Research fatigue was 56.3% in Mosoriot, tied to repeated studies

In a heavily studied Kenyan community, older age, being male, hospital-based studies, and personal questions sharply increased fatigue and dropout desire.

More than half of surveyed community members in Mosoriot, Kenya reported research fatigue (56.3%). In the journal article “Kuchoka”: Investigation of research fatigue in Mosoriot, Kenya, fatigue was more likely among people repeatedly recruited into studies, especially in hospital settings and when asked personal questions. The strongest signal was a stated desire to drop out, which was linked to markedly higher odds of fatigue.

Quick summary

What the study found: Research fatigue affected 56.3% of participants and was associated with being at least 35 years old, being male, being self-employed, hospital-based research participation, participating in two or more studies, wanting to drop out, and being asked personal questions.
Why it matters: Fatigue is not just discomfort; it can increase dropout risk and push people toward protective answering that threatens data quality and ethical standards.
What to be careful about: This was a cross-sectional study (a one-time snapshot), so it shows associations, not proof that any factor causes fatigue.

What was found

This cross-sectional survey sampled 327 community members in Mosoriot, Kenya, using self-administered and/or guided questionnaires. The study estimated the prevalence of research fatigue and tested which characteristics were associated with it using logistic regression (a statistical method that estimates how strongly factors relate to an outcome).

Overall, 56.3% of participants reported research fatigue. When broken down, 47.4% reported physical fatigue, 42.8% emotional fatigue, and 15.3% mental fatigue. In addition, over half of participants (62.4%) reported spending more than one hour at the research site.

In adjusted analyses, higher odds of research fatigue were associated with being at least 35 years old (odds ratio 2.28, 95% confidence limits 1.27 to 4.15) and being male (odds ratio 2.80, 95% confidence limits 1.59 to 5.00). Self-employment was also associated with higher odds (odds ratio 2.05, 95% confidence limits 1.06 to 4.01).

Research context and repetition mattered. Participating in hospital-based studies was linked to higher odds of fatigue (odds ratio 3.59, 95% confidence limits 1.88 to 7.09), as was involvement in two or more studies (odds ratio 3.86, 95% confidence limits 1.87 to 8.27).

Two participant-experience variables stood out. Wanting to drop out of a study had the strongest association with research fatigue (odds ratio 11.49, 95% confidence limits 3.69 to 43.83). Being asked personal questions was also strongly associated (odds ratio 6.23, 95% confidence limits 3.28 to 12.23).

What it means

Research fatigue is best understood as a form of accumulated burden: repeated demands on time, attention, privacy, and emotional energy from participation in studies. When that burden crosses a threshold, people disengage. Disengagement shows up as exhaustion, irritability, reduced concentration, and—most practically for researchers—dropout intentions and lower-quality responses.

Two mechanisms are especially important for readers who care about mental health and ethics. First is depletion: long sessions, repeated visits, and travel time can drain self-control and patience, making participants less able to thoughtfully engage with questions. Second is threat management: when questions feel too personal, people may become guarded and shift into self-protection rather than openness.

The study also flags a problem that is easy to miss: the consent process itself. The only ethical-issue variable associated with fatigue in bivariate analysis was the length of the consenting process. Informed consent is meant to protect autonomy (the right to freely decide), but if it becomes long and exhausting, it can undermine the very clarity and voluntariness it is supposed to support.

Finally, the “desire to drop out” result is a practical red flag. In human terms, it can signal frustration, mistrust, or feeling trapped. In research-quality terms, it can forecast attrition (participants leaving a study) and biased samples, because the people who remain may differ systematically from those who want to leave.

Where it fits

This paper frames research fatigue as both an ethical issue and a methodological risk. Ethically, heavily researched communities can become overburdened, especially if certain groups are repeatedly targeted. Methodologically, fatigue can degrade data integrity through response bias and disengagement.

