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Key findings | Baseline medication reconciliation practice was poor at both sites. Post-general educational intervention, medication discrepancy was significantly reduced by 42.8% at the intervention site ( | PMC10652589 | ||
Conclusions | The educational interventions improved pharmacists’ medication reconciliation practice at the intervention site. It is expected that this research would help create awareness on medication reconciliation among pharmacists in developing countries, with a view to reducing medication-related patient harm. | PMC10652589 | ||
Keywords | PMC10652589 | |||
Background | ambulatory diabetes, hypertensive | EVENT | Medication error is defined as any preventable event that may cause or lead to inappropriate medication use or patient harm while the medication is in the control of the health care professional, patient, or consumer [Medication reconciliation, which is intended to minimize medication discrepancies and possible incidence of needless hospital readmissions,[Medication reconciliation is an effective strategy to alleviate the danger and cost linked with medication errors during hospital admission and avoidable readmission [The purpose of this study was to evaluate background extent of medication reconciliation practice, and the effect of educational intervention on pharmacists’ practice of medication reconciliation among ambulatory diabetes and hypertensive patients in two tertiary hospitals in Nigeria. | PMC10652589 |
Methods | PMC10652589 | |||
Study design and setting | A mixed-method non-randomised clinical trial was carried out at two teaching healthcare facilities in Nigeria. The study was carried out at the University College Hospital, Ibadan (intervention site), a 950-bed teaching hospital affiliated with University of Ibadan. The University of Ilorin Teaching Hospital, Ilorin (control site) is a 650-bed teaching hospital affiliated with University of Ilorin. Both sites are major referral centers and centers of excellence for undergraduate and postgraduate training for physicians, pharmacists, nurses, and other healthcare practitioners in Nigeria. The study was carried out for a duration of 12 months. | PMC10652589 | ||
Inclusion and exclusion criteria | hypertension, diabetes | HYPERTENSION, DIABETES | Pharmacists who gave their informed consent to participate in the study were recruited at both sites. Undergraduate pharmacy students on experiential rotation were excluded from the study. Patients (18 years and above) diagnosed with diabetes and/or hypertension who visited the Endocrinology or Cardiology Clinics were enrolled for the study. Patients who were not on medications for diabetes or hypertension, or those who did not consent to participate in the study were excluded. | PMC10652589 |
Data collection instruments | hypertensive, diabetes | DIABETES | Three semi-structured questionnaires (Q1, Q2 and Q3) were used as the data collection instrument. The questionnaires were developed by the authors based on their teaching and practise experience, and extensive literature review [The data collection instrument for patients was pretested for face validity among 34 diabetes and/or hypertensive patients at Catholic Hospital, Oluyoro, Ibadan. Also, pharmacists’ data collection instruments were pretested among 12 pharmacists at the University Health Services, University of Ibadan and Military Hospital, Ojoo, Ibadan. Content validity of all the questionnaires was done by three faculties at the Department of Clinical Pharmacy and Pharmacy Administration, Faculty of Pharmacy, University of Ibadan. | PMC10652589 |
Recruitment of participants and data collection | hypertensive, intervention-46, 140 diabetes, hypertension, diabetes | HYPERTENSION, DISEASES, DIABETES | Sequel to acquiring ethics approval from each hospital review board, the approvals of heads of different units/departments where the study was undertaken were also secured. Total sampling of the entire pharmacists at the control and intervention sites was adopted for the study. The purpose of the study was explained to all the pharmacists and consulting physicians in each hospital. Thereafter, pharmacists were visited in their different units and the questionnaire administered to those who gave informed consent. The study, which was a mixed-method non-randomised clinical trial utilised self-administered questionnaire (Q2) among 146 pharmacists (intervention site-85; control site-61) to directly evaluate their medication reconciliation practice. Ambulatory diabetes and/or hypertensive patients were visited on their respective clinic days during which the purpose of the study was explained in English and Yoruba (local language), as required. Interviewer-administered semi-structured questionnaires (Q1 and Q3) were employed to carry out medication reconciliation for a total of 660 ambulatory patients (Q1 for 520 and Q3 for 140 diabetes and/or hypertension patients) throughout the study in different cohorts to indirectly evaluate the pharmacists’ medication reconciliation practice, at both sites. Patients with diabetes and/or hypertension were targeted because of the prevalence of the two diseases in Nigeria (5.77% for diabetes and 30.6% for hypertension) [Baseline medication reconciliation practice of the pharmacists was indirectly evaluated as the principal investigator carried out medication reconciliation among a cohort of 334 (intervention-183; control-151) out of the 660 patients. Thereafter, a general educational intervention was carried out among the 85 pharmacists, hereafter referred to as intervention pharmacists, in the intervention group to address the medication reconciliation practice gaps observed at baseline. The semi-structured questionnaire (Q2) was administered to pharmacists at the intervention site at one, three and six months to assess their medication reconciliation practice of the pharmacists after the general educational intervention. The effect of the intervention on pharmacists’ medication reconciliation practice was also indirectly evaluated using Q1 as the principal investigator carried out medication reconciliation among cohorts of 96 (intervention-46; control-50) patients at three months and 90 (intervention-44; control-46) at six months postintervention. This was followed by a focused educational intervention for 15 pharmacists (a subset of the initial 85 intervention pharmacists) at the Geriatric Center of the intervention site. The data collected by the 15 pharmacists after carrying out medication reconciliation for a cohort of 140 patients was independently reviewed by three experts, who were faculties at the Department of Clinical Pharmacy and Pharmacy Administration of the University of Ibadan. The three experts in this study were faculties at the Department of Clinical Pharmacy and Pharmacy Administration, Faculty of Pharmacy, University of Ibadan, Nigeria. They were selected based on their competence in the core areas of medication reconciliation, which includes comprehensive medication history taking, documentation of clinical care activities, identification and resolution of drug therapy problems as well as medication discrepancies. The criteria to define them as experts in clinical pharmacy includes the fact that they have several years of teaching and research experience in Clinical Pharmacy. The three experts comprised an Associate Professor and two Senior Lecturers who are well versed in intervention studies aimed at improving the quality of care provided by pharmacists for patients. They have also made extensive contributions in Clinical Pharmacy with several articles published in both local and international peer-reviewed journals. Outcomes measured included identification and resolution of drug therapy problems, medication discrepancies and patients who brought their medication packs for clinic appointment. | PMC10652589 |
