This systematic review aimed to provide a comprehensive overview of the validity and reliability of existing measurement instruments for quantifying head and neck lymphedema.Four databases were searched on January 31st, 2022. The COnsensus-based Standards for selecting health Measurement INstruments (COSMIN) checklists were used for the risk of bias (ROB) assessment.Out of 3362 unique records, eight studies examined the reliability and validity of five measurement instruments of which one patient reported outcome. The Patterson scale for internal lymphedema and the patient reported head and neck external lymphedema and fibrosis (LIDS-H&N) demonstrated validity and reliability. For external lymphedema, none of the instruments had good reliability for all measuring points.There is a lack of sufficiently reliable and valid measurement instruments for external head and neck lymphedema. The Patterson scale and the patient reported LIDS-H&N seem reliable for clinical practice and research. The often extensive treatment for head and neck cancer can cause long-term side effects. Well-known and extensively reported side effects include dysphagia , 2, xeroHead and neck lymphedema is a chronic accumulation of fluid and proteins in external structures (soft tissue) as well as in internal anatomical sites (mucous membranes and underlying soft tissues of the upper aerodigestive tract) , 11. HeaReliable screening, treatment, and follow-up of HNC-related lymphedema is important to optimize lymphedema care in head and neck cancer survivors. Therefore, the presence of a valid and reliable measurement instrument is needed to establish severity of lymphedema and to measure effectiveness of treatment. A few methods have been proposed and evaluated in terms of validity and reliability, and some external head and neck lymphedema measurement instruments have found their way into clinical practice –24. ExamWhile there is consensus on how to measure and evaluate lymphedema of the limbs, this is not the case for head and neck lymphedema, and to our knowledge evidence based recommendations are currently lacking. This causes practice variation and lack of standardization of outcome measures in clinical studies. The purpose of this systematic review is to (1) provide a comprehensive overview of the literature on clinician-administered and patient-reported measurement instruments for the assessment of external- and internal lymphedema in the head and neck area in patients after HNC; (2) to determine the validity and reliability of the different methods; and (3) to provide recommendations for clinical practice based on evidence.This systematic review was reported in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) . The proWe performed a systematic literature search, with the support of a medical information specialist (E.A.W.), in four electronic databases: MEDLINE (PubMed), Embase (OVID), SCOPUS, and PEDro. The last search date was January 31th, 2022. The search strategy included combinations of free-text keywords, equivalent words in title/abstract, and standardized keywords (MeSH and Emtree). The search strings were translated according to the standards of each separate database. Search terms included (“head and neck cancer” and “lymphedema”) or  (the full search strategy is shown in supplement 1), with no limits for date, study design, or language. Duplicate articles were removed according to the method of Bramer et al. . In addiStudies that assessed the properties of measurement instruments for the severity of lymphedema in the head and neck area, either scored by clinicians or patient-reported (PROMs), were eligible for inclusion. Studies on patients with lymphedema due to non-oncological etiologies in the head and neck area or including only healthy participants were excluded. Two reviewers (C.R.A. and J.E.L.) independently screened the retrieved records based on title and abstract, blinded to each other, using Rayyan QRCI . Next, fTo extract the data from each study, a structured data collection form was used. Data extraction included: sample size, study population, name of the measurement tool/technique assessed, reported results of intra- and interrater reliability, and validity indices.The COnsensus-based Standards for selecting health Measurement INstruments (COSMIN) risk of bias (ROB) checklist was used for the risk of bias assessment of the PROM studies –30. For Our systematic search identified 5091 records, and an additional 74 records were identified through backward and forward reference checking. After resolving duplicates, 1803 records, the in- and exclusion criteria were applied to 3362 abstracts. In this step, 3337 articles were excluded and we retrieved the remaining 25 articles for full-text. Of these, eight articles explicitly reported the reliability and validity of measurement instruments for lymphedema in the head and neck area and were included in the final analysis. Figure The included articles were published between 2007 and 2021. Seven of the eight studies were single-center cross-sectional studies. Only the study of Deng et al. was a prospective study, with six follow-up moments . The numexternal lymphedema; all were clinician-rated [internal lymphedema, using a rating scale on laryngopharyngeal video endoscopic images[n = 6) [n = 3) [Four studies used an instrument for assessing an-rated , 36, 37.an-rated , 34, theic images, 38. In ic images, 39 was s[n = 6) –38 and iFor ROB assessment we used the COSMIN tools. Content validity of the LSIDS-H&N was assessed in one study, by Deng et al. This study received a score of ‘doubtful’, because it was unclear whether there were at least two researchers involved in the analysis . Two stuThe measurement properties of the MoistureMeterD, a device estimating extracellular water percentage using dielectric constant analysis, was studied by Purcell et al. using a single submental measuring point. The interrater reliability was excellent (ICC 0.973) . They alTwo studies used tape measurements to quantify lymphedema , 34. Chop < 0.001, in one study [The HN-ELAF criteria classify lymphedema and fibrosis into four phenotypes, dependent on palpable thickening, tightness of the dermis, visible swelling, reducibility and persistency. The HN-ELAF showed an interrater reliability absolute agreement of 83%, with a kappa 0.75, ne study . The HN-ne study .For validity assessment, Deng et al. grouped ultrasound measurements in five regions  corresponding with the HN-LEF sites. The area under the curve (AUC) from the ultrasound value on each region was used as the reference standard to enable known group comparisons. Known group validity was largely confirmed for the submental, cheek, and neck regions, but expected associations of the peri-orbital and supraclavicular regions with HN-LEF typing was not confirmed .The two studies on internal lymphedema assessed the measurement properties of the Patterson scale and the Revised Patterson Scale, respectively , 38. BotThe revised Patterson Scale has been shortened, and to improve reliability, a description for each severity level was added with photographic examples. These additions improved the weighted kappa for the epiglottis (0.63 to 0.78), vallecula (0.34 to 0.68), pharyngoepiglottic folds (0.40 to 0.52), arytenoid (0.65 to 0.69), false vocal fold (0.61 to 0.66) and pyriform sinuses (0.53 to 0.54). The aryepiglottic folds (0.65 to 0.63) and the true vocal folds (0.43 to 0.23) showed less favorable agreement compared to the first study. The mean weighted kappa was 0.54 in the original scale, and 0.64 for the revised scale , 38.A single PROM was identified, reported in two studies. Deng et al. developed the LSIDS-H&N, a patient reported outcome for head and neck lymphedema and fibrosis. They started with 64 items, which were eventually reduced to 33 items in seven symptom domain clusters with good internal consistency; Cronbach’s alpha for each cluster ranged between 0.49 and 0.90 .Activity cluster. Also, scores on Lymphedema Quality of Life Inventory (LyQOLI) correlated with the practical cluster strongest with the clusters Activity and Sexuality, as hypothesized.In a subsequent study, Ridner et al. revised the LSIDS-H&N to include 48 items in seven clusters. The Cronbach’s alpha for the clusters varied between 0.83 and 0.95. Construct validity of the scale was assessed by correlating the LSIDS-H&N cluster scores to other questionnaires . Out of n sample size, HNC head and neck cancer, HN-ELEF Head and neck External lymphedema and fibrosis assessment criteria, HN-LEF Head and neck External lymphedema and fibrosis assessment criteria, ALOHA Assessment of Lymphedema of the Head and Neck, LSIDS-H&N Lymphedema Symptom Intensity and Distress Survey-Head and Neck, ICC Intraclass Correlation Coefficient, Sw within-subject standard deviation, MMD MoistureMeterD, FASQ Functional Assessment Screening Questionnaire, PROMS-SF Profile of Mood States-Short Form, LyQLI Lymphedema Quality of Life InventoryThe purpose of this systematic review was to provide a structured overview of the literature on the validity and reliability of measurement instruments for the assessment of external—and internal lymphedema in the head and neck area. In total, eight studies met the inclusion criteria. These studies were scored on their ROB and results were summarized. The measurement tools identified were tape measurements, the MoistureMeterD, the clinician rated HN-LEF criteria for external lymphedema, the Patterson scale for internal lymphedema, and a single PROM: the LSIDS-H&N.In an earlier ‘state of the art’ scoping review on measurement instruments for head and neck lymphedema, by Deng et al., the authors pointed out that there was a lack of reliable measurement tools and that adequate measuring of lymphedema in the head and neck area remained a challenge . FollowiBased on the findings of our review, this challenge is still unmet for measurement instruments assessing external lymphedema. While tape measurement, the MoistureMeterD, and the HN-LEF have all shown some promising results for some, but not all, measuring locations in the head and neck area, reliability of each of these instruments could be further improved, and evidence for validity is still limited at best. With regard to measuring internal lymphedema, the revised Patterson scale showed a moderate to good interrater reliability except for the true vocal folds, and thus seems adequate for clinical practice and research.The only patient-reported outcome identified; the LSIDS-H&N, showed good reliability in two studies, and some evidence to support validity.Quality of study design and reporting could be improved in future studies, as evidenced by the high number of ‘doubtful’ scores assigned to the studies included in this review. For example, three studies did not describe their hypothesis in the methods, but rather in the results or discussion section. As a result, it is unclear if these were a-priori or post-hoc hypotheses , 36, 39.Standardization of measures for assessment of external lymphedema in the head and neck area in clinical practice would be highly desirable. Nevertheless, the current evidence is still too limited to recommend any single instrument for this purpose. Given the small number of studies and the methodological limitations in many of them, further studies are needed to strengthen the evidence base for existing measurement instruments as well as to improve the reliability of measurement procedures. Future studies would benefit from careful design and reporting, taking into account all quality recommendations for conduct and reporting of clinimetric research as proposed by COSMIN .The revised Patterson scale seems adequate for assessing internal lymphedema in clinical practice and research. To this end we would recommend to make recordings when laryngoscopies are conducted in daily practice, making sure that the anatomical locations used in the Patterson scale are clearly visualized, and to save the images in the electronic hospital records for future reference and research.Although the validity of the LSIDS-H&N requires further confirmation, this patient reported outcome measure is the only one currently available. Since it has sufficiently promising measurement properties, it could be considered for use in clinical practice when appropriate.This systematic review was done according to rigorous methodological standards: two independent authors conducted all steps of the systematic review (C.R.A. and J.E.L.), and no constraints were put on language, resulting in a broad search with a limited risk of missing relevant publications. Unfortunately, for two articles, no full text could be obtained, which is a limitation of our results. The quality rating was based on reported information. Due to shortcomings in meeting reporting standards, our ROB assessment may have been overly strict, however, we preferred to err on the side of caution instead of relying on benefit of the doubt. Finally, a meta-analysis of the reliability of the measurement instruments was not possible due to the heterogeneous nature of the studies and limited available data.To date, no single clinician-rated measurement instrument for external lymphedema can be recommended for clinical practice without caution due to limitations in reliability and heterogeneity in the applied measurement protocols. The MoistureMeterD is reliable for the measurement of extracellular water in the submental region. The only currently available PROM—the LSIDS-H&N—has shown promising reliability and can be used in clinical environments. The revised Patterson scale is very promising for measuring internal lymphedema and seems to be useful in clinical practice.


