The assessment of improvement or maintenance of joint health in healthy subjects is a great challenge. The aim of the study was the evaluation of a joint stress test to assess joint discomfort in subjects with activity-related knee joint discomfort (ArJD).Forty-five subjects were recruited to perform the single-leg-step-down (SLSD) test (15 subjects per group). Subjects with ArJD of the knee (age 22–62 years) were compared to healthy subjects (age 24–59 years) with no knee joint discomfort during daily life sporting activity and to subjects with mild-to-moderate osteoarthritis of the knee joint . The subjects performed the SLSD test with two different protocols: (I) standardization for knee joint discomfort; (II) standardization for load on the knee joint. In addition, range of motion (ROM), reach test, acute pain at rest and after a single-leg squat and knee injury, and osteoarthritis outcome score (KOOS) were assessed.In OA and ArJD subjects, knee joint discomfort could be reproducibly induced in a short time interval of less than 10 min (200 steps). In healthy subjects, no pain was recorded. A clear differentiation between study groups was observed with the SLSD test  as well as KOOS questionnaire, ROM, and reach test. In addition, a moderate to good intra-class correlation was shown for the investigated outcomes.These results suggest the SLSD test is a reliable tool for the assessment of knee joint health function in ArJD and OA subjects to study the improvements in their activities. Further, this model can be used as a stress model in intervention studies to study the impact of stress on knee joint health function. The single-leg-step-down (SLSD) test is an appropriate test to induce joint discomfort in subjects with activity-related joint discomfort (ArJD) of the knee.The test shows a good test-retest reliability when applying the test for two standardization approaches: (I) standardization for pain level with focus on endpoints of mobility, (II) standardization for load on the knee joint with focus on endpoints of joint discomfort.The SLSD test is able to discriminate between healthy subjects , subjects with ArJD of the knee, and subjects with osteoarthritis (OA) of the knee joint.These results confirm the suitability of the SLSD test in intervention studies to study the impact of stress on knee joint health functionJoint discomfort and joint overload are a common problem not only in daily life of athletes, but also in daily life of recreationally active people. These symptoms can limit mobility and flexibility also in healthy people without diagnosed joint diseases, like osteoarthritis (OA) or rheumatoid arthritis. The functional joint discomfort occurs because of short-term degradation of cartilage and increased stress on the joints –3. StresMeasuring an improvement or maintenance of joint health in healthy subjects is a great challenge. A collective of people with activity-related joint discomfort (ArJD) but without any diagnosed knee joint diseases is a potential cohort for observation and evaluation of joint health. Some research groups already worked with people with ArJD and their research showed promising results , 7. HoweTherefore, stress models (joint or muscle related) have been used in the past to induce standardized load on physical structures to be able to observe changes on perceived stress as accurately as possible –10. For Aim of the study was the evaluation and specification of a joint stress test to evaluate joint discomfort in healthy subjects with ArJD of the knee compared to healthy subjects without ArJD and subjects with diagnosed mild-to-moderate OA as control groups. An easy test set up with a targeted load on the knee joint without high impact on cardiovascular fitness was a precondition for the test protocol. Focus of the clinical set up was set on symptomatic endpoints as well as on joint function. The supportive assessment of systemic biomarkers was not focus of the study. To qualify a test protocol for future studies, the test set up with suitable movements needed to (a) induce high mechanical load on the knee joint, (b) be suitable to differentiate between study groups, (c) show high degree of reproducibility, (d) be easy to carry out, (e) show no or low training effects, and (f) offer a time-efficient approach to induce joint discomfort without harm on joint structures.A step-down movement was finally selected for the study set up, as this movement has the highest biomechanical load on the knee joint. During walking upstairs, the knee joint is loaded with up to 3.0× of the body weight and during walking downstairs with up to 3.5× of the body weight –14.Standardization for joint discomfort, allowing to measure the impact on joint mobility.Standardization for load, allowing to measure the impact on joint discomfort and investigate parameters of recovery.In the present study, the joint stress test was performed with respect to two standardization approaches:The study was a prospective, 3-arm, non-interventional study at the study site of BioTeSys GmbH . The study was conducted in accordance with the guidelines for Good Clinical Practice (GCP) set forth by the International Conference on Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH), and the Declaration of Helsinki regarding the treatment of human subjects in a study.During day 1 (test 1), after 14 days (test 2) and after 28 days (test 3), subjects had to do a joint stress test. A detailed test procedure was defined for each subject individually. The range of motion (ROM) and most distance walk during a reach test were recorded to characterize the study group. Acute pain at rest and after a single-leg squat on an 11-point numeric rating scale (NRS-11) at different time points pre- and post-joint-stress-test (only test 2 and test 3) were determined. The assessment of the study parameters was done on the leg with usually more intense pain after physical activity (target knee). In healthy subjects, the target knee was the non-dominant leg, which was assessed with a shove-test. In OA patients, the target knee was the knee with higher pain. During the 2 weeks prior to test 2 and test 3, subjects recorded their sporting activities in a diary and documented if knee pain occurred during activity. Average knee pain during regular sporting activity was recorded using NRS-11.To reduce the effect of confounding factors, study subjects were asked to maintain their usual diet during the study time. Further, subjects were not allowed to use any pain medication  or supplements influencing joint pain. Forty-eight hours before to the test days, the subjects were not allowed to perform any sporting activities or other exhaustive physical activity, like heavy gardening or hiking.From January to July 2018, subjects were screened for their eligibility at BioTeSys GmbH . Informed consent from each subject participating in the study was obtained after written and oral explanation of the aims, methods, benefits, and potential hazards of the study.2 were eligible to participate. Healthy and ArJD subjects had to perform joint bearing sports for at least 2 times per week . Subjects with activity-related pain and healthy subjects were matched according to age and BMI. Subjects in the ArJD group additionally had to suffer from reversible knee joint discomfort during or immediately after physical activity over a period of at least 3 months. OA subjects had to have diagnosed with mild or moderate osteoarthritis in the knee without planned surgical treatment (known X-ray finding Kellgren score of 2 to 3). The diagnosis was confirmed by a study orthopaedist prior to the subject’s inclusion in the study.Healthy non-smoking men and women at the age of 18–65 years with a BMI between 19 and 29.9 kg/mExclusion criteria included joint replacement of the knee, planned surgical intervention during the study conduct, intra-articular therapy within the last 3 months prior to the study start and relevant history or presence of any medical disorder potentially interfering with this study, like active cancer, heavy cardiovascular diseases  as well as pregnancy and lactation. Additionally, subjects in the OA group were excluded if they had diagnosed rheumatoid arthritis or other inflammatory, infectious, or metabolic joint disorders except OA. Subjects from the healthy and ArJD group were neither supposed to have acute knee pain at rest or during daily life activities nor having acute or a history of musculoskeletal injury or previous severe joint injury at the knee joints , if not completely recovered without remaining functional impairment and if not more than 2 years ago. Subjects with hip, spine, and foot pathology were also excluded from study participation.Only those subjects with no identified exclusion criteria were invited to take part at screening (test 1).The single-leg-step-down (SLSD) test is an unilaterally performed test used in literature as a functional performance test in patients with patellofemoral pain syndrome . As the Subjects had to step forward and down from a platform with adjustable height. The down limb only brushed the floor with the heel and then returned back up to the platform to full knee extension. Figure During test 1 and test 2, subjects performed the test until they experienced a pain of 5 on NRS-11. Additional aim of this set up was to derive cut-off values for the number of steps to discriminate between OA and ArJD.During test 3, subjects performed the same number of steps as during test 2. The maximum discomfort was assessed to investigate if the movement induces a comparable discomfort level by same load. After test 2 and test 3, acute joint discomfort was additionally monitored to test the impact on recovery. Figure The step test was conducted three times with two different approaches:A joint’s ROM, used to assess the joint flexibility, is usually measured by the number of degrees from the starting position of a segment to its position at the end of its full range of movement. In the current study, range of motion measurement was assessed by using a digital goniometer . The positioning of the goniometer was determined by the two most prominent bone protrusions of the lower leg. They include the lateral condyle of the femur to determine the knee joint centre and the lateral malleolus for the ankle joint.For the measurement subjects lied in prone position with shanks hanging free over the edge of the examination couch in maximal extension. Active ROM was assessed by measurement of the maximum possible flexion in the knee joint. To measure the passive ROM, the flexion of the knee was performed by examiner pressing against the upper shank to the maximum possible flexion.ROM measurement was performed prior the stress test performance.The reach test was adapted based on the balance and reach test . The subThe KOOS (knee injury and osteoarthritis outcome score) questionnaire is used for knee injury that can result in post-traumatic osteoarthritis; i.e., ACL (anterior cruciate ligament) injury, meniscus injury, and chondral injury , 16. KOOFor the retrospective measurement of joint discomfort during daily life and physical activity, the KOOS was completed prior to the