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Higher Education Quality Assessment and University Change: A Theoretical Approach

• First Online: 04 May 2016

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• Shuiyun Liu 6  

Part of the book series: Higher Education in Asia: Quality, Excellence and Governance ((HEAQEG))

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This chapter is devoted to finding theoretical approaches to understand how quality assessment causes transformation of higher education institutions. Firstly, studies on quality assessment in higher education are reviewed. The context in which quality assessment emerged in the higher education system, its purposes and the approaches to operating it are discussed. Based on previous empirical studies, the main impact of quality assessment on evaluated universities is summarised, and the factors determining the impact are tentatively proposed. Then, the focus turns to a theoretical exploration of how quality assessment, as an external force, causes university change. Theories about organisational change, the working processes and structures of higher education systems and the operating mechanism of quality assessment are examined. Building on these theories, a perspective is proposed to help to understand the ways in which quality assessment processes interact with universities to generate change.

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Liu, S. (2016). Higher Education Quality Assessment and University Change: A Theoretical Approach. In: Quality Assurance and Institutional Transformation. Higher Education in Asia: Quality, Excellence and Governance. Springer, Singapore. https://doi.org/10.1007/978-981-10-0789-7_2

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Madrasa Education System in India

Last updated on November 8, 2024 by ClearIAS Team

Madrasa education in India is a traditional Islamic educational institution that focuses on teaching Islamic theology, jurisprudence, language (particularly Arabic), and sometimes other subjects. The word “madrasa” means “school” in Arabic, but it is often associated with religious education. Read here to learn more.

Madrasas have a long history in India, dating back to the medieval period, and have evolved to incorporate various reforms in their curriculum.

They play an important role in providing education within Muslim communities, especially in areas where access to mainstream education may be limited.

A madrasa is an Islamic educational institution, traditionally focused on teaching religious studies, including the Quran , Hadith (sayings of the Prophet Muhammad), Fiqh (Islamic jurisprudence), and Arabic language .

In many regions, madrasas also teach secular subjects and have historically been key institutions for broader educational and scholarly pursuits in various fields, such as science, philosophy, and mathematics.

Table of Contents

History of madrasas in India

The history of madrasas in India dates back centuries, primarily emerging during the Delhi Sultanate (1206–1526) and the Mughal period (1526–1857) .

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These educational institutions have historically played an essential role in providing religious education, primarily focusing on Islamic theology, jurisprudence, Arabic, and Persian studies.

Over time, they have evolved to reflect changes in governance, socio-cultural needs, and educational demands in India.

Early Period (Delhi Sultanate and Mughal Era) :

• The madrasa system has been in existence since the era of the Delhi Sultanate, receiving patronage from the Khilji and Tughlaq dynasties.
• During the Delhi Sultanate, madrasas gained prominence as centres for Islamic learning and administration, fostering scholars proficient in theology, law, and governance.
• The Madrasa of Khwaja Nizamuddin and the Madrasa of Firoz Shah Tughlaq are prominent examples from this period.
• Under the Mughals, madrasas expanded significantly, with rulers like Akbar and Aurangzeb establishing institutions that combined religious and secular subjects, including philosophy, astronomy, and mathematics.
• These madrasas were crucial for training administrators, legal experts, and scholars within the empire.

Colonial Period :

• British colonial rule (1757–1947) brought significant shifts. The colonial government set up modern , English-medium educational institutions, often overlooking traditional madrasa education.
• However, some madrasas adapted to the new conditions; for instance, the Darul Uloom Deoband , founded in 1866, aimed to preserve Islamic traditions amidst the challenges of British colonial influence.
• Madrasas like Aligarh Muslim University (initially a madrasa) incorporated modern subjects alongside religious education, adapting to the demands for a modernized Muslim intelligentsia capable of navigating British India.

Post-Independence Period :

• After India gained independence in 1947 , madrasas continued to serve as educational institutions for the Muslim community, particularly in areas where access to mainstream schools was limited.
• The focus remained on Islamic studies, though some madrasas have since incorporated a broader curriculum to enhance vocational and academic prospects.
• In the 21st century, there have been calls for madrasa modernization, including introducing subjects like science, mathematics, and English.
• Various state governments and the central government have launched programs to support madrasa education and facilitate its integration with mainstream educational standards.

Madrasa Education in India

According to data presented by the Union government in Parliament on February 3, 2020, India has a total of 24,010 madrasas. These include 11,621 recognised and 2,907 unrecognised madrasas.

• Madrasas traditionally follow the Dars-e-Nizami curriculum, introduced in the 18th century by scholar Mullah Nizamuddin Sihalvi, focusing on Islamic theology, Arabic, Persian, and Islamic jurisprudence ( fiqh ).
• Over time, many madrasas have added secular subjects like mathematics, science, and English to align with modern educational needs. Government-recognized madrasas may also follow state or central board syllabi for secular subjects while maintaining their religious curriculum.
• Maktabs : Local schools attached to mosques where children receive basic religious instruction.
• Kuttab : Similar to maktabs but often have a broader curriculum.
• Full-fledged madrasas are divided into primary , secondary , and higher levels, with higher institutions granting degrees equivalent to secular university education in Islamic studies.
• The Indian government has programs to support madrasa modernization. For instance, the Scheme for Providing Quality Education in Madrasas (SPQEM) offers financial assistance to madrasas to introduce science, math, social studies, Hindi, and English.
• In some states, such as Assam and West Bengal, madrasas are part of the state board, allowing their students to receive formal certification and pursue higher education or government jobs.
• Additionally, under the National Institute of Open Schooling (NIOS) , students from madrasas can gain certification equivalent to formal schooling.
• Madrasas provide free or low-cost education, room, and board, which can be critical for children from low-income backgrounds, particularly in rural and underserved areas.
• Many madrasas offer vocational training and skill development programs to improve employability, recognizing the need for economic opportunities beyond religious education.