Educational interventions | BEST | Two educational interventions were carried out by the principal investigator during the study, who is a faculty and a doctorate student working on medication reconciliation at the Department of Clinical Pharmacy and Pharmacy Administration, Faculty of Pharmacy, University of Ibadan, Nigeria. He underwent a two-week training on “Best Clinical Practices” organized by University of Nigeria Teaching Hospital (UNTH) in collaboration with the Nigerian Association of Pharmacists and Pharmaceutical Scientists in the Americas (NAPPSA) at UNTH, Nigeria in 2015. He also had a 6-week International Pharmacists’ Enrichment Programme at Howard University, Washington DC, with focus on medication reconciliation, jointly organized by FIP-Pharmabridge and Howard University in 2016.The first intervention was a general intervention carried out among the entire 85 pharmacists recruited for the study at the intervention site. This intervention was aimed at educating the pharmacists on comprehensive medication history taking, effective communication with patients and other healthcare team members, identification and resolution of drug therapy problems, and the concept and practice of medication reconciliation. The intervention, which lasted for four hours, comprised didactic lectures, role-plays, and case-reviews on skills required for medication reconciliation. The second intervention was a focused intervention which involved a detailed follow up educational intervention with emphasis on consistent documentation of medication reconciliation practice. The intervention, which lasted for one hour, consisted of hands-on practice on medication reconciliation, documentation of clinical practices, detection and resolution of drug therapy problems and medication discrepancies. The questionnaire (Q3) for consistent medication reconciliation data collection designed for this phase was utilized by the 15 pharmacists for data collection. Both educational interventions were carried out at the Pharmacy Department of the intervention site. | PMC10652589 | |
Data analysis | comorbidity | Data was summarized with descriptive and inferential statistics using SPSS for Windows Version 23.0 (IBM Corp, New York, USA). Normal distribution of the data was evaluated using Kolmogorov-Smirnov test. Inferential statistics such as Fisher’s exact test was done to compare associations between absence/presence of medication discrepancy among patients at the intervention and control sites. Pearson product-moment correlations analysis was carried out to investigate relationships between patients’ medication discrepancy and comorbidity, number of medication(s) and educational level. Independent-samples t-test was used to evaluate the difference between average medication discrepancy among patients at the intervention and control sites. One-way analysis of variance compared patients’ medication discrepancies at the intervention and control sites over the study period. The level of significance was set at | PMC10652589 | |
Discussion | This study revealed poor baseline medication reconciliation practice among pharmacists at both study sites. The focused educational intervention especially improved the practice of medication reconciliation by pharmacists at the intervention site. There was a reduction in medication discrepancies and an increase in detection and resolution of drug therapy problems by the intervention pharmacists.As evidenced by the lack of documented cases on medication reconciliation at baseline, and the slow build up to the adoption of the process during the study, the practice of medication reconciliation was a non-deliberate, haphazard, and unmonitored practice which was seldom done by a few pharmacists at both sites. This might be because medication reconciliation is not yet an established component of pharmacy practice in Nigeria [One of the processes during medication reconciliation is the review of patients’ past medications and comparing it with the newly prescribed medications. Healthcare practitioners, especially pharmacists, need to regularly educate their patients on the need to always bring their medications along for hospital visits. Studies from developed countries show that patients bringing their medication packs along for hospital appointments vary from one place to another [Medication reconciliation is a tool for detecting discrepancies in prescribed medications in diverse healthcare settings, or at different levels of care to update patient’s medication and avoid medication errors [A significant reduction in the occurrence of medication discrepancy from 43.75% to 25.0% at the intervention site, after the general educational intervention. Medication discrepancy observed among patients in related studies showed a range of 33.2% - 86.1% with a range of six to ten medications taken by the patients [19, 39, 22-24,]. It is expected that a much lower occurrence of medication discrepancy would be reported in developed countries where medication reconciliation is already an institutionalized practice. However, in the present study with a range of two to ten medications, the occurrence of medication discrepancies was significantly reduced from 43.75% to 25.0%. The higher average number of medications taken by patients in developed countries could be responsible for the higher medication discrepancies. This is logical, since medication discrepancy is likely to increase with increasing number of medications, especially as found in a geriatric population [Since patient safety is the primary goal of medication reconciliation, this study equipped pharmacists in the intervention site with knowledge and skill to improve patient safety by reducing medication discrepancies. Therefore, an institutionalized medication reconciliation practice will help to eventually reduce medication discrepancy in the long run. | PMC10652589 | ||
Study limitations | hypertensive, diabetes | DIABETES | The attrition rate observed among pharmacists at both sites was quite high. This level of attrition was because a few pharmacists were posted out of the study sites, some dropped out for personal reasons, while some were on leave at different times during the study period. Since the study was carried out among ambulatory diabetes and hypertensive patients alone, the results may not be generalizable to diabetes and/or hypertensive patients in other transitions of care. In the practice of medication reconciliation, self-report was used by the pharmacists. This method of data collection could be susceptible to bias [ | PMC10652589 |
Conclusion | The educational interventions improved intervention pharmacists’ medication reconciliation practice and led to prevention of medication-related harm to patients. It is recommended that this intervention be replicated in more hospitals in Nigeria to encourage implementation of best practices. | PMC10652589 | ||
Acknowledgements | Not applicable. | PMC10652589 | ||
Authors’ contributions | Akinniyi A., Oluwakemi A. | Dr. Akinniyi A. Aje: Principal investigator and corresponding author. Contributions: Study design, data collection and analysis, manuscript writing. Dr. Segun J. Showande: Independent assessment of medication reconciliation data form retrieved from the study participants at the Geriatric Center, data analysis, manuscript review. Dr. Rasaq Adisa: Independent assessment of medication reconciliation data form retrieved from the study participants at the Geriatric Center, manuscript review. Professor Titilayo O. Fakeye: Study design, independent assessment of medication reconciliation data form retrieved from the study participants at the Geriatric Center, manuscript review. Oluwakemi A. Olutayo: Assistance with data collection, medication reconciliation educational intervention at the Geriatric Center, patients’ case note review. Lawrence A. Adebusoye: Family Physician and Geriatrician at the Geriatric Center. Review of recommendations made by pharmacists after medication reconciliation, manuscript review. Olufemi O. Olowookere: Family Physician and Geriatrician at the Geriatric Center. Review of recommendations made by pharmacists after medication reconciliation, manuscript review. | PMC10652589 | |