The use of artificial intelligence (AI) for national defense is a matter of high societal significance and ongoing public discourse, but very little is known about public acceptance of AI in defense contexts. Currently, there is no reliable and valid measure of attitudes towards AI in defense, and more general attitudinal measures on AI use are unlikely to capture relevant perceptions and opinions. A measure was therefore developed for the assessment of Attitudes towards AI in Defense (AAID), and this work presents the initial validation of this scale.M = 45.7, SD = 16.1) completed a self-report questionnaire which included an initial item pool of 29 attitudinal statements related to the use of AI in defense. An additional general attitude towards AI scale was also included to assess the concurrently validity of the AAID scale. The AAID underwent initial statistical validation via exploratory factor analysis and confirmatory factor analysis to test the underlying structure of the newly developed scale.A total of 1,590 participants . Factor 1 was termed “Positive outcomes” and reflected the potential and anticipated consequences of implementing AI in defense. Factor 2 was termed “Negative Outcomes” and reflected the potential negative outcomes for AI in defense. The scale also exhibited acceptable internal reliability and current validity.The newly developed AAID presents a new measurement tool which has the capacity to assess current attitudes towards AI in defense. Such work is essential if further developments in AI in defense are to continue with the support of the public. However, the work also notes that there are some key concerns and barriers that could block further developments in the area, with further work needed to explore how such anxieties are driven by narratives related to the topic. The use of Artificial Intelligence (AI) continues to grow exponentially, permeating throughout many aspects of society . A promiThe development of AI has opened up a variety of potential uses for military and defense purposes . ApplicaDefense-specific definitions of AI have been offered but are not frequently discussed in the research literature. For example, the •“An artificial system that performs tasks under varying and unpredictable circumstances without significant human oversight, or that can learn from its experience and improve performance when exposed to data sets;•An artificial system developed in computer software, physical hardware, or other context that solves tasks requiring human-like perception, cognition, planning, learning, communication, or physical action;•An artificial system designed to think or act like a human, including cognitive architectures and neural networks;•A set of techniques, including machine learning that is designed to approximate a cognitive task;•An artificial system designed to act rationally, including an intelligent software agent or embodied robot that achieves goals using perception, planning, reasoning, learning, communicating, decision-making, and acting.”Such descriptions indicate that, other than the specific area of application and the types uses therein, there is little difference in what qualifies as AI within and outside civilian versus defense contexts. A summative working definition for more general purposes is provided, for example, by “Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction. Particular applications of AI include expert systems, speech recognition, and machine vision. Examples of AIs include personal helpers (like Siri and Alexa), medical diagnostic aids, and self-driving vehicles” , p.1.Given that the purpose of the present work is not to capture accurately the technicalities of AI, but to address public perceptions of it, the general working definition by Potential uses of AI in defense have been categorized according to three types of uses: (1) Sustainment and Support; (2) Adversarial and Non-Kinetic; and (3) Adversarial and Kinetic Uses . In brieWhilst the technical feasibility of AI is the focus of much research and development across several defense domains e.g., , concernA number of countries have started to address concerns surrounding AI systems with an openly communicated commitment toward transparent and ethical uses in defense contexts. For example, the While strategic commitments are an important element in outreach and engagement activities, it remains to be seen whether commitments hold the power to shape and influence opinions and attitudes. In democratic societies, citizens’ views often play a key role in informed decision-making and policy development, including the regulation and funding of science and technology . UltimatAt present, there is no bespoke, relevant, contemporary scale that measures attitudes toward AI in defense. Creating such a scale will help to identify key trends in public attitudes and understand factors that underlie these views, as well as being able to identify potential influences on attitudes due to information bias and misinformation. This will enable, it is hoped, technology developers to consider the reputational aspects of their practice; it will help those responsible for policy to shape relevant strategies; it will support those concerned with AI implementation to communicate appropriately to both the public and the personnel affected. Therefore, the key aim of the current study is to develop a robust, reliable, and psychometrically valid scale that is capable of measuring attitudes toward AI use in defense.n = 2,000). This research is noteworthy, as it included detailed discussion around the preferred governance of AI. In general, participants expressed mixed feelings about the development of AI, with 41% supporting the development of AI, and a smaller number of respondents (22%) opposing further development in the field. A substantial number (82%) strongly believed that robots and AI should be carefully managed. In terms of those aspects that were rated most highly in terms of governance concerns, four key themes emerged  military, law enforcement, intelligence, and search and rescue operations.M = 45.7, SD = 16.1) completed a self-report questionnaire. The sample comprised 777 male and 800 female participants, with 13 participants preferring not to disclose their gender. 58% reported to be in employment (excluding self-employment), 42% reported an annual income of £30,000 and above. 89% self-identified as White.A total of 1,590 participants, aged between 18–75  and negative aspects  prominent in work so far. Given that AI in defense does not refer to a homogenous set of established technologies, it was decided not to commit to specific applications and devices in the items wording. Instead, the focus was placed on the expected or anticipated positive and negative consequences of further development and use of AI in defense, defined in general terms. This approach means that the attitudinal construct is not represented by a direct evaluation of technology (which may or may not exist in the present), but instead by the perceived balance of positive and negative outcomes.In a second step, draft items were generated to capture such positive and negative consequences. Negative items tapped into the dystopian narratives surrounding AI use in defense, including concerns about the threats to human existence, privacy, an escalating arms race, and ethical concerns . PositivDraft items were then, in a third step, reviewed by two independent reviewers with domain knowledge. Reviewers provided feedback on general suitability and on wording such that the item pool could be further refined. This process yielded a total of 29 items, with 14 positive and 15 negative statements associated with attitudes toward AI in Defense see . Items wAll 29 items were included in the survey. The section of the study was introduced with the following paragraph:“In this part of the study, we want to now focus on the use of AI in a Defense setting. These are general statements that could relate to the use of AI for Defense purposes, and are not directly based on a particular application or type of AI. For each of the statements, please choose the option that most closely represents your attitude toward it.”This was then followed by the stem sentence: “The use of AI in Defense could.” Participants were asked to rate the extent to which they agree with the statements on a five-point Likert-scale .Next to the demographic information  and the 29 preliminary items of the AAID, the General Attitudes Toward AI Scale GAAIS;  was incln = 100), but the authors reported adequate Cronbach’s Alpha of 0.88 for the positive attitudes toward AI sub-scale, and 0.83 for the negative attitudes sub-scale, indicating good internal reliability  and includes questions such as “There are many beneficial applications of Artificial Intelligence”  and “I shiver with discomfort when I think about future uses of Artificial Intelligence” . The scale is split into two, 10-item sub-scales, measuring positive and negative attitudes. The scale has been previously used with a small sample .All data was cleaned at the point of collection to remove participants who exhibited response acquiescence , and participants that completed the survey too quickly or took an excessive amount of time (in comparison to a mean time to complete). There were also attentional checks built into the questionnaire, where participants were asked to respond in a direct way to items to ensure they were still engaged in the survey . Participants who failed this attentional check had their participation terminated.Prior to analysis, data were screened for missing values and normality of distributions. The data contained no missing values, and skew and kurtosis values were within acceptable ranges .n = 794) and testing (n = 795) sub-samples of roughly equal size. A table of the descriptive attributes for each sub-sample are presented in The factor structure of the new Attitudes Toward AI in Defense scale was examined via Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). All analyses were conducted in R 4.1.3 . Prior tWhilst a two-factor solution was hypothesized based on current literature and existing similar measures, parallel analyses  was alsoConfirmatory factor analysis was conducted using the testing sub-sample to evaluate the solution obtained via the EFA. The model fit of the CFA was evaluated using the following goodness-of-fit indices and cut-off criteria: the chi-square per degree of freedom (χ2/df) ratio (less than three deemed acceptable), robust comparative fit index , robust Tucker-Lewis Index , robust root mean square error of approximation , and the standardized root mean square residual  Cut-off criteria were taken from Reliability of the AAID measure was assessed with Construct validity was further tested by inspecting Average Variance Extracted values. Concurrent validity was evaluated via the association between the new AAID scale and the