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        new Paragraph({
          text: 'AUTOMATED ESTRUS DETECTION USING ACTIVITY MONITORS AND PEDOMETERS',
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          children: [new TextRun('Course: Theriogenology and Herd Reproductive Management')],
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          children: [new TextRun('Focus: Practical use of precision livestock technologies for heat detection')],
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        bullet('Explain the physiology of estrus and behavior changes that form the basis of automated detection.'),
        bullet('Describe principles and field use of pedometers, accelerometers, and multi-sensor systems.'),
        bullet('Interpret activity alerts and integrate them with clinical examination and farm records.'),
        bullet('Critically evaluate benefits, limitations, economics, and welfare implications in dairy herds.'),

        new Paragraph({ text: '1. Introduction', heading: HeadingLevel.HEADING_2 }),
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          text: 'Efficient estrus detection is central to reproductive success in dairy and buffalo herds. Traditional visual heat detection is labor-intensive and prone to missed heats, especially in high-producing cows where estrus signs may be shorter and less intense. Automated estrus detection systems use continuous behavioral monitoring to identify cows with increased activity associated with the peri-estrus period and provide timely alerts for insemination planning.',
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          text: 'In modern herd management, activity-based estrus detection does not replace veterinary judgment; instead, it augments decision-making by improving surveillance and reducing dependence on intermittent human observation.',
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        new Paragraph({ text: '2. Physiological Basis of Activity-Based Heat Detection', heading: HeadingLevel.HEADING_2 }),
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          text: 'Estrus behavior is driven by endocrine changes, especially preovulatory estradiol rise and low progesterone. Cows approaching estrus show greater locomotion, increased mounting behavior, restlessness, and altered rumination patterns. Activity monitors detect these deviations from each cow’s baseline and convert them into risk scores or alert categories.',
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          children: [new TextRun({ text: 'Figure 1. Hormonal dynamics relevant to estrous cyclicity and return to cyclic activity.', italics: true, size: 20 })],
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        bullet('Usually leg-mounted devices that count steps and walking intensity.'),
        bullet('Generate an estrus alert when step activity rises above cow-specific thresholds.'),
        bullet('Simple and widely adopted, but can be confounded by lameness, regrouping, and flooring effects.'),

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        bullet('Use tri-axial motion sensing to capture neck, leg, or body movement patterns.'),
        bullet('Often integrated with cloud dashboards and herd software for real-time alerting.'),
        bullet('Can combine activity with rumination, feeding, and lying-time metrics for higher precision.'),

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        new Paragraph({ text: '4. Workflow for On-Farm Use', heading: HeadingLevel.HEADING_2 }),
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          text: 'A robust automated estrus detection program should be protocol-driven. The following practical workflow can be used in dairy farms:',
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        bullet('Step 1: Ensure proper sensor assignment, battery health, and data synchronization for all eligible cows.'),
        bullet('Step 2: Exclude cows with current disease, severe lameness, or recent stressful events from immediate breeding decisions.'),
        bullet('Step 3: Review daily estrus alerts by priority (high, moderate, low confidence).'),
        bullet('Step 4: Confirm high-value cows with secondary checks (visual signs, vaginal mucus, ovarian ultrasonography if needed).'),
        bullet('Step 5: Perform AI at timing recommended by the system protocol and farm SOP.'),
        bullet('Step 6: Record outcomes and periodically calibrate alert thresholds based on herd fertility data.'),

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          text: 'Not every activity spike is estrus. Differential interpretation is required. Increased activity may occur due to social regrouping, heat stress, feed changes, handling, or discomfort. False negatives may occur in silent heats, lame cows, or cows in poor body condition. Therefore, veterinarians must interpret alerts in context with DIM (days in milk), postpartum disorders, uterine health, body condition, and previous cyclic history.',
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        new Paragraph({ text: '6. Role of Ultrasonography and Reproductive Examination', heading: HeadingLevel.HEADING_2 }),
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          text: 'Automated systems are strongest when paired with periodic reproductive examinations. Ovarian and uterine ultrasonography can validate cyclic status, detect anovulation, cysts, endometritis, and optimize breeding decisions in cows with ambiguous alerts.',
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          children: [new TextRun({ text: 'Figure 3. Example ultrasonographic view relevant to reproductive assessment in cattle.', italics: true, size: 20 })],
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        new Paragraph({ text: '8. Recent Evidence Summary (for critical appraisal)', heading: HeadingLevel.HEADING_2 }),
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          text: 'Recent peer-reviewed evidence supports the clinical value of activity-based estrus alerts. A 2026 meta-analysis in lactating Holstein cows reported better reproductive outcomes in cows with automated estrus alerts during voluntary waiting period compared with cows without alerts. Odds of AI at detected estrus, pregnancy at first service, and pregnancy by 150 DIM were significantly improved in alert-positive cows.',
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        bullet('Meta-analysis (PMID: 41297596): Alerts associated with improved AI and pregnancy outcomes.'),
        bullet('Review (PMID: 38798195): Multi-modal and automated methods improve detection efficiency when integrated with management.'),
        bullet('Review (PMID: 37106988): Infrared thermography discussed as adjunct non-invasive technology.'),

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        bullet('Compare visual estrus detection versus activity monitor-based detection in terms of sensitivity, labor, and economics.'),
        bullet('Design an estrus alert response protocol for a 200-cow dairy farm with limited labor.'),
        bullet('List five major causes of false positive and false negative alerts and propose mitigation steps.'),
        bullet('Interpret a case: Cow at 65 DIM with repeated moderate alerts but no conception after two AI attempts.'),
        bullet('Propose key performance indicators (KPIs) to evaluate success after 6 months of implementation.'),

        new Paragraph({ text: '10. Conclusion', heading: HeadingLevel.HEADING_2 }),
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          text: 'Automated estrus detection using pedometers and activity monitors is now a core component of precision reproductive management in dairy systems. For veterinarians, the greatest value lies in combining digital alerts with clinical examination, herd health records, and reproductive physiology. This integrated approach improves breeding decisions, fertility outcomes, and farm sustainability while supporting better animal welfare.',
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        new Paragraph({ text: 'References', heading: HeadingLevel.HEADING_2 }),
        new Paragraph('1. Mörig F, Drillich M, Burnett TA, et al. Evaluation of the association between automated estrus alerts from activity monitoring systems in early lactation with reproductive performance in lactating Holstein cows: A meta-analysis. J Dairy Sci. 2026;109. PMID: 41297596.'),
        new Paragraph('2. Rajput AS, Mishra B, Rajawat D, Bhakat M. Early prediction of oestrus for herd fertility management in cattle and buffaloes - a review. Reprod Domest Anim. 2024. PMID: 38798195.'),
        new Paragraph('3. Riaz U, Idris M, Ahmed M, et al. Infrared Thermography as a Potential Non-Invasive Tool for Estrus Detection in Cattle and Buffaloes. Animals (Basel). 2023;13(9):1471. PMID: 37106988.'),
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