stress test.Acute joint discomfort was assessed at rest at timepoints prior, immediately after (0 h), 0.5 h, 2 h, 4 h, 6 h, 12 h, and 24 h after the stress test (only test 2 and test 3). Further, joint pain was additionally assessed while performing a single-leg squat . For this purpose, a 10-cm visual analog scale (VAS) with 0 no pain and 10 maximal imaginable pain was used.Between the test days, subjects had to document the intensity of pain felt during or immediately after their normal sports activities using NRS-11, as well as the duration of pain after sports in a diary. Average duration of knee pain was also assessed via diary and mean values were calculated from the reporting. In ArJD group, the time until full recovery from pain was additionally reported.During the study, the subjects documented any adverse events (AEs) and concomitant medication in diaries. These entries were monitored and judged by the delegated study staff.Further, blood routine parameters and vital signs were assessed prior to test 1. Blood pressure and heart rate were determined after 5 min of rest in a sitting position.If parameters of blood routine and vital signs were evaluated as clinically not relevant, subjects were allowed to participate.Source documents were filled in by authorized staff to collect information about the compliance to study standards, adverse events, study progress and efficacy parameters, and data was afterwards transcribed into the electronic case report form (eCRF).n = 15 in each target group. For test-retest reliability, within each study arm, the intra-class correlation coefficient (ICC) was calculated. For the assessment of intra-class correlation, the two-way mixed model was used with type absolute agreement. To describe precision, the 95% confidence interval (CI) was calculated. For interpretation of ICC, the guideline by Koo and Li 2016 was followed with ICC < 0.5: poor, between 0.50 and 0.75: moderate, between 0.75 and 0.90: good and above 0.90: excellent ICC [The current study was planned as pilot study for evaluation of a suitable joint stress test for further research studies and to demonstrate test-retest reliability with lent ICC .t test or Wilcoxon signed rank test, as appropriate. Acute joint discomfort was investigated within each study arm with baseline corrected results from joint stress test using a linear mixed model with repeated measures and the appropriate baseline level as covariate.Furthermore, within each study arm results from joint stress test for approach (I) standardization for joint discomfort (test 1 vs. test 2) and (II) standardization for load (test 2 vs. test 3) were analyzed using paired Changes within each study test and study arm for parameters with repeated measures were analyzed with repeated measurement ANOVA or Friedman test.t test or Wilcoxon rank sum test was applied.Comparison of study arms was performed for assessments at test 2 using one-way ANOVA or Kruskal-Wallis test. In case parameters could not be determined in the healthy control group, unpaired All statistical tests were performed two-sided. If not indicated otherwise, a significance level of 0.05 was used. Statistical analysis was performed using the software Prism Version 5.4, SPSS 24.0 and SAS 9.3Out of 58 screened subjects, 45 subjects were enrolled in the study. There were no drop outs during the study and all subjects completed the study in its entirety. The overview of the subject disposition as well as primary reasons for non-inclusion of screening failures  are summarized in Fig. p < 0.001) and the healthy group (p < 0.001). Baseline characteristics of the study subjects at the screening timepoint are presented in Table Overall, 25 women and 20 men participated in the study. The sex breakdown was evenly represented in each group. Subjects in the OA group were significantly older in comparison to the ArJD p < 0.00 and the In the OA group, only subjects with mild-to-moderate osteoarthrosis as confirmed by X-ray were eligible to participate. Eleven subjects were diagnosed with Kellgren score 2 and 4 subjects with Kellgren score 3. Majority of subjects of this group suffered from knee pain since more than 2 years.Overall, all subjects in the study groups healthy and ArJD reported to regularly perform sporting activities (at least twice per week). In the OA group, only two subjects stated not to do regular sports.Subjects in the ArJD group reported to have knee joint discomfort for more than 3 months (60% of subjects at least 1 year) but only during the regular sporting activities, especially during knee joint bearing sports like badminton, cycling, fitness, handball, running, and soccer. The majority of subjects however reported to have recovered from pain at rest within 1–2 h after the ending of the sporting activities.p < 0.01), function in sport and recreation (p < 0.01) and knee related quality of life (p < 0.01).KOOS questionnaire was filled in by all subjects. As expected, subjects in the healthy study arm showed nearly no impairment in the different subscales. For the ArJD, subject’s impairment was identified and the OA group showed the strongest symptoms  as well as ArJD and OA   during test 1 and 37.7 (95%CI 28.1–47.4) during test 2. In the OA group, the mean number of steps between pain 2 and pain 5 was 14.3 (95% CI 9.1–19.4) during test 1 and 13.2 (95% CI 9.5–19.9) during test 2. In one subject of the OA group, the assessment of step count until the pain 2 was not possible as the subject already started the test with pain level of 2.p < 0.01) as well as for the pain level of 5 (p < 0.001).Subjects of the ArJD group performed significantly more steps in comparison to the OA group. This was confirmed for the pain level of 2 on NRS-11 ) and OA subjects (median 20 repetitions of steps (25th–75th percentile 16–26)), a lower cut-off value of 30 steps was defined to reliably discriminate between ArJD and OA . In the OA group, the level was slightly higher (p = 0.6082) compared to test 2  in the ArJD group and 5.2 (95% CI 4.6–5.8) in the OA group. In the ArJD group, the pain level at test 3 was slightly lower , 0.5 h, 2 h, 4 h, 6 h, 12 h, and 24 h after the stress test.Healthy subjects did not feel any knee pain at rest or during the single-leg squat prior and after the SLSD test. Therefore, no data are presented for the healthy group.Subjects of ArJD group also did not feel any pain at rest. However, when performing a single-leg squat, a low level of pain was caused in 13 of 15 subjects at test 2 during the pre-assessment (mean pain 1.55 95% CI 0.85–2.25)). The acute joint discomfort was also monitored after the SLSD test. As pain level of 5 was induced by the stress test, subjects also indicated some pain at rest, which was most pronounced directly after the test. Within 2 h after the tests, the majority of subjects recovered from pain at rest . p = 0.0242).Interestingly, the pain level at rest increased again between the time points 4 h and 24 h. This can be explained by the fact, that subject left the study site after the 2 h measurement, so that the load on the knee joint increased due to daily life activity, whereas within the first 2 h after the stress test subjects spend at rest at the study site. In the OA group, 6 of 15 subjects reported to suffer from pain at rest at test 2 with a maximum pain level of 4.6. All subjects felt a pain when doing the single-leg squat (mean pain 3.19 (95% CI 1.89–4.49)). The level of pain during single-leg squat was significantly higher in the OA group in comparison to the ArJD groups , test 3: 1.49 (95% CI 0.83–2.14); OA test 2: 1.33 (0.59–2.07), test 3: 1.64 (95% CI 0.50–2.78)), and VAS after the single-leg squat (ArJD: test 2: 2.65 (95% CI 1.95–3.36), test 3: 2.11 (95% CI 1.43–2.80); OA: test 2: 3.69 (2.50–4.87), test 3: 3.57 (95% CI 2.23–4.92))  are recognized approaches to study exercise recovery. In the field of joint health, different biomarkers of cartilage were investigated and the expression of several biomarkers was found to be sensitive to mechanical loading –21. HoweAim of the present study was to set up a stress test with focus on joint health to assess joint discomfort and joint function parameters. In literature, different approaches are described in healthy subjects with joint discomfort like pain during intensive soccer play, change of pain intensity during activity or an aerobic step test with consecutive training at alternating days but with focus to endpoints of cartilage turn over , 6, 24. The SLSD movement is a movement that is very easy to perform by everyone and does not require any special training. It is a movement that poses a high targeted load on the knees without cardiopulmonary exhaustion. Furthermore, the involvement of other muscle groups and their loading to fatigue as well as induction of sore muscles is less pronounced. Even though some other muscle groups are involved in the performance of the step test, i.e., predominantly the hip musculature, the load on the knee joints is paramount. The performance of almost every other conceivable movement  will require more than one physical performance factor  possibly limiting the use of the test for different target groups. Additionally, biomarkers might be influenced by such other factors, which complicates interpretation of joint specific parameters. To overcome these limitations, the step-down test was developed. This test seems to be a suitable approach to study joint health specifically by induction of knee joint discomfort under standardized conditions.The SLSD test could be easily performed by all subjects independent of training status and was therefore suitable for the different subject groups of ArJD and OA. Out of all screened subjects, only 4 subjects could not be included due to endurance or circulatory problems  as well as back pain (1× ArJD) when performing the test. This underlines the suitability of the test for a broad range of subjects.Another advantage of the SLSD test is that due to high load on the knee, joint discomfort could be reproducibly induced in ArJD and OA subjects within a short period of time, less than 10 min. A maximum level of joint discomfort of 5 corresponding to moderate joint discomfort was selected based on ethical considerations as well as on cut-off levels defined for other sport models .The stress test was performed following a standard protocol with standardized intervals for the steps. The height of steps was adjusted to shank length to better inter-individually standardize the load on joints. The key to the reliability of the test is that standard protocols were followed to keep the impact by observer as low as possible.Prior to the induction of pain by the stress test, the function impairment was additionally measured with different approaches. The range of motion measurement was performed under active and passive conditions. ROM measurement is a widely used method for the assessment of flexibility of different joint types. It is also used for the evaluation of changes of knee joint flexibility in the context of physical or surgical therapy. The findings of the current study support the reliability of the ROM measurement, as both parameters, passive and active ROM, were