Advanced KoboToolbox for Health Data Training Course

Duration 10 days 18 modules · 65 contact hours
Level Advanced Certificate on completion
Who attends M&E Specialists, Health Information Officers and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 16 Nov 2026 Course code SD-ME-133

Before you join: Participants should have practical experience creating or managing basic KoboToolbox forms and be comfortable using spreadsheets.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Health programmes depend on timely, credible field data for outbreak response, service delivery monitoring, supervision, beneficiary follow-up and donor reporting. Yet many KoboToolbox deployments produce incomplete records, inconsistent clinical classifications, duplicate submissions, weak location data or datasets that cannot be reconciled with indicator definitions. Advanced practitioners need to design forms that protect data quality at source, operate reliably offline and produce analysis-ready outputs without exposing sensitive health information.

This course develops advanced KoboToolbox skills for health monitoring and evaluation. Participants design complex XLSForms using relevance logic, repeat groups, calculations, constraints, media, cascading selections and external choice files. They configure projects, user permissions, data collection channels and quality controls; manage submissions; apply cleaning rules; and prepare datasets for routine health indicator analysis. The course also addresses informed consent, data minimisation, data protection, safeguarding and field protocols for collecting personally identifiable and health-related information.

Instruction combines focused technical briefings with health programme cases, guided form-building, data-quality investigations, peer reviews and structured application planning. Participants work with realistic scenarios such as mobile clinic monitoring, community health worker supervision, vaccination campaign follow-up and nutrition screening. They leave with a tested health data collection package: an XLSForm, data dictionary, fieldwork protocol, quality-assurance checklist and analysis-ready export specification that can be adapted for their own programme.

The course suits experienced M&E, health information, research and programme staff who already use KoboToolbox or have worked with digital data collection. It is particularly relevant where teams need stronger controls over complex forms, sensitive data and routine reporting quality.

What you will be able to do

  • Design advanced health monitoring forms using XLSForm logic, repeats, constraints and calculations
  • Configure KoboToolbox projects, user roles, collection channels and secure submission workflows
  • Build indicator-driven questionnaires aligned with health M&E plans and data dictionaries
  • Apply relevance, validation and skip-pattern rules to prevent common field data errors
  • Manage sensitive health data using consent, minimisation, access-control and retention procedures
  • Clean, reconcile and document KoboToolbox submissions for analysis-ready health datasets
  • Produce routine health monitoring outputs using disaggregation, completeness and timeliness checks
  • Present a tested KoboToolbox health data collection package and implementation plan

Benefits of attending

For you

  • Design complex health forms without relying on external form developers
  • Diagnose validation, logic and deployment faults before field rollout
  • Produce cleaner datasets that require less post-collection correction
  • Apply defensible consent and confidentiality controls to sensitive health records
  • Lead technical reviews of KoboToolbox tools used by health programmes

For your organisation

  • Fewer incomplete, invalid and duplicated records from mobile health data collection
  • Stronger alignment between field questions, indicators and donor reporting requirements
  • Faster availability of cleaned data for programme management decisions
  • Reduced privacy risks through controlled collection and access to sensitive data
  • Reusable KoboToolbox standards for future surveys, supervision and campaign monitoring

Course modules

18 modules over 10 days

Participants can define a KoboToolbox project architecture that links health monitoring questions, users, workflows and outputs.

  • Health monitoring workflows mapped from respondent contact to reporting output
  • KoboToolbox project settings matched to survey, supervision and routine monitoring needs
  • Data-flow diagrams identifying collection, review, correction and analysis responsibilities
  • Form architecture decisions for household, facility and community health data
  • Unique identifier strategy for clients, facilities, visits and field teams
  • Indicator reference sheets translated into questionnaire data requirements

Participants can construct a structured XLSForm workbook for a multi-section health monitoring questionnaire.

  • Survey, choices and settings worksheets structured for KoboToolbox deployment
  • Question names designed for stable exports and reproducible data dictionaries
  • Required columns, question types and appearance settings used correctly
  • Group sections organising household, clinical, service and follow-up questions
  • Note fields providing interviewer instructions without creating data variables
  • Version control conventions for form files, revisions and deployment records

Participants can build and test relevance logic that presents only applicable health questions.

  • Relevant expressions controlling eligibility, referral and service-specific question paths
  • Nested groups used to organise conditional maternal and child health sections
  • Selected() syntax applied to multi-select symptoms and service options
  • Boolean operators combining age, sex, consent and screening criteria
  • Handling unanswered parent questions without breaking downstream relevance rules
  • Logic maps documenting every respondent pathway before field deployment

Participants can implement validation controls that prevent invalid or implausible health records.