existing GAAIS.p < 0.001, and the size of the Kaiser–Meyer–Olkin measure of sampling adequacy (KMO = 0.95) indicated that the remaining 28 items had sufficient common variance for factor analysis  met this criterion. Item 5 was subsequently removed from the item pool. The significance of Bartlett’s test of sphericity, χ2 (378) = 20062.26, analysis .The parallel analysis suggested a maximum of four factors, although only the first two factors had an empirical eigenvalue greater than one . When examining the pattern matrix of a four-factor solution, poor interpretability of factors and weak loadings  were observed for Factor 3 and Factor 4. Furthermore, the pattern matrix indicated poor interpretability of the solution with only two items loading onto Factor 4 and only three items loading onto Factor 3, with evidence of item cross-loading onto two or more factors. Given these observations and the conceptual debate on attitudes presented in the introduction, a two-factor solution was explored next. Interpretability of the two-factor solution was improved; however, there was still evidence of low loading items (below 0.50), cross-loading, and poor communality values (below 0.40). Subsequently several items were removed from the item pool - Item 6, Item 13, Item 16, Item, 19, and Item 21 due to low loadings, Item 23 and Item 20 due to cross-loadings, and Item 3, Item 22, Item 10, and Item 9 due to low communality values. A final two-factor solution was then obtained with acceptable pattern loadings and communality values with the remaining fifteen items.r = 0.11).The final two-factor solution explained 42.52% of the variance, with Factor 1 explaining 22.35% of the variance [Eigenvalue (λ) = 6.26], and Factor 2 explaining 20.17% of the variance [Eigenvalue (λ) = 5.64]. Nine items loaded onto Factor 1 and six items loaded onto Factor 2 at 0.50 of greater. Rotated factor loadings and communalities are presented in p < 0.001, χ2/df = 2.24; CFIrobust = 0.96, TLIrobust = 0.95; RMSEArobust = 0.05, and SRMRrobust = 0.05. Furthermore, the two-factor solution provided a better fit to the data than a more parsimonious, one factor solution  values were computed. Next to the results of the CFA already reported, AVE values indicate how much of the observed variance is captured by a construct compared to the amount that is due to measurement error . Values b = 0.27, β = 0.64, p < 0.001) and negative attitudes toward AI in defense . General negative attitudes toward AI were also associated with both positive  and negative attitudes toward AI in defense . Associations among the two positive and the two negative factors were stronger than among pairings of positive and negative factors, which was taken as first evidence of concurrent validity.To examine concurrent validity, the AAID factors were examined next to the factors of the GAAIS. As the GAAIS represents a broader, top-level domain of AI applications, associations between both instruments cannot be expected to be very strong. Still, some systematic association is desirable, and this should be strongest for the two positive and the two negative factors across instruments. A full SEM model was specified, whereby both factors of the AAID and the GAAIS were modeled as latent factors, therefore accounting for measurement error. General positive attitudes toward AI were associated with both positive  and Anticipated Negative Outcomes . These two factors are taken to represent positive and negative attitudes, respectively. The two factors are weakly correlated with each other, which indicates that individuals can hold, to a certain extent, both positive and negative attitudes toward the use of AI in defense. This empirical finding has implications both for scale application and implementation. First, it means that next to a consideration of an overall scale score, the scores of both sub-scales should also be routinely considered for any interpretation. Second, in some contexts that are concerned with clustering respondents or identifying different attitudinal constellations, differences can be explored between four types of responses: higher levels of positive and negative outcomes anticipated; higher levels of positive and lower levels of negative outcomes; lower levels of positive and higher levels of negative outcomes; lower levels of positive and negative outcomes.The AAID is free to use in research. The scale presented here consists of 15 items distributed over two sub-scales: Anticipated Positive Outcomes . Without more dedicated and robust attitudinal measures, there is little possibility of progressing our understanding of societal support of and opposition toward AI implementation. The present work attempts to ease some of these challenges: The AAID scale is a reliable and valid measure for further research and awaits further use.lee.hadlington@ntu.ac.uk.The datasets presented in this article are not readily available because data is protected via Official Level through DSTL. Requests to access the datasets should be directed to LH, Ethical approval for the current study was given a favourable opinion by the Ministry of Defense Research Ethics Committee (MODREC), ref 2109/MODREC/21. All participants provided their informed consent to participate in this study.LH: conceptual design and planning, data collection, manuscript preparation, project management, and lead researcher. JB and MK-M: data collection and manuscript preparation. SG: data collection, data analysis, and manuscript preparation. SK: manuscript preparation, approval for manuscript publication, and technical partner for funder. All authors contributed to manuscript revision, read, and approved the submitted version.


In recent decades, more and more attention has been paid to the influence of nutrition on reproductive health. Nevertheless, the imminent association between diet-related inflammation and the risk of infertility has not yet been established. The aim of the current study was to investigate the ability of the Dietary Inflammatory Index (DII) to estimate infertility incidence in women.This cross-sectional study was conducted using data from Ravansar non-communicable diseases (RaNCD) cohort study on 4437 participants. The DII was calculated based on the reported consumption of up to 31 food parameters measured via a validated and reproducible 118-item food-frequency questionnaire (FFQ). Multiple logistic regression analysis was applied to estimate the multivariable odds ratio (OR) adjusted for potential confounding variables.Out of all participants, 411 women (9.26%) were infertile. The mean ± SD age and weight of infertile women were 43.67 ± 7.47 years and 72.86 ± 13.02 kg, respectively. Statistical analyses showed the odds ratio of infertility in the fourth quartile (pro-inflammatory diet) was 1.76 times higher than in the first quartile (anti-inflammatory diet) of DII (95% CI: 1.57–2.02).The findings of this study provide compelling evidence about the association between infertility and the quality of diet in women. Therefore, interventions and programs aimed at promoting a healthy lifestyle and using healthy diets can be considered as one of the effective approaches in the prevention and treatment of infertility in women. Infertility is defined as a reproductive failure after at least one year of unprotected sex, which affects approximately 10–15% of women worldwide. Approximately 15.5% of married women aged 15 to 44 years in Iran are unable to become pregnant after one year of unprotected sex (infertility) . AlthougAlthough infertility therapeutic techniques have been developed, the high cost of these methods can lead individuals to come to alternative and inexpensive treatments . Most reThe dietary inflammatory index has been designed to recognize the inflammatory potential of individuals’ dietary patterns . The proTo date, no study has examined the association between DII and the risk of women’s infertility. Therefore, to illuminate this uncharted area, the current study was performed using data from a cohort study in a RaNCD cohort study to examine the relationship between women’s infertility and DII.This cross-sectional study was performed by using data from the RaNCD cohort study. The RaNCD cohort study is part of the PERSIAN  Cohort that has been conducted to investigate non-communicable diseases in 10,047 Kurdish participants aged 35–65 years living in Ravansar city, Kermanshah Province, West Iran since 2014. Details of this study were defined in previous studies , 22. AllInfertility was determined based on a self-administered questionnaire about fertility history that was completed for all participant women. All women aged 20–50 years old who had the inclusion criteria and were willing to collaborate were included in the study. Written consent has been obtained from all participants. The collected data were investigated by the center supervisor and ultimately recorded after ensuring their accuracy on the same day. In case of any problem, follow-up was performed and the subjects were asked to present in the cohort center and complete the questionnaire again. In this study, the online version of the standard questionnaires was used to collect information, in accordance with the PERSIAN cohort protocol. However, the questionnaires used in this study were revised after performing a pre-pilot phase to improve their validity and reliability. The exclusion criteria were the absence of any malignancy, following a special diet, and having conditions such as neurological, hepatic, and endocrine diseases was our inclusion criteria. Among the all participants, 4764 were men and excluded from the study. Also, other excluded participants were as follows; 447 single and divorced women, 162 women with a history of hysterectomy, 105 pregnant women, 112 women who intake more than 4200 calories, and 20 who intake less than 800 calories. Finally, 4437 women (411 infertile and 4026 non-infertile) were included in this study.Baseline questionnaires including demographic, socioeconomic status, physical activity, smoking status, alcohol intake, dietary habits, medical history, family medical history, and FFQ were completed in face-to-face interviews by a trained interviewer for all participants recruited to RaNCD.In RaNCD cohort study, physical activity was assessed by the standard questionnaire designed by PERSIAN mega cohort study with twenty two questions about the amount of daily activities. After collecting data, the participants’ responses were reported based on the metabolic equivalent of task per hour per day (MET/h per day). Detail of this questionnaire was described in the previous study . FinallyWeight was measured by an InBody 770 device while the participant was wearing light and without shoes with a precision of 100 gr. Height measurement was performed by using the automatic stadiometer BSM 370 in a standing position without shoes. Body mass index (BMI) was determined by dividing the weight (kg) by squared height (meter).1, B2, B3, B6, B12, folate, beta-carotene, total fat, saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), omega 3 and 6, cholesterol, Mg, Fe, Se, and Zn. These food item intakes were linked to the world database mean and SD from 11 studies worldwide [At the RaNCD study site, a 118-item FFQ was completed, and the validity and reliability of this questionnaire have been assessed in previous studies , 27. We orldwide . FinallyData are accessible as the mean (standard deviation) and frequency (%) for continuous and categorical variables. The normality of the data was assessed and proven by the Kolmogorov–Smirnov test. Independent samples t-tests and one-way ANOVA tests were used for between-group differences of continuous variables. Additionally, the chi-square/Fisher exact test was used to assess the associations between two categorical variables, as well as the chi-square test was used for testing multiple categorical variables. Logistic regression was performed to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for infertility as the outcome adjusting for age, BMI, SES, and physical activity. P-values for trends were calculated across DII quartiles. All statistical analyses were performed using STATA version 14.1 . A P-value < 0.05 was considered statistically significant.Among 4437 women enrolled in this study, 411 (9.26%) were infertile. Almost 60% of participants were urban and 299 (12.51%) of them were infertile. Among infertile women, 252 (61.3%) of them had low physical activity (3 > MET/ h per day) which was significantly different from the fertile women (P = 0.001).  