Funding | Not applicable. | PMC10652589 | ||
Availability of data and materials | The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. | PMC10652589 | ||
Declarations | PMC10652589 | |||
Ethics approval and consent to participate | Approval for the study was granted by University of Ilorin Teaching Hospital Ethics Research Committee (ERC/PAN/2018/08/1814) and the joint University of Ibadan/University College Hospital Health Research and Ethics Committee (UI/EC/15/0308). The study was registered on ClinicalTrials.gov (ID NCT03182972) on 09/06/2017.The study was explained to the pharmacists and the patients. Only those who gave informed consent were recruited for this study. The research was carried out in accordance with the Declaration of Helsinki. | PMC10652589 | ||
Consent for publication | Not applicable. | PMC10652589 | ||
Competing interests | The authors declare no competing interests. | PMC10652589 | ||
References | PMC10652589 | |||
Subject terms | Combined oral contraceptives (COC) are among the most commonly used contraceptive methods worldwide, and mood side effects are the major reason for discontinuation of treatment. We here investigate the directed connectivity patterns associated with the mood side effects of an androgenic COC in a double-blind randomized, placebo-controlled trial in women with a history of affective COC side effects ( | PMC10276024 | ||
Introduction | depressed mood, adverse mood side effects, depressive symptoms | Combined oral contraceptives (COC) are among the most commonly used contraceptive methods worldwide [Although mood-related side effects are usually more pronounced in women with a history of depressive symptoms, depressed mood is not consistently reported to change after treatment in RCTs [Recent years have witnessed an increased integration of psychiatry and neurosciences, which allows us to change our perspective on mental well-being and further understand its underlying neural correlates. The triple network model proposes that a balanced connectivity between three ‘core’ networks, i.e., the default mode network (DMN), the salience network (SN) and the executive control network (ECN), is crucial for mental health [Given the sparsity of literature, it is challenging to draw a general conclusion regarding the differences between COC users and naturally cycling women in resting state fMRI (for a review, see [Despite the advantages of functional connectivity, these approaches may be too coarse and insufficient to capture within and between network connectivity dynamics. In general, they do not capture changes in the large-scale organization of the brain, given that (i) functional connectivity measures do not provide information about the directionality of connectivity changes and/or (ii) intrinsic connectivity approaches are inadvertently restricted to changes in connectivity within a given network, while information processing requires the balanced integration of information across different networks. We here aim to disentangle the directed connectivity patterns particularly in women experiencing adverse mood side effects to an androgenic COC. Given that the triple network model has already been proved useful to investigate connectivity changes related to hormonal fluctuations [Given that previous research hints at an enhanced connectivity within the DMN (especially medial areas), but decreased connectivity within the ECN related to depressive symptoms [ | PMC10276024 | |
Materials and methods | PMC10276024 | |||
Experimental design | Participants were scanned twice, first during a pre-treatment cycle (day 4 ± 3 after onset of menses) and secondly, during the last week of the treatment cycle (day 15–21 after start of treatment). The participants started taking the pill on the first day of menses (Fig. | PMC10276024 | ||
Experimental design. | Each participant had two sessions, before and during treatment (day 1–10 and day 15–21 respectively after onset of menses). Therefore, for the placebo group endogenous hormone levels increased from the first to the second appointment, while in the COC group synthetic hormone levels were stable and endogenous hormones low. Expected hormonal variation in a physiological menstrual cycle and during COC treatment is represented by green (estradiol), and yellow (progesterone) lines. Actual values for each of the MRI-session are displayed in Table Additionally, participants filled out daily mood and physical symptoms on the Cyclicity Diagnoser (CD) scale [ | PMC10276024 | ||
Data acquisition | BEST | Functional and structural images were acquired on a Philips Achieva 3.0 T scanner using an 8-channel head coil (Philips Medical Systems, Best, The Netherlands). For the 5 min resting state a single shot echo planar imaging sequence was used to collect 100 volumes of BOLD data with a voxel size of 3.0 × 3.0 × 3.0 mm | PMC10276024 | |
Preprocessing | Scanner DICOM images were first converted to NIfTI files with MRIcron ( | PMC10276024 | ||
Spectral dynamic causal modeling and parametric empirical bayes | Resting state functional images were modelled using a (Bayesian) hierarchical random effects framework and spectral DCM was specified and inverted using DCM12 as implemented in SPM12 (In order to compare changes in the treatment group to the changes observed in the placebo group, we ran a 3-level hierarchical analysis using a Parametric Empirical Bayes (PEB)-of-PEBs approach. We first modelled the treatment effect on each group separately, and then fit those parameters to the next level of the hierarchy (Fig. SIn order to relate effective connectivity to mood symptoms during treatment, we modelled another PEB including the changes in CD-scale ratings that differed significantly between the COC and placebo group, i.e., PEB results were thresholded to only include parameters from the A matrix that had a 95% posterior probability of being present vs. absent, which represents strong evidence for treatment-related changes, and thresholded to an estimated value Ep > 0.10. Only results surviving this threshold are reported in the results section. | PMC10276024 | ||
Cross-validation | SIDE EFFECT | In order to check whether the mood side effect related effective connectivity could predict the assignment of participants to one group or another (COC vs. placebo) we used a leave-one-out scheme (spm_dcm_loo.m) as described in [ | PMC10276024 | |
Changes only in the placebo group | From the follicular to the luteal phase, within-network connectivity increased in the DMN (from the right AG to the mPFC), and in the SN (from the left AI to the right AI). Within the ECN, we found a lateralized pattern: while connectivity increased from parietal to frontal ECN in the right hemisphere, connectivity decreased from frontal to parietal ECN in the left hemisphere.Regarding the between-networks changes, we observed an increased connectivity between the DMN and the SN (from the right AG to bilateral AI), and in turn, from the right AI to the PCC. While frontal ECN increased its connectivity to the DMN (from the left MFG to the mPFC and to the left AG; and from the right MFG to bilateral AG), parietal ECN decreased its connectivity to the DMN (from the left SMG to bilateral AG). Connectivity from parietal ECN to SN also decreased (from the right SMG to bilateral AI). | PMC10276024 | ||
