-DOPPS data [ 61 ].

Current KDIGO guidelines recommend an individualized approach to anemia management for PD patients, with an aim to prioritize their QoL during dialysis [ 62 ].

ESA hyporesponsiveness may present as an issue for the older PD patient living with kidney failure and other co-morbidities however. Escalating ESA doses to achieve hemoglobin targets is likely to pose greater cardiovascular, thrombotic, and mortality risks in this group [ 63 ]. Nevertheless, in view of the adverse effects that hypoxia-induced factor prolyl-hydroxylase inhibitors may possess notably its potential cardiovascular risks, these agents are currently either under consideration for regulatory approval or remain not approved for use in numerous countries for dialysis patients.

It is currently under investigation what clinical assessment tools best measure the various levels of symptom burden in older PD patients. Symptom burden is usually complex, multifactorial, and may be difficult to assess and quantify, as older patients often experience multiple symptoms simultaneously, and the attributes of symptoms may change over time during PD treatment [ 69, 70 ].

Symptom burden is also not systematically assessed in most outpatient settings. Previous systematic reviews describing symptom burden in maintenance dialysis concluded that patients commonly experience multiple symptoms, with at least pain, fatigue, pruritus, and constipation presenting in half of the individuals receiving long-term dialysis [ 71, 72 ].

Respite care or having alternative caregiver may be essential in this setting to relieve caregiver stress. Family members of older patients may have full-time employment and taking up caregiver roles and responsibilities additionally may add further stress and become unmanageable for caregivers.

It is therefore essential to develop a feasible long-term care plan with shared decision-making for older PD patients and their caregivers together to prevent caregiver overstress and burnout, such as having regular respite care and community nursing support.

PEW syndrome is highly prevalent in older PD patients as a result of multifactorial causes and is shown to predict an increased mortality and hospitalization risk in PD patients.

It is essential to monitor nutritional status regularly in older PD patients. Key biochemical parameters to monitor for include hypokalemia and hypoalbuminemia. The P-DOPPS showed that hypokalemia may reflect underlying PEW in PD patients and predict peritonitis and mortality risk [ 88 ].

Serum albumin alone is not a reliable marker of nutrition status as overhydration may cause hypoalbuminemia and serum albumin is also a negative acute phase reactant.

The presence of hypoalbuminemia may reflect the presence of underlying inflammation, co-morbidities, or extracellular volume overload. Nutrition management in older PD patients require an individualized, multi-disciplinary approach involving nephrologists, dietitians, and PD nurses to evaluate the causes of PEW and develop a nutrition care plan with or without the need for oral, enteral, or parenteral nutrition support, depending on the causes and severity of PEW [ 86 ].

Other than monitoring and improving nutritional status, optimization of physical conditioning is also an important component of PD care for older patients. Being physically active is instrumental for life participation, one of the key prioritized outcomes in the SONG-PD initiative [ 17 ].

Sedentary behavior is very common in older people and physical inactivity is especially prevalent in those living with multi-morbidities [ 89 ]. Sarcopenia is an age-related process of involuntary skeletal muscle mass loss which adversely impacts physical functioning and mobility in older individuals [ 72 ].

Sarcopenia contributes to the frailty syndrome, and may be exacerbated from the presence of multi-morbidities in older PD patients [ 90 ]. Objective tools to screen for sarcopenia is currently undergoing further validation [ 91, 92 ].

Promoting physical activity may be challenging for many older PD patients. Some of the key perceived barriers to promote physical activity in older PD patients may relate to fatigue symptoms and a lack of energy, sarcopenia with poor muscle function, poor balance with potential risk of falls in the presence of pre-existing frailty, poor physical functioning, multi-morbidities, poor cardiorespiratory reserves, and tolerability [ 93 ].

While there is emerging data to suggest better clinical outcomes with exercise therapy in HD, there is a paucity of randomized intervention trial data on the effect of exercise among PD patients [ 94, 95 ]. Several studies have explored the efficacy of exercise programs among older patients with advanced CKD and frailty [ 96, 97 ].

Physiotherapists, occupational therapists, exercise professionals, and caregiver support groups in the community play important roles in assisting older PD patients to develop an individualized, realistic goal on graduated exercise programs so to gradually improve their physical functioning, balance, tolerance, and cardiorespiratory fitness while minimizing the risk of falls.

Recent advances in telehealth may assist PD care in older people. It enables the setting up of virtual multi-disciplinary clinic consults to facilitate remote monitoring of clinical progress, exit site, nutrition status, BP and volume status, and well-being of older PD patients and minimize their travel needs [ 99, ].

However, there are limitations in that virtual consultations may limit the extent clinicians can provide full assessments for their patients. Older patients may not have the literacy and capability to handle the mobile devices required for telehealth consultations and would require caregiver support [ ].

Moreover, there are concerns with patient security and confidentiality, as well as socio-economic factors impacting equity of telehealth access in older patients [ ]. Creating user-friendly telehealth systems for older people will be important, considering its emerging role in home dialysis care amidst the COVID pandemic.

Further study will be required to evaluate the cost effectiveness of telehealth application in caring for older PD patients. There is a growing burden of an aging population with kidney failure and receiving PD treatment worldwide. Many of these older patients also have multi-morbidities and frailty.

Patient-reported outcomes relating to PD care in the older population is generally lacking and remains infrequently reported across most randomized clinical trials and large sample observational studies at present [ ]. The international nephrology community will need to provide initiatives in continually striving to address this issue.

What matters most for many older patients is living comfortably within a familiar home environment while receiving PD. Many would like to continue pursuing life goals and enjoy life with physical activity, a good appetite, and minimal symptom burden as well as hospitalizations and treatment-associated complications, rather than just surviving.

Caregiver support is essential in the management of older PD patients, and the multi-disciplinary team will need to work hand-in-hand with caregivers to optimize outcomes for this patient population.

The author contributions are as follows: conceptualization, H. and R. writing — original draft preparation, H. writing — review and editing, H. All authors have read and agreed to the submitted version of this manuscript. Sign In or Create an Account. Search Dropdown Menu. header search search input Search input auto suggest.

filter your search All Content All Journals Kidney Diseases. Advanced Search. Skip Nav Destination Close navigation menu Article navigation. Volume 9, Issue 5. Frailty Screening and Comprehensive Geriatric Assessment in Older Patients Initiating on PD. Employing a Personalized, Goal-Directed PD Prescription Strategy in Older Patients.

Preserving RKF in Older PD Patients. Optimizing BP Management in Older PD Patients. Managing Anemia in Older PD Patients. Addressing Symptom Burden in Older PD Patients.

Managing Nutritional Intake and Promoting Physical Exercise in Older PD Patients. Telehealth Opportunities in Facilitating PD Care for Older Patients. Conflict of Interest Statement.