is significantly 86%  higher than the first quartile (anti-inflammatory diet). After adjusting for main confounding factors including age, BMI, marital status, smoking, education, alcohol drinking, energy intake, and physical activity, the odds of infertility in the fourth quartile (pro-inflammatory) was 76%  higher than first quartile (anti-inflammatory) . Consistent with our study, they concluded that greater adherence to a healthy diet may enhance oocyte quality and quantity. On the other hand, an unhealthy diet could negatively affect the chance of pregnancy . Unisa eIn addition, a randomized clinical trial showed that a healthy diet rich in fruits and vegetables, similar to the Dietary Approaches to Stop Hypertension (DASH), can have a positive effect on total antioxidant capacity (TAC). In this study, 60 women were randomly assigned to the DASH diet group  and a control group. After 12 weeks of follow-up, women in the DASH diet group had increased TAC. Additionally, another study showed that an increase in the DASH diet was associated with an increase in plasma TAC in obese subjects with PCOS. In this study, the “healthy diet” had higher vegetables, fruits, and nuts which these food groups are rich in antioxidants . It has Inflammation is one of the main causes of infertility in endometriosis. It prevents decasualization, lowers progesterone levels, and disrupts endometrial function. Moreover, hormonal imbalances caused by the different diseases play a significant role in infertility, as inflammation increases aromatase activity and creates an estrogen-dominant phase. Besides, oocyte maturation and fertilization are influenced by prostaglandins and cytokines from inflammatory cells. In this way, not only the reproductive system of the woman is affected, but also the sperm entering through the uterus and fallopian tubes, their movement is reduced and their attachment to the ovary is damaged. Reactive oxygen species also damage the endometrium, Oocyte, and sperm, resulting in oxidative stress that affects the molecular level .Chronic inflammation is a popular explanation of the positive association between DII and infertility, which may occur through the effect of a pro-inflammatory diet on increasing systemic inflammation . Some stThe present study had some limitations in terms of methodology and nature. Firstly, the DII score was calculated using only 31 dietary parameters due to a limitation in the number of parameters. A second limitation of the present study was that, because of its cross-sectional design, there was no way to examine the causal relationship between DII and the progression of infertility. However, this study had some strengths. An important strength of this study was its large sample size, which allowed it to determine the relationship between DII and infertility among Kurdish women in Iran. Other strengths of this study are the high quality of data collection, population-based study design, and adjustment for all known confounders such as age, BMI, SES, and physical activity. As well, using DII instead of inflammatory markers to assess the effect of inflammation may help directly measure the impact of diet on clinical outcomes through inflammation and reduce the overall cost of the study. Additionally, the calculation of the DII by an inexpensive and non-invasive method (FFQ) makes it possible to evaluate the inflammatory properties of food and its relationship with some diseases. In summary, these novel findings from the cohort study provide evidence that a more pro-inflammatory diet as indexed by the DII score increased the odds of infertility. Our findings showed that a diet rich in vegetable fibers and omega-3 fatty acids and low in unhealthy dietary factors such as high simple sugars and saturated fats can be considered an appropriate approach for reducing the risk of infertility. However, more investigations are necessary to validate the kinds of conclusions that can be drawn from this study. Our data suggest that we still have a long way to go, and future research should also examine whether altering the inflammatory potential of the diet can reduce chronic inflammation and the risk of infertility.


The median LDH level in the survival-to-discharge group was 376 U/L (range: 171–1620), which was significantly lower than that in the death group (p < 0.001). Using the adjusted model, the odds ratio of the LDH value (≤634 U/L) for primary outcomes was 2.418 (1.665–3.513) and the odds ratio of LDH value (≤553 U/L) for secondary outcomes was 4.961 (2.184–11.269). Conclusions: In conclusion, the serum LDH levels of patients with OHCA measured in the ED can potentially serve as a predictive marker for clinical outcomes such as ROSC and survival to discharge, although it may be difficult to predict neurological outcomes.Introduction: Out-of-hospital cardiac arrest (OHCA) is complex, and risk stratification tools have the potential to include components other than clinical risk indicators, thus requiring extensive studies. Simple and accurate biomarkers for OHCA patients with poor prognoses are still needed. Serum lactate dehydrogenase (LDH) has been identified as a risk factor in patients with various diseases, such as cancer, liver disease, severe infections, and sepsis. The primary aim of this study was to assess the accuracy of LDH values at initial presentation in the emergency department (ED) in predicting the clinical outcome in OHCA. Methods: This retrospective multicenter observational study was performed in the ED of two tertiary university hospitals and one general hospital between January 2015 and December 2021. All patients with OHCA who visited the ED were included. The primary outcome was the sustained return of spontaneous circulation  after advanced cardiac life support (ACLS). The secondary outcome was survival to discharge (including home care and nursing care discharge) among patients with ROSC. The neurological prognosis was considered a tertiary outcome in patients who survived to discharge. Results: In total, 759 patients were enrolled in the final analysis. The median LDH level in the ROSC group was 448 U/L (range: 112–4500), which was significantly lower than that in the no-ROSC group ( Out-of-hospital cardiac arrest (OHCA) results in 185,000–450,000 deaths each year, representing 7–18% of all deaths in the United States ,3. The cSerum lactate dehydrogenase (LDH) has been identified as a risk factor in patients with various diseases, such as cancer, liver disease, severe infections, and sepsis . Very hiCurrently, no information is available on the clinical usefulness of serum LDH in the ED with respect to the prediction of clinical outcomes in OHCA in the early phase. Therefore, the primary aim of this study was to assess the accuracy of LDH values at initial presentation in the ED in predicting the clinical outcome in OHCA. We hypothesized that LDH levels might be elevated in OHCA patients in the ED, along with a high risk of poor clinical outcome.This retrospective multicenter observational study was performed in the ED of two tertiary university hospitals and one general hospital between January 2015 and December 2021. This study was approved by the Institutional Review Board (IRB) of Wonju Severance Christian Hospital (IRB No. CR322004), Dongguk University Ilsan Hospital, Dongguk University (IRB No. DUIH 2022-02-027), and that of National Health Insurance Service Ilsan Hospital (IRB No. NHIMC 2022-03-030-001). The study protocol conformed to the ethical guidelines of the Declaration of Helsinki (1975) and its amendments. As the study involved retrospective and observational analyses, the requirement for informed consent was waived, and patient records and information were anonymized before analysis. Emergency medical technicians provide both basic and advanced life support for a minimum of 5 min at the scene. If the return of spontaneous circulation (ROSC) cannot be achieved, the patient is transported to the nearest ED. After ROSC is achieved, the patient is referred to the nearest hospital to receive post-cardiac arrest care, including targeted temperature management.From January 2015 to December 2021, all patients with OHCA who visited the ED were included. The total number of enrolled patients was 2375. All patients underwent treatment in accordance with the current ACLS guidelines. The exclusion criteria were as follows: (1) aged < 18 years, (2) traumatic arrest, (3) missing LDH values, and (4) death on arrival or patients with do-not-resuscitate orders.The following epidemiologic and biomarker parameters were obtained from the medical records retrospectively: age, sex, the witness of cardiac arrest, bystander cardiopulmonary resuscitation (CPR), initial shockable rhythm, total CPR time, in-hospital CPR time, total epinephrine dose, and LDH level at the ED. LDH was measured within 2 to 3 cycles of CPR during in-hospital ACLS.The primary outcome was sustained ROSC (>20 min) after ACLS. Secondary outcomes were divided into two patient groups: death after ROSC (including death in the ED or intensive care unit) and survival to discharge (including home care and nursing care discharge) among patients with ROSC. However, patients were excluded when they were referred to other institutions in ED or could not receive post-cardiac