Developing hypotheses for scientific research in musculoskeletal conditions: A guide for health-care professional

Directional hypothesis

The directional hypothesis describes the association between variables in terms of a difference but also gives a direction to that difference. The directional hypothesis is also known as the unidirectional hypothesis. Different study designs develop directional hypotheses, which predict the relationship between one variable and another. They may be supported by solid theoretical justification or consistent earlier findings, particularly in experimental settings or meta-analyses. However, careful presentation is essential, recognizing that unexpected outcomes are possible. Remember that both directional and non-directional (predicting only a difference) hypotheses maintain value based on existing knowledge, even though the studies that generate them vary between fields.^[11,12] For example, children with cerebral palsy who receive direct myofascial release technique in combination with functional training will have a significantly faster resolution of contracture, greater reduction of spasticity, and greater improvement in gross motor function when compared with children who receive functional training alone.

Non-directional hypotheses

Non-directional hypotheses, predicting a relationship without direction, frequently emerge when knowledge is constrained (new phenomenon, conflicting previous finding) or exploratory is key (exploratory studies, many variables). A non-directional hypothesis, also known as a bi-directional hypothesis, suggests that two variables change each other. It implies an equal relationship where both variables can affect each other. Even without guidance, they remain helpful since they can lead to unexpected findings and a deeper understanding, particularly in controversial or developing fields of study.^[11,13] For example, there is an association between social media use and mental health, but the direction of the effect is uncertain. Brown et al. (2021)^[14] conducted research comparing manual and exercise therapy’s effectiveness in treating shoulder pain. They hypothesized that there would be differences in the outcomes between the two therapies. Involving 150 patients randomly assigned to either manual or exercise therapy over 12 weeks, the study measured outcomes such as pain intensity, range of motion, and patient-reported functional status. The results indicated significant differences between the treatment groups, with varying directions and magnitudes across different measures. This non-directional hypothesis facilitated an unbiased assessment of both therapies’ effectiveness.

Simple hypothesis

A simple hypothesis forecasts a relationship or difference between variables without specifying the direction and nature of the relationship. It consists of one independent variable and one dependent variable. It is commonly used in observational (cross-sectional and case–control) studies,^[15] experimental (pre-test/post-test control group and pilot) studies,^[16] and sometimes in case reports or descriptive studies; formulation of a simple hypothesis is needed for further exploration.^[17] While simple hypotheses’ clarity and focused approach are advantageous, they are inappropriate for addressing complicated relationships or interactions between multiple variables. For example, to illustrate the application of simple hypotheses in MSK research, we can consider a few specific examples. In a study by O'Bryan et al. (2020), researchers hypothesized that a 12-week strength training program would reduce pain in patients with chronic low back pain (LBP).^[18] The hypothesis was straightforward: Participating in the strength training program would lead to a decrease in pain levels. The study involved 100 participants who engaged in supervised strength training sessions; with pain levels measured using a standardized pain scale before and after the intervention. The results supported the hypothesis, showing a significant reduction in pain among participants.

Complex hypotheses

A complex hypothesis contains multiple variables and predicts more complex relationships between them. It may specify the influence of the interface among variables. The choice of study design for research using a complex hypothesis can be determined by various circumstances involving the definite nature of the hypothesis, the research questions being addressed, and the available resources. However, some common designs that are well-suitable for complex hypotheses include:

• Experimental (randomized controlled trials [RCTs], factorial designs) studies,

• Observational (cohort and case–control) studies, and

• Mixed method studies combine qualitative and quantitative methods to collect data because they involve both subjective and objective factors.^[16,19]

For example, several detailed examples can be explored to demonstrate the use of complex hypotheses in MSK research. In a study by Thompson et al. (2020), researchers proposed that combining strength training with dietary supplementation would improve muscle mass and functional performance in elderly patients with sarcopenia compared to either intervention alone.^[20] This hypothesis suggested that the interaction between exercise and nutritional support would provide synergistic benefits. The study included 200 participants who were randomly assigned to one of four groups: Strength training, dietary supplementation, combined interventions, or a control group. Outcomes such as muscle mass, grip strength, and walking speed were measured before and after a 12-week intervention period. The results confirmed the hypothesis, showing that the combined intervention group experienced the most significant improvements across all measures.

Null hypothesis

When there is no significant relationship or association between independent and dependent variables, the statistical hypothesis is called the null hypothesis (H₀).^[4] In hypothesis testing, it is the default position, and researchers try to invalidate it using statistical analysis. Here is a summary of the most common types of study design and how they use H0:^[16,21]

• Observational (cohort, case–control, and cross-sectional) studies,

• Experimental RCT studies and

• Systematic reviews and meta-analysis – They do not usually do H₀ testing. However, it is a useful tool for determining the overall strength of the evidence in favor of or against a specific hypothesis.

For example, in a recent investigation by Garcia et al. (2021), researchers explored how various stretching techniques affect hamstring flexibility among athletes.^[22] The H₀ posited that there would be no discernible variance in hamstring flexibility enhancement between athletes engaging in static stretching versus those practicing dynamic stretching. The study encompassed 80 athletes, each randomly allocated to one of three groups: static stretching, dynamic stretching, or a control group. Evaluations of hamstring flexibility were conducted using the sit-and-reach test both before and after a four-week intervention phase. The outcomes demonstrated no statistically significant disparity in flexibility enhancement between the two stretching regimens, thereby corroborating the H₀.