Challenges, criticisms and reforms

While madrasas serve as essential institutions for religious education and provide access to education for underprivileged students, criticisms often centre on the following aspects:

• Outdated curriculum: The curriculum in many madrasas is often centred around Islamic theology and classical texts, with limited inclusion of contemporary subjects like science, mathematics, and technology. This narrow focus can hinder students from gaining skills applicable to broader career opportunities.
• Lack of modern subjects : Educationalists argue that a balanced curriculum with modern subjects would enhance students’ employability and allow them to contribute more effectively to society.
• Lack of Vocational Training and Skill Development: Critics highlight that madrasas often do not emphasize vocational training or skill-based education, which limits graduates’ employment prospects outside of religious teaching roles. This lack of employable skills contributes to economic disadvantages for students who rely solely on madrasa education.
• Isolation from Mainstream Education : Madrasas are often seen as operating independently of India’s mainstream education system, which can isolate students from diverse perspectives and limit their exposure to India’s multicultural society. Critics argue that this isolation can reinforce cultural and social divides rather than promote integration.
• Concerns Over Extremist Ideology : In some cases, madrasas have been associated with concerns over extremist ideology. Although this is not representative of all madrasas, instances of radicalization in certain institutions have led to heightened scrutiny and calls for regulatory oversight to ensure that students receive a balanced and moderate education.
• Gender Disparity in Access and Curriculum: Many madrasas offer separate or limited access to education for girls, and the curriculum for female students can be even more restricted than for male students. Critics argue this limits women’s economic opportunities and contributes to the perpetuation of traditional gender roles.
• Inadequate Government Oversight and Standardization: Inconsistent government oversight means that madrasas often vary widely in educational quality and curriculum content. There are calls for standardized regulation to ensure quality control while respecting religious freedom, allowing students to transition more easily into higher education and mainstream employment sectors.
• Efforts and Potential Solutions: Some policymakers and educationists advocate for madrasa modernization by introducing state-supervised curriculums that include secular subjects and vocational training while preserving the religious education component.
• Integrating madrasas into the broader educational framework could allow for a dual system where religious and secular education coexist, thus providing madrasa students with greater economic and social mobility.

Significance of Madrasa Education

Notable figures such as Raja Ram Mohan Roy , the father of the Indian Renaissance, Rajendra Prasad , India’s first President, and the renowned writer Munshi Premchand are historically believed to have gained their foundational knowledge from madrasas and their teachers, known as maulvis.

Madrasa education remains significant for India’s educational landscape, particularly for promoting literacy within Muslim communities and preserving Islamic knowledge and heritage.

In recent years, reforms and government policies have aimed to ensure madrasas can contribute more effectively to the socio-economic upliftment of their students while maintaining their religious character.

Countries with Madrasa Education Systems

• Pakistan has a significant madrasa system, with institutions regulated by the Wafaq ul Madaris and other boards. Some Pakistani madrasas offer secular subjects alongside religious education.
• Concerns over militancy led to reforms aimed at modernizing the madrasa curriculum and improving oversight.
• In Bangladesh, madrasas are divided into two main categories: Qawmi and Aliya . Qawmi madrasas are more traditional and privately managed, while Aliya madrasas receive government oversight and include secular subjects.
• Aliya madrasas follow a curriculum that blends religious studies with secular education, including English, science, and social studies.
• Indonesia has a large number of madrasas under the Ministry of Religious Affairs. They offer both religious and general education, serving as an alternative to public schools.
• Madrasas in Indonesia are often seen as complementary to the state educational system, blending Islamic values with modern education.
• Afghanistan’s madrasa system primarily provides Islamic education but also includes subjects like Pashto, Dari, and mathematics in some schools.
• Since the Taliban regained control, there has been an emphasis on madrasa education, though reforms are underway to modernize the curriculum.
• Egypt, Saudi Arabia, and other Arab countries have madrasas, with notable institutions like Al-Azhar University in Egypt, a leading centre of Islamic learning worldwide.
• These madrasas often integrate subjects such as mathematics, history, and geography, especially in prominent urban areas.
• European countries generally do not have madrasa education systems integrated into their national education frameworks in the same way that some Islamic-majority countries do.
• However, certain European countries with sizable Muslim populations, such as France, the UK, Germany, and the Netherlands , have institutions or private Islamic schools where elements of a madrasa-like education, particularly religious studies , are offered alongside the standard curriculum.

The Supreme Court on November 5, 2024, upheld the constitutional validity  of the  Uttar Pradesh Board of Madarsa Education Act, 2004 (2004 Act) except for its provisions allowing the Board to award higher degrees like Kamil (undergraduate studies) and Fazil (post-graduate studies).

• A three-judge Bench headed by Chief Justice of India D.Y. Chandrachud opined that granting these degrees contravened the University Grants Commission (UGC) Act, 1956, rendering it unconstitutional.
• Accordingly, the top court set aside the Allahabad High Court’s decision, which had deemed the 2004 Act to be in breach of the principles of secularism.

Madrasas today are undergoing significant reform efforts in many countries. Some governments aim to modernize madrasas by introducing secular subjects, such as science, technology, and languages, to better prepare students for diverse career opportunities.

In many Muslim-majority countries, madrasas provide affordable education, particularly in underserved areas, making them crucial for educational access.

European countries face challenges in balancing religious freedom with educational standards and secular principles. Consequently, madrasa-like education often happens outside of formal schooling hours and within regulated frameworks that limit these schools’ operations to ensure alignment with national standards.

In India, the primary challenge is the modernization of the curriculum, with some stakeholders arguing that traditional madrasa education may not prepare students adequately for broader job markets.

There are also concerns about the varying quality of education, as some madrasas have limited resources and lack trained teachers in secular subjects.

Reforms have been introduced to bridge the gap between religious and secular education. Efforts are also being made to improve transparency in madrasa administration and ensure alignment with national educational goals.

This complex issue involves balancing religious freedom with educational quality and employability. Reforms that respect madrasas’ cultural role while addressing these criticisms could help integrate students more fully into India’s evolving socio-economic landscape.

Frequently Asked Questions (FAQs)

Q. What is madrasa education?

Ans: Madrasa is an Arabic word that means an educational institution. Madrasa education seems to be working on old traditional patterns as there is no emphasis on any research. Madrasas are centres of free education. They are the nucleus of the cultural and educational life of Muslims.

Q. What is the recent development in the regulation of madrasas?

Ans: The Supreme Court on November 5, 2024, partially upheld the constitutional validity of the Uttar Pradesh Madrasa Education Board Act of 2004 while confirming that the State can regulate madrasa education to ensure standards of excellence.