reliable measures to characterize the study groups. In OA subjects, ROM active and passive was significantly impaired in comparison to the healthy and ArJD groups. However, to measure changes of interventions by nutraceuticals, the parameter might lack sensitivity because of measurement variability.The same applies to the reach test. This test is an appropriate function test to characterize the different study groups. Differences between the healthy as well as ArJD group in comparison to the OA group were significant. The test is an accepted test also for subjects with patellofemoral pain syndrome. However, to investigate intervention effects in ArJD subjects the test might be not sensitive enough .Additionally, the KOOS questionnaire was used to assess knee joint discomfort at baseline measurement. There was a clear differentiation between the ArJD and OA group. The KOOS questionnaire confirmed in all subscales the clear differentiation between subjects with ArJD and healthy subjects. Further, the KOOS questionnaire indicated that even if the knee pain only occurred during heavy load or sporting activities, this already had some impact on knee related quality of life and especially the subscale sport and recreation. This indicates that the KOOS questionnaire is sensitive enough to cover the impairment in this borderline collective and clearly differentiates between impaired and healthy populations. In a study with amateur football players of mean age of 22 years comparable levels to the ArJD group of our study were reported for the subscale symptoms and ADL . KOOS scWithin a given time interval, subjects had to perform as many repetitions as possible until a subjective pain/joint discomfort of 5 on NRS-11. This approach focuses on the increase or decrease of mobility. Translated to daily life: if an intervention could increase the number of repetitions until pain 5, this means that more stairs can be climbed or soccer can be played longer before any joint discomfort occurs, which has a great impact on quality of life.In the ArJD group, the number of repetitions was significantly higher in comparison to OA subjects. Thus, a clear differentiation of ArJD and OA subjects was possible. Therefore, the joint stress test is also suggested as a tool to select subjects for participation in clinical trials. A cut-off value of 30 steps, which is based on the distribution of the data in subjects with activity-related joint discomfort and osteoarthritic subjects, seems to be appropriate to quite reliably discriminate between these two populations. Applying the cut-off value of ≥ 30 steps, sensitivity was calculated with 87% to truly identify subjects with ArJD, with a specificity of 83%.The data also indicate that the number of repetitions clearly depends on the individual endurance and the kind of sport-related stress the subject is used to, and also explains the overlap between target groups. Interestingly, no impact of age or BMI was seen in our study data, neither in subjects with ArJD nor in the OA group (data not shown). Based on this background as upper cut-off level, 120 to 150 steps are suggested with respect to minimize heterogeneity of subjects but also to allow subjects to participate with great endurance. Such a cut-off level would also allow the determination of intervention success if the maximum step number of 200 from the healthy subjects is applied.For the healthy subjects, the test was stopped after 200 repetitions. The subjects did not complain from knee pain, but due to the single side movement, some subjects reported back pain and muscle soreness. Taking this under consideration, the study experience confirms the suitability of the upper limit of 200 steps for a healthy and athletic population to keep other confounding factors, e.g., muscle soreness from other muscle groups as low as possible.Overall, ICC of the SLSD test for number of repetitions in ArJD and OA group was moderate to good. It is commonly known that sportive challenge tests are prone to training effects or adaptation reactions especially with eccentric muscular loads . In contThe second approach was the evaluation of the response of step test on joint function and pain while keeping the load on joints identical between the two repeated measurements (test 2 and test 3).Keeping the load identical indicated that during both occasions the movement induced a comparable pain/discomfort level. The reported pain range at test 3 was slightly higher in OA group with a range of 3–8 in comparison to ArJD (range 3–6) which may be attributed to the fluctuating symptoms known for OA. Despite the subjective assessment of pain, the reproducibility of the stress test was very good. The induction of pain was confirmed in all participating subjects.Next to the maximum pain at the end of the stress test, the pain at rest was assessed at specified time points. Healthy subjects and subjects of the ArJD group did not have any pain at rest when they started the stress test. After the stress test, the majority of subjects of the ArJD group indicated some pain at rest, which was most pronounced directly at the end of the step test. Majority of subjects recovered from the pain within 2 h after the test. The reliability for maximum knee joint discomfort after the stress test was good to excellent for pain at rest in both study groups.Further, the pain level after a single-leg squat was evaluated prior and after the SLSD test. Despite a good reliability between the tests, performing a squat was a challenge for some subjects due to balance issues. Therefore, subjects were allowed to stabilize but only with little force. These test conditions were individually standardized to avoid external impact factors. Anyhow, it turned out that there was some factor of variability with regard to how deep the squat was performed which might have had an impact on the pain level. Therefore, in future assessments, we would highly recommend to better standardize the movement, e.g., with a goniometer.The current study could confirm that the SLSD test is a reliable tool for the assessment of knee joint health in subjects with ArJD.Joint discomfort was induced with an easy to handle movement targeted to the knee joint allowing for high standardization. The test was able to discriminate between different subject groups and showed a good test-retest reliability applying the test with focus to endpoints of mobility as well as endpoints of joint discomfort. Requirements for a stress model were confirmed to allow the use in future intervention studies to measure impact on joint health. Thus, the SLSD test seems to be an appropriate test to measure knee joint function parameters in a healthy population with ArJD by inducing inter-current discomfort. The sensitivity to capture joint health benefits within intervention studies needs to be investigated in future studies and compared to response to other assessments, like quality of life changes or reporting from situations during which activity-related joint discomfort normally occurs.


Both the incidence and survival rate of head and neck cancer (HNC) is increasing, making quality of life of HNC survivors an important issue.In this cross-sectional study we compared the oral health related quality of life (OHRQoL) of long-term HNC survivors to that of a general population cohort from the seventh survey of the Tromsø study with the Oral Impact on Daily Performances questionnaire. Comparisons were done with frequency analyses and cross tabulation. We also assessed OHRQoL’s association to sociodemographic and oral health related variables in both cohorts as well as with cancer related variables in the HNC cohort with regression analyses.The HNC survivors had four times the risk of reporting problems with daily performances compared with the general population cohort. The ability to eat and enjoy food was most frequently affected in both cohorts. Moderate-poor self-rated dental health and general health as well as high frequency of dental visits were significantly associated with poorer OHRQoL. To have a history of oral or pharyngeal cancer was associated with more problems than having a history of laryngeal cancer.Our study shows that HNC treatment is associated with a strong and lasting impairment of OHRQoL, highlighting the need to find less toxic, yet effective ways to treat the disease, and to provide easy access to expert dental care at all stages of the disease to minimize morbidity. Given the widespread side effects of cancer treatment, a multidisciplinary approach might be required to improve the OHRQoL of HNC survivors.The online version contains supplementary material available at 10.1186/s12903-022-02140-2. Worldwide, almost 1,000,000 persons are diagnosed with head and neck cancer (HNC) each year. The number of long-term HNC survivors is rising due to an increase in cancer incidence along with improved survival rates . These tThe side effects of HNC treatment may disturb several of the vital functions that the head and neck region harbors, including chewing, swallowing, speech and expression, and thus cause nutritional problems, isolation and a reduced quality of life . SeveralThere are several validated questionnaires to evaluate OHRQoL, including the Oral Impact on Daily Performances (OIDP) . This quThis cross-sectional study aimed at assessing OHRQoL and associated factors in HNC survivors and in a general adult population in Norway. The study included two separate cohorts, one with persons treated for HNC (HNC cohort) and one general, adult population from the seventh survey of the Tromsø study (T7 cohort), which is a community-based cohort study. The characteristics of both cohorts are summarized in Table In May 2020, a questionnaire was sent by mail to the 577 members of the “Head and neck cancer association”. This is an association that works for the interests of HNC patients in Norway, and have members who have or have had cancer in the head and neck region themselves, their next of kin, as well as professionals working with HNC. One reminder was sent by SMS, and the last response was received by the end of August 2020. Responses from members without a history of HNC  were excluded.The questionnaire assessed sociodemographic variables, general health and oral health related variables by the same questions as in the T7 cohort, but also included questions associated with the cancer diagnosis and treatment.The seventh survey of the Tromsø Study was carried out in 2015 and 2016. An invitation was sent by mail to all 32,591 persons 40 years or older in Tromsø municipality. In the present study, we used data from a questionnaire in T7 assessing sociodemographic variables, general health and oral health related variables. We excluded persons who had reported a current or previous cancer diagnosis . Further, we excluded persons younger than 50 years of age  to increase the match with the HNC cohort, which included only five persons younger than 50 years.The following variables were included from both cohorts:We assessed OHRQoL by the Norwegian version of the OIDP questionnaire , where tAge was trichotomized into (1) 50–59 years, (2) 60–69 years and (3) 70 years or