  • Constraint expressions checking plausible age, weight, height and dosage values
  • Constraint messages written for fieldworkers to correct entries immediately
  • Regular expressions validating phone numbers, facility codes and identification formats
  • Soft warnings distinguished from hard stops for exceptional clinical observations
  • Required responses applied selectively to avoid forced inaccurate answers
  • Range checks aligned with programme protocols and indicator definitions

Participants can calculate derived variables and classification fields within KoboToolbox forms.

  • Calculate fields deriving age from date of birth
  • Body mass index calculations using height and weight measurements
  • Mid-upper arm circumference classifications generated from programme thresholds
  • Date arithmetic calculating follow-up windows and missed appointment intervals
  • Indexed repeat calculations summarising household member screening results
  • Hidden variables storing standardised classifications for downstream analysis

Participants can use repeat groups to capture multiple services, household members and follow-up visits.

  • Repeat groups recording household members, consultations and referral actions
  • Repeat count expressions controlling expected numbers of child records
  • Nested repeats avoided where exports would become difficult to manage
  • Parent-child identifiers maintained across repeat tables and follow-up submissions
  • Longitudinal visit structures designed for treatment adherence monitoring
  • Repeat exports reviewed for row structure and relational data integrity

Participants can create maintainable choice structures for geographic, facility and service classifications.

  • Cascading selects filtering districts, facilities and service delivery points
  • Choice filters using geographic hierarchy codes rather than text labels
  • External CSV files managing large facility registries and commodity lists
  • Standardised code lists preserving compatibility with reporting and analysis systems
  • Search appearances improving selection from long health facility lists
  • Choice-list maintenance procedures documenting additions, closures and boundary changes

Participants can configure location, media and barcode fields appropriate for health fieldwork.

  • Geopoint capture configured with accuracy thresholds and enumerator guidance
  • Geotrace and geoshape questions used for outreach catchment mapping
  • Photo fields applied to verification tasks without collecting unnecessary identities
  • Barcode scanning linked to facility assets, sample labels and stock records
  • Audio audit considerations balanced against consent and confidentiality requirements
  • Metadata fields reviewed for device, timestamp and enumerator accountability

Participants can map health indicators to data elements, questions, calculations and reporting outputs.

  • Indicator reference sheets defining numerators, denominators and required disaggregation
  • Questionnaire variables mapped to each indicator calculation and source
  • Reporting periods distinguished from service dates and interview dates
  • Disaggregation fields specified for sex, age, disability and location
  • Data dictionaries documenting definitions, formats, labels and permitted values
  • Proxy indicators assessed for validity before inclusion in mobile forms

Participants can design KoboToolbox collection processes that protect consent, confidentiality and participant safety.

  • Informed consent scripts separating participation, contact and media permissions
  • Data minimisation removing identifiers unnecessary for monitoring decisions
  • Sensitive questions sequenced to protect privacy during household interviews
  • Safeguarding referral protocols linked to disclosure and protection concerns
  • Retention schedules defining deletion, archival and restricted-access decisions
  • Pseudonymous identifiers designed for follow-up without exposing client identities

Participants can administer KoboToolbox projects with appropriate roles, settings and release controls.

  • Project sharing permissions assigned by collection, review and management responsibilities
  • Form deployment procedures separating draft, test and production versions
  • User access registers recording account purpose, approval and review dates
  • Submission edit settings evaluated against audit and correction requirements
  • Collection links managed for anonymous, authenticated and enumerator-led workflows
  • Change-control logs documenting question revisions and operational implications

Participants can prepare a KoboToolbox form and field team for reliable offline data collection.

  • KoboCollect configuration tested for offline forms, updates and submissions
  • Enketo web forms assessed for browser-based collection and remote follow-up
  • Device readiness checks covering storage, battery, date settings and permissions
  • Field deployment checklists covering accounts, languages, form versions and support
  • Enumerator briefing materials explaining identifiers, consent and validation messages
  • Pilot protocols documenting defects, revisions and acceptance decisions

Participants can conduct structured testing that identifies form defects before production deployment.

  • Test scripts covering normal, boundary, invalid and exceptional response paths
  • Scenario-based testing of referral, eligibility and repeat-group workflows
  • Boundary-value analysis applied to numeric constraints and date calculations
  • Cross-device checks comparing Android collection and browser form behaviour
  • User acceptance criteria agreed with programme, M&E and field stakeholders
  • Defect logs recording severity, owner, correction and retest status

Participants can monitor incoming submissions and direct corrective action during data collection.

  • KoboToolbox table views filtered by date, team, facility and completeness
  • Daily completeness checks comparing expected and received submissions
  • Timeliness metrics calculating delays between interview, submission and review
  • Duplicate detection using identifiers, timestamps and matching demographic fields
  • Outlier review flags applied to impossible or unusual health measurements
  • Enumerator feedback loops documenting corrections, coaching and recurring errors

Participants can clean KoboToolbox exports using documented rules that preserve an auditable data trail.