Related articles:

• Education in India
• Education in State list: Should it be restored?
• National Education Policy 2020
• Multilingualism in education

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Adherence Support Strategies for Physical Activity Interventions in People With Chronic Musculoskeletal Pain—A Systematic Review and Meta-Analysis

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Background : Chronic musculoskeletal (MSK) pain significantly impacts individuals’ quality of life. Regular physical activity is an important key to therapy. However, adherence to exercise is often below the intended levels. This systematic review aims to assess the effectiveness of adherence support strategies in physical activity randomized controlled trials (RCTs) for patients with chronic MSK pain, to address the challenge of motivation and adherence in this population. Methods : Ovid and CINAHL databases were searched for MSK “chronic pain,” “adherence” and “physical activity” (in various synonyms) for RCTs with a follow-up of at least 6 months. After a screening process that included several team members, a narrative synthesis and a meta-analysis on adherence and effect sizes of different outcomes were performed. Results : An initial database output of 5816 articles resulted in 76 RCTs that remained for analysis. These RCTs showed high heterogeneity of intervention design and outcome data. Adherence to exercise varied widely, with 21 RCTs achieving rates of 80% or higher using strategies like education, goal setting, self-monitoring, and personal feedback. The meta-analysis, based on 29 out of 76 RCTs, indicated a nonsignificant correlation between adherence levels and an improved outcome (eg, pain or quality of life). No individual strategy emerged as superior, emphasizing the complexity and diversity of intervention designs. Conclusions : This review offers strategies, such as information/education and individual goal setting/feedback that may be beneficial for enhancing physical activity in patients with chronic MSK pain. Future research should focus on including promising adherence support strategies and providing data for adherence calculations.

Chronic musculoskeletal (MSK) pain affects millions of individuals worldwide. Approximately 1 billion people globally live with MSK disorders, including rheumatoid arthritis, osteoarthritis, low back pain, neck pain and gout with the vast majority experiencing MSK pain. 1 Chronic MSK pain has a severe impact on a person’s quality of life, social relationships, mood, sense of self, and work ability. 2 – 4 Among the various therapeutic interventions available, regular physical activity (PA) is encouraged in patients with chronic pain as it can improve pain severity, physical function and quality of life. 5 , 6 This is supported by a meta-analysis indicating that PA interventions significantly improved chronic nonspecific low back pain in comparison to placebo medication, no therapy, or treatment as usual. 7 For chronic low back pain, which is the most prevalent type of MSK pain, 1 , 8 , 9 exercise should always be recommended, at least as a part of a multimodal therapy. 10

Despite overall health benefits, physical inactivity is globally prevalent: in 2016, 28% of people over the age of 18 did not meet the recommendations for regular exercise. 11 Adherence to exercise intervention often remains below the intended exercise level of the intervention, 12 independent of type of exercise used in the intervention. 13 According to the World Health Organization report from 2003, adherence is defined as “the extent to which a person’s behavior corresponds with agreed recommendations from a health care provider.” 14 Adherence is a key link between recommendations and outcome of health interventions and consists of 4 domains: fidelity, dose, reach, and adaptations. 15

Motivation for PA is even more challenging for patients with chronic MSK pain, especially for patients with high levels of disability. 13 , 16 People with chronic pain are more likely to be motivated to engage in activities if it suits their preferences, external circumstances, fitness level, and experience with exercise. 17 , 18 However, overall the proportion of people engaging in exercise as a self-management strategy is low (26%) although exercise reduces the risk of having high levels of pain-related disability. 19 Support strategies aim to improve adherence to exercise interventions, but their specific efficacy in chronic pain patients is uncertain.

Therefore, the aim of this review is to evaluate adherence support strategies implemented in intervention designs of PA randomized controlled trials (RCTs) for people with chronic MSK pain in a community setting.

The objectives are to

• 1. Describe adherence support strategies.
• 2. Determine which support strategies lead to high (>80%) adherence to PA recommendations.
• 3. Determine the association between adherence to the PA intervention and study outcomes.

We conducted a systematic review and meta-analysis. The protocol was registered on PROSPERO (CRD42021233916).

• Eligibility Criteria

We included peer-reviewed RCTs with follow-up measurements of >6 months; RCTs examining the efficacy of PA interventions; RCTs reporting on adherence support strategies for PA interventions; RCTs including patients with chronic pain (MSK, arthritis, rheumatism, and fibromyalgia).

We excluded RCTs entailing only elite athletes, children, and teenagers under 18 years of age, RCTs including in-patients; RCTs also including participants without chronic MSK pain, RCTs including postsurgery patients, editorials, case-reports, clinical guidelines, discussion papers, protocols, and comments (unless relating to an included article); RCTs not in English, German, Italian, Spanish, French, or Dutch (no funds available for professional translation). We also excluded reviews but searched their reference list for eligible publications.

• Information Sources

Searches were performed up to November 7, 2022, on Ovid with the databases MEDLINE, Embase, PsycInfo, and AMED. Additionally, CINAHL was searched from beginning to November 7, 2022. The search was updated on May 21, 2024, using the same search strategy. Databases were PubMed, Embase, and PsycInfo as the access options for our institution changed.

• Search Strategy

Search terms were (chronic pain OR CLBP OR fibromyalgia OR CWP OR osteoarthritis OR rheumatoid arthritis OR rheumatic diseas* OR chronic lumbago OR chronic backache) AND (adherence OR adhering OR compliance OR complying OR motivat* OR attendance OR participation OR engagement OR user* experience*) AND (physical activit* OR exercise* OR physiotherap* OR physical therap* OR rehabilitation). These terms were searched for in the abstract and title (for Ovid, PubMed, Embase, and PsycInfo) and in the abstract only (for CINAHL). The search was limited to humans. The updated search in May 2024 was limited to RCTs and reviews.