arrest care because they were transferred to other institutions within the ICU.The neurological prognosis was considered a tertiary outcome in patients who survived to discharge and was graded using Cerebral Performance Categories (CPCs), which was measured at the time of hospital discharge and assessed for outcomes. Favorable neurological outcomes were defined as CPC 1–2 and poor neurological outcomes were defined as CPC 3–5.t- or Mann–Whitney U test, as appropriate. Categorical data were analyzed using the chi-squared test or Fisher’s exact test, as appropriate. To calculate the effect size, Cramer’s v and eta squared coefficients were calculated. To evaluate the factors contributing to clinical outcomes, including ROSC, survival to discharge, and favorable neurologic outcomes, univariable and multivariable logistic regression analyses were performed, which are presented with odds ratios (OR) and 95% confidence intervals (CI). Variables with a p-value < 0.2 in univariable logistic regression analysis were included in the multivariable logistic regression analysis. A restricted cubic spline curve was fitted to visualize differences in the OR of ROSC, survival to discharge, and neurologic outcome according to LDH. p-values of <0.05 were considered statistically significant, all analyses were performed using SPSS ver. 23  and R statistical software .Continuous data are presented as means with standard deviations or medians (interquartile ranges), followed by normality tests. Normally distributed data were assessed using the Shapiro–Wilk test. Categorical variables are presented as counts and percentages. Continuous data were analyzed using Student’s n = 700), trauma (n = 222), age < 18 years (n = 20), death on arrival or those with do-not-resuscitate orders (n = 674). Therefore, 759 patients were enrolled in the final analysis (n = 34).During the study period, 2375 patients with OHCA were admitted to the ED. Among them, 1616 patients were excluded from the analysis because of the following factors: absence of laboratory biomarker data . The total CPR time was lower in the ROSC group than in the no-ROSC group (p = 0.000), and the total administered epinephrine dose was lower in the ROSC group (p = 0.000). The median LDH level in the ROSC group was 448 U/L (range: 112–4500), which was significantly lower than that in the no-ROSC group  . The total CPR time and total administered epinephrine doses were lower in the survival-to-discharge group . The median LDH level in the survival-to-discharge group was 376 U/L (range: 171–1620), which was significantly lower than that in the death group  . The out-of-hospital CPR time and total CPR time were lower in the favorable neurologic outcome group . The median LDH level in the favorable neurologic outcome was 354 U/L (range: 171–1620), which was not significantly different from that in the poor neurologic outcome group   . Of the 12–4500) . Of the 211–829) . CalculaROC curves were used to assess the predictive ability of LDH for clinical outcomes in OHCA patients . The optLogistic regression analysis was performed to assess the correlation between LDH and clinical outcomes of out-of-hospital cardiac arrest. The probabilities of primary and secondary outcomes increased significantly when the LDH level was below the respective cut-off value. However, the probability of tertiary outcome was not significantly associated with LDH. Using the adjusted model, including age, witness, initial shockable rhythm, in-hospital CPR time, out-of-hospital CPR time, total CPR time, and total epinephrine dose, the OR of LDH (≤634 U/L) for primary outcomes was 2.418 (1.665–3.513); the OR of LDH (≤553 U/L) for secondary outcomes was 4.961 (2.184–11.269); and the OR of LDH (≤483 U/L) for tertiary outcomes was 3.192 (0.691–14.743) .In this study, we verified LDH measured in the ED as an independent predictive factor associated with the clinical outcome of patients with cardiac arrest, and suggested a cut-off value for each clinical outcome. In this cohort, the group of patients with serum LDH ≤ 634 U/L measured in the ED was an independent factor associated with ROSC, with an adjusted OR of 2.418 . Among patients with ROSC, serum LDH ≤ 553 U/L was an independent factor for survival to discharge, with an adjusted OR of 4.961 (2.184–11.269). However, in the multivariate logistic analysis of favorable neurological outcome in patients who survived to discharge, serum LDH in the ED was not an independent variable, with an adjusted OR of 3.192 . According to the cubic spline curve, the OR trend for the primary and secondary outcomes decreased proportionally with serum LDH in the ED. The key difference between previous studies and the current study is that this study was conducted in the ED, whereas previous studies were conducted in the intensive care unit. This study aimed to determine the clinical implication of LDH level that was measured during ACLS in terms of predicting clinical outcomes in patients with OHCA. In this cohort, LDH was confirmed to be an independent predictor of ROSC and survival to discharge. However, its accuracy is difficult to use as a single parameter, and it has been identified as a factor that is difficult to use, especially for neurologic outcomes. As in previous studies, it may be helpful to provide accurate information through integrated hematological evaluation of the patient’s expected clinical outcome rather than a single biomarker . The onsAccording to the CPR guidelines, a multimodal approach is recommended for determining the clinical prognosis. Biomarkers are more stable and allow for objective patient assessment . ProcalcIn determining the prognosis of cardiac arrest, various scoring systems have been designed for different stages of cardiac arrest  and for This study had a few limitations. First, this is a retrospective study with many excluded patients. Second, the time of LDH measurement was different in all patients. All LDH levels were measured within the ACLS of the patient visiting the ED. However, caution is advised when interpreting our results because the durations of OHCA and CPR were different. Third, unlike most previous studies, no follow-up data on LDH levels were included in our study. Further follow-up studies are needed to properly establish the values indicated in our results. Fourth, as a multicenter study, a limitation of this study is that 700 OHCA patients were not enrolled because serum LDH levels could not be measured at the initial visit in all cases. This might have caused a selection bias, which could also limit the generalizability of our study. Despite these limitations, the measurement of serum LDH levels in the emergency room has the potential to be a valuable predictive tool for clinical outcomes in patients with OHCA.In conclusion, serum LDH levels of patients with OHCA measured in the ED are associated with ROSC and survival to discharge, but not with favorable neurologic outcome and therefore cannot be used to predict neurologic outcome.


We employed a previously developed smart glove device mounted with multiple inertial measurement unit (IMU) sensors on the upper limbs of healthy and chronic stroke individuals. The recorded ROMs were used to predict subjective spasticity through generalized estimating equations (GEE) for the affected side. Results: The models have significant (p ≤ 0.05 *) prediction of spasticity for the elbow, thumb, index, middle, ring, and little fingers. Overall, during SBS and FFE activities, the maximum number of upper limb joints attained the greater average ROMs. For large joints, the elbow during CS and the wrist during FFE have the highest average ROMs, but smaller joints and the wrist have covered the highest average ROMs during FFE, FBS, and SBS activities. Conclusions: Thus, it is concluded that CS can be used for spasticity assessment of the elbow, FFE for the wrist, and SBS, FFE, and FBS activities for the thumb and finger joints in chronic stroke survivors.Background: We aim to study the association between spasticity and active range of motion (ROM) during four repetitive functional tasks such as cone stacking (CS), fast flexion–extension (FFE), fast ball squeezing (FBS), and slow ball squeezing (SBS), and predicted spasticity models. Methods: An experimental study with control and stroke groups was conducted in a Medical Center. A total of sixty-four participants, including healthy control ( Spasticity is generally present in the stroke population; upper limb spasticity is more common than lower limb . It impaA study by Jeanette et al.  reportedFor this purpose, different systems with inertial measurement units (IMUs) and surface electromyography (sEMG) sensors are being developed. One proposed by Ang et al.  used thrAlthough the relations between spasticity and voluntary movements have been discussed in the above studies, they require lots of feature engineering on the raw data to build a model for spasticity, which is a time-consuming process. Furthermore, it is difficult for the therapists to know the relationship between voluntary movements and spasticity because the sensor data are not easily understood. Owing to the spasticity effect on ROM, the ROM of the stroke patients’ upper limb joint while performing voluntary movements should be used to determine the spasticity level instead of using sensor data. Moreover, most of the research has built the models with the data from both the left- and right-affected sides, which might have different motion characteristics. Haaland et al.  reportedHence, to test our hypothesis for the AROM and MAS score correlation for the affected upper limb joints, and to solve the aforementioned limitation of using sensor data to assess spasticity in chronic stroke, we used a laboratory-developed wearable system and performed four voluntary tasks to record the AROM and to assess the spasticity level of the upper limb joints in stroke survivors, by wearing a smart-glove device on both upper limbs. We used generalized estimating equation (GEE) models to avoid time-consuming processes like dimension reduction and feature selection techniques on sensor data. Hence, the main goal of our study was to predict the spasticity using AROM following the four specified tasks in stroke survivors and computed models for both the right and left sides. Despite the limitations of the MAS scale, in our current study, we used this scale to obtain the subjective MAS grades and recorded the AROM during four voluntary tasks. Through this research, we aimed to highlight a robust and convenient method for the evaluation of spasticity and the limitation of AROM in the clinical population.The data were collected through an upper limb motion capture device (UMCD) comprised of a sensory glove and motion tracking device for the upper arm (MTD-UA), in which a total of 19 IMUs were mounted. The hardware and software design with the device calibration procedure and