older. In the HNC cohort, there were five respondents (1.9%) younger than 50 years, and they were included in the 50–59-year group. Gender had the response options man or woman.Highest level of education was dichotomized into (1) no college/university degree  and (2) college/university degree (including college/university degree ≤ 3 years and college/university > 3 years).Self-rated general health and self-rated dental health was assessed by one question each with five response options from 1 (very poor) to 5 (very good). For analyses, we trichotomized the responses into: (1) poor (option 1 and 2); (2) moderate (option 3); and (3) good (option 4 and 5). Dental attendance was assessed with one question with six options. For regression analyses, we trichotomized the options into (1) more often than once a year; (2) once a year; and (3) every second year or rarer. We calculated the body mass index (BMI = weight in kilograms divided by the square of height in meters), and trichotomized it into (1) underweight (< 18.5); (2) normal weight (18.5–24.99), and (3) overweight (≥ 25).For the HNC cohort we also included cancer-associated variables:Cancer location in the head and neck region according to the WHO classification were listed in the questionnaire, and the respondents checked all relevant location. For analyses the locations were categorized as (1) oral cavity, (2) pharynx (including oropharynx and nasopharynx) and (3) larynx (including hypopharynx and larynx) according to the 4th edition of the WHO classification of head- and neck tumors . NasophaWe assessed cancer treatment with one question with 5 options: (1) surgery; (2) radiation; (3) chemotherapy; (4) other (with free-text option) and (5) do not know, and instructed the respondents to check all relevant options. We re-categorized cancer treatment as (1) surgery without radiation, (2) radiation only and (3) radiation and surgery with or without adjuvant chemotherapy. No respondents had received chemotherapy only.Time of first cancer diagnosis was categorized as (1) before 2010; (2) 2010–2014; and (3) 2015–2020. Cancer recurrence was reported as yes or no.p < 0.05 for all analyses. Data were missing for less than 5% of the variables included in regression analyses, and these were excluded from analyses.We used the Statistical package for Social Sciences (SPSS) for Windows version 26  for statistical analyses. We assessed differences among groups by cross-tabulation, and the statistical significance of the observed differences were assessed by Pearson’s Chi-square test. In relative risk analyses by cross-tabulation as well as in univariate and multivariate logistic regression analyses  we used OHRQoL dichotomized into no problems versus problems as the dependent variable, with no problem as the reference value. The variables listed in Table We received 349 (59%) answers from the HNC cohort, of which 216 were from members with a history of HNC themselves. The remaining 133 responders were either next of kin to HNC patients or health care professionals working with HNC, whom we excluded. The attendance rate of the T7 study was 65% , of whom 12,647 were included in this study after exclusion of respondents with a current or previous cancer diagnosis or age younger than 50 years.Table Compared to the T7 cohort, the HNC cohort included a higher proportion of men and people of older age, whereas educational level was similar. The HNC survivors reported significantly worse general health and lower BMI than did respondents in the T7 cohort. Furthermore, the HNC survivors had poorer self-rated dental health than respondents in the T7 cohort had, and reported a much higher frequency of dental visits with 71% having regular visits more than once a year, compared to 25% in the T7 cohort. The most common cancer location was the pharynx, and more than 90% of the HNC survivors had been treated with radiation therapy (Table Table For each of the eight daily activities assessed by the OIDP instrument, respondents in the HNC cohort reported a much higher frequency of problems than did those of the T7 cohort, with only 16% of the respondents reporting no problems with any of the activities, compared to 81% in the T7 cohort. The daily activity that was most frequently affected in both cohorts was the ability to eat and enjoy food, with which more than two thirds reported problems in the HNC cohort and 13% in the T7 cohort. The ability to speak and pronounce words clearly was the second most affected daily performance in the HNC cohort, with which more than half of the respondents reported problems. The ability to smile and show teeth without embarrassment was the second most affected daily performance in the T7 cohort, but the least affected in the HNC cohort. Still, a much higher proportion of the HNC cohort than of the T7 cohort reported problems with smiling or showing teeth (29% vs 9%, Table As the HNC- and the T7 cohorts were unmatched for age and gender, we performed a cross tabulation of OIDP frequency score and these variables, which confirmed that the HNC cohort had much higher OIDP scores than the T7 cohort had, across gender and age (Additional file To explore how the panorama of problems varied with tumor location and treatment, we performed cross-tabulation of OIDP with tumor location Table  as well Respondents who were treated without radiation therapy generally reported better OHRQoL than those who had received radiation, with poorest OHRQoL among respondents who had received radiation in combination with surgery and/or chemotherapy Table . RespondTo assess the associations between the outcome variable OIDP and the explanatory variables, we performed regression analyses for both cohorts, with OIDP dichotomized into no problems and problems Table . For theIrrespective of cohort and model, self-rated dental health showed the strongest association with OIDP. In the fully adjusted model for the HNC cohort, those who rated their dental health as poor had nine times the odds of reporting problems with one or several daily activities compared to those rating their dental health as good. The association was even stronger in the T7 cohort with an odds ratio of eleven. Having poor or moderate self-rated general health was also associated with reporting problems with daily performances in both cohorts, as was having dental visits more than once a year. In the adjusted model for the T7 cohort, lower age and higher educational level also increased the odds of reporting problems with daily performances. In the fully adjusted model for the HNC cohort, cancer location in the oral cavity or the pharynx was strongly associated with reporting problems, in addition to the significant variables from the partly adjusted model. The fully adjusted model for the HNC cohort explained almost 40% of the variance in OIDP, whereas the partly adjusted model explained almost 30%. For the T7 cohort, the adjusted model explained almost 20% of the variance.In the current study, we have assessed OHRQoL by the OIDP questionnaire in a cohort of patients treated for HNC and in a general adult population cohort in Norway. Respondents in the HNC cohort had about 4 times the relative risk of reporting problems with oral functions compared with those in the general population cohort. The HNC survivors reported most problems with eating and speech, but problems related to emotional and social functioning were also common. Moderate-to-poor self-rated dental health and general health as well as high frequency of dental visits were all significantly associated with poorer OHRQoL in both cohorts. HNC survivors who had a history of oral or pharyngeal cancer had more problems than those with a history of laryngeal cancer.The long-term side effects of HNC treatment, especially related to radiation therapy, are well documented . It is tWe found that the proportion reporting problems was somewhat lower among head and neck cancer survivors who were diagnosed a long time before the survey compared to those diagnosed within the past 10 years of the survey. However, the association between time of diagnosis and OHRQoL was not statistically significant, neither in univariate nor in multivariate regression analyses. The majority of those who were diagnosed 0–10 years prior to the survey would have been treated with intensity-modulated radiotherapy or volumetric modulated arc therapy. Although these treatments reduce the radiation dose to the healthy tissue surrounding the cancer , many ofRegular dental visits have been associated with good dental health and OHRQoL in many studies –29. ThisWe found that OHRQoL was significantly associated with general health in both study cohorts, which is in accordance with findings from several other studies –37. OralThere are several limitations to this study. It is based on self-reported measures, relying on the memory of the respondents. This limits the information about clinical parameters such as tumor stage at diagnosis and details regarding therapy including type of surgery and radiation protocol and radiation field. We did not include such questions in the study as we considered it unlikely that the study participants had accurate knowledge of this. Furthermore, we lack clinical data on dental status and include only self-rated dental health as a measure of the dental status of the participants. In the T7 cohort respondents with a cancer diagnosis were excluded, and one could therefor argue that it is not a proper general population. Our study may therefore overestimate the OHRQoL of the actual general population. However, a previous study using OIDP to measure OHRQoL in Norwegian adults found that about 18% of adults between 45 and 79 years reported problems with at least one daily performance [The number of respondents in the two cohorts was very different, and this will affect the power of the statistical analyses, which is much higher for the T7 cohort than for the HNC cohort. This may explain why fewer associations were statistically significant in regression analyses for the HNC cohort than for the T7 cohort, as well as the range of the 95% CI of the OR. Furthermore, the HNC cohort is probably not representative for all persons with a history of HNC in Norway. We surveyed members of the organization working for HNC patients in Norway. The rationale for selecting members of this organization was to avoid contacting persons with a HNC cancer history who did not want to be reminded of their illness, because that may be stressful . As the Our study shows that HNC survivors have a strong and lasting impairment of OHRQoL, highlighting the need to find less toxic, yet effective ways to treat the disease. Tight follow-up of dental professionals does not seem to alleviate the problems. Given the widespread negative effects associated with cancer treatment, a multidisciplinary approach including dental hygienists, general dentists, various dental specialists, nutritional specialists, speech therapists and psychologists might be required to improve the OHRQoL in HNC survivors.Additional file 1.Table S1. Additional treatment information Head and neck cancer cohort. Table S2. Oral impact on daily performances by age and gender.