Changes only in the COC group | From pre- to during treatment MRI-session, within-network connectivity increased in the DMN (from the PCC to the right AG and viceversa), while it decreased in the ECN (from the right SMG to the left MFG). No significant changes were observed in the within-network connectivity of the SN.Regarding the between-networks changes, we observed an increased connectivity between the DMN and the SN, with increased connectivity from the left AG to the right AI, and, in turn, increased connectivity from the left AI to the right AG. Between the DMN and the ECN several connectivity changes were observed during treatment. Connectivity increased from the left AG to the right MFG, and from the right AG to bilateral parietal ECN. In turn, the left MFG decreased its connectivity to the DMN (right AG and PCC). An increased connectivity between the SN and ECN (from the dACC to the left SMG) was observed, during treatment. | PMC10276024 | ||
Changes in opposite direction for COC and the placebo group | In the following, an increase refers to increased connectivity in the COC, but decreased connectivity in the placebo group; while a decrease refers to decreased connectivity in the COC, but increased connectivity in the placebo group.Regarding the within-networks changes, we observed an increased connectivity in the DMN (from the left AG to the mPFC); whereas in the SN, the dACC decreased its self-connectivity, and in the ECN, connectivity from the frontal to the parietal right hemisphere also decreased.Regarding the between-networks connectivity, most of the changes occurred between the DMN and the SN. While connectivity decreased from the right AI to the mPFC, it increased from the dACC to the medial nodes of the DMN (PCC and mPFC). In turn, the right AG decreased its connectivity to the dACC. Connectivity was increased from parietal ECN to posterior DMN (from the left SMG to the PCC), and from the frontal ECN to the medial SN (from the right MFG to the dACC). | PMC10276024 | ||
Summary of findings | Taking into account all the above, in the COC group compared to the placebo group, the within-network connectivity increased during treatment in the DMN, whereas it decreased in the SN and ECN. Regarding the between-network connectivity, specifically from the dACC (SN) to medial nodes of DMN, effective connectivity was increased in the COC group compared to the placebo group. From the rAG (posterior DMN) to the SN, effective connectivity increased in the placebo group compared to the COC group. Conversely, effective connectivity increased from the rAG to the posterior ECN in the COC group compared to the placebo group. Effective connectivity from the frontal ECN to the DMN was in general stronger during treatment in the placebo than in the COC group, while those connections originating in the lSMG followed the opposite pattern. In general, effective connectivity between the ECN and the SN was stronger in the COC group compared to the placebo group. | PMC10276024 | ||
Associations of effective connectivity mood side effects | When making the previous distinction among the connectivity changes, some consistent patterns could be distinguished in the relation to the side effects (see Table Among those mood lability-related connections, the following connectivity changes surpassed the threshold of Ep > 0.20: from dACC to PCC, from rMFG to dACC, from rAG to rAI, from rAG to dACC, and from rAG to rSMG. In order to maximize the predictive accuracy, only these connections were selected in the subsequent cross-validation analysis. | PMC10276024 | ||
Prediction of treatment by mood-related effective connectivity | The leave-one out cross-validation based on those connections identified above as showing the highest effect size and relation to mood lability (dACC → PCC, rMFG → dACC, rAG → rAI, rAG → dACC, rAG → rSMG) showed a significant association between the actual and predicted group of | PMC10276024 | ||
Leave-one-out cross-validation analysis. | Left: scatter plot displaying the correlation between the actual treatment group in the left-out-subject’s design matrix and the predicted treatment group based on the left-out-subject’s connectivity. Centre: the resulting posterior probability for each treatment group for each subject. Right: Differential connectivity strength for the interactive effect of group by treatment. The differential connectivity strengths (Ep) are depicted by the width of the arrow. Black arrows reflect positive values and red arrows reflect negative values. | PMC10276024 | ||
Discussion | mood deterioration, fatigue, anxiety, mood disorders, depressed mood, depressive, depressive symptoms | The main goal of the current manuscript was to characterize the changes in directed connectivity during COC treatment related to concurrent mood symptoms. Our results showed how effective connectivity changes noted during COC treatment related to mood deterioration. Mood lability was the most prominent COC-induced symptom [Overall, we found the expected pattern of enhanced connectivity within the DMN and decreased connectivity within the ECN during COC use [However, and contrary to our hypothesis, only the connectivity from rAG to PCC was positively related to depressive symptoms. In that respect, different sub-items in depressive scales have been shown to be associated with abnormal activity of various brain areas [Alongside the decreased ECN within-network connectivity, which is line with previous findings [Remarkably, an important role seems to emerge for the dACC. Better emotional regulation and lower anxiety levels have been related to stronger dACC activity [The above-described changes and related experienced side effects could be a consequence of the synthetic hormones, the abolishment of cyclic endogenous hormonal fluctuations, or both. Although animal research shows a differential binding affinity of synthetic compared to endogenous hormones (levonorgestrel has a 5-fold affinity vs. progesterone for progesterone receptors) [Some potential limitations need to be noted. First, an exclusion criterion was the use of hormonal contraceptives within the previous two months. While it could be argued that this represents a short wash-out period, it should be noted that prior to randomization, every participant had regular menstrual cycles. Second, the pre-treatment mood assessment was obtained during the luteal phase, while the pre-treatment MRI session was performed during menses. Correspondingly, differences in mood lability, fatigue and depressed mood were operationalized as the difference between the third week of treatment and the luteal pre-treatment phase, in order to avoid potential confounding effects of menstrual-cycle related variations. Last, although five minutes of resting state scanner sequence may seem short, and statistical significance is higher with increased sequence length, longer scans have been associated with similar or even diminished reliability [In summary, the present randomized placebo-controlled trial showed effective connectivity changes during COC treatment related to worsened mood in women with a history of mood COC side effects. The most confident effects corresponded to connections that changed during COC treatment compared to placebo and were related to an increased in mood lability. These differences during COC treatment in the triple network model may affect cognitive processes important for mood stability and mental health and similar disruptions have been reported across mood disorders [ | PMC10276024 | |
Supplementary information | The online version contains supplementary material available at 10.1038/s41398-023-02470-x. | PMC10276024 | ||
Acknowledgements | This research was funded by the Swedish Research Council project K2008–54X-200642–01–3, the Swedish Council for Working Life and Social Research projects 2007–1955, and 2007–2116, the Family Planning Foundation, and an unrestricted research grant from Bayer AB. The European Research Council (ERC) Starting Grant 850953 supported BP and EH-L. | PMC10276024 | ||