sensitivity were presented in previous studies ,21. The The study recruited 22 healthy and 42 stroke participants through convenience sampling  and was An experienced physical therapist performed the baseline evaluation of the participants, and spasticity was assessed using a 6-point MAS scale with the following scores, 0 = no increase in muscle tone; 1 = slight increase in muscle tone, manifested by a catch and release or by minimal resistance at the end of the range of motion when the affected part is moved in flexion or extension; 1+ = slight increase in muscle tone, manifested by a catch, followed by minimal resistance throughout the remainder  of the ROM; 2 = more marked increase in muscle tone through most of the ROM, but the affected part easily moved; 3 = considerable increase in muscle tone, passive movement difficult; and 4 = affected part rigid in flexion or extension  for the Each participant performed the task at their preferred speed with a resting period between each task; however, the FFE and FBS tasks were performed at higher speeds than the preferred pace. The participants put the test hand on a table that was 75–80 cm high before each task. The table’s legs were secured to the floor and its height was adjusted according to the participant’s height to avoid any unforeseen movement. For the CS task, two cone bases were arranged 20 cm from the edge of the table. Before the task, ten cones were placed near the side opposite to the participant’s test side, that is, if the left side were to be tested, the ten cones were placed on the cone base on the right side and vice versa. The participants transferred the ten cones from one cone base to the other as rapidly as they could after getting the signal to begin the test. The participants were not allowed to lean their trunks to any side during the test. After moving all the cones, the participant returned the test hand to the initial position. They performed 50 rapid flexions and extensions for the FFE and squeezed a ball rapidly and slowly 50 times for the FBS and SBS tasks. The voluntary tasks are explained in more detail in this study .The time series data of each joint collected with a sampling rate of 50 Hz  was procA statistical package for social sciences (SPSS) was used for further analysis and model prediction. The normality of the data was checked through a bell-shaped histogram, which represented a normal distribution. A GEE  method wTwo types of models were computed, namely, the model for the right-dominant right-affected side and the right-dominant left-affected side. Each activity’s ROM was correlated with each joint MAS score in the GEE model to predict the spasticity. The working correlation matrix chosen was first-order autoregressive (AR1), which means repeated measurements have a first-order autoregressive relationship in which immediately preceding values are used to predict the value at the present time .p-value [p ≤ 0.05 *, p ≤ 0.001 **, and p ≤ 0.0001 ***. The AROMs of four tasks for healthy control, unaffected and affected sides in stroke survivors are presented through box and bar plots.The GEE models were evaluated through the estimate (β), standard error (SE), 95% confidence interval (CI), and p-value . A validp-value . The GEEp-value . The levThe main characteristics of the participants showed that 12 out of 22 were males and the rest were females in the healthy group, while in the stroke group, 32 were male and 10 were female participants. The average age for both groups ranged from 54–57 years. Most of the participants were right-handed in both groups. Among the stroke participants, 19 were right-side affected, 22 were left-side affected, and 1 with both sides affected, as presented in Out of 42 stroke patients, 17 were right-dominant right-affected, 19 were right-dominant left-affected, 2 were left-dominant right-affected, 3 were left-dominant left-affected, and 1 had both sides affected. The GEE models were predicted only for the right-dominant affected patients due to the small sample size for the left-dominant affected patients.p = 0.001 **~0.0001 ***). For finger 2, finger 3, and finger 4, the ROMs of all joints predicted spasticity significantly during four tasks, and the QIC of the models were 22, 19, and 22, respectively. For finger 5, only the ROMs of PIP predicted spasticity significantly while performing FBS and SBS, and the QIC was 12.p = 0.001 **~0.0001 ***). The ROM of the elbow predicted spasticity significantly only during FBS, and the QIC of the model was 9. The ROM of the IP on the thumb predicted spasticity significantly only while performing FFE, and the QIC was 8. For finger 2, the ROMs of MP, PIP, and DIP on finger 2 predicted models while performing CS, FFE, FBS, and SBS, and the QIC was 15. For finger 3, the ROMs of PIP and DIP on finger 3 predicted spasticity while performing CS, FFE, FBS, and SBS, and the QIC was 14. For finger 4, the ROMs of MP, PIP, and DIP on finger 4 predicted spasticity significantly while performing FFE, FBS, and SBS, and the QIC was 11.The healthy participants in The right-affected side had large average ROMs for the elbow during CS and FFE, thumb during SBS, finger 2 during FFE, FBS, and SBS, finger 3 and finger 4 during FFE, and finger 5 during FBS, respectively. The left affected side had greater average ROMs for the elbow during SBS, wrist during all four activities, thumb during CS, FFE, and FBS, finger 2 during CS, finger 3 and finger 4 during CS, FBS, and SBS, and finger 5 during CS, and FFE, respectively . Table 5This is the first study to explore the relationship between spasticity through MAS score and AROM in chronic stroke survivors, and to classify three categories  of ROM for stroke participants’ unaffected and affected sides. Two different conditions, including the right-dominant right-affected and the right-dominant left-affected-sides’ GEE models, were built. According to our results, the AROM of upper limb joints can assist the subjective spasticity assessment (MAS) in stroke survivors. The stiffness or weakening of the affected side’s muscles contributes to the development of contractures, leading to increased muscle spasticity in stroke. In this way, the measured ROM of the upper limb joints during four functional activities was related to the degree of spasticity. As a result, this relationship between ROM and spasticity can help physicians and physical therapists with an objective measure in the early diagnosis of the extent of spasticity.The GEE models for stroke survivors have good significance and prediction for finger 2, finger 3, and finger 4, for both the right and left affected sides. Although most of the participants were right-handed, the left-affected side models predicted spasticity with more upper limb joints than the right-affected side. The reason could be that more stroke survivors’ affected sides were left-based in our study. While both affected sides reached a significant level, there was a lower QIC and narrower CI for the left-affected side  and  than the right-affected side  and  respectively, makes it more predictable ,27,28 T. A previp = 0.738) and wrist  vs. affected, 27.60 (10.71), p = 0.704) joints, but finger joints did not show this trend possibly due to the lack of individual joint spasticity assessment  vs. affected, 51.26 (21.22), sessment . Becausesessment .Based on the literature, it is difficult to state clearly that the GEE method can predict significant models for only the left-affected side but not for the right-affected side. This may be due to the smaller number of participants for the right-affected side, different levels of spasticity, and varying degrees of activity limitations on each side. Several studies ,30 have p < 0.05 *) correlation (r = 0.94), we have computed significant GEE models (p < 0.05 *) for right- and left-affected sides for upper limb spasticity. Another study predicted the severity of spasticity of the elbow joint using tonic stretch reflex threshold (TSRT) from upper limb muscles’ EMG with a significant prediction (p = 0.0002 **~0.0003 **) [p = 0.001 **~0.0001 ***) for upper limb joints. Hence, our study’s strength can be explained twofold: (1) the prediction of significant spasticity models for the right and the left-affected sides of stroke survivors and (2) the utilization of a common type of activities for spasticity assessment, which is the main component of the rehabilitation program in stroke survivors.All the previous studies ,11,13,15on r = 0.4, we havAccording to the definition of spasticity, the correlation between the limitation in the AROM during these four voluntary tasks and the TSRT, sEMG, or speed of movement is unclear. Even though we have tested slow and fast-speed tasks in stroke survivors and found out that fast-speed tasks (FFE and FBS) utilized more upper limb joints as compared to slow-speed tasks (CS and SBS) in CS can be used for spasticity assessment of the elbow, FFE for the wrist, and SBS, FFE, and FBS activities for thumb and finger joints in chronic stroke survivors.(1) Reproducibility and sensitivity of spasticity models for stroke and healthy people were not tested; (2) spasticity models only computed for MAS grades 0–2 spasticity and MAS grade 3 was not included; (3) the sample size of both groups did not balance and control group has fewer participants than stroke group; (4) no muscle strength was recorded for the stroke survivors; (5) spasticity was assessed only once, it should be reassessed after four functional tasks; (6) did not include Brunnstrom recovery stage; and (7) did not collect sEMG and TSRT during four functional tasks.Future research with a balanced sample size of both groups, calculated through G-power and randomized controlled design, should be conducted. The TSRT should be measured in future studies, along with sEMG activation of the upper limb muscles in stroke survivors. A future study will be required to test each task at different speeds for the exact involvement of each upper limb joint and muscle in chronic stroke survivors. It will also focus on explaining the fact that either left- or right-affected side models could be better to predict spasticity, or only one side, and to predict individual models for the healthy/no, mild, moderate, and severe spastic stroke survivors. Firstly, this study found a relationship between AROM and spasticity through MAS scores with four voluntary activities, which had not been researched in previous studies. By using AROM, therapists can easily know the relationship between the patients’ movements and their spasticity level objectively in the early stage of stroke and design an appropriate rehabilitation program. Secondly, we built the GEE models for the right- and left-affected sides separately because the extent of spasticity is different for both affected sides and ensues the varying limitations in AROMs of the upper limb joints; this has also not been researched in previous studies.