Internet-based programs can help provide accessible and inexpensive behavioral health care to those in need; however, the evaluation of these interventions has been mostly limited to controlled trials. Data regarding patterns of use and effectiveness of self-referred, open-access online interventions are lacking. We evaluated an online-based treatment designed to address stress, depression, and conflict management, the Dartmouth PATH Program, in a freely available and self-guided format during the COVID-19 pandemic.The primary aim is to determine users’ levels of stress and depression, and the nature of problems and triggers they reported during the COVID-19 pandemic. A secondary objective is to assess the acceptability and usability of the PATH content and determine whether such a program would be useful as a stand-alone open-access resource. The final objective is understanding the high dropout rates associated with online behavioral programs by contrasting the use pattern and program efficacy of individuals who completed session one and did not return to the program with those who came back to complete more sessions.Cumulative anonymous data from 562 individuals were analyzed. Stress triggers, stress responses, and reported problems were analyzed using qualitative analysis techniques. Scores on usability and acceptability questionnaires were evaluated using the sign test and Wilcoxon signed rank test. Mixed-effects linear modeling was used to evaluate changes in stress and depression over time.P=.03), and there was a significant decrease in depressive symptoms over all time points (P=.01). Depression severity decreased on average by 20%  between sessions one and two.A total of 2484 users registered from April through October 2020, most of whom created an account without initiating a module. A total of 562 individuals started the program and were considered in the data analysis. The most common stress triggers individuals reported involved either conflicts with family or spouses and work or workload. The most common problems addressed in the mood module were worry, anxiousness, or stress and difficulty concentrating or procrastination. The attrition rate was high with 13% (21/156) completing the conflict module, 17% (50/289) completing session one of the mood module, and 14% (16/117) completing session one of the stress module. Usability and acceptability scores for the mood and stress modules were significantly better than average. In those who returned to complete sessions, symptoms of stress showed a significant improvement over time (Conflicts with others, worry, and difficulty concentrating were some of the most common problems people used the programs to address. Individuals who completed the modules indicated improvements in self-reported stress and depression symptoms. Users also found the modules to be effective and rated the program highly for usability and acceptability. Nevertheless, the attrition rate was very high, as has been found with other freely available online-based interventions.ClinicalTrials.gov NCT02726061; https://clinicaltrials.gov/ct2/show/NCT02726061 Community epidemiological surveys estimate that as many as 30% of adults in the United States are affected by a mental disorder, yet less than half see a physician, and only a quarter are treated properly ,2. A numTechnology can increase access to care by providing secure, inexpensive, and easily accessible treatment tools. Internet-delivered CBT (ICBT) has existed for 20 years, and a number of controlled trials and meta-analytic reviews have demonstrated the effectiveness of this approach -10. AlthOur study evaluated a web-based program in a freely available format. The Dartmouth PATH Program is a multimedia-based computerized CBT tool designed to address stress, depression, and conflict management. The program was developed as a psychosocial training and treatment resource for the National Aeronautics and Space Administration (NASA) with the aim of addressing psychological challenges endured by astronauts on long duration spaceflights . The opeComparably, the COVID-19 pandemic has also been associated with mental health challenges related directly to the virus’ morbidity and mortality, and indirectly by the impact of physical distancing and stay-at-home orders . AccordiThe program was freely available as part of NCT02726061. Participants needed to agree to participate using an online consent form. We evaluated responses to the PATH program during the COVID-19 pandemic from April through October 2020. The data collected were fully anonymous and self-reported. Our objectives were to determine the levels of stress and depression as well as the nature of problems affecting individuals during the pandemic, assess the acceptability and usability of the PATH content and determine whether such a program would be useful as a stand-alone open-access resource, and understand the high dropout rates associated with online behavioral programs by contrasting the use pattern and program efficacy of individuals who completed session one and did not return to the program with those who came back to complete more sessions.The PATH program is an interactive, media-intensive CBT-based program that interacts with users in real time and delivers individualized feedback based on self-reported responses . In addiThe conflict module teaches participants how to approach conflict and reach effective solutions using CBT principles. It includes a conflict briefing, an interactive conflict simulation, a cognitive restructuring exercise, and a training module on interest-based negotiation . An evalAt the end of each one of the four sections in the conflict module, users received a survey on how valuable, feasible, and realistic the simulation was on a scale of 0 to 4 ranging from “Strongly disagree” to “Strongly agree.” The questions covered whether the activity had too much or too little information and if it was interesting or valuable.Like the conflict module, the stress module also uses CBT principles and focuses on stress management and resilience training. The module consists of six approximately 1-hour sessions. A randomized controlled trial of a version of the stress module reported significant reductions in perceived stress and increases in perceived control over stress . Each seUsers’ progress was tracked at the beginning of each session through a self-reported survey regarding satisfaction with their progress since the last session on a binary scale . Participants were also asked how often they practiced each skill learned in the previous session on a scale ranging from 0 to 3 .Participants completed the Perceived Stress Scale-14 (PSS) questionnaire at the beginning of each session to assess the degree to which events were perceived as stressful since their last visit. The questions in the PSS were meant to convey feelings and thoughts experienced in the last month on a 0 to 4 scale ranging from “never” to “very often.” The questionnaire contains 14 questions, and the final score is obtained by reversing the scores on seven of the positively stated items and then summing across all 14 questions. The PSS has established adequate internal and test-retest reliability, and correlates with life event scores .The depression module uses problem-solving treatment and consists of six sessions lasting 30 to 60 minutes. In these sessions, a mentor guides users through a step-by-step problem-solving therapy tool. Participants were first asked to identify and clarify a problem, establish an achievable goal, brainstorm solutions to the problem, evaluate pros and cons of each solution, develop an action plan to implement the selected solutions, and finally schedule enjoyable activities they will do during the next week. At the end of each session, a summary printout was available containing the problem selected, the action plan developed in that session, and a list of the selected scheduled enjoyable activities to be completed before the following session . The proParticipant progress was tracked through surveys regarding satisfaction with the amount of effort spent trying to solve the problem on a 1 to 10 scale ranging from “not satisfied at all” to “extremely satisfied” at the beginning of each session.Users completed the Patient Health Questionnaire-9 (PHQ-9) Item Depression Scale at the beginning of each session to assess depression symptoms they had experienced for the past 2 weeks. The questionnaire had participants rating nine questions concerning their depression symptoms on a 0 to 3 scale ranging from “not at all” to “nearly every day.” The final score was calculated by adding up the total scores from each question, which ranges from 0 to 27 . The questionnaire has demonstrated strong test-retest reliability and internal consistency .The study was approved by the Dartmouth Committee for the Protection of Human Subjects. The online program was made freely available to anyone interested in participating in the study. Users were required to accept an online consent form and then create a username and password of their choice. The data were acquired during the period of the COVID-19 pandemic from April through October 2020.Users were able to browse the program’s website freely and to choose to go through the cognitive behavioral modules they selected at their own pace. The main page contains all the potential choices for users to choose from, which includes the three primary modules of depression, stress, and conflict management; a guide on how to use the program; self-assessments to guide users on finding relevant content based on individual needs; and other resources and publications.Participant age and gender were collected upon signing up in the study.The Post-Study System Usability Questionnaire (PSSUQ) is a 19-item self-report questionnaire used to assess user satisfaction with system usability at the end of a study.The items are scored on a 7-point scale (1-7) on the strength of agreement with each statement . The scale ranges from “Strongly agree” (1) to “Strongly disagree” (7), and a “Not applicable” indicates answers outside the scale. The overall score is obtained by summing across scores for all questions (1-19), and the evaluation is further subdivided into three subscales: system usefulness (questions 1-8), information quality (questions 9-15), and interface quality (questions 16-18). The overall scale and its subscales have shown adequate levels of reliability, validity, and sensitivity ,31. InitThe Acceptability of Self-Guided Treatment (AST) is a 16-item self-report questionnaire developed in previous research on an earlier version of the depression module as a stand-alone treatment for depression . It compR2020a (v9.8.0).To evaluate for statistically significant improvements in PSS (stress module) and PHQ-9 (mood module) scores, we performed the nonparametric Wilcoxon signed rank test using MATLAB (MathWorks). Since the dropout rate of sessions three to six were too high, this test was only performed to assess the median of differences of paired samples for both PSS and PHQ-9 questionnaires from sessions one and two at a 5% significance level . The PSSUQ and AST analyses were also performed using the sign test to evaluate whether the given scores were statistically significant against the neutral mean value of each questionnaire’s scale. A right-sided test for the median of the AST scores and a left-sided test for the median of the PSSUQ scores were performed at a 5% significance level. This tested the hypothesis that the median scores of the AST questionnaires were higher than neutral (more acceptable) and that the median scores of the PSSUQ questionnaire were lower than neutral (more usable). To determine whether stress and depression would show statistically significant improvement over time, we conducted linear mixed-effects modeling to account for the time-varying natures of the variables and missing data. We entered time as the fixed effect and the intercept of subjects as random effects into the model. ATLAS.ti software (ATLAS.ti Scientific Software Development GmbH) was used to synthesize the qualitative data for both the stress and mood modules . The