To Track or not to Track? Employees’ Privacy in the Age of Corporate Wellness, Mobile Health, and GDPR
Notice bibliographique
Résumé
The latest digital health developments have allowed for a better tracking of individuals’ health through wearable devices and health apps, also known as ‘mobile health’ (mHealth). mHealth companies do not only target individual consumers, but also businesses, as they see a market in corporate health and wellness programs. As such, some employers now offer employees to use fitness wristbands or smartwatches so that employees can monitor their health at work and beyond. These devices and apps enable users to track their exercise, number of steps, sleep patterns, eating habits and a myriad of other health-related activities, which are often non work-related. Employers present mHealth devices and apps as company ‘perks’ for employees. However, mHealth may come at a price for employees, who may unwillingly share their most personal information (health information) with their employer and third parties, such as mHealth developers, and/or insurance companies. Therefore, this article investigates the lawfulness of the use of mHealth devices and apps in the context of corporate wellness programs, in particular in light of employees’ rights to privacy, data protection, and non-discrimination under European Union (EU) law and under the European Convention on Human Rights (ECHR) and related case law. First, the article analyzes the conditions for a valid consent given by an employee to the processing of her health data, as set under the EU General Data Protection Regulation (GDPR), and related interpretative guidelines and opinions. The current regime seems very protective of employees’ privacy: in an advisory opinion on data processing at work issued in 2017, the European Data Protection Working Party stated that employees’ free consent to the processing of mHealth data is highly unlikely because of the sensitive nature of health data and the unequal relationship between employers and employees. The article argues that this highly protective regime is not only a way to protect employees’ right to privacy, but also to protect them against any potential discrimination on prohibited grounds, such as pregnancy, disability or health status, as such discrimination in the workplace is often indirect and difficult to prove. Therefore, measures which are less intrusive of employees’ privacy, namely, which do not track employees’ health information, may be deemed more proportionate under EU law and under the ECHR. Secondly, in the event where an employee’s consent to use mHealth technology were found valid in the employer-employee relationship, the article analyzes how third parties developing mHealth apps and devices also need to respect employees’ privacy. This question is answered in light of the recent Draft Code of Conduct on privacy for mobile health applications, as well as EU and ECHR law. The article concludes that although the European privacy regime may seem overly protective of employees’ privacy and data, this may benefit mHealth developers in the long-run by fostering a culture of trust by users of these technologies, who will know that their data cannot be used against them.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,027 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,031 |
| Communication savante | 0,012 | 0,014 |
| Science ouverte | 0,001 | 0,009 |
| Intégrité de la recherche | 0,014 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».