Author contributions | JE, IS-P, and MG designed and performed the clinical trial and acquired the data. EH-L was responsible for data curation and analysis, interpreting the results, drafting and revising the manuscript. MG, BP, and IS-P supervised the analysis, contributed in the results’ interpretation, and revised the manuscript. Both MG and BP should be considered shared senior authors. All authors approved the final version and are accountable for all aspects of the work. | PMC10276024 | ||
Competing interests | Over the past three years, I. Sundstrom-Poromaa has served occasionally on advisory boards or acted as invited speaker at scientific meetings for Bayer Health Care, Gedeon Richter, Peptonics, Shire/Takeda, and Sandoz. None of the other authors has any conflicts of interest. | PMC10276024 | ||
References | PMC10276024 | |||
Background | Snacking is a common diet behaviour which accounts for a large proportion of daily energy intake, making it a key determinant of diet quality. However, the relationship between snacking frequency, quality and timing with cardiometabolic health remains unclear. | PMC10799113 | ||
Design | Demography, diet, health (fasting and postprandial cardiometabolic blood and anthropometrics markers) and stool metagenomics data were assessed in the UK PREDICT 1 cohort ( | PMC10799113 | ||
Results | Participants were aged (mean, SD) 46.1 ± 11.9 years, had a mean BMI of 25.6 ± 4.88 kg/m | PMC10799113 | ||
Conclusion | Snack quality and timing of consumption are simple diet features which may be targeted to improve diet quality, with potential health benefits. | PMC10799113 | ||
Clinical trial registry number and website | NCT03479866, | PMC10799113 | ||
Supplementary Information | The online version contains supplementary material available at 10.1007/s00394-023-03241-6. | PMC10799113 | ||
Keywords | PMC10799113 | |||
Introduction | Snacking can account for a large proportion of daily energy intake, making it a key determinant of diet quality [Snacks can be defined based on the time of day when consumed [The inconsistencies of snacking research render the impact of snacking on health unclear. Further investigation of snacking behaviour is warranted, specifically, whether snacking frequency, quality and/or timing are key determinants of cardiometabolic health [ | PMC10799113 | ||
Subjects and methods | SECONDARY | The This secondary analysis is a cross-sectional analysis of the baseline data and weighted logged diet data obtained as part of the original intervention trial. Out of the | PMC10799113 | |
Diet data | In the ZOE PREDICT 1 cohort, participants recorded all diet intakes during the entire study period on the specialised ZOE study app, yielding comprehensive records of timed intakes. Participants were trained to accurately record ad libitum diet intake using photographs, product barcodes, product-specific portion sizes and weighed intakes using digital scales. Data logged into the study app were uploaded onto a digital dashboard in real time and assessed for logging accuracy and study compliance by study staff (Criteria for accuracy assessment were previously described in Berry et al. [Participants self-reported meal type (i.e. snack, breakfast, lunch, dinner or drink) when they logged food items. An eating occasion was defined as any occasion where a food or beverage was separated in time from the preceding and succeeding eating occasion by 30 min. Foods or drinks consumed within the same 30 min window of a meal were considered part of the meal. Where snacks were consumed with a main meal, they were relabelled as a component of the main meal. After aggregating foods and drinks into eating occasions, 86% of eating occasions contained a single meal type (i.e. snack only) and 13% contained multiple meal types (i.e. breakfast and snack). Calorie and nutrient information were summed within each eating occasion.During the study home-phase, participants consumed multiple standardised test meals over a 9–11 day period, differing in macronutrient composition and order (See online protocol for further detail [ | PMC10799113 | ||
Assessment of snacking | EVENTS | Snacks were defined as foods or drinks consumed between meals. Snacking events contained (1) a single food type, e.g. apple, or (2) multiple food types, e.g. apple, nut butter and coffee. For the single food snack type, drinks ≤ 50 kcal were excluded to ensure low-calorie drinks did not inflate snacking frequency. To determine the average snack frequency, the number of snacking occasions per participant per day was summed and the number of snacking occasions was averaged across all free-living days. Snacks were mapped onto a “Food Tree” consisting of a database of nutrient information arranged according to a hierarchical tree structure as follows: level 1 (9 food groups); level 2 (52 food groups); and level 3 (195 food groups). These foods were mapped on to the Composition of Foods Integrated Datase | PMC10799113 | |
Diet quality scores | In order to capture the overall quality of the snacks an individual consumes, we created a snack diet index (SDI) in snackers (those consuming ≥ 1 snack/day). This used an adapted version of the plant-based diet index [ | PMC10799113 | ||
Hunger ratings | Participants reported their hunger levels on a visual analogue scale daily. App notifications appeared at | PMC10799113 | ||
Activity levels | Physical activity was self-reported, captured using the following question “In the past year, how frequently have you typically engaged in physical exercises that raise your heart rate | PMC10799113 | ||
Gut microbiome | Stool samples were collected by participants at home prior to the clinic visit using an EasySampler collection kit (ALPCO) and put into faecal collection tubes containing DNA/RNA Shield buffer (Zymo Research). A total of | PMC10799113 | ||
Cardiometabolic blood and anthropometric measures | The methods for anthropometric and biochemical measures are described in full elsewhere [ | PMC10799113 | ||
Statistical analysis | REGRESSION | Data analysis was performed using Python 3.8.3 edition (Pandas 1.3.3, statsmodel 0.13.2, scipy 1.7.1). Descriptive characteristics of the cohort and diet intakes were examined. The relationship between snacking frequency, quantity from energy and timing with the cardiometabolic health outcomes was assessed using analysis of covariances (ANCOVA). Linear regression analysis was used to examine the associations between snacking quality (SDI) with the cardiometabolic blood and anthropometric measures. All analyses (ANCOVA and linear models) were adjusted for age, sex, BMI, physical activity, education and main meal quality (oPDI). Participants were stratified across sexes (males and females), age groups (18–35 years, 36–45 years and 46–65 years) and physical activity levels (< 1/week, 1–4/week and ≥ 5/week), and differences in energy intake from snacks between the groups were examined using ANCOVA for the three variables separately. Participants were also stratified across BMI categories (healthy weight; < 25 kg/mThe analysis of the microbiome data was undertaken using a machine learning framework, the same as developed in Asnicar et al. [To ensure the impact of snacking quality on the microbiome was independent of the whole diet, we defined 100 random subsets each of 200 individuals containing 100 good and 100 bad snackers (according to their SDI score). The requirements for identifying these 100 random subsets were that they should not be statistically significantly different between their meal quality scores (according to the Mann–Whitney | PMC10799113 | |
Results | Participants were aged (mean, SD) 46.1 ± 11.9 years, had a mean BMI of 25.6 ± 4.88 kg/mCharacteristics of the cohortSnacking habits in the PREDICT 1 cohort ( | PMC10799113 | ||