numerical values are presented in this study with mean (SD), median (range), or both. The statistical analyses were conducted using MATLAB Between April and October 2020, a total of 2484 users registered with the program website in response to media coverage of the program during the COVID-19 pandemic. Of those who registered, 1321 (53.2%) self-identified as females and 1042 (41.9%) as males with a mean age for the entire group of 44 (SD 15.2) years. The majority of those who registered did not interact with the program and therefore were not included in the treatment dropout rate. The dropout rate involves leaving treatment before its completion  and may A total of 562 participants interacted with the program modules and were included in the data analysis. At baseline, 156 users interacted with the conflict module, 289 with the mood module, and 117 with the stress module. The conflict module had four subsections and was completed by 21 users . SessionThe Conflict Introduction Questionnaire was completed by 102 participants  and was P=.19). The individual analysis of the PSS scores across sessions one through six is shown in The mean PSS score baseline for session one, including all participants (n=61), was 30.0 (SD 6.6). The average PSS of session one for those who returned for more than one session (n=8) was 28.6 (SD 5.9) while session two (n=8) was 27.3 (SD 9.1). The difference in scores for each participant between the first and last completed sessions was analyzed using the Wilcoxon signed rank test and was found to not be significant (P=.03).To determine whether stress symptoms would improve significantly over time, we used a linear mixed-effects analysis of the relationship between time and PSS scores for each participant throughout all sessions completed. We entered time as the fixed effect and the intercept of subjects as random effects into the model. The decrease in symptoms in relation to time was significant  versus those who completed multiple sessions (n=7). Individuals were able to select multiple responses .The stress triggers were grouped into 10 major categories: (1) conflicts with family or spouse; (2) work or workload; (3) conflicts with friends or neighbors; (4) financial concerns; (5) health concerns; (6) boredom and lack of productivity; (7) time away from friends and family; (8) internal stressors, which included negative views about oneself, uncertainty of future, and fear of failure; (9) COVID-19 and isolation; and (10) current political climate. The responses were categorized into each of the major groups, and only one category was counted per person .Conflict with family or spouse and work or workload were the two most common stressful triggers among participants. In contrast, conflicts with friends or neighbors and financial concerns were ranked third by those who completed multiple sessions, while health concerns and boredom or lack of productivity were ranked third by those who completed just one session. The response percentage rate for each category can be found in The emotional response to perceived stressors were also grouped into 10 major categories: (1) hopelessness, (2) worry or anxiety, (3) anger, (4) irritability, (5) frustration, (6) sadness or depression, (7) fear or panic, (8) obsessions or overthinking, (9) cynicism, and (10) burnout or overwhelmed. The responses recorded allowed for multiple responses, and each response was placed into the appropriate category . The most common responses were hopelessness, worry or anxiety, anger, irritability, and frustration among participants. The response breakdown can be found in The time spent on each part of session one in the mood module was calculated in addition to the retention rate throughout the session. It had five major steps, including completing the PHQ-9, defining the problem, selecting a goal, brainstorming solutions, and developing an action plan. The time elapsed began when the participant started each section until the section was completed, including participant idle time and time away from the computer. The calculation included both individuals who completed the section in one and multiple sittings. For this reason, participant completion times totaling over 100 hours were not included in the calculation, and the mode and range are included in The type of problems selected by participants in the program was evaluated to better understand the kind of issues experienced by participants and differences, if any, among individuals who only completed one session (n=145) versus those who completed multiple sessions (n=8). The problems were grouped into 13 categories: (1) worried, anxious, overthinking, or stressed; (2) difficulty concentrating or procrastination; (3) problems with overeating or undereating; (4) not enough exercise; (5) negative feelings about oneself; (6) loss of interest or lack of motivation; (7) anger, irritability, or frustration; (8) problems with work; (9) problems with sleep; (10) lack of social activities or hobbies, or isolation; (11) problems with relationships ; (12) problems with weight; and (13) financial problems. Those who completed multiple sessions ranked worried, anxious, overthinking, or stressed and difficulty concentrating or procrastination as the highest among the major problems. In contrast, those who completed just one session had problems with work and not enough exercise as the highest ranked. The complete analysis is shown in P=.06). The depression severity levels between the two sessions decreased by an average of 20.0% (SD 35.2%). The mean PHQ-9 score for session three (n=4) was 5.8 (SD 3.1), session 4 (n=1) was 5.0, session 5 (n=1) was 6.0, and session 6 (n=1) was 5.0. The individual analysis of the PHQ-9 scores across sections one through six is shown in The change in depression severity levels was analyzed using the PHQ-9. The mean PHQ-9 score baseline for session one, including all participants (n=289), was 9.98 (SD 6.23). The average PHQ-9 of session one for those who returned for more than one session (n=8) was 11.0 (SD 5.4) while session two (n=8) was 8.0 (SD 4.2). The difference in scores between the first two sessions, analyzed using the Wilcoxon signed rank test, was not significant .The linear mixed-effects model analysis of the relationship between time and PHQ-9 scores with time as the fixed effect and the intercept of subjects as random effects showed depressive symptoms decreased significantly over time  selected more activities on average than those with lower PHQ-9 scores who completed only one session (n=35). A visual analysis is shown in The most common type of activities selected among participants were entertainment, social, and physical activities. The response breakdown for each category is shown in This secondary analysis consisted of evaluating the usability and acceptability of the program and differences perceived by those who completed session one and did not return to the program versus those who came back to complete more than one session.Lower PSSUQ scores indicated a more agreeable usability experience. A total of 22 individuals completed this questionnaire at the end of session one in the mood module, of which 5 individuals went on to complete more than one session. The mean overall usability score after session one given by those who completed one session (n=17) versus those who completed multiple sessions (n=5) was 2.7 (SD 1.8) and 3.2 (SD 2.2), respectively.A total of 10 individuals completed the usability questionnaire at the end of session one in the stress module, of which 4 of those participants completed more than one session. The mean overall score after session one given by those who completed one session (n=6) and multiple sessions (n=4) was 3.4 (SD 2.5) and 4.1 (SD 2.4), respectively. Mean scores for each one of the PSSUQ subscales are presented in Higher scores on the AST questionnaire indicate a higher degree of acceptability. A total of 25 individuals completed the questionnaire at the end of session one in the mood module. The mean acceptability score given by those who completed session one only (n=20) was 5.7 (SD 0.7), and the mean score given by individuals who completed multiple sessions (n=5) was 5.9 (SD 0.4). Both of these were significantly different from neutral.A total of 12 individuals completed the questionnaire at the end of session one in the stress module, of which 5 individuals completed multiple sessions. The mean acceptability score for the stress module after session one given by those who completed one session (n=7) was 5.5 (SD 0.7), and the mean score given by those who completed multiple sessions (n=5) was 5.3 (SD 0.4). These scores were significantly different from neutral.The mean acceptability score for the conflict module (n=20) was 5.3 (SD 1.2) and statistically significant from neutral. Mean scores for each AST item are presented in The difference in response patterns was further evaluated between individuals who completed the first session only and those who completed multiple sessions for the item “I would rather use a computer to help myself privately than go to a therapist.” In the stress module, 43% (3/7) of individuals who completed only one session chose “agree,” while 57% (4/7) chose “neither agree nor disagree.” A third of participants who completed multiple sessions chose “somewhat agree,” 50% (3/6) chose “neither agree nor disagree,” while 17% (1/6) chose “somewhat disagree.” In the mood module, 45% (9/20) of participants who completed only the first session agreed with the statement and 30% (6/20) were neutral, while 25% (5/20) disagreed with the item. Individuals who completed multiple sessions in the mood module mostly agreed with the item at a rate of 60% (3/5), while 40% (2/5) remained neutral. The complete analysis can be found in This study evaluated the use and effectiveness patterns of a free and unrestricted web-based program designed to provide cognitive behavioral–based approaches to stress, depression, and conflict management during the COVID-19 pandemic. The program provided insight into the kinds of problems people were experiencing during the pandemic and seeking help for. In the stress module, the greatest source of stress was conflicts with family or spouse. Feelings of hopelessness, worry, anxiety, and anger were common. The problems selected for the mood program focused on issues of worry, anxiety, overthinking, and stress. Those who returned to complete more than one mood session tended to have problems with worry, stress, and anxiety.Those who returned to complete more than one stress or mood module showed significant reductions in self-reported stress and depression, although they did not rate the program as high for usability as those who completed only one session. Individuals who completed multiple sessions of the mood module also rated the program higher for acceptability than those who completed only the first session and tended to agree that they would rather help themselves privately with a computer rather than go to a therapist. For the stress module, individuals who completed only one session rated the program with slightly higher acceptability scores than those who returned to complete multiple subsequent sessions. On average, users thought the conflict training was useful and would recommend it to a friend. In the conflict module, the interest-based negotiation exercise was the highest rated.The usability score describes the perceived ease of use of the technology applied in this program. It is based on a scale of 1 to 7 (ranging from “strongly agree” to “strongly disagree”), where a higher usability score infers a lower perceived ease of use by the participant. A psychometric evaluation of the PSSUQ established norms for this questionnaire . The amoThe PSSUQ score results provided one surprising finding. Those who completed more sessions  perceived the program as less usable than those who completed just one session . This result was unforeseen, as one would expect participants rating the programs with a low usability score would be likely not to continue. The significance of this finding is limited by the small number of people in these groups who have usability