• Selection Process

All articles identified in the databases were imported into the data management program Mendeley (Elsevier). Duplicates were automatically filtered and verified duplicates were manually removed (Heisig). Based on the eligibility criteria, titles and abstracts were screened by 4 members of the research team (Heisig, Lindner, Kornder, and Reichert). Only articles that were clearly not eligible were excluded at this stage. For all other articles, full-text versions were retrieved, if available. Four members of the research team (Heisig, Lindner, Kornder, and Reichert) checked the eligibility of the full-text articles using the inclusion and exclusion criteria. In case of disagreement, the researchers first discussed eligibility of the article, if no solution could be found, a fifth member (van der Wardt) of the research team decided on eligibility. Numbers of articles were recorded at all stages.

• Study Risk of Bias Assessment

Quality appraisal for each included study was independently assessed by 2 members of the research team (Reichert and Heisig) based on the Critical Appraisal Skills Program checklist for RCTs. 20

• Data Extraction

A custom-made table including all relevant data items supported data extraction to describe adherence support strategies (objective 1). The data items included: author, year, country; study design, participants (MSK group, sample size at baseline, age, gender); adherence support strategy; adherence measurements; and effectiveness of adherence strategies. Two members of the research team (Heisig, Reichert) independently extracted the data items from the included articles. In case of disagreement, the researchers discussed the issue, if disagreement remained, a third research team member (van der Wardt) decided on the appropriate data extraction. These data were the basis for a narrative synthesis according to Popay et al. 21

To determine which support strategies lead to high adherence (objective 2), adherence was extracted as percent adherence. This was defined as the percentage of participants who met the recommendation for PA, percentage of requested sessions of PA completed or percentage of PA performed depending on previously defined PA goals. Either the values are stated in the article or are calculated with given values (goal of PA and actual PA at specific time points in percent). Adherence was defined as high, if 80% or more of the intended intervention was completed. 12

All other adherence measurements were extracted, but not used for analysis due to low incidence and limited comparability: percentage of participants who performed PA at least once per week 22 ; percentage based on number of adherent participants 23 ; self-reported adherence by participants with questionnaires that are stated as mean values on a visual analogue scale (number of adherent participants). 24 – 28

For correlation analysis of adherence and study outcomes (objective 3), the following outcomes were assessed: pain (visual analogue scale, Western Ontario and McMaster Universities Osteoarthritis Index, Medical Outcomes Study 36-Item Short-Form Health Survey questionnaire, Knee Injury and Osteoarthritis Outcome Score, Health Assessment Questionnaire, Fibromyalgia Impact Questionnaire), quality of life (Assessment of Quality of Life instrument, EuroQol health survey (quality of life) with 5 dimensions and 5 levels, Knee Injury and Osteoarthritis Outcome Score), self-efficacy (Arthritis Self-Efficacy Scale, Lorig, Bandura, Physical Activity Scale for the Elderly), function (Western Ontario and McMaster Universities Osteoarthritis Index, Knee Injury and Osteoarthritis Outcome Score, Health Assessment Questionnaire, Medical Outcomes Study 36-Item Short-Form Health Survey questionnaire), disability (Oswestry Disability Questionnaire, Health Assessment Questionnaire, 13-item questionnaire), and PA (minutes/day, minutes/week, steps/day, days/week, Physical Activity Scale for the Elderly). Abbreviations and references are listed in Table  1 . One hundred level Likert scale values were divided by 10, 5-level Likert scales were multiplied by 2 to reach comparable values with a 10-level scale. All scales were reversed if they were negative (from 0 = best to 100 = worst for the patient). In case of missing values for adherence measurements or study outcomes, motivational strategies were screened and tabularized (for objectives 1 and 2), but the effectiveness of these strategies was not evaluated.

Data Used for Correlation Analysis

Abbreviations: AQoL, Assessment of Quality of Life instrument 60 ; ASESpain, ASES pain subscale; ASES, Arthritis Self-Efficacy Scale 61 ; CI, confidence interval; EQ-5D-5L, EuroQol health survey (quality of life) with 5 dimensions and 5 levels 62 ; FIQ, Fibromyalgia Impact Questionnaire 63 ; HAQ, Health Assessment Questionnaire 64 , 65 ; KOOS, Knee Injury and Osteoarthritis Outcome Score 66 ; ODQ, Oswestry Disability Questionnaire 67 ; PASE, Physical Activity Scale for the Elderly 68 ; SF-36, Medical Outcomes Study 36-Item Short-Form Health Survey questionnaire 69 ; OA, osteoarthritis; RA, rheumatoid arthritis; SD, standard deviation; SE, standard error; SMD, standardized mean difference; VAS10, VAS100, Visual Analogue Scale 70 ; WOMAC, Western Ontario and McMaster Universities Osteoarthritis Index. 71

a Higher scores indicate better outcome for the patient; scales were reversed (−) when higher scores indicated worse outcome for the patient.  b These SDs were calculated from 95% CI.  + all WOMAC subscales combined.

The means and the 95% confidence intervals (CIs) or standard deviations (SD) of intervention and control groups were extracted. Furthermore, sample sizes in intervention and control group were collected for calculation of standardized mean differences (Hedges g) and the respective standard errors. Time points (3–24 mo after intervention) were noted based on adherence measurements. Mean differences between groups were not extracted, as the calculation of standardized mean differences is based on mean values.

In case an article did not report SD for intervention group and control group, but only the point estimates and corresponding 95% CIs, we calculated the SD using recommended methods. 72

• Definition “Control Group”

In RCTs with 2 or more intervention groups (without no-treatment control group), the control group is defined as the group with less intervention or without PA.

• Data Synthesis

All RCTs with both of these 2 criteria were included in correlation analysis (objective 3):

• • Adherence was given (or could be calculated) as percentage of participants who met the recommendation for PA or percentage of requested sessions of PA completed.
• — Means and SD or 95% CI of the outcomes pain, quality of life, self-efficacy, function, disability, or PA were stated.