Response bias means answers are systematically distorted rather than fully accurate. In this study, fatigue was associated with “answering questions to protect personal information.” That is a specific pathway from participant discomfort to compromised data: people may provide partial truths, minimize sensitive behaviors, or choose socially safer options.

The results also highlight that “fatigue” is not one thing. Physical fatigue (tiredness in the body), emotional fatigue (feeling drained, irritable, or overwhelmed), and mental fatigue (difficulty concentrating) can coexist but may arise from different parts of the research experience. Long days and travel plausibly load physical fatigue, while personal questions and repeated probing plausibly load emotional fatigue; the study reports the prevalence of each but does not test separate predictors by fatigue type.

Context appears central. Hospital-based research participation was associated with higher fatigue than other settings. Without adding claims beyond the study, the implication is straightforward: setting shapes the participant experience, and that experience shapes fatigue and willingness to continue.

How to use it

If you run studies, manage research programs, or sit on an ethics review committee, this paper offers clear operational signals for reducing burden without weakening science. Start by treating fatigue as a measurable risk, not a vague complaint.

Prioritize recruitment and scheduling practices that avoid repeatedly drawing from the same people. The study found higher fatigue among those involved in multiple studies (two or more). A practical response is community-level coordination: keep a participation log (with appropriate privacy protections) or coordinate across research teams to distribute invitations more evenly across eligible households.

Reduce “time tax” wherever possible. Bivariate results linked fatigue to longer travel time and perceptions that research took longer than expected. That points to concrete fixes: shorten sessions, provide clearer time estimates upfront, and design data collection that can be completed in fewer visits. When time on site must be long, build in breaks and provide comfortable waiting conditions.

Handle personal questions with more care than you think you need. Being asked personal questions had a strong association with fatigue in the adjusted model. In plain terms, sensitive questions should earn their place: ask only what you truly need, explain why you need it, and strengthen privacy protections during data collection. Also consider question order: placing the most sensitive items after trust is built can reduce immediate defensiveness, even if the study did not test this directly.

Watch for early warning signs and respond in real time. The strongest correlate was a desire to drop out. Train staff to treat withdrawal talk as a welfare signal, not a compliance problem. Offer pauses, answer questions, restate the right to stop, and make it easy to decline specific items without penalty when ethically and methodologically feasible.

Limits & what we still don’t know

This study was cross-sectional, meaning it measured fatigue and related factors at one point in time. That design can identify associations but cannot show that any factor causes fatigue. For example, people who are already exhausted for other reasons might be more likely to experience research as burdensome.

Fatigue was measured by questionnaire, which reflects self-report. Self-report is essential for subjective states like emotional fatigue, but it can also be shaped by mood, expectations, and the immediate experience of the survey itself.

The data also come from one community and time period. The paper itself notes the importance of local cultural and social roles, and it cautions against generalizing fatigue patterns across settings. Readers should assume the prevalence estimate and the strongest predictors could look different in communities with different research histories and power dynamics.

Finally, the study reports mental, emotional, and physical fatigue prevalence, but it does not provide separate multivariate models for each type. That leaves an open practical question: do different stressors uniquely drive physical versus emotional versus mental fatigue, and would interventions need to be tailored accordingly?

Closing takeaway

This journal article documents a clear signal: research fatigue was common in Mosoriot (56.3%) and clustered around repeated participation, hospital settings, and experiences that strain time and privacy. The strongest marker—wanting to drop out—should be treated as an ethical and data-quality alarm. If researchers want sustained participation and trustworthy responses, reducing participant burden is not optional; it is part of doing valid, respectful research.

Data in this article is provided by PLOS.

Tagged Classical mechanics, Clinical medicine, Damage mechanics, Emotions, Fatigue, Health care, Material fatigue, Materials physics, Materials science, Mathematical and statistical techniques, Mathematics, Multivariate analysis, Physics, Public and occupational health, Questionnaires, Research assessment, Research design, Research ethics, Research integrity, Signs and symptoms, Socioeconomic aspects of health, Statistical methods, Statistics, Survey research