Does resistance training make a difference to the quality of life or heart health for older adults compared to aerobic exercise? A systematic review protocol from The People's Review

1 Does resistance training make a difference to the quality of life or heart health for older adults 1 compared to aerobic exercise? A systematic review protocol from The People's Review 2 Éle Quinn1,2*, Laura Bosner3, Patricia Logullo4, Kevin Murray5, KM Saif-Ur-Rahman2,6, Charlene 3 Young7, Derek Stewart7,8, Maureen Smith7, Jeremy Holt7, Shaun Treweek9 Chris Noone10, David 4 Moher11, Sinéad M. Hynes1 & The People. 5 6 1. Discipline of Occupational Therapy, School of Health Sciences, University of Galway, Galway, 7 Ireland. 8 2. Evidence Synthesis Ireland and Cochrane Ireland, University of Galway, Galway, Ireland. 9 3. School of Medicine, University of Galway, Galway, Ireland. 10 4. Independent Researcher 11 5. School of Pharmacy and Medical Sciences, University of Galway, Galway, Ireland. 12 6. Centre for Health Research Methodology, School of Nursing and Midwifery, University of 13 Galway, Galway, Ireland. 14 7. Public Partner 15 8. HRB Trials Methodology Research Network, University of Galway, Galway, Ireland. 16 9. Aberdeen Centre for Evaluation, University of Aberdeen, United Kingdom. 17 10. School of Psychology, University of Galway, Galway, Ireland. 18 11. Centre for Journalology, Methodological and Implementation Research, Ottawa Hospital 19 Research Institute, Ottawa, Canada 20 *Corresponding author. Éle Quinn, [email protected], Discipline of Occupational 21 Therapy, School of Health Sciences, Áras Moyola, University of Galway, University Road, Galway, 22 Ireland, H91TK33 23 24 2 ABSTRACT 25 Background 26 Systematic reviews bring together all the evidence on a health topic in an organised and careful way. 27 The People's Review aims to help the public understand what systematic reviews are and why they 28 matter by designing and conducting their own systematic review. The question chosen for The 29 People’s Review is: Does resistance training make a difference to quality of life and/or heart health 30 for older adults compared to aerobic exercise? This paper outlines how we will carry out this review. 31 Methods 32 This is a systematic review involving the public throughout. This review will search for, include and 33 summarise: 34 • randomised controlled trials 35 • with older adults (50+ years) 36 • that compare resistance training (e.g. lifting weights) with aerobic exercise (e.g. walking or 37 running) 38 • and measure quality of life or heart health. 39 First, the technical team will search research databases to find possible studies. The public will look 40 at summaries of these to find studies that might be relevant. Then, two members of the technical 41 team will read the full studies and decide which ones to include. Next, the public will help collect 42 some of the key information from the included studies. The technical team will record the rest. The 43 public and the technical team will work together to check for biases (or flaws in how the studies were 44 done) in the studies. Finally, if possible, the team will combine the study results using a method 45 called meta-analysis (a way of pooling numbers together). If we can't combine the numbers, we will 46 write a summary of what the studies found. 47 48 Discussion 49 3 This review will summarise all the available evidence that addresses the review question. This 50 review could support the public to make decisions about what type of exercise to engage in as they 51 age, and influence exercise guidelines, clinical practice and future research. 52 KEYWORDS 53 Systematic review protocol; Citizen Science; Older adults; Resistance training; Aerobic exercise; 54 Evidence synthesis; Quality of life; Heart health; Cardiac outcomes 55 56 INTORDUCTION 57 This is a systematic review protocol from The People's Review – an online, novel citizen science 58 approach to involving members of the public in a systematic review. The overall aim of The People's 59 Review is to support the public in understanding what systematic reviews are and why they matter 60 (1). With this in mind, this protocol is written in plain English to make it accessible to as many 61 people as possible. 62 63 This protocol was produced with 366 members of the public, cited in this protocol as “the People”. 