To examine the relationship between the effect sizes for different outcomes and adherence, we fitted a mixed 3-level meta-analytic model. Such a model allows effect sizes to randomly vary between study participants (level 1), several outcomes (level 2), and individual studies (level 3). 73 , 74 Compared to other approaches, a multilevel model has several advantages. If a study reports effect sizes on several studies, a traditional 2-level model would not be appropriate since it does not account for dependencies between effect sizes. However, conducting the analysis stratified by outcomes would be highly inefficient. Compared with other approaches that account for dependencies between effect sizes, the multilevel approach requires no assumptions on the correlation between several outcomes. 75 To explore the relationship between adherence and effect sizes, we included adherence percentage as a fixed term in the 3-level meta-analytic model assuming that the effect of adherence would be fixed across studies and outcomes. To quantify the different sources of heterogeneity, we calculated a multilevel version of I 2 . 76 In a conventional 2-level meta-analysis, I 2 represents the amount of variation that is not attributable to sampling error. In the 3-level model, there are 2 I 2 values, quantifying the percentage of total variation associated with either level 2 (variance in effect sizes for different outcomes within a study, ie, within-study heterogeneity) or level 3 (variance in effect sizes between studies, ie, between-study heterogeneity).

All statistical analyses were performed using R (version 4.1.2, R Foundation for Statistical Computing, 2021) and the statistical software metafor. 77

• Study Selection

The search returned 5816 articles (Figure  1 ). After removal of duplicates, 4718 studies were screened for eligibility based on the information in their title and/or abstract. 746 studies remained for full text retrieval. The reference list of 122 review articles, which were part of the original search result, were searched for relevant RCTs and 23 articles are included in this review. Altogether, 76 RCTs were included based on the eligibility criteria.

Flow diagram. # Reports assessed for eligibility: 672 studies and 74 reviews (from these reviews 23 articles were then included in this review). MSK indicates musculoskeletal; RCT, randomized controlled trial.

Citation: Journal of Physical Activity and Health 2024; 10.1123/jpah.2024-0099

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Articles were published from 1989 to 2024 (Table  2 ) and were from Australia, 28 , 31 , 33 – 36 , 38 , 53 Austria, 40 , 41 Brazil, 30 Canada, 50 , 84 , 94 , 94 , 101 China, 25 , 27 Denmark, 37 , 113 Finland, 43 , 99 France, 46 , 97 , 106 Germany, 96 Ireland, 26 , 54 , 90 Italy, 86 The Netherlands, 23 , 39 , 47 , 48 , 82 , 85 , 93 , 114 , 116 , 118 New Zealand, 29 Norway, 52 , 57 , 81 , 87 , 102 Singapore, 111 Spain, 59 , 103 Sweden, 32 , 80 , 91 , 92 , 109 Taiwan, 107 the United Kingdom 22 , 112 , 117 and the United States. 24 , 42 , 44 , 45 , 49 , 51 , 55 , 56 , 78 , 79 , 88 , 98 , 100 , 104 , 105 , 110 , 115 Participants had chronic MSK pain deriving from lower back pain, 26 , 31 , 40 , 46 , 49 , 50 , 55 , 80 , 90 fibromyalgia, 51 , 56 , 59 , 79 , 87 , 94 , 103 neck pain, 86 , 92 osteoarthritis, 22 – 25 , 27 – 30 , 32 – 39 , 42 , 44 , 45 , 47 , 51 – 53 , 57 , 78 , 82 , 84 , 88 , 91 , 98 – 102 , 104 – 106 , 110 – 112 , 115 – 118 and rheumatoid arthritis. 42 , 43 , 48 , 51 , 54 , 81 , 85 , 95 – 97 , 101 , 107 , 109 , 113 , 114 , 118 Sample size ranged from 40 to 855 participants. The mean age of participants was 59.0 years (SD 8.4) and 73.0% (SD 15.9) were women. The characteristics of the included RCTs are shown in Table  2 .

Study Characteristics

Abbreviations: 6MWT, 6-minute walk test; ActivA, ActiveOA–Active living with OsteoArthritis; ADL, activities of daily living; AS, ankylosing spondylitis; AE, aerobic exercises; AHI, Arthritis helplessness index; AIMS, Arthritis impact measurement scale; ANOVA, analysis of variance; ASES, Arthritis Self-Efficacy Scale; ARS, adherence to recommendations scale; ASMP, arthritis self-management program; BBAT, Basic Body Awareness Therapy; BGA, behavioral graded activity program; BMI, body mass index; BOA, Better Management of Osteoarthritis; BOOST, Boston Overcoming Osteoarthritis through Strength Training; CAROT, Influence of Weight Loss or Exercise on Cartilage in Obese Knee Osteoarthritis Patients Trial; CG, control group; CI, confidence interval; CHAMPS, community healthy activities model program for older adults; CONNECT, Communication Style and Exercise Compliance in Physiotherapy; CPCI, chronic pain coping inventory; CRF, cardiorespiratory fitness; CVD, cardiovascular disease; DAS-28, disease activity score; DBP, diastolic blood pressure; EARS, Exercise Adherence Rating Scale; ES, effect size; EX, exercise program; EQ-5D, EuroQol health survey (quality of life) with 5 dimensions 62 ; FACIT-F, functional assessment of chronic illness therapy–fatigue; HCQ, health cognition questionnaires; FSHC, fibromyalgia self-help course; FIQ, Fibromyalgia Impact Questionnaire; FIQ-PI, Fibromyalgia Impact Questionnaire-Physical Impairment; FPAQ, Freiburg physical activity questionnair; FM, fibromyalgia; FSHC, fibromyalgia self-help course; HADS, hospital anxiety and depression scale; HFAQ, Hannover functional ability questionnaire; HHS, Harris Hip Score; HOOS, Hip Osteoarthritis Outcome Score; IBET, internet-based exercise training; IG, intervention group; IIC, implementation intention condition; IMPAACT, Improving Motivation for Physical Activity in Arthritis Clinical Trial; IPAQ,  International Physical Activity Questionnaire; IQR, interquartile range; KOA, knee osteoarthritis; KOOS, Knee Injury and Osteoarthritis Outcome Score; LAPAQ, Longitudinal Aging Study Amsterdam Physical Activity Questionnaire; LBP, low back pain; MET, metabolic equivalents; MHAQ, Modified Health Assessment Questionnaire; MI, motivational interviewing; MACTAR, McMaster Toronto Arthritis Patient Preference Disability Questionnaire; MIIC, motivational and implementation intention condition; MMSE, minimental state examination; MITI, motivational interviewing treatment integrity; MSK, musculoskeletal; MV, moderate vigorous; MVPA, moderate-vigorous physical activity; MWP, minimum walking program; NDI, neck disability index; NIFS, national institute for fitness and sport; NEMEX, neuromuscular exercise; NRS, Numeric Rating Scale; OA, osteoarthritis; OMERACT-OARSI, Outcome Measures in Rheumatology Society—Osteoarthritis Research Society International; PA, physical activity; PAM, patient activation measure; PASE, Physical Activity Scale for the Elderly; PCS, physical component score; PCST, pain coping skills training; PE, patient education; PFJ, plants for joints; PGA, patient global assessment; PGIC, patient global impression of change; PT, physiotherapist; QALYs, quality adjusted life years; RA, rheumatoid arthritis; RAQoL, Rheumatoid Arthritis Quality of Life; RDQ, Roland Morris Disability Questionnaire; RAPIT, rheumatoid arthritis patients in training; RCT, randomized controlled trials; RoM, range of motion; SDT, Self-Determination Theory; SEE, Self-Efficacy for Exercise; SF, short form; SIRAS, Sports Injury Rehabilitation Adherence Scale; ST, strength training; SQUASH, Short Questionnaire to Assess Health Enhancing Physical Activity; ST-FSHC, strength training and fibromyalgia self-help course; SWP, standard walking program; TLC, telephone linked communication; TMBC, transtheoretical model of behavior change; TR, telephone reinforcement; TTM, transtheoretical model; VAMS, visual analogue mood scale; VAS, visual analogue scale; VTP, Vitality Training Program; WL, weight loss; W, walking intervention 84 ; WB, walking and behavioural intervention 84 ; WOMAC, Western Ontario and McMaster Universities Osteoarthritis Index.