Diet and snacking in the ZOE PREDICT 1 cohort | The average daily snack intakes in people who snack (95% of the cohort) were 2.28 snacks/day (95% CI 2.21–2.35) (Fig. The most popular foods consumed as snacks included drinks (milk, tea, coffee, fruit drinks), candy, cookies and brownies, nuts and seeds, fruits (apples, bananas, citrus fruits), crisps, bread, cheese and butter, cakes and pies and granola or cereal bars (Fig. | PMC10799113 | ||
Snacking quality versus habitual diet | Average snacking quality (SDI; lower scores are indicative of poorer snack quality) was 5.73 ± 2.09, range; 1–11, and IQR; 4–7 (Fig. | PMC10799113 | ||
Snacking frequency and energy quantity are not associated with cardiometabolic health | Across the snacking frequency groups (0, 1, 2 and > 2 snacks/day), there were no differences in cardiometabolic blood or anthropometric markers including anthropometric traits (height, weight, BMI, visceral fat, waist-to-hip ratio), or fasting and postprandial blood markers (see Supplementary Table 3) (all adjusted for age, sex, BMI, physical activity level and main meal quality). Similarly, we saw no differences in the same cardiometabolic blood or anthropometric markers across the quartiles of quantity of energy from snacks (Q1 < 25, Q2; 25–50, Q3; 50–75, Q4 > 75%). These trends did not change when examining food-only snacking. | PMC10799113 | ||
Snacking quality is associated with cardiometabolic health | TG | Participants consumed on average 74% of their snacking calories and 18% of their total daily calories from unhealthful foods. An inverse association was found between snacking frequency and quality (1 snack/d; 6.29 ± 1.67, 2 snack/day; 5.81 ± 2.11 and > 2 snacks/day; 5.23 ± 2.19, A sensitivity analysis was performed where only 2 free-living days were selected for all participants, and the associations between snacking quality (SDI) with the cardiometabolic blood and anthropometric measures were repeated. The relationships between snack quality and TG, insulin and HOMA-IR persisted (Supplementary Table 6). | PMC10799113 | |
Minimally processed snacking is associated with cardiometabolic health | The SDI was inversely correlated with ultra-processed snacks (% of snacking energy from NOVA 4) (rho; -0.41, | PMC10799113 | ||
High-quality snacking versus low-quality snacking | Frequently snacking on high-quality foods (SDI Q1; ≥ 7, | PMC10799113 | ||
The relationship between timing of snacks and cardiometabolic health | Four clear temporal snacking patterns, capturing the timing and frequency of snack intake across the day, were evident (Fig. Patterns of snacking across the day. Furthermore, individuals who snack after 9 pm (32%), classified as late-evening snackers, had higher HbA1c concentrations (5.54 ± 0.42% vs 5.46 ± 0.28%, | PMC10799113 | ||
The relationship between snacking and the gut microbiome | The microbiome composition differentiated individuals based on their snacking quality (AUC = 0.617). As the frequency of snacking might also be related to the quality of the snack, we tested whether the gut microbiome was able to discriminate participants that snack rarely versus those that snack regularly, but we did not see an association (AUC = 0.521). We matched participants for meal quality (see Methods), and the average AUC was reduced to 0.555. This suggests overall diet may have stronger effects on the microbiome than snaking alone. | PMC10799113 | ||
Discussion | obesity, diet-related diseases | OBESITY | This research demonstrates snacking is a common dietary behaviour in a UK population accounting for 24% of daily energy intake. The relationship between snacking quality and main meal quality was low, highlighting the discordance between these two behaviours and their capturing of different dietary attributes suggesting that snacking may be a key diet strategy to improve health. We address unanswered questions relating to the importance of snacking frequency, quantity, quality and timing to cardiometabolic health, taking into account the whole day’s diet. Contrary to public perception, we find that the act of snacking, in terms of both frequency and quantity of energy from snacks, was not associated with unfavourable cardiometabolic blood or anthropometric markers. Instead, we observed that snack quality matters and is associated with favourable lipemic and insulinemic responses, as well as decreased hunger. Frequent high-quality snack intake was also associated with favourable weight and BMI compared to non-snackers and frequent low-quality snackers. We identified four temporal snacking patterns based on the time of day and show late snacking is associated with unfavourable outcomes, potentially due to a reduced overnight fasting interval. These findings support the view that snacking on high-quality foods earlier in the day can be part of a healthy lifestyle.High-quality snacks include whole fresh fruit and vegetables, nuts and seeds which are typically high in fibre and other healthful food components, while retaining their food matrix structure. These foods play roles in mediating hunger and appetite [Circadian regulation of metabolic pathways implies that foods may be metabolised differently throughout the day and evidence suggests late snacking is associated with adverse health [Large diversity exists in the definitions and approaches used across studies to capture snacks, whether as an eating occasion or as a specific collection of foods, contributing to conflicting evidence on the health effects of snacking. In this study, 24% of total daily energy was derived from snacks, similar to previously observed levels in other countries, including Norway (men; 17% and women; 21%) [In line with previous research, our findings showed total energy intake increased with snacking frequency [Strengths of this study include high-resolution diet data, weighted and checked by nutritionists in real time, to evaluate snacking intakes in a UK cohort and the densely phenotyped PREDICT cohort with postprandial metabolic responses. Limitations include the cross-sectional nature of the study which does not allow the assessment of causality owing to the uncertain temporality of the association. The associations of snacking with health outcomes may be confounded by possible under-reporting of eating frequency (that is, meal and/or snack intake) concomitant with the under-reporting of energy intake particularly by people who are overweight or living with obesity. Sample size numbers were limited when stratifying participants based on snacking frequency and quality. Finally, our data were limited to 2–4 days of logged diet data, did not have information on work versus work free days and did not permit the examination of seasonality on snacking behaviours within individuals. Compared to the average UK population, PREDICT 1 participants were older, had a lower BMI, were less likely to smoke and had a lower proportion of males (Supplementary Table 10). However, snacking intakes (% energy and frequency) were similar to previously reported surveys (24%/2.28/day vs 20%/2.55/day) (the UK and Ireland) [In conclusion, snacking behaviour may be a key diet target to ameliorate risk factors for diet-related diseases and snacking on high-quality foods earlier in the day can be part of a healthy lifestyle. However, when people have sufficient diet information, food knowledge and healthy eating intentions, the current food environment makes it difficult for them to change their snacking behaviour [ | PMC10799113 |
Supplementary Information | Below is the link to the electronic supplementary material.Supplementary Figure 1. CONSORT diagramSupplementary Figure 2. Correlations between diet quality indices calculated using meals and snacks. uPDI, unhealthful plant diet index; oPDI, original plant-based diet index; hPDI, healthful plant-based diet index; and SDI, snack diet indexSupplementary file3 (XLSX 294 KB) | PMC10799113 | ||