64 There is also a group of researchers and Patient and Public Involvement (PPI) contributors working 65 behind the scenes. Throughout this protocol, we refer to this group as the “Technical Team”. We are 66 all one large review team who have created this protocol together and will go on to produce a 67 systematic review together. We make this distinction throughout this protocol so that it is clear and 68 transparent who is doing what parts of the review. 69 70 What this review is about: the importance of exercising 71 As we age, our bodies change. Ageing gradually impacts our heart health and fitness. A decline in 72 heart health is closely linked to the onset of diseases, including heart failure (2), diabetes (3), and the 73 narrowing of arteries (4,5). Therefore, it is really important to find the best ways to support healthy 74 4 ageing in older adults, slowing the decline of heart health, preventing the onset of diseases, and 75 ultimately leading to a better quality of life (6).Regular exercise has a positive impact on healthy 76 ageing (7,8). Exercise prevents the onset of disease, maintains function, improves mental health, and 77 brings social benefits. In particular, regular exercise supports heart health for older adults (9–11). 78 79 Types of exercise we will investigate 80 Different types of exercise are promoted to improve heart health, quality of life, muscle strength, 81 mobility and many other outcomes (10–12). Aerobic exercise is an activity that increases the heart 82 rate and breathing, while engaging large muscle groups over a sustained period. These exercises 83 improve heart health, fitness and endurance. Examples include brisk walking, running, swimming, 84 cycling, or dancing. Resistance training is another form of exercise that involves using external force 85 or load to challenge and strengthen muscles. The external force could be lifting weights, pulling 86 against elastic resistance bands, or using one’s body weight to work against gravity. 87 88 Traditionally, aerobic training was recommended as the best form of exercise to support heart health 89 (10,12,13). However, in recent years, emerging research suggests that resistance training may also 90 play an important role in improving heart health (14–17). Although the main goal of resistance 91 training is to increase muscle strength (18), the American Heart Association (AHA) recommends 92 engaging in regular resistance training to lower the risk of heart disease and maintain a healthy heart 93 (19). 94 95 However, there is still uncertainty about how resistance and aerobic training compare to each other 96 for supporting heart health (20) and quality of life (21). A recent systematic review explored the 97 effects of aerobic versus resistance training on heart health in older adults (22). However, that review 98 did not focus on quality of life, and had several limitations. Therefore, it is still unclear whether 99 5 resistance training makes a difference to the quality of life and heart health for older adults compared 100 to aerobic exercise. This review will compare these two types of exercise: resistance and aerobic 101 training. 102 103 How resistance training might improve heart health and quality of life 104 Resistance training causes a person’s blood pressure to lower, meaning there is less pressure put on 105 the heart as it pumps blood around the body (15,16). There is also an increase in the amount of blood 106 the heart can hold and pump out with each beat, meaning the heart works more efficiently (17). The 107 ability for blood vessels and the body’s muscles and cells to transport and use oxygen is improved 108 through resistance training, therefore reducing the effort on the heart to supply oxygen (23–25). 109 Resistance training can lower cholesterol, balance blood sugars and reduce body fat, which can 110 impact heart health (14,26). Through these effects, resistance training has been shown to reduce the 111 risk of developing heart disease (19). 112 113 Resistance training may improve more than just heart health for people over the age of 50. It can also 114 enhance overall physical health (27,28), lower the risks of falls (29), and improve mental health in 115 older adults (21,30–32). Any type of physical activity can improve quality of life (33), including 116 resistance training, as likely due to the release of feel-good chemicals in the brain and strengthening 117 pathways in the brain that control our emotions and mood [35–37]. Additionally, resistance training 118 can help older adult’s brain function (34) such as concentration and memory. 