• Quality Appraisal

We appraised quality with the Critical Appraisal Skills Program -checklist, a tool developed for assessing RCTs. 119 , 120 Although an allocation concealment was performed in most RCTs, blinding of study investigators, physical therapists, physicians, and participants after randomization was not possible in most study designs (Table  3 ). All but one RCT 55 reported data and participant loss throughout the course of study. Most RCTs reported the intervention effects precisely and comprehensively. The reported harms were minor and did not influence the outcome or outweighed the benefits of the intervention. Although all interventions included a strategy to increase the motivation for PA, not all RCTs reported sufficiently detailed results for the meta-analysis.

Abbreviations: CG, control group; IG, intervention group; RCT, randomized controlled trial.

1 Yes (assessors, caretakers [GPs, surgeons]), statisticians. 2 Yes (physiotherapists and investigators assessing outcomes), no (participants, therapist). 3 Yes (participants, researchers), no (trial clinicians). 4 Yes (assessor), no (physiotherapist, participants). 5 Yes (participants, study personnel, analysts), no (physiotherapist). 6 Participants in intervention group were 2 years younger than in control group; was brought up in the discussion and concluded that it has no effect on outcome. 7 But data were missing: Table 3, steps/day at months 12 and 18. 8 Adverse events: mild transient knee pain. 9 No (participants), no (physiotherapists), yes (statistician). 10 Yes (participants, statistical analysis), no (nurses, conductors). 11 As patients started with the center specific rehab first and then switched to the standardized program, the initial treatment could have been different. 12 Only analyst. 13 Only assessor. 14 Only assessor. 15 Yes (assessors, participants), no (physiotherapists). 16 Yes (assessors, statistician, treatment allocator), no (physiotherapists, participants). 17 But data missing at 10-week follow-up. 18 Only participants. 19 Patients. 20 Patients. 21 No (physical therapists), all other unclear. 22 Yes (physician providing physical activity), no (participants). 23 Yes (pain and function), no (motivational interviewing). 24 Yes (participants), no (physical therapists). 25 Yes (assessor, analyst), no (participants, program trainer). 26 Significant higher median age and symptoms duration (in years) in intervention group compared to control group. 27 Yes (participants, nurses who delivered care to intervention and control group), no (physiotherapists who delivered care only to intervention group). 28 But more control group participants tended to report more problems in other joints. 29 Yes (participants), no (physical therapists). 30 Yes (participants), no (physical therapists). 31 No (participants, physiotherapists), yes (study personnel at all follow-up, statistician). 32 But adverse effects, mild increase of LBP, groin or knee pain for only a few days. 33 No randomization in terms of age, sex, gender and so on. 34 Yes (main investigator), no (physical therapists). 35 In IG were more people with low education level than in CG, no other differences. 36 Yes (participants, analyst), no (physical therapist). 37 Only assessor. 38 Differences in quality of life, physical activity self-efficacy scale measures. 39 Yes (therapists in terms of evaluation status, outcome assessor), no (patients, therapists to treatment status). 40 Research personnel. 41 Assessor. 42 Case-control assignment. 43 Physicians blinded to group assignment. 44 Single blind. 45 Single blind (participants, study coordinator blinded). 46 Yes (principal investigator, statistician), no (participants, assessor). 47 Not much on adherence. 48 Assessors. 49 Interviewers. 50 Assessor. 51 Assessor and education seminar leader. 52 Triple blind. 53 Control group received postural exercises that the intervention group did not receive. 54 Single blind (participants). 55 Assessor. 56 Participant and researcher were blinded until baseline questionnaire was completed. 57 Yes but not in length of time since diagnosis. 58 Yes (research coordinator), no (physical therapists). 59 Yes (research team before allocation), no (participants). 60 All differences are shown. 61 Assessor. 62 Yes (assessors), no (participants, staff delivering intervention). 63 Intervention group had 1 hour more daily sitting time versus control group. 64 Single investigator. 65 But research assistants had to guess assigned treatment after 13 weeks. 66 Assessors (information from study protocol). 67 But drop out was larger than expected, missing data for psychosocial variables. 68 Yes (investigator for evaluations), no (participants). 69 Assessor. 70 Yes (assessor blinded, statistician), no (participants).