data. Nevertheless, it is interesting that those who chose to continue were not necessarily those who rated the program highly for usability. For the mood module, one possible explanation for continuing was the participants feelings about whether they wanted to use a computer rather than a therapist. Although 25% of those who completed only one mood session disagreed with the statement “I would rather use a computer to help myself privately than go to a therapist,” none of those who did more than one session did.Ratings on the AST showed that participants found all three modules to be acceptable. The mean score  for the mood, stress, and conflict modules were 5.7 (SD 0.7), 5.5 (SD 0.7), and 5.3 (SD 1.2), respectively, which were all significantly different than average. These results are similar to mean scores for the same modules when they were used in isolated and confined environments  . The HI-P<.001 on a 0-4 scale). From the Hypothesis Testing Questionnaire, participants indicated that the program was somewhat easy to understand , and the feedback somewhat helped them understand why responses were correct or incorrect . Lastly, the conflict-based negotiation activity was found to be both enjoyable  and highly valuable for learning about conflict management .The conflict module consisted of four sections, each concluding with a short questionnaire. The Conflict Introduction and Conflict Simulation Questionnaires were found to be interesting and valuable ; however, the scores indicated a significant improvement over time (P=.03). There was no difference in the response pattern of the selected stressful triggers and perceived emotional response to stress between those who completed one session versus multiple sessions. The major stressful triggers selected by participants included conflict with family or spouse and work or workload. Noticeably, COVID-19 and isolation, political climate, and time away from friends and family were also mentioned as stressful triggers. Lastly, the most common emotional response to perceived stressors were hopelessness, worry or anxiety, anger, and irritability.Symptoms of stress as measured by PSS scores between sessions one and two did not significantly decrease . However, there was a significant decrease in the depressive symptoms over time (P=.01). Finally, the average number of enjoyable activities selected by participants indicated that those with higher depression levels and who completed multiple sessions selected more enjoyable activities on average than those with lower PHQ-9 scores that completed only one session.There was a mean 20.0% (SD 35.2%) decrease in depression severity level, as measured by PHQ-9 scores between sessions one and two, which was not statistically significant  of participants who completed only one session and 60% (3/5) of those who completed multiple sessions of the mood program indicated that they either agreed or strongly agreed with the statement that “I would rather use a computer to help myself privately than go to a therapist,” indicating that a significant number of people want to use a computer-based or web-based approach for issues like depression. The acceptability and usability of the programs was good, and those who completed activities showed improvement in self-reported stress and depression. Despite all this, however, very few people returned to continue their work on problem solving or stress management after the first visit. One possible explanation for this is that, no matter how acceptable, usable, or effective a program may be, CBT requires time and effort to be successful. Just like other activities that provide benefits but require effort , maintaining the motivation to complete the programs is difficult. Programs like these likely need to be used within a supportive environment with a human touch that can provide encouragement and ongoing support .Data collected was uncontrolled since this was essentially a pragmatic trial during a time when there was heightened interest in the program. In contrast to previous studies of computer-based and online-based interventions evaluated in the setting of clinical trials, we analyzed anonymous data of a self-guided freely available program where users were self-referred.This evaluation of the PATH program has many limitations, with the most significant likely being the high attrition rate, which is consistent with other research on self-help interventions ,34,39-42Additionally, the data reported here are only from those who completed an activity in the program and then opted to complete a questionnaire, resulting in a selection bias. We do not have data from those who did not complete activities or who may have stopped because of difficulties with either usability or acceptability. The small sample size for those completing multiple sessions restricted our interpretations as to whether this program had a significant effect on reducing mental health symptoms. Given these limitations, any generalization from these results needs to be evaluated with caution. Nevertheless, the opportunity to evaluate a program like this during the COVID-19 pandemic was unique.The programs were initially developed for use at NASA, and the conflict module included examples and situations based in the space program. The high acceptability and usability ratings for the conflict program indicate that these examples could be understood and applied to the public outside of NASA. Nevertheless, feedback from some participants who completed the conflict module included questions regarding the meaning of some of the words used, such as “EVA” (extravehicular activities). The vocabulary included in some of the scenarios could have created a barrier, hindering the full understanding of the module.The sample was also not well characterized because, to maintain participant anonymity, only minimal data were collected about the users. This makes it difficult to gauge how confounding variables such as ethnicity, socioeconomic status, or education level would affect the results and attrition rate of this study. Additionally, the Dartmouth PATH program had a high exposure within the Dartmouth hospital and college campus, which could have contributed to a sample skewed toward well-educated individuals. These factors could affect the results and dropout rates. Those with more experience with computers may be more likely to continue with the program compared to those with lower education backgrounds and less technological capability . Future During the COVID-19 pandemic, those who came to the program seeking self-help for behavioral programs and engaged with the program rated conflicts with family or spouse as a major source of stress. Feelings of hopelessness, worry, anxiety, and anger were commonly reported. The problems selected for the mood program focused on worry, anxiety, overthinking, and stress. Despite the high attrition rate, this study shows that an open-access online behavioral program aiming to treat depression, stress, and conflict management can be effective and rated highly for usability and acceptability by users. This suggests the main issue is not content but instead finding the best implementation strategy. A significant proportion of users reported that they preferred to address behavioral health programs on their own using an online resource. Those who returned for additional visits tended to report more issues with worry, stress, and anxiety, and on average rated the programs less usable than those who completed just one session. For acceptability, there were differences in response patterns between the mood and stress module. Although those who returned for more visits in the mood module rated the program as more acceptable, the opposite trend was true in the stress module. The ultimate value of this program as a stand-alone resource will depend on understanding the reasons for the low completion rates and addressing them effectively. Strategies such as progress tracking, professional support, and weekly reminders may help increase users’ adherence to online programs . Attriti


Numerous ligand-based drug discovery projects are based on structure-activity relationship (SAR) analysis, such as Free-Wilson (FW) or matched molecular pair (MMP) analysis. Intrinsically they assume linearity and additivity of substituent contributions. These techniques are challenged by nonadditivity (NA) in protein–ligand binding where the change of two functional groups in one molecule results in much higher or lower activity than expected from the respective single changes. Identifying nonlinear cases and possible underlying explanations is crucial for a drug design project since it might influence which lead to follow. By systematically analyzing all AstraZeneca (AZ) inhouse compound data and publicly available ChEMBL25 bioactivity data, we show significant NA events in almost every second assay among the inhouse and once in every third assay in public data sets. Furthermore, 9.4% of all compounds of the AZ database and 5.1% from public sources display significant additivity shifts indicating important SAR features or fundamental measurement errors. Using NA data in combination with machine learning showed that nonadditive data is challenging to predict and even the addition of nonadditive data into training did not result in an increase in predictivity. Overall, NA analysis should be applied on a regular basis in many areas of computational chemistry and can further improve rational drug design.The online version contains supplementary material available at 10.1186/s13321-021-00525-z. The similarity and additivity principles represent the basis of various well-established areas in computer-aided drug design (CADD) such as Free-Wilson (FW)  analysisYet, both these principles are subject to frequent disruptions. The exceptions to the similarity principle often complicate SAR analysis. So-called ‘activity cliffs’ refer to structurally very similar compound pairs with large alterations in potency –14. ExceAdditivity and NA of ligand binding have been studied for many years , 24 and NA is calculated from so-called double-mutant or double-transformation cycles (DTC) . These cExtensive work on experimental uncertainty and NA has been carried out by Kramer et al. , 34–36. Despite the clear need for NA analysis it is generally not incorporated in classical QSAR applications and publications. NA clearly creates difficulties for linear SAR analysis approaches, such as standard MMP and FW analysis. These classical QSAR models will not work if the effect of introducing group R1 in the molecule is influenced by R2 or R3 .Apart from classical CADD approaches, many machine learning (ML) and deep learning (DL) techniques became popular and are applied to a diverse range of questions—from generation of new molecules –40, to pIn this work we show a systematic analysis of AZ inhouse and public ChEMBL physicochemical and biological data with the aim to quantify and compare NA in assays and compounds in public and inhouse data. Nonlinear events occur in 57.8% of all the AZ inhouse and in 30.3% of all public assays, indicating the need for constantly integrating NA analysis in drug discovery projects and understanding the structural reasons behind it. Additionally, we trained ML models to evaluate the predictability of nonadditive data and could show their poor performance in all trained models.https://github.com/KramerChristian/NonadditivityAnalysis) [1-4):The open-source NA analysis code provided by Christian Kramer was used in this study  . The codnalysis)  as well nalysis)  which isnalysis) . DTCs arNonadditivity analysis is performed for each assay independently.In this study both public and inhouse data are analyzed in order to compare the occurrence of NA. By understanding both types of data valuable information can be concluded for CADD projects.Assay data was downloaded from ChEMBL version 25  . A ChEMBAll assays with an existing target gene ID were extracted from the internal AZ screening and test database .. pIC50) but had an indicated unit were discarded. (5) All compounds with multiple measurements in one assay, where the difference between the minimum and the