119 120 Why it is important to do this systematic review 121 Finding out whether resistance training is equally good or better than aerobic exercise may be useful 122 to the public, healthcare workers (such as doctors, nurses, or physiotherapists), policymakers, and 123 6 researchers to inform recommendations about what older people can do to support their physical and 124 mental well-being. 125 126 Perhaps the most important reason for conducting this systematic review is that the question was 127 selected by the public. Through three rounds of priority-setting surveys, the People decided the final 128 question for this review. 129 130 What is the aim of this review? 131 This systematic review aims to find out if resistance training makes a difference to quality of life 132 and/or heart health for older adults (aged 50 years or older) compared to aerobic exercise. 133 METHODS 134 This is a systematic review protocol with public involvement throughout. This protocol follows the 135 PRISMA-P reporting checklist (35). It is important to follow this checklist so that the protocol is 136 clear, complete and easy for everyone to understand. This protocol is registered in PROSPERO: 137 CRD420251156252. See Fig. 1 for an overview of the methods. 138 What is a systematic review protocol? A systematic review protocol is a plan for how a systematic review will be done. A protocol should be written up and shared before the authors of the review begin searching for and analysing the studies to be included in the review. A systematic review protocol improves the quality of the review, holds authors accountable to the set out plan, and allows others to take a look at the work before it starts (36). 7 Fig 1. A flow diagram of this systematic review’s methods What studies will we include in our 139 review? 140 141 8 What type of studies will we include in our review – the eligibility criteria? 142 A study will be included in this systematic review if it: 143 1. Is a randomised controlled trial (RCT) 144 2. Includes people over the age of 50 145 3. Compares resistance training with aerobic exercise 146 4. And measures quality of life and/or heart health 147 The study must meet all four of these criteria in order for it to be included in the review. Each of 148 these are described in further detail below. 149 150 Randomised controlled trials (RCTs) only – the study design 151 We will only include randomised controlled trials (RCTs) in our review. Randomised trials are 152 widely considered the most reliable way to evaluate whether healthcare interventions work or not. 153 We will include all types of randomised trials, including the traditional and simple ones (like those 154 with two or more groups) and more sophisticated designs, such as cluster-randomised trials (where 155 participants are randomised in a cluster or group, such as by clinic or nursing home unit) and cross156 over trials (where participants are randomly assigned to either the resistance training or aerobic 157 exercise group and then switched). All other types of studies will be excluded from our review. 158 159 Older adults – the population 160 We define older adults as people over the age of 50. Trials with people aged 49 years or younger will 161 be excluded from our review. If a trial includes both people over 50 and under 50, we will include it 162 only if the data for people over 50 is reported separately. 163 164 There is some disagreement in the literature about what we mean by an ‘older adult’ (37,38). For 165 example, the World Health Organization defines older adults as people over the age of 60 (6). Others 166 9 argue that older adults include people over the age of 50, especially where life expectancy is lower 167 (39,40). For this review, the People decided to focus on adults over the age of 50. 168 169 Resistance training and aerobic exercise – the intervention and comparator 170 We will include trials that compare resistance training with aerobic exercise. For our review, we 171 consider resistance training to be the main intervention being studied and aerobic exercise to be the 172 comparator. We define resistance training as a form of exercise that involves using external force or 173 load to challenge and strengthen your muscles. The external force could be lifting weights, pulling 174 against elastic resistance bands, lifting weighted objects (such as bricks or bottles of water), or using 175 your own body weight (such as squats, or push-ups). 176 177 We define aerobic exercise as any activity that increases heart rate and breathing, while engaging 178 large muscle groups over a sustained period. These exercises improve heart health, fitness and 179 endurance. Examples include brisk walking, running, swimming, cycling, dancing, or sports. The 180 People decided these definitions of resistance training and aerobic exercise. 