• Description of Adherence Support Strategies

Most RCTs tested the effect of adherence strategies on regularly performed physical exercises with no walking component (56 RCTs), exercise together with walking (6 RCTs 44 , 45 , 84 , 88 , 90 , 104 ), and walking without other exercise components (4 RCTs 49 , 50 , 103 , 110 ). Two RCTs investigated stretching. 86 , 92

Several interventions used cognitive theories as part of their adherence support strategies (Table  4 ). In 8 RCTs, the intervention was based on cognitive behavioral theory, 34 , 44 , 46 , 81 , 101 , 109 , 113 in 2 RCTs intervention was based on cognitive behavioral pain coping skills training. 33 , 35 Sixteen RCTs included Motivational Interviewing in their intervention 24 , 34 , 40 , 42 , 48 , 51 , 54 , 79 , 81 , 86 – 88 , 95 , 103 , 113 and 4 RCTs the Transtheoretical Model. 24 , 25 , 27 , 96 Eleven interventions aimed at developing self-management skills. 46 , 48 , 52 , 56 , 81 , 91 , 94 , 106 , 107 , 114 Twelve RCTs used an internet-based platform, including a personal account for record tracking or online community exchange. 28 , 30 , 35 , 47 , 49 , 53 , 78 , 82 , 95 , 105 , 114 , 118 Forty-five RCTs provided educational and informative group sessions or books/handouts that included information about the participants’ diseases or instructions for specific exercises (Table  4 ). Multiple individual approaches were used to enhance adherence for PA: 27 interventions used goal setting, 17 RCTs barriers or problem-solving, 4 RCTs reinforcement meetings. 40 , 85 , 88 , 116 These were delivered in personal conversations, phone or video calls with trained physiotherapists, physicians, nurses, or study personnel. Thirty-three interventions included a self-monitoring strategy, for example, logbooks, diaries, or pedometer/accelerometer. Seventeen RCTs used generic emails or SMS as reminders. Several original adherence support strategies stood out: study T-Shirt and a buddy system, 98 certificates and a graduation ceremony, 44 , 88 signed contract between patient and therapist, 40 peer storytelling, 107 exergame with a virtual rehabilitation tool, 59 Basic Body Awareness Therapy, 102 or communication skills training for physiotherapists. 26

Adherence Support Strategies

1 Education/information: group session or book/handout. 2 Self-monitoring: logbooks, diaries, pedometer (self-monitoring or blinded). 3 Feedback: personal (phone calls or face-to-face), progression discussion, meetings with physiotherapist. 4 Reminders: generic email, SMS. 5 Multimodal, individualized, supervised exercise physiotherapy; prescription of home exercises. 6 Graded sensorimotor retraining. 7 Physical activity on prescription. 8 Text messages based on the Behavior Change Wheel. 9 Fitness center training before home exercises. 10 Favorite activity. 11 Plan own regimen. 12 Gradually translation from supervised facility-based exercises to unsupervised home-based exercises; intervention after weight loss program. 13 Physiotherapy and home exercises individually tailored on comorbidities. 14 Exercise therapy and home exercises tailored on comorbidities. 15 Vitality Training Program with various topics, for example, resources and values, arts and creativity, meditation, exercises. 16 Graduation certificate. 17 Graduation certificate. 18 Graduation certificate. 19 Session with affected person. 20 e-Community. 21 Multidisciplinary rehabilitation (rheumatologist, physical therapist, exercise therapist, massage therapist, psychologist, dietician). 22 Communicational skills training for physiotherapists. 23 Study T-shirt, buddy system, social gatherings. 24 Therapeutic aquatic exercises. 25 Personalized pacing schedule based on home monitoring period. 26 Basic Body Awareness Therapy: movement habits, increase awareness, movement strategies in daily life. 27 If-then plans. 28 Behavioral Graded Activity. 29 Face-to-face monitoring for 6 months, 4 times/wk. 30 Peer story telling. 31 Smartphone app. 32 Two-year exercise program, home visits, self-paced, progressively more challenging. 33 Internet forum. 34 Behavioral Graded Activity. 35 Behavior change techniques grounded in Self-Determination Theory, behavior change counselor. 36 Exergame (virtual reality rehabilitation tool, VirtualEx-FM). 37 Plants for Joints lifestyle intervention from dietician and physiotherapist, webinars, and personal group meetings.

All adherence rates above 80% are shown in bold.

• Which Support Strategies Lead to High Adherence?

All adherence support strategies are summarized in Table  4 . Forty-nine interventions included a combination of different support strategies, only 5 RCTs investigated single strategies. 26 , 55 , 80 , 99 , 100 Adherence rates ranged from 15% to 190%. The differences in adherence measurements should be clearly emphasized here: in some RCTs, adherence percentages of over 100% can be achieved, for example, if the exercise goal is exceeded. In other RCTs adherence is measured with class attendance and an adherence rate of over 100% cannot be accomplished. Twenty-one RCTs achieved an adherence rate of 80% and above. Adherence support strategies in these RCTs included education and information (delivered in a group session or with printed material/website or both), goal setting (face-to-face or in a group discussion or with diaries), self-monitoring (pedometers or diaries or both), and personal feedback (phone calls or face-to-face or group discussion). Since these strategies are also included in other, less effective interventions regarding adherence, no single strategy can be deemed superior to the others. The range of adherence support strategies also reflects the heterogeneous intervention designs of the RCTs included.

• Correlation Between Adherence to the Intervention and Study Outcomes

To analyze the correlation between adherence and effect on study outcomes, we excluded 46 of 76 RCTs. Reasons for exclusion were: no percentage for adherence reported (29 RCTs, thereof 5 RCTs with visual analogue scale values for adherence); no mean values reported for outcome measurements (32 RCTs); adherence calculation not comparable to other RCTs (3 RCTs). In total, data on 87 effect sizes nested within 6 outcome categories (pain, quality of life, self-efficacy, function, disability, and PA) and 29 RCTs were included in meta-analysis (Table  1 ). The number of effect sizes reported in a single study varied between 1 and 6.

Correlation analysis (Figure  2 ) indicated that for a 10% increase in adherence, the standardized mean difference increases by 0.009 (95% CI, −0.021 to 0. 038). The effect is not significant with a P value of .58. About 28.9% of the total variance was attributable to sampling error variance ( I 2 total  = 71.1%). The I 2 level 3 was 57.3%, indicating a moderate-high between-study heterogeneity and the I 2 level 2 was 13.7%, indicating the proportion of total variance, which can be explained by differences between the effects for different outcomes within the respective RCTs.