Abbreviations: | haemoglobinBody mass indexFood | MAY | Snack diet indexTriglyceridesIncremental area under the curveHomeostatic Model Assessment for Insulin ResistanceGlycated haemoglobinBody mass indexFood frequency questionnaireKate M. Bermingham, Anna May, and Sarah E. Berry have contributed equally to this work. | PMC10799113 |
Author contributions | SEB, AMV, JW, NS, PWF, TDS, GH and JC designed research. SEB, JW, GH and TDS conducted research. AM, KMB and FA performed statistical analysis. KMB, SEB, AMV, JW, TDS, NS, FA, ERL and LMD wrote the paper. SEB, TDS and KMB had primary responsibility for final content. All authors read and approved the final manuscript. | PMC10799113 | ||
Funding | Arthritis, Thomas’ NHS | ARTHRITIS, CHRONIC DISEASE | This work was supported by ZOE Ltd and TwinsUK which is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation (CDRF), ZOE Ltd, and the National Institute for Health Research (NIHR) Clinical Research Network (CRN) and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. | PMC10799113 |
Declarations | PMC10799113 | |||
Conflict of interest | TDS, GH and JW are co-founders of ZOE Ltd. TDS, FA, NS, LMD, PWF, AMV and SEB are consultants to ZOE Ltd. TDS, SEB, JW, GH, LMD, PWF, AMV, JC and AM are in receipt of ZOE options. KMB, AM and JC are employed by ZOE Ltd. Other authors have no conflict of interest to declare. | PMC10799113 | ||
References | PMC10799113 | |||
Objective | SE, multiple sclerosis | MULTIPLE SCLEROSIS, CHRONIC INSOMNIA | Mindfulness is an established approach to reduce distress and stress reactivity by improving awareness and tolerability of thoughts and emotions. This study compares mindfulness training to sleep hygiene in persons with multiple sclerosis (PWMS) who report chronic insomnia, examining sleep efficiency (SE), self-reported sleep quality and quality of life. | PMC10334613 |
Methods | SE, Insomnia | MULTIPLE SCLEROSIS | Fifty-three PWMS were randomized (1:1) in a single-blinded, parallel group design to ten, two-hour weekly sessions of Mindfulness Based Stress Intervention for Insomnia (MBSI-I) over a span of ten weeks or a single, one hour sleep hygiene (SH) session over one day. The primary outcome measure was SE, measured by the Fitbit™ Charge 2 wrist device, at 10 and 16 weeks from the start of study interventions. Self-report outcomes included the Pittsburg Sleep Quality Rating Scale (PSQI), Insomnia Severity Index (ISI) and the Multiple Sclerosis Quality of Life Inventory (MSQLI). Nineteen participants in the MBSI-I group and 24 in the SH group completed the primary study. Subsequently, ten participants in the original SH group participated in the 10-week MSBI-I course and their data was added to the MBSI-I cohort (eMSBI-I). | PMC10334613 |
Results | SE | While neither SE nor the PSQI showed significant differences between MBSI-I, eMBSI-I and SH groups, ISI improved in both the MSBI-I and eMBSI-I vs SH at 10 weeks ( | PMC10334613 | |
Conclusion | insomnia | This pilot study demonstrates beneficial effects of MBSR on insomnia, sleep quality and quality of life in PWMS. | PMC10334613 | |
Trial registration | MAY | NCT03949296. 14 May 2019. | PMC10334613 | |
Keywords | PMC10334613 | |||
Introduction | anxiety, multiple sclerosis, Insomnia, SE, stress reduction | MULTIPLE SCLEROSIS, CHRONIC INSOMNIA | Twenty to fifty percent of persons with multiple sclerosis (PWMS) report having chronic insomnia (CI) [The clinical impact of chronic insomnia in PWMS, while frequently overlooked by clinicians, is supported by several studies demonstrating an overall lower quality of life [Insomnia is often treated pharmacologically with antidepressants, anxiolytics, antihistamines, and benzodiazepines. Some PWMS self-medicate with cannabis [Clearly, effective non-pharmacological treatments need to be explored to avoid these hazards. Two such programs, Mindfulness Based Stress Reduction (MBSR) and the Sleep Hygiene (SH) index have been used to treat CI. MBSR has its origins in non-Western Buddhist philosophy, and was developed by John Cabot Zin, PhD for stress reduction and anxiety [The purpose of this pilot study is to contrast the effectiveness of two therapies to treat CI in PWMS and their impact on subjective and objective measures of sleep, quality of life and actigraphy using the Fitbit™ Charge 2 band. We hypothesize that MBSI-I is superior to SH in improving sleep efficiency (SE) in PWMS with CI, and this will be associated with significant benefits in self-reported quality of life outcome measures compared to SH. | PMC10334613 |
Methods | PMC10334613 | |||
Study design | Insomnia | MAY, RECRUITMENT | This randomized parallel, single-blinded clinical study enrolled 53 participants with MS who were randomly assigned (1:1) to attend ten, two-hour weekly sessions of MBSI-I or a one-hour counseling session on SH. Repeated assessments were performed at baseline, 10 and 16 weeks. The evaluator was blinded to treatment group assignment. The study was conducted at Griffin Hospital, a community hospital in the lower Naugatuck valley, in central CT, USA, in collaboration with the Yale Stress Center at the Yale School of Medicine in New Haven, CT. The study was approved by the Griffin Hospital Institutional Review Board (IRB) and registered on clinicaltrials.gov (NCT03949296) before initiating recruitment. The recruitment period commenced from May, 2019 to September, 2019.Participants were assigned to one of two cohorts: one comprised of small groups of six to 11 persons who attended ten weekly sessions of MBSI-I (Mindfulness Based Stress Intervention for Insomnia) and the other, similar groups of participants who attended one sleep hygiene session conducted by the Griffin Hospital Sleep Wellness Center staff. MBSI-I is an adaptation of MBSR. Eighty percent attendance at the MBSI-I program was considered good compliance. Because of the lower than expected number of participants at the end of the randomized portion of the study, participants from the sleep hygiene cohort were offered participation in the MBSI-I course and repeated 10 and 16 week assessments after the course, i.e., the expanded MBSI-I, or eMBSI-I cohort. This was done to increase the statistical power of the results. | PMC10334613 |
Treatment groups | PMC10334613 | |||
MBSI-I | In MBSR, participantsare taught under supervision to concentrate on the present moment intentionally and without judgment in order to reduce distress and emotional reactivity [ | PMC10334613 | ||
SH | This group attended a one-hour group counseling session based on a handout enumerating 15 sleep hygiene tips, published by the Centre for Clinical Intervention in Australia. The SH tips were as follows: (1) maintaining a consistent sleep pattern of going to bed and arising at about the same time each day; (2) attempting sleep only when feeling tired or sleepy; (3) getting up to do something calm until feeling sleepy and returning to bed if unable to sleep; (4) avoiding caffeine and nicotine for at least four to six hours before going to bed; (5) avoiding alcohol for at least four to six hours before going to bed; (6) using the bed only for sleeping and sex, which would preclude, among other activities, reading, watching television, or using a laptop; (7) avoiding naps during the day, or limiting them to less than an hour prior to 3 p.m.; (8) developing personalized rituals to relax and prepare for sleep; (9) taking a warm bath one to two hours before bedtime; (10) avoiding the tendency to check the clock frequently during the night; (11) using a sleep diary for a few weeks to track progress; (12) avoiding strenuous exercise within four hours of bedtime; (13) avoiding heavy meals before bedtime, and, if hungry, restricting oneself to a light snack; (14) creating a sleep environment that is quiet, comfortable, and dark, and (15) conducting a regular daytime routine, even after a night of poor sleep. | PMC10334613 | ||