To investigate the seasonal, monthly, and daily distributions of the incidence of Stanford type-A acute aortic dissection (TAAAD) and identify seasonality in the duration of hospital stay and in-hospital mortality of TAAAD in south-eastern China.2 test was used to compare in-hospital mortality among the four groups. Non-parametric methods were used for all comparisons of the duration of hospital stay. Univariate logistic and multivariable logistic regression analyses were performed to assess the duration of hospital stay.We enrolled patients diagnosed with TAAAD between 1 June 2017 and 31 May 2021. Participants were divided into seasonal, monthly, and daily groups according to the need for analysis. Analysis of variance was applied to compare the number of TAAAD in different seasons, months, and days. χP = 0.04, P < 0.01, and P < 0.01, respectively). This study did not identify any significant decrease in maximal, mean, or minimum temperatures between the three days before TAAAD and the day of TAAAD. No seasonal variations associated with in-hospital mortality was observed (P = 0.89). However, significant differences were observed in the seasonal distribution of the duration of hospital stay for TAAAD . Multiple factor analysis showed that winter was the independent risk factor for the increased duration of hospital stay. The odds ratio for winter was 2.21 .Of the 485 patients, 154 were diagnosed in winter (31.8%), 115 in spring (23.7%), 73 in summer (15.1%), and 143 in autumn (29.5%). The daily, monthly, and seasonal distributions of TAAAD were significantly different (Our study confirmed that the incidence of TAAAD exhibits seasonal, monthly, and daily variations in south-eastern China. Moreover, the daily incidence of TAAAD is higher on weekdays than that on weekends.The online version contains supplementary material available at 10.1186/s13019-023-02222-4. Type-A acute aortic dissection (AAD) (TAAAD) involves the ascending aorta, and is a life-threatening disease, with an incidence of approximately 2.0 cases per 100,000/year . TherefoThis study included all consecutive patients admitted to our department with a diagnosis of TAAAD between 1 June 2017 and 31 May 2021. We included patients who (1) came from Fujian province and nearby areas and (2) had a diagnosis of TAAAD, as confirmed by computed tomography angiography or magnetic resonance angiography. We excluded patients if (1) the exact date of TAAAD symptom onset was unknown, (2) they had a clear aetiology such as iatrogenic AD secondary to cardiac surgery, Loeys-Dietz syndrome, Marfan syndrome, or a history of surgery for AD, and (3) they had a history of other cardiac surgery or chronic aortic dissections. This retrospective study was approved by the Ethics Committee of our Hospital and was conducted in accordance with the Declaration of Helsinki. The Strengthening the Reporting of Observational studies in Epidemiology guidelines were followed in this retrospective study , laboratory inspections, and hospital admission . We gathered data on in-hospital outcomes from medical records.The patients in our study were divided into seasonal groups in south-eastern China, , monthly groups, or daily groups according to our need for analysis.This study was conducted in Fuzhou (south-eastern China in Fujian province). This area has a subtropical monsoon climate. The mean daily temperatures range between 3 and 10 °C in winter and between 22 and 32 °C in summer. Daily regional meteorological data were retrieved from the Fuzhou Meteorological Service. The following parameters were assessed each day: minimum, mean, and maximum air temperatures (°C).2 test was used to compare baseline characteristics among the seasonal groups. χ2 test was used to analyse the differences in in-hospital mortality among the seasonal groups. Non-parametric methods were used for all comparisons of the duration of hospital stay. The cut-off point was considered according to the average duration of hospital stay of all patients, and variables with P<0.20 on univariate analysis were selected to perform multivariate logistic regression analysis on the related risk factors of patients, using generalized additive models to explore changes in weather and aortic dissection. The statistical significance of all tests was defined as p < 0.05 (two-sided). Statistical analyses were performed using SPSS V.22.0 and R software V.3.4.2. For individual cases of lost data, we used the mode principle of statistics to replace the missing values. The cases with several missing values (more than three) were omitted.Continuous variables were reported as mean±standard deviation (SD) or median and interquartile range (IQR) according to whether they followed a Gaussian distribution. Analysis of variance or χP < 0.01).Between 1 June 2017 and 31 May 2021, 485 consecutive patients diagnosed with TAAAD in our department were enrolled in this study; of these, 154 cases of TAAAD occurred in winter (31.8%), 115 in spring (23.7%), 73 in summer (15.1%), and 143 in autumn (29. 5%) . Moreover, the frequency of TAAAD in January was three times higher than that in July (P < 0.01), and the daily distribution in TAAAD was significantly different (P = 0.04). The frequency of TAAAD was higher on weekdays than that on weekends (P < 0.01). The generalized additive models showed that months and average temperature have a non-linear relationship with the incidence of TAAAD. The lower the average temperature, the more the cases of TAAAD. Similarly, the colder the months (winter), the more the cases of TAAAD , as presented in Fig. Significant variations in TAAAD symptom onset were investigated; the onset of TAAAD was 2.11 times higher in winter than that in summer (The variations in atmospheric temperatures during the three days preceding TAAAD are presented in Table P = 0.89). However, the seasonal distribution of the duration of hospital stay for TAAAD revealed significant differences (P < 0.01). The duration of hospital stay for TAAAD was higher in winter than that in spring (P = 0.04) and autumn (P = 0.03). Univariate analysis of the duration of hospital stay showed that winter, autumn, coronary malperfusion, cerebral malperfusion, acute renal failure, acute aortic regurgitation, and serum creatinine are risk factors for the increased duration of hospital stay. Multiple factor analysis showed that winter was the independent risk factor for the increased duration of hospital stay. The odds ratio of winter was 2.21 , P < 0.01 (Table The in-hospital mortality and duration of hospital stay in patients with TAAAD onset in different seasons are presented in Tables 01 Table .Table 3IOur findings confirm that the incidence of TAAAD varies seasonally, monthly, and daily in south-eastern China. In this study, the occurrence of TAAAD was independent of the rapid change in atmospheric temperature. Moreover, the distribution of in-hospital mortality in patients with TAAAD did not show seasonal variations. However, the seasonal distribution of the duration of hospital stay for TAAAD was inhomogeneous in this study.A series of studies showed that the fall and winter months represent a high-risk temporal frame for the occurrence of aortic dissections , 21, 22,In the present study, we found a distribution of the daily incidence of AAD, with a significantly higher incidence on weekdays than that on weekends. This finding is similar to that reported by Karangelis et al. . A similBenouaich et al. suggested that the relative change in temperature, rather than absolute temperature, is a mechanistic factor of TAAAD . HoweverPrevious studies investigated whether the onset season acted as a trigger in the prognostic value of patients with AAD; however, no association with seasonal variability could be found on the patients’ outcome and in-hospital mortality , 29. In Our analysis is the first to suggest that the duration of hospital stay is affected by the season of disease onset. Compared with spring and autumn, the duration of hospital stay in winter was the lowest. The in-hospital mortality was the highest in winter, and many patients were in serious condition and were extremely late to be cured; this is in accordance with the duration of hospital stay in winter, which was the lowest. However, there was no significant difference in the duration of hospital stay between winter and summer. There is a possibility that the number of hospitalized patients in summer is relatively less compared to that during other seasons, meaning that the duration of hospital stay is lesser. Further studies are needed to identify the factors that affect seasonal changes in hospitalization duration.This study has some important limitations. A major limitation of our study is that our meteorological data did not include atmospheric pressure, humidity, and other specific climate data, which may provide more details regarding the reason for seasonal variability. Second, the number of preceding days that were investigated may be insufficient, meaning that the influence of rapid alterations in atmospheric temperature on TAAAD were not accurately investigated. Third, our study had a single-centre design in one province, and the sample size of this study was small. Additionally, the findings might not necessarily be reflected in other countries or cities with different climates. China has a large land area; thus, follow-up studies on the differences between the south and north and the east and west, and a multi-centre study are our targets. At present, the research period is four years, which may lead to some bias in the research results.Our study confirmed that the incidence of TAAAD exhibits seasonal, monthly, and daily variations in south-eastern China. Moreover, the daily incidence of TAAAD is higher on weekdays than that on weekends. This could promote the formulation of effective prevention strategies during the colder months, and further studies should seek to clarify the pathophysiology of seasonality in the incidence of TAAAD.Additional file 1. STROBE Guidelines.


Social determinants of health [SDOH] and happiness have received meaningful consideration as foundational concepts in the field of public health. However, the relationship between the SDOH and happiness of older adults have not received the requisite recognition in Ghana. This study examined the relationship between the SDOH and happiness of older adults in Ghana.The study used data from the 2014/2015 Ghana Study on Global Ageing and Adult Health (SAGE) Wave 2. Data was analysed using the Structural Equation Modeling (SEM) technique to investigate the direct, indirect and covariances of the SDOH and happiness of older adults.β = 0.07), neighbourhood and built environment , access to quality education , access to healthy food  social and community context , and access to quality healthcare  had direct relationship with happiness of the older adults in Ghana.The results showed positive relationship between the SDOH and happiness among older adults. The economic stability  positively impact their happiness in later life. Neighbourhood and physical environment influence the effect of quality education on happiness of older adults. Social policies and interventions aiming at happiness of older adults should consider the social determinants of health and the mediating effects of food on happiness through quality education, and quality of healthcare system. Social determinants of health (SDOH) and happiness of older adults is a pivotal area in healthy ageing. The past decades have received noteworthy consideration of the concept of happiness in the field of public health . HoweverEarlier studies have shown the relationships between the specific indicators of SDOH and happiness –11. TherBesides, access to high quality healthcare services contributes positively towards healthy living and happiness . Thus, aFurthermore, the neighbourhood and built environment [NPE] or living in a healthy and safer location is linked to health and wellbeing  –24. LivEvidence shows that educational attainment increases the level of happiness through independence and freedom of choice . Highly Imperatively, close relationships and interactions with family, friends, colleagues at work, and community members have key impact on people’s happiness. Access to safer open social spaces for recreational purposes like the community centers, aged friendly drinking spots, church places, and places where older adults can gather to play games are very important characteristics of the social and community context that are directly related to happiness . These eStudies in Ghana have shown that family social capital, including family sense of belonging, autonomy support, control, and social support, vary with self-reported happiness . HappineThe Objective List Theory (OLT) strengthened the premise of this study. The theory holds that happiness consists of a human life that achieves certain things from a list of worthwhile pursuits which may include career accomplishments, friendship, freedom from disease and pain, material comforts, civic spirit, beauty, education, love, knowledge, and good conscience , 42. TheThe data used for this study was obtained from the 2014 Ghana Study on Global Ageing and Adult Health (SAGE) Wave 2, which is supported by the US National Institute on Ageing, Division of Behavioural and Social Research and National