Correlation analysis between adherence and effect on study outcomes (3-level model, random effects with predictor “adherence” as linear predictor). Each endpoint is shown as a bubble, the size of the bubble reflects the relative group size of the intervention group.

This review presents a wide variety and combination of different adherence support strategies to encourage people with chronic MSK pain to engage in PA. Most RCTs used multiple strategies, and the type and duration of interventions varied greatly, making it difficult to determine individual factors that may promote PA adherence. Education and information, goal setting, self-monitoring and personal feedback were part of interventions that had an adherence rate over 80%. Correlation analysis showed that, despite the heterogeneity of RCTs, high adherence to PA might lead to an improvement of outcome measurements, although this improvement was not significant.

• Comparison to Existing Literature

Physicians counseling patients to engage in more PA alone may not be sufficient to change PA behavior, as it has been shown to have limited impact on patient’s behavior. 121 The behavior change wheel is a framework that has been developed to guide behavior change interventions to support motivation, capability, and opportunity. 122 Behavior change techniques that are most likely to be effective to change behavior, such as increasing PA, are education, persuasion, incentives, training, and enablement (5 out of 9 intervention functions from the behavior change wheel). 122 Counseling by physicians could be able to cover a few dimensions in the behavior change wheel, especially education or persuasion, but this can happen only selectively. Contacts with the patients are usually too few and too short for a long-lasting motivation and real behavior change. Patients’ interests, motivations, and comorbidities vary widely, and the same intervention can be advantageous for some patients, and not feasible for others. 83 , 123

Nevertheless, counseling and a reliable and consistent working alliance between doctor and patient is a positive predictor of effective treatment. 124 Therapeutic alliance is one of the 5 key dimensions of patient-centered medical care and is characterized by developing common therapeutic goals and enhancing the personal bond between doctor and patient. 125 Empathy, congruence, and unconditional positive regard are both necessary and sufficient for effecting therapeutic change in clients. 126

Advice from general practitioners together with an additional support strategy (like individual feedback or follow-ups) promotes PA better than advice alone in patients with no chronic pain conditions. 127 – 129 These all-encompassing strategies are even more important, but also more challenging, in patients with higher levels of disability who tend to engage in lower levels of PA. 16 Some physicians reported that their patients with chronic back pain often did not follow their PA recommendations, while some patients felt they were stigmatized and not taken seriously. The doctor–patient relationship might then be perceived as unbalanced and conflictual due to feelings of failure and frustration on both sides. 130

While higher adherence was related to improvement of study outcomes, this association was not significant. On average, people with chronic MSK pain engage less in PA than the general population. 131 World Health Organization standards advise adults to engage in at least 150 minutes of moderate-intensity aerobic exercise per week but in Europe only about a third of the population achieves this. 14 , 132 , 133 Given the low levels of PA in people with chronic MSK pain, even some PA might be beneficial, even if it is below the intended intervention. 133

• Strengths and Limitations

An important strength is that, to our knowledge, this is the first study to identify successful adherence support strategies for people with chronic MSK pain, which also investigated the relationship between adherence and study outcomes. In addition, the literature search was performed with a wide range of different types of chronic MSK pain. RCTs used similar adherence support strategies independent of the pain location. Therefore, it was deemed reasonable to investigate adherence beyond individual health backgrounds.

Some limitations must be considered. A few RCTs lacked percentage values for adherence which led to elimination of several RCTs from the meta-analysis. RCTs also used different measures to report adherence. We decided against a meta-analysis stratified by adherence calculation or different outcomes, as small sample sizes do not allow for well-founded conclusions. The heterogeneity between RCTs exceeded that of adherence calculations or outcomes, so we aggregated these outcomes.

Furthermore, positive adherence rates were not tied to specific strategies if used in combination; it was not possible to differentiate which one of these jointly used strategies was particularly effective. In addition, the RCTs were difficult to compare due to different intervention contexts. Example: Ang et al 79 used Motivational Interviewing delivered by phone calls (6 phone calls in a 12-wk period) and showed an adherence rate of 128% to home exercises at 6 months. Gilbert et al 42 also tested Motivational Interviewing either in person or by phone calls, 4 times within a year and twice in the second year and achieved 53% adherence to PA at 2 years. Another important limitation is that we considered adherence as an aggregated, study-level variable instead of a participant-level variable. This might have introduced an ecological fallacy. One should be careful to draw conclusions about characteristics of the individual participant that are based on group data. Individual participants’ data on adherence and study outcomes would be the optimal data basis for a reliable correlation analysis.

Limitations of the review process itself must also be considered. Due to a very high number of outputs from databases, only RCTs with a follow-up period of at least 6 months were included in this review. Publication bias is likely as most RCTs have a small group size with less than 100 participants and show low effect sizes for their outcomes. 134 Therefore, the probability for unpublished negative RCTs or not MEDLINE listed publications is high.

• Implications for Practice and Future Research

Education and information, goal setting, self-monitoring, and personal feedback were promising adherence support strategies and should be further tested in interventions for people with chronic MSK pain. Upcoming trials investigating exercise interventions should also include assessments of adherence and report those results. 135 Furthermore, sharing data on patient level, or displaying raw data in the publications and supplements will make additional analysis more feasible.

• Acknowledgments

Funding : This work was funded by the “Britta und Peter-Wurm-Stiftung.” Registration : This study is registered at PROSPERO (CRD42021233916).

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Lindner https://orcid.org/0000-0003-2755-5615

Kornder https://orcid.org/0000-0003-0176-6967

Becker https://orcid.org/0009-0008-9172-8473

Haasenritter https://orcid.org/0000-0001-6766-4627

Viniol https://orcid.org/0000-0002-5591-7399

van der Wardt https://orcid.org/0000-0003-3995-7056

* Heisig ( [email protected] ) is corresponding author, https://orcid.org/0000-0002-6167-1049

Identification of individual strategies that lead to high adherence to physical activity in people with chronic musculoskeletal pain remains challenging due to their combined use in interventions.

While increased adherence to physical activity is associated with better outcomes, the correlation was not statistically significant, suggesting the need for more detailed participant-level data for future research.

Journal of Physical Activity and Health

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• Julia Heisig
• Nicole Lindner
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• Wiebke Reichert
• Annette Becker
• Jörg Haasenritter
• Annika Viniol
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