181 182 We will include trials that compare resistance training with aerobic exercise where the programme 183 includes at least eight sessions (as chosen by the People). Trials with fewer than eight sessions will 184 not be included in the review. 185 Quality of life and/or heart health – the outcomes 186 We are interested in two main outcomes for this review: quality of life and heart health. Therefore, 187 we will include trials that measured either quality of life or heart health (and compare resistance 188 training with aerobic exercise). 189 190 16 using a computer programme. and with similar characteristics). Allocation concealment Making sure the people enrolling participants do not know or cannot predict which group the participant will be assigned to. This domain is about the method used to hide the allocation. It is important that the people running the trial are not be able to influence which treatment each participant gets. For example, if the person enrolling a participant knows that the next assignment is to intervention group (consciously or unconsciously) assign a participant they think will do better in the intervention group. At the beginning of the trial, before and during the randomisation process. Blinding of participants and personnel Making sure that both the participants receiving either the intervention or comparator (the participants), and the people running the trial (the personnel) are unaware of which group participants are in. Participants and/or the people running the trial might act differently if they know whether participants are getting the intervention or comparison. During the trial when the participants are receiving the intervention or comparison. Blinding of outcome assessors Making sure that the people measuring the outcomes are unaware which group participants are in. If the outcome assessor knows whether the participant is in the intervention or comparison group they might change the outcome to make the results more favourable. When the outcomes are measured (usually before and after the intervention) Incomplete outcome data When there is missing data because participants may have dropped out of the trial, or did not complete all of the outcome measures. This domain looks at how much data is missing and if it was handled properly. It is normal to have some missing information in trials. But if there is lots of missing information especially in one group and not another, it can influence the trial results. When the results of the trial are being analysed. Selective reporting Whether the researchers reported all the results they Leaving out results that researchers didn’t like can give a false impression of Happens when the results of the trial are being written 17 planned to in the protocol. how well something worked. up and published. Other bias This is a place to note any other problems that may have influenced the results of the trial; for example, conflicts of interest, not following the plan, or stopping the trial early. Sometimes there are problems that don’t fit neatly into the other domains. At any stage during the trial. 326 For each domain, we will assign a judgement of either low risk of bias, high risk of bias or some 327 concerns. As previous stages, the public can assess the risk of bias in as many studies as they choose; 328 however, they will be asked to assess the risk of bias in at least one study. 329 330 The public will look at the first four risk of bias domains, with the remaining three domains to be 331 assessed independently by two members of the Technical Team. Each study will be assessed by at 332 least four different members of the public. If there is less than 80% agreement by the members of the 333 public about the risk of bias judgement, a member of the Technical Team will act as a resolver to 334 make the final decision. If discrepancies arise in the Technical Team, they will resolve it through 335 discussion or with a third assessor. 336 337 If cluster randomised trials are included, the Technical Team will assess all seven risk of bias 338 domains for these trials. This is because cluster randomised trials require additional considerations, 339 such as whether the analysis accounts for clusters, whether effects are overestimated, or if there were 340 recruitment issues (56). 341 342 18 The results from the public’s assessment and the Technical Teams’ assessment will be combined and 343 summarised in tables and graphs. We will also summarise the risk of bias within each study in a 344 table. 