Governments. The core SAGE collects a comprehensive data on adults aged 18+, with an emphasis on populations aged 50+, from nationally representative samples in six countries: China, Ghana, India, Mexico, Russian Federation and South Africa. The SAGE Wave 2 for Ghana focused on the health and wellbeing as well as happiness level of older adults (50+) adults. SAGE surveys follow the standard procedures  which allow cross-country comparison. The survey employs a stratified two-stage sampling technique. The initial stage involves the selection of Primary Sampling Units (PSUs) by region and location  across the six countries. The second stage involves the systematic selection of enumeration areas (EAs), households (HH) with 50 + adults, and households (HH) with persons aged 18–49. For this study, only those 50 years and over (n = 781) who had complete information on the variables of interest were included . RespondIn general, the dependent variable assessed if respondents lived happy life or were satisfied with life. This was measured by three variables including satisfaction with living condition (1 = very satisfied to 5 = very dissatisfied), overall quality of life (from 1 = very good to 5 = very bad), and level of happiness (from 1 = very happy to 5 = very unhappy). For the analysis of this study, “happy in life” was recoded into a binary variable, “very happy”, “happy”, and “moderately happy” were coded into 1 representing older adults who rated themselves as happy (1 = happy) while “unhappy” and “very unhappy” coded into 0 indication older adults who rated themselves as unhappy (0 = unhappy).The independent variables as adopted from the Healthy People  were divEconomic stability denotes living out of poverty to afford basic things like healthy food, healthcare, housing, quality education. It was defined in this study as having steady employment and earning enough to pay for food, housing, health care, and education can reduce poverty and improve health and well-being. It was measured in this study by earnings/income (yes/no), currently working, renamed employment (yes/no).Access to quality education is where learners who are well-nourished, are ready to participate and learn, are supported by their families and communities; healthy, safe, and supportive environments. For the analysis, it was defined as high-quality educational achievement and or opportunities for older adults in their entire life environment. it was measured by highest level of education (from 1 = Primary/JHS to 3 = Tertiary), and years educated (from 1 = < 10 years to 4 = > 30 years).Access to quality healthcare is an indication that an individual gets the healthcare services needed where they live. In this study, it was considered as older adults’ access to timely and high-quality healthcare services in the places where they have spent their entire lives. It was measured to include healthcare provider , quality of healthcare (from 1 = very good to 5 = very bad), and satisfaction with quality of care (from 1 = very satisfied to 5 = very dissatisfied).Neighbourhood and built environment (NBE) are the areas that promote the health and wellbeing of and individual. For this study, NBE denote improved health and safety in the place where older adults are born, grow, live, learn, work, play, worship, and aged. it comprised place of residence , always lived here in the community, renamed childhood residence (yes/no), as well as safe on the street, renamed safety (from 1 = completely safe to 5 = not safe).Social and community context(SCC) refer to the relationships and interactions with family, friends, working colleagues and community members. In this study it is defined as the social support that older adults need in the places where they were born, live, learn, work, play, worship, and aged/grow up. It was measured by community meetings renamed social integration (from 1 = never to 5 = daily), religious services renamed community engagement (from 1 = never to 5 = daily) and feel left out which was renamed social isolation (from 1 = never to 4 = often).Access to healthy food included number of fruit servings per a day, and number of servings of vegetable per a day, renamed fruit and vegetable consumption (from 1 = < 2 servings to 4 = > 6 servings), hungry, no money to buy food (from 1 = every month to 5 = never).Three confounders were included in this study. These were sex , age , and marital status .2) test, the Non-normed Fit Index (NFI), Tucker-Lewis Index (TLI), Comparative Fit Index (CFI), and the Root Mean Square Error of Approximation (RMSEA). All the three relative fit indices  exceeded the 0,90 criterion [P< 0.01) in the modified hypothesised model.Statistical analyses were performed with SPSS version 26 and Amos version 23. Data analysis was conducted by the used of the cross-tabulation, Confirmatory Factor Analysis (CFA) and the Structural Equation Modelling (SEM) techniques. First, the chi-square test was used to examine if the percentage distribution of the older adults who were happy in life were statistically significantly (p < 0.05) different by the selected background characteristics. SEM uses factor analysis to create an indicator score to measure a latent variable. Therefore, SEM was applied because the indicators used to measure the SDOH, and happiness were latent. The model was setup using a theory-based conceptual model see  used forriterion . The valriterion . All theApproximately 56 percent of older adults were females with 63.3 percent being relatively youngest (50–64 years). Sixty percent were married, 50.6 percent had attained Secondary/SHS education, 49.9 and 56.3 percent had 2 to 3 servings of fruits and vegetables respectively. Majority of older adults (76.8%) had lived in the same community/neighbourhood from childhood. Seventy one percent were working, and 68 percent were earning income. Almost all older adults (91.5%) were never hungry. Approximately 73 percent had walkable environment while 97.4 percent had access to modern healthcare. Sixty-five percent were satisfied with quality of healthcare services, while 45.6 percent had good healthcare. Approximately 47 percent had never attended public meetings, 82.5 percent were never lonely, and 40.8 percent felt completely safe. Roughly 65 percent were satisfied with their living conditions, 52.8 percent had good overall quality of life whilst 72.5 percent were happy.The goodness of fit of the fitted SEM model was examined using Chi-squared test, Non-normed Fit Index (NFI), Tucker-Lewis Index (TLI), Comparative Fit Index (CFI), and Root Mean Square Error of Approximation (RMSEA). The Chi-squared statistics for the fitted models was 1916.266, (degrees of freedom = 180), with a corresponding p-value < 0.001. The NFI, TLI, CFI and RMSEA for the fitted model were 0.92, 0.95, 0.93, and 0.07, respectively. The NFI, TLI, and CFI for the fitted model were all greater than 0.90 criterion, while the RMSEA was lower than 0.08. Inferring from Byrne (2009), the goodness of fit statistics indicate that the model fits the data well.Factor Score = 0.35, P < 0.01), and employment . Walkability Factor Score = 0.73, P < 0.001), safety , and place of residence  were significantly related to neighbourhood and built environment. Access to quality education was highly measured by higher educational attainment , and years educated . Being hungry affected access to healthy food negatively . However, fruit consumption  and vegetable consumption  significantly caused access to healthy food. Community engagement , and social integration  positively affected social and community context, but stress negatively caused social and community context .As shown in Factor Score = 0.20, P < 0.001), availability of health care provider  and healthcare coverage  were positively related to health care system. Happiness of older adults was strongly caused by level of happiness  and satisfaction with living condition .Satisfaction with health care (β = 0.07), neighbourhood and built environment , access to quality education , access to healthy food  social and community context , and access to quality healthcare  had direct relationship with happiness of older adults.The social determinants of health as were analysed in this study were directly and positively related to happiness of older adults. The results show that economic stability  to affect happiness of older adults. Also, the results showed an indirect effect of healthcare system on happiness of older adults through access to healthy food . Also, healthy food mediated the effect of quality education on happiness of older adults  while access to quality education operated through neighbourhood and built environment  to influence happiness of older adults.Further, the findings show that economic stability, social and community context covary  affect happiness of older adults has not received the requisite attention in Ghana. This study examined the relationships between the SDOH and happiness of older persons in Ghana. Direct and positive relationships are shown between the SDOH and happiness of older people. Economic stability (income and employment) of older persons was directly related to happiness, indicating the possibility of higher happiness of older persons, especially the youngest-old who are still capable of working and earning to afford their basic needs such as paying utility bills, and leisure activities. Our analysis supports results of Frey and Stutzer . Diener We found that neighbourhood and built environment had direct and a positive effect on happiness of older adults. Although the socio-gerontological study by Lykken  argues tOne of the important factors that influence happiness is education . Our resWe found positive relationship between eating healthy food and happiness. This possibly explains the fact that older adults who have access to nutritious food with adequate  servings of fruits and vegetables are happier than those who do not. The result of this study further confirms results of Blanchflower et al.  and FaraConsistent with earlier studies , 37, ourBesides, we realized that access to quality healthcare is positively related to happiness of older adults. Happiness is an important factor that contributes significantly to the efficiency of the healthcare system . A plausThe study also revealed that food mediates the effects of healthcare access and quality on happiness. This possibly explains that access to quality healthcare in a country helps to educate older people on health benefits of eating nutritious food rich in fruits and vegetables which could lead to living a happy life. Also, findings of this study show that food operate through access to quality education to impact happiness. A plausible explanation could be that higher educational attainment ensures having a good job, higher income and better understanding of nutrition with the means to afford nutritious food and therefore, improved happy life of older adults. Lastly, neighbourhood and build environment mediated the effect of quality education on happiness. This could be because higher educational attainment is influenced by social integration and engagement. Our observation is further supported the finding of a study by Ariana  which shThis study draws its conclusions from a nationally representative sample of aged people in Ghana. It also utilised multistage cluster sampling method in the selection of the respondents, whilst rigorous statistical analysis was conducted. The questionnaire and methods of data collection have also been validated. Moreover, the study included one more component  to the indicators of SDOH by Healthy People and uncovers the direct and indirect effects of the social determinants of health on happiness of aged people that are important for policies and social interventions for older adults using SEM techniques. However, due to the cross-sectional nature of the survey, the study could not establish causality. Further, only variables with complete cases for this analysis were used in this study, which has the potential of producing some biased estimates. Results are generalisable to only older adults in Ghana. Lastly, the model should be tested for applicability in other countries.In examining the social determinants of happiness of older adults, this study shows that the conditions in which older adults were born, live, learn, work, play, worship, and age (SDOH) positively impact their happiness in later life. Specifically, economic stability and social and community context have significant linkages or intercorrelate to influence happiness of older adults. Most importantly, effects of quality education and healthcare on happiness of older adults was mediated by access to healthy food. Indicating that eating healthy food plays a vital role for older adults who had access to quality education and healthcare. Lastly, neighbourhood and physical environment play important role between quality education and happiness of older adults in Ghana. Whatever, social policies and interventions targeting happiness among older adults should consider the social determinants of health, most importantly, the mediating effects of food on happiness through access quality education, and healthcare system, as well as the important role that neighbourhood and build environment could play between quality education and happiness of older adults.