  • Cleaning plans defining source fields, rules, owners and approval thresholds
  • Missing-data codes distinguished from blanks, refusals and not-applicable responses
  • Duplicate records resolved using documented matching and retention rules
  • Facility names reconciled against approved master facility lists
  • Query logs tracking corrections, unresolved issues and final decisions
  • Raw and cleaned datasets stored separately with versioned documentation

Participants can produce well-documented KoboToolbox exports for analysis, reporting and system exchange.

  • XLS and CSV exports selected according to analysis and archival needs
  • Repeat tables joined using parent keys and relational export structures
  • Variable labels, codebooks and value labels prepared for handover
  • DHIS2 aggregate reporting requirements compared with individual-level Kobo records
  • Indicator extracts structured for monthly, quarterly and campaign reporting
  • Data handover packages including files, dictionaries, cleaning notes and caveats

Participants can specify routine monitoring outputs that turn KoboToolbox data into management evidence.

  • Completeness and timeliness indicators calculated from submission metadata
  • Disaggregated coverage tables structured by age, sex, location and service
  • Data-quality dashboards distinguishing operational signals from reporting artefacts
  • Trend charts checked for denominator changes and reporting-period distortions
  • Action trackers linking findings, decisions, owners and due dates
  • Data review meeting agendas built around exceptions and management questions

Participants can build, review and present a complete KoboToolbox health monitoring package for a defined programme use case.

  • Health monitoring use case defined with respondents, indicators and decisions
  • Complete XLSForm built with logic, constraints, repeats and calculations
  • Consent, safeguarding and access controls documented for sensitive variables
  • Testing evidence compiled from scripted scenarios and defect resolution
  • Data dictionary, field protocol and cleaning plan assembled for handover
  • Peer review presentation defends design choices, risks and implementation actions

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Advanced health monitoring XLSForm template
  2. Health indicator-to-question mapping matrix
  3. KoboToolbox field deployment checklist
  4. Sensitive data governance plan
  5. Data-quality review and cleaning log
  6. Analysis-ready export specification

Who should attend

  • M&E Specialists — need reliable digital tools for indicator reporting and data-quality assurance
  • Health Information Officers — manage facility, community and campaign data collection systems
  • Programme Managers — require usable monitoring data for operational decisions and donor reporting
  • Research Coordinators — design structured mobile surveys with defensible validation controls
  • Community Health Supervisors — oversee field teams collecting household and service-delivery records
  • Data Managers — prepare KoboToolbox extracts for cleaning, analysis and reporting

Skills and tools covered

Advanced XLSForm designHealth indicator measurementMobile data qualitySensitive data governanceKoboToolbox administrationDataset quality assurance

Tools and standards

KoboToolbox, XLSForm, KoboCollect, DHIS2

How the course runs

Training methodology

The course uses short technical briefings followed by guided configuration, case-based form design and structured peer review. Participants build components of a health monitoring package from the first week, using scenarios involving facility reporting, household follow-up and community health supervision. Group exercises test logic paths, field conditions and data-quality failures. Simulations cover offline collection, submission review and correction workflows. Trainers provide technical feedback on XLSForms, indicator mappings and governance decisions, while application planning connects each participant’s work to a current programme requirement.

Requirements and prerequisites

Participants should have practical experience creating or managing basic KoboToolbox forms and be comfortable using spreadsheets. They should understand common health programme concepts such as indicators, denominators, disaggregation, baseline and routine monitoring. Prior exposure to mobile data collection, field supervision or health information systems is useful. Participants should bring a laptop with spreadsheet software and access to a KoboToolbox account if organisational policy permits. Programming, database administration, statistical coding and prior XLSForm authoring are not required; the course introduces advanced XLSForm techniques step by step.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

Participants should have used KoboToolbox or understand basic form concepts such as questions, choices and submissions. The course assumes spreadsheet confidence but does not require prior XLSForm coding or programming experience.

Bring a laptop with spreadsheet software and, where permitted, access to a KoboToolbox account. Participants may also bring a current health questionnaire, indicator matrix or data-collection problem for use during application planning.

It is designed for experienced health M&E staff, data managers, programme teams, researchers and field supervisors. It is most useful for people responsible for designing, reviewing or managing KoboToolbox tools rather than only completing interviews.

Basic courses focus on creating simple forms and collecting submissions. This course concentrates on XLSForm logic, repeat groups, calculations, sensitive-data controls, deployment governance, cleaning procedures and health indicator reporting.

Participants build a tested health monitoring package that can be adapted to a live programme requirement. The templates support form reviews, pilot preparation, field deployment, submission monitoring and data handover.

Each training day runs from 08:30 to 16:30, providing about 6.5 contact hours. The first week develops forms and health data specifications; the second week covers governance, deployment, quality assurance, exports and the capstone package.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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