345 How will we analyse and summarise the results from each trial? 346 Once we have extracted the data from the included trials and assessed them for risk of bias, we will 347 then synthesise the results. Synthesising means carefully combining the results data from all the 348 individual trials to provide an overall picture of the evidence. 349 350 In systematic reviews that compare two treatments, we typically use a statistical method known as 351 meta-analysis to synthesise numerical data. Meta-analysis combines the results data from each 352 individual trial and produces a summary, or overall result. The result of synthesising the data using 353 meta-analysis produces a graph called a forest plot (described in Fig. 2). 354 Fig. 2. What is a forest plot? 355 356 19 Sometimes it is not always appropriate or possible to do a meta-analysis and produce a forest plot. In 357 this case, we will use other ways (57) to summarise the results, including words and tables. 358 359 Measuring the effects of the intervention versus the comparator 360 We want to find out how resistance training compares to aerobic exercise on quality of life and heart 361 health (measured by functional fitness). To do this, we must calculate the difference between the 362 intervention and the comparator. What calculation we use is based on two factors: 1) the type of data 363 we are working with, and 2) whether the studies measure the outcomes in the same way. 364 365 The type of data for both of our outcomes, quality of life and heart health, is continuous. Continuous 366 outcomes are based on scales with values that are in a specified range – everyday examples of 367 continuous data include height, weight, and temperature. 368 369 We will use either the mean difference or the standardised mean difference to measure the difference 370 between the two groups. If the outcomes are measured in the same way in all trials, using the same 371 test, we will calculate the mean difference and 95% confidence interval. Mean difference is the 372 average difference between the two groups. 373 374 How to calculate mean difference – a worked example Say people doing resistance training rate their quality of life as 70 out of 100. People who do aerobic exercise rate it as 65 out of 100. The mean difference would then be 5. A 95% confidence interval is the range of numbers that is likely to include the true mean difference. For example, if resistance training improves quality of life by 5 points more than aerobic exercise, with a confidence interval of 3-7 points, this suggests the real 20 difference is likely between 3 and 7. The 95% confidence interval means that if we repeated the study 100 times, in 95 of those studies, the range we would find (like 3-7 points) would include the true mean difference. 375 If the outcomes are not measured in the same way, using the same scale (which is quite likely for 376 both quality of life and functional fitness), we will calculate the standardised mean difference. For 377 example, one study might score quality of life out of 10, and another out of 100. The standardised 378 mean difference puts all the results on the same scale, so we can still compare them fairly. 379 How will we account for problems with study designs that may affect the results? – unit of analysis 380 issues 381 Data collected in randomised trials should be analysed in ways that match how the data was 382 collected. For simple trials where each participant is randomly allocated to either the intervention or 383 the comparator, there are usually no issues. However, for more sophisticated trial designs there are 384 some considerations that we need to make, to ensure that we do not over or underestimate the effects 385 of the intervention. These considerations are described in table 2. 386 387 Table 2. Study designs that need to be accounted in this review 388 Randomised trial designs Explained What we will do about these designs in this review Randomised trials with more than two groups For example, one group of participants could do resistance training, another could do aerobic exercise, a third could do both resistance training and aerobic exercise. We will extract data only from the resistance training group and the aerobic exercise group. We will not analyse data from combined interventions (for example interventions that use aerobic and resistance training). Crossover trials Where participants are randomly assigned to either resistance training (the intervention) or aerobic exercise (the comparator). Then the groups swap