Datasets:

electricsheepafrica
/

africa-synth-mhealth-mobile-apps-all

id int64	age int64	sex string	location string	education string	phone_type string	data_plan int64	app_category string	channel string	enrolled int64	active_30d int64	messages_received_month int64	messages_read int64	appointment_reminder int64	medication_reminder int64	health_info_received int64	symptom_checker_used int64	data_submitted int64	adherence_improved int64	appointment_kept int64	cost_barrier int64	literacy_barrier int64	language_barrier int64	privacy_concern int64	network_coverage_issue int64	satisfaction_score int64	year int64
1	35	female	remote	secondary	smartphone	0	chw_tool	whatsapp	1	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	3	2,023
2	30	female	urban	secondary	smartphone	1	chw_tool	whatsapp	0	0	0	0	0	0	0	0	1	0	0	0	1	1	1	0	0	2,022
3	22	female	peri_urban	tertiary	smartphone	0	data_collection	ivr	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	2,022
4	15	female	rural	secondary	smartphone	1	nutrition	ussd	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	2,019
5	26	male	peri_urban	tertiary	smartphone	1	immunization	sms	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,023
6	24	male	urban	secondary	smartphone	1	ncd_management	sms	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,021
7	15	male	remote	tertiary	feature_phone	0	hiv_art_adherence	web	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	2,023
8	42	male	peri_urban	secondary	smartphone	0	chw_tool	whatsapp	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	2,022
9	42	male	urban	secondary	smartphone	0	mental_health	sms	0	0	0	0	0	0	0	1	0	0	0	0	1	0	1	0	0	2,022
10	66	female	rural	secondary	smartphone	0	ncd_management	ussd	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	6	2,023
11	17	male	rural	primary	feature_phone	0	data_collection	sms	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	1	0	2,019
12	17	female	remote	secondary	feature_phone	0	data_collection	ussd	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	2,021
13	17	male	urban	tertiary	smartphone	1	mental_health	web	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	6	2,020
14	31	female	rural	primary	feature_phone	0	chw_tool	whatsapp	1	1	8	6	0	0	0	0	0	0	0	0	1	0	1	1	3	2,023
15	15	female	rural	tertiary	feature_phone	0	family_planning	sms	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	2,021
16	18	female	urban	none	feature_phone	0	maternal_health	web	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2,021
17	30	male	urban	none	smartphone	1	mental_health	whatsapp	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,019
18	30	male	rural	primary	smartphone	0	ncd_management	sms	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2,023
19	35	female	remote	none	feature_phone	0	ncd_management	ivr	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	2,022
20	35	male	peri_urban	primary	smartphone	0	chw_tool	sms	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0	0	2,023
21	28	female	peri_urban	secondary	smartphone	0	data_collection	native_app	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	2,023
22	50	female	remote	tertiary	smartphone	0	maternal_health	ussd	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1	2,020
23	36	male	remote	secondary	smartphone	0	chw_tool	sms	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0	0	2,022
24	22	female	rural	primary	no_phone	0	chw_tool	web	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	2,021
25	37	female	remote	none	no_phone	0	data_collection	sms	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	1	0	2,023
26	38	male	urban	secondary	smartphone	0	nutrition	native_app	1	1	10	5	0	0	1	0	0	1	0	0	1	0	1	0	2	2,019
27	30	female	urban	none	smartphone	0	ncd_management	ivr	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	2,022
28	28	female	urban	secondary	smartphone	0	maternal_health	native_app	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,021
29	15	female	peri_urban	none	smartphone	0	hiv_art_adherence	ussd	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,022
30	18	female	peri_urban	secondary	smartphone	0	general_health	ussd	0	0	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	2,019
31	45	male	peri_urban	primary	smartphone	0	chw_tool	native_app	0	0	0	0	0	0	0	0	1	0	0	1	1	1	0	0	0	2,023
32	61	female	peri_urban	secondary	smartphone	0	nutrition	native_app	1	0	0	0	1	1	0	0	0	0	1	0	1	1	0	0	4	2,020
33	33	female	rural	primary	smartphone	0	maternal_health	sms	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	2,022
34	29	female	remote	none	feature_phone	0	ncd_management	sms	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,023