maximum measurement was larger than 2.5 log units, were removed. For those kept, the median of the logged activity values was calculated. Only compounds with large measurement differences were removed, the assay itself was kept. (6) All compounds with different IDs and the same simplified molecular-input line-entry system (SMILES) strings were filtered out and only the compound with the highest activity value was kept. (7) The molecular size was restricted to 70 heavy atoms (atomic number > 1). (8) Last, small assays with less than 25 compounds were removed.Molecules were standardized with PipelinePilot  and molecules were standardized with the included RDKit standardization code.Data curation was conducted with the Jupyter notebook  to assess general model performance in a classification scenario. For all models 500 trial runs were performed using a 5-fold cross-validation to avoid overfitting. We used ECFP6 counts (as implemented in REINVENT [2 and RMSE as implemented in scikit learn.The models are trained to predict the compounds’ pICREINVENT ), which The following protocol was applied to ChEMBL data for training RF, SVM and PLS models. Herein, additive data refers to those compounds that had NA below the experimental uncertainty cut-off of 1.0 log unit and were thus not significant.Models were trained based on different data selection strategies. First, compounds were considered that occur in DTCs (‘DTC-split’). For those compounds, their NA value is known and they can be classified as either additive or nonadditive. Second, we formed a data set based on all compounds , in which compounds that are not in DTCs are assumed to be additive. For the first two selections further separation into training and test data is based on stratified splitting with 80% training and 20% testing, herein, only additive data is used for training, while different test sets are compiled consisting of additive or nonadditive data. Third, a splitting strategy was applied to construct the training data consisting of A or B compounds, while the testing data contained AB compounds (‘A-B-AB-split’). For this third set the information from the DTCs was leveraged to assign compounds to either training or test set. This splitting strategy was once applied using DTC data only and once adding those compounds, for which no DTC information is available. Due to a random starting point of assigning compounds to the additive test set, this strategy was performed twice using two different random seeds (4 and 7) in order to exclude the starting point being responsible for the performance of the model. For further information on the selection see Additional file (1.1)Optimization of hyper-parameters based on the training set (80% additive observations) with 5-fold cross-validation (i.e. mean performance of 5 models trained on 80% of the training set).(1.2)Train final model on all of the training set using the best hyper-parameters from (1.1).(1.3)Prediction of test setsDTC-split and all-split: predict two test sets – the non-significant test (20%), i.e. additive data only and the significant hold-out sets , i.e. nonadditive data only.A-B-AB-split: predict the non-significant AB test (20%), the significant AB test set, all remaining significant compounds not assigned as AB, and  the non-significant test (20%). Three tests sets are used for DTC data, four test sets for all data.(1.4)2 and RMSE to quantify performance.Use RFor all three data splits the following model training and testing strategy was applied:(2.1)The predictions from (1.3) were dichotomized  and then compared to the true class (same threshold).(2.2)Matthews correlation coefficient (MCC from scikit learn) is used to quantify performance. MCC is used due to several advantages for binary classification problems : The MCCThe effect of NA data during training and on the model performance on the test data was analyzed by adding increasing fractions of NA observations in the respective training sets see . TherefoDTC-split.Overall, for each selected ChEMBL data set 12 RF models were trained  in AZ assays have been measured more than once Table , which iata sets –36 0.3 aFigure According to Fig. In order to compare the distribution of NA in two groups, two tests have been performed: (1) Kruskal-Wallis H Test , that doImportantly, public data has a larger number of assays with fewer measurements and unique compounds Table . The numLess than half of the assays 41.7%) in AZ screening and test database are either additive or no DTCs were assembled  the coverage of pIC50 values between additive and nonadditive  is used for training the models. For the NA set only a minor improvement in RMSE values can be observed, while overall the model is still non-predictive for this data set, i.e. negative R2 and an RMSE > 1.2.Based on the automatic hyper-parameter training using Optuna, individual RF models were generated for each of the three selected ChEMBL assays  can be seen for ChEMBL1613777 but not for the already bad performing ChEMBL1613797  large molecules, such as peptides are not compatible with the NA analysis since it is impossible to track small functional groups; (2) performing calculations on large molecules is computationally expensive; (3) cases where the functional group represents too large a proportion of the molecule will most likely result in NA since almost the whole compound is transformed and the corresponding binding mode is more likely to change.In addition to the size restrictions of molecules, limiting assay size after all the data-cleaning steps is also crucial. On one hand, small assays should be discarded, because there is a lower probability of DTCs assembling. In this research project, 25 was set as the lowest number of unique compounds per assay. Since most of the assays are small , it also influences the general statistics resulting in no NA output. One might argue that the majority of the assays are additive, however, most of them are too small to draw any meaningful conclusions regarding their NA.According to the results, significant nonlinearity occurs once in every second assay in AZ inhouse and once in every third biological and physico-chemical assays in ChEMBL databases. Importantly, significant nonadditive events are less frequent in public data sets. The reasons for this can be: (1) potential bias in reporting single series or positive SAR results; (2) the smaller size of public bioactivity assays, resulting in less DTCs; (3) a higher threshold of the experimental uncertainty for the entire data, as some assays have significantly higher experimental noise. An additional influence is the reliability of the compound measurements. Since in the inhouse database a majority of compounds is measured several times in each assay the measurements are more reliable. This is not the case in the public data sets, where only 5% of the compounds are measured more than once in each assay.Prior to the analysis, it is crucial to carefully set the thresholds for the experimental noise to point out true NA cases. Strong NA stands out from the rest of the data and it is easy to spot, while weaker NA is usually blended with the experimental noise. As described by Kramer et al. . NA anal2 values. ChEMBL1613797 assay data on the other hand proved to be difficult and the models do not generalize well. Thus, main conclusions are drawn from results based on ChEMBL1613777 and ChEMBL1614027. A clear trend for all three selected assays was a bad prediction of NA compounds independent of the models’ training performance. This observation remains true for different selection of training data, i.e. only based on compounds occurring in DTCs or based on all assay data. Employing a different splitting strategy (A-B-AB-split) by leveraging the information from DTCs resulted in a better performance for additive compounds but no or a slight drop in performance for NA compounds. The reason for this might be that the model learns the additivity from compounds A and B. Thus it can only predict compound AB correctly, if the additivity assumption holds true. If, however, compound AB displays an unexpected change in affinity, i.e. the compound is nonadditive, the model has even more problems predicting this compound compared to other models trained on data where the compounds were randomly assigned to either training or test set. Overall, this analysis shows how important a careful analysis of nonadditivity in data is. Even though the inclusion of those compounds does not affect the performance too much, nonadditive compounds cannot be predicted correctly and thus display a problem for QSAR models. Here, one also has to keep in mind that especially those NA compounds might have an interesting SAR that can be further leverage in the drug design process.Based on three showcases, we elucidated the impact of NA data in QSAR models and how well NA compounds can be predicted by those models. Herein, ChEMBL1613777 and ChEMBL1614027 achieve good generalization during training the models as shown by high cross validation RNA can be a problem for linear SAR techniques. Yet, if used intentionally, it can be an important tool for drug discovery. This study provides a detailed picture of the NA pattern amongst the inhouse and public databases, providing the global distribution of nonlinear events amongst assays and unique compounds. A careful understanding of the data is the key to successful decision-making. By conducting NA analysis one can easily identify outliers, detect potential assay artefacts, or key conformational changes. It is crucial to understand the possible experimental noise, that can be underlying most of NA cases. Therefore, one must always keep in mind the origin of a given assay, the reliability of the measurements, and a possible upper limit of experimental uncertainty.By systematically incorporating the NA analysis into the drug discovery projects, detection of interesting interactions and key SAR features will be easier and will eventually provide more structural insights for rational drug design.Identifying NA in the SAR data sets can be crucial by suggesting important structural features for the compound optimization. However, nonadditive events can be caused by the random addition of experimental uncertainty, which is important to consider during the interpretation of results. The impact of the experimental noise increases with the size of the assay, as more double-transformation cycles can be assembled. NA analysis in the AZ compound database suggests that significant nonlinear events are more frequent in AZ inhouse data than public ChEMBL data. By considering only public data one might assume that NA is a rare event and important cases can be neglected. AZ data points out the fact that this is not true and the statistical framework of the NA analysis should be systematically implemented in SAR projects and discussed in publications for rational drug design.Retrospectively, it is difficult to identify whether a specific change lead to a general increase or decrease in activity. From MMP studies we know that 100-fold improvements are very rare events of about 1% . Our numCurrently, the sign of a NA value does not provide valuable information since the order of compounds does not indicate the effect of a given transformations. In other words, one cannot establish which feature leads to the gain or loss of activity from investigating a specific double-transformation cycle. It would add another level of information to see the pattern of NA distribution in terms of boosting or decreasing the biological effect, whether the cases are equal or mostly lead to the loss of biological activity. For further follow-up work it would be of interest to draw conclusions about patterns in NA, i.e. if target-specific or non-target specific modification can be identified that always lead to NA, both on a per dataset basis and across public and inhouse data.Additional file 1. Additional figures.Additional file 2. ChEMBL1613797 data set with obtained NA values for ML approach.Additional file 3. ChEMBL1614027 data set with obtained NA values for ML approach.Additional file 4. ChEMBL1613777 data set with obtained NA values for ML approach.