and do the other exercise type. However, the To prevent the impact of the carryover effect we will use the results from the first period before the groups swap. 21 effects of the first type of exercise might still be there when people start the second type. That makes it harder to tell which exercise is really causing the results. Cluster randomised trials When participants are randomised in a cluster or group, such as by clinic, school or nursing home. The Technical Team will assess if the authors of the cluster trials have appropriately accounted for clustering and get advice from a statistician if needed. If the authors have not appropriately accounted for clusters, we will adjust for this using methods recommended in the Cochrane handbook (58). 389 What will we do if there is information missing that we need? – dealing with missing data 390 It is common for trials not to report all the information needed for a review. If this happens, we will 391 initially contact the trial authors to request the missing information. We will record the amount of 392 missing data, specify what data is missing, and the reasons for missing data (if known). If the authors 393 have filled in missing data by using estimated guesses (known as imputation), we’ll decide if the 394 approach is appropriate based on best practice (59). 395 396 If the authors do not respond to us with the missing data, the Technical Team may use accepted 397 methods to estimate the missing values (known as imputation). For example, if standard deviations 398 (which show how spread out the results are from the average) are missing and cannot be calculated 399 from the data, the Technical Team will follow Furukawa’s guidance on imputing this data (60). We 400 will only fill in missing values if there is a small amount (less than 10%) of data missing. If there are 401 large amounts of missing data, we will exclude this from the meta-analysis (the statistical method of 402 combining the results). We will, however, include the trials in our review and summarise them in 403 writing. We will make note of any missing data and discuss the potential impact of this on the results 404 of our review in the discussion section. 405 406 22 How will we assess whether the studies are similar or different to each other? - heterogeneity 407 Heterogeneity is the extent to which the trials are different from each other. It is important to check 408 how different the trials are so we have a clear picture of the evidence. There are also practical 409 reasons for the review because if they vary too much, combining the results in a meta-analysis 410 (shown in a forest plot) could give misleading results. In systematic reviews, heterogeneity can be 411 measured in several ways. Each test is different and has strengths and weaknesses. We will use three 412 different tests to account for these limitations. The Technical Team will conduct all three tests, and 413 the People will help interpret the results of the tests. 414 415 For our review, we will first look closely at the forest plot produced from the meta-analysis. We will 416 look out for: 417 1. The point estimate distribution 418 2. Overlapping confidence intervals 419 3. The width of the confidence intervals 420 These concepts are explained further in Fig. 3. 421 Fig. 3. Visually inspecting a forest plot 422 423 23 After visually inspecting the forest plot, we will also measure the heterogeneity using a calculation 424 called the I2 estimate, which is represented as a percentage (%). We will use the thresholds outlined 425 in table 3 (61). There is overlap across the thresholds, as I2 estimates should be interpreted cautiously 426 based on the context. 427 428 Table 3. I2 estimate thresholds 429 Threshold Meaning 75-100% Considerable heterogeneity - meaning the studies may be very different from each other 50-90% Substantial heterogeneity - meaning the studies may be somewhat different from each other. 30-60% Moderate heterogeneity - meaning there is some difference that may be important. 0-40% The studies are fairly similar and the amount of difference is possibly not relevant. 430 We will also complete a statistical calculation called the chi squared test. The chi squared test can 431 assess whether the difference between the studies is due to chance alone or for other reasons. It is 432 presented as a number called p-value. The lower the p-value (the closer to zero), the higher the 433 probability that the heterogeneity is likely not due to chance and is statistically significant (61). 