Recruitment procedures and participants | insomnia | Participants were recruited widely throughout the state of Connecticut via press releases distributed via paper and email to newspapers for articles and advertisements, MS support groups, neurologists, the Yale-Griffin Prevention Research Center electronic Newsflash, health magazines, and current and previous patients of the MS Treatment Center at Griffin Hospital (MSTC). Interested participants underwent an initial telephone screening to determine eligibility.Inclusion criteria included a diagnosis of MS, based on the 2014 revised McDonald diagnostic criteria [After preliminary eligibility was established, a clinical screening was scheduled to determine final eligibility. These procedures included vital signs measurements, using calibrated equipment, of height, weight, waist circumference and blood pressure. A neurological exam and a brief physical exam were performed by the PI. The medical assessment included a description of insomnia symptoms and history of the diagnosis and treatment for MS as well as current medications and other pertinent medical information. Participants were consented using an IRB-approved Consent Form and told they could discontinue participation at any time during the study without penalty. | PMC10334613 | |
Randomization and blinding | The former was carried out using SAS software for Windows version 9.4 (SAS Institute, Cary, NC) by dividing participants into blocks of 14, 17, and 22. The study coordinator enrolled the participants and assigned them to one of the two treatment groups based on the randomization algorithm. Therefore, the coordinator was unblinded and aware of the randomization scheme. The Principal Investigator (PI), statistician and study personnel assessing outcome measures were blinded to the treatment assignments throughout the study. Participants were labelled as receiving either treatment A or B. Only the study coordinator knew the treatment allocations that each participant received. Participants’ group assignment was unmasked by the study coordinator at conclusion of statistical analyses. | PMC10334613 | ||
Outcome measures | PMC10334613 | |||
Primary outcome | The study’s primary outcome was sleep quality defined by sleep efficiency, as measured by the Fitbit™ Charge 2 wrist device. This is a consumer wristband-tracking device that embeds a heart rate monitor and three-axis accelerometer to report heart rate, exercise and sleep. Raw data from the device was uploaded to Fitbit, which processed it using a proprietary algorithm. Data reported back from Fitbit included subject ID, date of sleep, start time, end time, minutes asleep, minutes in sleep period and sleep efficiency. Other parameters, were also reported, including sleep stages, but this data was not deemed reliable enough to use in the analysis. All statistics on sleep data were performed by our Study Statistician as noted below.The FitBit™ Charge 2 was introduced in 2016 and replaced in 2019 by improved devices. Cost, ease of use by persons in a natural sleep environment, the amount of data collected, the inessential requirement for specialized technicians to interpret the date are some of the advantages of consumer actigraphy over the gold standard polysomnography (PSG) [Sleep efficiency is defined as the percentage of time asleep while in bed during a specified sleep period. A normal sleep efficiency is at least 85%. This was calculated from the longest recorded sleep period (> = two hours) during a 24 h period that occurred within or overlapped between 8:00 pm and 8:00 am. In order to distinguish a sleep period from a daytime nap, the onset, but not the end of the sleep period, had to fall within the sleep window [ | PMC10334613 | ||
Secondary outcomes | death, Muscle spasticity, insomnia, Insomnia, disability | SECONDARY, MULTIPLE SCLEROSIS | These included self-reported sleep quality as measured by the Pittsburgh Sleep Quality Index (PSQI) at baseline, the end of the 10-week intervention, and 16-weeks post-intervention. The PSQI is a self-rated questionnaire to assess perceived sleep quality and disturbances over the prior one-month time interval [The Insomnia Severity Index (ISI) is a brief self-report screening tool of seven questions assessing sleep over the previous two weeks, including three questions rating difficulties with: 1) falling asleep; 2) staying awake and 3) early morning awakening on a five-point Likert scale ( ‘0’ = none to ‘4’ = very severe). Other questions rate dissatisfaction with current sleep pattern (‘0’ = very satisfied to ‘4’ = very dissatisfied); how noticeable sleep problems are to others (‘0’ = not at all to ‘4’ = very much); worried/distressed about current sleep problem (‘0’ = not at all to ‘4’ = very much) and interference with daily function (‘0’ = not at all to ‘4’ = very much). Total scores of 15 to 21 indicate moderate and scores of 22–28 indicate severe insomnia (Other secondary outcome measures included the self-reported Multiple Sclerosis Quality of Life Inventory (MSQLI) [Muscle spasticity was measured by the Modified Ashworth Scale (MAS) [The Expanded Disability Status Scale (EDSS) is a standard measure of physical and mental impairment in MS that is universally employed in MS studies and clinical practice. It consists of a set of subscale, measuring different neurological functions, and a ten-point ordinal scale that grades neurological findings in MS, ranging from no impairment (0), moderate disability (3.0 or higher), reliance on a unilateral assistive device to walk 100 m (6.0), wheelchair bound (7.0) and death from MS (10) (reference). The Principal Investigator, a Board Certified neurologist with more than two decades of experience in treating MS and participating in MS clinical trials, performed the EDSS examinations. | PMC10334613 |
Exploratory outcome measures | Within-group comparisons comparing baseline to 10 weeks and 16 weeks were done for the MBSI-I and SH cohorts. At the end of the randomized phase, participants in the sleep hygiene group were offered the same MBSI-I training and analyzed as a group, the expanded MBSI-I cohort (eMBSI-I). The eMBSI-I outcomes analyses included data from the original MBSI-I cohort as well as the crossover SH participants. Within-group and between group (SH) analyses were performed for the eMBSI-I cohort at the same time points. | PMC10334613 | ||
Adverse events reporting scheme | ADVERSE EVENT | Adverse events, including MS relapses, were recorded throughout the study by the coordinator. These were presented to the PI, who would inform the IRB as per the protocol. | PMC10334613 | |
Statistical analysis | REGRESSION | The sample size estimate allowed for 20% attrition and noncompliance to provide ≥ 80% power and maximum type I error of 5% to detect a minimal difference of 1.6 point improvement in subjective sleep quality as measured by the ISI sleep scale between cohorts. Generalized linear models were used to compare scores of the outcome measures between cohorts. Paired student t-tests were used to assess difference from baseline to endpoints for each group. Regression models were used to control for covariates (i.e., age, gender, race, compliance, and medication use). All analyses at endpoints were based on intention-to-treat principle. SAS software for Windows version 9.4 (SAS Institute, Cary, NC) was used to carry out all statistical analyses. | PMC10334613 | |
Role of the funding sources | Neither Fitbit, Inc., which provided the Fitbit™ Charge 2 device as well as data tabulation free of charge, nor the funder of the study, had any role in study design, data collection, data analysis, data interpretation, or writing of the report. | PMC10334613 |
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