35	22	female	peri_urban	secondary	smartphone	1	family_planning	web	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	2,023
36	38	male	rural	none	feature_phone	0	chw_tool	ussd	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,023
37	28	female	urban	secondary	smartphone	0	nutrition	web	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	2,019
38	26	female	remote	secondary	no_phone	0	family_planning	ivr	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	5	2,022
39	37	female	rural	tertiary	smartphone	0	nutrition	ivr	1	0	0	0	0	1	1	0	0	0	0	1	1	0	0	1	1	2,021
40	28	female	urban	none	smartphone	0	data_collection	web	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	2,021
41	27	female	urban	primary	smartphone	1	maternal_health	ussd	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,022
42	27	female	urban	primary	feature_phone	0	ncd_management	whatsapp	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,021
43	36	female	peri_urban	secondary	feature_phone	0	data_collection	ussd	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,023
44	26	female	urban	secondary	no_phone	0	ncd_management	ussd	1	0	0	0	1	1	1	0	0	0	1	0	1	0	1	0	2	2,021
45	44	female	urban	primary	smartphone	0	maternal_health	native_app	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	2,023
46	19	male	peri_urban	primary	feature_phone	0	chw_tool	sms	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2,020
47	38	male	remote	none	smartphone	1	maternal_health	ussd	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	4	2,022
48	56	female	peri_urban	tertiary	smartphone	0	nutrition	sms	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	2,020
49	16	female	rural	primary	feature_phone	0	mental_health	sms	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	5	2,023
50	29	female	peri_urban	none	feature_phone	0	immunization	whatsapp	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2,023
51	32	female	rural	primary	feature_phone	0	chw_tool	ussd	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	2,022
52	25	male	remote	primary	feature_phone	0	maternal_health	web	1	1	12	8	1	0	1	0	0	0	1	0	1	0	0	0	6	2,021
53	43	female	rural	primary	feature_phone	0	family_planning	sms	1	0	0	0	1	0	1	0	0	0	1	0	1	0	0	1	4	2,022
54	31	male	rural	tertiary	feature_phone	0	ncd_management	sms	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	5	2,021
55	26	male	remote	secondary	feature_phone	0	hiv_art_adherence	sms	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	3	2,020
56	29	male	peri_urban	tertiary	smartphone	0	family_planning	sms	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2,021
57	28	female	remote	secondary	feature_phone	0	ncd_management	ussd	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	2,022
58	51	male	rural	primary	feature_phone	0	maternal_health	sms	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2,022
59	36	female	remote	secondary	feature_phone	0	hiv_art_adherence	web	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	2,023
60	36	male	remote	primary	feature_phone	0	maternal_health	web	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	2,022
61	34	female	peri_urban	secondary	smartphone	0	mental_health	ussd	0	0	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	2,021
62	35	female	remote	secondary	feature_phone	0	immunization	web	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,022
63	33	male	rural	primary	feature_phone	0	family_planning	sms	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	2	2,020
64	28	male	peri_urban	secondary	feature_phone	0	hiv_art_adherence	ussd	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,022
65	44	female	peri_urban	secondary	smartphone	0	data_collection	whatsapp	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	2,021
66	21	female	remote	tertiary	no_phone	0	chw_tool	whatsapp	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	1	0	2,021
67	37	male	urban	tertiary	feature_phone	0	hiv_art_adherence	native_app	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	2,023
68	18	male	remote	primary	smartphone	0	nutrition	web	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	2,020
69	15	male	peri_urban	secondary	smartphone	0	nutrition	web	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	2,022
70	39	male	rural	primary	feature_phone	0	data_collection	ivr	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	2,020
71	32	male	urban	secondary	smartphone	1	maternal_health	ussd	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	2,020
72	15	female	rural	tertiary	smartphone	0	chw_tool	sms	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,020