434 435 We will summarise our assessment of heterogeneity in a written summary, including possible causes 436 of heterogeneity. If we observe high levels of heterogeneity, indicating that the studies are too 437 dissimilar to pool together, we will not complete a meta-analysis. Performing a meta-analysis of 438 trials that are too different would not be appropriate and may misrepresent the study’s results. We 439 will report the results in this case in written and table format, following best practice guidelines (57). 440 441 How will we check if trials are missing? - assessment of reporting biases 442 A common problem with research is that sometimes trials with less favourable results are simply not 443 published at all and the results are hidden from the public. This is known as reporting bias. Reporting 444 24 bias can impact the results of a systematic review, as it might seem like one treatment (the one with 445 more published trials) is more effective than it actually is. 446 447 To check for reporting bias, we will follow the guidance in the Cochrane Handbook (62). We will 448 use a funnel plot if there are more than 10 studies included in our meta-analysis (63,64). A funnel 449 plot (Fig. 4) can be used to explore whether reporting bias is present. If the funnel plot’s shape is 450 symmetrical, then this indicates an absence of reporting bias. If it is asymmetrical, then this suggests 451 that results are grouped, telling “one side” of the story, or showing only the positive findings - there 452 may be some reporting bias or hidden results, and, therefore, missing evidence. Besides analysing the 453 plot visually, we will check for funnel plot asymmetry using a statistical test called an Egger test 454 (65). The Technical Team will prepare the funnel plot and complete the Egger test, and the People 455 will help interpret the funnel plot. 456 Fig. 4. How to interpret a funnel plot 457 458 How will we combine the results from each study together? - Data synthesis 459 If the studies are sufficiently similar, they will be combined using meta-analysis. We will perform a 460 separate meta-analysis for each outcome – quality of life and heart health (measured by functional 461 25 fitness). We will use a website called MetaAnalysisOnline.com (66) to conduct the meta-analysis as 462 it is freely available and open access. 463 464 One member of the Technical Team will enter the data into MetaAnalysisOnline.com and another 465 will check that the data was inputted accurately. The meta-analysis will then be conducted in two 466 steps. 467 468 • Step 1: Calculate the measure of effect for each study using either the mean difference or 469 standardised mean difference (as described in the section: Measuring the effects of the 470 intervention versus the comparator). 471 • Step 2: Combine the effect measures using the random-effects model using the inverse472 variance methods. There are different types of statistical models to use for a meta-analysis – 473 we will use the random-effects model because it takes into consideration any difference 474 (heterogeneity) across the studies. 475 If meta-analysis is not appropriate, for example, if there are lots of missing data, or if the studies are 476 very different from each other, then we will describe the results through written word, tables, and 477 graphs. We will follow best practice guidelines for explaining the results in this way – this guidance 478 is known as SWiM (Synthesis without meta-analysis; (57). 479 Will we look at groups of studies with specific aspects? - Subgroup analysis 480 As decided by the People, we will conduct a sub-group analysis to explore differences: 481 • Across sex and/or genders (based on what is most commonly reported in the trials). 482 • In age categories between 50-60 years and 60+ years. 483 • Between healthy participants and participants with reported health conditions. 484 485 32 Evidence Synthesis Ireland and Cochrane Ireland. Éle Quinn’s PhD studentship is funded by the 612 College of Medicine, Nursing and Health Sciences, University of Galway, Ireland, through Evidence 613 Synthesis Ireland. Laura Bosner is supported by an Evidence Synthesis Ireland Summer Studentship 614 2025. 615 616 Data availability 617 Not applicable. 618 619 Ethics approval and consent to participate 620 Ethics approval is not required for a systematic review. However, we have received ethical approval 621 from the University of Galway Research Ethics Committee (2023.06.012) to involve the public in 622 The People's Review. 