Alternate hypothesis

In contrast to the H₀, the alternative hypothesis suggests that there is a significant association or difference between the variables. It is represented as H₁ or H_a. Accepting the alternative hypothesis instead of the H₀ implies that there is a difference between the groups or that the variables are connected.^[23] The alternative hypothesis cannot be tested directly; it is accepted by rejection if the test of statistical significance rejects the H₀. Although most studies do not generate alternate hypotheses directly, they can be developed with the help of a variety of research techniques. Various studies used alternative hypotheses, and these are: ^[24]

Inductive hypotheses

Using specific observations as a starting point, inductive reasoning generates new concepts and investigates poorly understood phenomena. For example, imagine viewing white swans and concluding that they are all white. This method, meanwhile, may lead to oversimplification.^[24]

Many studies use inductive hypotheses for generalized conclusions and these are:

Exploratory studies

In these types of studies, the aim is to identify novel occurrences and provide a preliminary understanding of them. For instance, the inductive hypothesis that a new medication increases healing rates may be supported by the observation that multiple patients recover more quickly after using it.^[31]

Grounded theory

In grounded studies, theories are generated directly from qualitative data through systematic theme and pattern analysis. For example, conducting interviews with participants regarding their experiences with the counseling programs regarding health may result in the inductive premise that health depends heavily on peer support.^[32]

For example, a recent Garcia et al. (2020) investigation examined the connection between physical activity levels and bone mineral density (BMD)in postmenopausal women. Utilizing inductive reasoning, the researchers postulated that heightened physical activity levels would correlate with increased BMD in this demographic.^[33] The study comprised 150 postmenopausal women whose physical activity levels were gauged through self-reported surveys, while BMD was measured through DEXA scans. The outcomes unveiled a positive correlation between physical activity and BMD, suggesting that regular physical activity may contribute to preserving bone health in postmenopausal women.

Deductive hypothesis

A deductive hypothesis is derived from theory and is designed to establish evidence that supports, expands, or contradicts properties of a given theory. Conversely, deductive hypotheses undermine accepted ideas.^[5,31]

Studies using deductive hypothesis are as follows:

Hypothesis testing

Aims to validate or refuse hypotheses that have been derived from previous research. For instance, a scientist might put out the deductive idea that light bends around big objects based on the theory of relativity. After that, they would design experiments to verify this hypothesis.^[34]

Confirmation bias research

It investigates the tendency of people to look for evidence to support their pre-existing ideas. A healthcare professional might, for instance, propose the deductive hypothesis that individuals who believe in conspiracy theories are more prone to believe in self-exercise than in evidence-based guidelines for chronic shoulder pain. Then, to verify this prediction, they conduct surveys and experiments.^[31]

To demonstrate the application of deductive hypotheses in MSK research, consider these instances. In a study conducted by O'Bryan et al. (2021), researchers aimed to validate a deductive hypothesis suggesting that individuals with a consistent history of resistance training would display higher BMD than those without prior engagement in such activities.^[18] The hypothesis was rooted in the understanding that resistance training, known for its capacity to stimulate bone growth, would enhance BMD over time. The study enrolled 200 participants, half with a documented history of regular resistance training, while the remainder had not participated in such exercises. The BMD was quantified using DEXA scans. The findings affirmed the deductive hypothesis, demonstrating notably elevated BMD levels among individuals with a history of resistance training compared to those without.

Associative hypothesis

An associative hypothesis suggests that a modification in one variable results in a change in another variable, but it does not determine which variable is causing the changes in the other. Interdependency between two variables is termed an associative hypothesis but cannot establish causality (cause-and-effect).^[35] For example, there is an association between long-term sloughed sitting and an increased risk of neck pain.

Studies that use associative hypothesis are as follows:

Observational studies

Gather data through observation without active manipulation of variables. Examples: Surveys, case–control studies, and cohort studies. For example, improved cardiovascular health positively correlates with increased activity levels (Note: Other factors, such as food, may contribute to improved cardiovascular health; exercise is not the sole cause).^[36]

In MSK research, associative hypotheses are vital in exploring potential relationships between variables. Lee and Patel (2021) investigated the association between physical activity levels and joint health in individuals with OA. The associative hypothesis proposed that higher levels of physical activity would be correlated with improved joint function and reduced pain in this population. The study involved 200 OA participants whose physical activity levels were assessed using activity trackers, while joint function and pain levels were measured through standardized assessments. The results revealed a significant association between higher physical activity levels, better joint function, and reduced pain, supporting the associative hypothesis.

Causal hypothesis

The causal hypothesis proposes a cause-and-effect interaction between variables. It indicates that manipulating one variable (independent) directly causes changes in another variable (dependent).^[35] For example, not treating anterior cruciate ligament tears properly will cause OA.

Studies that use causal hypothesis are as follows:

Experimental studies

Actively change an independent variable and examine its effects on the dependent variable. Examples: RCTs and A/B testing (type of RCT experiment in which you run two different exercises simultaneously and see which performs better). For example, a study by Nguyen et al. (2020) aimed to establish a causal relationship between smoking and BMD in young adults.^[37] The causal hypothesis suggested that smoking would lead to decreased BMD over time due to its detrimental effects on bone metabolism. The study involved 200 young adults, half smokers, and half non-smokers, with BMD measured using DEXA scans. The results showed a significant reduction in BMD among smokers compared to non-smokers, supporting the causal hypothesis.^[36]

Various types of hypotheses and their corresponding study designs are summarized in Table 1.