73	37	male	urban	tertiary	smartphone	0	maternal_health	whatsapp	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,020
74	27	male	rural	secondary	feature_phone	0	data_collection	whatsapp	1	1	10	6	1	0	0	0	1	0	1	0	1	0	1	1	8	2,020
75	20	male	urban	primary	feature_phone	0	hiv_art_adherence	native_app	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	2,021
76	35	female	urban	tertiary	no_phone	0	maternal_health	native_app	1	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	6	2,021
77	25	female	remote	primary	no_phone	0	ncd_management	ussd	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	2,021
78	39	male	remote	secondary	smartphone	0	family_planning	ussd	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	2,022
79	23	male	peri_urban	primary	smartphone	1	chw_tool	sms	1	0	0	0	0	1	0	0	1	0	0	0	1	1	0	0	4	2,023
80	24	male	urban	secondary	smartphone	1	chw_tool	web	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	2,021
81	41	male	urban	secondary	smartphone	0	general_health	web	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	2,023
82	41	female	remote	secondary	feature_phone	0	immunization	sms	1	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	3	2,022
83	31	female	urban	secondary	smartphone	1	maternal_health	sms	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,023
84	37	female	remote	secondary	feature_phone	0	family_planning	native_app	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	2,021
85	30	male	remote	tertiary	smartphone	1	data_collection	sms	0	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	0	2,021
86	27	male	remote	none	feature_phone	0	chw_tool	ivr	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	2,022
87	35	female	rural	secondary	feature_phone	0	family_planning	sms	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	2,023
88	49	male	urban	primary	no_phone	0	hiv_art_adherence	ussd	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	2,020
89	26	male	rural	tertiary	feature_phone	0	nutrition	sms	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	2,020
90	58	female	remote	primary	feature_phone	0	ncd_management	sms	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	2,023
91	40	female	urban	none	feature_phone	0	ncd_management	ussd	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,021
92	36	female	urban	secondary	smartphone	0	ncd_management	web	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,022
93	59	female	peri_urban	secondary	smartphone	0	family_planning	ussd	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,020
94	45	female	rural	tertiary	smartphone	1	general_health	sms	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	5	2,021
95	31	female	rural	none	feature_phone	0	data_collection	whatsapp	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	1	0	2,022
96	37	male	remote	primary	feature_phone	0	maternal_health	ussd	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	2,022
97	28	female	remote	secondary	feature_phone	0	general_health	ivr	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	2,021
98	35	female	remote	none	smartphone	0	data_collection	web	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	2,022
99	38	male	urban	none	no_phone	0	family_planning	web	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	2,021
100	27	male	rural	tertiary	smartphone	0	general_health	sms	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	2,020

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⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

mHealth & Mobile Health Apps

Abstract

Synthetic dataset modeling mHealth and mobile health app usage across three SSA scenarios. Captures phone type, channels (app/SMS/USSD/WhatsApp), enrollment, engagement, use cases, adherence improvement, barriers, and satisfaction. Parameterized from SSA mHealth research.

Parameterization Evidence

Parameter	Value	Source	Year
Mobile penetration SSA	>80%	GSMA/Mechael et al.	2023
SMS improves ART adherence	12-20%	WHO mHealth	2021
SMS-based programs	MomConnect, mTrac	Agarwal et al. BMJ Glob Health	2015
Smartphone penetration SSA	~50%	GSMA	2023

Validation

Usage

from datasets import load_dataset
ds = load_dataset("electricsheepafrica/mhealth-mobile-apps", name="mhealth_basic")
df = ds['train'].to_pandas()

References

Mechael P et al. (2010). mHealth developing countries. Earth Institute
WHO (2021). mHealth public health programmes
Agarwal S et al. (2015). mHealth maternal newborn. BMJ Glob Health. DOI: 10.1136/bmjgh-2016-000190

License

CC-BY-4.0

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