623 624 Competing interest 625 ÉQ, LB, PL, CY, DS, MS, JH, ST, CN, and SMH have no competing interests to declare. DM is a 626 founding Editor-in-Chief of the journal. KMSUR is an Associate Editor of the journal. Competing 627 interests for members of the group author ‘The People’ are listed in Supplementary Material 3. 628 629 Acknowledgements 630 We want to thank The People's Review Steering Group for their commitment to The People's 631 Review. We would also like to thank the team at Cochrane Crowd and Metaxis (Anna Noel-Storr, 632 Gordon Dooley, and David Anstee) for their support in facilitating public involvement in this 633 protocol. ‘The People’ are listed as a group author on this protocol. The People is made up of the 634 following individuals: Oscar Soden, Christian Cortés Armijo, Paul K Bateman, Mehram Khaiser, 635 Jhoselin Marian Castro Rodriguez, Pareekshith Hirenallur Lohithaswa, Stella Hassan, Kavessh 636 33 Vandaiyar, V Nikhila Priya, Dr. Sushant Swaroop Das, Fredrik Nyman, Shannon Cheng, Ana Laura 637 Afonso Garcia, Virgilio Blandon, Johanna Pope, Gerard Mangan, Naam Kadhim, Jehath Syed, 638 Rebecca M. Lane, Sri Harsha Chalasani, Belén Morales Franco, Hanadi H. Almintish, Kirthana Nair, 639 Aisha Elnagi, Barbara Molony-Oates, Diarmuid Verrier, Ivana Turudic, Raiza Rai, Abhijit Dutta, 640 Ulrich Schmitz, Mahmoud Rashad Kamal, Anup Rai, Aduak Israel Bassey, Rayan Haidar, Thora El641 Sayed, Skarlet Marcell Vásquez, Megha Bhattacharyya, Ali Abud, Jakub Ruszkowski, Simone Ryan, 642 Aliasghar Fakhri-Demeshghieh, Declan Devane, Olivia Smith, Elaine Lorigan McSweeney, Aishath 643 Mala, Nirvi Sharma, Wajida Perveen, Elizabeth Deane McCarthy, Johannes Wagner, Anthony 644 Rimmer, Bláthnaid Quinn, Mónica Rosalía Loera Pulido, Dr. Nikhil Sisodiya, Mirza Ammar Arshad, 645 Eugene Farrell, Maria Elena Asdrubali, Alaa AM Osman, Kapil Paiwal, Gursimer Jeet, Simone 646 Lepage, Mahla Azizzadeh Herozi, Lasse Østengaard, Richard Lohmann, Radheshyam Meher, Louise 647 van der Merwe, Shirley Hall, Aragaw Hamza Yimer, Dr Kanimozhi V, Neil Canham, Dr Orton 648 Lungu, Laura Aparecida Martins Albino, Chang Helly, Braa Bushra Gorani Hamid, Ashraf Sadeq 649 Nasser Yahya Qadesh, Fábio André Miranda Viana, Pierpaolo Giordano, Abir Romdhani, Sameeksha 650 Shetty, Jose D. Cruz-Cuevas, Paul Owusu, Amanda Doherty-Kirby, Gary Hoang, Kin Fung SO, Dr. 651 Denny Mathew John, Ayman Nadeem, Hariklia Nguyen, Mengqi Li, Merlyn Joseph Stalin Vellalar, 652 Yuna Kato, Haoling Wang, Devarajulu Reddy Sirasanambati, Michelle I M Paynter, Akuma 653 Ifeanyichukwu, Anuj Kumar Pandey, Agnes Falconer, Amit Ahuja, Muhammad Zohaib, Basavaraj 654 Poojar, Hosna Khazaei, Karla Elizabeth Duque Jacome, Holly Southall, Sunjuri Sun, Federico 655 Capriles, Lyria Arcari, Annemarie Sheehan, Alexia Jeayes, Karan Sethi, M. Dulce Estêvão, Dejana 656 Krajacic, Mohammed S. Abujayyab, Nicole Askin, Amanda de Carvalho Robaina, Jen Smith, Shruti 657 Bora, Deepak Singh-Ranger, Adi Zeba, Giovana Vesentini, Dr. Orla Mooney, Christopher James 658 Graham, Kim Locke, Jeanna Pillainayagam, Dr Abdul Shakoor, Qamar Uz Zaman Khan, Marwa 659 Yahia Elbasosy, Emily Shepherd, Marina Maria Manea, Danielle Lawrence, Cristhian Gonzalo 660 Aspiazu-Briones, Quynh Thuong Huynh, Zarah Janda, Jesus Salvador Garcia Lopez, Tint Hla Hla 661 34 Htoo, A Dennington-Price, Patrick De Neve, Ahmadu Inuwa, Thu-Huong Nguyen, Manon Hubert, 662 Richard Sinert, DO, Lenora Murphy, Christopher W. Roche, Conor McKinney, Aidan Quinn, 663 Michael-Dharma Irwin, Dr. Adedoyin Adeosun, Chandan Kumar, Mai Babenko, Fatma Alagelli, 664 Moni Choudhury, Mona Lee, Aswathi Surendran, Renee Jarrett, MPH, Williams, R.J.C., Zeev 665 Konstantin G. Gurevich, Therese Kristine Dalsbø, Oluwaseun Abigail Newton, Alice Fognani, 666 Feargal Quinn, Ciara Smith, Janani Surya Ravichandran, Oyewale Johnson Akande, Evangelia 667 Papadimou, Anwyn Johnson, Aswin K Mohan, Parichehr Hayatdavoudi, Brenda Golden, Dr. 668 Srikanth S., Shahd Ashraf Izzeldin Abdalla, Alaa Mohamed, Rose Nasser, João Morgadinho, 669 Stephanie Skeffington, Matthieu Gaiotti-Gras, Lucas HCC Santos, April English, Eliane Denise 670 Bahbouth, Yasmine Ahmed Mourad Asaad, Eva O'Byrne, Neha Singh, Syeda Safura Sultana, Marci 671 Kay Livingston, Jenny Bui, Adeola Ajayi, Faith Armitage, Silvia Maria Martins Bernardo, Grace 672 Walsh and Omar Alhaman. All authors listed on this protocol have provided consent for their names 673 to be listed, have reviewed or edited the manuscript, thereby complying with the International 674 Committee of Medical Journal Editors (ICMJE) authorship guidance (95). 675 References 676 1. 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